Electronic nose for microbial quality classification of grains

Electronic nose and wireless sensor network for environmental monitoring application in pulp and paper industry: a review

Pulp and paper industries emit various odorous gases during the pulp production and paper-making phase, which are unpleasant and have harmful effects on the human body. The working staffs are continuously exposed to these gases and develop various health issues. Hence, regular monitoring and analysis of such gases are necessary to avoid any sudden high concentration exposure and to prevent adverse health effects on the staff. An electronic nose (EN) has an array of gas sensors with an alert system for early detection of gases. Various ENs have been developed for varying applications till date. The detailed knowledge of the sensors used, their sensitivity and technology is helpful in development of any EN. The objective of this study is to comprehensively review various developed ENs with respect to their gas sensing and pattern recognition (PR) technologies. The information on gases released from pulp and paper industries is also compiled. The evolution of EN technology, its various applications, challenges in developing EN and its utility in safeguarding the industrial workers' life have been described. Further, gap analysis among previously developed EN, contemporary EN and wireless sensor network (WSN) is elaborated. It will facilitate future researchers for better selection of sensors and PR technologies while developing EN. The commonly used sensing technologies are described with their advantages, disadvantages and working principles. Metal oxide semiconductor (MOS) gas sensor and ANN algorithm show better result and hence recommended in the development of EN, whereas ZigBee protocol has been widely used for WSN.

Research advance in gas detection of volatile organic compounds released in rice quality deterioration process

Rice quality deterioration will cause grievous waste of stored grain and various food safety problems. Gas detection of volatile organic compounds (VOCs) produced by deterioration is a nondestructive detection method to judge rice quality and alleviate rice spoilage. This review discussed the research advance of VOCs detection in terms of nondestructive detection methods of rice quality deterioration, applications of VOCs in grain detection, inspection of characteristic gas produced during rice spoilage, rice deterioration prevention and control, and detection of VOCs released by rice mildew and insect attack. According to the main causes of rice quality deterioration and major sources of VOCs with off-odor generated during rice storage, deterioration can be divided into mold and insect infection. The results of literature manifested that researches mainly focused on the infection of Aspergillus in the mildew process and the attack of certain pests in recent years, thus the research scope was limited. In this paper, the gas detection methods combined with the chemometrics to qualitatively analyze the VOCs, as well as the correlation with the number of colonies and insects were further studied based on the common dominant strains during rice mildew, that is, Aspergillus and Penicillium fungi, and the common pests during storage, that is, Sitophilus oryzae and Rhyzopertha dominica. Furthermore, this paper pointed out that the quantitative determination of characteristic VOCs, the numeration relationship between VOCs and the degree of mildew and insect infestation, the further expansion of detection range, and the application of degraded rice should be the spotlight of future research.

A Barcoded Polymer-Based Cross-Reactive Spectroscopic Sensor Array for Organic Volatiles

The development of cross-reactive sensor arrays for volatile organics (electronic noses, e-noses) is an active area of research. In this manuscript, we present a new format for barcoded polymer sensor arrays based on porous polymer beads. An array of nine self-encoded polymers was analyzed by Raman spectroscopy before and after exposure to a series of volatile organic compounds, and the changes in the vibrational fingerprints of their polymers was recorded before and after exposure. Our results show that the spectroscopic changes experienced by the porous spectroscopically encoded beads after exposure to an analyte can be used to identify and classify the target analytes. To expedite this analysis, analyte-specific changes induced in the sensor arrays were transformed into a response pattern using multivariate data analysis. These studies established the barcoded bead array format as a potentially effective sensing element in e-nose devices. Devices such as these have the potential to advance personalized medicine, providing a platform for non-invasive, real-time volatile metabolite detection.

Non-destructive techniques for the detection of fungal infection in cereal grains

Infection of cereal grains by fungi is a serious problem worldwide. Depending on the environmental conditions, cereal grains may be colonised by different species of fungi. These fungi cause reduction in yield, quality and nutritional value of the grain; and of major concern is their production of mycotoxins which are harmful to both humans and animals. Early detection of fungal contamination is an essential control measure for ensuring storage longevity and food safety. Conventional methods for detection of fungal infection, such as culture and colony techniques or immunological methods are either slow, labour intensive or difficult to automate. In recent years, there has been an increasing need to develop simple, rapid, non-destructive methods for early detection of fungal infection and mycotoxins contamination in cereal grains. Methods such as near infrared (NIR) spectroscopy, NIR hyperspectral imaging, and electronic nose were evaluated for these purposes. This paper reviews the different non-destructive techniques that have been considered thus far for detection of fungal infection and mycotoxins in cereal grains, including their principles, application and limitations.

