Integration of a low-cost electronic nose and a voltammetric electronic tongue for red wines identification

From Traditional to Intelligent, A Review of Application and Progress of Sensory Analysis in Alcoholic Beverage Industry

Sensory analysis is an interdisciplinary field that combines multiple disciplines to analyze food qualitatively and quantitatively. At present, this analysis method has been widely used in product development, quality control, marketing, flavor analysis, safety supervision and inspection of alcoholic beverages. Due to the changing needs of analysis, new and more optimized methods are still emerging. Thereinto, intelligent and biometric technologies with growing attention have also been applied to sensory analysis. This work summarized the sensory analysis methods from three aspects, including traditional artificial sensory analysis, intelligent sensory technology, and innovative technologies. Meanwhile, the application sensory analysis in alcoholic beverages and its industrial production was scientifically emphasized. Moreover, the future tendency of sensory analysis in the alcoholic beverage industry is also highlights.

Analysis of the Correlation between Persimmon Fruit-Sugar Components and Taste Traits from Germplasm Evaluation

Persimmon fruits are brightly colored and nutritious and are fruits that contain large amounts of sugar, vitamins, mineral elements, and phenolic substances. The aim of this study was to explore the differences in fruit-sugar components of different persimmon germplasms and their relationships with phenotypic and flavor indices through the determination of phenotypes and sugar components and through electronic-tongue indices, which provided the basis and inspiration for the selection of different sugar-accumulating types of persimmon fruits and the selection of high-sugar persimmon varieties. Our results showed that persimmon germplasm fruit-sugar components were dominated by sucrose, glucose and fructose and that the remaining sugar components were more diverse but less distributed among the various germplasm types. Based on the proportion of each sugar component in the fruit, persimmon germplasms can be categorized into sucrose-accumulating and reduced-sugar-accumulation types. Sucrose-accumulating types are dominated by sucrose, galactose, fucose and inositol, while reduced-sugar-accumulation types are dominated by glucose, fructose, mannose-6-phosphate, and xylose. The content of sugar components in the germplasm persimmon of fruits of different types and maturity periods of also differed, with significant differences in sugar components between PCNA (pollination-constant non-astringent) and PCA (pollination-constant astringent) fruits. Cluster analysis classified 81 persimmon germplasms into three clusters, including cluster I-A, with low glucose and fructose content, and cluster I-B, with medium glucose, fructose, and sucrose contents. Cluster II was high in sucrose and fructose. Cluster III had high contents of glucose and fructose and low contents of sucrose and inositol.

Electronic Tongues and Noses: A General Overview

As technology advances, electronic tongues and noses are becoming increasingly important in various industries. These devices can accurately detect and identify different substances and gases based on their chemical composition. This can be incredibly useful in fields such as environmental monitoring and industrial food applications, where the quality and safety of products or ecosystems should be ensured through a precise analysis. Traditionally, this task is performed by an expert panel or by using laboratory tests but sometimes becomes a bottleneck because of time and other human factors that can be solved with technologies such as the provided by electronic tongue and nose devices. Additionally, these devices can be used in medical diagnosis, quality monitoring, and even in the automotive industry to detect gas leaks. The possibilities are endless, and as these technologies continue to improve, they will undoubtedly play an increasingly important role in improving our lives and ensuring our safety. Because of the multiple applications and developments in this field in the last years, this work will present an overview of the electronic tongues and noses from the point of view of the approaches developed and the methodologies used in the data analysis and steps to this aim. In the same manner, this work shows some of the applications that can be found in the use of these devices and ends with some conclusions about the current state of these technologies.

Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry

Electronic nose devices stand out as pioneering innovations in contemporary technological research, addressing the arduous challenge of replicating the complex sense of smell found in humans. Currently, sensor instruments find application in a variety of fields, including environmental, (bio)medical, food, pharmaceutical, and materials production. Particularly the latter, has seen a significant increase in the adoption of technological tools to assess food quality, gradually supplanting human panelists and thus reshaping the entire quality control paradigm in the sector. This process is happening even more rapidly in the world of wine, where olfactory sensory analysis has always played a central role in attributing certain qualities to a wine. In this review, conducted using sources such as PubMed, Science Direct, and Web of Science, we examined papers published between January 2015 and January 2024. The aim was to explore prevailing trends in the use of human panels and sensory tools (such as the E-nose) in the wine industry. The focus was on the evaluation of wine quality attributes by paying specific attention to geographical origin, sensory defects, and monitoring of production trends. Analyzed results show that the application of E-nose-type sensors performs satisfactorily in that trajectory. Nevertheless, the integration of this type of analysis with more classical methods, such as the trained sensory panel test and with the application of destructive instrument volatile compound (VOC) detection (e.g., gas chromatography), still seems necessary to better explore and investigate the aromatic characteristics of wines.

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Application of colorimetric sensor array coupled with machine-learning approaches for the discrimination of grains based on freshness

BACKGROUND

Volatile organic compounds (VOCs) in grain fluctuate depending on the degree of grain freshness. A new colorimetric sensor array (CSA) was developed as capture probes for the quantification of VOCs in grains in this work, and it was designed to monitor the variation of grain VOCs. CSA spectral data acquisition using visible-near-infrared spectroscopy and image processing of CSA's image imformation by computer were used comparatively. Then, machine-learning-based models - for example, synergistic interval partial least squares, genetic algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and ant colony optimization (ACO) algorithm - were introduced to optimize variables. Moreover, principal component analysis, and linear discriminant analysis (LDA), and K-nearest neighbors (KNN) were used for the classification. Ultimately, quantitative models for detecting grain freshness are developed using various variable selection strategies.

RESULTS

Compared with the pattern recognition results of image processing, visible-near-infrared spectroscopy could better separate the grains with different freshness from principal component analysis, and the prediction set of LDA models could correctly identify 100% of rice, 96.88% of paddy, and 97.9% of soybeans. In addition, compared with CARS and ACO, the LDA model and KNN model based on genetic algorithms show the best prediction performance. The prediction set could correctly identify 100% of rice and paddy samples and 95.83% of soybean samples.

CONCLUSION

The method developed could be used for non-destructive detection of grain freshness. © 2023 Society of Chemical Industry.

Characterization of different grades of Jiuqu hongmei tea based on flavor profiles using HS-SPME-GC-MS combined with E-nose and E-tongue

In order to distinguish different grades of Jiuqu hongmei tea (black tea), four different grades of Jiuqu hongmei tea were used as materials in this study: Super Grade (SuG), First Grade (FG), Second Grade (SG), and Third Grade (TG). HS-SPME-GC-MS combined with electronic nose (E-nose) and electronic tongue (E-tongue) technology was used to detect and analyze tea samples. The results showed that 162 volatile substances were identified, mainly alcohols, followed by hydrocarbons, aldehydes, ketones and esters. Twenty-nine volatile compounds were found in all grades of tea samples. The results of heat map analysis showed that the relative contents of five volatile compounds in different grades of Jiuqu hongmei tea were positively correlated with the grades of Jiuqu hongmei tea. By orthogonal partial least squares discriminant analysis (OPLS-DA), 35 different compounds of SuG and FG, 30 different compounds of SG and TG, 34 different compounds of FG and SG were found. Overall, the results indicated that there were significant differences in volatile compounds among different grades of Jiuqu hongmei tea, and the use of HS-SPME-GC-MS combined with E-nose and E-tongue could provide a scientific reference method as an effective tool for detecting flavor characteristics of other types of black tea grades.

