Discrimination and growth tracking of fungi contamination in peaches using electronic nose

The potential of glucosidase and glucose oxidase for aroma improvement in concentrated peach puree based on volatilomics and metabolomics

Cooked off-flavor was produced during the processing of concentrated peach puree (CPP), which led to aroma deterioration. Enzymatic treatment was beneficial in eliminating off-flavors and improving the aroma quality. Herein, the efficacy of glycosidase (AR2000), glucose oxidation (GOD), and their combination on the inhibition of off-flavors and aroma enhancement were evaluated. Compared with CPP, contents of benzaldehyde, benzyl alcohol, nonanal, and linalool increased by 198%, 1222%, 781%, and 71% after AR2000 treatment via the metabolisms of shikimate, glucose, linoleic acid, and linolenic acid, leading to the strengthening of floral and grassy. Due to the removal of 1-octen-3-one via linolenic acid metabolism, cooked off-flavor could be significantly weakened by GOD. Furthermore, Furthermore, the combination of AR2000 and GOD could not only inhibit the production of 1-octen-3-one to weaken the cooked note but also enhance grassy and floral attributes via the increase of aldehydes and alcohols.

Advancement of metal oxide nanomaterials on agri-food fronts

The application of metal oxide nanomaterials (MOx NMs) in the agrifood industry offers innovative solutions that can facilitate a paradigm shift in a sector that is currently facing challenges in meeting the growing requirements for food production, while safeguarding the environment from the impacts of current agriculture practices. This review comprehensively illustrates recent advancements and applications of MOx for sustainable practices in the food and agricultural industries and environmental preservation. Relevant published data point out that MOx NMs can be tailored for specific properties, enabling advanced design concepts with improved features for various applications in the agrifood industry. Applications include nano-agrochemical formulation, control of food quality through nanosensors, and smart food packaging. Furthermore, recent research suggests MOx's vital role in addressing environmental challenges by removing toxic elements from contaminated soil and water. This mitigates the environmental effects of widespread agrichemical use and creates a more favorable environment for plant growth. The review also discusses potential barriers, particularly regarding MOx toxicity and risk evaluation. Fundamental concerns about possible adverse effects on human health and the environment must be addressed to establish an appropriate regulatory framework for nano metal oxide-based food and agricultural products.

Discrimination of internal browning in pineapple during storage based on changes in volatile compounds

Internal browning (IB) is a physiological disorder without external symptoms of postharvest pineapples, but whether and how IB influences pineapple volatiles remain unknown. We examined eigenvalues of volatiles in 'Comte de Paris' pineapples with or without IB using electronic nose (E-nose) and gas chromatography-mass spectrometry (GC-MS). Correlation coefficients between the responses of E-nose sensors S7 and S9 and IB were 0.836 and 0.804, respectively. The multilayer perceptron neural network and radial basis function neural network models classified IB degree with accuracy of 94.77% and 91.67%. GC-MS analysis revealed 30 volatile substances upregulated in pineapple with IB compared to those without, of which 15 were esters. IB regulated volatile compound synthesis through the lipoxygenase pathway which involved lipoxygenase, pyruvate decarboxylase 1, alcohol dehydrogenases, acyl-CoA oxidase 1, and alcohol acyltransferase genes and their related enzymes. These results suggested that volatiles regulated by IB could be used to discriminate IB severity in pineapples.

Effects of cholesterol removal treatment on the flavor and physicochemical properties of hot gel egg yolk

The aim of this study was to investigate effects of cholesterol removal treatment (CRT) on the flavor, taste, texture, color, and nutritional value of hot gel egg yolk (EY). The off-odor, volatile components and taste of EY treated with CRT were studied by electronic nose (E-nose), gas chromatography-mass spectrometry (GC-MS) and electronic tongue (E-tongue). The effect of CRT on the nutritional value of EY was studied by amino acid and fatty acid analysis. The CRT significantly reduced the content of hexanal, 2-amyl-furan, 1-octene-3-ol, styrene and heptanal in EY1-EY4, also decreased its bitter taste without affecting other taste and elasticity. In addition, the CRT did not affect the essential amino acids (EAA) content and L*, a* and b* values of EY1-EY4, but it led to the reduction in polyunsaturated fatty acids (PUFA) content. In general, the CRT is an effective way to reduce the off-odor of EY without affecting consumer acceptance.