Cross-reactive, self-encoded polymer film arrays for sensor applications

Simple and versatile spectroscopically-encoded styrene-based polymers are the basis for advanced e-Nose sensor array technology.

Kiwi fruit (Actinidia chinensis) quality determination based on surface acoustic wave resonator combined with electronic nose

In this study, electronic nose (EN) combined with a 433 MHz surface acoustic wave resonator (SAWR) was used to determine Kiwi fruit quality under 12-day storage. EN responses to Kiwi samples were measured and analyzed by principal component analysis (PCA) and stochastic resonance (SR) methods. SAWR frequency eigen values were also measured to predict freshness. Kiwi fruit sample's weight loss index and human sensory evaluation were examined to characteristic its quality and freshness. Kiwi fruit's quality predictive models based on EN, SAWR, and EN combined with SAWR were developed, respectively. Weight loss and human sensory evaluation results demonstrated that Kiwi fruit's quality decline and overall acceptance decrease during the storage. Experiment result indicated that the PCA method could qualitatively discriminate all Kiwi fruit samples with different storage time. Both SR and SAWR frequency analysis methods could successfully discriminate samples with high regression coefficients (R = 0.98093 and R = 0.99014, respectively). The validation experiment results showed that the mixed predictive model developed using EN combined with SAWR present higher quality prediction accuracy than the model developed either by EN or by SAWR. This method exhibits some advantages including high accuracy, non-destructive, low cost, etc. It provides an effective way for fruit quality rapid analysis.

Food analysis using artificial senses

Nowadays, consumers are paying great attention to the characteristics of food such as smell, taste, and appearance. This motivates scientists to imitate human senses using devices known as electronic senses. These include electronic noses, electronic tongues, and computer vision. Thanks to the utilization of various sensors and methods of signal analysis, artificial senses are widely applied in food analysis for process monitoring and determining the quality and authenticity of foods. This paper summarizes achievements in the field of artificial senses. It includes a brief history of these systems, descriptions of most commonly used sensors (conductometric, potentiometric, amperometic/voltammetric, impedimetric, colorimetric, piezoelectric), data analysis methods (for example, artificial neural network (ANN), principal component analysis (PCA), model CIE L*a*b*), and application of artificial senses to food analysis, in particular quality control, authenticity and falsification assessment, and monitoring of production processes.

Diverse applications of electronic-nose technologies in agriculture and forestry

Electronic-nose (e-nose) instruments, derived from numerous types of aroma-sensor technologies, have been developed for a diversity of applications in the broad fields of agriculture and forestry. Recent advances in e-nose technologies within the plant sciences, including improvements in gas-sensor designs, innovations in data analysis and pattern-recognition algorithms, and progress in material science and systems integration methods, have led to significant benefits to both industries. Electronic noses have been used in a variety of commercial agricultural-related industries, including the agricultural sectors of agronomy, biochemical processing, botany, cell culture, plant cultivar selections, environmental monitoring, horticulture, pesticide detection, plant physiology and pathology. Applications in forestry include uses in chemotaxonomy, log tracking, wood and paper processing, forest management, forest health protection, and waste management. These aroma-detection applications have improved plant-based product attributes, quality, uniformity, and consistency in ways that have increased the efficiency and effectiveness of production and manufacturing processes. This paper provides a comprehensive review and summary of a broad range of electronic-nose technologies and applications, developed specifically for the agriculture and forestry industries over the past thirty years, which have offered solutions that have greatly improved worldwide agricultural and agroforestry production systems.

Electronic Nose for Microbiological Quality Control of Food Products

Electronic noses (ENs) have recently emerged as valuable candidates in various areas of food quality control and traceability, including microbial contamination diagnosis. In this paper, the EN technology for microbiological screening of food products is reviewed. Four paradigmatic and diverse case studies are presented: (a)Alicyclobacillusspp. spoilage of fruit juices, (b) early detection of microbial contamination in processed tomatoes, (c) screening of fungal and fumonisin contamination of maize grains, and (d) fungal contamination on green coffee beans. Despite many successful results, the high intrinsic variability of food samples together with persisting limits of the sensor technology still impairs ENs trustful applications at the industrial scale. Both advantages and drawbacks of sensor technology in food quality control are discussed. Finally, recent trends and future directions are illustrated.

