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This study aimed to investigate the volatile flavor compounds and tastes of six kinds of sauced pork from the southwest and eastern coastal areas of China using gas chromatography–ion mobility spectroscopy (GC-IMS) combined with an electronic nose (E-nose) and electronic tongue (E-tongue). The results showed that the combined use of the E-nose and E-tongue could effectively identify different kinds of sauced pork. A total of 52 volatile flavor compounds were identified, with aldehydes being the main flavor compounds in sauced pork. The relative odor activity value (ROAV) showed that seven key volatile compounds, including 2-methylbutanal, 2-ethyl-3, 5-dimethylpyrazine, 3-octanone, ethyl 3-methylbutanoate, dimethyl disulfide, 2,3-butanedione, and heptane, contributed the most to the flavor of sauced pork (ROAV ≥1). Multivariate data analysis showed that 13 volatile compounds with the variable importance in projection (VIP) values > 1 could be used as flavor markers to distinguish six kinds of sauced pork. Pearson correlation analysis revealed a significant link between the E-nose sensor and alcohols, aldehydes, terpenes, esters, and hetero-cycle compounds. The results of the current study provide insights into the volatile flavor compounds and tastes of sauced pork. Additionally, intelligent sensory technologies can be a promising tool for discriminating different types of sauced pork. [ABSTRACT FROM AUTHOR]

Fish is a highly demanding food product and the determination of fish freshness is crucial as it is a fundamental factor in fish quality. Therefore, the fishery industry has been working on developing rapid fish freshness determination methods to monitor freshness levels. Artificial senses that mimic human senses are developed as convenient emerging technologies for fish freshness determination. Computer vision, electronic nose (e‐nose), and electronic tongue (e‐tongue) are the emerging artificial senses for fish freshness determination. This review article is uniquely worked upon to investigate the current applications of the artificial senses in fish freshness determination while describing the steps, and fundamental principles behind each artificial sense, comparing them with their advantages and limitations, and future trends related to fish freshness determination. Among the artificial senses, computer vision determines the freshness of fish in a completely nondestructive way while the e‐tongue determines the freshness of fish in a completely destructive way. There are developed e‐noses for fish freshness determination in both destructive and nondestructive ways. By analyzing visual cues such as color, computer vision systems can assess fish quality without the need for physical contact and it makes computer vision suitable for large‐scale industrial fish quality assessing applications. Overall, this review study reveals artificial senses as a proven replacement for traditional sensory panels in determining fish freshness precisely and conveniently. As future trends, there is a demand for developing applications for consumers to determine fish freshness based on artificial senses. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: Roasting processing was commonly applied to enhance flavour characteristics, and thus this study was to investigate the taste and aroma compounds between broccoli floret and stem during oven‐roasting processes. In electronic tongue analysis, sourness, saltiness, umami, and sweetness in florets showed an increasing tendency during the roasting, however, decreasing bitterness. Besides, In electronic nose analysis, methanethiol was reduced after the roasting processes and other sulfur compounds were generated in oven‐roasted florets and stems. Furthermore, furans were generated after the roasting. Pyridine and pyrazines were only identified in oven‐roasted florets, however, pyrrole was only identified in oven‐roasted stems. Principal component analysis and cluster analysis were conducted based on combined taste and aroma profiles. Broccoli floret roasted at 15 min was affected by numerous variables compared with the others, and broccoli stems roasted at 15 and 20 min were affected by numerous variables. Additionally, the floret roasted at 15 min was the highest dissimilarity amongst all florets, and the stems roasted at 15 and 20 min were the highest dissimilarity amongst all stems. Accordingly, oven‐roasting induced higher changes in taste and aroma characteristics with increasing times. In addition, this research may provide a basis for enhancing the flavour characteristics of broccoli. [ABSTRACT FROM AUTHOR]

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

This study investigated the effects of four brewing methods (cold and hot brew, espresso, and cezve) on the chemical sensory properties of green and roasted coffee beans (cv. Yellow Bourbon) extract. The caffeine and chlorogenic acid contents of the coffee were analyzed using HPLC. The taste and volatile aromatic compounds of coffee were analyzed using an electronic tongue and nose, respectively, and the results were analyzed using principal component analysis. For the taste components analyzed using the electronic tongue, the degree of separation was relatively large depending on the extraction method, and the degree of separation was larger depending on roasting for the volatile compounds analyzed using the electronic nose. Our findings provide basic data for the coffee industry. Practical Application: The use of an electronic sensor will provide flavor characteristics for four different types of coffee extracted from green beans and roasted beans. In this study, it was confirmed that the extraction method had a greater effect on the taste of coffee, and in the case of the volatile aromatic compounds of coffee, there was a large difference depending on the green beans and roasted beans. Therefore, our findings will provide data based on the sensory properties of coffee. [ABSTRACT FROM AUTHOR]

This work investigated the effect of gelatinization on the sensory traits of common (Fagopyrum esculentum) and Tartary (Fagopyrum tataricum) buckwheat flours in relation to their phenolic composition. Flours from two samples of common and Tartary buckwheat were heat-treated in the laboratory under controlled conditions to promote starch gelatinization; in addition, a common buckwheat sample heat-treated industrially was considered. The aromatic profiles of buckwheat samples were evaluated by electronic nose, while the electronic tongue was applied to assess taste. Rutin and quercetin concentrations were evaluated by HPLC; total phenolic compounds and antioxidant activity were also determined before and after thermal treatment. The heat treatment carried out in laboratory decreased both the total phenolic content and antioxidant activity and promoted the almost total hydrolysis of rutin into quercetin. The same effect was not detected in the sample treated industrially. Concerning sensory traits, a decrease in bitterness and astringency and a less intense aromatic profile were observed after gelatinization at lab scale; on the contrary, the industrially gelatinized sample was less affected by the thermal treatment, showing a sensory profile similar to the raw sample. [ABSTRACT FROM AUTHOR]

The volatile profiles and taste properties of Lanzhou beef bouillons prepared with traditional (A1–A8) and modern (B1, B2) processing methods were evaluated. A total of 133 volatiles were identified: olefins, aldehydes and alcohols from spices in traditional bouillons were significantly higher (p < 0.05) than those in instant bouillons. The characteristic volatile substances in traditional beef bouillons were eucalyptol, linalool, 2-decanone, β-caryophyllene and geraniol; instant bouillons lacked 2-decanone and β-caryophyllene, and the contents of the other three substances were low. PCA (principal component analysis) and CA (clustering analysis) showed that the instant bouillons have a similar volatile profile to traditional bouillons, and the results of E-nose and sensory evaluation also supported this conclusion. The E-tongue showed that the taste profiles of instant bouillons were significantly different from those of traditional bouillons, mainly due to lack of umami; however, sensory evaluation revealed that taste differences were not perceptible. [ABSTRACT FROM AUTHOR]

