Accepted Manuscript

Title: Biosensing technology for sustainable food safety

Author: V. Scognamiglio, F. Arduini, G. Palleschi, G. Rea

PII: S0165-9936(14)00164-2

DOI: http://dx.doi.org/doi:10.1016/j.trac.2014.07.007

Reference: TRAC 14291

To appear in: Trends in Analytical Chemistry

Please cite this article as: V. Scognamiglio, F. Arduini, G. Palleschi, G. Rea, Biosensing

technology for sustainable food safety, Trends in Analytical Chemistry (2014),

http://dx.doi.org/doi:10.1016/j.trac.2014.07.007.

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1

Biosensing technology for sustainable food safety

V. Scognamiglio a,

*, F. Arduini b, G. Palleschi

b, G. Rea

a

a IC-CNR Istituto di Cristallografia, AdR1 Dipartimento Agroalimentare - Via Salaria Km 29.3, 00015 Monterotondo

Scalo, Rome, Italy b Università di Roma Tor Vergata, Dipartimento di Scienze e Tecnologie Chimiche - Via della Ricerca Scientifica

00133, Rome, Italy

HIGHLIGHTS

We deal with quality and safety claims related to human health

Biosensing technologies can be exploited to address food safety

We consider food safety along the entire food production and distribution chain

Nanotechnology and synthetic biology are moving biosensors from research to market

ABSTRACT

Food and diet are closely linked to human health, and new emerging research fields are attempting

to guarantee improvements in food quality and safety. Biosensor technology represents a cutting-

edge frontier in environmental and biomedical diagnosis and is at the forefront in the agrifood

sector. Smart monitoring of nutrients and fast screening of biological and chemical contaminants

are some of the key evolving issues challenging the assessment of food quality and safety.

Advances in materials science and nanotechnology, electromechanical and microfluidic systems,

protein engineering and biomimetics design are boosting the sensing technology from bench to

market. This review highlights current and future trends in analytical diagnostic tools focused on

the food industry and target analytes to support healthier nutrition.

Keywords:

Biosensing

Biosensor

Contaminant

Embedded system

Fast screening

Food quality

Food safety

Nanotechnology

Smart monitoring

Transduction system

*Corresponding author. Tel.: +39 06 90672480; Fax: +39 06 90672630.

E-mail address: viviana.scognamiglio@mlib.ic.cnr.it (V. Scognamiglio)

1. Introduction

Nowadays, several research efforts are devoted to developing control systems ensuring food

quality and safety [1]. Awareness of food control also increased recently, due to estimations

suggesting significant global population growth in the next 30 years (“World Population to 2300”,

Department of Economic and Social Affairs, United Nations). This global increase poses marked

challenges to the agrifood sector, since intensive agriculture and animal farming, food handling,

processing and distribution may hamper food safety and quality and, as a consequence, human

health. Innovation and development in the agrifood sector and the recent globalization of agro-

industrial markets point to fundamental belief in the need for food safety and quality, which have

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become of great concern for human and environmental health, so that various efforts have been

committed to guarantee food safety and quality [2].

The term food quality relates to appearance, taste, smell, nutritional value content, functional

ingredients, freshness, flavor, texture and chemicals. The analysis of food composition allows us to

characterize food and prove if it contains all the desired constituents, including natural components

(e.g., sugars, amino acids and alcohols) and additives (e.g. vitamins and minerals). Furthermore, the

evaluation of food composition enables comprehensive estimation of freshness, revealing the

presence and/or the concentration of microorganisms and toxins produced as a result of damage.

The food-safety concept entails the production and the commercialization of food which do not

represent a risk to the consumer, so it must be free from allergens, pesticides, fertilizers, heavy

metals, organic compounds, pathogens and toxins. These contaminants could seriously affect

human health and well-being, giving rise to foodborne diseases with serious consequences for the

health-care system and economic productivity (Appendix 1, Supplementary material). It is

necessary to identify and to set up an ensemble of procedures, inspections, and control systems to

minimize threats that cause unsafe or off-quality end products.

The diagnostics industry is key to the development of analytical methodologies endowed with

high sensitivity, speed and portability. Among diagnostics, biosensors combine a high functional

performance (in terms of specificity, sensitivity and short response time) with ease in building

technical capacity (including modularity, integration, and automation) (Appendix 2, Supplementary

material). Hazard analysis and critical control point (HACCP), generally accepted as the most

effective system to ensure food safety, can utilize biosensors for process control.

Biosensing R&D had an estimated a market of US$8.5 billion in 2012 and is projected to reach

US$16.8 billion by 2018. However, this market comprises mainly devices for medical diagnosis,

and there is no quantitative analysis of the agrifood market [3–5].

This review provides a global overview on recent advances in biosensor technology for the

agrifood field, enabling the development of reliable, robust and selective biosensors. In this context,

we propose a selection of biosensing configurations with improved performance in terms of

sensitivity, stability, reliability, multiplex analyses and time response compared with older

generation biosensors or conventional analytical devices. Nevertheless, although a huge assortment

of biosensors have been reported in the literature, only a few prototypes reached the market, dealing

mainly with glucose, lactose and microorganism detection. We aim to stimulate research activity in

developing innovative, tailor-made biosensors for agrifood diagnosis and to move this technology

from bench to market to reduce the gap between research and industries.