Characterization of the Pure Black Tea Wine Fermentation Process by Electronic Nose and Tongue-Based Techniques with Nutritional Characteristics

Wine is an alcoholic beverage, consisting of several compounds in various ranges of concentrations. Wine quality is usually assessed by a sensory panel of trained personnel. Electronic tongues (e-tongues) and electronic noses (e-noses) have been established in recent years to assess the quality of beverages and foods. Response surface and electronic analysis tools were used to examine the quality of black tea wine. The results indicated the optimum initial sugar level (25 °Brix), yeast addition (0.5%), and fermentation temperature (25 °C) for Golden Peony black tea wine. The black tea wine produced under these conditions with 14.0% vol alcohol has as an orange-red color, full wine and tea flavor, and mild and mellow taste. The sourness of the wine was most affected by fermentation factors-yeast addition, fermentation temperature, and initial sugar level. Alcohols, aldehydes, ketones, and alkanes contributed to most of the volatile components under the influence of yeast addition and fermentation temperature. In contrast, nitrogen oxides, aromatics, and organic sulfides contributed under the influence of the initial sugar level. This study provided a facilitated strategy for obtaining the optimum black tea wine fermentation process through electronic nose and tongue-based techniques. The analysis of wines requires new technologies able to detect various different compounds simultaneously, providing worldwide information about the sample instead of information about specific compounds.

Non-Invasive Digital Technologies to Assess Wine Quality Traits and Provenance through the Bottle

Due to increased fraud rates through counterfeiting and adulteration of wines, it is important to develop novel non-invasive techniques to assess wine quality and provenance. Assessment of quality traits and provenance of wines is predominantly undertaken with complex chemical analysis and sensory evaluation, which tend to be costly and time-consuming. Therefore, this study aimed to develop a rapid and non-invasive method to assess wine vintages and quality traits using digital technologies. Samples from thirteen vintages from Dookie, Victoria, Australia (2000–2021) of Shiraz were analysed using near-infrared spectroscopy (NIR) through unopened bottles to assess the wine chemical fingerprinting. Three highly accurate machine learning (ML) models were developed using the NIR absorbance values as inputs to predict (i) wine vintage (Model 1; 97.2%), (ii) intensity of sensory descriptors (Model 2; R = 0.95), and (iii) peak area of volatile aromatic compounds (Model 3; R = 0.88). The proposed method will allow the assessment of provenance and quality traits of wines without the need to open the wine bottle, which may also be used to detect wine fraud and provenance. Furthermore, low-cost NIR devices are available in the market with required spectral range and sensitivity, which can be affordable for winemakers and retailers and can be used with the machine learning models proposed here.

Authentication of duck blood tofu binary and ternary adulterated with cow and pig blood-based gel using Fourier transform near-infrared coupled with fast chemometrics

This work aims to investigate a feasible and practical technique for the authentication of edible animal blood food (EABF) using Fourier transform near-infrared (FT-NIR) coupled with fast chemometrics. A total of 540 samples were used, including raw duck blood tofu (DBT), cow blood-based gel (CBG), pig blood-based gel (PBG), and DBT binary and ternary adulterated with CBG and PBG. The protein, fat, total sugar, and 16 kinds of amino acids were measured to validate the difference in basic organic matters among EABFs according to species. Fisher linear discriminate analysis (Fisher LDA) and extreme learning machine (ELM) were implemented comparatively to identify the adulterated EABF. To predict adulteration levels, four extreme learning machine regression (ELMR) models were constructed and optimized. Results showed that, by analyzing 27 crucial spectral variables, the ELM model provides higher accuracy of 93.89% than Fisher LDA for the independent samples. All the correlation coefficients of the optimized ELMR models' training and prediction sets were better than 0.94, the root mean square errors were all less than 3.5%, and the residual prediction deviation and the range error ratios were all higher than 4.0 and 12.0, respectively. In conclusion, the FT-NIR paired with ELM have great potential in authenticating the EABF. This work presents amino acids content in EABFs for the first time and built tracing models for rapid authentication of DBT, which can be used to manage the EABF market, thereby preventing illegal adulteration and unfair competition.

