Monitoring black tea fermentation quality by intelligent sensors: Comparison of image, e-nose and data fusion

Abstract

To scientifically and objectively monitor the fermentation quality of black tea, a computer vision system (CVS) and electronic nose (e-nose) were employed to analyze the black tea image and odor eigenvalues of Yinghong No. 9 black tea. First, the variation trends of tea polyphenols, volatile substances, image eigenvalues and odor eigenvalues with the extension of fermentation time were analyzed, and the fermentation process was categorized into three stages for classification. Second, principal component analysis (PCA) was employed on the image and odor eigenvalues obtained by CVS and e-nose. Partial least squares discriminant analysis (PLS-DA) was performed on 117 volatile components, and 51 differential volatiles were screened out based on variable importance in projection (VIP ≥1) and one-way analysis of variance (P < 0.05), including geraniol, linalool, nerolidol, and α-ionone. Then, image features and odor features are fused by using a data fusion strategy. Finally, the image, smell and fusion information were combined with random forest (RF), K-nearest neighbor (KNN) and support vector machine (SVM) to establish the classification models of different fermentation stages and to compare them. The results show that the feature-level fusion strategy integrating the SVM was the most efficient approach, with classification accuracy rates of 100% for the training sets and 95.6% for the testing sets. The performance of Support Vector Regression (SVR) prediction models for tea polyphenol content based on feature-level fusion data outperformed data-level models (Rc, RMSEC, Rp and RMSEP of 0.96, 0.48 mg/g, 0.94, 0.6 mg/g).