Screening of deoxynivalenol contamination in durum wheat by MOS-based electronic nose and identification of the relevant pattern of volatile compounds

Machine Learning Applied to the Detection of Mycotoxin in Food: A Systematic Review

Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds can contaminate a variety of crops, leading to economic losses and health risks to both humans and animals. Traditional lab analysis methods for mycotoxin detection can be time-consuming and may not always be suitable for large-scale screenings. However, in recent years, machine learning (ML) methods have gained popularity for use in the detection of mycotoxins and in the food safety industry in general due to their accurate and timely predictions. We provide a systematic review on some of the recent ML applications for detecting/predicting the presence of mycotoxin on a variety of food ingredients, highlighting their advantages, challenges, and potential for future advancements. We address the need for reproducibility and transparency in ML research through open access to data and code. An observation from our findings is the frequent lack of detailed reporting on hyperparameters in many studies and a lack of open source code, which raises concerns about the reproducibility and optimisation of the ML models used. The findings reveal that while the majority of studies predominantly utilised neural networks for mycotoxin detection, there was a notable diversity in the types of neural network architectures employed, with convolutional neural networks being the most popular.

Low-Cost Pocket Fluorometer and Chemometric Tools for Green and Rapid Screening of Deoxynivalenol in Durum Wheat Bran

Cereal crops are frequently contaminated by deoxynivalenol (DON), a harmful type of mycotoxin produced by several Fusarium species fungi. The early detection of mycotoxin contamination is crucial for ensuring safety and quality of food and feed products, for preventing health risks and for avoiding economic losses because of product rejection or costly mycotoxin removal. A LED-based pocket-size fluorometer is presented that allows a rapid and low-cost screening of DON-contaminated durum wheat bran samples, without using chemicals or product handling. Forty-two samples with DON contamination in the 40-1650 µg/kg range were considered. A chemometric processing of spectroscopic data allowed distinguishing of samples based on their DON content using a cut-off level set at 400 µg/kg DON. Although much lower than the EU limit of 750 µg/kg for wheat bran, this cut-off limit was considered useful whether accepting the sample as safe or implying further inspection by means of more accurate but also more expensive standard analytical techniques. Chemometric data processing using Principal Component Analysis and Quadratic Discriminant Analysis demonstrated a classification rate of 79% in cross-validation. To the best of our knowledge, this is the first time that a pocket-size fluorometer was used for DON screening of wheat bran.

Nanotechnology and E-Sensing for Food Chain Quality and Safety

Nowadays, it is well known that sensors have an enormous impact on our life, using streams of data to make life-changing decisions. Every single aspect of our day is monitored via thousands of sensors, and the benefits we can obtain are enormous. With the increasing demand for food quality, food safety has become one of the main focuses of our society. However, fresh foods are subject to spoilage due to the action of microorganisms, enzymes, and oxidation during storage. Nanotechnology can be applied in the food industry to support packaged products and extend their shelf life. Chemical composition and sensory attributes are quality markers which require innovative assessment methods, as existing ones are rather difficult to implement, labour-intensive, and expensive. E-sensing devices, such as vision systems, electronic noses, and electronic tongues, overcome many of these drawbacks. Nanotechnology holds great promise to provide benefits not just within food products but also around food products. In fact, nanotechnology introduces new chances for innovation in the food industry at immense speed. This review describes the food application fields of nanotechnologies; in particular, metal oxide sensors (MOS) will be presented.

Microbial volatile organic compounds as novel indicators of anaerobic digestion instability: Potential and challenges

The wide application of anaerobic digestion (AD) technology is limited by process fluctuations. Thus, process monitoring based on screening state parameters as early warning indicators (EWI) is a top priority for AD facilities. However, predicting anaerobic digester stability based on such indicators is difficult, and their threshold values are uncertain, case-specific, and sometimes produce conflicting results. Thus, new EWI should be proposed to integrate microbial and metabolic information. These microbial volatile organic compounds (mVOCs) including alkanes, alkenes, alkynes, aromatic compounds are produced by microorganisms (bacteria, archaea and fungi), which might serve as a promising diagnostic tool for environmental monitoring. Moreover, mVOCs diffuse in both gas and liquid phases and are considered the language of intra kingdom microbial interactions. Herein, we highlight the potential of mVOCs as EWI for AD process instability, including discussions regarding characteristics and sources of mVOCs as well as sampling and determination methods. Furthermore, existing challenges must be addressed, before mVOCs profiling can be used as an early warning system for diagnosing AD process instability, such as mVOCs sampling, analysis and identification. Finally, we discuss the potential biotechnology applications of mVOCs and approaches to overcome the challenges regarding their application.