Application progress of intelligent flavor sensing system in the production process of fermented foods based on the flavor properties

Fermented foods are sensitive to the production conditions because of microbial and enzymatic activities, which requires intelligent flavor sensing system (IFSS) to monitor and optimize the production process based on the flavor properties. As the simulation system of human olfaction and gustation, IFSS has been widely used in the field of food with the characteristics of nondestructive, pollution-free, and real-time detection. This paper reviews the application of IFSS in the control of fermentation, ripening, and shelf life, and the potential in the identification of quality differences and flavor-producing microbes in fermented foods. The survey found that electronic nose (tongue) is suitable to monitor fermentation process and identify food authenticity in real time based on the changes of flavor profile. Gas chromatography-ion mobility spectrometry and nuclear magnetic resonance technology can be used to analyze the flavor metabolism of fermented foods at various production stages and explore the correlation between flavor substances and microorganisms.

Quick classification of strong-aroma types of base Baijiu using potentiometric and voltammetric electronic tongue combined with chemometric techniques

Nowadays, the classification of strong-aroma types of base Baijiu (base SAB) is mainly achieved by human sensory evaluation. However, prolonged tasting brings difficulties for sommeliers in guaranteeing the consistency of results, and may even cause health problems. Herein, an electronic tongue (E-Tongue) combined with a gas chromatography-mass spectrometry (GC-MS) method was successfully developed to grade high-alcoholic base SAB. The E-tongue was capable of identifying base SAB samples into four grades by a discriminant function analysis (DFA) model based on human sensory evaluation results. More importantly, it could effectively and rapidly predict the quality grade of unknown base SAB with an average accuracy up to 95%. The differences of chemical components between base SAB samples were studied by the GC-MS analysis and 52 aroma compounds were identified. The qualitative and quantitative results showed that with the increase of base SAB grade, the varieties and contents of aroma compounds increased. Overall, the comprehensive analysis of E-tongue data and GC-MS results could be in good agreement with human sensory evaluation results, which also proved that the newly developed method has a potential to be a useful alternative to the overall quality grading of base Baijiu.

Physicochemical, Electronic Nose and Tongue, Sensory Evaluation Determination Combined with Chemometrics to Characterize Ficus hirta Vahl. (Moraceae) Beer

Ficus hirta Vahl. (FHV) is widely consumed because of its functional and aromatic compounds. The incorporation of adjuncts contributes to the functional and flavor properties of beers. This study aims to enrich FHV extractions to develop beers with satisfactory physicochemical, antioxidant, and sensory characteristics. As a result, beers with 0.1 g/mL (P1) and 0.067 g/mL (P3) FHV extraction showed the highest values of physicochemical properties including °Brix, antioxidant activity, foam, lightness, and color intensity. Electronic nose and tongue results show that the aroma of P1 and taste of P3 were quite different from those of other FHV beers, resulting in substantially high consumer preference. The liking drivers of FHV beers were color appearance, hop and malty odor, sweet and malty flavor, thickness, and carbonation mouthfeel. However, the astringency flavor attribute was the disliking factor for beers. The results of this study may provide some references and guidelines for the development of Ficus hirta Vahl. functional beer to control the physicochemical, antioxidative, and sensory properties of the beer.

Machine-learning assisted modelling of multiple elements for authenticating edible animal blood food

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The critical elements for identifying species of the animal blood food were selected.

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Elemental fingerprint coupled with ELM were proposed for species identification of the animal blood food.

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The optimal ELM model for identifying the species of the animal blood food was constructed.

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The absolute and relative content of 25 elements in animal blood food were reported for the first time.