2. Discussion

Conventional methodologies for food analysis provide high reliability and very low limits of

detection (LODs). Among them chromatography, spectrophotometry, electrophoresis,

immunoassays, polymerase chain reaction (PCR) assays and ATP detection methods promise

results within 24 h, but they are expensive and time consuming, and need samples to be sent to

laboratories, and most of them require the use of highly trained personnel.

For these reasons, there is increasing demand for robust, rapid, cost-effective alternative

technologies for in-situ, real-time monitoring. Several biosensors have been designed and realized

for the detection of food components [5] and chemical species in food and water products [6], in

order to satisfy all the requirements of the diagnostic industry.

The following sections provide an overview of biosensor technology in the past 10 years, which

was intended for application in the agrifood to address control of food quality and safety (Table 1).

2.1. Glucose

Food content and composition change during storage, especially the main carbohydrate

constituents, such as glucose and fructose, which could be responsible for food-browning processes.

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For this reason, glucose monitoring is important as it is an indicator of food freshness [7].

Biosensors started in the 1960s with the pioneering work of Clark and Lyons and the first enzyme-

based glucose sensor reported by Updike and Hicks in 1967 [8]. Since then, widespread

investigation of biosensors was done for the production of novel systems for glucose monitoring.

Most electrochemical biosensors (amperometric, potentiometric, impedimetric or conductometric)

are based on glucose oxidase (GO) enzyme that catalyzes the oxidation of glucose to produce

gluconic acid, as shown in Fig. 1. Glucose monitoring by glucose oxidase was performed with

different LODs by:

Goriushkina et al. [9] for wine analysis in the linear range 0.04–2.5 mM;

Shan et al. [10] with a polyvinylpyrrolidone-protected graphene/polyethylenimine-functionalized

ionic liquid/GO for detection up to 14 mM; and,

Xu et al. [11] with PPy-nanowire GO arrays showing an LOD of 50 µM. A novel trend in glucose sensing is the use of receptors as an

alternative to enzymes. In this context, research efforts were

dedicated to the development of non-consuming biosensors, based on

the use of inactive apo-enzymes or binding proteins for

reversible, implantable and/or in-line sensing systems. Scognamiglio et

al. [12], reported the use of an inactive form of glucose oxidase from Aspergillus niger (Fig. 2), in

which the flavin adenine dinucleotide (FAD) cofactor, required for glucose oxidation, was removed.

Fluorescence measurements showed that the obtained apo-glucose oxidase was still able to bind

glucose without consuming it, so it was suitable for a reversible sensor.

Similarly, several binding proteins and receptors were exploited for the development of affinity

biosensors. Among them, a D-glucose/D-galactose binding protein (GGBP) from Escherichia coli

was produced by different research groups, extensively characterized by spectroscopic techniques

and exploited for the realization of optical biosensors [13–15].

Subsequently, several genetic variants were obtained with different affinity for glucose to

enhance sensor sensitivity [16–18]. In the same research field, Staiano et al. [19] employed a

thermostable sugar-binding protein (Ph-SBP) from archaeon Pyrococcus horikoshii as a more stable

variant protein to increase robustness.

In recent years, many biosensors for glucose monitoring were based on the latest research on

nanotechnologies and biocomposite materials, which provided devices with better performance in

term of stability and sensitivity. German et al. [20] studied the electrochemistry of glucose oxidase

immobilized on a graphite-rod electrode modified by gold nanoparticles (AuNPs), in comparison

with similar electrodes not containing AuNPs (GOx/graphite). They demonstrated that the

application of AuNPs could increase the rate of mediated electron transfer, providing an improved

sensitivity with an LOD within 0.1 mmol/L and 0.08 mmol/L, suitable for determination of glucose

in beverages and/or food.

Although a large number of articles report the development of biosensors conceived for food

industry [21], very few of them have been applied to the detection of glucose in real samples, with

limited exceptions, including analysis of wine [22], fruit juices [23] and soft drinks [24].

2.2. Glutamine

Glutamine is an essential amino acid that plays key roles in several metabolic pathways and

accomplishes crucial functions (e.g., signaling, transport and precursor in the biosynthesis of

nucleic acids, amino sugars and proteins). It represents a nitrogen source in mammals’ diet, since

they are unable to synthesize nitrogen-containing organic compounds from inorganic salts.

Glutamine supplementation in patients affected by critical pathologies such as malabsorptive

disorders or immunodepression seems to be essential to improve immune functions, preserve

intestinal functionality and reduce bacterial translocation [25].

The development of glutamine-enriched food and glutamine quantification is essential for critical

patients. Determination of glutamine in cell-culture fermentation processes is also crucial and

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several methodologies were successfully exploited for monitoring glutamine concentration (i.e., by

HPLC), but this technique is time consuming, expensive and not appropriate for on-line monitoring.