Effect of the Consolidation Level on Organic Volatile Compound Emissions from Maize during Storage

The aim of this study was to determine the emission of organic volatile compounds from maize grain as a function of granularity and packing density of bulk material in conditions imitating processes occurring in silos. The study was carried out with the use of a gas chromatograph and an electronic nose, which was designed and constructed at the Institute of Agrophysics of PAS and has a matrix of eight MOS (metal oxide semiconductor) sensors. A 20-L volume of maize grain was consolidated in the INSTRON testing machine with pressures of 40 and 80 kPa. The control samples were not compacted, and the maize bed had bulk density. The analyses were carried out at a moisture content of 14% and 17% (w.b.-wet basis). The measurement system facilitated quantitative and qualitative analyses of volatile organic compounds and the intensity of their emission during 30-day storage. The study determined the profile of volatile compounds as a function of storage time and the grain bed consolidation level. The research results indicated the degree of grain degradation induced by the storage time. The highest emission of volatile compounds was recorded on the first four days, which indicated a dynamic nature of maize quality degradation. This was confirmed by the measurements performed with electrochemical sensors. In turn, the intensity of the volatile compound emission decreased in the next stage of the experiments, which showed a decline in the quality degradation dynamics. The sensor responses to the emission intensity decreased significantly at this stage. The electronic nose data on the emission of VOCs (volatile organic compounds) as well as grain moisture and bulk volume can be helpful for the determination of the quality of stored material and its suitability for consumption.

E-Nose Technology for Mycotoxin Detection in Feed: Ready for a Real Context in Field Application or Still an Emerging Technology?

Mycotoxin risk in the feed supply chain poses a concern to animal and human health, economy, and international trade of agri-food commodities. Mycotoxin contamination in feed and food is unavoidable and unpredictable. Therefore, monitoring and control are the critical points. Effective and rapid methods for mycotoxin detection, at the levels set by the regulations, are needed for an efficient mycotoxin management. This review provides an overview of the use of the electronic nose (e-nose) as an effective tool for rapid mycotoxin detection and management of the mycotoxin risk at feed business level. E-nose has a high discrimination accuracy between non-contaminated and single-mycotoxin-contaminated grain. However, the predictive accuracy of e-nose is still limited and unsuitable for in-field application, where mycotoxin co-contamination occurs. Further research needs to be focused on the sensor materials, data analysis, pattern recognition systems, and a better understanding of the needs of the feed industry for a safety and quality management of the feed supply chain. A universal e-nose for mycotoxin detection is not realistic; a unique e-nose must be designed for each specific application. Robust and suitable e-nose method and advancements in signal processing algorithms must be validated for specific needs.

Type B Trichothecenes in Cereal Grains and Their Products: Recent Advances on Occurrence, Toxicology, Analysis and Post-Harvest Decontamination Strategies

Type B trichothecenes (deoxynivalenol, nivalenol, 3-acetyldeoxynivalenol, 15-acetyldeoxynivalenol) and deoxynivalenol-3-glucoside (DON-3G) are secondary toxic metabolites produced mainly by mycotoxigenic Fusarium fungi and have been recognized as natural contaminants in cereals and cereal-based foods. The latest studies have proven the various negative effects of type B trichothecenes on human health. Due to the widespread occurrence of Fusarium species, contamination by these mycotoxins has become an important aspect for public health and agro-food systems worldwide. Hence, their monitoring and surveillance in various foods have received a significant deal of attention in recent years. In this review, an up-to-date overview of the occurrence profile of major type B trichothecenes and DON-3G in cereal grains and their toxicological implications are outlined. Furthermore, current trends in analytical methodologies for their determination are overviewed. This review also covers the factors affecting the production of these mycotoxins, as well as the management strategies currently employed to mitigate their contamination in foods. Information presented in this review provides good insight into the progress that has been achieved in the last years for monitoring type B trichothecenes and DON-3G, and also would help the researchers in their further investigations on metabolic pathway analysis and toxicological studies of these Fusarium mycotoxins.

Electronic Nose for the Rapid Detection of Deoxynivalenol in Wheat Using Classification and Regression Trees

Mycotoxin represents a significant concern for the safety of food and feed products, and wheat represents one of the most susceptible crops. To manage this issue, fast, reliable, and low-cost test methods are needed for regulated mycotoxins. This study aimed to assess the potential use of the electronic nose for the early identification of wheat samples contaminated with deoxynivalenol (DON) above a fixed threshold. A total of 214 wheat samples were collected from commercial fields in northern Italy during the periods 2014−2015 and 2017−2018 and analyzed for DON contamination with a conventional method (GC-MS) and using a portable e-nose “AIR PEN 3” (Airsense Analytics GmbH, Schwerin, Germany), equipped with 10 metal oxide sensors for different categories of volatile substances. The Machine Learning approach “Classification and regression trees” (CART) was used to categorize samples according to four DON contamination thresholds (1750, 1250, 750, and 500 μg/kg). Overall, this process yielded an accuracy of >83% (correct prediction of DON levels in wheat samples). These findings suggest that the e-nose combined with CART can be an effective quick method to distinguish between compliant and DON-contaminated wheat lots. Further validation including more samples above the legal limits is desirable before concluding the validity of the method.

