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SENSORY AND VOLATILE ANALYSIS OF RED DRUM (SCIAENOPS OCELLATUS) GROWN IN RECIRCULATING AQUACULTURE SYSTEMS (RAS) AND INTEGRATED
MULTI TROPHIC AQUACULTURE SYSTEMS (IMTAS)
By
DANIEL W. CLARK
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
2017
© 2017 Daniel W. Clark
To my Mom and Dad
4
ACKNOWLEDGMENTS
First I would like to thank my advisor, Dr. George Baker, for giving me the
opportunity to pursue my master’s degree under his guidance, he has shown me how a
good leader and boss treats those he works with as he provided me an environment
that encouraged me to learn and grow as a person. I would like to thank the Food
Science and Human Nutrition Department for providing the stipend that allowed me to
focus on my research, the Yeomans group for paying for my tuition, and Dr. Baker for
providing the remainder of the funding for my graduate program. I would also like to
extend my sincere appreciation to the rest of my supervisory committee: Dr. Kevan
Main, Dr. Paul Sarnoski, and Dr. Charles Sims, for their guidance and expertise
throughout my studies. A special thanks goes to Sara Marshall, Dr. Asli Odabasi, and
the FSHN Taste Panel Staff for their assistance with sensory evaluations. I would also
like to thank my lab mates past and present for all of their help, suggestions, and
friendship which is what got me through to the end: Ruby and Caity for all of our
hilariously random, meaningful talks and adventures, Rui who always was good for a
laugh. I would also like to thank my best friend and gym partner, Zack Carter, as well as
Zach Goins, Devin Gregory, Conner Rogers, Joey Olivera and the rest of the Bromance
for being there for me through all of this and for all of the great adventures and late night
beers we’ve shared. And last but definitely not least, I would like to thank my parents,
Dale and Diane, and my brother, David, for their never ending love, patience, and
support in all of my endeavors.
5
TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 8
LIST OF ABBREVIATIONS ............................................................................................. 9
ABSTRACT ................................................................................................................... 11
CHAPTER
1 INTRODUCTION .................................................................................................... 13
2 LITERATURE REVIEW .......................................................................................... 16
Fish (Red Drum) ..................................................................................................... 16 Habitat and Spawning ...................................................................................... 17 Environmental Physical Tolerance ................................................................... 18
Red Drum Production ............................................................................................. 19 Types of Production Phases ............................................................................. 19
RAS at Mote Marine Aquaculture Park (MAP) ........................................... 20
Red drum market and trade ....................................................................... 20
Red drum composition ............................................................................... 21 Aquaponics ....................................................................................................... 22
Flavor Identification ................................................................................................. 24
Off-flavors in RAS ............................................................................................. 24 Gas Chromatography ....................................................................................... 25
Mass Spectrometry .......................................................................................... 27 Gas Chromatography / Mass Spectrometry (GC/MS) ...................................... 28 Solid Phase Microextraction ............................................................................. 29
Headspace aroma capture using a gas tight syringe ........................................ 34 Sensory Analysis .................................................................................................... 35
3 MATERIALS AND METHODS ................................................................................ 54
Sample Collection ................................................................................................... 54
Sample Preparation ................................................................................................ 54 Sensory Evaluation ................................................................................................. 56
Trained Panel ................................................................................................... 56 Trained Panel Testing and Data Collection ...................................................... 58 Consumer Panel ............................................................................................... 59
Volatile Analysis ...................................................................................................... 60 GC-GC/MS analysis ......................................................................................... 60
6
SPME Extraction of Headspace Volatiles .................................................. 60
Gas Tight Syringe Extraction ..................................................................... 60 Statistics ................................................................................................................. 61
4 RESULTS AND DISCUSSION ............................................................................... 64
Sensory Evaluation ................................................................................................. 64 Trained Panel ................................................................................................... 64 Consumer Panel ............................................................................................... 64
Volatile Analysis ...................................................................................................... 65
HS-SPME ......................................................................................................... 65 DI-SPME .......................................................................................................... 67 Gas Tight Syringe Headspace Extraction ......................................................... 68
5 CONCLUSIONS ..................................................................................................... 75
APPENDIX
A SAMPLE BALLOT FOR TRAINING SESSIONS FOR TRAINED TASTE PANEL .. 77
B SAMPLE BALLOT FOR TRAINED TASTE PANEL SENSORY EVALUATION ...... 78
C BALLOT FOR CONSUMER PANEL SENSORY EVALUATION ............................. 79
LIST OF REFERENCES ............................................................................................... 82
BIOGRAPHICAL SKETCH ............................................................................................ 94
7
LIST OF TABLES
Table page 2-1 Typical length vs age for red drum ..................................................................... 41
2-2 Vitamins in red drum fillet per ~200g .................................................................. 42
2-3 Minerals in red drum fillet per ~200g .................................................................. 42
3-1 Attribute reference samples ................................................................................ 62
4-1 Mean aroma attribute scores for trained panels-raw samples ............................ 70
4-2 Mean aroma attribute scores for trained panels-cooked samples ...................... 70
4-3 Mean flavor attribute sensory score for trained taste panel ................................ 70
4-4 Consumer sensory data ..................................................................................... 71
4-5 Volatile compounds identified by GC-GC/MS samples extracted with HS-SPME ................................................................................................................. 72
4-6 Volatile compounds identified by GC-GC/MS samples extracted with DI-SPME ................................................................................................................. 73
4-7 Volatile compounds identified by GC-GC/MS samples extracted with a gas tight syringe ........................................................................................................ 74
8
LIST OF FIGURES
Figure page 2-1 Picture of red drum fish (Sciaenops ocellatus) (FAO 2017) ................................ 42
2-2 Coastal map of red drum habitat (GBIF 2017) .................................................... 43
2-3 Map of Volusia county (World Maps 2017) ......................................................... 43
2-4 Map of Volusia county (FL Maps 2017) .............................................................. 44
2-5 Map of Indian River Lagoon (FHWA 2017) ......................................................... 44
2-6 Anatomy of fish (Fishing Target 2017) ................................................................ 45
2-7 External anatomy of a fish with soft dorsal fin and spiny dorsal fin identified (Fishing Target 2017) ......................................................................................... 45
2-8 Male fish fertilizing eggs from female (Zoology 2017) ........................................ 46
2-9 Mariculture ponds (Alloyance 2017) ................................................................... 46
2-10 Production cycle of red drum (FAO 2017) .......................................................... 47
2-11 Graph of age vs length of red drum (adapted from USM 2017) .......................... 47
2-12 Global aquaculture production for red drum (FAO 2017) .................................... 48
2-13 Diagram of a gas chromatograph (Gas chromatography 2017) ......................... 50
2-14 Inner diagram of a mass spectrometer (Overview of Mass Spectrometry 2017) .................................................................................................................. 50
2-15 Inside of a gas chromatograph/mass spectrometer (Gas chromatography–mass spectrometry 2017) ................................................................................... 51
2-16 Number of published articles in recent years related to SPME and SPME/MS applications (Vas 2004) ...................................................................................... 51
2-17 Inner design of a SPME fiber (Kataoka 2000) .................................................... 52
2-18 SPME fiber properties, retention vs polarity (Fanali 2017) ................................. 52
2-19 SPME process for headspace vs direct immersion SPME (Vas 2004) ............... 53
2-20 Extraction time and analyte absorption rate on SPME fiber (Vas 2004) ............. 53
3-1 Consumer panel screener .................................................................................. 63
9
LIST OF ABBREVIATIONS
ANOVA analysis of variance
DVB/CAR divinylbenzene/carboxen
DI direct immersion
FAO Food and Agriculture Organization
FID flame ionization detector
GC gas chromatography
GC/MS gas chromatography/mass spectrometry
GC-GC/MS multidimensional gas chromatography/mass spectrometry
GSM geosmin
HPLC high performance liquid chromatography
HS headspace
IMTAS integrated multi trophic aquaculture system
LC-MS liquid chromatography-mass spectrometry
MAP Mote marine aquaculture park
MIB 2-methylisoborneol
MD microwave distillation
MS mass spectrometry
MUFA monounsaturated fatty acids
NIST National Institute of Standards and Technology
m/z mass to charge ratio
PAH polycyclic aromatic hydrocarbons
PDMS polydimethylsiloxane
ppb parts per billion
10
ppt parts per trillion
PUFA polyunsaturated fatty acids
QDA quantitative descriptive analysis
RAS recirculating aquaculture system
SPME solid phase micro extraction
TCD thermal conductivity detector
VAS visual analog scale
μm
micro meter
11
Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science
SENSORY AND VOLATILE ANALYSIS OF RED DRUM (SCIAENOPS OCELLATUS)
GROWN IN RECIRCULATING AQUACULTURE SYSTEMS (RAS) AND INTEGRATED MULTI TROPHIC AQUACULTURE SYSTEMS (IMTAS)
By
Daniel W. Clark
August 2017
Chair: George Baker Major: Food Science and Human Nutrition
An increasing global demand for aquatic food products has led to the need to
grow fish using an established method like recirculating aquaculture systems (RAS) as
a means to provide safe and sustainable seafood but the presence of dirty and muddy
off-flavors like geosmin (GSM) and 2-methylisoborneol (MIB) negatively impact quality.
It is hypothesized that Integrated Multi Trophic Aquaculture (IMTA) systems may
actively suppress the production of dirty and muddy off-flavor when compared to
conventional RAS or that elimination of off-flavor compounds in aquaponics systems is
more efficient when compared to conventional RAS (Diver 2010). The objective of the
study was to compare the presence of off-flavors and sensory quality of red drum grown
in IMTAS and RAS.
Descriptive analysis and hedonic consumer panels were conducted on red drum
fillets sourced from RAS and IMTAS for sensory attribute intensity of common aromas
and flavors associated with finfish. A hedonic consumer panel tested 60 panelists using
a 9-point scale for likeability and preference. Volatile analysis was conducted using HS-
12
SPME, DI-SPME and gas tight syringe extraction, headspace volatiles were then
analyzed in a GC/MS.
Trained panelists found no significant difference in aroma attributes between raw
or cooked RAS and IMTAS samples. Steamed RAS samples had a significantly higher
presence of “dirt” off-flavor compared to IMTAS (17.13 vs 10.54, respectively). A
consumer panel showed no significant difference in likeability between samples.
Preference results were evenly split (29:31 for RAS: IMTAS).
Volatile analysis detected the presence of 1, 2-Propadiene-1, 3-dione in all
samples and (S)-(+)-1-Cyclohexylethylamine in all samples except the raw IMTAS.
These compounds have an acrolein (burned fat) and amine (fishy) odor respectively. It
is assumed that the presence of these compounds, adversely affect the quality of the
fish.
Sensory results suggest that there is no significant statistical differences between
aroma of RAS and IMTAS reared red rum. However, descriptive analysis trained
panelists were able to discern a “dirty” off-flavor in RAS reared red drum when the
samples were tasted. Consumer panels could not find noticeable differences between
the samples, raw or cooked.
13
CHAPTER 1 INTRODUCTION
Due to the increased need for aquatic food products globally, seafood products
grown in aquaculture have increased. Globally, China, Indonesia, the United States,
and Russia are the top producers of aquacultured products (FAO-UN, 2016).