This study proposes an efficient method for monitoring the submerged fermentation process of Tremella fuciformis (T. fuciformis) by integrating electronic nose (e-nose), electronic tongue (e-tongue), and colorimeter sensors using a data fusion strategy. Chemometrics was employed to establish qualitative identification and quantitative prediction models. The Pearson correlation analysis was applied to extract features from the e-nose and tongue sensor arrays. The optimal sensor arrays for monitoring the submerged fermentation process of T. fuciformis were obtained, and four different data fusion methods were developed by incorporating the colorimeter data features. To achieve qualitative identification, the physicochemical data and principal component analysis (PCA) results were utilized to determine three stages of the fermentation process. The fusion signal based on full features proved to be the optimal data fusion method, exhibiting the highest accuracy across different models. Notably, random forest (RF) was shown to be the most accurate pattern recognition method in this paper. For quantitative prediction, partial least squares regression (PLSR) and support vector regression (SVR) were employed to predict the sugar content and dry cell weight during fermentation. The best respective predictive R2 values for reducing sugar, tremella polysaccharide and dry cell weight were found to be 0.965, 0.988, and 0.970. Furthermore, due to its ability to capture nonlinear data relationships, SVR had superior performance in prediction modeling than PLSR. The results demonstrated that the combination of electronic sensor fusion signals and chemometrics provided a promising method for effectively monitoring T. fuciformis fermentation. [Display omitted] • A data fusion system based on optimal sensor array was developed. • The fermentation process was identified by physicochemical parameter results. • The visualization results of PCA confirmed the fermentation process. • Multiple feature sets were evaluated by DT、RF、SVC for effective classification. • SVR and PLSR successfully predicted crucial physicochemical parameters. [ABSTRACT FROM AUTHOR]

The intrinsic chemical components and sensory characteristics of Gardeniae fructus Praeparatus (GFP) directly reflect its quality and subsequently, affect its clinical curative effect. However, there is little research on the correlation between the appearance traits and chemical compositions of GFP during heat processing. In this study, the major components of five typical processed decoction pieces of GFP were determined. With the deepening of processing, the contents of geniposidic acid and 5-HMF gradually increased, while the contents of deacetyl-asperulosidic acid methyl ester, gardenoside, and two pigments declined. Moreover, the electronic eye, electronic tongue, and electronic nose were applied to quantify GFP's sensory properties. It was found that the chroma values showed a downward trend during the processing of GFP. The results of odor showed that ammonia, alkenes, hydrogen, and aromatic compounds were the material base for aroma characteristics. Complex bitterness in GF was more obvious than that in other GFP processed products. Furthermore, one mathematical model was established to evaluate the correlation between the sensory characteristics and chemical composition of GFP during five different stages. A cluster analysis and neural network analysis contributed to recognizing the processing stage of GFP. This study provided an alternative method for the exterior and interior correlation-based quality evaluation of herbs. [ABSTRACT FROM AUTHOR]

In this study, the bacterial flora structure, sensory quality and culturable lactic acid bacteria resources of sour porridge in Bayannur area were analyzed. Furthermore, these bacterial sequencing data were combined with the high-throughput sequencing data of Ordos sour porridge to analyze the microbiota differences. MiSeq results showed that Lactiplantibacillus , Acetobacter , Lacticaseibacillus , Lactobacillus , and Limosilactobacillus were the main bacterial genera in sour porridge. Further comparative analysis showed that the average relative abundance of 7 dominant bacteria genera was significantly higher in sour porridge from Bayannur area (P < 0.05). Electronic nose and electronic tongue demonstrated that the primary volatile flavor substances in sour porridge were terpene compounds, and the change in taste characteristics was mainly reflected in the sour taste index. Correlation showed that most of LAB genera and Acetobacter were positively correlated with the formation of aromatic compounds. Pure culture showed that Lacticaseibacillus paracasei and Lactiplantibacillus plantarum were the dominant LAB in sour porridge. Overall, certain differences in the bacterial flora of sour porridge from different regions were identified, with Lactiplantibacillus , Acetobacter , Lacticaseibacillus , Lactobacillus , and Limosilactobacillus being characteristic of sour porridge in Bayannur area. Moreover, some LAB genera and Acetobacter were found to have a positive effect on flavor formation in sour porridge. • Lactiplantibacillus , Acetobacter , Lacticaseibacillus , Lactobacillus , and Limosilactobacillus are the main bacterial genera. • Lacticaseibacillus paracasei and Lactiplantibacillus plantarum are the dominant LAB strains. • Lactiplantibacillus is a biomarker in Bayannur sour porridge, Limosilactobacillus is a biomarker in Ordos sour porridge. • LAB genera and Acetobacter have a positive effect on flavor formation. [ABSTRACT FROM AUTHOR]

• The quality and flavor of Noni during ripening were analyzed by different methods. • The optimum stage of nutrient accumulation was 7M−8M. • The volatile odor changed markedly from 8M to 9M. • The sour, astringent and fresh tastes contributed to the dominant taste. • The shared and unique compounds can be used as references for ripeness markers. Different ripeness plays a key role in the final quality and flavor of Noni products, but the variations of quality and flavor during Noni ripening are still unclear. In this study, physicochemical analysis combined with electronic nose, electronic tongue and gas chromatography-mass spectrometry (GC–MS) were used to investigate the effect of different ripeness levels on Noni fruit quality and flavor chemistry. The results showed that the optimum stage of nutrient accumulation is 7–8 ripe (7M−8M), in which especially the higher contents were 144Mg/100 g vitamin C, 11.08 g/kg polyphenols, 15.32 g/kg flavonoids and 2420Mg/kg potassium. Octanoic acid and hexanoic acid with an unfriendly odor began to decrease significantly after 8 ripe (8M), the lowest contents were 256.76 and 231.26 ng/g at 9 ripe (9M). Sour, astringent and fresh tastes are the dominant tastes of Noni fruit. Therefore, the optimum harvesting time of Noni fruit is 8M. [ABSTRACT FROM AUTHOR]

To address the problem of the lack of rapid discrimination technology for comprehensive evaluation of product quality and grade in the field of tea quality control, this study uses near-infrared spectroscopy, electronic eye, electronic tongue, and electronic nose technology to sense the external and internal quality sensing characteristics of tea leaves, and realizes comprehensive evaluation of four quality factors such as "color, aroma, taste and shape" of tea leaves through cross-sensor multimodal quality characteristics data fusion. For the first time, a comprehensive method for assessing the grade quality of tea based on deep learning algorithms combining cross-sensor multimodal fusion features such as color, texture, shape, optical, electronic tongue, and electronic nose is established. The results showed that the constructed deep convolutional neural network (CNN) classification model had a misclassification rate of only 0.86% in the prediction set, which was better than the modeling results based on traditional classification methods. This study reveals that the CNN algorithm can effectively evaluate the quality grade of tea samples and realize the digital evaluation of multiple evaluation factors of tea sensory quality. [Display omitted] • Description of tea quality using deep learning and multi-sensor feature fusion. • A comprehensive evaluation for tea "color, aroma, taste and shape" is proposed. • The constructed CNN model has a misclassification rate of only 0.86%. [ABSTRACT FROM AUTHOR]