Advancements in Improving Selectivity of Metal Oxide Semiconductor Gas Sensors Opening New Perspectives for Their Application in Food Industry

Volatile compounds not only contribute to the distinct flavors and aromas found in foods and beverages, but can also serve as indicators for spoilage, contamination, or the presence of potentially harmful substances. As the odor of food raw materials and products carries valuable information about their state, gas sensors play a pivotal role in ensuring food safety and quality at various stages of its production and distribution. Among gas detection devices that are widely used in the food industry, metal oxide semiconductor (MOS) gas sensors are of the greatest importance. Ongoing research and development efforts have led to significant improvements in their performance, rendering them immensely useful tools for monitoring and ensuring food product quality; however, aspects related to their limited selectivity still remain a challenge. This review explores various strategies and technologies that have been employed to enhance the selectivity of MOS gas sensors, encompassing the innovative sensor designs, integration of advanced materials, and improvement of measurement methodology and pattern recognize algorithms. The discussed advances in MOS gas sensors, such as reducing cross-sensitivity to interfering gases, improving detection limits, and providing more accurate assessment of volatile organic compounds (VOCs) could lead to further expansion of their applications in a variety of areas, including food processing and storage, ultimately benefiting both industry and consumers.

Suanyu fermentation strains screening, process optimization and the effect of thermal processing methods on its flavor

Suanyu is a famous traditional fermented aquatic food in south China. However, the quality of Suanyu is unstable due to natural fermentation based on the environment. This work screened suitable microbial fermenters (Enterococcus rivorum and Enterococcus lactis) from traditional fermented fish and optimized a suitable fermentation process. Effects of different fermentation (natural and mixed starters fermentation) and thermal treatments (microwave, frying and roasting) on the flavor of Suanyu were investigated. Compared to the natural fermentation group, the TVB-N content (31.5 mg/100 g) was lower, the total acidity (5.12 g/kg) and flavor compounds content were richer in the mixed starters fermentation group (P < 0.05). But there was no significant difference in histamine content (P > 0.05). The roasting treatment group contained higher contents of free amino acids, organic acids, nucleotides and richer key aroma components. The electronic nose was able to distinguish between the differently treated samples. The sensory evaluation result showed that roasted and fried samples had a more acceptable flavor and color. This work will provide a theoretical reference for the standardized production of Suanyu and the development of pre-cooked products.

Classification and Identification of Contaminants in Recyclable Containers Based on a Recursive Feature Elimination-Light Gradient Boosting Machine Algorithm Using an Electronic Nose

Establishing an excellent recycling mechanism for containers is of great importance for environmental protection, so many technical approaches applied during the whole recycling stage have become popular research issues. Among them, classification is considered a key step, but this work is mostly achieved manually in practical applications. Due to the influence of human subjectivity, the classification accuracy often varies significantly. In order to overcome this shortcoming, this paper proposes an identification method based on a Recursive Feature Elimination-Light Gradient Boosting Machine (RFE-LightGBM) algorithm using electronic nose. Firstly, odor features were extracted, and feature datasets were then constructed based on the response data of the electronic nose to the detected gases. Afterwards, a principal component analysis (PCA) and the RFE-LightGBM algorithm were applied to reduce the dimensionality of the feature datasets, and the differences between these two methods were analyzed, respectively. Finally, the differences in the classification accuracies on the three datasets (the original feature dataset, PCA dimensionality reduction dataset, and RFE-LightGBM dimensionality reduction dataset) were discussed. The results showed that the highest classification accuracy of 95% could be obtained by using the RFE-LightGBM algorithm in the classification stage of recyclable containers, compared to the original feature dataset (88.38%) and PCA dimensionality reduction dataset (92.02%).

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.

The Dissipation Behavior and Risk Assessment of Carbendazim Under Individual and Joint Applications on Peach (Amygdalus persica L.)

Dissipation, residue levels, and ingestion risks of carbendazim in peach (Amygdalus persica L.) were investigated with individual and joint applications in the present study. The dissipation kinetics of carbendazim, chlorpyrifos, prochloraz, and imidacloprid were evaluated by the first-order kinetics. When carbendazim was individually applied, the final residual concentration was 2.97 mg kg-1 and the half-life was 17.4 d. In the joint application of carbendazim with chlorpyrifos, prochloraz, and imidacloprid, the residual concentrations at 35 d after spraying were 7.16, 7.50, and 4.26 mg kg-1 and the half-lives were 30.8, 23.7, and 23.2 d, respectively, which showed an increase of 1.3-1.8 times compared with the single application of carbendazim. In addition, the effects of household processing of rinsing and peeling were investigated, and a high removal rate of 54.6% and 76.5% were found. Furthermore, the carbendazim ingestion risk assessment was conducted, which indicated that the acute health risk (aHI) and hazard quotient (HQ) of carbendazim were all within acceptable levels ranging from 21.7% to 40.9%. However, a higher ingestion risk of carbendazim was found under the joint application. This study provides some preliminary guidance for the joint application and risk assessment of carbendazim in peach, which is worth further investigation.

