Search

Bear bile powder (BBP) is a valuable animal-derived product with a huge adulteration problem on market. It is a crucially important task to identify BBP and its counterfeit. Electronic sensory technologies are the inheritance and development of traditional empirical identification. Considering that each drug has its own specific odor and taste characteristics, electronic tongue (E-tongue), electronic nose (E-nose) and GC-MS were used to evaluate the aroma and taste of BBP and its common counterfeit. Two active components of BBP, namely tauroursodeoxycholic acid (TUDCA) and taurochenodeoxycholic acid (TCDCA) were measured and linked with the electronic sensory data. The results showed that bitterness was the main flavor of TUDCA in BBP, saltiness and umami were the main flavor of TCDCA. The volatiles detected by E-nose and GC-MS were mainly aldehydes, ketones, alcohols, hydrocarbons, carboxylic acids, heterocyclic, lipids, and amines, mainly earthy, musty, coffee, bitter almond, burnt, pungent odor descriptions. Four different machine learning algorithms (backpropagation neural network, support vector machine, K-nearest neighbor, and random forest) were used to identify BBP and its counterfeit, and the regression performance of these four algorithms was also evaluated. For qualitative identification, the algorithm of random forest has shown the best performance, with 100% accuracy, precision, recall and F1-score. Also, the random forest algorithm has the best R2 and the lowest RMSE in terms of quantitative prediction. [ABSTRACT FROM AUTHOR]

Codonopsis Radix (CR) is an edible food and traditional Chinese herb medicine in China. Various varieties of Codonopsis Radix have different tastes. To make the flavor of processed food stable, two kinds of electronic sensory devices, electronic nose and electronic tongue, were used to establish a discrimination model to identify the botanical origin of each sample. The optimal model built on the 88 batches of samples was selected from the models trained with all combination of two pretreatment methods and three classification methods. A comparison were performed on the models trained on the data collected by electronic nose and electronic tongue. The results showed that the model trained on the fused dataset outperformed the models trained separately on the electronic nose data and electronic tongue data. The two preprocessing approaches could improve the prediction performance of all classification methods. Classification and Regression Tree approach performed better than Partial Least Square Discriminant Analysis and Linear Discriminant Analysis in terms of accuracy. But Classification and Regression Tree tends to assign the samples of minority class to the majority class. Meanwhile, Partial Least Square Discriminant Analysis keeps a good balance between the identification requirements of all the two groups of samples. Taking all the results above, the model built using the Partial Least Square Discriminant Analysis method on the fused data after z-score was used to identify the botanical origin of Codonopsis Radix. [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]

Sweeteners of natural sources, such as sugar alcohols, are in the centre of growing interest. Their impact on the phytochemicals, antioxidant and sensory properties of blackberry jams were investigated during a 9-months storage period. Measurements on jams prepared with different sugars and sugar alcohols (sucrose, fructose, xylitol and erythritol) were performed at the date of preparation and in the 1st, 3rd, 6th, 9th month. Total polyphenol content, individual polyphenols, antioxidant properties, anthocyanin content and CIE L*a*b* colour were determined. Sensory profiles were determined by a trained panel. Sensory attributes were compared by the results of the electronic tongue and nose. Sweeteners had a significant impact on physicochemical properties and sensory attributes. Storage time also affected the sensory and compositional properties of jams. Changes in antioxidant properties did not follow a clear trend during the storage period, and antioxidant capacity was not affected significantly by the sweetening agent, but showed a significant decline from the 6th month. A positive effect of xylitol was observed in terms of a low degradation rate of anthocyanins, while their decomposition was the fastest in the fructose-containing preparation. Jams sweetened with erythritol reached significantly lower values for some sensory attributes (blackberry flavour and general taste intensity), however, they showed more intense red colour. Multiple factor analysis enabled the identification of the effect of sweetener and storage time on the pattern of the sensory data matrix. Classification according to individual sweeteners was performed successfully by the electronic tongue, but not by electronic nose. [ABSTRACT FROM AUTHOR]

This study developed principal component analysis and radial basis function neural networks (PCA-RBFNNs) for predicting freshness in tilapia fillets stored at different temperatures by integrating an electronic nose and electronic tongue. Total volatile basic nitrogen (TVB-N), total aerobic counts (TAC), and K value increased at 0, 4, 7, and 10 °C, while sensory scores decreased significantly. The electronic nose and tongue acquired the volatiles and dissolved chemical compounds in the stored samples. Gas chromatography-mass spectrometry (GC-MS) verified the changes in gas species and contents in fillets stored for different periods of time at different temperatures. PCA-RBFNNs based on data fusion were developed and presented good performance for prediction of TVB-N, TAC, K value, and sensory score in tilapia fillets. The established PCA-RBFNNs based on feature variables of the electronic nose and tongue is a promising method to predict changes in the freshness of fillets stored from 0 to 10 °C in the cold chain. [ABSTRACT FROM AUTHOR]

Electronic noses (e-noses) are based on arrays of different sensor types that respond to specific features of an odorant molecule, mostly volatile organic compounds (VOCs). Differently from gas chromatography and mass spectrometry, e-noses can distinguish VOCs spectrum by pattern recognition. E-nose technology has successfully been used in commercial applications, including military, environmental, and food industry. Human-exhaled breath contains a mixture of over 3000 VOCs, which offers the postulate that e-nose technology can have medical applications. Based on the above hypothesis, an increasing number of studies have shown that breath profiling by e-nose could play a role in the diagnosis and/or screening of various respiratory and systemic diseases. The aim of the present study was to review the principal literature on the application of e-nose technology in respiratory diseases. [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]