Invited review article: an odor-sensing system--powerful technique for foodstuff studies

This work examines gas sensor array technology combined with multivariate data processing methods and demonstrates a promising potential for rapid, non-destructive analysis of food. Main attention is focused on detailed description of sensor used in e-nose instruments, construction, and principle of operation of these systems. Moreover, this paper briefly reviews the progress in the field of artificial olfaction and future trends in electronic nose technology, namely, e-nose based on mass spectrometry. Further discussion concerns a comparison of artificial nose with gas chromatography-olfactometry and the application of e-nose instruments in different areas of food industry.

Differential detection of potentially hazardous Fusarium species in wheat grains by an electronic nose

Fungal infestation on wheat is an increasingly grave nutritional problem in many countries worldwide. Fusarium species are especially harmful pathogens due to their toxic metabolites. In this work we studied volatile compounds released by F. cerealis, F. graminearum, F. culmorum and F. redolens using SPME-GC/MS. By using an electronic nose we were able to differentiate between infected and non-infected wheat grains in the post-harvest chain. Our electronic nose was capable of distinguishing between four wheat Fusaria species with an accuracy higher than 80%.

Metal oxide sensors for electronic noses and their application to food analysis

Electronic noses (E-noses) use various types of electronic gas sensors that have partial specificity. This review focuses on commercial and experimental E-noses that use metal oxide semi-conductors. The review covers quality control applications to food and beverages, including determination of freshness and identification of contaminants or adulteration. Applications of E-noses to a wide range of foods and beverages are considered, including: meat, fish, grains, alcoholic drinks, non-alcoholic drinks, fruits, milk and dairy products, olive oils, nuts, fresh vegetables and eggs.

Near infrared and fluorescence spectroscopic methods and electronic nose technology for monitoring foods

There is a clear need for application of proper methods for measuring food quality and safety in the globalized food-webs. Numerous instrumental methods have been established in the course of the 20th century and are developing further, together with data analysis techniques, for such purposes. Among them, near-infrared and fluorescence spectroscopic methods and chemical sensor arrays called electronic noses show particular promise for rapid, non-destructive, non-invasive and cost-effective ways for assessing changes and enhancing control during processing and storage of foods. Their key advantages as analytical tools are 1) their relatively high speed of analysis, 2) the lack of a need to carry out complex sample preparation or processing, 3) their relatively low cost, and 4) their suitability for on-line monitoring or quality control. The present survey attempts to demonstrate examples from the above areas, limiting itself mainly to monitoring some quality indices which contribute to the functionality or acceptability of foods as affected by alternative processing technologies, or loss of freshness/microbial safety, or developing spoilage during storage and marketing. These instrumental methods are correlative techniques: they must be calibrated first against (traditional) reference properties, and the instrumental data are evaluated with the help of chemometric methods. Near-infrared (NIR) spectroscopy can be used in either the reflectance or the transmittance mode. NIR spectra transformed to mathematical derivatives allows subtle spectrum changes to be resolved. Selected examples from the extensive NIRS literature relate to assessment of the quality of frozen fish, predicting cooking loss of chicken patties, detecting complex physico-chemical changes of minced meat as a function of the intensity of high hydrostatic pressure treatment, comparing changes of NIR spectrometric “fingerprints” caused by gamma radiation or high pressure pasteurization of liquid egg white. Changes of NIR spectra reflect several parameters which suit the evaluation of loss of freshness, and onset of spoilage of various foods. NIR spectroscopy shows an application potential for rapid detection of bacterial or mould contamination. It may serve as a tool for detecting initial stages of mobilization processes during germination of cereal grains, or even for GMO screening. Spectrofluorometic measurements have shown potential, e.g. to monitor lipid oxidation and development of meat rancidity, to differentiate between raw and processed milks, and to monitor fish and egg freshness. Electronic noses containing chemical sensor arrays offer a rapid method for evaluation of head-space volatiles of food samples, important for characterizing quality and safety. Such gas sensors may be able to classify storage time, and determine spoilage, either earlier or at the same time as the human senses, or “sniffing out” bacterial pathogens or (toxigenic) fungal growth on certain foods. Electronic nose sensing is also a promising method for detecting quality changes of fruit- and vegetable products non-destructively. In relation to some examples to be presented in the paper, certain software developments as qualitative classification tools made by Hungarian scientists will be pointed out.