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.

Application of Machine Learning Algorithms to Classify Peruvian Pisco Varieties Using an Electronic Nose

Pisco is an alcoholic beverage obtained from grape juice distillation. Considered the flagship drink of Peru, it is produced following strict and specific quality standards. In this work, sensing results for volatile compounds in pisco, obtained with an electronic nose, were analyzed through the application of machine learning algorithms for the differentiation of pisco varieties. This differentiation aids in verifying beverage quality, considering the parameters established in its Designation of Origin". For signal processing, neural networks, multiclass support vector machines and random forest machine learning algorithms were implemented in MATLAB. In addition, data augmentation was performed using a proposed procedure based on interpolation-extrapolation. All algorithms trained with augmented data showed an increase in performance and more reliable predictions compared to those trained with raw data. From the comparison of these results, it was found that the best performance was achieved with neural networks.

A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae

Introduction

Anthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and taking preventive and control measures.

Methods

This study presented an approach for tracking growth and identifying different infection stages of the C. musae in bananas using Vis/NIR spectroscopy. A total of 330 banana reflectance spectra were collected over ten consecutive days after inoculation, with a sampling rate of 24 h. The four-class and five-class discriminant patterns were designed to examine the capability of NIR spectra in discriminating bananas infected at different levels (control, acceptable, moldy, and highly moldy), and different time at early stage (control and days 1-4). Three traditional feature extraction methods, i.e. PC loading coefficient (PCA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), combining with two machine learning methods, i.e. partial least squares discriminant analysis (PLSDA) and support vector machine (SVM), were employed to build discriminant models. One-dimensional convolutional neural network (1D-CNN) without manually extracted feature parameters was also introduced for comparison.

Results

The PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively.

Discussion

These results indicate the feasibility of identifying banana fruit infected with C. musae using Vis/NIR spectra, and the resolution can be accurate to one day.

Transcriptional profiling of the terpenoid biosynthesis pathway and in vitro tests reveal putative roles of linalool and farnesal in nectarine resistance against brown rot

The most devastating fungal disease of peaches and nectarines is brown rot, caused by Monilinia spp. Among the many plant responses against biotic stress, plant terpenoids play essential protective functions, including antioxidant activities and inhibition of pathogen growth. Herein, we aimed to characterize the expression of terpenoid biosynthetic genes in fruit tissues that presented different susceptibility to brown rot. For that, we performed artificial inoculations with Monilinia laxa at two developmental stages (immature and mature fruit) of two nectarine cultivars ('Venus' -mid-early season cultivar - and 'Albared' -late season cultivar-) and in vitro tests of the key compounds observed in the transcriptional results. All fruit were susceptible to M. laxa except for immature 'Venus' nectarines. In response to the pathogen, the mevalonic acid (MVA) pathway of the 'Venus' cultivar was highly induced in both stages rather than the methylerythritol phosphate (MEP) pathway, being the expression of some MEP-related biosynthetic genes [e.g., PROTEIN FARNESYLTRANSFERASE (PpPFT), and 3S-LINALOOL SYNTHASE (PpLIS)] different between stages. In 'Albared', both stages presented similar responses to M. laxa for both pathways. Comparisons between cultivars showed that HYDROXYMETHYLGLUTARYL-CoA REDUCTASE (PpHMGR1) expression levels were common in susceptible tissues. Within all the terpenoid biosynthetic pathway, linalool- and farnesal-related pathways stood out for being upregulated only in resistant tissues, which suggest their role in mediating the resistance to M. laxa. The in vitro antifungal activity of linalool and farnesol (precursor of farnesal) revealed fungicidal and fungistatic activities against M. laxa, respectively, depending on the concentration tested. Understanding the different responses between resistant and susceptible tissues could be further considered for breeding or developing new strategies to control brown rot in stone fruit.

Preliminary Studies on Detection of Fusarium Basal Rot Infection in Onions and Shallots Using Electronic Nose

The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae, which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with F. oxysporum f. sp. cepae, to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops.