BACKGROUND: Waxy maize (Zea mays L. sinensis Kulesh) is a good material for brewing. Waxy maize wine, a kind of Chinese rice wine, is strongly affected by a fermentation starter named Qu. In this study, an innovative mixed Qu, consisting of two yeasts and three molds, was produced and the raw‐starch brewing method was applied in winemaking. Three other waxy maize wines fermented by three kinds of commercial Qu were also analyzed for comparison. RESULTS: Due to superb growth and fermentation characteristics, Saccharomyces cerevisiae CICC1009 and Pichia anomala CICC1851 were chosen to produce yeast Qu. The addition amount of yeast Qu was determined to be 30 g kg−1. In terms of chemical properties, mixed Qu was more suitable for making maize wine by the raw‐starch brewing method than the three kinds of commercial Qu with which it was compared. The most influential components for the overall aroma profile in maize wines fermented by mixed Qu and Mifeng Qu were ethyl butyrate and β‐damascenone, respectively, while in maize wines fermented by Angel Qu and Like Qu the most influential component was ethyl octanoate. Obvious differences were found among four maize wines regarding bitterness, umami, richness, saltiness, and sourness by the electronic tongue. The olfactory characteristics of maize wine fermented by Mifeng Qu were quite different from the other three according to the electronic nose. CONCLUSION: The innovative mixed Qu can be considered as an excellent starter for raw‐starch brewing of waxy maize. The chemical indices and volatile flavor compounds of waxy maize wines were greatly affected by different kinds of Qu. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Pufferfish (Takifugu spp.) are rapidly becoming a valuable food commodity across Asia, making the flavour characterization of the different species economically significant. In this study, the flavour characteristics of four cultured pufferfish varieties (T obscurus, T flavidus, T bimaculatus and T rubripes) were evaluated using headspace‐gas chromatography‐ion mobility spectrometry (HS‐GC‐IMS) coupled with an electronic nose and electronic tongue. A total of 37 volatile compounds, primarily aldehydes and alcohols, were identified by HS‐GC‐IMS and used to distinguish the four pufferfish species. Principal component analysis (PCA) of the volatile compound profiles indicated that the four cultivated varieties had unique chemical compositions. PCA of the electronic nose and tongue response data was consistent with HS‐GC‐IMS evaluation and revealed that the aroma and taste of T rubripes were distinct from the other varieties. The collaborative analysis method combining HS‐GC‐IMS and intelligent sensory systems could significantly advance the evaluation of flavour differences. [ABSTRACT FROM AUTHOR]

Introduction-- The evaluation of water quality remains a challenge for public health institutions today. One of the most abundant bacteria and the one that is mainly related with the sanitary risk of water is Escherichia coli (E. coli). The incidence of this bacteria shows that there is an increased risk of the presence of other bacteria and viruses of fecal origin, many of which are pathogenic. Nowadays, standardized and regulated conventional techniques are used for the detection of E. coli in water. These techniques require at least 24-28 hours of incubation for detection; in addition to require reagents and qualified personnel, among other requirements. Objective-- This article presents an analysis of the ability of the sensory perception systems (e-nose and e-tongue) to determine and discriminate E. coli from other related bacteria in water samples. Methodology-- To verify discrimination between bacteria, water samples contaminated with three bacteria were prepared: E. coli, Klebsiella oxytoca and Pseudomonas aeruginosa. Sterilized drinking water was used as a negative control. On the other hand, to evaluate the potential of the systems under study for the detection of E. coli in drinking water, water samples from the drinking water treatment plant (DWTP) of the municipality of Toledo (N. S) were analyzed. For this, the microbiological membrane filtration method was used as reference in this study. Results-- The water samples discrimination was carried out through the Principal Components Analysis (PCA), reaching 97.6% of variance captured through the electronic nose. On the other hand, with the electronic tongue, the discrimination of the bacteria was 99.4% variation in the data set, obtaining a similar response with both methods. Conclusions-- The results confirmed that the methodology allowed an effective evaluation between the contaminated samples and control samples. It is obtained a good discrimination of the categories for the samples acquired from the water treatment plant. [ABSTRACT FROM AUTHOR]

The electronic nose and electronic tongue were used to detect adulterated chicken rapidly. In this research, electronic nose and electronic tongue were used to detect the content of soybean protein and starch in chicken meat. Soy protein (0, 2.5%, 5.0%, 7.5%, and 10.0%) was mixed into minced chicken meat to prepare adulterated chicken samples, the total sample weight was 10 g. The 2 mL of distilled water and 0.006 g of neutral protease were added in sequence in a 50 ℃ water bath. After 15 min, the above samples were raised to 90 ℃ to inactivate the enzyme. And incorporated with 0.1 g of D-ribose, Maillard reaction was carried out for 20 min. Then the reaction was terminated at 20 ℃. The processed samples were put into the measuring bottle for electronic nose detection. The 50 mL of 0.1 mol/L potassium chloride extract was added to each group of prepared electronic nose samples. After 30 min, the filtrate was taken for electronic tongue detection. Twenty-four samples in each group were made in parallel, including 18 modeling sets and 6 detection sets. Starch (0, 2.5%, 5.0%, 7.5%, 10.0% and 15.0%) was mixed into minced chicken meat to prepare adulterated chicken samples, the total sample weight was 10 g. The 2 mL of distilled water and 0.006 g of α-amylase were added to each group of samples in a 45 ℃ water bath. After 15 min, the above samples were raised to 90 ℃ to inactivate the enzyme. When incorporating with 0.1 g of glycine, the Maillard reaction was carried out for 20 min. Then the reaction was terminated at 20 ℃. The processed samples were put into the measuring bottle for electronic nose detection. The 50 mL of 0.1 mol/L potassium chloride extract was added to each group of prepared electronic nose samples. After 30 min, the filtrate was taken for electronic tongue detection. Twenty-four samples in each group were made in parallel, including 18 modeling sets and 6 detection sets. The data was statistically analyzed by Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR). By PCA, the results showed that the contribution rates of first principal component and second principal component combinedly detected by electronic nose and electronic tongue for minced chicken meat adulterated with soy protein were 99.2% and 0.6%, respectively, and the total contribution rate was 99.8%. By PLSR, the coefficient of determination detected by electronic nose or electronic tongue was 0.989 and 0.972, the root mean square error was 3.9% and 5.4%, respectively. The coefficient of determination and the root mean square error of combinedly detected by electronic nose and electronic tongue were 0.992 and 2.8%. By PCA, the results showed that the contribution rates of first principal component and second principal component combinedly detected by electronic nose and electronic tongue for minced chicken meat adulterated with starch were 97.0% and 2.1%, respectively, and the total contribution rate was 99.1%. By PLSR, the coefficient of determination detected by electronic nose or electronic tongue was 0.977 and 0.976, the root mean square error was 5.0% and 5.2%, respectively. The coefficient of determination and the root mean square error of combinedly detected by electronic nose and electronic tongue were 0.996 and 2.4%. The performance of combined detection by electronic nose and electronic tongue for soy protein was better than for starch. The combined detection using electronic nose and electronic tongue sensors has a potential ability to distinguish and predict soy protein-based or starch-based adulteration in minced chicken meat and has also been proved to be a useful authentication method for meat adulteration detection with high efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Platycodon is a ubiquitous plant widely grown in Asia. This study investigated changes in odor/aroma associated sensory attributes and chemical properties in Platycodon grandiflorum roots upon roasting. Amino acid analysis, electronic tongue analysis, chemical property analysis, volatile compound analysis, GC‐olfactometry‐assisted sensory attributes, and electronic nose analysis were performed. In results, amino acid profiles showed diverse patterns. Electronic tongue analysis somewhat corresponded to the free amino acid profiles. Total phenolic content, antioxidant capacity, and browning intensity significantly increased up to 4 min and slightly decreased afterward. Various pyrazines relevant to roasted odor such as 3‐ethyl‐2, 5‐dimethyl‐pyrazine, and 2, 6‐dimethyl‐pyrazine were generated by roasting. In electronic nose analysis, positive odor parameter significantly increased and potential unpleasant odorants significantly decreased over time. This is believed to be the first study demonstrating overall insight on odor/aroma and chemical characteristics and utilizing objective sensory measures on roasted Platycodon grandiflorum roots for food applications. Practical applications: This study will be utilized (a) to researchers and food companies who are interested in medicinal foods (b) to individuals and food industry that search for changes in sensory characteristics and chemical changes of foods induced by roasting, and (c) to farmers and crop producers who look for utilization and applications of Platycodon grandiflorum roots as food sources and ingredients. [ABSTRACT FROM AUTHOR]