Impact of Coffee Bean Roasting on the Content of Pyridines Determined by Analysis of Volatile Organic Compounds

The aim of the study was to analyze the process of roasting coffee beans in a convection-conduction roaster (CC) without a heat exchanger and a convection-conduction-radiation roaster (CCR) with a heat exchanger for determination of the aroma profile. The aroma profile was analyzed using the SPME/GC-MS technique, and an Agrinose electronic nose was used to determine the aroma profile intensity. Arabica coffee beans from five regions of the world, namely, Peru, Costa Rica, Ethiopia, Guatemala, and Brazil, were the research material. The chemometric analyses revealed the dominance of azines, alcohols, aldehydes, hydrazides, and acids in the coffee aroma profile. Their share distinguished the aroma profiles depending on the country of origin of the coffee beans. The high content of pyridine from the azine group was characteristic for the coffee roasting process in the convection-conduction roaster without a heat exchanger, which was shown by the PCA analysis. The increased content of pyridine resulted from the appearance of coal tar, especially in the CC roaster. Pyridine has an unpleasant and bitter plant-like odor, and its excess is detrimental to the human organism. The dominant and elevated content of pyridine is a defect of the coffee roasting process in the CC roaster compared to the process carried out in the CCR machine. The results obtained with the Agrinose showed that the CC roasting method had a significant effect on the sensor responses. The effect of coal tar on the coffee beans resulted in an undesirable aroma profile characterized by increased amounts of aromatic volatile compounds and higher responses of Agrinose sensors.

Odorant binding proteins fromHermetia illucens: potential sensing elements for detecting volatile aldehydes involved in early stages of organic decomposition

Organic decomposition processes, involving the breakdown of complex molecules such as carbohydrates, proteins and fats, release small chemicals known as volatile organic compounds (VOCs), smelly even at very low concentrations, but not all readily detectable by vertebrates. Many of these compounds are instead detected by insects, mostly by saprophytic species, for which long-range orientation towards organic decomposition matter is crucial. In the present work the detection of aldehydes, as an important measure of lipid oxidation, has been possible exploiting the molecular machinery underlying odour recognition inHermetia illucens(Diptera: Stratiomyidae). This voracious scavenger insect is of interest due to its outstanding capacity in bioconversion of organic waste, colonizing very diverse environments due to the ability of sensing a wide range of chemical compounds that influence the choice of substrates for ovideposition. A variety of soluble odorant binding proteins (OBPs) that may function as carriers of hydrophobic molecules from the air-water interface in the antenna of the insect to the receptors were identified, characterised and expressed. An OBP-based nanobiosensor prototype was realized using selected OBPs as sensing layers for the development of an array of quartz crystal microbalances (QCMs) for vapour phase detection of selected compounds at room temperature. QCMs coated with four recombinantH. illucensOBPs (HillOBPs) were exposed to a wide range of VOCs indicative of organic decomposition, showing a high sensitivity for the detection of three chemical compounds belonging to the class of aldehydes and one short-chain fatty acid. The possibility of using biomolecules capable of binding small ligands as reversible gas sensors has been confirmed, greatly expanding the state-of the-art in gas sensing technology.

In Vitro Systems for Toxicity Evaluation of Microbial Volatile Organic Compounds on Humans: Current Status and Trends

Microbial volatile organic compounds (mVOC) are metabolic products and by-products of bacteria and fungi. They play an important role in the biosphere: They are responsible for inter- and intra-species communication and can positively or negatively affect growth in plants. But they can also cause discomfort and disease symptoms in humans. Although a link between mVOCs and respiratory health symptoms in humans has been demonstrated by numerous studies, standardized test systems for evaluating the toxicity of mVOCs are currently not available. Also, mVOCs are not considered systematically at regulatory level. We therefore performed a literature survey of existing in vitro exposure systems and lung models in order to summarize the state-of-the-art and discuss their suitability for understanding the potential toxic effects of mVOCs on human health. We present a review of submerged cultivation, air-liquid-interface (ALI), spheroids and organoids as well as multi-organ approaches and compare their advantages and disadvantages. Furthermore, we discuss the limitations of mVOC fingerprinting. However, given the most recent developments in the field, we expect that there will soon be adequate models of the human respiratory tract and its response to mVOCs.

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

The quality and safety of wheat flour are of public concern since they are related to the quality of flour products and human health. Therefore, efficient and convenient analytical techniques are needed for the quality and safety controls of wheat flour. Near-infrared (NIR) spectroscopy has become an ideal technique for assessing the quality and safety of wheat flour, as it is a rapid, efficient and nondestructive method. The application of NIR spectroscopy in the quality and safety analysis of wheat flour is addressed in this review. First, we briefly summarize the basic knowledge of NIR spectroscopy and chemometrics. Then, recent advances in the application of NIR spectroscopy for chemical composition, technological parameters, and safety analysis are presented. Finally, the potential of NIR spectroscopy is discussed. Combined with chemometric methods, NIR spectroscopy has been used to detect chemical composition, technological parameters, deoxynivalenol, adulterants and additives of wheat flour. Furthermore, NIR spectroscopy has shown great potential for the rapid and online analysis of the quality and safety of wheat flour. It is anticipated that the current review will serve as a reference for the future analysis of wheat flour by NIR spectroscopy to ensure the quality and safety of flour products.

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NIR spectroscopy is an ideal technique for analysis of wheat flour due to its rapid and nondestructive nature.

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Use of NIR spectroscopy for chemical composition, technological parameters, and safety analysis.

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Online and handheld NIR spectrometers for wheat flour detection are the future trends.