Applications and advances in electronic-nose technologies

Electronic-nose devices have received considerable attention in the field of sensor technology during the past twenty years, largely due to the discovery of numerous applications derived from research in diverse fields of applied sciences. Recent applications of electronic nose technologies have come through advances in sensor design, material improvements, software innovations and progress in microcircuitry design and systems integration. The invention of many new e-nose sensor types and arrays, based on different detection principles and mechanisms, is closely correlated with the expansion of new applications. Electronic noses have provided a plethora of benefits to a variety of commercial industries, including the agricultural, biomedical, cosmetics, environmental, food, manufacturing, military, pharmaceutical, regulatory, and various scientific research fields. Advances have improved product attributes, uniformity, and consistency as a result of increases in quality control capabilities afforded by electronic-nose monitoring of all phases of industrial manufacturing processes. This paper is a review of the major electronic-nose technologies, developed since this specialized field was born and became prominent in the mid 1980s, and a summarization of some of the more important and useful applications that have been of greatest benefit to man.

Differentiation of closely related fungi by electronic nose analysis

In this work the potential of electronic nose analysis for differentiation of closely related fungi has been described. A total of 20 isolates of the cheese-associated species Geotrichum candidum, Penicillium camemberti, P. nordicum, and P. roqueforti and its closely related species P. paneum, P. carneum as well as the noncheese-associated P. expansum have been investigated by electronic nose, GC-MS, and LC-MS analysis. The isolates were inoculated on yeast extract sucrose agar in 20-mL headspace flasks and electronic nose analysis was performed daily for a 7-d period. To assess which volatile metabolites the electronic nose potentially responded to, volatile metabolites were collected by diffusive sampling overnight onto tubes containing Tenax TA, between the 7th and 8th day of incubation. Volatiles were analyzed by gas chromatography coupled to mass spectrometry and the results indicated that mainly alcohols (ethanol, 2-methyl-1-propanol, and 3-methyl-1-butanol) and ketones (acetone, 2-butanone, and 2-pentanone) were produced at this stage. The volatile metabolite profile proved to be species specific. Nonvolatile metabolites were collected on the 8th day of incubation and mycotoxin analysis was performed by high pressure liquid chromatography coupled to a diode array detector and a time of flight mass spectrometer. Several mycotoxins were detected in samples from the species P. nordicum, P. roqueforti, P. paneum, P. carneum, and P. expansum. Differentiation of closely related mycotoxin producing fungi incubated on yeast extract sucrose agar has been achieved, indicating that there is a potential for predicting production of mycotoxins on food and feedstuffs by electronic nose analysis.

A Multipurpose receptor composed of promiscuous proteins. Analyte detection through pattern recognition

A multipurpose receptor akin to the "electronic nose" was composed of coumarin-labeled mutants of human glutathione transferase A1. We have previously constructed a kit for site-specific modification of a lysine residue (A216K) using a thiol ester of glutathione (GSC-Cou bio) as a modifying reagent. In the present investigation, we scrambled the hydrophobic binding site (H-site) of the protein scaffold through mutations at position M208 via random mutagenesis and isolated a representative library of 11 A216K/M208X mutants. All of the double mutants could be site-specifically labeled to form the K216 Cou conjugates. The labeled proteins responded to the addition of different analytes with signature changes in their fluorescence spectra resulting in a matrix of 96 data points per analyte. Ligands as diverse as n-valeric acid, fumaric acid monoethyl ester, lithocholic acid, 1-chloro-2,4-dinitrobenzene (CDNB), glutathione (GSH), S-methyl-GSH, S-hexyl-GSH, and GS-DNB all gave rise to signals that potentially can be interpreted through pattern recognition. The measured K d values range from low micromolar to low millimolar. The cysteine residue C112 was used to anchor the coumarin-labeled protein to a PEG-based hydrogel chip in order to develop surface-based biosensing systems. We have thus initiated the development of a multipurpose, artificial receptor composed of an array of promiscuous proteins where detection of the analyte occurs through pattern recognition of fluorescence signals. In this system, many relatively poor binders each contribute to detailed readout in a truly egalitarian fashion.

