Rapid Detection of Tea Polyphenols in Fresh Tea Leaves Based on Fusion of Visible/Short-Wave and Long-Wave near Infrared Spectroscopy and Its Device Development  
[PERSON]  
1 School of Future Technology, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Furzhou 350002, China 12 [PERSON] 1233  
2 Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou 350002, China 3  
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Visible and near infrared (Vis/NIR) spectroscopy indicates the octave and ensemble absorption characteristics of the vibrations of hydrogen-containing groups (e.g., C-H, _n_-H, and O-H) in substances, and shows great promise for applications in the fields of tea quality, aquatic products, and food testing [10; 11; 12]. The quantitative model was established by using an XDS near-infrared spectrometer (400-2498 nm) to detect the content of tea polyphenols and caffeine in green tea processing, and the sensitive wavebands were extracted by the successive projections algorithm (SPA). The results showed that the prediction performance of the multiple linear regression (MLR) model based on SPA for detecting the content of tea polyphenols and caffeine was better, and the prediction coefficient of determination (R\({}_{\text{P}}^{2}\)) of this model were all greater than 0.834 [13]. [PERSON] et al. (2020) collected spectral and image information of different tea varieties using a hyperspectral imaging spectrometer (900-1700 nm) and combined it with chemometric methods to estimate phosphorus and potassium contents within tea leaves. The results indicated that the predicted correlation coefficient (R\({}_{\text{P}}\)) of MLR models based on the SPA to extract spectral data in sensitive wavebands were all higher than 0.91 [14]. [PERSON] et al. (2021) used the Vis/NIR spectrometer (400-2498 nm) to collect spectral data of tea leaves during fermentation, with the modeling determination coefficient of calibration (R\({}_{\text{c}}^{2}\)) of the modified partial least-squares regression (MPLSR) developed for both total catechins and theanine contents greater than 0.94 [15]. The above results demonstrated the feasibility of applying spectroscopic techniques in tea quality testing; however, the expensive spectroscopic instruments and the strict operating environment limited their laboratory utilization. Fortunately, several low-cost portable visible spectrometers and near-infrared spectrometers have been developed [16; 17]. To overcome the shortage of information obtained by a single spectrometer, [PERSON] et al. (2018) presented a portable optical instrument integrating a AvaSpec-2048 fiber optic spectrometer (200-1100 nm) with a AvaSpec-NIR256-2.5 TEC NIR spectrometer (1000-2500 nm). A \"response correction\" algorithm was then proposed to fuse the two independent spectral regions into a dual-band region (400-2400 nm) that continuously covers the Vis/short-wave and long-wave NIR spectra. The results indicated that the detection accuracy of quality parameters, including protein, adipose, and potential of hydrogen (PH), of pork based on the dual-band spectral region was better than that of the single-band region (400-1100 nm or 1074-2400 nm), and the coefficients of determination (R\({}^{2}\)) of the partial least squares regression (PLSR) models all reached above 0.90 [18]. [PERSON] et al. (2016) used Vis/NIR and X-ray fluorescence (XRF) coupled for agronomic soil prediction, which improved the prediction accuracy with a relative improvement in the root mean square error (RMSE) ranging from 4% to 44% [19]. [PERSON] et al. (2022) combined three micro-spectrometers: SCIO (740-1070 nm), NIRscan (901-1701 nm), and NIRone (1750-2150 nm), and acquired the spectral information of the sequential and orthogonalized partial least squares (SO-PLS) model to achieve a prediction of the total sugar content of sugarcane with comparable performance to the commercial instrument ASD (350-2500 nm) [20].