Hermetia illucens (L.) (Diptera: Stratiomyidae) Odorant Binding Proteins and Their Interactions with Selected Volatile Organic Compounds: An In Silico Approach

The black soldier fly (BSF), Hermetia illucens (Diptera: Stratiomyidae), has considerable global interest due to its outstanding capacity in bioconverting organic waste to insect biomass, which can be used for livestock, poultry, and aquaculture feed. Mass production of this insect in colonies requires the development of methods concentrating oviposition in specific collection devices, while the mass production of larvae and disposing of waste may require substrates that are more palatable and more attractive to the insects. In insects, chemoreception plays an essential role throughout their life cycle, responding to an array of chemical, biological and environmental signals to locate and select food, mates, oviposition sites and avoid predators. To interpret these signals, insects use an arsenal of molecular components, including small proteins called odorant binding proteins (OBPs). Next generation sequencing was used to identify genes involved in chemoreception during the larval and adult stage of BSF, with particular attention to OBPs. The analysis of the de novo adult and larval transcriptome led to the identification of 27 and 31 OBPs for adults and larvae, respectively. Among these OBPs, 15 were common in larval and adult transcriptomes and the tertiary structures of 8 selected OBPs were modelled. In silico docking of ligands confirms the potential interaction with VOCs of interest. Starting from the information about the growth performance of H. illucens on different organic substrates from the agri-food sector, the present work demonstrates a possible correlation between a pool of selected VOCs, emitted by those substrates that are attractive for H. illucens females when searching for oviposition sites, as well as phagostimulants for larvae. The binding affinities between OBPs and selected ligands calculated by in silico modelling may indicate a correlation among OBPs, VOCs and behavioural preferences that will be the basis for further analysis.

Development of Attenuated Total Reflectance Mid-Infrared (ATR-MIR) and Near-Infrared (NIR) Spectroscopy for the Determination of Resistant Starch Content in Wheat Grains

The chemical method for the determination of the resistant starch (RS) content in grains is time-consuming and labor intensive. Near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy are rapid and nondestructive analytical techniques for determining grain quality. This study was the first report to establish and compare these two spectroscopic techniques for determining the RS content in wheat grains. Calibration models with four preprocessing techniques based on the partial least squares (PLS) algorithm were built. In the NIR technique, the mean normalization + Savitzky-Golay smoothing (MN + SGS) preprocessing technique had a higher coefficient of determination (R c 2 = 0.672; R p 2 = 0.552) and a relative lower root mean square error value (RMSEC = 0.385; RMSEP = 0.459). In the ATR-MIR technique, the baseline preprocessing method exhibited a better performance regarding to the values of coefficient of determination (R c 2 = 0.927; R p 2 = 0.828) and mean square error value (RMSEC = 0.153; RMSEP = 0.284). The validation of the developed best NIR and ATR-MIR calibration models showed that the ATR-MIR best calibration model has a better RS prediction ability than the NIR best calibration model. Two high grain RS content wheat mutants were screened out by the ATR-MIR best calibration model from the wheat mutant library. There was no significant difference between the predicted values and chemical measured values in the two high RS content mutants. It proved that the ATR-MIR model can be a perfect substitute in RS measuring. All the results indicated that the ATR-MIR spectroscopy with improved screening efficiency can be used as a fast, rapid, and nondestructive method in high grain RS content wheat breeding.

The Existing Methods and Novel Approaches in Mycotoxins' Detection

Mycotoxins represent a wide range of secondary, naturally occurring and practically unavoidable fungal metabolites. They contaminate various agricultural commodities like cereals, maize, peanuts, fruits, and feed at any stage in pre- or post-harvest conditions. Consumption of mycotoxin-contaminated food and feed can cause acute or chronic toxicity in human and animals. The risk that is posed to public health have prompted the need to develop methods of analysis and detection of mycotoxins in food products. Mycotoxins wide range of structural diversity, high chemical stability, and low concentrations in tested samples require robust, effective, and comprehensible detection methods. This review summarizes current methods, such as chromatographic and immunochemical techniques, as well as novel, alternative approaches like biosensors, electronic noses, or molecularly imprinted polymers that have been successfully applied in detection and identification of various mycotoxins in food commodities. In order to highlight the significance of sampling and sample treatment in the analytical process, these steps have been comprehensively described.

Effect of Supplementation of Flour with Fruit Fiber on the Volatile Compound Profile in Bread

This paper presents the analyses of the effect of fiber additives on volatile organic compounds in bread. The bread was baked from wheat flour with the addition of 3% of fruit fiber, following common procedures. After baking, volatile organic compounds contained in the control bread and breads supplemented with cranberry, apple, and chokeberry fiber were determined. The SPME/GC-MS technique was used for the identification of the odor profile, and the electronic nose Agrinose (e-nose) was used to assess the intensity of the aroma. The results of the analyses revealed the profile of volatile organic compounds in each experimental variant, which was correlated with responses of the electronic nose. The results indicate that the volatile compound profile depends on the bread additives used and influences the intensity of bread aroma. Moreover, the profile of volatile organic compounds in terms of their amount and type, as well as the intensity of their interaction with the active surface of the electrochemical sensors, was specific exclusively for the additive in each case.