Potential for detection and discrimination between mycotoxigenic and non-toxigenic spoilage moulds using volatile production patterns: A review

There has been interest in the development of techniques for the rapid early detection of mycotoxigenic moulds in the food production chain. The development of sensor arrays that respond to the presence of different volatiles produced by such moulds has been examined as a potential method for the development of such detection systems. Commercial devices based on such sensor arrays, so-called 'electronic noses', have been examined extensively for the potential application of determining the presence of mycotoxigenic moulds in food raw materials. There is also interest in using the qualitative volatile production patterns to discriminate between non-mycotoxigenic and mycotoxigenic strains of specific mycotoxigenic species, e.g. Fusarium section Liseola, Penicillium verrucosum and Aspergillus section Nigri. This paper reviews the technology and available evidence that the non-destructive analysis of the headspace of samples of food raw materials or the discrimination between strains (mycotoxigenic and non-mycotoxigenic) can be determined using volatile fingerprints.

Applications of artificial neural networks (ANNs) in food science

Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decades, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs hold a great deal of promise for modeling complex tasks in process control and simulation and in applications of machine perception including machine vision and electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in the field.

Modelling fungal growth using radial basis function neural networks: The case of the ascomycetous fungus Monascus ruber van Tieghem

A radial basis function (RBF) neural network was developed and evaluated against a quadratic response surface model to predict the maximum specific growth rate of the ascomycetous fungus Monascus ruber in relation to temperature (20-40 degrees C), water activity (0.937-0.970) and pH (3.5-5.0), based on the data of Panagou et al. [Panagou, E.Z., Skandamis, P.N., Nychas, G.-J.E., 2003. Modelling the combined effect of temperature, pH and aw on the growth rate of M. ruber, a heat-resistant fungus isolated from green table olives. J. Appl. Microbiol. 94, 146-156]. Both RBF network and polynomial model were compared against the experimental data using five statistical indices namely, coefficient of determination (R(2)), root mean square error (RMSE), standard error of prediction (SEP), bias (B(f)) and accuracy (A(f)) factors. Graphical plots were also used for model comparison. For training data set the RBF network predictions outperformed the classical statistical model, whereas in the case of test data set the network gave reasonably good predictions, considering its performance for unseen data. Sensitivity analysis showed that from the three environmental factors the most influential on fungal growth was temperature, followed by water activity and pH to a lesser extend. Neural networks offer an alternative and powerful technique to model microbial kinetic parameters and could thus become an additional tool in predictive mycology.

Direct integration of pervaporation as a sample preparation method for a dedicated "electronic nose"

The present study investigates the possibility of monitoring the bioproduction of a complex aroma profile with an analytical electronic aroma-sensing technique, the so-called "electronic nose", combined with a pervaporative sample enrichment method necessary to overcome the ethanol interference on the sensors' response. It presents in detail the development of a direct integrated pervaporation-electronic nose unit for a simple and fast analysis, which are key criteria for this technique to be broadly implemented. The system developed was investigated using model solutions simulating the muscatel wine must fermentation. It proved to be able to evaluate different relevant aroma compounds in solutions of varying degree of complexity, and also in the presence of ethanol, which is a major interference on the sensors' response to the aromas. The transient sensors' response was investigated in detail, revealing information for sample discrimination and reducing the analysis time. The system developed allowed a simple, fast, and selective analysis, therefore permitting a high sample throughput over time, with the possibility of fully automation.

Use of a MS-electronic nose for prediction of early fungal spoilage of bakery products

A MS-based electronic nose was used to detect fungal spoilage (measured as ergosterol concentration) in samples of bakery products. Bakery products were inoculated with different Eurotium, Aspergillus and Penicillium species, incubated in sealed vials and their headspace sampled after 2, 4 and 7 days. Once the headspace was sampled, ergosterol content was determined in each sample. Different electronic nose signals were recorded depending on incubation time. Both the e-nose signals and ergosterol levels were used to build models for prediction of ergosterol content using e-nose measurements. Accuracy on prediction of those models was between 87 and 96%, except for samples inoculated with Penicillium corylophilum where the best predictions only reached 46%.

