biosensing technology for sustainable food safety
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articulo cientificoTRANSCRIPT
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: [email protected] (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
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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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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