Study Of Qality Detection Approaches For Cherry Tomato Juices Based On Electronic Nose And Electronic Tongue

Nowadays, consumers demand high-quality, additive-free, minimally-processed, nutritious, and fresh-like products. Freshly-squeezed fruit juice labeled as100%fruit is typically one of these products. This research employed an electronic nose (e-nose) and an electronic tongue (e-tongue) to detect the quality of cherry tomato juices. The tasks of tracing material freshness, discriminating pretreatments, evaluating sterilization persistence and indenfying adulteration levels were considered. The whole structure of this paper is as follows:(a) explore data structure of the e-nose and e-tongue datasets of cherry tomato juices;(b) establish quality evaluation models for cherry tomato juices based on the e-nose and e-tongue detections, respectively;(c) optimize those quality evaluation models by combining the e-nose and e-tongue signals; and (d) present a semi-supervised approach to improve the quality discrimination classifiers. The main conclusions of this research are as follows:(1) Spectral clustering, single Linkage clustering (SL), complete Linkage clustering (CL), Ward’s clustering, Fuzzy c-means (FCM), κ-means clustering and iterative selforganizing data analysis techniques algorithm (ISODATA) were employed to explore the underlying data structure of three e-nose and three e-tongue datasets. Both2D principal components analysis (PCA) plots and three cluster validation criteria (CVC)-precision, mutual information (MI) and rand index (RI)-demonstrated that the spectral clustering outperformed traditional clustering methods at reflecting the real underlying structure of datasets.(2) The e-nose and e-tongue were applied to authenticate cherry tomato juices with different adulteration levels. In addition to directly e-nose measurement, a pretreatment of employing anhydrous sodium carbonate as desiccant was also conducted to observe if reducing of water vapor would improve the authentication ability of e-nose. The results of PCA, canonical discriminant analysis (CDA), learning vector quantization (LVQ), support vector machines (SVM) and principal component regression (PCR) all demonstrate that employing anhydrous sodium carbonate as desiccant did not improve the performance of e-nose when detecting liquid samples; on the contrary, directly e-nose measurement was better than e-nose with desiccant at authenticating cherry tomato juices. Meanwhile, either the e-tongue or the direct e-nose could authenticate cherry tomato juices as well as predict quality indices (pH and solube solid contents (SSC)).(3) The e-nose and e-tongue were applied to measure the freshness of cherry tomato under different storage conditions (16days at4℃and8days at25℃). Both qualitative and quantitative results demonstrate it is possible to trace the freshness of original cherry tomatoes through detecting the squeezed juices. Meanwhile, freshness discrimination models as well as quality indices prediction models built based on the e-tongue were better than those based on the e-nose.(4) A combination of the e-nose and e-tongue was tried to trace the freshness of cherry tomatoes by detecting the squeezed juices. Six data fusion approaches-simple concatenation, stepwise selection, PCA, factor F with Log F values higher than3,2.5and2, respectively-were employed. A second batch of experiments was conducted to produce an independent dataset for validation of the material freshness tracing models. The results demonstrated that quality regression models built based on single usage of e-nose or e-tongue were lack of generalization. On the contrary, models built based on fusion datasets could predict quality indices (pH, SSC, vitamin C and firmness) for the second batch of experimental data.(5) A semi-supervised classification approach-cluster-then-label-was compared with supervised approaches-probabilistic neural networks (PNN) and LVQ. In both cases of tracing material freshness and authenticating adulteration levels, cherry tomato juice quality evaluation models built using spectral clustering and FCM based cluster-then-label approaches outperformed the supervised approaches. In the cases that only a few labeled data is available for training, supervised classifiers tend to overfit the particular training dataset and thus lacking generalization ability, i.e., it tends to fail to classify unknown data. On the contrary, the semi-supervised classifier is more stable and of generalization. Classification accuracy for the training and testing datasets were almost the same.In consideration of the above conclusions, it is obvious that establishing quality evaluation models for cherry tomato juices based on the e-nose and e-tongue techniques is possible. The proposed spectral clustering offers a better approach to explore underlying structures of e-nose and e-tongue datasets; and based on this clustering approach, the semi-supervised cluster-then-lable approach could build a reliable classifier with only a few labeled training data. Meanwhile, with appropriate feacture extraction approaches, the fusion use of both instruements could be better than individual usage of the e-nose and e-tongue.