Elemental fingerprint coupled with machine learning modelling was proposed for species authentication of the edible animal blood gel (EABG). A total of 25 elements were determined by inductively coupled plasma mass spectrometry (ICP-MS) and atomic absorption spectroscopy (AAS) in 150 EABG samples prepared from five species of animals, namely duck, chicken, bovine, pig, and sheep. Extreme learning machine (ELM) models were constructed and optimized. Principal component analysis and Fisher linear discriminant analysis were comparatively utilized for dimension reduction of the crucial input elements selected via stepwise discriminant analysis and one-way ANOVA. The optimal ELM model was obtained with the crucial elements selected by one-way ANOVA from the relative content of the measured elements, which afforded accuracies of 98.0% and 96.0% for the training and test set, respectively. All findings suggest that elemental fingerprint accompanied by ELM have great potential in authenticating the edible animal blood food.

Fusion of a low-cost electronic nose and Fourier transform near-infrared spectroscopy for qualitative and quantitative detection of beef adulterated with duck

A low-cost electronic nose (E-nose) based on colorimetric sensors fused with Fourier transform-near-infrared (FT-NIR) spectroscopy was proposed as a rapid and convenient technique for detecting beef adulterated with duck. The total volatile basic nitrogen, protein, fat, total sugar and ash contents were measured to investigate the differences of basic properties between raw beef and duck; GC-MS was employed to analyze the difference of the volatile organic compounds emitted from these two types of meat. For variable selection and spectra denoising, the simple T-test (p < 0.05) separately intergraded with first derivative, second derivative, centralization, standard normal variate transform, and multivariate scattering correction were performed and the results compared. Extreme learning machine models were built to identify the adulterated beef and predict the adulteration levels. Results showed that for recognizing the independent samples of raw beef, beef-duck mixtures, and raw duck, FT-NIR offered a 100% identification rate, which was superior to the E-nose (83.33%) created herein. In terms of predicting adulteration levels, the root means square error (RMSE) and the correlation coefficient (r) for independent meat samples using FT-NIR were 0.511% and 0.913, respectively. At the same time, for E-nose, these two indicators were 1.28% and 0.841, respectively. When the E-nose and FT-NIR data were fused, the RMSE decreased to 0.166%, and the r improved to 0.972. All the results indicated that fusion of the low-cost E-nose and FT-NIR could be employed for rapid and convenient testing of beef adulterated with duck.

Freshness Identification of Oysters Based on Colorimetric Sensor Array Combined with Image Processing and Visible Near-Infrared Spectroscopy

Volatile organic compounds (VOCs) could be used as an indicator of the freshness of oysters. However, traditional characterization methods for VOCs have some disadvantages, such as having a high instrument cost, cumbersome pretreatment, and being time consuming. In this work, a fast and non-destructive method based on colorimetric sensor array (CSA) and visible near-infrared spectroscopy (VNIRS) was established to identify the freshness of oysters. Firstly, four color-sensitive dyes, which were sensitive to VOCs of oysters, were selected, and they were printed on a silica gel plate to obtain a CSA. Secondly, a charge coupled device (CCD) camera was used to obtain the “before” and “after” image of CSA. Thirdly, VNIS system obtained the reflected spectrum data of the CSA, which can not only obtain the color change information before and after the reaction of the CSA with the VOCs of oysters, but also reflect the changes in the internal structure of color-sensitive materials after the reaction of oysters’ VOCs. The pattern recognition results of VNIS data showed that the fresh oysters and stale oysters could be separated directly from the principal component analysis (PCA) score plot, and linear discriminant analysis (LDA) model based on variables selection methods could obtain a good performance for the freshness detection of oysters, and the recognition rate of the calibration set was 100%, while the recognition rate of the prediction set was 97.22%. The result demonstrated that the CSA, combined with VNIRS, showed great potential for VOCS measurement, and this research result provided a fast and nondestructive identification method for the freshness identification of oysters.