Recirculating aquaculture systems (RAS) allow for the production of locally sourced
seafood in regions that are not near bodies of water or where there are water
restrictions. Global demand for safe, nutritious, sustainable seafood has increased due
to the projected worldwide population exceeding nine billion people by 2050 (FAO-UN,
2016). Seafood and specifically fish is recognized as an efficient source of protein
requiring 1.2 pounds of feed to attain 1.0 pound of edible flesh, compared to cattle
which require upwards of 8.7 pounds per pound of edible protein (Gerber 2007).
A problem that plagues fish grown in RAS is the development of fish off-flavors
which tend to be muddy, dirty or musty flavor (Schrader 2003). Research conducted to
identify the source of off-flavors in RAS points most commonly to the presence of 2-
methoisoborneol (MIB) and geosmin (GSM) (Yamprayoon 2003). MIB and GSM are
compounds formed by cyanobacteria and Gram-positive filamentous bacteria called
actinomycetes which produce spores and are capable of surviving multiple conditions
(Tucker 1999). The threshold of sensory detection for these compounds is low, 0.015
and 0.035 ppb for GSM and MIB, respectively (Leo 2012). At the levels present in RAS,
those compounds are not considered toxic yet negatively impact the quality of the fish.
Common techniques managing the off-flavors in RAS fish includes purging them with
approximately 0.3 m3 clean treated water/kg feed, which increases the total amount of
water, energy and chemical cost for the grower (Seginer 2008).
14
A novel form of RAS incorporates the use of hydroponics to create a sustainable
recirculating system where plants being grown utilize ammonia and nutrient rich water
provided by the fish to act as fertilizer (Tisdell 1999). Research conducted has been
linked to the conclusion that these marine aquaponics systems, also known as
Integrated Multi-Trophic Aquaculture (IMTA) systems, offer fish farmers good
commercial potential due to its decreased operational cost when compared to traditional
RAS (Diver 2010). A commercial-scale, zero-discharge (whereby no water leaves the
system during the production process) aquaponics system is currently being used at
Mote Marine Aquaculture Research Park (MAP) in the production of red drum
(Sciaenops ocellatus) and sea vegetables Sea Purslane (Sesuvium portulacastrum) and
Saltwort (Batis maritimia), as well as fertilizer to be used in wetland plant nursery
farming (Boxman et al., 2016).
An informal taste panel conducted at the University of Florida, following the
lexicon and preparation procedure outlined by the Food and Agriculture Organization of
the United States (FAO) provided evidence that red drum produced in IMTA systems
have decreased levels of off-flavor compounds, while red drum grown in traditional RAS
from the same facility require fresh water purging of the fish to remove off-flavors prior
to harvesting (Davidson 2014, Schrader 2016). Informal taste panel using six panelists
who are experts in sensory description, yielded results that showed that IMTA grown
red drum had a “cleaner” flavor that had less noticeable off-flavors when compared to
the RAS samples, while the fish grown using conventional RAS had dirty, muddy,
earthy, and bitter off-flavors. IMTA systems may actively suppress the production of off-
flavor in fish muscle when compared to conventional RAS or that elimination of off-flavor
15
compounds in aquaponics systems is more efficient when compared to conventional
RAS (Rimando 2003).
16
CHAPTER 2 LITERATURE REVIEW
Fish (Red Drum)
Red drum (Sciaenops ocellatus) (Figure 2-1), are natural inhabitants of Florida’s
Atlantic coast, with a large population in the Indian River Lagoon as well as a habitat
that range from central Mexico and extends to Massachusetts, including the Gulf of
Mexico (Figure 2-2) (Robins 1986). The Indian River Lagoon runs from Volusia County
down through to Jupiter Inlet in Palm Beach County (Figures 2-3 and 2-4), incorporated
with Cape Canaveral, the St Lucie River, and Lake Okeechobee and connected by the
Okeechobee Waterway (Figure 2-5). Red drum are a prominently estuary dwelling fish,
living most of its life near shore (Reagan 1985). Reaching up to five feet (150 cm) and
90 lbs (41kg) in weight, the red drum is a compressed, rugged member of the Sciaenid
family (Hoese and Moore 1977). Red drum are a resilient fish and are easily raised in
aquaculture conditions due to their high tolerance of wide temperature variances (35.6°-
99.5°F), differences in salinity of the water ranging from 0.14-50 ppt, as well as
acclimating to freshwater conditions (Arnold et al 1977). Red drum are saltwater
dwelling fish that thrive when the water temperatures are warm (~72°F) and contain
both marine and estuary habitats (Sweat 2010). Since the near depletion of red drum
occurred in the late 1980s due to commercial fishing. Limitations such as size and bag
limits were implemented along with commercial fishing of red drum eliminated along the
coasts of Florida (ASFMC 2010). Red drum have a straight head, large mouth with
villiform teeth, and a blunt, cone shaped snout (Reagan 1985). The body color is
primarily coppery brown or reddish with a white underside. Red drum lack barbells
which are whisker like organs located at the base of the mouth used to locate food by
17
acting as nostrils or taste buds when in low visibility conditions unlike other species
similar to the drum, but have two dorsal fins that contain ten hard spines, with the first
hard spine and a grouping of soft rays in the second. The soft dorsal (soft ray) and
caudal fins are composed of rays that are less rigid and typically branched (FFWC
2017). The soft rays are typically used to support the fins of the fish and sometimes can
play a defensive role. Near the base of the slightly concave caudal fin are one or more
dark spots that lay above the lateral line (Figures 2-6 and 2-7).
Habitat and Spawning
Red drum spend most of their life in estuaries but move to the deeper waters on
the inner coast, bay areas, and inlets during mating season (Crocker 1981). The mating
season occurs during the fall around mid-August through October depending on where
the fish dwell in Florida. Shorter daylight hours and cooler water temperatures trigger
mating habits of the red drum and peak around 9:30-10:00 p.m. Typically, spawning
occurs from its peak in September or October but cases have been found where
spawning can occur in August and December depending upon environmental
temperature (Robins 1986). The spawning process typically occurs around the bay
areas as red drum larvae have not been reported more than 12 miles from the beach or
bays. Red drum males during spawning produce a characteristic drumming sound by
rubbing specific muscles against their air bladder which produces a drumming sound
hence the name. To attract females during mating season, males begin making the
drumming sound and increases around dusk. When the males come within the
proximity of females they proceed to nudge the female’s abdomens causing a release of
the eggs which is then fertilized by the males. After the males use their drumming
sounds to attract females, the spawning process begins at night near the offshore
18
waters during the summer and fall months. The larger females produce up to two million
eggs in a season which are then fertilized by the males (Figure 2-8). The fertilized eggs
take 24-36 hours to hatch after being spawned and the larvae are then carried by the
action of the tides into shallow estuaries that typically serve as low salinity nursery
areas (ASM 2017). After the larvae hatch they live off the attached yolk sac for three
days then feed on plankton while living in the water column for approximately 20 days
(USM 2017).
Mariculture is the process of producing aquaculture products by means of
creating an aquatic medium that can be completely marine or have various degrees of
marine and brackish water incorporations (Mariculture 2017). These environments can
be free floating cages, net enclosures, earth ponds, or constant water recirculating
systems (Figure 2-9) (Sivalingam 2017). In Florida mariculture ponds, red drum have
been shown to produce 20,000 to two million eggs per spawn (Roberts et al 1978).
Environmental Physical Tolerance
Though red drum can withstand substantial changes to their aquatic
environment, the mortality rate increases with drastic changes in temperature (Gunter
1941). Optimized breeding and hatching of red drum occurs at temperatures around
25°C and 30 ppt salinity, with the best growth and survival occurring in water
temperatures of 25-30°C (Holt et al 1981). Though capable of living in freshwater
conditions, red drum eggs tend to sink when the salinity is below 20 ppt, leading to a
low survival rate.
19
Red Drum Production
Types of Production Phases
Two production phases are utilized for the cultivation of red drum in aquaculture,
(Figure 2-10). The broodstock phase is the production process where four to six fish are
combined in a tank, typically the male to female ratio is 1:1 Male: Female, to spawn in
tanks ranging from 10 to 17 cubic meters in width and 1.5 m in height (Holt 1990).
These tanks are connected to an external recirculating aquaculture system (RAS). RAS
Filtration systems are used to help maintain water quality by removing such
contaminants as ammonia, nitrites, dissolved organic solids, carbon dioxide, excess
nitrogen, and other suspended solids (Smith 2003). Using a broodstock production
system allows fish growers to provide a constant supply of red drum larvae that are
available for the grow-out production phase. Red drum growth is rapid during the early
part of their life spans, until 5 years when growth slows down significantly (Table 2-1
and Figure 2-11). The male and female red drum grow at the same rate and the growth
rate can vary upon location as gulf coast fish typically grow faster due to the warmer
water (USM 2017).
During the red drum spawning process, broodstock are fed a diet of primarily
squid, fish, pellet food, and shrimp (Holt 1990). After the red drum larvae become
juvenile fish, their natural diet shifts to shrimp, worms, small crabs, and other smaller
fish. Wild adult red drum are opportunistic feeders that will feed on prey such as mullet,
pinfish, spot, and other small fish. During the winter months when the water temperature
is colder the red drum diet consists primarily of fish while during the warmer summer
and fall, more shrimp and crustaceans are eaten (USM 2017).
20
RAS at Mote Marine Aquaculture Park (MAP)
Aquaculture utilized at Mote Marine Aquaculture Research Park (MAP), uses a
closed aquaculture system which uses a treatment system to clean the water so it
becomes reusable instead of emitting it into the environment (Cottee 2009). Closed
aquaculture systems help to eliminate negative environmental impacts, introduction of
exotic species, and pollution of waters with chemicals and other nutrients (Naylor 2000).
A typical RAS like the one at MAP will feature oxygenation, aeration, and solids
removal through the addition of biofiltration (Piedrahita 2003). Biofilters comprise of a
collection of bacteria on the surface of a biofilm carrier media. This bacteria present in
the biofilm is the major source of nitrification which is responsible for the break down
and removal of ammonium. Biofilters have been used in conjunction with plants or algae
to successfully remove ammonium in IMTAS (van Rijn 1996).
Though effective, biofilters are costly to operate and it is required that high value
fish be grown to balance out the high cost of operation for the biofilter (Zucker 1999).
The utilization of plants and algae in conjunction with biofilters can help to provide
additional revenue opportunities as well as overall cost reduction (Graber 2009, Neori et
al., 2004).
Red drum market and trade
In 2016, the price of red drum raised in commercial aquaculture was USD $4.19-
4.63/kg, though the price fluctuates with market supply and demand (FAO 2017). These
recent sale prices reflect domestic red drum that are frozen or fresh boned fillets and
steaks ranging in weight from 170 to 340 grams.
21
Red drum composition
Fish like red drum contain fat-soluble vitamins A, D and E as well as omega-3
fatty acids (Borgstrom 1961). Omega-3 and Omega-6 fatty acids are polyunsaturated
fatty acids (PUFA) and are essential in that they are not produced independently by the
human body. PUFAs play a major role in reducing the risk of heart disease and aiding in
the role of brain function (Ehrlich 2015). PUFA and monounsaturated fatty acids
(MUFA) tend to undergo oxidation due to the presence of double bonds in their
structure, while saturated fatty acids are less prone to oxidation (Delany 2000). When
compared to plant based sources of Omega-3 fatty acids, marine sources are more
prone to oxidation by the presence of long carbon chain PUFAs which are chemically
unstable (Albert 2013). Autoxidation is known to cause flavor deterioration as well as
the formation of toxic materials (Paquette 1985). Compounds like hydroperoxides, a
main product of autoxidation, breaks down to form a variety of volatile and non-volatile
secondary products when extensive lipid oxidation occurs (Paquette 1985).
