A feature selection strategy of E-nose data based on PCA coupled with Wilks Λ-statistic for discrimination of vinegar samples

Yin, Yunfei; Zhao, Yuzhen

doi:10.1007/s11694-019-00161-0

Cited by 35 publications

(10 citation statements)

References 30 publications

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“…(2) The multi-omics technologies, including metagenomics, metaproteomics, and metabolomics, may be the future direction to elucidate how VVOCs are produced by microorganisms, and what are contributions of different microorganisms to VVOCs. (3) The multivariate statistical analysis methods, including principal component analysis, partial least squares discriminant analysis, and so on, have been applied to identify different kinds of vinegar, establish aroma fingerprints, and analyze characteristic aroma components of kinds of vinegar (Yin and Zhao, 2019 ; Zhang et al, 2019 ), therefore in the future the multivariate analysis of the VVOCs profile for discriminant features might be carried out to distinguish the different kinds of vinegar, especially PGI vinegar products.…”

Section: Discussion and Perspectivementioning

confidence: 99%

Vinegar Volatile Organic Compounds: Analytical Methods, Constituents, and Formation Processes

et al. 2022

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Vinegar is an acid condiment shared all over the world. According to the raw materials, vinegar can be mainly divided into fruit and cereal ones, both of which possess unique aroma and flavor characteristics and corresponding volatile organic compounds (VOCs). Many studies about vinegar VOCs' (VVOCs) sorts, analytical methods, and forming mechanisms have been done. In this review, the main categories of vinegar and their distribution in the world are briefly introduced, then VVOCs' analytical and identified methods, types, and forming processes are summarized. Additionally, the VVOCs' research directions are discussed and prospected. According to the searched literatures, this study is the first to systematically review the analytical methods, sorts, and formation mechanisms of VVOCs, which will make the readers better understand the vinegar's aromas and flavors and their producing mechanisms.

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Section: Discussion and Perspectivementioning

confidence: 99%

Vinegar Volatile Organic Compounds: Analytical Methods, Constituents, and Formation Processes

et al. 2022

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“…The taste quality of the blank and triazole-pesticide-treated samples was analyzed by electronic nose technology. Principal component analysis (PCA) was applied to the data analysis as a multivariate method of generating principal component (PC) variables by investigating the correlation among several variables [23], which was used to eliminate the correlation among original characteristic variables. According to the PCA analysis (Figure 1a), the first component (PC1) explained 99.76% of the total system variance.…”

Section: Electronic Nose Analysismentioning

confidence: 99%

Influence of Triazole Pesticides on Wine Flavor and Quality Based on Multidimensional Analysis Technology

Xiao

Chen

et al. 2020

Molecules

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Triazole pesticides are widely used to control grapevine diseases. In this study, we investigated the impact of three triazole pesticides—triadimefon, tebuconazole, and paclobutrazol—on the concentrations of wine aroma compounds. All three triazole pesticides significantly affected the ester and acid aroma components. Among them, paclobutrazol exhibited the greatest negative influence on the wine aroma quality through its effect on the ester and acid aroma substances, followed by tebuconazole and triadimefon. Qualitative and quantitative analysis by solid-phase micro-extraction gas chromatography coupled with mass spectrometry revealed that the triazole pesticides also changed the flower and fruit flavor component contents of the wines. This was attributed to changes in the yeast fermentation activity caused by the pesticide residues. The study reveals that triazole pesticides negatively impact on the volatile composition of wines with a potential undesirable effect on wine quality, underlining the desirability of stricter control by the food industry over pesticide residues in winemaking.

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“…In order to achieve quantitative evaluation of multiple features, parameters including variance, integrals, steady state average, mean differential value, skewness, and kurtosis were selected as time domain features, as shown in Table 3. Variance describes the degree of data dispersion acquired by different sensors, the integral value reflects the total response of the sensor to the gas, the steady state Sensors 2020, 20, 50 5 of 18 average reflects the characteristic information of the sample, the average differential value reflects the average speed of the sensor's response to the gas, and skewness and kurtosis reflect the distribution of signals [49][50][51][52]. Table 3.…”

Section: Feature Extraction From E-nose Systemmentioning

confidence: 99%

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

Yang

Wang

et al. 2019

Sensors

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Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R2 = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea.

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A feature selection strategy of E-nose data based on PCA coupled with Wilks Λ-statistic for discrimination of vinegar samples

Cited by 35 publications

References 30 publications

Vinegar Volatile Organic Compounds: Analytical Methods, Constituents, and Formation Processes

Vinegar Volatile Organic Compounds: Analytical Methods, Constituents, and Formation Processes

Influence of Triazole Pesticides on Wine Flavor and Quality Based on Multidimensional Analysis Technology

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

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