Patulin and Trichothecene: characteristics, occurrence, toxic effects and detection capabilities via clinical, analytical and nanostructured electrochemical sensing/biosensing assays in foodstuffs

Patulin and Trichothecene as the main groups of mycotoxins in significant quantities can cause health risks from allergic reactions to death on both humans and animals. Accordingly, rapid and highly sensitive determination of these toxics agents is of great importance. This review starts with a comprehensive outlook regarding the characteristics, occurrence and toxic effects of Patulin and Trichothecene. In the following, numerous clinical and analytical approaches have been extensively discussed. The main emphasis of this review is placed on the utilization of novel nanomaterial based electrochemical sensing/biosensing tools for highly sensitive determination of Patulin and Trichothecene. Furthermore, a detailed and comprehensive comparison has been performed between clinical, analytical and sensing methods. Subsequently, the nanomaterial based electrochemical sensing platforms have been approved as reliable tools for on-site analysis of Patulin and Trichothecene in food processing and manufacturing industries. Different nanomaterials in improving the performance of detecting assays were investigated and have various benefits toward clinical and analytical methods. This paper would address the limitations in the current developments as well as the future challenges involved in the successful construction of sensing approaches with the functionalized nanomaterials and also allow exploring into core-research works regarding this area.

Targeted versus Nontargeted Green Strategies Based on Headspace-Gas Chromatography-Ion Mobility Spectrometry Combined with Chemometrics for Rapid Detection of Fungal Contamination on Wheat Kernels

Conventional methods for detecting fungal contamination are generally time-consuming and sample-destructive, making them impossible for large-scale nondestructive detection and real-time analysis. Therefore, the potential of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was examined for the rapid determination of fungal infection on wheat samples in a rapid and nondestructive manner. In addition, the validation experiment of detecting the percent A. flavus infection presented in simulated field samples was carried out. Because the dual separation of HS-GC-IMS could generate massive amounts of three-dimensional data, proper chemometric processing was required. In this study, two chemometric strategies including: (i) nontargeted spectral fingerprinting and (ii) targeted specific markers were introduced to evaluate the performances of classification and prediction models. Results showed that satisfying results for the differentiation of fungal species were obtained based on both strategies (>80%) by the genetic algorithm optimized support vector machine (GA-SVM), and better values were obtained based on the first strategy (100%). Likewise, the GA-SVM model based on the first strategy achieved the best prediction performances (R² = 0.979-0.998) of colony counts in fungal infected samples. The results of validation experiment showed that GA-SVM models based on the first strategy could still provide satisfactory classification (86.67%) and prediction (R² = 0.889) performances for percent A. flavus infection presented in simulated field samples at day 4. This study indicated the feasibility of HS-GC-IMS-based approaches for the early detection of fungal contamination in wheat kernels.

Influence of Changes in the Level of Volatile Compounds Emitted during Rapeseed Quality Degradation on the Reaction of MOS Type Sensor-Array

This study presents the applicability of a three-parameters method for digital description of spoiled rapeseed odor based on the use of an electronic nose. The method consists of the use of three parameters to describe the sensor response, i.e., the maximum resistance value, the response time and the cleaning time of the active surface of the sensor. Reference chemical methods, i.e., determination of the ergosterol content and analysis of volatile compounds by gas chromatography-mass spectrometry, were used to monitor qualitative changes occurring in the stored material. A 31-day profile of volatile compounds and changes in the ergosterol content was determined in the study. A total of 18 chemical groups of volatile organic compounds was identified. There was a strong positive correlation between the cleaning time and the percentage content of alcohols and alkenes, as well as ergosterol, as a marker of qualitative changes. The maximum response was another parameter that effectively described the changes occurring in the seeds. This parameter was strongly negatively correlated with esters and amides in the case of six sensors, and with ergosterol, alkenes and to a lesser degree with alcohols in the case of the other two sensors. The study results clearly demonstrated a relationship between the sensor responses and the percentage content of alcohols and alkenes, which provided novel practical information for the oilseed branch.

Detection and Differentiation of Volatile Compound Profiles in Roasted Coffee Arabica Beans from Different Countries Using an Electronic Nose and GC-MS

This paper describes the possibility of electronic nose-based detection and discrimination of volatile compound profiles of coffee from different countries roasted in a Gothot roaster under identical time and thermal regimes. The material used in the study was roasted Arabica coffee beans from Brazil, Ethiopia, Guatemala, Costa Rica, and Peru. The analyses were carried out with the use of the Agrinose electronic nose designed and constructed at the Institute of Agrophysics of the Polish Academy of Sciences in Lublin. The results of the volatile compound profile analysis provided by the Agrinose device were verified with the GC-MS technique. Chemometric tests demonstrated a dominant role of alcohols, acids, aldehydes, azines, and hydrazides in the coffee volatile compound profile. The differences in their content had an impact on the odor profile of the coffees originating from the different countries. High content of pyridine from the group of azines was detected in the coffee from Peru and Brazil despite the same roasting conditions. The results of the Agrinose analysis of volatile substances were consistent and correlated with the GC-MS results. This suggests that the Agrinose is a promising tool for selection of coffees based on their volatile compound profile.