Fish like red drum are also good sources of sodium, magnesium, cobalt, iodine,
fluorine, and magnesium which are helpful for bone growth, muscle building, and heart
health (Venugopal 2009). The amount of vitamins and minerals per 200 grams of red
drum can be seen in Tables 2-2 and 2-3.
Red drum flesh has a unique firmness when cooked, with an odor reminiscent of
the ocean with a fresh and fatty note (USM 2017). The color of the flesh is slightly
amber, but when cooked turns ivory. When presented to sensory evaluation panelists, it
is generally liked and has a high overall acceptance (Klanian 2015). Wang (2009),
noted a fishy, grassy, fatty and melon odor in cultured red drum linked to hexanal and 1-
penten-3-ol, likely sourced from environmental factors (Wang 2009). In the Wang
22
study, the dorsal muscle of the fish tended to have the highest concentration of odor. 1-
octen-3-ol, 4-heptanal, and benzaldehyde lent strongly to the characteristic odor notes
in red drum. Cucumber odor has been associated with fresh fish and used as a quality
indicator for red drum (Josephson 1983).
Due to the high resiliency of red drum and their ease of cultivation from a
spawning, nutrition, and environmental tolerance perspective, there has been a steady
rise in production of red drum worldwide (Figure 2-12) (FAO 2017). Further research
conducted in temperature tolerance, disease resistance, and feed efficiency, may
enhance red drum as an economically and ecologically sound species to produce for
human food (FAO 2017).
Aquaponics
Despite the known health benefits of fish and vegetables, the average American
consumes less than four pounds of fish per year and an average of 92 pounds of fresh
vegetables per year which equates to one serving of vegetables per day (USDA 2010).
Systems like Aquaponics systems are able to integrate the production of fish and plants
while reducing the environmental impact of aquaculture (Adler et al., 2003; Tyson et al.,
2011). Aquaponic systems are capable of removing low concentrations of dissolved
nitrogen and phosphorous that typically accumulates in aquaculture. When plants take
up and sequester nitrogen and phosphorus, along with other nutrients, water quality is
improved (Buzby 2014). RAS are designed to grow large quantities of fish in small
volumes of water but the environmental water must be purged and treated to prevent
the buildup of waste products before the water is reused. Systems that grow secondary
vegetative crops in conjunction to the primary fish species by utilizing the by-products of
23
the primary growth species are referred to as Integrated Multi-Trophic Aquaculture
Systems (IMTAS) (Rakocy 2006).
In aquaponics, aquaculture water can be treated through the production of
hydroponic plants which grow without soil or roots and are in regular contact with water
in the system (Rakocy 2012). To apply aquaponics to a marine setting, the growth of
salt tolerant vegetables must be incorporated.
The IMTAS at MAP is a marine aquaponics system and grows salt tolerable
vegetables in conjunction with red drum. Saltwort (Batis maritimia) contains high
concentrations of essential amino acids and grows in salt marshes in both North and
South America (Debez 2010). Sea purslane (Sesuvium portulacastrum) is a vegetable
with a salty taste that is popular in India and grows along coastlines in tropical and
subtropical environments (Kathiresan 1997).
The plants remove nutrients and potential sources of toxins from the culture
water, which eliminates the need for a separate biofilter (Rakocy 2006). Fish produce
nitrogen waste through excretion that is distributed in the water supply as ammonia
(NH3). Nitrifying bacteria present in the IMTA system convert the ammonia into nitrite
(NO2-) and then ultimately into nitrate (NO3-) (Madigan et al., 2003). Plants used in
IMTAS can absorb the nitrate and ammonium (NH4+) as a source of food, due to
nitrogen being the main nutrient required by most plants (Marschner 2003). Most of the
waste the fish generate is removed by a filtering system before entering the hydroponic
tanks. If waste weren’t removed, deep deposits of sludge would form and produce
methane and hydrogen sulfide, which are known to be toxic to some fish at certain
concentrations (Rakocy 2006). Research has found that nitrate toxicity occurs at 0.9 ±
24
0.14 mg/L after 24 hours and 0.8 ± 0.16 mg/L after 48 hours (Wise 1989). Studies
stated that hydrogen sulphide which forms in low oxygen environments, caused
mortality in red drum at levels ≤2.0 µg/L (Lucas 2013).
Flavor Identification
Off-flavors in RAS
Earthy and muddy off-flavors and aromas have been documented since 1891,
where it was thought originally that the earthy odor came from the soil but was later
discovered to be sourced from actinomycetes (Gerber 1967). It was also noted that fish
that were subjected to water containing trace amounts of the odorous compounds would
pick up the taint and store it in the flesh. This absorption occurred through the mouth
and gills of fish and not through the skin, which required keeping them in pure running
water to eliminate the off-flavors (Gerber 1967). Research since then has confirmed that
the major source of earthy-musty taste and odor is caused by metabolites of
actinomycetes (Henley 1969, Rosen 1970). Actinomycetes in addition to fungi, bacteria,
and algae, are known to produce geosmin (GSM) and 2-methylisoborneol (MIB) which
are semi volatile compounds that have a muddy, musty odor that is identifiable in
concentrations as low as 0.015 and 0.035 µg L-1 for geosmin and MIB, respectively
(Lloyd 1998, Leo 2012). The most common taste and odor compounds in fish and
freshwater aquaculture systems are GSM and MIB as both are readily absorbed into the
lipid tissue of the aquatic organism in concentrations present in the tissue at greater
than 0.6 g/kg, which make the fish unfit for retail or commercial sale (Petersen 2011,
Persson 1980). Both compounds are saturated cyclic tertiary alcohols which makes
them resistant to oxidation by conventional water treatment methods (Figure 2-13)
(Izaguirre 1981).
25
Though geosmin and MIB are identified as the dominant source of off-flavors in
RAS, some research indicates that other compounds can be the source of off-flavor
(Petersen et al. 2014). These compounds have been linked to the presence of
Actinomycetes which have been identified not only in terrestrial aquaculture systems but
also in ocean water as part of the run off from rivers (Weyland 1969). Some researchers
have found that other bacteria, like the species found in the Myxococales order, can
produce musty and earthy off-flavors in aquaculture systems (Zaitlin & Watson 2006).
Factors like phosphate concentrations, micronutrients, and organic matter in the system
can affect water quality and promote the growth of off-flavor producing microorganisms
(Auffret 2013). In RAS, water quality can be negatively affected by the presence of
nitrogen compounds like ammonium and nitrite (Wickins, 1980; Russo & Thurston,
1991). Conditions where phosphorous concentrations were increased lead to enhanced
GSM production, while environments with increased zinc, iron and copper decreased
the production of GSM with copper being the most effective (Schrader 2001). It has
been identified that GSM and MIB grow best in aerobic, organic-rich conditions which
tend to stimulate the growth of actinomycetes, while a reduction of the compounds was
present in an anaerobic water treatment condition (Guttman 2008). Temperature,
salinity, and phosphate levels positively correlated to the growth of the microorganisms
responsible for GSM and MIB (Robertson 2006).
Gas Chromatography
Food aroma and flavors are a combination of various volatile compounds that are
sensed by the olfactory portion of the mouth and nose (Figure 2-14). Odorant receptors
are localized on the olfactory nerve in the nasal epithelium. Upon receiving an odor
compound, signals are relayed to the brain (Rinaldi 2007). Gas chromatography (GC) is
26
useful for the separation and identification of specific volatile compounds that make up
aromas. GC operates by separating the volatile compounds based upon the amount of
time it takes for them to elude from the column after being inserted. Gas
chromatography (GC) is reliant upon the different partitioning behavior that each
substance possess based upon the flowing mobile phase and stationary phase being
used to separate the component from a mixture. After being injected into the GC, the
sample is taken up by the moving gas (mobile phase) through a tube that is lined with a
film of a liquid stationary phase. As the sample travels through the column, the
components are separated in the column based upon their polarity and will elude out of
the column at different rates. As the compounds are eluted out, the detector measures
the quantity of the eluted components. If a compound has an unknown concentration, a
standard with a known concentration is introduced into the instrument and the retention
time of the unknown compounds are compared to the sample being tested to estimate
the concentration (Shimadzu 2017). The different components of a GC consist of the
column inlet, column, and detector (Figure 2-15). The column inlet is narrow metal
tubing that serves as the head of the column and is commonly considered split or
splitless which control how much sample enters the column. After the sample is injected
into the chamber using a syringe, in split mode a portion of the sample is swept by the
carrier gas while in splitless mode the entire sample is swept by the gas. For most food
samples where the concentration of the analytes are low, it is advised to use splitless
injection. Capillary columns used in GC consider length and column materials to dictate
proper usage in experiments. Capillary columns range from 10 to over 120 meters with
their inner walls being active, flexible materials which allow the column to be coiled. The
27
detector most commonly used in flavor identification are Flame Ionization Detectors
(FID) and Thermal Conductivity Detectors (TCD) (Begnaud 2016). FIDs and TCDs work
well with wide concentration ranges and are sensitive to most types of compounds. The
TCD requires the thermal conductivity of a compound to differ from the carrier gas,
while the FID works well with hydrocarbons, but is not capable of detecting water (de
Saint Laumer 2010).
Mass Spectrometry
Mass spectrometry (MS) uses the mass to charge ratio (m/z) of various ions to
quantify and identify the molecules in both simple and complex mixtures (Downard
2004). MS uses an ion source, an ion detector, and a mass analyzer to acquire the
physical property data of the sample it is testing. The MS accelerates ions though the
remainder of the system based upon the charge the molecule receives. The mass
analyzer exudes an electric or magnetic field on the ion which will cause it to deflect the
path of the individual ions based upon with charge and mass (m/z). MS analyzers can
both filter specific ions towards the detector as well as separate all the analytes for an
analysis of the whole sample (Thermofisher 2017). The ions hit the ion detector after
being deflected off the mass analyzer, and typically the ion detector are electron
multipliers which will emit a cascade of electrons as each ion comes in contact with the
detector plate (Figure 2-16) (Finehout 2004) which results in improved sensitivity as
each amplification occurs. Inside the MS is a vacuum at (10-6 to 10-8 torr) which will
remove gas molecules, neutrals, and non-sample ions that would contaminate the
results as they collide with the intended sample ions and alter their path or create non-
specific reaction products (Hoffman 2001). A computer that is connected to the MS
analyzes data the ion detector receives and produces a graph that is used to organize
28
the ions detected based upon their unique m/z and relative amount. These results are
then compared against databases collected and used to identify a molecule based upon
its specific m/z (Downard 2004).