The purpose of this present study was to develop a rapid and effective approach for identification of red wines that differ in geographical origins, brands, and grape varieties, a multi‐sensor fusion technology based on a novel cost‐effective electronic nose (E‐nose) and a voltammetric electronic tongue (E‐tongue) was proposed. The E‐nose sensors was created using porphyrins or metalloporphyrins, pH indicators and Nile red printed on a C2 reverse phase silica gel plate. The voltammetric E‐Tongue with six metallic working electrodes, namely platinum, gold, palladium, tungsten, titanium, and silver was employed to sense the taste of red wines. Principal component analysis (PCA) was utilized for dimensionality reduction and decorrelation of the raw sensors datasets. The fusion models derived from extreme learning machine (ELM) were built with PCA scores of E‐nose and tongue as the inputs. Results showed superior performance (100% recognition rate) using combination of odor and taste sensors than individual artificial systems. The results suggested that fusion of the novel cost‐effective E‐nose created and voltammetric E‐tongue coupled with ELM has a powerful potential in rapid quality evaluation of red wine. [ABSTRACT FROM AUTHOR]

The impacts of the vacuum freeze (VFD), hot air (HAD), microwave (MD) and vacuum microwave (VMD) drying on the flavour of golden pompano fillets were evaluated using an electronic nose (E-nose), an electronic tongue (E-tongue) and simultaneous distillation extraction (SDE) - gas chromatography - mass spectrometry (GC–MS). The results showed that the E-nose and E-tongue systems could effectively differentiate volatile compounds of four samples. A total of 86 volatile flavour components were identified in the dried fillets; the main flavour components contained hydrocarbons (39), aldehydes (15), esters (10) and alcohols (9). HAD, MD and VMD processing promoted a gradual reduction in ketones and the generation of esters, while the fillets that were processed by VFD contained more hydrocarbon (29.68%) and alcohol (2.64%) compounds. The volatile compounds of dried golden pompano fillets were developed through four potential pathways, including the Maillard reaction, lipid oxidation and degradation, protein hydrolysis, and Strecker degradation. Unlabelled Image • Golden pompano fillets were dehydrated by four drying methods. • E-nose and E-tongue systems could effectively differentiate the four samples. • HAD, MD and VMD processing decreased ketones and the generation of esters. • VFD fillets contained more hydrocarbon and alcohol compounds. • The mechanisms of volatile compound changes in dried fillets were explained. [ABSTRACT FROM AUTHOR]

Highlights • Tea quality was jointly detected by E-nose, E-tongue and E-eye. • The signals of E-nose, E-tongue and E-eye were fused for analysis. • PLSR, SVM and RF were applied for qualitative and quantitative assessment. • The efficiency of the fusion signals outperformed that of independent signals. • RF based on the fusion signals performed perfectly in tea quality detection. Abstract Electronic nose (E-nose), electronic tongue (E-tongue) and electronic eye (E-eye) combined with chemometrics methods were applied for qualitative identification and quantitative prediction of tea quality. Main chemical components, such as amino acids, catechins, polyphenols and caffeine were measured by traditional methods. Feature-level fusion strategy for the integration of the signals was introduced to integrate the E-nose, E-tongue and E-eye signals, aiming at improving the performances of identification and prediction models. Perfect results with an accuracy of 100% were obtained for qualitative identification of tea quality grades, based on fusion signals by support vector machine and random forest. Quantitative models were established for predicting the contents of the chemical components based on independent electronic signals and fusion signals by partial least squares regression, support vector machine and random forest. Random forest based on the fusion signals achieved the best performance in predicting the concentration of those chemical components. [ABSTRACT FROM AUTHOR]

[Display omitted] • The volatile compounds of four grades of Chinese black tea (Jiuqu hongmei tea) were studied by E-nose, E-tongue and HS-SPME-GC-MS. • The volatile compounds of different grades of black tea varied greatly. • The grade of tea could be successfully separated by E-nose and E-tongue. • The volatile compounds of different grades of tea were modeled by orthogonal partial least squares discriminant analysis (OPLS-DA). 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. [ABSTRACT FROM AUTHOR]

Agricultural sector plays a key role in the economy of any country due to rapidly increasing food demand. In recent years, among emerging novel trends in agriculture, multisensor systems called electronic nose (EN) and electronic tongue (ET) have attracted much interest because they could be considered as potential alternatives or supplements to widely used analytical methods. Thanks to their design such devices are capable of examining samples without separation and identification of individual compounds, but through generating their unique so-called fingerprints, that are further processed and encoded thanks to various chemometric methods. This paper summarizes the achievements in the field of the applications of EN and ET systems to agricultural purposes during last 15 years – it discusses implementation of these devices to soil and water analysis, detection of pest and pesticides/fungicides residues, monitoring of food storage and transport conditions, as well as diseases diagnosis. [Display omitted] • State-of-art in the field of electronic noses and electronic tongues for the agricultural purposes. • Multisensor systems for: water and soil analysis, diseases' and pests' identification, and pesticides' residues detection. • Critical aspects of multisensor systems including sampling handling systems, sensor arrays and chemometric methods. • Potential limitations, future trends and prospects of artificial olfaction/taste to cope with agricultural challenges. [ABSTRACT FROM AUTHOR]