Detection of Escherichia coli in packaged alfalfa sprouts with an electronic nose and an artificial neural network

A rapid method for the detection of Escherichia coli (ATCC 25922) in packaged alfalfa sprouts was developed. Volatile compounds from the headspace of packaged alfalfa sprouts, inoculated with E. coli and incubated at 10 degrees C for 1, 2, and 3 days, were collected and analyzed. Uninoculated sprouts were used as control samples. An electronic nose with 12 metal oxide electronic sensors was used to monitor changes in the composition of the gas phase of the package headspace with respect to volatile metabolites produced by E. coli. The electronic nose was able to differentiate between samples with and without E. coli. To predict the number of E. coli in packaged alfalfa sprouts, an artificial neural network was used, which included an input layer, a hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. The network was shown to be capable of correlating voltametric responses with the number of E. coli. A good prediction was possible, as measured by a regression coefficient (R2 = 0.903) between the actual and predicted data. In conjunction with the artificial neural network, the electronic nose proved to have the ability to detect E. coli in packaged alfalfa sprouts.

Volatile compounds of Aspergillus strains with different abilities to produce ochratoxin A

Volatile compounds emitted by Aspergillus strains having different abilities to produce ochratoxin A were investigated. Thirteen strains of Aspergillus ochraceus, three belonging to the A. ochraceus group, and eight other species of Aspergillus were examined for their abilities to produce volatile compounds and ochratoxin A on a wheat grain medium. The profiles of volatile compounds, analyzed using SPME, in all A. ochraceus strains, regardless of their toxeginicity, were similar and comprised mainly of 1-octen-3-ol, 3-octanone, 3-octanol, 3-methyl-1-butanol, 1-octene, and limonene. The prevailing compound was always 1-octen-3-ol. Mellein, which forms part of the ochratoxin A molecule, was found in both toxigenic and nontoxigenic strains. Volatile compounds produced by other Aspergillus strains were similar to those of A. ochraceus. Incubation temperatures (20, 24, and 27 degrees C) and water content in the medium (20, 30, and 40%) influenced both volatile compounds formation and ochratoxin A biosynthesis efficiency, although conditions providing the maximum amount of volatiles were different from those providing the maximum amount of ochratoxin A. The pattern of volatiles produced by toxigenic A. ochraceus strains does not facilitate their differentiation from nontoxigenic strains.

Differentiation of species from the Penicillium roqueforti group by volatile metabolite profiling

Species from the Penicillium roqueforti group were differentiated by volatile metabolite profiling primarily of sesquiterpenes. A total of 24 isolates from species P. roqueforti, Penicillium carneum, and the recently described species Penicillium paneum were inoculated on yeast extract sucrose agar. Volatile metabolites were collected by diffusive sampling onto tubes containing Tenax TA, overnight between the fifth and sixth days of incubation. Volatiles were thermally desorbed and analyzed by gas chromatography coupled to mass spectrometry. The sesquiterpene area of the chromatogram was investigated, and potential sesquiterpenes were tabulated by comparison of their Kovats retention index and mass spectrum. In general, P. carneum isolates produced the lowest number of sesquiterpenes, all of which were unique for P. carneum within the P. roqueforti group. P. roqueforti and P. paneum produced a larger variety of volatile metabolites, some of which they have in common and some of which are unique for the two species. (+)-Aristolochene was found in samples from P. paneum and P. roqueforti. Other Penicillium species in which (+)-aristolochene was also detected were P. commune, P. glandicola, and P. solitum.

Rapid method for prediction of Escherichia coli numbers using an electronic sensor array and an artificial neural network

An electronic sensor array with 12 nonspecific metal oxide sensors was evaluated for its ability to monitor volatile compounds in super broth alone and in super broth inoculated with Escherichia coli (ATCC 25922) at 37 degrees C for 2 to 12 h. Using discriminant function analysis, it was possible to differentiate super broth alone from that containing E. coli when cell numbers were 10(5) CFU or more. There was a good agreement between the volatile profiles from the electronic sensor array and a gas chromatography-mass spectrometer method. The potential to predict the number of E. coli and the concentration of specific metabolic compounds was investigated using an artificial neural network (ANN). The artificial neural network was composed of an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. Good prediction was found as measured by a regression coefficient (R2 = 0.999) between actual and predicted data.