Gas Chromatography / Mass Spectrometry (GC/MS)
Gas Chromatography Mass Spectrometry (GC/MS) is helpful in the determination
of the structure and molecular weights of unknown organic compounds in complex
solutions by comparing their spectra against a known reference spectra for the
identification and quantitation of volatile and semi-volatile organic compounds (Settle
1997). GC/MS is a combination of gas chromatography (GC) which is capable of
separating volatile compounds from an unknown solution but is not able to identify
them. Alternatively, mass spectrometry (MS) provides specific structural data with high
specificity but cannot separate compounds from solutions (Figure 2-17) (Settle 1997). In
recent years, gas chromatographs use capillary columns to separate analytes in a
mixture and their effectiveness varies on the dimensions and phase properties based
upon what compounds are being targeted. The separation of molecules occurs during
the travel down the length of the column, causing them to elute at different times the
mass spectrometer captures, ionizes, accelerates, deflects and detects the ionized
molecules individually. The mass-to-charge (m/z) ratio of the molecules is determined
as the mass spectrometer breaks down each molecule into its ionized fragments and
then detects those fragments based upon the ratio. This combination of the two
detection methods allows for a more accurate identification of a particular molecule than
with GC or MS separately. When a molecule has a particular mass spectrum and
appears at a specific retention time in a GC/MS analysis, it can be concluded that the
molecule of interest is present in the sample. (Stashenko 2014).
29
Solid Phase Microextraction
Flavor is a combination of both taste and retro nasal olfaction and crucial to the
acceptance that a consumer experiences for food (Kataoka 2000). Most foods contain
complex food matrices and consist of a wide range of organic compounds that possess
various polarities while comprising extremely low concentrations of aroma and flavor
compounds (Wilkes 2000). Solid phase microextraction (SPME) is a simple and low-
cost technique designed to analyze a wide variety of volatile and semi-volatile
compounds found in food, water and other environmental sources (Lloyd 1998). SPME
which was created by (Pawliszyn 1995), utilizes both static headspace (HS) and
dynamic headspace to allow for the accumulation of analytes through diffusion
(Pawliszyn 1995 and Jelen 2006). The primary advantages of SPME lie in its simplicity,
solvent removal, increased sensitivity, small sample volume requirement, low cost,
relative sample speed, and simple automation. The qualitative and quantitative
information is needed for accurate characterization of aroma producing compounds that
are used to determine the criteria of quality in most foods. Due to SPMEs incorporation
of multiple types of fibers and integration with GC, it can be successfully used to extract
polar and non-polar compounds that are present in gaseous, liquid, and solid samples
while easily being coupled with GC/MS, HPLC, and LC/MS. (Kataoka 2000). Since its
creation, the popularity of SPME and its utilization with MS, has been increasingly
illustrated and its experiments published about since 1990 (Figure 2-18).
SPME has been widely utilized in scientific analysis of flavors and volatiles
organic compounds using GC and GC-MS to extract them from the environment,
biological and food samples (Vas 2004). Scientists reviewed the use of SPME in the
analysis of volatile flavors in milk and dairy product utilizing on-column injection and
30
‘purge and trap’ systems (Mariaca 1997). SPME was used to analyze the volatile
compounds in fish muscle that are associated with oxidative off-flavors (Iglesias
2008).Investigation was conducted into the new trends developing around SPME and
highlighted the potential the unique microsampling technique has for the analysis of
volatile compounds in the environment (Eisert 1997). Researchers reviewed the recent
advances in SPME and discussed how though the intent was for the analysis of
environmental samples, SPME has grown to incorporate many additional applications
like flavor analysis in food products (Lord 1998). Prosen reviewed the technical facets of
SPME and detailed some applications that SPME could be applied to such as semi-
volatile constituents of vodka and organochlorine pesticides in water (Posen 1999).
Investigation was done on the analysis and binding of flavor volatiles in various dairy
products while using SPME to establish real flavor profiles as perceived by human
panelists (Stevenson 1996). Scientists reviewed the combination of SPME for aqueous
samples using capillary gas chromatography, specifically heart-cut oriented reversed-
phase liquid chromatography GC, which is useful for separating selected portions of the
main separation into a secondary column for further analysis, and analyte-isolation-
oriented analyte extraction GC in which water is directly introduced on the GC column in
where the water is eliminated and the remaining analytes are analyzed (Louter, 1999).
Experiments were conducted investigating the experimental conditions and SPME
method development that have improved fibers, matrixes, acceleration method, and
extraction time (Junting 1998). Research was conducted that reviewed further the
application of SPME when used in the analysis of organic micropollutants such as
31
toluene, pesticides, phenols, polycyclic aromatic hydrocarbons (PAH), and benzene
(Eisert 1996).
The SPME device contains a fiber assembly with a fiber holder that houses a
built-in fiber which mimics a modified syringe (Figure 2-19). The fiber consists of fused-
silica that is coated with a thin layer of several polymeric stationary phases which act as
a “sponge” as it concentrates the organic analytes on its surface during the absorption
or adsorption from the sample matrix (Kataoka 2000). An absorbent phase is coated
onto a fused silica fiber then attached to the tip of a syringe plunger. Due to the delicate
nature of the fiber, the plunger is retracted into the needle as it is being used to
penetrate the septum of a sealed vial, the fiber is then extended into the headspace
(Lloyd 1998). The fiber is usually exposed to the headspace of the sample and extracts
the volatile compounds to be used in qualitative analysis during which the sample must
undergo equilibrium. An alternative is for the tip to be immersed into a liquid sample to
then further be analyzed. During equilibrium the analyte molecules absorb onto the fiber
coating, then the fiber is retracted into the sheath. The needle is then inserted into a
heated injection port of a gas chromatograph (GC) in which the analytes are desorbed
thermally and transferred onto the head of the GC capillary column for separation and
analysis (Lloyd 1998).
The type of stationary phase selected can affect the absorption of the sample
analytes, stationary phases are defined by non-bonding, bonding, partial crosslinking or
high crosslinking (Figure 2-20). The principle of “like dissolves like” is used to assess
the affinity of a fiber for an analyte and the thickness and properties that various fibers
exhibit determine which type of fiber is most effective for different compounds. The non-
32
polar polydimethylsiloxane (PDMS) fiber is effective for extracting non-polar analytes
such as many flavor volatiles in food samples and is able to withstand high injector
temperatures around 300°C. The polarity and volatility of the analytes are the two
factors which effect sampling and chromatography. Most analytes are studied using HS-
SPME and followed by GC-GC/MS, typically a splitless injection is used since there is
no solvent and the analysis is considered very sensitive (Vas 2004).
Optimization of extraction relies on factors such as exposure time, concentration
of the analyte, and injector temperature. The extraction rate is determined by not only
the agitation rate but also the partition coefficient of the analyte between the fiber
coating and the sample matrix (Kataoka 2000). The sensitivity factor for SMPE is at a
maximum when the sample is at its equilibrium point but do to the linear relationship
between the amount of analyte adsorbed by the fiber and the initial concentration in the
sample matrix in non-equilibrium conditions, true analyte equilibrium is not necessary
for accurate and precise SPME analysis (Ai 1997). The salting out effect caused by the
addition of soluble salts like sodium chloride or potassium carbonate by supersaturating
the sample with salts to maximize the analyte extraction (Vas 2004). The time of
extraction is increased along with the fiber thickness as well as the lowering the
diffusion coefficients of the analyte molecule in the sample (Vas 2004).
Optimization of desorption is determined by factors such fiber coating, injection
depth, exposure time, and injector temperature. To ensure a high linear flow the fiber
needs to be exposed immediately after the needle is introduced to the narrow-bore GC
injector and the depth of the needle exposure should be adjusted so that the fiber is
positioned in the center of the hot injector zone (Kataoka 2000). Most analytes have an
33
optimum desorption temperature approximately equal to the boiling point of the least
volatile compound. The initial temperature of the GC column should be low to prevent
peak broadening in addition to achieving a concentration of analytes at the head of the
column. When desorbing in to the GC, a narrow-bore (0.75 mm i.d.) unpacked injection
liner helps to ensure a high gas flow that is also linear while reducing desorption time
and aiding to prevent peak broadening. Dynamic and static desorption are the two
techniques for removing analytes from the fiber during SPME interfaces. Dynamic uses
the moving mobile phase to remove analytes from the fiber while static uses
theαsoaking in the mobile phase or a strong solvent to remove analytes that are
strongly absorbed into the fiber (Vas 2004).
Direct Immersion (DI)-SPME is conducted when the fiber is immersed directly
into liquid samples, after enough time has passed for a sufficient extraction rate, the
fiber is withdrawn into the sheath of the needle, and the needle is removed from the
septum and directly injected into the injection port of the GC (Kataoka 2000). The
analytes are then desorbed from the fiber coating as the fiber is heated in the injection
port and the analytes are transferred directly to the GC column for analysis (Figure 2-
21). Though direct immersion into the sample shortens the life of the fiber in DI-SPME
by concentrating and remaining on the fiber, it is considered that DI-SPME is more
sensitive to semi or less volatile analytes present in liquid when compared to headspace
(HS)-SPME (Kataoka 2000).
Traditional sample preparation methods attempt to completely remove the
analytes of interest from sample but SPME fibers remove only an amount proportional
to the concentration of the compounds in the sample. SPME’s ability to quantitate data
34
before reaching equilibrium allows for a shorter sampling time and produces a more
economical and adaptable testing procedure. When sampling complex matrices, an
internal standard or standard additions is advised for calibration (Vas 2004). As with
many sampling techniques extraction time is a critical factor and (Figure 2-22) shows
the typical relationship between extraction time and the analytes absorption rate on the
fiber (Sigma-Aldrich 2017).
Headspace aroma capture using a gas tight syringe
The use of a gas tight syringe when conducting volatile analysis is intended to
create a cost effective, faster method of analysis while only requiring a small amount of
sample and still being able to detect low concentrations of analytes (Cho 2015). Gas
tight syringes are constructed of borosilicate glass and due to the interference fit of the
plunger and the barrel, its gas sealing properties have made the syringes a consistent
choice for GC equipment (Sigma Aldrich 2017). Static headspace analysis with MS is
rapid and useful method for the identification of off-flavor and their chemical constituents
(Marsilli 1997). To quantify odorants in boiled salmon and cod, gas tight syringes were
used to extract headspace volatiles and injected into a GC for further analysis (Milo
1996). Static headspace dilution analysis in conjunction with a gas chromatography
olfactometry utilized a gas tight syringe to quantify impactful aroma components in Thai
fish sauce (Lapsongphon 2015). It is suggested by researchers that slowly injecting the
sample and using a cold trap after extraction could help increase sensitivity of the
analysis (Schreier 1984). The cryo trapping helps to narrow the chromatographic band
and improve the detection limit.
35
Sensory Analysis
Standard sensory evaluation techniques allow for the accurate measurement of
human responses to sensory characteristics found in foods while minimizing biases
created by brands, consumer trends, and consumer perception (Lawless 2010).
Information extracted from sensory testing can then be used by food scientists to modify
or improve potential food products so they can better appeal to the consumer. (Stone
and Sidel, 2004) state that sensory evaluation is a scientific method designed to
measure, analyze, and interpret the perceived responses of products through sight,
smell, taste and hearing. Statistical methods of analysis must be conducted to
compensate for the high variability found in human observations. Variables, such as the
mood of the panelist, sensory stimulation, and past exposure to the product, must
attempt to control those variables. Prescreening panelists attempts to eliminate outside
bias but proper sensory evaluation relies on correct interpretation test results (Lawless
2010). Meiselman states that sensory evaluation is concerned with accuracy, sensitivity,
precision, and avoiding false positive data (Meiselman 1993).