The aim of this work was to investigate the applicability of e-senses (electronic nose, electronic tongue and electronic eye) for the characterization of edible olive oils (extra virgin, olive and pomace) and for the assessment of extra virgin olive oil and olive oil quality decay during storage at different temperatures. In order to obtain a complete description of oil samples, physico-chemical analyses on quality and nutritional parameters were also performed. Data were processed by PCA and a targeted data processing flow-sheet has been applied to physico-chemical and e-senses dataset starting from data pre-processing introducing an innovative normalization method, called t0 centering. On e-senses data a powerful mid-level data fusion approach has been employed to extract relevant information from different analytical sources combining their individual contributions. On physico-chemical data, an alternative approach for grouping extra virgin olive oil and olive oil samples on the basis of their freshness was applied and two classes were identified: fresh and oxidized. A k-NN classification rule was developed to test the performance of e-senses to classify samples in the two classes of freshness and the average value of correctly classified samples was 94%. Results demonstrated that the combined application of e-senses and the innovative data processing strategy allows to characterize edible olive oils of different categories on the basis of their sensorial properties and also to follow the evolution during storage of extra-virgin olive oil and olive oil sensorial properties thus assessing the quality decay of oils. [ABSTRACT FROM AUTHOR]

The consumption of whole-grain food-including pasta-has been increasing steadily. In the case of whole-grain pasta, given the many different producers, it seems important to have some objective parameters to define its overall quality. In this study, commercial whole-grain pasta samples representative of the Italian market have been characterized from both molecular and electronic-senses (electronic nose and electronic tongue) standpoint in order to provide a survey of the properties of different commercial samples. Only 1 pasta product showed very low levels of heat damage markers (furosine and pyrraline), suggesting that this sample underwent to low temperature dry treatment. In all samples, the furosine content was directly correlated to protein structural indices, since protein structure compactness increased with increasing levels of heat damage markers. Electronic senses were able to discriminate among pasta samples according to the intensity of heat treatment during the drying step. Pasta sample with low furosine content was discriminated by umami taste and by sensors responding to aliphatic and inorganic compounds. Data obtained with this multidisciplinary approach are meant to provide hints for identifying useful indices for pasta quality. Practical Application As observed for semolina pasta, objective parameters based on heat-damage were best suited to define the overall quality of wholegrain pasta, almost independently of compositional differences among commercial samples. Drying treatments of different intensity also had an impact on instrumental sensory traits that may provide a reliable alternative to analytical determination of chemical markers of heat damage in all cases where there is a need for avoiding time-consuming procedures. [ABSTRACT FROM AUTHOR]

Electronic nose and tongue sensors and chemometric multivariate analysis were applied to characterize and classify 7 Chinese robusta coffee cultivars with different roasting degrees. Analytical data were obtained from 126 samples of roasted coffee beans distributed in the Hainan Province of China. Physicochemical qualities, such as the pH, titratable acidity (TA), total soluble solids (TSS), total solids (TS), and TSS/TA ratio, were determined by wet chemistry methods. Data fusion strategies were investigated to improve the performance of models relative to the performance of a single technique. Clear classification of all the studied coffee samples was achieved by principal component analysis, K-nearest neighbour analysis, partial least squares discriminant analysis, and a back-propagation artificial neural network. Quantitative models were established between the sensor responses and the reference physicochemical qualities, using partial least squares regression (PLSR). The PLSR model with a fusion data set was considered the best model for determining the quality parameters. [ABSTRACT FROM AUTHOR]

The quality changes of RJ at different storage conditions are evaluated from various perspectives, including non-target volatile compound profiling, multi-index (physicochemical parameters and flavor characteristics), and multi-technology (HS-SPME-GC/MS, electronic nose, and electronic tongue) integration. [Display omitted] • A total of 37 volatile compounds in royal jelly are identified using HS-SPME-GC–MS. • Seven volatile compounds contribute largely to the overall flavor of royal jelly. • Electronic nose/tongue exhibit useful value in the quality control of royal jelly. • Combination of multiple methods enhance the evaluation of royal jelly quality. Royal jelly (RJ) is known for its unique flavor and nutritional value. However, the flavor changes of RJ during storage remain unclear. In this work, the flavor profiles of RJ during storage were evaluated by using headspace solid-phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) combined with both electronic nose and electronic tongue analyses. Results revealed that the moisture, water-soluble protein, and whiteness were changed significantly at 25 °C. The holistic variation of RJ flavor exhibited evident distinction based on principal component analysis with electronic nose and electronic tongue. Among the total of 37 volatile compounds identified in RJ, the octanoic acid showed the highest contents of 47.61 % at 25 °C in 21 d. Seven volatile compounds, i.e., 2(3H)-furanone,5-butyldihydro-, 2-heptanone, trans -β-ocimene, 2-nonen-4-one, 2-nonanone, methyl benzoate, and 2-octenoic acid (E), contributed largely to the typical overall flavor of RJ. This work provides an improved understanding of the flavor change of RJ during storage. [ABSTRACT FROM AUTHOR]

Electronic tongues (ETs) are multisensor systems based on the coupling of different sensors with high stability and cross-sensitivity towards different species, along with an appropriate chemometric tool that allow the classification of samples and/or the quantification of selected analytes. Despite the great advantages that the data processing stage may offer (e.g. allowing to offset matrix effects from the sample itself, or to resolve the presence of interferences, drifts or non-linearities obtained with the sensors), the stability of the sensors response together with its varied cross-sensitivity are of utmost importance. In this regard, despite the progress that has been made over the last years and the increasing number of publications dealing with ETs, researchers have focused mainly on the developed application itself or on the data treatment stage, but very few report on the choice of the sensors. In this direction, herein we explore on the development of a simple methodology that allows the a priori selection of the optimal sensor array to carry out a specific application. Concretely, the usage of principal component analysis (PCA) in combination with different clustering indices is proposed as a simple approach to simultaneously assess the cross-response and the reproducibility of the different sensors in a single step, what allows to define which sensors may constitute an ET array for a given qualitative or quantitative application. The above ideas have been applied to the study case of aromatic essential oils. [Display omitted] • A simple methodology for the a priori tailored selection of optimal sensor arrays is proposed. • Selection is based on the combination of PCA with different clustering indexes. • Clustering improvement for qualitative applications is visually and numerically confirmed. • PLS was used to assess also the improvement in quantitative applications. • The same approach can be extrapolated to electronic noses or other array-based systems. [ABSTRACT FROM AUTHOR]

Flavor perception involves a mixing phenomenon between smell and taste. This phenomenon is known as the olfactory-taste synesthesia effect (including halo, horn, nasal, olfactory-taste coexistence, and posterior nasal effects). Research on the synesthesia effect of electronic nose (e-nose) and electronic tongue (e-tongue) for flavor substance analysis is limited, which affects the analysis performance. Therefore, in this study, an olfactory-taste synesthesia model (OTSM) is proposed for human nervous systems to integrate e-nose and e-tongue data for flavor substance identification. Next, the synesthesia effect is simulated to demonstrate the effectiveness of the OTSM for flavor substance analysis. Finally, four flavor substances are identified using the OTSM. The results suggest that, first, excellent recognition results in the simulation of nasal, olfactory-taste coexistence, and posterior nasal effects are acquired with the OTSM. Second, in the simulation of halo and horn effects, a high correlation between the OTSM output and sensory scores of the halo and horn effects is obtained. The simulation results indicate that the OTSM is effective for flavor substance analysis. Finally, compared with the identification results for signal preprocessing and multipattern recognition models, the best identification results for four flavor substances, including an accuracy of 95.56%, Kappa coefficient of 94.17%, and F 1 -score of 95.58%, are acquired by the OTSM. In conclusion, effective identification of flavor substances with the e-nose and e-tongue is achieved using the OTSM. [Display omitted] • E-nose and e-tongue are applied to obtain overall properties of flavor substances. • Flavor responses of the e-nose and e-tongue are processed from nerve conduction. • Olfactory-taste synesthesia effect is simulated by the e-nose, e-tongue, and OTSM. • The OTSM shows the best performance for flavor substance identification. [ABSTRACT FROM AUTHOR]