Interactions among Fungal Community, Fusarium Mycotoxins, and Components of Harvested Wheat under Simulated Storage Conditions

Economic loss of postharvest wheat under poor storage conditions due to fungal spoilage and mycotoxin contamination is severe. In order to study the influencing factors of the aggravation of mildew in natural wheat during storage, we assessed changes in Fusarium mycotoxins by high performance liquid chromatography, changes in fungal communities by high-throughput sequencing, and changes in biochemical components in wheat stored under artificial simulation conditions. Deoxynivalenol was the dominant Fusarium mycotoxin, reaching 1103 μg/kg at 25 °C with 75% relative humidity after 30 weeks. Under these conditions, Fusarium dominated the fungal communities, and Fusarium graminearum was significantly negatively correlated with glutenin (p < 0.05). Low storage temperatures and low humidity result in lower levels of Fusarium mycotoxins. Different fungi tended to consume different wheat components, and the interaction between environmental and biological factors eventually leads to the deterioration of wheat quality. These findings might provide valuable information for control strategies of mildew occurrence during grain storage.

Identification of Volatile Organic Compounds and Their Concentrations Using a Novel Method Analysis of MOS Sensors Signal

Volatile organic compounds (VOCs) are natural markers useful in rapid assessment of adverse changes occurring in biological material. The use of an electronic nose seems to be a good, fast, and cheap method to determine particular VOCs. This paper presents a new method determination for VOCs and their concentration based on three sensorgram parameters: maximum of normalized sensor response, reaction time, and cleaning time measured from the end of the test to the half value of the maximum of normalized sensor response. The novelty of the method consists in the use for the first time of two parameters: reaction time and cleaning time measured from the end of the test to the half value of the maximum of normalized sensor response. The VOC sensorgrams at different VOC concentrations (26 to 3,842 ppm) were measured by an electronic nose Food Volatile Compound Analyzer (Agrinose) equipped with eight metal oxide semiconductor sensors dedicated to detect different gases. In the present studies, only six sensors that best respond to the VOCs were used. The highest responses to VOCs were obtained for two sensors-TGS2602 and AS-MLV-P2. The results showed that the dependence between the sensorgram parameters on VOC concentration was well described by a logarithmic curve in the whole range of concentrations. Detailed analysis revealed that the cleaning time increases with an increase in the number of carbon atoms in aliphatic molecules. The principal component analysis (PCA) was used to verify the utility of the new three parameters method in VOCs differentiation. The PCA analysis of these parameters showed that maximum of the normalized sensor response alone, which has been used for identification of particular VOCs so far, could not be regarded as a good parameter used for this purpose. Application of all the three parameters gave the best results in VOC identification. The research indicates that the use of three parameters of a volatile compound instead of only one parameter can allow precise determination of substances. Moreover, the results indicate that the analyzed parameters depend on the chemical structure of VOCs.

Electronic Nose-Based Technique for Rapid Detection and Recognition of Moldy Apples

In this study, the PEN3 electronic nose was used to detect and recognize fresh and moldy apples inoculated with Penicillium expansum and Aspergillus niger, taking Golden Delicious apples as the model subject. Firstly, the apples were divided into two groups: individual apple inoculated only with/without different molds (Group A) and mixed apples of inoculated apples with fresh apples (Group B). Then, the characteristic gas sensors of the PEN3 electronic nose that were most closely correlated with the flavor information of the moldy apples were optimized and determined to simplify the analysis process and improve the accuracy of the results. Four pattern recognition methods, including linear discriminant analysis (LDA), backpropagation neural network (BPNN), support vector machines (SVM), and radial basis function neural network (RBFNN), were applied to analyze the data obtained from the characteristic sensors, aiming at establishing the prediction model of the flavor information and fresh/moldy apples. The results showed that only the gas sensors of W1S, W2S, W5S, W1W, and W2W in the PEN3 electronic nose exhibited a strong signal response to the flavor information, indicating most were closely correlated with the characteristic flavor of apples and thus the data obtained from these characteristic sensors were used for modeling. The results of the four pattern recognition methods showed that BPNN had the best prediction performance for the training and testing sets for both Groups A and B, with prediction accuracies of 96.3% and 90.0% (Group A), 77.7% and 72.0% (Group B), respectively. Therefore, we demonstrate that the PEN3 electronic nose not only effectively detects and recognizes fresh and moldy apples, but also can distinguish apples inoculated with different molds.

Fourier transform near-infrared and mid-infrared spectroscopy as efficient tools for rapid screening of deoxynivalenol contamination in wheat bran

BACKGROUND

Deoxynivalenol (DON) is the most common Fusarium mycotoxin occurring in wheat and wheat-derived products, with several adverse and toxic effects in animals and humans. Although bran fractions produced by milling wheat have numerous health benefits, cereal bran is the part of the grain with the highest concentration of DON, thus representing a risk for consumers. Increased efforts have been made to develop analytical methods suitable for rapid DON screening.

RESULTS

The applicability of Fourier transform near-infrared (FTNIR), or mid-infrared (FTMIR) spectroscopy, and their combination for rapid analysis of DON in wheat bran, was investigated for the classification of samples into compliant and non-compliant groups regarding the EU legal limit of 750 µg kg^-1 . Partial least squares-discriminant analysis (PLS-DA) and principal component-linear discriminant analysis (PC-LDA) were employed as classification techniques using a cutoff value of 400 µg kg^-1 DON to distinguish the two classes. Depending on the classification model, overall discrimination rates were from 87% to 91% for FTNIR and from 86% to 87% for the FTMIR spectral range. The FTNIR spectroscopy gave the highest overall classification rate of wheat bran samples, with no false compliant samples and 18% false noncompliant samples when the PC-LDA classification model was applied. The combination of the two spectral ranges did not provide a substantial improvement in classification results in comparison with FTNIR.