The ability of a test to generate data that is considered “true” is defined as
accuracy in sensory science (Lawless 2010). Data gathered from the result of a sensory
test conducted with a group of consumers should translate to a generalization of the
opinion the population has as a whole (Lawless 2010). Type II error and minimization of
β-risk is a statistical measure to ensure the sensitivity of a test and that there are no
missing differences present between two products. A type II error is also labeled as a
false negative in which it is the error of not rejecting the null hypothesis when the
alternative hypothesis is the correct statement. It is essentially the error of failing to
notice a difference between objects when one actually exists. From a statistical
36
standpoint, beta risk is the acceptance of the null hypothesis when the alternative
hypothesis is true. This is essentially assuming there is a difference between samples
when no true difference actually exists. Missing differences can be prevented by proper
panelist training and careful experimental controls, where necessary (Lawless 2010).
There is often an error adjustment caused by the case that when human
perceptions are testing during sensory evaluations, after repeating the same test, the
results are not the same. This error variance is desired to be minimized by ensuring the
precision, accuracy, and sensitivity of the tests (Lawless 2010). Meiselman states that
some sensory tests might be adequate for some products, but not others (Meiselman
1993). Difference tests are useful to determine if consumers can tell if a product has
changed or if they prefer the newer version. Sensory perception and instrumental
measurements correlate consumer perception and quality control considerations
(Szczesniak 1987). Prevention of panelist bias is accomplished by ensuring principles of
good sensory practice. Panelist bias is the panelist’s ability to be within the accepted
range of intensity for the attributes or characteristics in the presented sample or a
control. The panelist bias can be shown in a formula (insert screen shot) where “d” is
the deviation or bias, “x” is the observed panelist value, and mu is the value of the
control or intended attribute (Meilgard 2006).
Good sensory testing principles include ensuring the testing environment is
clean, climate controlled with a 50% relative humidity (the amount of water vapor in the
air at any given time that is required to saturate the air) and temperature of around 20-
22°C, noise and distraction free, free of odors with adequate ventilation (Meilgaard and
others 2006). Panelists should be instructed how to consume and rate products
37
presented to them and all products should be properly labeled with random numbers
presented in a random order to prevent bias, a preconceived opinion of the quality of a
sample based on the brand recognition, outside source of information or influence from
other panelists (Meilgaard et. al. 2006). Samples should be presented at a standardized
temperature controlled by the panel staff that allows proper time between samples.
Additionally, water and unsalted crackers should be provided to allow panelists to
cleanse their palettes between products (Lawless 2010).
Types of Sensory Evaluation of Foods: The three types of discrimination
testing conducted in the evaluation of foods in sensory science are difference,
descriptive, and affective testing (Lawless 2010). Discrimination testing determines if
there are any perceivable differences between products using an analytical type of test
with panelists that are screened for sensory acuity. Sensory acuity is the ability of a
panelist to detect a certain limit or threshold of an attribute in a presented sample. In
some sensory tests panelists are trained to be able to generate panel terms,
comprehend panel testing concepts, and panel testing phases (Lawless 2010).
Discrimination testing is the process of utilizing sensory analysis to establish if a
detectable difference exists between two or more products. From a statistical
standpoint, the principle behind a discrimination test would be reject a null hypothesis
that says there is no detectable difference between the products presented. A triangle
test is a classic example of a difference test, typically presented to 25-40 participants
who are familiar with test procedures and have demonstrated the ability to detect
differences in products presented to them (Lawless 2010). The popularity of these tests
come from the simplicity of data analysis, given an adequate sample size to properly
38
document clear sensory differences. Replicate testing is also performed to allow for
statistical measurement. Descriptive tests determine how products differ in regard to
specific sensory characteristics utilizing panelists that are highly trained and screened
for sensory acuity and motivation, panelists are typically motivated monetarily or by
other means of compensation (Lawless 2010).
Descriptive analysis does not rely on a single sensory expert to collect sensory
data as that would present too much internal bias and would deprive the statistical data
of variance that comes from utilizing multiple panelists. Instead, descriptive analysis
uses a panel of trained individuals to extract sensory data to more accurately represent
a larger population. Descriptive sensory analysis is a complex method of sensory
discrimination and acceptance that results in a connection of similarities and differences
of a sample when compared to the complete description of the product. Results from
the descriptive analysis measure the correlation between sample ingredients or specific
production process variables with various sensory attributes (Stone and Sidel 2004).
Descriptive analysis tests are currently thought to be the most comprehensive and
informative sensory evaluation tool, as it helps to characterize changes in samples
being tested to answer questions derived from the research of food products during
their development (Lawless 2010). Sensory acceptance data generated from descriptive
analysis is often correlated to instrumental measures by means of statistical analysis, by
regression, the process of estimating the relationship between independent or
dependent variables present in samples being presented, and correlation, a measure of
the relationship between two variables as expressed by a single number to represent
the degree of the relationship (Lawless 2010). Types of notable descriptive tests
39
include: Flavor Profile method (Caul 1957), the Spectrum Method® (Meilgaard and
others 2006), and Qualitative Descriptive Analysis® (QDA®) (Stone and Sidel 2004). In
QDA® multiple product assessments are provided to panelists to assist them in making
relative evaluations to specific attributes with a high degree of precision. This is useful
since human panelists are better at assessing relative sensory characteristic
differences, and less with absolute differences (Stone and Sidel 2004).
A panel size of 10 to 12 people is suggested to gain consensus when comparing
test products used as training tools designed to illustrate specific product attributes.
Reference samples are used to help determine cohesive terminology used during the
training. Panels moderated by a panel leader, who functions as a facilitator without
interference of panelists discussion (Sensory Society 2017). Line scales are used
sometimes to help in data collection and analysis in QDA®, where a six-inch line with
anchor points on each end assist panelists during training. Panelists are able to use any
part of the scale to determine attribute intensities, resulting in relative differences
between product attributes. Relative measurements and the importance of an absolute
scale is then negated (Lawless 2010). Panelist reliability is evaluated based upon their
repeated measurements of sample attributes (Sensory Society 2017). QDA® data is
analyzed using a one-way analysis of variance (ANOVA) based on attributes and allows
for the examination of product and panelist (Sensory Society 2017). An analysis of
variance is test of the hypothesis that is used to compare the means of a variable that is
continuous and present in two or more independent comparison groups (Sullivan 2017).
ANOVA is used to test whether or not the means of several various groups are equal
and generalizes the t-test to more than two groups. The t-test is the analysis of two
40
population means through the use of statistical examination and used to test when the
variance of two normal distributions are unknown (Staff 2017). Other statistical
procedures can be applied to QDA®, such as principle component analysis, factor
analysis, multivariate analysis, and cluster analysis (Lawless 2010). Factor analysis is
useful for taking large amounts of data and condensing it into more manageable smaller
sets that are easier to extract results from while a multivariate analysis is the analysis of
data that arises from the statistical observation of more than one variable. A cluster
analysis is the grouping of a set of data points in a way that points in the same group or
cluster are more similar to each other than to those in other groups.
Visual analog scales (VAS) are commonly used for ranking the intensity of a
sensory characteristic by the panelist making a mark or slash on a measured line to
indicate intensity (Lawless 2010). An advantage of the line scale is that due to the
continuousness of the line, panelists feel less limited in their responses. The numeric
scale is limited as the panelist is only able to pick between 9 preselected values ranging
from one to nine. An issue arises if a panelist feels that a sample falls in the range
between a set of numbers as they are not able to place a mark between those points
like 4.5. Line scale allows a panelist to freely select a point anywhere along the 150cm
line without restricting the panelist to nine preselected points. When using VAS it is
suggested to employ a panel moderator to guide panelists in using the scale or
panelists will default to using sections of the scale. Anderson and Lawless conducted
experiments where they presented their panelists with examples of high and low
attributes stimuli that they perceive, which aided the panelist’s ability to orient their
responses to the scale (Anderson 1974 & Lawless 2010).
41
Affective testing aids sensory scientists in the determination of preferred products
or how well they are liked by using a hedonic test with untrained panelists, panelists are
screened for their familiarity of the products presented, an untrained panelist is a
panelist that is not able to consistently reproduce data points when given a similar
sample over different test (Lawless 2010). Affective tests are considered to be one of
the most straightforward sensory tests, as it identifies the degree of liking or disliking of
a product. A typical affective test would utilize a 9-point numeric hedonic scale given to
60-90 consumers who identified themselves as regular consumers of the product
(Lawless 2010). The Hedonic scaling provides a balanced 9-point scale for liking with a
central neutral point and increasing levels of likeability the right of the scale where (9 is
“like extremely”) and decreasing levels of likeability to the left of the scale where (1 is
“dislike extremely”). Descriptive words are equally spaced by a predetermined distance
or numeric value (Peryam and Pilgrim 1952).
Table 2-1. Typical length vs age for red drum
Age (years) Typical Length
1 12-13 2 19-21 3 24-25 4 27-29 5 30-33
USM 2017
42
Table 2-2. Vitamins in red drum fillet per ~200g
Vitamin Amount present %DV
Vitamin A 337 IU 7% Retinol 101 mcg Retinol Activity Equivalent 101 mcg Vitamin C 2.0 mg 3% Thiamin 0.1 mg 9% Riboflavin 0.3 mg 20% Niacin 4.7 mg 23% Vitamin B6 0.6 mg 30% Folate 29.7 mcg 7% Food Folate 29.7 mcg Folic Acid 0.0 mcg Dietary Folate Equivalents 29.7 mcg Vitamin B12 4.0 mcg 66% Pantothenic Acid 1.5 mg 15%
Nutrition Data 2017 Table 2-3. Minerals in red drum fillet per ~200g
Mineral Amount present %DV
Calcium 119 mg 12% Iron 1.8 mg 10% Magnesium 59.4 mg 15% Phosphorous 356 mg 36% Potassium 545 mg 16% Sodium 148 mg 6% Zinc 1.3 mg 9% Copper 0.5 mg 23% Manganese 1.4 mg 69% Selenium 24.9 mcg 36%
Nutrition Data 2017
Figure 2-1. Picture of red drum fish (Sciaenops ocellatus) (FAO 2017)
43
Figure 2-2. Coastal map of red drum habitat (GBIF 2017)
Figure 2-3. Map of Volusia county (World Maps 2017)
44
Figure 2-4. Map of Volusia county (FL Maps 2017)
Figure 2-5. Map of Indian River Lagoon (FHWA 2017)
45
Figure 2-6. Anatomy of fish (Fishing Target 2017)
Figure 2-7. External anatomy of a fish with soft dorsal fin and spiny dorsal fin identified
(Fishing Target 2017)
46
Figure 2-8. Male fish fertilizing eggs from female (Zoology 2017)
Figure 2-9. Mariculture ponds (Alloyance 2017)
47
Figure 2-10. Production cycle of red drum (FAO 2017)
Figure 2-11. Graph of age vs length of red drum (adapted from USM 2017)
48
Figure 2-12. Global aquaculture production for red drum (FAO 2017)
Figure 2-13. Chemical structure of Geosmin and 2-Methylisoborneol (Juttner 2007)
49
Figure 2-14. Diagram of human olfactory nerves (Rinaldi 2007)
50
Figure 2-13. Diagram of a gas chromatograph (Gas chromatography 2017)
Figure 2-14. Inner diagram of a mass spectrometer (Overview of Mass Spectrometry
2017)
51
Figure 2-15. Inside of a gas chromatograph/mass spectrometer (Gas chromatography–
mass spectrometry 2017)
Figure 2-16. Number of published articles in recent years related to SPME and
SPME/MS applications (Vas 2004)
52
Figure 2-17. Inner design of a SPME fiber (Kataoka 2000)
Figure 2-18. SPME fiber properties, retention vs polarity (Fanali 2017)
53
Figure 2-19. SPME process for headspace vs direct immersion SPME (Vas 2004)
Figure 2-20. Extraction time and analyte absorption rate on SPME fiber (Vas 2004)
54
CHAPTER 3 MATERIALS AND METHODS
Sample Collection
Sixty (60) pounds each of red drum fish that were grown out in a zero-discharge
IMTA system and a traditional zero-discharge RAS were collected from Mote Marine
Aquaculture Research Park (Sarasota, FL), layered in ice in insulated plastic coolers,
and transported to the University of Florida, Gainesville, for preparation and
analysis.. Insulated plastic coolers (Igloo MaxCold 98x45x41cm) were immediately
placed in a -15°F AmeriKooler (Hialeah, FL) freezer upon arrival. After complete
freezing (3-5 days), red drum samples were removed from coolers, handled with sterile
nitrile gloves, and vacuum sealed using a Kenmore Seal-n-Save (Chicago, IL) in
Kenmore Fresh Seal-n-Save Uncut 8in Wide Rolls (Chicago, IL) for continued frozen
storage at -15°F.
