Early detection of microbial contamination in processed tomatoes by electronic nose

A review of the emerging technologies and systems to mitigate food fraud in supply chains

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

On-Chip Annealing Using Embedded Micro-Heater for Highly Sensitive and Selective Gas Detection

The demand for gas sensing systems that enable fast and precise gas recognition is growing rapidly. However, substantial challenges arise from the complex fabrication process of sensor arrays, time-consuming data transmission to an external processor, and high energy consumption in multi-stage data processing. In this study, a gas sensing system using on-chip annealing for fast and power-efficient gas detection is proposed. By utilizing a micro-heater embedded in the gas sensor, the sensing material of adjacent sensors in the same substrate can be easily varied without further fabrication steps. The response to oxidizing gas is constrained in metal oxide (MOX) sensing material with small grain sizes, as the depletion width of grain cannot extend beyond the grain size during the gas reaction. On the other hand, the response to reducing gases and humidity, which decrease the depletion width, is less affected by grain sizes. A readout circuit integrating a differential amplifier and dual FET-type gas sensors effectively emphasizes the response to oxidizing gases by canceling the response to reducing gases and humidity. The selective on-chip annealing method is applicable to various MOX sensing materials, demonstrating its potential for application in commercial fields due to its simplicity and expandability.

Design of a Multisensory Device for Tomato Volatile Compound Detection Based on a Mixed Metal Oxide-Electrochemical Sensor Array and Optical Reader

Insufficient control of tomato ripening before harvesting and infection by fungal pests produce large economic losses in world tomato production. Aroma is an indicative parameter of the state of maturity and quality of the tomato. This study aimed to design an electronic system (TOMATO-NOSE) consisting of an array of 12 electrochemical sensors, commercial metal oxide semiconductor sensors, an optical camera for a lateral flow reader, and a smartphone application for device control and data storage. The system was used with tomatoes in different states of ripeness and health, as well as tomatoes infected with Botrytis cinerea. The results obtained through principal component analysis of the olfactory pattern of tomatoes and the reader images show that TOMATO-NOSE is a good tool for the farmer to control tomato ripeness before harvesting and for the early detection of Botrytis cinerea.

Juice Quality Evaluation with Multisensor Systems-A Review

E-nose and e-tongue are advanced technologies that allow for the fast and precise analysis of smells and flavours using special sensors. Both technologies are widely used, especially in the food industry, where they are implemented, e.g., for identifying ingredients and product quality, detecting contamination, and assessing their stability and shelf life. Therefore, the aim of this article is to provide a comprehensive review of the application of e-nose and e-tongue in various industries, focusing in particular on the use of these technologies in the fruit and vegetable juice industry. For this purpose, an analysis of research carried out worldwide over the last five years, concerning the possibility of using the considered multisensory systems to test the quality and taste and aroma profiles of juices is included. In addition, the review contains a brief characterization of these innovative devices through information such as their origin, mode of operation, types, advantages and disadvantages, challenges and perspectives, as well as the possibility of their applications in other industries besides the juice industry.

Artificial olfactory sensor technology that mimics the olfactory mechanism: a comprehensive review

Artificial olfactory sensors that recognize patterns transmitted by olfactory receptors are emerging as a technology for monitoring volatile organic compounds. Advances in statistical processing methods and data processing technology have made it possible to classify patterns in sensor arrays. Moreover, biomimetic olfactory recognition sensors in the form of pattern recognition have been developed. Deep learning and artificial intelligence technologies have enabled the classification of pattern data from more sensor arrays, and improved artificial olfactory sensor technology is being developed with the introduction of artificial neural networks. An example of an artificial olfactory sensor is the electronic nose. It is an array of various types of sensors, such as metal oxides, electrochemical sensors, surface acoustic waves, quartz crystal microbalances, organic dyes, colorimetric sensors, conductive polymers, and mass spectrometers. It can be tailored depending on the operating environment and the performance requirements of the artificial olfactory sensor. This review compiles artificial olfactory sensor technology based on olfactory mechanisms. We introduce the mechanisms of artificial olfactory sensors and examples used in food quality and stability assessment, environmental monitoring, and diagnostics. Although current artificial olfactory sensor technology has several limitations and there is limited commercialization owing to reliability and standardization issues, there is considerable potential for developing this technology. Artificial olfactory sensors are expected to be widely used in advanced pattern recognition and learning technologies, along with advanced sensor technology in the future.