Optical methods, such as near-infrared spectroscopy (NIR) and chemiluminescence, were also

described. In this scenario, a biosensor provides real-time, cost-effective process monitoring. As an

example, a sophisticated glutaminase-based microfluidic biosensor chip coupled to flow-injection

analysis for electrochemical detection was realized by Bäcker et al. [26] to detect glutamine in

fermentation processes. Glutaminase (EC 3.5.1.2) was integrated into a platinum thin-film electrode

and the resulting assembly had an LOD of 0.1 mM. However, due to the chemical similarity of

glutamine to glutamate, interfering phenomena could occur. These interfering signals were

minimized by exploiting receptor proteins able to bind glutamine with high affinity and selectivity,

without consuming substrate. In this context, De Stefano et al. [27] described the development of an

optical microsensor, based on the glutamine-binding protein (GlnBP) from E. coli [28,29]

immobilized on a porous silicon Fabry-Perot layer, for the detection of glutamine (Fig. 3). Finally,

commercial biosensors for glutamine detection in food were produced by several companies,

including Universal Sensors Company (USA, http://www.usisensor.com/en/) and Yellow Springs

Instruments Co (USA, http://www.ysi.com/index.php).

2.3. Gliadin

Gliadins are the alcohol-soluble fraction of gluten, the storage proteins occurring in wheat,

barley, rye and oats. They are a heterogeneous family of polypeptides characterized by repetitive

domains of proline and glutamine (prolamines). They are classified according to their structure into

β and gliadins [30]. Unfortunately, gliadins are strong food allergens that cause celiac

disease, defined as a permanent intolerance of the small intestine, affecting genetically susceptible

people following consumption of gluten. As therapy and treatment for celiac disease are not

available, a strict, permanent gluten-free diet is required for celiac patients to achieve intestinal

mucosal recovery and to prevent complicating disorders. Nevertheless, it is hard to observe such a

diet, essentially because a very low amount of gluten can affect the patient. For this reason, afflicted

people must avoid to eat gluten by limiting their diet to gluten-free foods and determining if certain

foods are safe or have acceptably low levels of gluten. The availability of gluten-free or low-gluten

(below 4 mg of gluten/100 g of food, 40 ppm) foodstuffs is crucial for the quality of life of celiac

patients. However, commercial food products declared gluten free can be relatively contaminated

by gluten in the range of 20–200 ppm [31]. In January 2009, the European Commission published a

new European Regulation concerning the composition and labelling of foodstuffs suitable for

people intolerant to gluten; this regulation indicates that foods may display the term “gluten-free” if

the gluten content does not exceed 20 mg/kg as sold to the final consumer [32].

To fine tune the sensitivity of the biosensors and achieve low LODs, a recombinant glutamine-

binding protein was produced in E. coli [33] and successfully exploited to construct an optical assay

for the detection of traces of gluten in food (Fig. 4) [34]. Furthermore, Varriale et al. [35] described

a higher sensitive fluorescence correlation spectroscopy (FCS) assay based on measuring the

fluctuations of fluorescein-labelled gliadin peptides in a focused laser beam, in the absence and in

the presence of anti-gliadin peptide antibodies. The results indicated that the combination of gluten

antibodies together with the innovative fluorescence immunoassay strategy resulted in a gluten

LOD of 6 ppb, which was lower than the values previously reported in the literature.

Several ELISA assays were also applied in recent years for the detection of gluten in food, but

the principal limitation of these methods was that, in hydrolyzed foods (e.g., baby foods, syrups and

beers), gluten proteins are fragmented during food processing and converted into peptides in which

only one toxic peptide may appear. As a consequence, the quantification of gluten would be

incorrect, yielding less than the real gluten content. The Codex Alimentarius Commission stated

that “for the detection of hydrolyzed gluten a modification of the competitive ELISA assay has to

be applied” [36]. In order to overcome the drawbacks of ELISA assays, Laube et al. [37] developed

an electrochemical immunosensor with the advantage of quantifying gliadin or small gliadin

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fragments in natural or pre-treated food samples with excellent LODs (of the order of µg/L), in line

with the legislation for gluten-free products.

2.4. Pesticides

Pesticides are relevant pollutants due to the large amounts released into the environment. For this

reason, many countries have established maximum residue levels (MRLs) in food products [38].

Among the several pesticides applied in agriculture, organophosphorus and carbamic insecticide

species are largely used due to their high insecticidal activity and relatively low persistence in the

environment. Several analytical methodologies were developed in recent years for the detection of

pesticides, in order to provide valuable diagnostic tools for control of food and water.

Immunosensors confirmed their potential usefulness for high-speed agrifood and environmental

monitoring. They are sensitive, with very low LODs, but they need a label to detect the immune

reaction. However label-free transduction systems have obvious advantages (e.g., electrochemical

immunosensors could be competitive thanks to their simplicity and cost-effective technology) [39].

The approach used most for the detection of these contaminants lies in the development of

cholinesterase (ChE)-based biosensors. The toxicity of these compounds is the ability to inhibit

irreversibly a key enzyme of the nervous transmission: the acetylcholinesterase (AChE).