Sample Preparation
Vacuum-sealed red drum fish which were held for under 12 months at -15°F
before analysis, were segregated by aquaculture environment, removed from frozen
storage using sterile nitrile gloves, and the vacuum packaging was released enabling
aerobic thawing for C. botulinum control. The fish were then transferred to clear plastic
tubs, and thawed in a commercial refrigerator at 35°F for 48 hours before filleting.
Thawed red drum fish were filleted from the bone using a filet knife and the skin was
removed. After fileting, the fish to be analyzed by sensory panels-were cooked in a
Cleveland Range Steamer (Cleveland, OH) until a minimum temperature of 145°F,
measured by thermocouple (Fluke Thermometer 52II, Everett, WA). For descriptive
analysis panel training, an additional cooking method used a Kenmore (Korea) 1200
55
watt microwave oven to cook the red drum for 2 minutes and 15 seconds or until an
internal temperature of 145°F was registered using and electronic thermometer (Fisher
Scientific, Waltham, MA). Cooked red drum fillets were immediately apportioned into
one ounce (1 oz.) serving sizes and placed into 2 oz. Dart soufflé cups with lids for
sensory analysis by the panel moderator and an assistant. Estimated red drum edible
fillet yield is about 40% of total whole fish weight. Cooking loss is estimated to
contribute to another 20% weight loss.
Samples prepared for GC-GC/MS analysis, an estimated 50 grams was needed
per treatment in triplicate plus extra sample was added to accommodate for method
development and loss. SPME samples were made by taking defrosted red drum from
RAS and IMTAS and pureeing in a blender (Hamilton Beach, Glen Allen, VA) until
homogenous with 50 grams of fish sample and 50 grams of deionized water. Samples
were then labeled accordingly and the headspace was flushed with nitrogen. Samples
were then accurately weighed and transferred to 40-mL amber glass vials capped with
polypropylene caps fitted with specialized septa (Supelco, Bellefonte, PA) for DI-SPME
and gas tight syringe analysis .
Geosmin dilution samples were purchased from Supelco (Bellefonte, PA). (+/-) –
Geosmin 100µg/ml standards were serially diluted to create 1.0ppb, 0.1ppb, and
0.01ppb GSM solutions. Only the GSM has been reported but MIB solutions were also
made in the same manner and tested under the same conditions.
Additionally, approximately two pounds of intact fillets per treatment were
vacuum packaged and frozen at -20°F for further analysis.
56
Sensory Evaluation
Trained Panel
The sensory quality of red drum fish grown in RAS and IMTAS was assessed by
testing the odor sensory perception and taste intensity of fish samples. The sensory
panel used in this study was a selected and trained panel of 16 assessors with previous
experience in sensor evaluation who were familiar with the quality attributes of fresh fish
as well as being familiar with VAS when being used to assess attribute intensity.
The preliminary panel session was conducted five months after collection was
carried out to improve the ability of the panelists to recognize off-flavors and aromas
and consistently quantify sensory properties of the red drum samples grown in RAS or
IMTAS. Attributes were selected based upon their connection with quality indicators of
fresh fish by a preliminary panel of six sensory experts. The preliminary panel session
occurred six weeks before the first trained taste panel. The sensory experts were given
samples that were cooked in a microwave, unseasoned, and asked to assess the
sample for any attributes that were perceived that could be used as flavor and aroma
anchors on a VAS.
The first panel training assigned a scaled value of where the attribute anchor
would be placed on a Visual Analog Scale (VAS) which measured the intensity of the
attribute from 0-100 on a 150 mm line. A sample ballot for the trained panel can be
found in Appendix A. Reference samples were created using information received from
the preliminary taste panel conducted. Seven attributes were identified that were
present in both the RAS and IMTAS samples in varying concentrations (Table 3-1).
Reference samples were purchased 24 hours before each panel training to ensure
optimum freshness. Reference samples were prepared in an environment free of odor
57
contaminants and an estimated 70% humidity. All samples were prepared two hours
before each panel and refrigerated until the panel in a two ounce soufflé cup with a lid
immediately upon preparation. These reference samples were given to the panelists
during the panel training sessions. The red drum fish samples were profiled for aroma
intensities of the raw sample for each treatment of fish. Each treatment was labeled with
different randomly generated numbers without the panelists knowing which sample they
received but all panelists were given the same sample. After the panelists assessed
each raw sample for aroma intensity, a facilitated discussion was conducted by the
trained panelist moderator on how the sample intensities ratings occurred, which was
intended to align the sensory perception of the panelists. The samples were then
collected from the panelists and cooked in a microwave at 1200 watts (Kenmore Elite
Model 721, Chicago, IL) until an internal minimum temperature of 145° was registered
using a thermocouple (Fluke Thermometer 52II, Everett, WA); the sample cup was then
immediately capped with a lid. Samples were microwaved following the procedure
outlined for sampling aquaculture in production which uses a microwave to cook the
unseasoned fish samples until an internal temperature of 145°F is reached (Huss 1995).
The panelists then received their now cooked original samples and were asked to
assess the aroma and taste intensity of both samples, water and saltine crackers were
provided to cleanse their pallets between samples. After tasting the cooked samples, a
discussion was held with the same procedure as the raw sample. After four one-hour
training sessions, the trained panelists assessed the RAS and IMTAS samples in the
University of Florida sensory facility. Panelists were chosen for the data collection
portion of the testing after they had shown that their responses to the intensities of the
58
samples were within one standard deviation of the group’s responses. Panelists who did
not fall inside the one standard deviation of the group’s responses were invited to
participate in the testing procedure but their data was not used.
Trained Panel Testing and Data Collection
Both fish samples were analyzed by descriptive analysis test to assess the
aroma and taste intensity of red drum fish grown in RAS side-by-side with red drum fish
grown in IMTAS. All sensory evaluations were conducted in private booths with white
light. Each panelist received deionized water and unsalted crackers to cleanse their
palates between samples. A VAS was presented to trained panelists on a computer
screen using Compusense sensory analysis software with the predetermined attributes
present with 0-100 as anchors. A sample for the trained panel computer ballot can be
found in Appendix B. The trained panel test was conducted in duplicate and 16
panelists participated in the trained panel studies each.
Fish samples were randomly assigned three digit numbers using a random
number generator and sample trays used were labeled by the panelist number and
samples were arranged by corresponding numbers following the panel design layout to
ensure the orders of presentation were randomized and presented to panelists an equal
number of times. Each panelist received approximately one ounce of raw red drum fish
per environment, RAS and IMTAS. Panelists were asked to assess the aroma of the
raw samples and rate the intensity of the attributes without the use of the attribute
anchors. After completing the raw attribute assessment, the samples were steamed for
approximately three minutes until a temperature of 145°F and served to panelists within
10 minutes. Samples were then returned to panelists and they were asked to assess
the aroma and taste intensity of both samples.
59
Consumer Panel
A 60-person hedonic panel was performed at the University of Florida by
untrained, consumer panelists pre-screened for regular finfish consumption and fish
allergies by answering a questionnaire before starting the panel (Figure 3-1). Red drum
samples were steamed for approximately three minutes to an internal temperature of
145°F and served to panelists within five minutes. Fish samples were randomly
assigned three digit numbers using a random number generator and sample cups used
were labeled by the corresponding numbers. Each panelist received approximately one
ounce of cooked red drum fish per environment, RAS and IMTAS. Orders of
presentation were randomized and presented to panelists an equal number of times. In
one testing session, both fish samples were analyzed by a consumer hedonic likeability
and preference test to assess the acceptability of red drum fish grown in RAS side by
side IMTAS. All consumer evaluations were conducted in private booths with white light.
Each panelist received deionized water and unsalted crackers to cleanse their palates
between samples.
A one to nine hedonic likeability scale was presented to consumer panelists on a
computer screen using Compusense sensory analysis software, with anchors presented
for dislike extremely (1) on the far left, neither like / nor dislike (5) in the middle, and like
extremely (9) on the far left. A sample for the consumer panel computer ballot can be
found in Appendix C. After both RAS and IMTAS samples were rated, panelists were
then asked to pick which sample they preferred. Sixty panelists participated in this
study.
60
Volatile Analysis
GC-GC/MS analysis
SPME Extraction of Headspace Volatiles
Volatiles were extracted from the RAS and IMTAS samples by SPME using a 2
cm fiber (Supelco, Bellefonte, PA) comprised of coated divinylbenzene/Carboxen on
polydimethylsiloxane (DVB/CAR/PDMS) 50/30 μm via a manual SPME fiber assembly
holder for 30 minutes in a heating block at 55°C.
HS-SPME and DI-SPME extracted volatile compounds were sheathed by the
fiber assembly and immediately transferred to the injection port of a Shimadzu GC/MS
(Shimadzu, Columbia, MD) and held at 270°C in the injection port for 12 minutes
allowing volatile compound release from fiber into a 30 meter RTx-5MS capillary column
(Restek, Bellfonte, PA) with a 0.25 mm internal diameter and a 0.25 µm stationary
phase. Helium was used as the carrier gas at a flow rate of 1.1 mL/min. The oven was
initially held at 60°C for 1 minutes, then increased by 10°C/min to 300°C and held for 11
minutes. Samples were then cut by Dean’s Switch to a second DB-5 column under the
same temperature program and analyzed by MS in scanning mode. Compounds
detected by MS were compared to National Institute of Standards & Technology (NIST)
and Shimadzu Flavor & Fragrance databases for matching mass fragmentation patterns
to known compounds.
Gas Tight Syringe Extraction
Amber vials containing RAS and IMTAS samples were placed on a heating block
at 55°C for 15-20 minutes until an internal temperature read 55°C. A 1ml volume of
headspace gas was taken from the sample vial using a gas tight syringe and
immediately injected into the GC/MS. A 100 meter, CP-Sil 88 (Agilent, Santa Clara, CA)
61
capillary column with a 0.25mm internal diameter and a 0.25µm stationary phase was
used. Helium was used as the carrier gas at a flow rate of 1.31mL/min. The oven was
initially held at 150°C for zero minutes, then increased by 10°C/min to 300°C and held
for 5 minutes. Samples were then analyzed and cut based upon peaks present from FID
analysis.