Monitoring Fish Freshness in Real Time under Realistic Conditions through a Single Metal Oxide Gas Sensor

The realization of an unobtrusive and effective technology able to track fish freshness in real time and inform on its edibility is highly demanded, but still unachieved. In the present paper, we address this issue through a single metal oxide gas sensor working in temperature modulation mode. The system can work without an external reference air source, which is an appealing feature for its possible integration in domestic refrigerators. Tests were carried out using fresh sea bream fillets as case study and working both inside the refrigerator and at room temperature. Parallel gas chromatography-mass spectrometry and microbiological characterization indicated the marked dependence of both the microbiological condition and the gas-phase composition from the individual sample and from the storage temperature. Despite such a large variability, which may be expected in real applications, the proposed system provided similar responses whenever the total bacterial population approached and exceeded the edibility threshold of 107 CFU/g.

E-Nose and Olfactory Assessment: Teamwork or a Challenge to the Last Data? The Case of Virgin Olive Oil Stability and Shelf Life

Electronic nose (E-nose) devices represent one of the most trailblazing innovations in current technological research, since mimicking the functioning of the biological sense of smell has always represented a fascinating challenge for technological development applied to life sciences and beyond. Sensor array tools are right now used in a plethora of applications, including, but not limited to, (bio-)medical, environmental, and food industry related. In particular, the food industry has seen a significant rise in the application of technological tools for determining the quality of edibles, progressively replacing human panelists, therefore changing the whole quality control chain in the field. To this end, the present review, conducted on PubMed, Science Direct and Web of Science, screening papers published between January 2010 and May 2021, sought to investigate the current trends in the usage of human panels and sensorized tools (E-nose and similar) in the food industry, comparing the performances between the two different approaches. In particular, the focus was mainly addressed towards the stability and shelf life assessment of olive oil, the main constituent of the renowned “Mediterranean diet”, and nowadays appreciated in cuisines from all around the world. The obtained results demonstrate that, despite the satisfying performances of both approaches, the best strategy merges the potentialities of human sensory panels and technological sensor arrays, (i.e., E-nose somewhat supported by E-tongue and/or E-eye). The current investigation can be used as a reference for future guidance towards the choice between human panelists and sensorized tools, to the benefit of food manufacturers.

Data Science in the Food Industry

Food safety is one of the main challenges of the agri-food industry that is expected to be addressed in the current environment of tremendous technological progress, where consumers' lifestyles and preferences are in a constant state of flux. Food chain transparency and trust are drivers for food integrity control and for improvements in efficiency and economic growth. Similarly, the circular economy has great potential to reduce wastage and improve the efficiency of operations in multi-stakeholder ecosystems. Throughout the food chain cycle, all food commodities are exposed to multiple hazards, resulting in a high likelihood of contamination. Such biological or chemical hazards may be naturally present at any stage of food production, whether accidentally introduced or fraudulently imposed, risking consumers' health and their faith in the food industry. Nowadays, a massive amount of data is generated, not only from the next generation of food safety monitoring systems and along the entire food chain (primary production included) but also from the Internet of things, media, and other devices. These data should be used for the benefit of society, and the scientific field of data science should be a vital player in helping to make this possible.

Prediction of Fruity Aroma Intensity and Defect Presence in Virgin Olive Oil Using an Electronic Nose

The organoleptic profile of a Virgin Olive Oil is a key quality parameter that is currently obtained by human sensory panels. The development of an instrumental technique capable of providing information about this profile quickly and online is of great interest. This work employed a general purpose e-nose, in lab conditions, to predict the level of fruity aroma and the presence of defects in Virgin Olive Oils. The raw data provided by the e-nose were used to extract a set of features that fed a regressor to predict the level of fruity aroma and a classifier to detect the presence of defects. The results obtained were a mean validation error of 0.5 units for the prediction of fruity aroma using lasso regression; and 88% accuracy for the defect detection using logistic regression. Finally, the identification of two out of ten specific sensors of the e-nose that can provide successful results paves the way to the design of low-cost specific electronic noses for this application.