Electronic nose (E-nose) and electronic tongue (E-tongue) can mimic the sensory perception of human smell and taste, and they are widely applied in tea quality evaluation by utilizing the fingerprints of response signals representing the overall information of tea samples. The intrinsic part of human perception is the fusion of sensors, as more information is provided comparing to the information from a single sensory organ. In this study, a framework for a multi-level fusion strategy of electronic nose and electronic tongue was proposed to enhance the tea quality prediction accuracies, by simultaneously modeling feature fusion and decision fusion. The procedure included feature-level fusion (fuse the time-domain based feature and frequency-domain based feature) and decision-level fusion (D-S evidence to combine the classification results from multiple classifiers). The experiments were conducted on tea samples collected from various tea providers with four grades. The large quantity made the quality assessment task very difficult, and the experimental results showed much better classification ability for the multi-level fusion system. The proposed algorithm could better represent the overall characteristics of tea samples for both odor and taste. [ABSTRACT FROM AUTHOR]

Biomimetic crossreactive sensor arrays have been used to detect and analyze a wide variety of vapor and liquid components in applications such as food science, public health and safety, and diagnostics. As technology has advanced over the past three decades, these systems have become selective, sensitive, and affordable. Currently, the need for noninvasive and accurate devices for early disease diagnosis remains a challenge. This Opinion article provides an overview of the various types of biomimetic crossreactive sensor arrays (also referred to as electronic noses or tongues in the literature), their current use and future directions, and an outlook for future technological development. [ABSTRACT FROM AUTHOR]

Eight kinds of commercial rice vinegars from Korea, China and Japan were selected to evaluate their flavour-related discriminative characteristics based on solid-phase microextraction and gas chromatography with mass spectrometry (SPME/GC-MS), an electronic nose (e-nose), electronic tongue (e-tongue) and subjective sensory evaluation. Principal component analysis patterns based on SPME/GC-MS provided a clear categorical discrimination among the vinegars of three origins. The distribution patterns of the samples were similar between SPME/GC-MS and e-nose analyses and Korean vinegars were clearly discriminated. A high correlation coefficient was determined between SPME/GC-MS and e-nose on acetaldehyde (0.948) and diacetyl (0.970). e-Tongue-based principal component analysis patterns were subdivided into three regions according to the origin, and Chinese vinegars were clearly discriminated. Sensory panelists recorded origin-specific scores on flavours for all the samples. In conclusion, SPME/GC-MS, e-nose and e-tongue analyses may be useful for discrimination of flavour profiles of rice vinegars of different producer origin. Copyright © 2017 The Institute of Brewing & Distilling [ABSTRACT FROM AUTHOR]

Background There is a growing concern for the problem of food authenticity assessment (and hence the detection of food adulteration), since it cheats the consumer and can pose serious risk to health in some instances. Unfortunately, food safety/integrity incidents occur with worrying regularity, and therefore there is clearly a need for the development of new analytical techniques. Scope and approach In this review, after briefly commenting the principles behind the design of electronic noses and electronic tongues, the most relevant contributions of these sensor systems in food adulteration control and authenticity assessment over the past ten years are discussed. It is also remarked that future developments in the utilization of advanced sensors arrays will lead to superior electronic senses with more capabilities, thus making the authenticity and falsification assessment of food products a faster and more reliable process. Key findings and conclusions The use of both types of e-devices in this field has been steadily increasing along the present century, mainly due to the fact that their efficiency has been significantly improved as important developments are taking place in the area of data handling and multivariate data analysis methods. [ABSTRACT FROM AUTHOR]

When partaking of any food or beverage, we actually perceive its flavour, which is the combined effect of aroma and taste. This parallel processing of signals from our nose and tongue is possible due to the presence of large and complex biological neural networks in our brain. Artificial perception of odor and taste is attempted by the scientists using similar techniques, and is implemented with an array of sensors and an array of electrodes known as electronic nose and tongue, respectively. Fusion of electronic nose and tongue at various levels provides improved results for various applications This review article discusses different techniques of fusing electronic nose and tongue information as well as applications of the combined system in different fields. [ABSTRACT FROM AUTHOR]

Electronic tongue and electronic nose for food quality and safety in the production, supervision and daily life. It covers the whole process of food from production line to dining table. [Display omitted] • Electronic tongue and electronic nose play many roles for food quality and safety. • The proportion of electronic tongue and nose employed in food quality control and monitoring in production was the highest. • The recognition and prediction ability of the combined technology showed an increase of 8–25% compared with the single one. For the needs of food producers and consumers, electronic tongue and electronic nose play many roles for food quality and safety in food production, food supervision and daily life. The principles and progresses of electronic tongue and electronic nose were elaborated. The core hardware namely electronic tongue sensors and electronic nose sensors, and the core software namely intelligent sensory algorithms were summarized. It was found that the proportion of electronic tongue and electronic nose employed in food quality control and monitoring in production was the highest. Electronic tongue and electronic nose with the prediction accuracy of 80–96 % accounted for a large proportion in the applications of food. The superposition operation of two or more intelligent sensory algorithms has become routine. In addition, the combination of electronic tongue and electronic nose was highlighted. It was resulted that the ability of food category recognition and quality prediction of the combined technology of electronic nose and electronic tongue showed an increase of 8–25 % compared with the single technology. The future challenges and expectations of electronic tongue and electronic nose were discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • Aroma and taste of peanut butter were analyzed using electronic sensing. • Four different pretreatments were applied in the production of the butter. • Microwave treatment resulted in a stronger nutty aroma. • Radio-frequency treatments resulted in a finer texture. • Explosion puffing did not result in any improvements to the taste or aroma. Currently, the effect of different pretreatments (i.e., radio frequency (RF), explosion puffing (EP), microwave (MW) and oven heating (OH)) on the flavor characteristics of peanut butter is unclear. Consequently, this study identified volatile aroma and non-volatile taste using HS-SPME/GC–MS combined with the use of an electronic nose, electronic tongue, and sniffing. 53 volatile compounds in four peanut butters were identified, MW-treated samples exhibited the most aroma-active compounds (43), followed by samples treated using OH (42), EP (38) and RF (21). Different pretreatment resulted in significant flavor differences in the aroma and taste. The peanut butter under MW pretreatment had a strongest nutty notes among the treatments. RF methods yielded smaller particle sizes and better texture compared to conventional OH. However, instantaneous heating using EP did not result in improvements to the aroma or taste. A combination of MW and RF may improve the flavor quality of peanut butter. [ABSTRACT FROM AUTHOR]