Each sample was injected again under the same column parameters. The oven
was held at 100°C for zero minutes, then increased by 10°C/min to 225°C and held for
22.5 minutes. Samples were then cut by Dean’s Switch, based upon the peak times
from FID, to a second 30 meter SLB-IL11 (Supelco, Bellfonte, PA) column with 0.25mm
internal diameter and 0.2µm stationary phase under the same temperature program and
analyzed by MS in scanning mode.
Statistics
The number of trained panelists was established using a statistical table of
critical minimum values required for a significant difference ( α=0.05) from (Lawless
2010). Data from panelists during training was analyzed for statistical differences using
Microsoft Excel 2010 for Windows version 14.0.7015.1000 (Microsoft Corporation,
Redmond, WA) using one standard deviation from the collective group answers.
Trained panelist sensory scores were analyzed for statistical significance using a two-
way analysis of variance (ANOVA)(Statistical Analysis System, Cary, NC) which
allowed for panelist by sample interaction. Consumer sensory scores were analyzed
using a two-way (ANOVA) (Statistical Analysis System, Cary, NC).
62
Table 3-1. Attribute reference samples
Attribute Attribute anchor
Scale value
Verbal description Sensory definition Reference material
Cucumber 1A 90 Fresh cucumber, green The aroma associated with fresh cucumbers, similar aromas can be associated with certain species of very fresh raw fish
Diced seedless cucumbers
Seawater/ Seaweed
2A 80 Fishy, seawater, salty, musty
The aroma and flavor associated with the saltwater, specifically the sea
Chopped seaweed
Green/ Grassy
3A 70 Green, bright, grassy The aroma associated with the pronounced sharp odor of recently cut grass and green leaves
Diced green beans
Dirt 4A 30 Earthy, dirty The slightly musty aromatics associated with raw potatoes and damp humus, slightly musty notes.
Dry dirt Dirt 4B 100 Musty, muddy, dirty Moist dirt
Metallic 5A 100 Metallic odor An aromatic associated with an oxidized silver (or other oxidized metal) utensil when it is rubbed inside the mouth
Old penny
Hay 6A 30 Buttery, barn, decaying vegetation
Moldy/mildew-like aromatics associated with rotting plants.
Corn husk
Hay 6B 60 Sweet, mildew, decaying vegetation
Hay
Fresh pumpkin
7A 100 Melon rind, dirt, fruity, sweet, floral
The aroma associated with fresh melon rinds, flesh, and seeds
Diced fresh pumpkin
63
Figure 3-1. Consumer panel screener
64
CHAPTER 4 RESULTS AND DISCUSSION
Sensory Evaluation
Trained Panel
Tables 4-1 through 4-3 highlights the mean sensory scores of descriptive
analysis panelist results. There were no significant differences found between any
aroma attributes in either system. No statistically significant difference was found
between any of the aroma attributes for the raw or cooked sample of either system at
α=0.05.
The flavor attribute of “dirt” showed a significant difference in the cooked sample
with a mean value of 17.14 and 10.55 for RAS and IMTAS, respectively. The remaining
flavor attributes for the cooked RAS and IMTAS samples showed no significant
difference. The presence of a more pronounced flavor of “dirt” in the cooked RAS
sample, supports the idea that IMTAS removes the taste of “dirt” in fish flesh.
Consumer Panel
Results from the 60-person hedonic consumer sensory panel are found Table 4-
4. The likeability ratings for red drum fish cultivated in RAS and IMTAS were 6.02 and
6.05, respectively. Therefore, no significant difference in likeability was attributed
rearing environment at α=0.05.
In Table 4-4 preference differences between red drum from RAS and IMTAS
were nearly equal 29-RAS/31-IMTAS. There were no significant differences between
likeability ratings or preference.
One hypothesis was that red drum muscle tissue reared in RAS, would have a
dirtier, muddier flavor associated with it. It was also hypothesized that the dirty, muddy
65
flavor was associated with the presence of GSM and or MIB, although other aroma
compounds elicit off-flavors in fish.
Volatile Analysis
HS-SPME
Table 4-5 highlights some of the compounds extracted from the
headspace of a sample vial containing red drum raised in indoor RAS and IMTAS (and
equal amounts of salt) by HS-SPME. Table 4-5, lists 1-Penten-3-ol extracted by HS-
SPME is present in the cooked IMTAS red drum sample. 1-Penten-3-ol has an aroma
of green/grass, some descriptive analysis panelists associated this off-flavor for all of
the samples.
Aqueous samples containing GSM and MIB have used the addition of sodium
chloride (salt) and subjection to heating to aid analyte extraction from the liquid phase to
the gas phase (Grimm 2000). Separation and identification of HS-SPME extracts were
conducted by GC-GC/MS analysis.
HS-SPME didn’t prove to be very effective in identifying GSM or MIB.
Standard mixtures of GSM and MIB at varying concentrations (0.001 to 1 ppb) weren’t
identified by GC-GC/MS of SPME extraction of the red drum sample headspace. Other
researchers have reported similar results. For example, Cardin (2016) noted that
SPME encounters problems with differences in phase ratios between the SPME fiber
and the amount of sample required to low ppt concentrations, as well as the affinity of
the odor producing compounds to remain in the sample matrix. Other researchers have
concluded that SPME is not effective in the analysis of complex matrices like muscle
tissue due to the physical bonds that allow the prospective analyte to bond to the
sample matrix (Grimm 2000). Limited extraction by HS-SPME may also be associated
66
with GSM and MIB being lipophilic in nature will only partition from fish muscle into the
headspace in very low amounts making HS-SPME ineffective (Lloyd 1999). Compound
extraction limitations by SPME have been overcome by modifications of standard SPME
extraction procedures. For example, SPME recovery can be so low that isotope dilution
is needed for quantitative measurements of each targeted compound, with carryover
being as high as 10% making it difficult to establish a reliable method detection limit
(Cardin 2016). In addition, with the complex sample matrix of fish samples, it may be
unrealistic to report analyte concentrations below 0.001ppb regardless of how much
signal is present, due to the determination of practical limits of detection (Cardin 2016).
Another modification of traditional SPME techniques is microwave distillation.
Analytes of interest in complex matrices can be extracted using microwave distillation
(MD) (Grimm 2000). In MD the analytes are steam distilled from the matrix and
transferred to a flask in a chilled water bath where the steam effluent carrying the
analytes is collected. This process frees bound analytes from the food matrix (like fish
muscle tissue) and allows them to be collected in an aqueous matrix. In procedures
described by Lloyd (1999), SPME extraction is then used to concentrate the volatile
organic compounds from the aqueous solution. MD when combined with SPME allows
for a sensitive technique that analyzes the semi-volatile and thermally stable volatile
compounds in complex matrixes.
Other researchers found that steps were necessary to release volatile
compounds from fish samples. Jones (2013) conducted experiments on Barramundi
fish using static HS-SPME paired with GC/MS in which samples were homogenized
with deionized water and the volatiles were collected from the condensate after steam
67
was introduced to the sample and then cooled. Scores from the negatively associated
sensory attribute evaluation correlated with the instrumental analysis of flesh GSM
concentration when compared to a collection of key sensory attributes for the fish
samples. The Jones study suggests that instrumental analysis has the potential to be
used as a tool to estimate the impact that GSM levels will have on the overall flavor
attributes of fish samples.
However, SPME might still have some advantage over extraction systems like
purge and trap followed by solvent elution, is that it allows for subsequent analysis
without the introduction of solvents into the system (Lloyd 1999). Methods like purge
and trap have limited effectiveness due to the low recoveries and poor precision
resulting in unusable method detection limits (Cardin 2016).
DI-SPME
Table 4-6 highlights some of the compounds extracted from the aqueous portion
of a sample vial containing red drum raised in indoor RAS and IMTAS (and equal
amounts of salt) by DI-SPME. Table 4-6, lists carbamic acid extracted by DI-SPME is
present in all red drum samples raised in both environments. Carbamic acid has an
aroma of ammonia, although descriptive analysis panelists did not associate this off-
flavor for any of the samples. N-Hexadecanoic acid also found in the raw RAS and
IMTAS samples. N-Hexadecanoic has waxy like odor.
After unfavorable results were obtained from HS-SPME, a modification to the
traditional headspace method was examined. Some researchers have had success by
sticking the fiber in the aqueous portion of the sample matrix, as part of Direct
immersion (DI) – SPME. DI-SPME allows for balanced coverage of the fiber during
extraction of compounds in complex matrices (Gionfriddo 2015). However, the solid
68
porous coating has the possibility of surface saturation that may occur during the
adsorption from the matrix that contains a high concentration of analytes or interfering
compounds. During saturation, nonlinear adsorption isotherms result from competitive
extraction. Compounds that have a stronger affinity for the coating displace the
compounds with less affinity for the extraction phase (Gionfriddo 2015). This reaction is
likely due to matrix compounds like suspended solids and macromolecules binding to
the analytes, especially nonpolar compounds, and reducing their availability to the
coating. Another study noted how fiber life and performance was negatively impacted by
the presence of NaCl as well as the organic materials in the compound matrix baking
onto the fiber during repeated desorption (Watson 2000).
Other reasons that GSM and MIB were difficult to extract from red drum fish
muscle tissue include fat content and the sampling of muscle tissue for analysis. Fillet
fat has been linked with increased off-flavor present in fish (Grimm 2004). Fat content of
tested fish from 0.5 to 11%, had a significant effect on the absorption of MIB, lean fish
accrued less MIB during exposure to MIB than the fatter fish (Johnsen 1992). The
bioaccumulation of GSM is dictated by the compounds hydrophobicity which is related
to the compounds structure (Schuurman and Klein 1988). Another study stated that
sampling from different areas of fin fish has correlated the flesh from the dorsal shoulder
region to be relatively low in total lipids which could be responsible for the lowered
presence of GSM in this study (Jones 2013).
Gas Tight Syringe Headspace Extraction
Table 4-7 highlights some of the compounds extracted from the headspace of a
sample vial containing red drum raised in indoor RAS and IMTAS (and equal amounts
of salt) by collecting a volume in a gas tight syringe. Table 4-7, lists 1,2-Propadiene-
69
1,3-dione extracted by gas tight syringe (with no cryo-focusing) is present in raw and
cooked RAS and IMTAS red drum samples. 1,2-Propadiene-1,3-dione has an aroma of
acrolein (burnt fat/mustard oil), although descriptive analysis panelists did not associate
this off-flavor for any of the samples. (S)-(+)-1-Cyclohexylethylamine also found in
cooked RAS sample, raw RAS and cooked IMTAS samples. (S)-(+)-1-
Cyclohexylethylamine has an amine-like (fishy) odor.
After unfavorable results were obtained from HS-SPME and DI-SPME, another
modification to the method was examined. Gas tight syringe extraction has been used
in conjunction with a purge and trap system to analyze volatile compounds in salmon in
a GC/MS (Milo 1996). The purge and trap system operate by injecting a sample with an
inert gas which forces the volatile compounds out of the sample and are then retained in
an analytical trap. The volatile compounds are desorbed by heating the trap which is
injected into the GC by backflushing the trap with carrier gas. Lapsongphon used a gas
tight syringe to analyze fish sauce volatiles and used a cooled injection port, something
similar to a cryo trap allows for cryo-focusing of the compounds in the injection port.