Volatile Metabolic Markers for Monitoring Pectobacterium carotovorum subsp. carotovorum Using Headspace Solid-Phase Microextraction Coupled with Gas Chromatography-Mass Spectrometry

Identifying the extracellular metabolites of microorganisms in fresh vegetables is industrially useful for assessing the quality of processed foods. Pectobacterium carotovorum subsp. carotovorum (PCC) is a plant pathogenic bacterium that causes soft rot disease in cabbages. This microbial species in plant tissues can emit specific volatile molecules with odors that are characteristic of the host cell tissues and PCC species. In this study, we used headspace solid-phase microextraction followed by gas chromatography coupled with mass spectrometry (HS-SPME-GC-MS) to identify volatile compounds (VCs) in PCC-inoculated cabbage at different storage temperatures. HS-SPME-GC-MS allowed for recognition of extracellular metabolites in PCC-infected cabbages by identifying specific volatile metabolic markers. We identified 4-ethyl-5-methylthiazole and 3-butenyl isothiocyanate as markers of fresh cabbages, whereas 2,3-butanediol and ethyl acetate were identified as markers of soft rot in PCC-infected cabbages. These analytical results demonstrate a suitable approach for establishing non-destructive plant pathogen-diagnosis techniques as alternatives to standard methods, within the framework of developing rapid and efficient analytical techniques for monitoring plant-borne bacterial pathogens. Moreover, our techniques could have promising applications in managing the freshness and quality control of cabbages.

Storage effects on the quality quartet of orange juice submitted to moderate thermosonication: Predictive modeling and odor fingerprinting approach

The effects of moderate thermosonication (MTS) on the quality quartet: physico-chemical, microbial, nutritional and sensory qualities of orange juice (OJ) inoculated with Alicyclobacillus acidoterrestris (AAT) were studied during 24 days of storage at ambient and refrigerated temperatures. The bioactive compounds and antioxidant activity of OJ decreased with storage, while the pectin methyl esterase (PME) increased. Nonetheless, noticeable changes were observed from the 12th day of storage. There was no obvious (p > 0.05) variation in pH and total soluble solids. To determine the nutritional and microbial quality characteristics of OJ during storage, non-linear kinetic curves were successfully fitted with least square fitting polynomial and four-parameter log-logistic distribution models. The E-nose sensors succeeded in discriminating between the aroma of non-treated and treated OJ based on linear discriminant analysis (LDA). Furthermore, terpenes, alcohol and partially aromatic compounds were the main spoilage indicators of OJ during storage based on E-nose analysis and confirmed by HS-SPME-GC/MS analysis. Thus, MTS significantly extended the shelf life of the quality quartet of natural OJ at 4 °C. E-nose-GC/MS fusion offered odor fingerprints to AAT microorganisms that can be used as spoilage index without using traditional food analysis techniques. The proposed approach can be used as an alternative tool for rapid detection of spoilage microorganisms in OJ.

Application of electronic nose as a non-invasive technique for odor fingerprinting and detection of bacterial foodborne pathogens: a review

Food safety issues across the global food supply chain have become paramount in promoting public health safety and commercial success of global food industries. As food regulations and consumer expectations continue to advance around the world, notwithstanding the latest technology, detection tools, regulations and consumer education on food safety and quality, there is still an upsurge of foodborne disease outbreaks across the globe. The development of the Electronic nose as a noninvasive technique suitable for detecting volatile compounds have been applied for food safety and quality analysis. Application of E-nose for pathogen detection has been successful and superior to conventional methods. E-nose offers a method that is noninvasive, fast and requires little or no sample preparation, thus making it ideal for use as an online monitoring tool. This manuscript presents an in-depth review of the application of electronic nose (E-nose) for food safety, with emphasis on classification and detection of foodborne pathogens. We summarise recent data and publications on foodborne pathogen detection (2006-2018) and by E-nose together with their methodologies and pattern recognition tools employed. E-nose instrumentation, sensing technologies and pattern recognition models are also summarised and future trends and challenges, as well as research perspectives, are discussed.