In this study, the chemosensory components of microwave- or oven-roasted Coffea arabica L. were compared using high-performance liquid chromatography and electronic sensors. The contents of caffeine and chlorogenic acid were the highest in green bean extract and were decreased with micro- and oven-roasting time. The decreases in the content of the two components were observed more in oven roasting. In electronic tongue analysis, green bean extract had a relatively higher bitterness, sweetness, and sourness than others. After the roasting process, the bitterness decreased at 10 min, however, increased after 15 min. In addition, sourness tended to decrease over time. Microwave-roasted extracts were lower shifted than those of oven-roasted extracts during each roasting process. Accordingly, higher changes of taste patterns were represented during oven-roasting processes. In electronic nose analysis, volatile alcohols and esters were abundant in the green bean extract, and thus, these components were considered as the main volatiles in the green bean. Through the roasting process, the number of volatiles has increased. In particular, pyrazines were generated in those of roasted samples, and the amounts of furans increased. These results provide basic data regarding the chemosensory components of Yellow Bourbon and may be utilized to optimize the roasting conditions. • Chemical sensory properties of microwave- or oven-roasted coffee were studied. • Electronic tongue quantified five basic tastes in coffees roasted by microwave and oven. • Total 75 volatile compounds were identified using electronic nose. • Principal component analysis segregated coffee sample clusters with different thermal processing. [ABSTRACT FROM AUTHOR]

The feasibility of sensors (Electronic Nose, EN; Electronic Tongue, ET) and spectra (Near Infrared spectrum, NIR; Mid Infrared spectrum, MIR) to evaluate raw honey samples (Vitex, Jujube and Acacia) was explored. Partial least squares discriminant analysis (PLSDA) model, support vector machine (SVM) algorithms model and Interval partial least squares (iPLS) model were used to classify the botanical origin. The results indicate that spectra and sensors could classify the botanical origin of honey rapidly and accurately, since total accuracy for calibration and prediction sets was all almost 100% in EN and ET analysis by SVM model and in NIR and MIR analysis by iPLS model. Then principal components analysis (PCA) and PLSDA model were used to determine the adulterants. Total accuracy for calibration and prediction sets was all above 96% in NIR, MIR and ET by PLSDA model. The results indicate that ET is more suitable for detecting honey adulteration. [ABSTRACT FROM AUTHOR]

Electronic nose (E-nose), Electronic tongue (E-tongue) and Computer Vision System (CVS) are three analytical systems which have been used separately in the food and pharmaceutical industries as quality evaluation methods. The E-nose and E-tongue are designed to mimic the mammalian olfactory and taste systems, both containing sensors that non-selectively interact with flavor molecules to produce some sort of electronic signals. The produced signals are related to the material quality or its main chemical components. The major aim in the use of CVS is to realize the mode of operation of human vision and is based on image analysis to extract some specific attributes as quality features. The extracted color and flavor features are analyzed in a computer using multivariate data analysis to recognize patterns in the data. Since these three systems are non-destructive, rapid, economic and consistent, their fusion can form a powerful and objective inspection tool able to out-perform the individual constituting techniques. The combined evaluation technique has a variety of applications replacing the systems that may not have sufficient performance individually for specific uses. Application of these three techniques are individually reviewed with emphasis on the fusion of the artificial senses. Spectroscopy methods which are also covered can be coupled with these artificial senses to enrich the extracted information from the food industry. [ABSTRACT FROM AUTHOR]

In this study, electronic tongue (E-tongue), headspace solid-phase microextraction gas chromatography-mass spectrometer (GC-MS), electronic nose (E-nose), and quantitative describe analysis (QDA) were applied to describe the 2 types of citrus fruits (Satsuma mandarins [ Citrus unshiu Marc.] and sweet oranges [ Citrus sinensis {L.} Osbeck]) and their mixing juices systematically and comprehensively. As some aroma components or some flavor molecules interacted with the whole juice matrix, the changes of most components in the fruit juice were not in proportion to the mixing ratio of the 2 citrus fruits. The potential correlations among the signals of E-tongue and E-nose, volatile components, and sensory attributes were analyzed by using analysis of variance partial least squares regression. The result showed that the variables from the sensor signals (E-tongue system and E-nose system) had significant and positive (or negative) correlations to the most variables of volatile components (GC-MS) and sensory attributes (QDA). The simultaneous utilization of E-tongue and E-nose obtained a perfect classification result with 100% accuracy rate based on linear discriminant analysis and also attained a satisfying prediction with high coefficient association for the sensory attributes ( R2 > 0.994 for training sets and R2 > 0.983 for testing sets) and for the volatile components ( R2 > 0.992 for training sets and R2 > 0.990 for testing sets) based on random forest. Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods, such as GC-MS and sensory evaluation by human panel in the fruit industry. [ABSTRACT FROM AUTHOR]

BACKGROUND The aim of this study is to investigate the feasibility of a 'holistic' approach, using near infrared ( NIR) spectroscopy and electronic devices (electronic nose and electronic tongue), as instrumental tools for the classification of different coffee varieties. Analyses were performed on green coffee, on ground roasted coffee and on coffee beverage. Principal component analysis was applied on spectral and sensory data to uncover correlations between samples and variables. After variable selection, linear discriminant analysis was used to classify the samples on the basis of the three coffee classes: Robusta, natural Arabica and washed Arabica. RESULTS Linear discriminant analysis demonstrates the practicability of this approach: the external test set validation performed with NIR data showed 100% of correctly classified samples. Moreover, a satisfying percentage of correct classification in cross-validation was obtained for the electronic devices: the average values of correctly classified samples were 81.83% and 78.76% for electronic nose and electronic tongue, respectively. CONCLUSION NIR spectroscopy was shown to be a very reliable and useful tool to classify coffee samples in a fast, clean and inexpensive way compared to classical analysis, while the electronic devices could assume the role of investigating techniques to depict the aroma and taste of coffee samples. © 2014 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

The diagnosis of cancer and other diseases using data from non-specific sensors – such as the electronic tongues (e-tongues) - is challenging owing to the lack of selectivity, in addition to the variability of biological samples. In this study, we demonstrate that impedance data obtained with an e-tongue in saliva samples can be used to diagnose cancer in the mouth. Data taken with a single-response microfluidic e-tongue applied to the saliva of 27 individuals were treated with multidimensional projection techniques and non-supervised and supervised machine learning algorithms. The distinction between healthy individuals and patients with cancer on the floor of mouth or oral cavity could only be made with supervised learning. Accuracy above 80% was obtained for the binary classification (YES or NO for cancer) using a Support Vector Machine (SVM) with radial basis function kernel and Random Forest. In the classification considering the type of cancer, the accuracy dropped to ca. 70%. The accuracy tended to increase when clinical information such as alcohol consumption was used in conjunction with the e-tongue data. With the random forest algorithm, the rules to explain the diagnosis could be identified using the concept of Multidimensional Calibration Space. Since the training of the machine learning algorithms is believed to be more efficient when the data of a larger number of patients are employed, the approach presented here is promising for computer-assisted diagnosis. [Display omitted] • First use of an impedimetric electronic tongue (e-tongue) for the diagnosis of cancer. • E-tongue data were used in cancer diagnosis without needing a specific biomarker. • From saliva samples our e-tongue translated information of clinical importance. • Multidimensional calibration space created rules to explain the diagnosis results. [ABSTRACT FROM AUTHOR]

