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The Andean region is recognized today as one of the most important centers of crop origin and
diversity in the world. Quinoa, kañiwa and kiwicha are indegenous food plants of the Andean
region, dating back to 5000 years AD. The Incas appreciated their high nutritional value, and
the ease in milling these crops made it possible for the rural populations to take advantage of
their nutritional value, substituting for the lack of animal protein in these rural regions (Tapia,
1997). The protein content of quinoa, kiwicha and kañiwa is elevated, have balanced amino
acid composition and excellent nutritional value, especially when combined with other cereals
(Repo-Carrasco et al 2003; Pedersen et al 1987). These Andean grains have potential as
functional and bioactive ingredients in food products because their high dietary fiber content
and natural antioxidants such as phenolic compound (Gorinstein et al 2007). Andean grains
have potential agronomic importance across the world because they can adapt to different
environmental conditions. In particular, quinoa is a crop exhibiting a range of requirements for
humidity and temperature, with specific ecotypes adapted to diverse conditions. The crop has
been introduced to Europe, North America, Asia, Africa and Australia (Jacobsen 2003). Quinoa
was selected by FAO as one of the crops to offer food security in the current century (FAO
1998) and attention has been given to quinoa for people with celiac disease as an alternative
to the common cereals like wheat, rye, and barley, which all contain gluten (Schoenlechner et
al 2008). Because of their nutritional and economical importance there is a risk for economic
adulteration with less-expensive grains. Infrared spectroscopy is an attractive technology for
the rapid, sensitive, and high-throughput analysis of food authentication and detection of
economic adulteration.
Rapid Authentication of Andean Flours by Using a Portable Infrared Spectrometer
INTRODUCTION
METHODS
CONCLUSION SIGNIFICANCE
ACKNOWLEDGEMENTS
The authors would like to acknowledge the Ohio Agricultural Research
and Development Center and Universidad Nacional Agraria – La Molina
for their support towards this research.
RESULTS
Colleen Rossella, Gladys Tarazona Reyesb, and Luis E. Rodriguez-Saonaa
ABSTRACT
The objective of this study was to develop a rapid test combining ATR-MIR spectroscopy with
chemometrics to characterize and detect adulteration in native Andean flours
OBJECTIVE
Andean Grain Flour included Quinoa, Maca, Kiwicha,
and Cañihua. Potential adulterants were Wheat, Soy,
Barley, Faba beans & Corn
Diamond + pressure
ATR
Figure 1. Typical ATR-IR spectrum of
selected Andean grain flours
Figure 2. SIMCA classification and discriminating power based on
infrared spectra of grain flours samples using a portable system
(A,B) and a benchtop system (C,D).
Table 1. MIR Assignment of Protein Bands
x1
x2
Class 1
Class 2
Soft Independent
Modeling of Class
Analogy
Frequency
(cm-1)
Functional Group Assignment
1715 >C=O ester stretching; carboxylic acids
1695-1675 Amide I band components of proteins
1655 Amide I of α-helical structures of proteins
1637 Amide I of β-pleated sheet structures of
proteins
1550-1520 Amide II band of proteins
1515 Tyrosine band
1468 C-H deformation of >CH2 in lipids proteins
1415 C-O-H bending in CHOs, proteins
1400 C=O symmetric stretching of COO- group
1310-1240 Amide III band components of proteins
1200-1000 C–C, C–O stretching and C–O–H, C–O–C
deformation modes of carbohydrates
FT-IR spectroscopy combined with chemometrics showed
the ability to discriminate different types of commercial
flours including selected Andean grains
The most important bands explaining the discrimination
scores were in the 1000-1200 and 1500-1650 cm-1 region
associated with carbohydrates and proteins, respectively.
Interclass distances (ICD) showed the compositional
similarities between cañihua and kiwicha (ICD=2.6). It has
been reported their similar amino acid and fatty acid profiles.
Evaluation of samples obtained from several local markets
indicate some concern with adulteration of some of the
Andean grains, especially kiwicha flour.
3600 3200 2800 2400 2000 1600 1200 800
Wavenumber
Ab
so
rba
nc
e
C=
O g
rou
ps o
f lip
ids
Am
ide
I
Am
ide
II
CH
gro
up
s o
f lip
ids
CH
an
d c
arb
on
yl str
etc
hin
g
of acyl c
ha
in o
f tria
cylg
lyce
rols
O - H
gro
up
s
wa
ter
u C - O - C of
polysaccharides
3600 3200 2800 2400 2000 1600 1200 800
Wavenumber
Ab
so
rba
nc
e
C=
O g
rou
ps o
f lip
ids
Am
ide
I
Am
ide
II
CH
gro
up
s o
f lip
ids
CH
an
d c
arb
on
yl str
etc
hin
g
of acyl c
ha
in o
f tria
cylg
lyce
rols
O - H
gro
up
s
wa
ter
u C - O - C of
polysaccharides
3600 3200 2800 2400 2000 1600 1200 800
Wavenumber
Ab
so
rba
nc
e
C=
O g
rou
ps o
f lip
ids
Am
ide
I
CH
gro
up
s o
f lip
ids
CH
an
d c
arb
on
yl str
etc
hin
g
of acyl c
ha
in o
f tria
cylg
lyce
rols
O - H
gro
up
s
wa
ter
u C - O - C of
polysaccharides
Our results supports the use of IR spectroscopy
as a high-throughput technique that could be
easily adapted for in-field applications thanks to
the development of new portable devices.