Artificial bionic taste sensors coupled with chemometrics for rapid detection of beef adulteration

The purpose of this study was to investigate the potential of taste sensors coupled with chemometrics for rapid determination of beef adulteration. A total of 228 minced meat samples were prepared and analyzed via raw ground beef mixed separately with chicken, duck, and pork in the range of 0 ~ 50% by weight at 10% intervals. Total sugars, protein, fat, and ash contents were also measured to validate the differences between raw meats. For sensing the water-soluble chemicals in the meats, an electronic tongue based on multifrequency large-amplitude pulses and six metal electrodes (platinum, gold, palladium, tungsten, titanium, and silver) was employed. Fisher linear discriminant analysis (Fisher LDA) and extreme learning machine (ELM) were used to model the identification of raw and the adulterated meats. While an adulterant was detected, the level of adulteration was predicted using partial least squares (PLS) and ELM and the results compared. The results showed that superior recognition models derived from ELM were obtained, as the recognition rates for the independent samples in different meat groups were all over 90%; ELM models were more precisely than PLS models for prediction of the adulteration levels of beef mixed with chicken, duck, and pork, with root mean squares error for the independent samples of 0.33, 0.18, and 0.38% and coefficients of variance of 0.914, 0.956, and 0.928, respectively. The results suggested that taste sensors combined with ELM could be useful in the rapid detection of beef adulterated with other meats.

A Smartphone-Based Fluorescent Electronic Tongue for Tracing Dopaminergic Agents in Human Urine

The importance of tracing dopaminergic agents in the progression assessment of Parkinson's disease has boosted the demand for fast, sensitive, and real-time multi-analyte detection. Herein, visual and fingerprint fluorimetric patterns have been created by an optical sensor array to simultaneously detect and discriminate among levodopa, carbidopa, benserazide, and entacapone, as important dopaminergic agents. A dual emissive nanoprobe consisting of red quantum dots and blue carbon dots with an overall pink emission has been fabricated to provide unique emission patterns in the presence of the target analytes. The sensor elements in the array come from it's differential response in the absence and presence of cetyltrimethylammonium bromide under alkaline conditions. A smartphone camera was used to take photos from the solutions in the wells. Distinct changes in the spectral profiles along with vivid and concentration-dependent color variations led to visual discrimination of dopaminergic agents in a broad concentration range. The results of linear discriminant analysis revealed great discrimination accuracies. Different concentrations of the target analytes were excellently recognized in human urine. The high sensitivity of the array, which is a bonus to rapid, on-site, and visual discrimination of dopaminergic agents, holds great promise for routine analysis of real-world clinical samples.

Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling

Wine aroma is an important quality trait in wine, influenced by its volatile compounds. Many factors can affect the composition and levels (concentration) of volatile aromatic compounds, including the water status of grapevines, canopy management, and the effects of climate change, such as increases in ambient temperature and drought. In this study, a low-cost and portable electronic nose (e-nose) was used to assess wines produced from grapevines exposed to different levels of smoke contamination. Readings from the e-nose were then used as inputs to develop two machine learning models based on artificial neural networks. Results showed that regression Model 1 displayed high accuracy in predicting the levels of volatile aromatic compounds in wine (R = 0.99). On the other hand, Model 2 also had high accuracy in predicting smoke aroma intensity from sensory evaluation (R = 0.97). Descriptive sensory analysis showed high levels of smoke taint aromas in the high-density smoke-exposed wine sample (HS), followed by the high-density smoke exposure with in-canopy misting treatment (HSM). Principal component analysis further showed that the HS treatment was associated with smoke aroma intensity, while results from the matrix showed significant negative correlations (p < 0.05) were observed between ammonia gas (sensor MQ137) and the volatile aromatic compounds octanoic acid, ethyl ester (r = -0.93), decanoic acid, ethyl ester (r = -0.94), and octanoic acid, 3-methylbutyl ester (r = -0.89). The two models developed in this study may offer winemakers a rapid, cost-effective, and non-destructive tool for assessing levels of volatile aromatic compounds and the aroma qualities of wine for decision making.