Noninvasive, Nondestructive Measurement of Tomato Concentrate Spoilage in Large-Volume Aseptic Packages

Low frequency nuclear magnetic resonance (NMR) is used to noninvasively and nondestructively detect spoiled tomato concentrate stored in >200 L metal-lined containers. It is shown that longitudinal and transverse NMR relaxation times change as the tomato concentrate spoils. A rapid, viscosity-dependent spoilage detection method that takes advantage of the inherent inhomogeneity in single-sided NMR instruments is proposed. Here, the effective transverse magnetization decay rate is used as a parameter to determine tomato concentrate spoilage. Three different low frequency, single-sided NMR instruments are described and compared to determine the optimum sensor for spoiled tomato concentrate detection in large-format, metal-lined, aseptic containers. The most effective NMR sensor for this application is temperature stable and has large magnetic field gradients and a homogeneous magnetic field region offset >0.5 cm from the magnet surface. PRACTICAL APPLICATION: This manuscript describes a noninvasive and nondestructive tomato concentrate spoilage detector for application to large-format, sealed, commercial storage bins.

Research on Distinguishing Fish Meal Quality Using Different Characteristic Parameters Based on Electronic Nose Technology

In this paper, a portable electronic nose, that was independently developed, was employed to detect and classify a fish meal of different qualities. SPME-GC-MS (solid phase microextraction gas chromatography mass spectrometry) analysis of fish meal was presented. Due to the large amount of data of the original features detected by the electronic nose, a reasonable selection of the original features was necessary before processing, so as to reduce the dimension. The integral value, wavelet energy value, maximum gradient value, average differential value, relation steady-state response average value and variance value were selected as six different characteristic parameters, to study fish meal samples with different storage time grades. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and five recognition modes, which included the multilayer perceptron neural network classification method, random forest classification method, k nearest neighbor algorithm, support vector machine algorithm, and Bayesian classification method, were employed for the classification. The result showed that the RF classification method had the highest accuracy rate for the classification algorithm. The highest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the integral value, stable value, and average differential value. The lowest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the maximum gradient value. This finding shows that the electronic nose can identify fish meal samples with different storage times.

Electronic Noses in Medical Diagnostics

BACKGROUND

Electronic nose technology is being developed in order to analyse complex mixtures of volatiles in a way parallel to biologic olfaction. When applied in the field of medicine, the use of such devices should enable the identification and discrimination between different diseases. In this review, a comprehensive summary of research in medical diagnostics using electronic noses is presented. A special attention has been paid to the application of these devices and sensor technologies, in response to current trends in medicine.

METHODS

Peer-reviewed research literature pertaining to the subject matter was identified based on a search of bibliographic databases. The quality and relevance of retrieved papers was assessed using standard tools. Their content was critically reviewed and certain information contained therein was compiled in tabularized form.

RESULTS

The majority of reviewed studies show promising results, often surpassing the accuracy and sensitivity of established diagnostic methods. However, only a relatively small number of devices have been field tested. The methods used for sample collection and data processing in various studies were listed in a table, together with electronic nose models used in these investigations.

CONCLUSION

Despite the fact that devices equipped with arrays of chemical sensors are not routinely used in everyday medical practice, their prospective use would solve some established issues in medical diagnostics, as well as lead to developments in prophylactics by facilitating a widespread use of non-invasive screening tests.

Volatolomics: A broad area of experimentation

Chemical analysis (detection and monitoring) of compounds associated with the metabolic activities of an organism is at the cutting edge of science. Volatile metabolomics (volatolomics) are applied in a broad range of applications including: biomedical research (e.g. disease diagnostic tools, personalized healthcare and nutrition, etc.), toxicological analysis (e.g. exposure tool to environmental pollutants, toxic and hazardous chemical environments, industrial accidents, etc.), molecular communications, forensics, safety and security (e.g. search and rescue operations). In the present review paper, an overview of recent advances and applications of volatolomics will be given. The main focus will be on volatile organic compounds (VOCs) originating from biological secretions of various organisms (e.g. microorganisms, insects, plants, humans) and resulting fusion of chemical information. Bench-top and portable or field-deployable technologies-systems will also be presented and discussed.