Researchers then focused on the use of this enzyme to develop biosensing methods. The principle of

method involves measurement of the enzymatic activity before and after exposure to the

contaminated sample; in this way, a decrease in enzymatic response is ascribed to the presence of

these pesticides in the sample. Since the first biosensor based on ChE inhibition developed by

Guilbaut et al. in early 1962 [40], numerous biosensors were reported in literature, as confirmed in

several reviews in the past 10 years [41–45]. In this overall scenario, the electrochemical biosensors

based on ChE are the most developed.

Arduini et al. developed AChE and butyrylcholinesterase (BChE) biosensors for aldicarb,

carbaryl, paraoxon and chlorpyrifos-methyl oxon using miniaturized screen-printed electrodes [46].

A similar type of biosensor was also applied to detect this type of pesticide in wine and orange juice

[47,48]. AChE immobilized by glutaraldehyde in a pre-formed cystamine self-assembled monolayer

on a gold screen-printed electrode easily detected paraoxon in drinking water [49]. The coupling of

nanomaterials with the miniaturized sensor increased the sensitivity of the biosensor, as reported by

Ivanov et al., whose biosensor was developed using carbon nanotubes (CNTs) and electrochemical

mediator cobalt phthalocyanine for pesticide detection in sparkling and bottle water [50]. In order to

increase the sensitivity, the enzymes can be also engineered and used, as reported by the group of

Marty, who used different recombinant AChEs and also used Artificial Neural Network (ANN) as

the data-processing tool to resolve the insecticide mixtures [51].

In order to push biosensor prototypes from bench to market, it is necessary to provide automated

biosensing systems for real-time, in-field analyses of pesticides. For this purpose, an

electrochemical biosensor array integrated six AChE enzymes in a novel automated device

equipped with an efficient ANN program. The analytical device successfully identified

organophosphates in food and plant extracts with a very fast detection time of 6 min/analysis,

proving its efficiency and reliability also in real samples and in-field analyses. Furthermore, this

instrument was cost effective, when compared to conventional analytical methods, such as HPLC or

GC-MS, usually used for this purpose [52].

2.5. Herbicides

Among pesticides, herbicides represent the most common pollutants found in surface water and

groundwater, and their concentration limits are 0.1 μg/L for each single pollutant and 0.5 μg/L for

total pesticides [53]. Such low limits require highly sensitive analytical techniques, such as

biosensor technology, for easy, low-cost, and fast pre-screening. Examples of sensing systems to

reveal herbicides have been reported by several authors, including the molecular imprinting

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fluorescent chemosensor [54], surface-enhanced Raman spectroscopy [55], and chemiluminescence

immunoassay [56].

A critical requirement in herbicide monitoring is versatility, in terms of supporting multi-analyte

detection of a broad spectrum of compound classes [57]. Advances in biosensor technology have

been achieved in amplifying the range of recognition elements and measurement of a significant

number of dissimilar classes of pollutants, through the design of new engineered organisms/proteins

with different affinities towards herbicides. In this context, biosensors employing photosynthetic

organisms represent the most exploited analytical systems for herbicide monitoring. Indeed,

progress in the molecular biology of photosystems and algal chloroplast transformation has

produced a number of site-directed mutants characterized by amino-acid substitutions in the

sequence of the reaction center D1 protein (Appendix 3, Supplementary material). Modifications of

only one amino acid within the QB binding pocket can change the photosynthetic activity and

herbicide binding considerably.

In a bioinformatics study, Rea et al. [58] produced a set of mutant strains from photosynthetic

green alga Chlamydomonas reinhardtii, with higher affinity towards several classes of herbicides

(Fig. 5). A similar computational approach was adopted by several research groups [59–62] to

tackle issues related to low sensitivities and to provide new biological recognition elements able to

recognize a great number of chemicals at a wide range of concentrations. On the basis of these

efforts, several biosensors were assembled using recombinant microorganisms, such as the multi-

array optical biosensor based on a library of functional mutations of C. reinhardtii, as performed by

Giardi et al. [62], for monitoring diazines, triazines and urea herbicides with LODs of 0.8×10-11

–

3.0×10-9

M; the multi-biomediator fluorescence biosensor based on a versatile portable instrument

was assembled by Scognamiglio et al. [63] based on an array of engineered C. reinhardtii algae

with LODs of 1.0 x 10-9

–3.0 x 10-10

M towards a wide range of herbicide subclasses.

2.6. Toxins

Toxins are a heterogeneous group of compounds able to interfere with biochemical processes,

such as membrane function, ion transport, transmitter release and macromolecule synthesis. Human

exposure to toxins can lead to serious health problems, including immunosuppression and

carcinogenesis. A number of regulatory authorities have decreed maximum residue levels of several

mycotoxins and phycotoxins in food and water. Among them, the World Health Organization has

set a tolerable content of several toxins in foods. As an example, a tolerable weekly intake of 7 ppb

body weight was established for patulin. The content of patulin in foods has been restricted to 50

ppb in many countries: the European Union has set a limit of 10 ppb in children’s foods, but the

objective is to reach 25 ppb patulin in apple-containing products (European Commission. EC No.

1425/2003; Brussels, Belgium, 2003). For this reason, ready detection can be crucial to prevent the

entry of toxins into commercial food products.