Early Discrimination and Prediction of C. fimbriata-Infected Sweetpotatoes during the Asymptomatic Period Using Electronic Nose

Sweetpotato is prone to disease caused by C. fimbriata without obvious lesions on the surface in the early period of infection. Therefore, it is necessary to explore the possibility of developing an efficient early disease detection method for sweetpotatoes that can be used before symptoms are observed. In this study, sweetpotatoes were inoculated with C. fimbriata and stored for different lengths of time. The total colony count was detected every 8 h; HS-SPME/GC–MS and E-nose were used simultaneously to detect volatile compounds. The results indicated that the growth of C. fimbriata entered the exponential phase at 48 h, resulting in significant differences in concentrations of volatile compounds in infected sweetpotatoes at different times, especially toxic ipomeamarone in ketones. The contents of volatile compounds were related to the responses of the sensors. E-nose was combined with multiple chemometrics methods to discriminate and predict infected sweetpotatoes at 0 h, 48 h, 64 h, and 72 h. Among the methods used, linear discriminant analysis (LDA) had the best discriminant effect, with sensitivity, specificity, precision, and accuracy scores of 100%. E-nose combined with K-nearest neighbours (KNN) achieved the best predictions for ipomeamarone contents and total colony counts. This study illustrates that E-nose is a feasible and promising technology for the early detection of C. fimbriata infection in sweetpotatoes during the asymptomatic period.

Non-destructive Storage Time Prediction of Newhall Navel Oranges Based on the Characteristics of Rind Oil Glands

How to non-destructively and quickly estimate the storage time of citrus fruit is necessary and urgent for freshness control in the fruit market. As a feasibility study, we present a non-destructive method for storage time prediction of Newhall navel oranges by investigating the characteristics of the rind oil glands in this paper. Through the observation using a digital microscope, the oil glands were divided into three types and the change of their proportions could indicate the rind status as well as the storage time. Images of the rind of the oranges were taken in intervals of 10 days for 40 days, and they were used to train and test the proposed prediction models based on K-Nearest Neighbors (KNN) and deep learning algorithms, respectively. The KNN-based model demonstrated explicit features for storage time prediction based on the gland characteristics and reached a high accuracy of 93.0%, and the deep learning-based model attained an even higher accuracy of 96.0% due to its strong adaptability and robustness. The workflow presented can be readily replicated to develop non-destructive methods to predict the storage time of other types of citrus fruit with similar oil gland characteristics in different storage conditions featuring high efficiency and accuracy.

Advanced applications of chemo-responsive dyes based odor imaging technology for fast sensing food quality and safety: A review

Public attention to foodquality and safety has been increased significantly. Therefore, appropriate analytical tools are needed to analyze and sense the food quality and safety. Volatile organic compounds (VOCs) are important indicators for the quality and safety of food products. Odor imaging technology based on chemo-responsive dyes is one of the most promising methods for analysis of food products. This article reviews the sensing and imaging fundamentals of odor imaging technology based on chemo-responsive dyes. The aim is to give detailed outlines about the theory and principles of using odor imaging technology for VOCs detection, and to focus primarily on its applications in the field of quality and safety evaluation of food products, as well as its future applicability in modern food industries and research. The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods , poultry meat, aquatic products, fruits and vegetables, and tea. It has the potential for the rapid, reliable, and inline assessment of food safety and quality by providing odor-image-basedmonitoring tool. Practical Application: The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods, poultry meat, aquatic products, fruits and vegetables, and tea.

Early identification of Aspergillus spp. contamination in milled rice by E-nose combined with chemometrics

BACKGROUND

Rice grains can be contaminated easily by certain fungi during storage and in the market chain, thus generating a risk for humans. Most classical methods for identifying and rectifying this problem are complex and time-consuming for manufacturers and consumers. However, E-nose technology provides analytical information in a non-destructive and environmentally friendly manner. Two-feature fusion data combined with chemometrics were employed for the determination of Aspergillus spp. contamination in milled rice.

RESULTS

Linear discriminant analysis (LDA) indicated that the efficiency of fusion signals ('80^th s values' and 'area values') outperformed that of independent E-nose signals. Linear discriminant analysis showed clear discrimination of fungal species in stored milled rice for four groups on day 2, and the discrimination accuracy reached 92.86% by using an extreme learning machine (ELM). Gas chromatography-mass spectrometry (GC-MS) analysis showed that the volatile compounds had close relationships with fungal species in rice. The quantification results of colony counts in milled rice showed that the monitoring models based on ELM and the genetic algorithm optimized support vector machine (GA-SVM) (R²  = 0.924-0.983) achieved better performances than those based on partial least squares regression (PLSR) (R²  = 0.877-0.913). The ability of the E-nose to monitor fungal infection at an early stage would help to prevent contaminated rice grains from entering the food chains.

CONCLUSIONS

The results indicated that an E-nose coupled with ELM or GA-SVM algorithm could be a useful tool for the rapid detection of fungal infection in milled rice, to prevent contaminated rice from entering the food chain. © 2021 Society of Chemical Industry.