Determination of geosmin, 2-methylisoborneol, and a musty-earthy odor in wheat grain by SPME-GC-MS, profiling volatiles, and sensory analysis

Geosmin and 2-methylisoborneol-compounds responsible for the musty-earthy off-odor of wheat grain, were isolated by SPME and analyzed by GC-MS. Carboxen/PDMS/DVB fiber coating was selected because of its highest extraction efficiency. Concentrations of geosmin and 2-methylisoborneol as low as 0.001 microg/kg were detected in SIM mode using ion trap mass spectrometer. Apart from GC-MS determination of geosmin and 2-methylisoborneol, various methods for evaluating the musty-earthy off-odor caused by these compounds in wheat grain are presented. Sensory profile analysis differentiated wheat grain into sound and off-flavored, but the method was tedious. Similar groupings, however, were obtained using more rapid methods such as comparison of volatile profiles using SPME-fast GC with PCA projection of data and metal oxide (MOS) based electronic nose.

Pattern analysis techniques to process fermentation curves: application to discrimination of enological alcoholic fermentations

In fermentation processes, kinetic curves are generally aimed at control purposes. However, these curves could also contain information about inherent features of the product (such as origin, quality, etc.). This article presents several pattern analysis techniques used to classify fermentation curves. An application to alcoholic fermentation is presented as an illustration: it aims at retrieving the origin of a must from its fermentation curve. The fermentation kinetics of five vineyard musts, harvested over 9 years on the same parcels, were recorded. From these curves two sets of variables were generated: The first (p(1)) gathers all the kinetic curve points. The second (p(2)) contains a restrained number of variables, generated by the expert knowledge of the enologist. The set p(2) was processed by two very different techniques: a linear one (factorial discriminant analysis) and a nonlinear one (artificial neural networks). The set p(1) was processed by a new chemometric technique, the discriminant partial least-squares regression. For all the sets and the techniques used the selection of variables was studied. The interest in the latter is largely demonstrated both by theoretical and practical discussions. The discrimination results (up to 94% of good predictions) enhance the interest of the on-line measurements and their use in such pattern analysis tools.

Detection and quantification of ochratoxin A and deoxynivalenol in barley grains by GC-MS and electronic nose

Mycotoxin contamination of cereal grains can be detected and quantified using complex extraction procedures and analytical techniques. Normally, the grain odour, i.e. the presence of non-grain volatile metabolites, is used for quality classification of grain. We have investigated the possibility of using fungal volatile metabolites as indicators of mycotoxins in grain. Ten barley samples with normal odour, and 30 with some kind of off-odour were selected from Swedish granaries. The samples were evaluated with regard to moisture content, fungal contamination, ergosterol content, and levels of ochratoxin A (OA) and deoxynivalenol (DON). Volatile compounds were also analysed using both an electronic nose and gas chromatography combined with mass spectrometry (GC-MS). Samples with normal odour had no detectable ochratoxin A and average DON contents of 16 microg kg(-1) (range 0-80), while samples with off-odour had average OA contents of 76 microg kg(-1) (range 0-934) and DON contents of 69 microg kg(-1) (range 0-857). Data were evaluated by multivariate data analysis using projection methods such as principal component analysis (PCA) and partial least squares (PLS). The results show that it was possible to classify the OA level as below or above the maximum limit of 5 microg kg(-1) cereal grain established by the Swedish National Food Administration, and that the DON level could be estimated using PLS. Samples with OA levels below 5 microg kg(-1) had higher concentration of aldehydes (nonanal, 2-hexenal) and alcohols (1-penten-3-ol, 1-octanol). Samples with OA levels above 5 microg kg(-1) had higher concentrations of ketones (2-hexanone, 3-octanone). The GC-MS system predicted OA concentrations with a higher accuracy than the electronic nose, since the GC-MS misclassified only 3 of 37 samples and the electronic nose 7 of 37 samples. No correlation was found between odour and OA level, as samples with pronounced or strong off-odours had OA levels both below and above 5 microg kg(-1). We were able to predict DON levels in the naturally contaminated barley samples using the volatile compounds detected and quantified by either GC-MS or the electronic nose. Pentane, methylpyrazine, 3-pentanone, 3-octene-2-ol and isooctylacetate showed a positive correlation with DON, while ethylhexanol, pentadecane, toluene, 1-octanol, 1-nonanol, and 1-heptanol showed a negative correlation with DON. The root mean square error of estimation values for prediction of DON based on GC-MS and electronic nose data were 16 and 25 microg kg(-1), respectively.

