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• 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]

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]

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]

[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]

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]