Analysis of the Aroma Chemical Composition of Commonly Planted Kiwifruit Cultivars in China

The aroma chemical composition of commonly planted kiwifruit cultivars in China was analyzed. The combination of 2-octanone with 3-octanone was the most suitable dual internal standard for quantitative analysis in GC-MS. A total of 172 aroma components in 23 kiwifruit cultivars were detected, and ethyl butanoate, (E)-2-hexen-1-ol, and (E)-2-hexenal could be considered the core aroma components in kiwifruit, but still need further confirmation using Sensomics. E-nose could effectively distinguish different cultivars of kiwifruit. Clustering based on GC-MS and E-nose results tends to be consistent and demonstrate a certain degree of similarity. Kiwifruit cultivars with different flesh colors cannot be effectively distinguished by their aroma chemical compositions. Different species of kiwifruit can be distinguished to some extent by their aroma chemical compositions, but the effect was not satisfactory. These results could prove valuable in the breeding, planting, and marketing of kiwifruits.

A novel electronic nose for the detection and classification of pesticide residue on apples

Excessive pesticide residues are a serious problem faced by food regulatory authorities, suppliers, and consumers. To assist with this challenge, this work aimed to develop a method of detecting and classifying pesticide residue on fruit samples using an electronic nose, through the application of three different data-recognition algorithms. The apple samples carried various concentrations of two known pesticides, namely cypermethrin and chlorpyrifos. Data collection was performed using a PEN3 electronic nose equipped with 10 metal oxide semiconductor (MOS) sensors. In order to classify and analyze these pesticide residues on the apple samples, principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) results were combined with sensor output responses to realize MOS sensor array data visualization. The results indicated that all three data-recognition algorithms accurately identified the pesticide residues in the apple samples, with the PCA algorithm exhibiting the best classification and discrimination ability. Consequently, this work has shown that the MOS electronic nose, in combination with data-recognition algorithms, can provide support for the rapid and non-destructive identification of pesticide residues in fruits and can provide an effective tool for the detection of pesticide residues in agricultural products.

The MOS electronic nose in combination with data-recognition algorithms can provide an effective tool for the detection of pesticide residues in agricultural products.

Change Regularity of Taste and the Performance of Endogenous Proteases in Shrimp (Penaens vannamei) Head during Autolysis

This study evaluated the food safety and proximate composition of shrimp head (SH). Potentially toxic elements in SH were below European Union legislation limits. SH had a high content of tasting amino acids (sweet and umami amino acids was 57%) and a high content of functional amino acids (essential amino acids was 37%). Moreover, the changes of flavor and key umami molecules in SH were studied by sensory evaluation, electronic tongue, electronic nose, automated amino acid analyzer, and high performance liquid chromatography (HPLC). The results showed that the significant difference of flavor in SH happened during autolysis. SH with autolysis had the best umami taste at 6 h, which may result from the synergistic work of free amino acids and nucleotide related compounds. Additionally, the performance of endogenous proteases in SH was investigated to efficiently analyze autolysis. The optimum pH and temperature of endogenous proteases in SH were 7.5 and 50 °C, respectively. The autolysis of SH depends on two endogenous proteases (~50 kDa and ~75 kDa). These results suggest that the formation of flavor in SH during autolysis can be controlled, which could provide guidance for SH recycle. SH could consider as one of the food materials for producing condiments.

Effect of dielectric barrier discharge cold plasma treatments on flavor fingerprints of brown rice

A non-thermal processing method was developed to promote preservation of brown rice using dielectric barrier discharge cold plasma (DBD-CP). Physicochemical properties including free fatty acid (FFA) content, surface color change, volatile organic components (VOCs) and flavor fingerprints were evaluated in brown rice submitted to DBD-CP. FFA levels were 25.2% lower in treated samples compared to the control, and a more stable surface color was obtained at the end of the storage period. A total of 35 major VOCs could be detected in treated samples, and reduced levels of hexanal can be used as an indicator of DBD-CP treatment in brown rice during storage. Moreover, the flavor fingerprints in DBD-CP treated groups can be successfully distinguished through headspace gas chromatography ion mobility spectrometry. Collectively, application of DBD-CP treatment could be utilized as a feasible approach to promote stabilization of brown rice and preserve flavor during storage.