Even though analytical correlations of GC-GC/MS and human sensory data were
not discovered, the results of the descriptive analysis trained panel showed differences
between the RAS and IMTAS aquaculture systems. This result agrees with recent
research findings that IMTAS effectively reduces the amount of ammonia in the water
system using nitrifying bacteria to convert nitrites into nitrates (Rakocy 2006). The
Merck Index states that in acidic conditions, GSM decomposes to form odorless
compounds. Further investigation of GSM decomposition products would likely be
70
beneficial to the aquaculture industry to determine whether fish were raised in water
sources contaminated with algae responsible for GSM and MIB production.
Therefore, “dirt” flavor was associated with red drum fish raised in RAS.
However, consumers responses did not allow the measurement of “dirt” flavor, either
positively or negatively, only likeability. Thus, any correlations between the two panels
would not have statistical value. It can be said that consumer panelists weren’t
overwhelmingly positive because the mean rating was a six, where a five was “neither
like not dislike”. Even though there is no direct evidence that “dirt” or “muddy” flavor
factored into their response, it is a potential source for a diminished likeability rating.
Table 4-1. Mean aroma attribute scores for trained panels-raw samples
Table 4-2. Mean aroma attribute scores for trained panels-cooked samples
Attribute Mean score-RAS Mean score-IMTAS
Cucumber 8.52a 6.27a Seawater/ Seaweed 19.66a 17.23a Green/ Greasy 14.52a 13.96a Dirt 15.84a 14.02a Metal 6.41a 4.82a Hay 17.43a 16.55a Pumpkin 11.61a 10.32a
Table 4-3. Mean flavor attribute sensory score for trained taste panel
Attribute Mean score-RAS Mean score-IMTAS
Cucumber 8.98a 6.27a Seawater/ Seaweed 19.75a 16.43a Green/ Greasy 13.93a 11.23a Dirt 17.14a 10.55b
Attribute Mean score-RAS Mean score-IMTAS
Cucumber 19.43a 16.64a Seawater/ Seaweed 22.09a 18.64a Green/ Greasy 21.73a 19.75a Dirt 12.96a 10.39a Metal 6.18a 6.09a Hay 22.18a 21.77a Pumpkin 18.41a 17.64a
71
Metal 7.36a 6.86a Hay 14.86a 14.80a Pumpkin 7.07a 6.52a
Table 4-4. Consumer sensory data
Sample Overall liking Preference
RAS 6.02a 29 IMTAS 6.05a 31
72
Table 4-5. Volatile compounds identified by GC-GC/MS samples extracted with HS-SPME
Sample Compound Odor/ Aroma
Raw RAS Trifluoroethanol ethanol like
Raw RAS Benzaldehyde almond like
Raw RAS Phosphonoacetic acid** odorless
Raw IMTAS Heptane gasoline like
Raw IMTAS Propanedioic acid** odorless Raw IMTAS Nonanal* aldehyde like
Cooked RAS 1,2-benzisothiazol-3-amine N/A
Cooked RAS Nonanal* aldehyde like
Cooked RAS Phosphonoacetic acid** odorless
Cooked IMTAS Perfluorotributylamine odorless
Cooked IMTAS 1-Penten-3-ol green/grass
Cooked IMTAS Oxime strong, irritating
Cooked IMTAS Nonanal* aldehyde like
* compounds found in all samples except raw RAS ** compounds found in all samples except cooked IMTAS
73
Table 4-6. Volatile compounds identified by GC-GC/MS samples extracted with DI-SPME
Sample Compound Odor/ Aroma
Raw RAS Carbamic acid* ammonia
Raw RAS 1-Alanine ethylamide N/A
Raw RAS Orcinol earthy/ moss like
Raw RAS n-Hexadecanoic acid** waxy
Raw IMTAS Carbamic acid* ammonia Raw IMTAS 2-Hydroxy-4-methoxybezaldehyde vanilla Raw IMTAS n-Hexadecanoic acid** waxy
Cooked RAS Carbamic acid* ammonia
Cooked RAS 1-(2-dimethylaminoethoxy)-2-benzamido N/A
Cooked RAS Hasmigone warm, jasmine like
Cooked RAS 1,2-hydrazinedicarbothioamide N/A
Cooked IMTAS Carbamic acid* ammonia
Cooked IMTAS 1,2,4-Benzenetricarboxylic acid N/A
Cooked IMTAS 1-Pentene gasoline like
Cooked IMTAS 11-[(3,4-dimethoxyphenyl)methyl]-2,3,7 N/A
* compounds found all samples ** compounds only found in raw RAS and IMTAS samples
74
Table 4-7. Volatile compounds identified by GC-GC/MS samples extracted with a gas tight syringe
Sample Compound Odor/ Aroma Raw RAS 1,2-Propadiene-1,3-dione* acrolein/mustard oil Raw RAS (S)-(+)-1-Cyclohexylethylamine** Amine like Raw RAS cis-3-Methylcyclohexanol none Raw RAS 2-Formylhistamine N/A Raw RAS 3-Hexyn-2-ol none Raw RAS 3-Dimethylaminoacrylonitrile None Raw RAS 2-Trifluoroacetoxydodecane N/A Raw RAS 10-pentadecenol alcohol like Raw IMTAS 1,2-Propadiene-1,3-dione* acrolein/mustard oil Raw IMTAS Diethyl Phthalate slight odor (pesticides) Raw IMTAS Cycluron odorless Raw IMTAS dl-Phenylephrine none Raw IMTAS 3-Thiophenecarboxylic acid none Raw IMTAS Benzoxazol Pyridine like (nauseating, fish-like) Raw IMTAS 1-(4-Acetamidoanilino)-3,7-dimethylbenzo N/A Raw IMTAS 7-Hydroxyimino-4,5,6,7-tetrahdrobenzofuroxide N/A Raw IMTAS Imidazole Amine like Raw IMTAS Tricyclo[4.3.1.1(3,8)]undecane-1-carboxylic acid none Cooked RAS 1,2-Propanediamine Fishy, ammoniacal Cooked RAS 1,2-Propadiene-1,3-dione* acrolein/mustard oil Cooked RAS 1-(5-Bicyclo[2.2.1]heptyl)ethylamine none Cooked RAS (S)-(+)-1-Cyclohexylethylamine** Amine like Cooked RAS Thiophene-3-ol none/ benzene like (thiophene) Cooked RAS 1-(4-Acetamidoanilino)-3,7-dimethylbenzo N/A Cooked IMTAS 1,2-Propadiene-1,3-dione* acrolein/mustard oil Cooked IMTAS 3-Butyn-1-ol none Cooked IMTAS (S)-(+)-1-Cyclohexylethylamine** Amine like
* compounds found all samples ** compounds found in all samples except raw IMTAS
75
CHAPTER 5 CONCLUSIONS
Sensory analysis was conducted on red drum fish samples that were raised in
RAS or IMTAS environments. The trained taste panelists were trained to assess seven
different attributes that are associated with fresh fish, with one of the attributes (dirt)
highlighting the presence of GSM and MIB. The trained panel scores showed no
significant difference between the aroma attributes in either of the samples. However, a
significant difference was found in the flavor attribute of dirt with a higher average
intensity present in the RAS samples when compared to the IMTAS samples. The
higher score indicates that a more pronounced intensity of dirt flavor provides evidence
of an increased presence of dirty or muddy flavor, typically associated with GSM and
MIB, in RAS samples.
The consumer panel results showed no significant difference between the overall
likability or preference between the samples. This indicates that panelists could not
distinguish a difference between the two samples and that the difference between the
presence of GSM and MIB is not noticeable to the average consumer.
The SPME and gas tight syringe extracted volatiles showed the presence of
additional off-flavor compounds. Some of these additional compounds were noted to
have aromas of ammonia, green/grassy and fatty/waxy. Some of the aromas present
were not accounted for in consumer panels. For example, even though there is no
direct evidence that “dirt” or “muddy” flavor factored into their response, it is a potential
source for a diminished consumer likeability rating.
This study has revealed that there is a discernable difference in earthy and
muddy off-flavors between the two samples. Trained panelists were taught to masticate
76
by techniques accentuating retronasal perception of off-flavors, where “dirty” off-flavors
were detected in red drum fish harvested from the RAS. It is also noted that the average
consumer of fish did not distinguish a statistically relevant difference between the two
types of fish. Future research may involve the further analysis of volatile compounds in
the RAS and IMTAS samples to determine what is ultimately happening in the IMTAS
samples that lessen the impact of off-flavors commonly associated with RAS
environments.
77
APPENDIX A SAMPLE BALLOT FOR TRAINING SESSIONS FOR TRAINED TASTE PANEL
78
APPENDIX B SAMPLE BALLOT FOR TRAINED TASTE PANEL SENSORY EVALUATION
79
APPENDIX C BALLOT FOR CONSUMER PANEL SENSORY EVALUATION
TODAY'S SAMPLE: FISH
To start the test, click on the Continue button below:
Panelist Code: ________________________ Question # 1.
Please indicate your gender. Male Female Question # 2.
Please enter your age: Age __________ Question # 3.
How would you describe your racial or ethnic origin? Caucasian/White African-American/Black Hispanic/Latino Asian American Indian Other Would prefer not to say Question # 4.
How would you describe your racial background?
___________________________________________________________________________
80
Question # 5.
How often do you consume fish?
Daily 2-3 times a week Once a week 2-3 times a month Once a month 2-3 times a year Rarely I don't eat fish
Take a bite of cracker and a sip of water to rinse your mouth.
WHEN ANSWERING ANY QUESTION, MAKE
SURE THE NUMBER ON THE CUP MATCHES THE NUMBER ON THE MONITOR.
Please click on the 'Continue' button below.
Question # 6 - Sample ______
Please indicate how much you like or dislike sample 1 OVERALL. Overall Liking
Dislike extremely
Dislike very much
Dislike moderately
Dislike slightly
Neither like nor dislike
Like slightly
Like moderately
Like very much
Like extremely
1 2 3 4 5 6 7 8 9
Take a bite of cracker and a sip of water to rinse
your mouth.
WHEN ANSWERING ANY QUESTION, MAKE SURE THE NUMBER ON THE CUP MATCHES THE
NUMBER ON THE MONITOR.
81
Please click on the 'Continue' button below. Question # 6 - Sample ______
Please indicate how much you like or dislike sample 2 OVERALL. Overall Liking
Dislike extremely
Dislike very much
Dislike moderately
Dislike slightly
Neither like nor dislike
Like slightly
Like moderately
Like very much
Like extremely
1 2 3 4 5 6 7 8 9
Paired Comparison
In front of you are the two samples you have tasted.
Please taste both samples again and indicate which sample you
prefer.
__________ __________
82
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BIOGRAPHICAL SKETCH
Daniel Clark was born in Stuart, Florida and was raised by his parents, Dale and
Diane Clark, in Palm City, Florida. He received his Associate’s degree in business
administration from Indian River State College before receiving his Associate’s degree
in culinary arts from The Culinary Institute of America in 2011. Daniel then worked in the
culinary field before deciding to return to the University of Florida and pursue a degree
in food science. After finishing his bachelor’s degree, Daniel then decided to pursue a
Master of Science in food science and human nutrition under the direction of Dr. George
Baker.