We demonstrate the application of a rapid tool to
authenticate highly versatile and nutritious
Andean grains
Portable FT-IR (Agilent
Technologies)
aDepartment of Food Science and Technology, The Ohio State University, 2015 Fyffe Court, Columbus, OH 43210, USA. bDepartamento de Industrias Alimentarias, Universidad Nacional Agraria, Av. La Molina s/n. La Molina. Lima – Perú.
Andean indigenous grains such as Quinoa (Chenopodium quinoa), Cañihua (Chenopodium
pallidicaule), Kiwicha (Amaranthus caudatus L.) have high nutritional value for the Andean
region serving as principal protein sources of the region, substituting the scarce animal proteins.
The importance of these grains is based on their relatively high protein content with excellent
composition of essential amino acids, gluten-free type, good source of dietary fiber, bioactive
compounds and minerals (calcium, zinc and iron). Because of their nutritional and economical
importance there is a risk for economic adulteration with less-expensive grains. Our objective
was to develop a rapid analytical tool to characterize and detect adulteration of native Andean
indigeneous grains by combining infrared spectroscopy and pattern recognition analysis. Pure
flours produced from Andean (quinoa, cañihua, kiwicha, maca, sacha inchi) and other (maize,
soybean, wheat, linseed, algarroba, canary grass, sesame, barley, faba bean) ingredients were
provided by UNALM (Lima, Peru). In addition, commercial samples were obtained from various
local markets (Lima, Peru) and used for predictions. Unique spectral data was collected with a
portable attenuated total reflectance (ATR) mid-infrared spectrometer equipped with a diamond
crystal and analyzed by Soft independent modeling of class analogy (SIMCA). Pure flours
formed distinct clusters allowing the evaluation of commercial samples from Peruvian markets
showing some prevalence of adulteration. Spectral differences responsible for the separation of
classes were attributed to stretching vibrations of the ester (-C=O) linkage (1740 and 1780 cm-1)
of lipids and the amide (1450-1600 cm-1) region of proteins. ATR-IR spectroscopy in
combination with chemometrics was a viable tool in characterization of Andean flour samples
allowing for the rapid, “in-field”, and reliable detection of adulteration of food ingredients, making
it a great alternative to traditional testing methods.
Portable ATR-IR
Cary 630 series
PROBLEM
EXPLORATION
METHOD
SELECTION
spectra
DOE
Multivariate Analysis
Table 2. Interclass distances between flours using portable and benchtop FT-IR spectrometers
PC2
PC1
PC3
Maca
Soya
Cebada
Quinoa pred.
Kiwicha
pred. Haba
Cañihua
Algarrobo
Mashua
Linaza
Ajonjoli
Sacha Inchi
Maize
Trigo
Kiwicha std.
Alpiste Quinoa
std
B
Figure 3. SIMCA prediction plots of
market flours using a portable (A) and
a benchtop (B) FT-IR system
Flours Cañihua Cebada Haba Kiwicha Maca Quinoa Soya
Cañihua std. 3
Cebada std. 3
Haba std. 3 1
Kiwicha std. 2
Maca std. 3
Quinoa std. 2
Soya std. 3
Misclassified 2 1
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15
C1 0 9.6 8.2 3.6 17 9.6 12.3 7.5 10.6 9.8 14.8 22.5 12.8 15 6.8
C2 10.4 0 19.1 14.5 33.4 10.6 19.9 7.2 31.1 6.4 40.8 53.3 14 39.5 18.8
C3 6.2 17.3 0 13.8 28.8 19.2 20.2 18.5 23.2 15.2 31.6 38.7 23.8 37.1 15.2
C4 2.6 12 5.5 0 38.5 15.6 23.9 11 17.4 17.5 31.4 54.8 21.5 31.7 11
C5 15.7 25.2 22.4 20.3 0 35.4 19.1 39.3 34.2 37.1 46.4 24.9 55.8 48.5 21.9
C6 10.4 10 18.8 12.9 22 0 21 8.9 29.5 5.9 42.3 60.9 19 34.9 18.7
C7 14.5 11.1 21.9 17.1 8.3 11.2 0 26.8 8.9 18.5 39.6 40.2 31.9 41.4 19.3
C8 5.6 8.7 9.3 4.7 21.3 8.7 12.2 0 26.8 8.9 34.2 61 11.2 38.6 15.5
C9 8.9 24 9.3 9.1 23.7 25.3 27.8 16.5 0 34.5 31.8 39.2 38.3 17.1 14.3
C10 6 8.3 10.9 6.6 24.4 7.6 12.1 6.8 17.3 0 42.6 62.8 16.3 46.7 19
C11 5.5 12.1 5.3 5.9 12.3 12.1 13.8 9.7 3 10.2 0 50.7 46.7 38.6 16
C12 18.8 26.4 23.1 22.8 10.7 26.8 15.9 23.6 23.5 26.4 12.5 0 90.9 59.5 25.4
C13 13.8 9 19.3 13.9 19.5 10.1 13.6 5 25.8 7 14 22.6 0 51.7 20
C14 8 22.2 9.2 9.1 21.8 22.2 23.4 17.6 5.5 19.4 4.1 20.8 22.7 0 20.5
C15 11.1 31.9 7.7 11.6 32.8 31.3 32.2 21.9 5.3 26 3.3 29.4 28.2 7.4 0C1isCanihua,C2isCebada,C3isHaba,C4isKiwicha,C5isMaca,C6isMaize,C7isMashua,C8isQuinoa,C9isSoya,C10isTrigo,C11isAjonjoli,C12isAlgarrobo,C13isAlpiste,C14isLinaza,andC15isSachaInchi.
Figure 4. Prediction of market flours using a portable IR