Recent progress of nondestructive techniques for fruits damage inspection: a review

In the process of growing, harvesting, and storage, fruits are vulnerable to mechanical damage, microbial infections, and other types of damage, which not only reduce the quality of fruits, increase the risk of fungal infections, in turn greatly affect food safety, but also sharply reduce economic benefits. Hence, it is essential to identify damaged fruits in time. Rapid and nondestructive detection of fruits damage is in great demand. In this paper, the latest research progresses on the detection of fruits damage by nondestructive techniques, including visible/near-infrared spectroscopy, chlorophyll fluorescence techniques, computer vision, multispectral and hyperspectral imaging, structured-illumination reflectance imaging, laser-induced backscattering imaging, optical coherence tomography, nuclear magnetic resonance and imaging, X-ray imaging, electronic nose, thermography, and acoustic methods, are summarized. We briefly introduce the principles of these techniques, summarize their applicability. The challenges and future trends are also proposed to provide beneficial reference for future researches and real-world applications.

Distinguishing between long-term-stored and fresh chili pepper powder through fingerprinting of volatiles by headspace capillary-gas chromatography-ion mobility spectrometry

Long-term storage of chili pepper powder results in physicochemical and microbiological changes that decrease its commercial value; these changes occur owing to fungal growth and production of off-flavor compounds. Herein, long-term-stored chili pepper powder (LSCPP) and fresh chili pepper powder (FCPP) were analyzed using internal transcribed spacer sequencing and volatile organic compound fingerprinting by headspace capillary-gas chromatography-ion mobility spectrometry. Fungal analysis detected only Xeromyces bisporus with high accuracy in all the analyzed LSCPP samples. However, the proliferation of X. bisporus on nonspecific spots complicated the distinguishing process between the two groups based solely on fungal analysis. Therefore, nine compounds (three ketones, one alcohol, two aldehydes, one ester, one furan, and one sulfur compound) obtained by autoxidation and fungal metabolism were selected as potential markers for distinguishing LSCPP and FCPP. These above-mentioned substances, which were confirmed as off-flavor species owing to "stale" odor, emitted lipid fragrance and were used to successfully distinguish LSCPP from FCPP using principal component analysis and linear discriminant analysis. PRACTICAL APPLICATION: According to the research results, it was possible to discriminate between long-term stored and fresh chili pepper powders using nine VOC markers for quality control in industry. In addition, the fungus generated from long-term storage of chili pepper powder was Xeromyces bisporus, which was confirmed to be safe for intake because it does not form secondary toxic metabolites.

Analysis of Volatile Components of Auricularia auricula from Different Origins by GC-MS Combined with Electronic Nose

Auricularia auricula is a kind of nutrient-rich edible fungus, which has the reputation of “king of vegetarians.” In this paper, the electronic nose combined with GC-MS technology was used to analyze the volatile components of A. auricula in Heilongjiang, Jilin, Shanghai, and Sichuan provinces to investigate the differences and characteristics of A. auricula in different origins. The results showed that the electronic nose could obviously distinguish the samples from Jilin and Shanghai with a high degree of discrimination, while it was inappropriate to distinguish the samples from Heilongjiang and Sichuan Province. GC-MS was used to further analyze the volatile compounds in A. auricula qualitatively and quantitatively. The results showed that 98 volatile components were detected and 23 of them were common components, including alcohols, aldehydes, acids, esters, hydrocarbons, and other volatile components. The relative content of acetic acid and diethyl azodicarboxylate in A. auricula from the four origins was relatively high. According to the relative odor activity value (ROAV), it was found that the key compounds that caused the aroma difference between different origins were 1-octene-3-ol, cis-3-nonene-1-ol, (E)-2-octenal, (E)-2-nonenal, (E,E)-2,4-nonadienal, and 3-methyl butanal.

Portable Food-Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks

Artificial scent screening systems (known as electronic noses, E-noses) have been researched extensively. A portable, automatic, and accurate, real-time E-nose requires both robust cross-reactive sensing and fingerprint pattern recognition. Few E-noses have been commercialized because they suffer from either sensing or pattern-recognition issues. Here, cross-reactive colorimetric barcode combinatorics and deep convolutional neural networks (DCNNs) are combined to form a system for monitoring meat freshness that concurrently provides scent fingerprint and fingerprint recognition. The barcodes-comprising 20 different types of porous nanocomposites of chitosan, dye, and cellulose acetate-form scent fingerprints that are identifiable by DCNN. A fully supervised DCNN trained using 3475 labeled barcode images predicts meat freshness with an overall accuracy of 98.5%. Incorporating DCNN into a smartphone application forms a simple platform for rapid barcode scanning and identification of food freshness in real time. The system is fast, accurate, and non-destructive, enabling consumers and all stakeholders in the food supply chain to monitor food freshness.

Application of electronic nose and GC–MS for detection of strawberries with vibrational damage

Objectives

This study evaluated the potential of using electronic nose (e-nose) technology to non-destructively detect strawberry fruits with vibrational damage based on their volatile substances (VOCs).