Gene-reporter fusions allowed the creation of whole-cell based biosensors as early warning

detection systems. Usually, a bioluminescent, fluorescent or chemiluminescent reporter gene is

placed under the control of an inducible promoter responsive to a multiplicity of toxic chemicals,

and used to indicate the occurrence of environmental contaminants. Hence, recombinant cells

exposed to toxic substances can modulate the activation or the inhibition of production of a specific

reporter protein, leading to a general, non-specific response to the presence or the absence of toxins

[64]. Several types of toxins can affect different types of food (e.g., among mycotoxins, ochratoxin

A can be found in grapes and wine, patulin in apple, aflatoxin B1 in corn and barley, and aflatoxin

M1 in milk). In this scenario, biosensing systems for toxin detection in foods are important. The

detection of aflatoxin B1 in corn and barley was performed principally using immunosensors. An

indirect competitive immunoassay was developed by Ammida et al. using a screen-printed electrode

as electrochemical transducer [65]. This system was applied to barley samples, showing a low

matrix effect and good recovery values. A further step in the biosensing development was carried

out by Piermarini et al, who produced an indirect competition assay based on the use of an

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innovative electrochemical immunoplate with multichannel read-out. Also, in this case, a negligible

matrix effect and good recoveries were found using as corn matrix [66]. An alternative method for

aflatoxin B1 detection was developed by Arduini et al. based on AChE inhibition using a

spectrophotometric method. The developed colorimetric assay (Ellman’s method) can be performed

rapidly on treated samples (in less than 10 min), simply following the color of the analyzed

solution, which depends of the concentration of aflatoxin B [67]. The system was also applied to an

olive-oil matrix using an electrochemical amperometric transducer; in this case, attenuation of the

output current revealed the presence of aflatoxin B in the samples [68].

In the case of ochratoxin A detection, a wide number of different strategies were adopted in the

development of biosensing systems for food analysis {e.g. FCS immunosensor [69], polythionine

(PTH)/AuNP composite film-based electrochemical immunosensor [70], electrochemical

immunosensor for wine analysis [71], and a magnetic bead electrochemical biosensor [72]}.

An electrochemical transducer was also used by Micheli et al. to develop a direct competitive

immunosensor for aflatoxin M1 detection in milk [73].

For patulin detection, de Champdorè et al. [74] presented a competitive fluorescence

immunochemical sensor, employing new polyclonal antibodies for the specific toxin. In particular,

they described the synthesis of two new patulin derivatives characterized by their overall structure,

and exhibiting greater chemical stability. The synthesized toxins were conjugated to the bovine

serum albumin carrier protein to produce polyclonal antibodies in rabbits that were subsequently

labelled with a commercial fluorophore for the development of a patulin fluorescence

immunoassay. The competitive assay between bound and free patulin detected the toxin in the

concentration range of 10 µg/L.

Likewise, phycotoxins, mostly synthesized by marine algae, may produce undesirable ecological

effects also at low concentrations, causing intoxication syndromes throughout the food chain.

Several analytical methodologies have been developed for the detection of the main spread marine

toxins, including SPR biosensors, MIPs based biosensors and immune-biosensors for domoic acid

[75–77], electrochemical immunosensors and colorimetric test for okadaic acid [78,79]. Yotsu-

Yamashita et al. [80] discovered a novel soluble glycoprotein able to bind saxitoxin and

tetrodotoxin from plasma of the puffer fish Fugu pardalis. These binding proteins revealed high

affinity towards the marine toxins with dissociation constants in the nanomolar range, being optimal

candidate as biomediators for the development of optical biosensors. An interesting

electrochemiluminescent antibody nanobiosensor was developed for the detection of palytoxin with

very high sensitivity in the order of µg/kg [81].

2.7. Heavy metals

Heavy-metal monitoring in water and food and in the environment is a crucial task due to the

high toxicity of heavy metals, their increasing environmental levels, and their ability to

bioaccumulate in living organisms. Common analytical techniques, such as ion chromatography,

inductively coupled plasma, polarography, and ion-selective electrodes, are unable to distinguish

between potentially hazardous and non-hazardous fractions of metals in biological systems. Recent

progress was made by employing biosensors relying on whole cells able to sense general toxicity

and specific toxic metals, thanks to their capability to react with only the hazardous fraction of

metal ions [82]. Arsenic can be measured by means of bacteria-based bioassays, as reported in a

review of Diesel et al. [83]. Tests for total toxicity monitoring using whole cells have been

considered suitable for initial screening, but, due to the low specificity, they leave open the question

of what exact ion they are sensing and whether inhibition is caused by other pollutants too.

For this reason, enzymatic activity inhibition can be applied for the determination of hazardous

toxic elements by means of an inhibition mechanism, making the method very simple and sensitive

[84,85]. For example, As3+

was detected using an AChE electrochemical biosensor, demonstrating

that, in the experimental conditions used, As3+

is a more powerful inhibitor than other metal ions

tested, such as mercury, nickel and copper, so the system had satisfactory selectivity [86].

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Another type of bioassay developed for heavy-metal detection is the immunoassay system.