CONCLUSIONS

Fourier transform near-infrared spectroscopy in combination with classification models was an efficient tool to screen many DON-contaminated wheat bran samples and assess their compliance with EU regulations. © 2018 Society of Chemical Industry.

Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems

BACKGROUND

The instrumental evaluation of tea quality using digital sensors instead of human panel tests has attracted much attention globally. However, individual sensors do not meet the requirements of discriminant accuracy as a result of incomprehensive sensor information. Considering the major factors in the sensory evaluation of tea, the study integrated multisensor information, including spectral, image and olfaction feature information.

RESULTS

To investigate spectral and image information obtained from hyperspectral spectrometers of different bands, principal components analysis was used for dimension reduction and different types of supervised learning algorithms (linear discriminant analysis, K-nearest neighbour and support vector machine) were selected for comparison. Spectral feature information in the near infrared region and image feature information in the visible-near infrared/near infrared region achieved greater accuracy for classification. The results indicated that a support vector machine outperformed other methods with respect to multisensor data fusion, which improved the accuracy of evaluating green tea quality compared to using individual sensor data. The overall accuracy of the calibration set increased from 75% using optimal single sensor information to 92% using multisensor information, and the overall accuracy of the prediction set increased from 78% to 92%.

CONCLUSION

Overall, it can be concluded that multisensory data accurately identify six grades of tea. © 2018 Society of Chemical Industry.

A Novel Method for Generation of a Fingerprint Using Electronic Nose on the Example of Rapeseed Spoilage

The paper presents application of a new three-parameter method for identification of volatile organic compounds (VOCs) and creation of fingerprints based on the impregnation time (t_IM ), cleaning time (t_CL ), and maximum response ([ΔR/R]_max ) of chemically sensing sensors for detecting spoilage of agricultural commodities. The novelty of this method consists in the use of two additional parameters: an impregnation time and a cleaning time for the first time. An Agrinose built of eight metal oxide semiconductors was used for identification of loss in the rapeseed quality during a short period of storage after harvest. Principal component analysis was applied as a method of data analysis to verify the suitability of the new three-parameter method and visualization of groups of different quality of raw materials. Fourier transform infrared spectroscopy spectra for identification of the infrared bands of fungal polysaccharides and gas chromatography-mass spectrometry analysis of the headspace was applied to describe volatile metabolite contents in reference to the electronic nose technique. The investigations and analyses have demonstrated that the new three-parameter method for determination of volatile compounds ([ΔR/R]_max , t_IM , t_CL ) describes the changes in VOCs more efficiently than the single-parameter approach based only on the maximum sensor response ([ΔR/R]_max ). The proposed method for generation of electronic fingerprints clearly discriminated between rapeseed samples infected with field and storage microflora. Three-parameters method can be useful for quality control in food microbiology and safety, as a rapid method of analysis and detection, including electronic nose sensor technology. PRACTICAL APPLICATION: The use of the proposed method for generation of fingerprints requires no interference with the hardware of the electronic nose but necessitates modification of the software only. This facilitates implementation of the three-parameter method in available devices. This kind of methods and devices can be useful for example in storage process with active ventilation.

Combining E-Nose and Lateral Flow Immunoassays (LFIAs) for Rapid Occurrence/Co-Occurrence Aflatoxin and Fumonisin Detection in Maize

The aim of this study was to evaluate the potential use of an e-nose in combination with lateral flow immunoassays for rapid aflatoxin and fumonisin occurrence/co-occurrence detection in maize samples. For this purpose, 161 samples of corn have been used. Below the regulatory limits, single-contaminated, and co-contaminated samples were classified according to the detection ranges established for commercial lateral flow immunoassays (LFIAs) for mycotoxin determination. Correspondence between methods was evaluated by discriminant function analysis (DFA) procedures using IBM SPSS Statistics 22. Stepwise variable selection was done to select the e-nose sensors for classifying samples by DFA. The overall leave-out-one cross-validated percentage of samples correctly classified by the eight-variate DFA model for aflatoxin was 81%. The overall leave-out-one cross-validated percentage of samples correctly classified by the seven-variate DFA model for fumonisin was 85%. The overall leave-out-one cross-validated percentage of samples correctly classified by the nine-variate DFA model for the three classes of contamination (below the regulatory limits, single-contaminated, co-contaminated) was 65%. Therefore, even though an exhaustive evaluation will require a larger dataset to perform a validation procedure, an electronic nose (e-nose) seems to be a promising rapid/screening method to detect contamination by aflatoxin, fumonisin, or both in maize kernel stocks.

Rapid prediction of deoxynivalenol contamination in wheat bran by MOS-based electronic nose and characterization of the relevant pattern of volatile compounds

BACKGROUND

Deoxynivalenol (DON) is a mycotoxin, mainly produced by Fusarium sp., most frequently occurring in cereals and cereal-based products. Wheat bran refers to the outer layers of the kernel, which has a high risk of damage due to chemical hazards, including mycotoxins. Rapid methods for DON detection in wheat bran are required.