Digital Smoke Taint Detection in Pinot Grigio Wines Using an E-Nose and Machine Learning Algorithms Following Treatment with Activated Carbon and a Cleaving Enzyme

The incidence and intensity of bushfires is increasing due to climate change, resulting in a greater risk of smoke taint development in wine. In this study, smoke-tainted and non-smoke-tainted wines were subjected to treatments using activated carbon with/without the addition of a cleaving enzyme treatment to hydrolyze glycoconjugates. Chemical measurements and volatile aroma compounds were assessed for each treatment, with the two smoke taint amelioration treatments exhibiting lower mean values for volatile aroma compounds exhibiting positive ‘fruit’ aromas. Furthermore, a low-cost electronic nose (e-nose) was used to assess the wines. A machine learning model based on artificial neural networks (ANN) was developed using the e-nose outputs from the unsmoked control wine, unsmoked wine with activated carbon treatment, unsmoked wine with a cleaving enzyme plus activated carbon treatment, and smoke-tainted control wine samples as inputs to classify the wines according to the smoke taint amelioration treatment. The model displayed a high overall accuracy of 98% in classifying the e-nose readings, illustrating it may be a rapid, cost-effective tool for winemakers to assess the effectiveness of smoke taint amelioration treatment by activated carbon with/without the use of a cleaving enzyme. Furthermore, the use of a cleaving enzyme coupled with activated carbon was found to be effective in ameliorating smoke taint in wine and may help delay the resurgence of smoke aromas in wine following the aging and hydrolysis of glycoconjugates.

Application of untargeted volatile profiling and data driven approaches in wine flavoromics research

Traditional flavor chemistry research usually makes use of targeted approaches by focusing on the detection and quantification of key flavor active metabolites that are present in food and beverages. In the last decade, flavoromics has emerged as an alternative to targeted methods where non-targeted and data driven approaches have been used to determine as many metabolites as possible with the aim to establish relationships among the chemical composition of foods and their sensory properties. Flavoromics has been successfully applied in wine research to gain more insights into the impact of a wide range of flavor active metabolites on wine quality. In this review, we aim to provide an overview of the applications of flavoromics approaches in wine research based on existing literature mainly by focusing on untargeted volatile profiling of wines and how this can be used as a powerful tool to generate novel insights. We highlight the fact that untargeted volatile profiling used in flavoromics approaches ultimately can assist the wine industry to produce different wine styles and to market existing wines appropriately based on consumer preference. In addition to summarizing the main steps involved in untargeted volatile profiling, we also provide an outlook about future perspectives and challenges of wine flavoromics research.

E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review

Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.

Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach

Bushfires are increasing in number and intensity due to climate change. A newly developed low-cost electronic nose (e-nose) was tested on wines made from grapevines exposed to smoke in field trials. E-nose readings were obtained from wines from five experimental treatments: (i) low-density smoke exposure (LS), (ii) high-density smoke exposure (HS), (iii) high-density smoke exposure with in-canopy misting (HSM), and two controls: (iv) control (C; no smoke treatment) and (v) control with in-canopy misting (CM; no smoke treatment). These e-nose readings were used as inputs for machine learning algorithms to obtain a classification model, with treatments as targets and seven neurons, with 97% accuracy in the classification of 300 samples into treatments as targets (Model 1). Models 2 to 4 used 10 neurons, with 20 glycoconjugates and 10 volatile phenols as targets, measured: in berries one hour after smoke (Model 2; R = 0.98; R² = 0.95; b = 0.97); in berries at harvest (Model 3; R = 0.99; R² = 0.97; b = 0.96); in wines (Model 4; R = 0.99; R² = 0.98; b = 0.98). Model 5 was based on the intensity of 12 wine descriptors determined via a consumer sensory test (Model 5; R = 0.98; R² = 0.96; b = 0.97). These models could be used by winemakers to assess near real-time smoke contamination levels and to implement amelioration strategies to minimize smoke taint in wines following bushfires.