Direct competitive and indirect competitive immunosensors for Cd2+

detection in farm produce,

apple juice, rice flour, wheat flour, tea and spinach samples were reported in literature [87,88]. An

interesting example is based on the development of an immunochromatography system for

cadmium analysis using an anti-Cd-EDTA antibody, highly specific to Cd-EDTA. This allowed

cadmium detection in the range of 0.01–0.1 mg/L. The detection of Cd ions in solution can be easily

performed in situ [89]. Recently, biosensing based on the use of aptamers was also applied for

heavy-metal detection, with a novel fluorescence aptamer biosensor based on multi-walled CNT

(MWCNT) long-range energy transfer being developed by Wang et al. [90]; the system selectively

detected Hg, Ag and Pb

ions.

3. Conclusions

The global market of food analysis needs reliable, inexpensive methods for evaluating food

quality and safety. Biosensors offer the opportunity to satisfy this demand, since they are ideal

candidates for improving food diagnostics in terms of quality control and testing for genetically

modified constituents, authenticity and traceability, freshness, and presence of contaminants.

Several biosensors have been constructed and assembled for numerous target compounds of

agrifood and environmental interest, from food components to water pollutants, thanks to their

undeniable advantages. Biosensors could be considered as a forefront technology and represent a

potential alternative to conventional analytical methods, such as HPLC, GC and MS, being able to

provide probably one of the most promising ways to solve problems easily.

First, featuring high speed measurements, (generally from minutes to a few hours), specificity,

sensitivity (in nanomolar and sometimes femtomolar ranges), high degree of automation, biosensors

have the potential for real-time, on-line measurements, and high-throughput analysis along the

production chain.

Second, taking advantage of nanotechnology and materials science, new opportunities for

improving current technology have been investigated [91]. The integration of the high specificity of

biological recognition components with the unique optical and electrochemical properties of

nanomaterials provides novel interesting alternatives to conventional systems, improving the

sensitivity, the robustness and the performance of existing techniques.

Third, progress in bioinformatics and synthetic biology have paved the way to extend or to

modify the behavior of biological systems and to engineer them to perform new tasks, overcoming

some of the most important challenges for the last generation of biosensors, such as lowering their

LOD and increasing robustness.

In this context, the biosensors reported in this review probably exhibit the main crucial features

in terms of reliability, cost efficiency, stability, and multiplexing analysis, representing ideal

examples of new designs of sensing systems, able to overcome the problems of the old versions.

Nonetheless, a single biosensing system could be deficient in some characteristics, and so not be

ready as a commercial device. Indeed, although there has been huge variety of research on

biosensors for food industry, its application is still limited. Among the various drawbacks

restraining this technology are tests of prototypes in real samples remaining in a critical phase, due

to bioreceptor immobilization, sample preparation, stability, analysis in complex matrices, and real-

time measurements. These combination of challenges still limits the commercialization of

biosensors and consequently their application. Further detailed research is needed to move

biosensor technology from laboratory research to commercial products.

Crucial goals in manufacturing commercially feasible biosensors could be the isolation and

production of cost-efficient biological components, minimizing the costs of the devices, and

validation tests by regulatory agencies. In addition, they need to be highly sensitive, selective,

integrated and ready to use. A variety of new strategies must be considered to enhance commercial

applications of biosensors. Multiplexing could be essential for saving time and enlarging the range

of detected species, employing high-density arrays or lab-on-chip instruments able to perform a

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large number of measurements simultaneously. Food and water analysis constitute a promising

application field for multiplexed analysis based on a range of fluorescence, chemiluminescence,

electrochemical, hybrid electrochemical-optical systems in combination with a fluidics system.

Feasibly, one of the main tasks in which biosensor technology could be improved is the

recognition element. Enhancements in bioinformatics studies can model enzyme reactions and

receptor-binding interactions, enhancing knowledge on the biocomponent/transducer interface and

opening up new considerations for the design of new biological recognition elements. In this sense,

the use of synthetic materials represents an expanding research field focused on the design of new

molecules able to overcome elements missing in relation to biological components, such as

stability, sensitivity and production cost. Among synthetic materials, genetically-engineered

molecules, aptamers, artificial membranes, ribozymes and molecularly-imprinted polymers (MIPs)

recently received great attention as novel biosensing materials with better performance and/or

additional functions. In this context, biomimetic molecules are able to accelerate biosensor

development and applications. Poor stability of biological molecules could be overcome by

developing artificial molecular recognition elements with the desired selectivity and sensitivity

towards various compounds. Further, MIPs have been studied to obtain new low-cost, stable

receptors with high affinities for proteins, amino acids, sugars, vitamins, pesticides and antibiotics.

Furthermore, information and communication technology (ICT) in biosensing applications offers

a chance to construct biosensor-embedded systems, intended to integrate biological components

with transducers, microfluidics and network systems. Automated systems and wireless technologies

can be small, inexpensive and sustainable in energy use, reducing costs and significantly improving

food production and quality [92]. The goal is to develop multiple autonomous biosensing stations

able to provide information about key chemical, biological and physical parameters in agricultural

and industrial processes, rivers, lakes, wells, and water-treatment plants. Intelligent instrumentation,

electronics and signal-processing methods could have a key role in improving sampling, calibration

and data analysis and consequently providing instructions for a farmer or processor, so having

enormous impact on field-based agrifood and environmental measurements, as well as for

industrial, clinical, and security applications. A “biosensor embedded system” would integrate high-

density platforms, nanotechnology [93], microfluidics [94], new sensing molecules [95], and ICT.