RESULTS

A rapid screening method using an electronic nose (e-nose), based on metal oxide semiconductor sensors, has been developed to distinguish wheat bran samples with different levels of DON contamination. A total of 470 naturally contaminated wheat bran samples were analyzed by e-nose analysis. Wheat bran samples were divided in two contamination classes: class A ([DON] ≤ 400 µg kg^-1 , 225 samples) and class B ([DON] > 400 µg kg^-1 , 245 samples). Discriminant function analysis (DFA) classified wheat bran samples with good mean recognizability in terms of both calibration (92%) and validation (89%). A pattern of 17 volatile compounds of wheat bran samples that were associated (positively or negatively) with DON content was also characterized by HS-SPME/GC-MS.

CONCLUSIONS

These results indicate that the e-nose method could be a useful tool for high-throughput screening of DON-contaminated wheat bran samples for their classification as acceptable / rejectable at contamination levels close to the EU maximum limit for DON, reducing the number of samples to be analyzed with a confirmatory method. © 2018 Society of Chemical Industry.

Risks to human and animal health related to the presence of deoxynivalenol and its acetylated and modified forms in food and feed

Deoxynivalenol (DON) is a mycotoxin primarily produced by Fusarium fungi, occurring predominantly in cereal grains. Following the request of the European Commission, the CONTAM Panel assessed the risk to animal and human health related to DON, 3-acetyl-DON (3-Ac-DON), 15-acetyl-DON (15-Ac-DON) and DON-3-glucoside in food and feed. A total of 27,537, 13,892, 7,270 and 2,266 analytical data for DON, 3-Ac-DON, 15-Ac-DON and DON-3-glucoside, respectively, in food, feed and unprocessed grains collected from 2007 to 2014 were used. For human exposure, grains and grain-based products were main sources, whereas in farm and companion animals, cereal grains, cereal by-products and forage maize contributed most. DON is rapidly absorbed, distributed, and excreted. Since 3-Ac-DON and 15-Ac-DON are largely deacetylated and DON-3-glucoside cleaved in the intestines the same toxic effects as DON can be expected. The TDI of 1 μg/kg bw per day, that was established for DON based on reduced body weight gain in mice, was therefore used as a group-TDI for the sum of DON, 3-Ac-DON, 15-Ac-DON and DON-3-glucoside. In order to assess acute human health risk, epidemiological data from mycotoxicoses were assessed and a group-ARfD of 8 μg/kg bw per eating occasion was calculated. Estimates of acute dietary exposures were below this dose and did not raise a health concern in humans. The estimated mean chronic dietary exposure was above the group-TDI in infants, toddlers and other children, and at high exposure also in adolescents and adults, indicating a potential health concern. Based on estimated mean dietary concentrations in ruminants, poultry, rabbits, dogs and cats, most farmed fish species and horses, adverse effects are not expected. At the high dietary concentrations, there is a potential risk for chronic adverse effects in pigs and fish and for acute adverse effects in cats and farmed mink.

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.

Detecting internal quality of peanuts during storage using electronic nose responses combined with physicochemical methods

In this study, the changes in the quality of unshelled peanuts and peanut kernels during storage were analyzed using an electronic nose (e-nose). The physicochemical indexes (acid and peroxide values) of peanut kernels were tested by traditional method as a reference. The storage time of peanut kernels increases from left to right in the cluster analysis plot based on the physicochemical indexes. The "maximum values", "area values", and "70th s values" methods were applied to extract the feature data from the e-nose responses. Principal component analysis (PCA) results indicated that the "70th s values" method produced the most accurate results, furthermore, unshelled peanut and peanut kernel samples presented similar characteristics in the PCA plots; the partial least squares regression (PLSR) results showed that the features of unshelled peanuts and peanut kernels are highly correlated with acid and peroxide values, respectively.

Rapid analysis of deoxynivalenol in durum wheat by FT-NIR spectroscopy

Fourier-transform-near infrared (FT-NIR) spectroscopy has been used to develop quantitative and classification models for the prediction of deoxynivalenol (DON) levels in durum wheat samples. Partial least-squares (PLS) regression analysis was used to determine DON in wheat samples in the range of <50–16,000 µg/kg DON. The model displayed a large root mean square error of prediction value (1,977 µg/kg) as compared to the EU maximum limit for DON in unprocessed durum wheat (i.e., 1,750 µg/kg), thus making the PLS approach unsuitable for quantitative prediction of DON in durum wheat. Linear discriminant analysis (LDA) was successfully used to differentiate wheat samples based on their DON content. A first approach used LDA to group wheat samples into three classes: A (DON ≤ 1,000 µg/kg), B (1,000 < DON ≤ 2,500 µg/kg), and C (DON > 2,500 µg/kg) (LDA I). A second approach was used to discriminate highly contaminated wheat samples based on three different cut-off limits, namely 1,000 (LDA II), 1,200 (LDA III) and 1,400 µg/kg DON (LDA IV). The overall classification and false compliant rates for the three models were 75%–90% and 3%–7%, respectively, with model LDA IV using a cut-off of 1,400 µg/kg fulfilling the requirement of the European official guidelines for screening methods. These findings confirmed the suitability of FT-NIR to screen a large number of wheat samples for DON contamination and to verify the compliance with EU regulation.