An Electronic Nose for Royal Delicious Apple Quality Assessment - A Tri-layer Approach

Foodborne pathogens cause serious health issues and have a strong impact on the economy of the country. In this context, quality testing of royal delicious apple by detecting pathogen contamination using an electronic nose, which contains an array of six ready-made sensors, has been proposed. To estimate the types of pathogens, fresh, half and completely contaminated apple samples were considered for bacterial studies. This study revealed the presence of Staphylococcus, Salmonella and Shigella bacteria, which were in the order of zero, 10², 10³-10⁴ CFU/mL. Further, the recorded headspace GC-MS spectra of contaminated samples confirmed the presence of bacterial spoilage markers namely acetone, ethyl acetate, ethyl alcohol and acetaldehyde. Voltage swing of 0.2 and 0.5 V was observed for half and completely contaminated apple samples respectively with reference to the fresh sample. Voltage responses of the sensors fed to Principal component analysis and Ward's method of hierarchical cluster algorithms helped to assess the quality of apple samples. By correlating the results of tri-layers namely bacterial count, GCMS data and classification results, reference table was developed and embedded in the ATmega processor of the electronic nose for real-time quality estimation of apple samples.

Applications and Advances in Bioelectronic Noses for Odour Sensing

A bioelectronic nose, an intelligent chemical sensor array system coupled with bio-receptors to identify gases and vapours, resembles mammalian olfaction by which many vertebrates can sniff out volatile organic compounds (VOCs) sensitively and specifically even at very low concentrations. Olfaction is undertaken by the olfactory system, which detects odorants that are inhaled through the nose where they come into contact with the olfactory epithelium containing olfactory receptors (ORs). Because of its ability to mimic biological olfaction, a bio-inspired electronic nose has been used to detect a variety of important compounds in complex environments. Recently, biosensor systems have been introduced that combine nanoelectronic technology and olfactory receptors themselves as a source of capturing elements for biosensing. In this article, we will present the latest advances in bioelectronic nose technology mimicking the olfactory system, including biological recognition elements, emerging detection systems, production and immobilization of sensing elements on sensor surface, and applications of bioelectronic noses. Furthermore, current research trends and future challenges in this field will be discussed.

Assessment of a combined gas chromatography mass spectrometer sensor system for detecting biologically relevant volatile compounds

There have been a number of studies in which metal oxide sensors (MOS) have replaced conventional analytical detectors in gas chromatography systems. However, despite the use of these instruments in a range of applications including breath research the sensor responses (i.e. resistance changes w.r.t. concentration of VCs) remain largely unreported. This paper addresses that issue by comparing the response of a metal oxide sensor directly with a mass spectrometer (MS), whereby both detectors are interfaced to the same GC column using an s-swafer. It was demonstrated that the sensitivity of an in-house fabricated ZnO/SnO₂ thick film MOS was superior to a modern MS for the detection of a wide range of volatile compounds (VCs) of different functionalities and masses. Better techniques for detection and quantification of these VCs is valuable, as many of these compounds are commonly reported throughout the scientific literature. This is also the first published report of a combined GC-MS sensor system. These two different detector technologies when combined, should enhance discriminatory abilities to aid disease diagnoses using volatiles from e.g. breath, and bodily fluids. Twenty-nine chemical standards have been tested using solid phase micro-extraction; 25 of these compounds are found on human breath. In all but two instances the sensor exhibited the same or superior limit of detection compared to the MS. Twelve stool samples from healthy participants were analysed; the sensor detected, on average 1.6 peaks more per sample than the MS. Similarly, analysing the headspace of E. coli broth cultures the sensor detected 6.9 more peaks per sample versus the MS. This greater sensitivity is primarily a function of the superior limits of detection of the metal oxide sensor. This shows that systems based on the combination of chromatography systems with solid state sensors shows promise for a range of applications.

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.