Acknowledgements

This work was supported by grants from the ETB-2007-34 “MULTIBIOPLAT” Project and

COST Action TD1102 PHOTOTECH (http://www.phototech.eu). G.P. thanks EU Ocean 2013,

Project SMS, for financial support.

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Captions Fig. 1. The three generations of amperometric glucose biosensors, highlighting the evolution in biosensing research

over the years. The first generation (A) and the second generation (B) are based on the entrapment of glucose-oxidase

enzyme in polymers or membranes on a working electrode (metal or carbon) used as transducer, and are linked to a

mediator of electrons. The liberation of electrochemical species in the enzymatic reaction is measured at the working

electrode surface. The third generation (C) involves the possibility of achieving direct electrical communication

between the enzyme and the electrode surface.

Fig. 2. Structure of holo-glucose oxidase from Aspergillus niger as solved by X-ray spectroscopy. The enzyme was

rendered inactive by removing the FAD cofactor. Commercial fluorescent probe 8-anilino-1-naphthalene sulfonic acid

(ANS) was found to bind spontaneously to apo-glucose oxidase, as seen from the enhancement of the ANS

fluorescence. The steady state fluorescence intensity of the bound ANS decreased 25% upon binding of glucose. The

resulting apo-glucose oxidase was confirmed to be able to bind glucose, as observed from a decrease in its intrinsic

tryptophan fluorescence, without consuming it. These spectral changes occurred with a mid-point of 10–20 mM

glucose, which is comparable to the Kd of holo-glucose oxidase [12].

Fig. 3. The binding mechanism of glutamine-binding protein from Escherichia coli by interferometric detection on a

silicon layer. The binding protein penetrated and linked into the pores of the porous silicon matrix, thanks to the

hydrophobic interaction with the Si–H-terminated surface of the silicon. The sensor operated by measuring the

Page 14 of 17

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interferometric fringes in the reflectivity spectrum of the silicon layer, where the binding event was revealed as a shift in

wavelength of the fringes. The biosensor-signal response was measured in the range 9–36 µg/L and the sensitivity of

the method was calculated to be 0.23 nm µg/L [26].

Fig. 4. The Förster resonance-energy transfer (FRET) mechanism between fluorescein-labelled glutamine-binding

protein from Escherichia coli and rhodamine-labelled gliadin peptides. The binding protein specifically binds the

sequence of amino acids present in gliadin and other prolamins classified as toxic for celiac patients. Affinity

chromatography experiments together with mass spectrometry experiments demonstrated that the protein can bind the

amino-acid sequence XXQPQPQQQQQQQQQQQQL, exclusively representative of gliadin. These findings suggested

the development of an optical bioassay based on the FRET technique for easy, rapid detection of this sequence in raw

and cooked food. Glutamine-binding protein and gliadin peptides were labelled with fluorescein isothiocyanate and

rhodamine isothiocyanate, respectively. The FRET observed upon the addition of rhodamine-labelled gliadin peptides

indicated close interaction between the protein and gliadin. The results obtained indicated that the sensitivity of this

assay was up to 33 nM. Moreover, the protein was unable to bind a peptic-tryptic digest of zein, the corresponding

prolamin from corn that is a safe cereal for celiac patients, confirming the specificity of the assay [24].

Fig. 5. The modelled 3D structures of C. reinhardtii D1 and D2 proteins. QA and QB molecules are shown in ball-and-

stick representation and colored in red. The D1-D2 heterodimer present in photosystem II was examined to predict

mutations that enhance specificity and binding affinity for herbicides. In detail, the three-dimensional structure of the

protein was homology modelled on the basis of the high sequence homology with Thermococcus elongatus D1 and D2

proteins (87% and 89% amino-acid-sequence identity, respectively) and the affinity towards herbicides was predicted

by binding-energy calculations [55].

Table 1. Biosensor technology in the past 10 years, intended to control food quality and safety in

agrifood applications

Target analyte Matrix Analytical

parameters

Biosensor configuration Ref.

Glucose Wine 0.04-2.5 mM [9]

Glucose - 14 mM Polyvinylpyrrolidone-protected

graphene/polyethylenimine-

functionalized ionic liquid/GO

[10]

Glucose - 50 µM PPy nanowire GO [11]

Glucose - - Apo-GO from Aspergillus niger [12]

Glucose - - Sugar-binding protein (Ph-SBP)

from the Archaeon Pyrococcus

horikoshii

[19]

Glucose Beverages 0.1 mmol/L Glucose oxidase immobilized on

a graphite-rod electrode

modified with gold

nanoparticles (Au-NPs)

[20]

Ethanol

Glucose

Glycerol

Wines Pyrroloquinoline quinone-

dependent dehydrogenases

[23]

Glucose - Graphene oxide [24]

Glucose Soft drinks Electronic tongue [25]

Glutamine Fermentation 0.1 mM Glutaminase-based microfluidic

chip

[26]