Materials and methods

Four groups of strawberries with different durations of vibrations (0, 0.5, 1, and 2 h) were prepared, and their e-nose signals were collected at 0, 1, 2, and 3 days after vibration treatment.

Results

The results showed that when the samples from all four sampling days during storage were used for modelling, both the levels of vibrational damage and the day after the damage happened were accurately predicted. The best models had residual prediction deviation values of 2.984 and 5.478. The discrimination models for damaged strawberries also obtained good classification results, with an average correct answer rate of calibration and prediction of 99.24%. When the samples from each sampling day or vibration time were used for modelling, better results were obtained, but these models were not suitable for an actual situation. The gas chromatography–mass spectrophotometry results showed that the VOCs of the strawberries varied after experiencing vibrations, which was the basis for e-nose detection.

Limitations

The changes in VOCs released by other forces should be studied in the future.

Conclusions

The above results showed the potential use of e-nose technology to detect strawberries that have suffered vibrational damage.

Application of gas sensors for modelling the dynamic growth of Pseudomonas in pork stored at different temperatures

Pseudomonas have a faster growth rate over other bacteria in chilled meat under aerobic conditions. A non-destructive method for modelling the dynamic growth of Pseudomonas in pork stored at different temperatures using gas sensors was presented in our work. Based on selected gas sensor data, the first-order kinetic equations (Gompertz and Logistic Functions) combined with the secondary model (Square-root Function) effectively simulated Pseudomonas growth in pork at different temperatures with R² and RMSE values of 0.71-0.97 and 0.27-0.84, respectively. Additionally, these models showed high accuracy with correlation coefficients greater than 0.90, in addition to several individual accuracy values. Furthermore, HS-SPME/GC-MS results demonstrated the presence of identified key volatiles in samples inoculated with Pseudomonas, including three amine compounds (mercaptamine, 1-octanamine and 1-heptadecanamine), phenol and indole. Our work showed that gas sensors are a rapid, easy and non-destructive method with acceptable feasibility in modelling the dynamic growth of spoilage microorganisms in meat.

Volatile organic compounds gas sensor based on quartz crystal microbalance for fruit freshness detection: A review

In this review article, the state of the art of gas sensors based on quartz crystal microbalance (QCM) for fruit freshness detection is overviewed from the aspects of development history, working principle, selection and modification of sensitive materials, and volatile organic compounds detection of fruits. According to the characteristics of respiratory intensity at the stage of fruit ripening, fruits can be divided into respiration climacteric fruits and non-climacteric fruits. In recent years, research has mainly focused on respiration climacteric fruits, such as bananas and mangoes, etc., while related studies on non-climacteric fruits have been rarely reported, except for citrus fruits. The preparation methods and structure design of sensitive materials based on physical/chemical adsorption mechanisms are further discussed according to the odor components that affect the freshness of fruits, namely alkenes, esters, aldehydes and alcohols.

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.

Detection and identification of fungal growth on freeze-dried Agaricus bisporus using spectra and olfactory sensors

BACKGROUND

Fungal contamination in food products leads to mustiness, biochemical changes, and undesirable odors, which result in lower food quality and lower market value. To develop a rapid method for detecting fungi, hyperspectral imaging (HSI) was applied to identify five fungi inoculated on plates (Aspergillus niger, Aspergillus flavus, Penicillium chrysogenum, Aspergillus fumigatus, and Aspergillus ochraceus). Near-infrared (NIR) spectroscopy, mid-infrared (MIR) spectroscopy, and an electronic nose (E-nose) were applied to detect and identify freeze-dried Agaricus bisporus infected with the five fungi.

RESULTS

Partial least squares regression (PLSR) models were used to distinguish the HSI spectra of the five fungi on the plates. The A. ochraceus group had the highest calibration performance: coefficient of calibration (R_c ² ) = 0.786, root mean-square error of calibration (RMSEC) = 0.125 log CFU g^-1 . The A. flavus group had the highest prediction performance: coefficient of prediction (R_p ² ) = 0.821, root mean-square error of prediction (RMSEP) = 0.083 log CFU g^-1 . The ratio of performance deviation (RPD) values of all of the models was higher than 2.0 for the NIR, MIR, and E-nose results for freeze-dried A. bisporus infected with different fungi. The fungal species and degree of infection can be distinguished by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) using NIR, MIR, and E-nose, as the discrimination accuracy was more than 90%. The NIR methods had a higher recognition rate than the MIR and E-nose methods.

CONCLUSION

Principal component analysis (PCA) and PLSR models based on full spectra of HSI can achieve good discrimination results for these five fungi on plates. Moreover, NIR, MIR, and the E-nose were proven to be effective in monitoring fungal contamination on freeze-dried A. bisporus. However, NIR could be a more accurate method. © 2020 Society of Chemical Industry.