Fruits freshness is relatively easy to authenticate from their morphological characteristics while the act of processing fruits into juices makes it difficult to track/identify their freshness. Eight datasets, extracted from an e-nose and an e-tongue, and six sensor fusion approaches using both instruments, were applied to detect 100 % juices squeezed from cherry tomatoes with different post-harvest storage times (ST). Discrimination of the juices was mainly performed by canonical discriminant analysis (CDA) and library support vector machines (Lib-SVM). Tracking and prediction of physicochemical qualities (pH, soluble solids content (SSC), Vitamin C (VC), and firmness) of the fruit were performed using principle components regression (PCR). All eight datasets presented good classification results with classifiers trained by e-tongue dataset and fusion dataset 2 (stepwise selection) presented the best classification performances. Though quality regression models trained by either e-nose or e-tongue dataset were not robustness enough, sensor fusion approaches make it possible to build more robust prediction models that can correctly predict quality indices for a totally new juice sample. This study indicates the potential for tracking quality/freshness of fruit squeezed for juice consumption using the e-nose and e-tongue, and that sensor fusion approach would be better than individual utilization only if proper fusion approaches are used. [ABSTRACT FROM AUTHOR]

An electronic nose (E-nose) and an electronic tongue (E-tongue) have been used to characterise strawberry juices based on different processes (i.e. Microwave Pasteurisation, Steam Blanching, High Temperature Short Time Pasteurisation, Frozen-Thawed, and Freshly Squeezed). Quality parameters (vitamin C, pH, total soluble solid, total acid and sugar/acid ratio) were detected by traditional measuring methods. For qualitative discrimination, E-tongue system reached higher accuracy than E-nose did, and lower than the simultaneous utilisation. For quantitative prediction, E-tongue system had better performance ( R 2 ranged from 0.7294 to 0.8622 for the calibration, and R 2 ranged from 0.7362 to 0.9527 for the validation) than E-nose did ( R 2 ranged from 0.5344 to 0.8741 for the calibration, and R 2 ranged from 0.4444 to 0.7258 for the validation), and worse than the simultaneous utilisation ( R 2 ranged from 0.9627 to 0.9834 for the calibration, and R 2 ranged from 0.8553 to 0.8959 for the validation). Either E-nose or E-tongue was suited to classify strawberry juice, but was insufficient to predict the values of quality parameters (vitamin C, pH, total soluble solid, total acid, and sugar/acid ratio). This work indicates that the simultaneous utilisation of E-nose and E-tongue can discriminate processed juices and predict juice quality parameters successfully for the food industry. [ABSTRACT FROM AUTHOR]

Electronic nose (e-nose) and electronic tongue (e-tongue) instrumental systems were designed to crudely mimic human olfactory and taste sensory organs and are composed of an array of sensors. Complex data sets from electronic nose and electronic tongue signals combined with multivariate statistics represent rapid and efficient tools for classification, discrimination, recognition and identi-fication of samples, as well as for the prediction of concentrations of different compounds. A wide variety of sensors can be employed into the design of these instrumental systems, especially that of electronic tongues, offering numerous practical applications. In this study, characteristics of sensors and possibilities of electronic nose and electronic tongue applications in the dairy industry were re-viewed. [ABSTRACT FROM AUTHOR]

Highlights: [•] Our electronic panel discriminates wines with different polyphenolic and oxygen content. [•] The signals of the e-panel correlate well with oxygen level, polyphenols and colour values. [•] The combined use of an e-nose and an e-tongue improves the quality of the predictions. [Copyright &y& Elsevier]

Abstract: The study presented six approaches (two e-nose measurements, an e-tongue measurement and three fusion approaches using both of the instruments) for recognition and quantitative analysis of four tomato juice groups: unadulterated and three adulterated tomato juices with different adulteration levels. Recognition of the juices was performed by principle component analysis (PCA) and cluster analysis (CA). Quantitative calibration with respect to pH and soluble solids content (SSC) was performed using four regression methods (principle components regression (PCR) based on stepwise selection, multiple linear regression (MLR) based on raw feature vector, forward selection and stepwise selection features). CA based on different data standardization and distance calculation methods were compared, and precision-recall measure was applied to quantify clustering outcomes. The result implies that it is important to explore the optimum standardization and distance calculation methods for every dataset studied prior to CA. Humidity effect was also explored and the result showed that employing desiccant for e-nose measurement presented no improvement. The fusion dataset that consists of variables selected by analysis of variance (ANOVA) presented the best authentication ability, and the quality indices highly correlated to this dataset. [Copyright &y& Elsevier]

Highlights: [•] Seven techniques were applied for evaluation adulteration of tomato juices. [•] Sensor drift and humidity problems were considered. [•] Different feature selection and data fusion methods were discussed. [ABSTRACT FROM AUTHOR]

Highlights: [•] This review paper deals with the applications of electronic noses and electronic tongues to the monitoring of fermentation processes. [•] Positive and negative aspects of the different approaches reviewed are analyzed. [•] Current and future endeavors in this field are also commented. [Copyright &y& Elsevier]

Sensors are often organized in multidimensional systems or networks for particular applications. This is facilitated by the large improvements in the miniaturization process, power consumption reduction and data analysis techniques nowadays possible. Such sensors are frequently organized in multidimensional arrays oriented to the realization of artificial sensorial systems mimicking the mechanisms of human senses. Instruments that make use of these sensors are frequently employed in the fields of medicine and food science. Among them, the so-called electronic nose and tongue are becoming more and more popular. In this paper an innovative multisensorial system based on sensing materials of biological origin is illustrated. Anthocyanins are exploited here as chemical interactive materials for both quartz microbalance (QMB) transducers used as gas sensors and for electrodes used as liquid electrochemical sensors. The optical properties of anthocyanins are well established and widely used, but they have never been exploited as sensing materials for both gas and liquid sensors in non-optical applications. By using the same set of selected anthocyanins an integrated system has been realized, which includes a gas sensor array based on QMB and a sensor array for liquids made up of suitable Ion Sensitive Electrodes (ISEs). The arrays are also monitored from an optical point of view. This embedded system, is intended to mimic the working principles of the nose, tongue and eyes. We call this setup BIONOTE (for BIOsensor-based multisensorial system for mimicking NOse, Tongue and Eyes). The complete design, fabrication and calibration processes of the BIONOTE system are described herein, and a number of preliminary results are discussed. These results are relative to: (a) the characterization of the optical properties of the tested materials; (b) the performance of the whole system as gas sensor array with respect to ethanol, hexane and isopropyl alcohol detection (concentration range 0.1-7 ppm) and as a liquid sensor array (concentration range 73-98 µM). [ABSTRACT FROM AUTHOR]