Glutamine - - Glutamine-binding protein

(GlnBP) from E. coli porous

silicon Fabry-Perot layer

[27]

Gluten Food samples - Glutamine-binding protein

(GlnBP) from E. coli

[33], [34]

Gluten - 0.006 ppm Anti-gliadin peptide antibodies/

fluorescein-labelled gliadin

peptides by fluorescence

[35]

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16

correlation spectroscopy

Gliadin Natural or

pre-treated

food samples

µg/L Electrochemical magneto-

immunosensor

[37]

Aldicarb Carbaryl

Paraoxon Chlorpyrifos

Water 50 ppb

85 ppb

4 ppb

1 ppb

Acetylcholinesterase and

butyrylcholinesterase

immobilized on screen-printed

electrodes

[46]

Chlorpyrifos Coumaphos

Carbofuran

Orange juice

and water

2×10-8

mol/L

5×10-8

mol/L

8×10-9

mol/L

Acetylcholinesterase on Prussian

Blue screen-printed carbon

electrode

[47]

Aldicarb

Paraoxon

Parathion

Juice spiked 30 ppb

10 ppb

5 ppb

Acetylcholinesterase on Prussian

Blue screen-printed electrodes

[48]

Paraoxon Drinking

water

2 ppb Acetylcholinesterase biosensor

based on self-assembled

monolayer-modified gold-

screen-printed electrodes

[49]

Paraoxon

Malaoxon

Drinking

water

3 ppb

2 ppb

Acetylcholinesterase biosensor

based on screen-printed carbon

electrodes

[50]

Chlorpyrifos

Chlorfenvinphos

- - Acetylcholinesterase with

artificial neural network (ANN)

data analysis

[51]

Atrazine Drinking

water

1.8 μM Core-shell nanostructured

molecular imprinting fluorescent

chemosensor

[54]

Atrazine

Arsenic trioxide

Drinking

water

3 ppb

1 ppb

Surface-enhanced Raman

spectroscopy coupled with gold

nanostructures

[55]

2,4dichlorophenoxyacetic

acid

Water

samples

3 ng/mL Gold nanoparticle-catalyzed

chemiluminescence

immunoassay

[56]

Diazines, triazines and

urea herbicides

- 0.8×10-11

M–

3.0×10-9

M

D1 mutant variant from C.

reinhardtii

[62]

Diazines, triazines and

urea herbicides

- 1.0×10-9

M–

3.0×10-10

M

Array of engineered C.

reinhardtii algae

[63]

Aflatoxin B Spiked

samples

90 pg/mL

Indirect competitive

electrochemical enzyme-linked

immunosorbent assay (ELISA)

[65]

Aflatoxin B Corn 30 pg/mL Immunosensor array into

multichannel electrochemical

detection (MED) system

[66]

Aflatoxin B2

Aflatoxin G1

Aflatoxin G2

Aflatoxin M1

Fortified

barley

samples

10-60 ng/mL Cholinesterases (ChEs) with

Ellman’s method

[67]

Aflatoxin B Olive oil 10 ppb Choline oxidase on Prussian

Blue screen-printed electrodes

[68]

Ochratoxin A

Neomycin

- 0.0078 ng

0.0156 ng

Immunosensor with

fluorescence correlation

spectroscopy

[69]

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17

Ochratoxin A Ground corn 0.2 ng/mL Alkaline phosphatase (ALP)-

labeled horse anti-mouse IgG

antibodies into polythionine

(PTH)/gold nanoparticle

(nanoAu) composite film

[70]

Ochratoxin A Spiked

wine samples

180 pg/mL Electrochemical immunosensor [71]

Ochratoxin A Wines 0.11 ng/L Magnetic beads covered with

streptavidin and functionalized

with a monoclonal antibody

[72]

Aflatoxin M1 Milk 25 ppt Direct competitive

immunosensor

[73]

Patulin - 10 µg/L Immunochemical sensor [74]

Domoic acid - 3 ppb Surface-plasmon-resonance

biosensor

[76]

Domoic acid - 20 µg/g Immunobiosensor [77]

Okadaic acid Mussel 0.15 μg/L Amperometric immunosensor [78]

Okadaic acid - 0.0124 μg/L Protein phosphatase-based

colorimetric sensor

[79]

Saxitoxin - Kd = 14.6 nM Glycoprotein from Fugu

pardalis

[80]

Palytoxin - µg/kg range Electrochemiluminescent

antibody nanobiosensor

[81]

Heavy metals Soil samples - Metallothionein promoters from

Tetrahymena thermophila

[82]

Arsenic Water - Bacteria-based bioassays [83]

Hg2+

and Ag+ - - Conductometric biosensor [84]

Arsenic (III) Tap water 1.1×10-8

M Acetylcholinesterase on screen-

printed electrodes

[86]

Cadmium Farm produce 2.30 µg/L Enzyme-linked immunosensor [87]

Cadmium - 1.95 µg/L Enzyme-linked immunosensor [88]

Cadmium - 0.01–0.1 mg/L Immunochromatography sensor [89]

Hg2+

Ag+

Pb2+

- 15 nM

18 nM

20 nM

Aptamers on carbon nanotube [90]

Page 17 of 17