Metal Oxide Gas Sensors, a Survey of Selectivity Issues Addressed at the SENSOR Lab, Brescia (Italy)

This work reports the recent results achieved at the SENSOR Lab, Brescia (Italy) to address the selectivity of metal oxide based gas sensors. In particular, two main strategies are being developed for this purpose: (i) investigating different sensing mechanisms featuring different response spectra that may be potentially integrated in a single device; (ii) exploiting the electronic nose (EN) approach. The former has been addressed only recently and activities are mainly focused on determining the most suitable configuration and measurements to exploit the novel mechanism. Devices suitable to exploit optical (photoluminescence), magnetic (magneto-optical Kerr effect) and surface ionization in addition to the traditional chemiresistor device are here discussed together with the sensing performance measured so far. The electronic nose is a much more consolidated technology, and results are shown concerning its suitability to respond to industrial and societal needs in the fields of food quality control and detection of microbial activity in human sweat.

The prediction of food additives in the fruit juice based on electronic nose with chemometrics

Food additives are added to products to enhance their taste, and preserve flavor or appearance. While their use should be restricted to achieve a technological benefit, the contents of food additives should be also strictly controlled. In this study, E-nose was applied as an alternative to traditional monitoring technologies for determining two food additives, namely benzoic acid and chitosan. For quantitative monitoring, support vector machine (SVM), random forest (RF), extreme learning machine (ELM) and partial least squares regression (PLSR) were applied to establish regression models between E-nose signals and the amount of food additives in fruit juices. The monitoring models based on ELM and RF reached higher correlation coefficients (R²s) and lower root mean square errors (RMSEs) than models based on PLSR and SVM. This work indicates that E-nose combined with RF or ELM can be a cost-effective, easy-to-build and rapid detection system for food additive monitoring.

A new analytical platform based on field-flow fractionation and olfactory sensor to improve the detection of viable and non-viable bacteria in food

An integrated sensing system is presented for the first time, where a metal oxide semiconductor sensor-based electronic olfactory system (MOS array), employed for pathogen bacteria identification based on their volatile organic compound (VOC) characterisation, is assisted by a preliminary separative technique based on gravitational field-flow fractionation (GrFFF). In the integrated system, a preliminary step using GrFFF fractionation of a complex sample provided bacteria-enriched fractions readily available for subsequent MOS array analysis. The MOS array signals were then analysed employing a chemometric approach using principal components analysis (PCA) for a first-data exploration, followed by linear discriminant analysis (LDA) as a classification tool, using the PCA scores as input variables. The ability of the GrFFF-MOS system to distinguish between viable and non-viable cells of the same strain was demonstrated for the first time, yielding 100 % ability of correct prediction. The integrated system was also applied as a proof of concept for multianalyte purposes, for the detection of two bacterial strains (Escherichia coli O157:H7 and Yersinia enterocolitica) simultaneously present in artificially contaminated milk samples, obtaining a 100 % ability of correct prediction. Acquired results show that GrFFF band slicing before MOS array analysis can significantly increase reliability and reproducibility of pathogen bacteria identification based on their VOC production, simplifying the analytical procedure and largely eliminating sample matrix effects. The developed GrFFF-MOS integrated system can be considered a simple straightforward approach for pathogen bacteria identification directly from their food matrix. Graphical abstract An integrated sensing system is presented for pathogen bacteria identification in food, in which field-flow fractionation is exploited to prepare enriched cell fractions prior to their analysis by electronic olfactory system analysis.

Early detection of Zygosaccharomyces rouxii--spawned spoilage in apple juice by electronic nose combined with chemometrics

