Quantitative Analysis and Discrimination of Partially Fermented Teas from Different Origins Using Visible/Near-Infrared Spectroscopy Coupled with Chemometrics

Abstract

Partially fermented tea such as oolong tea is a popular drink worldwide. Preventing fraud in partially fermented tea has become imperative to protect producers and consumers from possible economic losses. Visible/near-infrared (VIS/NIR) spectroscopy integrated with stepwise multiple linear regression (SMLR) and support vector machine (SVM) methods were used for origin discrimination of partially fermented tea from Vietnam, China, and different production areas in Taiwan using the full visible NIR wavelength range (400–2498 nm). The SMLR and SVM models achieved satisfactory results. Models using data from chemical constituents’ specific wavelength ranges exhibited a high correlation with the spectra of teas, and the SMLR analyses improved discrimination of the types and origins when performing SVM analyses. The SVM models’ identification accuracies regarding different production areas in Taiwan were effectively enhanced using a combination of the data within specific wavelength ranges of several constituents. The accuracy rates were 100% for the discrimination of types, origins, and production areas of tea in the calibration and prediction sets using the optimal SVM models integrated with the specific wavelength ranges of the constituents in tea. NIR could be an effective tool for rapid, nondestructive, and accurate inspection of types, origins, and production areas of teas.

A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products

“…The development of quantitative predictive models was applied for the prediction or quantification of ACNs with non-destructive techniques. Artificial neural network (ANN), backward interval-PLS (Bi-PLS), non-parametric algorithm (NPA), principal component regression (PCR), chaotic neural network (KIII), partial least squares (PLS), competitive adaptive reweighted sampling (CARS)-PLS, response surface regression (RSR), multiple linear regression (MLR), back propagation neural network (BPNN), step multiple linear regression (SMLR), ant colony optimization (ACO)-PLS, genetic algorithm-PLS (GAPLS), and synergy interval-PLS (Si-PLS) (Figure 1) are generally used for quantification models (43)(44)(45)(46)(47).…”

Broad learning system with Takagi–Sugeno fuzzy subsystem for tobacco origin identification based on near infrared spectroscopy

“…Near infrared (NIR) spectroscopy has been known as a kind of fast, nondestructive and accurate data analysis technology for origin identification in recent years [10][11][12][13][14][15][16]. NIR spectroscopy combined with other soft computing methods has been widely applied for tobacco origin identification.…”