Volatile Fingerprinting (SPME-GC-FID) to Detect and Discriminate Diseases of Potato Tubers

Volatiles from Russet Burbank potatoes inoculated with Erwinia carotovora subsp. carotovora, E. carotovora subsp. atroseptica, Pythium ultimum, Phytophthora infestans, or Fusarium sambucinum were monitored by sampling the head space 3, 4, and 5 days after inoculation, using a solid phase microextraction (SPME) fiber to trap and gas chromatography with flame ionization detector (GC-FID) to fingerprint volatiles. Noninoculated (NON) potatoes served as the control. Volatile fingerprints varied among diseases. Within a disease, the fingerprints varied with time since inoculation and among blocks. In general, more volatiles were observed on the fourth and fifth day after inoculation than on the third day. The amount of volatile compounds produced (peak area) within a disease group increased with incubation time; however, the variation among blocks was much higher. The amount of volatiles produced, in general, was associated with disease severity. Disease-specific volatiles were observed. The F. sambucinum chromatogram had two unique peaks at retention time (RT) = 14.1 and 17.3 min. P. infestans produced few peaks and the profile was quite similar to NON. In contrast, E. carotovora subsp. carotovora, E. carotovora subsp. atroseptica, and Pythium ultimum produced many peaks, and the P. ultimum was different from the bacteria, in that the chromatogram peaks at RT = 4.04 and 8.76 min were absent. Instead, it produced a distinct peak at RT = 1.71 min. E. carotovora subsp. carotovora and E. carotovora subsp. atroseptica couldn't be discriminated based on unique peaks; however, they varied in concentration of volatiles produced. E. carotovora subsp. carotovora produced more of RT = 2.0 min and less of RT = 2.3 and 2.44 min than E. carotovora subsp. atroseptica. A back-propagation network (using neural networks) was developed to classify volatile profiles into six disease-groups. Cross-validation classification probabilities were NON = 71, E. carotovora subsp. carotovora = 71, E. carotovora subsp. atroseptica = 71, P. ultimum = 67, Phytophthora infestans = 46, and F. sambucinum = 75%.

The strengths and weaknesses of the electronic nose

Arrays of electronic sensors, capable of detecting and differentiating complex mixtures of volatile compounds, have been utilized to differentiate aromas of food and related materials. These sensor arrays have been dubbed "Electronic Noses" and have been commercially available in the USA for the past 4-5 years. Electronic nose technology is still in its development phase, both in respect to hardware and software development. The instruments contain an array of from one to 32 sensors, using a variety of different sensor technologies--from organic polymers to metal oxides to micro-balances. Electronic noses are being widely used by some companies as a quality control instrument. Strengths include high sensitivity and correlation to human sensory panels for many applications. Limitations to their full potential includes loss of sensitivity in the presence of water vapor or high concentrations of a single component like alcohol; sensor drift and the inability to provide absolute calibration: relatively short life of some sensors; necessity to do considerable method development work for each specific application; and lack of being able to obtain quantitative data for aroma differences. They do have a high sensitivity (ppt to ppm) and are often more sensitive than the human nose. There is some evidence that sensors differentiate aromas on the basis of relatively few compounds and in the future a relationship between specific chemicals and a single flavor attribute may be achievable. Also, the possibility exists to differentiate between "top" and "middle" notes of aroma.

Microbiological, sensory, and electronic nose evaluation of yellowfin tuna under various storage conditions

Microbiological assessment, sensory evaluation, and electronic nose (AromaScan) analysis were performed on yellowfin tuna stored at 0, 4, 10, and 22 degrees C for 0, 1, 3, 5, and 9 days. Fish color, texture, appearance, and odor were evaluated by a trained sensory panel, while aroma-odor properties were evaluated using an AromaScan. Bacterial enumeration was performed using plate count agar containing 1.5% NaCl. Tuna fillets stored at 22 degrees C for 3 days or longer had a bacterial load of over 10(7) CFU/g and were rated not acceptable for consumption (grade C) by the sensory panel. Tuna fillets stored at 4 degrees C for 9 days or 10 degrees C for over 5 days were rated as grade C products and also had a bacterial load of over 10(7) CFU/g. The change in fish quality as determined by AromaScan followed increases in microbiological counts in tuna fillets, indicating that bacterial load can serve as a useful and objective indicator of gross spoilage. Electronic nose devices can be used in conjunction with microbial counts and sensory panels to evaluate the degree of decomposition in tuna during storage.