Spoilage spawned by Zygosaccharomyces rouxii can cause sensory defect in apple juice, which could hardly be perceived in the early stage and therefore would lead to the serious economic loss. Thus, it is essential to detect the contamination in early stage to avoid costly waste of products or recalls. In this work the performance of an electronic nose (e-nose) coupled with chemometric analysis was evaluated for diagnosis of the contamination in apple juice, using test panel evaluation as reference. The feasibility of using e-nose responses to predict the spoilage level of apple juice was also evaluated. Coupled with linear discriminant analysis (LDA), detection of the contamination was achieved after 12h, corresponding to the cell concentration of less than 2.0 log 10 CFU/mL, the level at which the test panelists could not yet identify the contamination, indicating that the signals of e-nose could be utilized as early indicators for the onset of contamination. Loading analysis indicated that sensors 2, 6, 7 and 8 were the most important in the detection of Z. rouxii-contaminated apple juice. Moreover, Z. rouxii counts in unknown samples could be well predicted by the established models using partial least squares (PLS) algorithm with high correlation coefficient (R) of 0.98 (Z. rouxii strain ATCC 2623 and ATCC 8383) and 0.97 (Z. rouxii strain B-WHX-12-53). Based on these results, e-nose appears to be promising for rapid analysis of the odor in apple juice during processing or on the shelf to realize the early detection of potential contamination caused by Z. rouxii strains.

Winter jujube (Zizyphus jujuba Mill.) quality forecasting method based on electronic nose

Winter jujube (Zizyphus jujuba Mill.) quality forecasting method utilising electronic nose (EN) and double-layered cascaded series stochastic resonance (DCSSR) was investigated. EN responses to jujubes stored at room temperature were continuously measured for 8 days. Jujubes' physical/chemical indexes, such as firmness, colour, total soluble solids (TSS), and ascorbic acid (AA), were synchronously examined. Examination results indicated that jujubes were getting ripe during storage. EN measurement data was processed by stochastic resonance (SR) and DCSSR. SR and DCSSR output signal-to-noise ratio (SNR) maximums (SNR-MAX) discriminated jujubes under different storage time successfully. Multiple variable regression (MVR) results between physical/chemical indexes and SR/DCSSR eigen values demonstrated that DCSSR eigen values were more suitable for jujube quality determination. Quality forecasting model was developed using non-linear fitting regression of DCSSR eigen values. Validating experiments demonstrated that forecasting accuracy of this model is 97.35%. This method also presented other advantages including fast response, non-destructive, etc.

Application of Electronic Nose for Detection of Wine-Aging Methods

This study reports the application of an electronic nose for the identification and classification of red wines aged three different methods. The signals of the different wines detected by the 10 sensors present in the E-nose are significantly different from each other. The response to the signal generates a typical chemical fingerprint of the volatile compounds present in the wines. Principal Component Analysis can be applied for the dimensionality reduction of the collected signal. Since the total contribution rate of the first three principal components is up to 97.27%, different wines can be distinguished from each other by the three principal components. Euclidean distance, correlation analysis, Mahalanobis distance and linear discrimination analysis can offer 100% accuracy for known samples, and the accuracy rate can reach 88.9% for the 18 test samples. In addition, numerous advantages exist compared with sensory analysis in both authentication and quality control of wines.

Potential application of electronic olfaction systems in feedstuffs analysis and animal nutrition

Electronic Olfaction Systems (EOSs) based on a variety of gas-sensing technologies have been developed to simulate in a simplified manner animal olfactory sensing systems. EOSs have been successfully applied to many applications and fields, including food technology and agriculture. Less information is available for EOS applications in the feed technology and animal nutrition sectors. Volatile Organic Compounds (VOCs), which are derived from both forages and concentrate ingredients of farm animal rations, are considered and described in this review as olfactory markers for feedstock quality and safety evaluation. EOS applications to detect VOCs from feedstuffs (as analytical matrices) are described, and some future scenarios are hypothesised. Furthermore, some EOS applications in animal feeding behaviour and organoleptic feed assessment are also described.

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.

Nanostructured metal oxide gas sensors, a survey of applications carried out at SENSOR lab, Brescia (Italy) in the security and food quality fields

In this work we report on metal oxide (MOX) based gas sensors, presenting the work done at the SENSOR laboratory of the CNR-IDASC and University of Brescia, Italy since the 80s up to the latest results achieved in recent times. In particular we report the strategies followed at SENSOR during these 30 years to increase the performance of MOX sensors through the development of different preparation techniques, from Rheotaxial Growth Thermal Oxidation (RGTO) to nanowire technology to address sensitivity and stability, and the development of electronic nose systems and pattern recognition techniques to address selectivity. We will show the obtained achievement in the context of selected applications such as safety and security and food quality control.