Advances in Analysis and Detection of Major Mycotoxins in Foods

Mycotoxins are the most widely studied biological toxins, which contaminate foods at very low concentrations. This review describes the emerging extraction techniques and the current and alternatives analytical techniques and methods that have been used to successfully detect and identify important mycotoxins. Some of them have proven to be particularly effective in not only the detection of mycotoxins, but also in detecting mycotoxin-producing fungi. Chromatographic techniques such as high-performance liquid chromatography coupled with various detectors like fluorescence, diode array, UV, liquid chromatography coupled with mass spectrometry, and liquid chromatography-tandem mass spectrometry, have been powerful tools for analyzing and detecting major mycotoxins. Recent progress of the development of rapid immunoaffinity-based detection techniques such as immunoassays and biosensors, as well as emerging technologies like proteomic and genomic methods, molecular techniques, electronic nose, aggregation-induced emission dye, quantitative NMR and hyperspectral imaging for the detection of mycotoxins in foods, have also been presented.

Rapid and Non-Destructive Detection of Compression Damage of Yellow Peach Using an Electronic Nose and Chemometrics

The rapid and non-destructive detection of mechanical damage to fruit during postharvest supply chains is important for monitoring fruit deterioration in time and optimizing freshness preservation and packaging strategies. As fruit is usually packed during supply chain operations, it is difficult to detect whether it has suffered mechanical damage by visual observation and spectral imaging technologies. In this study, based on the volatile substances (VOCs) in yellow peaches, the electronic nose (e-nose) technology was applied to non-destructively predict the levels of compression damage in yellow peaches, discriminate the damaged fruit and predict the time after the damage. A comparison of the models, established based on the samples at different times after damage, was also carried out. The results show that, at 24 h after damage, the correct answer rate for identifying the damaged fruit was 93.33%, and the residual predictive deviation in predicting the levels of compression damage and the time after the damage, was 2.139 and 2.114, respectively. The results of e-nose and gas chromatography-mass spectrophotometry (GC–MS) showed that the VOCs changed after being compressed—this was the basis of the e-nose detection. Therefore, the e-nose is a promising candidate for the detection of compression damage in yellow peach.

Aheto J, Zhang D, Feng F. Detection of Beef Adulterated with Pork Using a Low-Cost Electronic Nose Based on Colorimetric Sensors

The present study was aimed at developing a low-cost but rapid technique for qualitative and quantitative detection of beef adulterated with pork. An electronic nose based on colorimetric sensors was proposed. The fresh beef rib steaks and streaky pork were purchased and used from the local agricultural market in Suzhou, China. The minced beef was mixed with pork ranging at levels from 0%~100% by weight at increments of 20%. Protein, fat, and ash content were measured for validation of the differences between the pure beef and pork used in basic chemical compositions. Fisher linear discriminant analysis (Fisher LDA) and extreme learning machine (ELM) were utilized comparatively for identification of the ground pure beef, beef–pork mixtures, and pure pork. Back propagation-artificial neural network (BP-ANN) models were built for prediction of the adulteration levels. Results revealed that the ELM model built was superior to the Fisher LDA model with higher identification rates of 91.27% and 87.5% in the training and prediction sets respectively. Regarding the adulteration level prediction, the correlation coefficient and the root mean square error were 0.85 and 0.147 respectively in the prediction set of the BP-ANN model built. This suggests, from all the results, that the low-cost electronic nose based on colorimetric sensors coupled with chemometrics has a great potential in rapid detection of beef adulterated with pork.

Discrimination of Volatiles of Shiitakes (Lentinula edodes) Produced during Drying Process by Electronic Nose

This study aimed to investigate the potential of electronic nose (E-nose) to differentiate volatiles of shiitakes produced at different drying stages. Shiitakes at different drying time slots were categorized into four groups (fresh, early, middle and late stage) by sensory evaluation. E-nose was used to analyze the volatiles and compared with headspace solid phase micro-extraction combined with gas chromatography-mass spectrometry (HS/GC-MS). The principal component analysis results showed that shiitakes at each stage could be successfully discriminated by E-nose and HS/GC-MS. The differences in volatile organic compounds produced at each stage were mainly caused by sulfurs and alcohols, leading to apparent changes of sensors sensitive to sulfurs, alcohols and aromatic compounds. The discriminant models were established by partial least squares discriminant analysis and support vector machine classification, with accuracy rates of 91.25 % and 95.83 %, respectively. The results demonstrated the potential use of E-nose in classifying and monitoring shiitakes during drying process.

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.