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

被引:10
|
作者
Xu, Jinchai [1 ,2 ]
Qu, Fangfang [1 ,2 ,3 ]
Shen, Bihe [2 ,3 ]
Huang, Zhenxiong [4 ]
Li, Xiaoli [4 ]
Weng, Haiyong [2 ,3 ]
Ye, Dapeng [1 ,2 ,3 ]
Wu, Renye [5 ]
机构
[1] Fujian Agr & Forestry Univ, Haixia Inst Sci & Technol, Sch Future Technol, Fuzhou 350002, Peoples R China
[2] Fujian Key Lab Agr Informat Sensing Technol, Fuzhou 350002, Peoples R China
[3] Fujian Agr & Forestry Univ, Coll Mech & Elect Engn, Fuzhou 350002, Peoples R China
[4] Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China
[5] Fujian Agr & Forestry Univ, Coll Agr, Fuzhou 350002, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 03期
基金
中国国家自然科学基金;
关键词
fresh tea leaves; tea polyphenols; visible and near-infrared spectroscopy; dual-band; non-destructive detection device; CAMELLIA-SINENSIS L; GREEN TEA; LEAF; NIR; CATECHINS;
D O I
10.3390/app13031739
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Tea polyphenols are considered as an important indicator of tea quality. Rapid detection of tea polyphenol content plays a valuable role for tea breeding and quality inspection during tea production. In this work, a portable rapid non-destructive detection device of tea polyphenols in fresh tea leaves was developed, which integrated the fusion technology of visible/short-wave (400-1050 nm) and long-wave (1000-1650 nm) near-infrared spectroscopy (Vis/NIR). Experimental results indicated that the spectra within the overlapping region (1000-1050 nm) were assembled by applying the spectral data fusing method. Followed by spectral data preprocessing with the Savitzky-Golay smoothing (SG) method, least squares support vector regression (LS-SVR) models were established for detecting the tea polyphenol content of fresh tea leaves. Based on the fused Vis/NIR spectra (dual-band), the correlation coefficient of calibration (R-C), root mean square error of calibration (RMSEC), correlation coefficient of prediction (R-P), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) reached 0.976, 0.679%, 0.893, 0.897%, and 2.230, respectively, which were better than the visible/short-wave or long-wave near infrared spectral data (single-band). The sensitive spectral wavebands of tea polyphenols extracted using the random frog (RF) algorithm were distributed in 402-448 nm, 555-600 nm, 810-1042 nm, 1056-1103 nm, 1219-1323 nm, 1406-1416 nm, and 1499-1511 nm. This demonstrated that the prediction of tea polyphenol content using fused spectral data combined with the LS-SVR model depended on various functional groups such as auxochromes, chromogenic groups, and hydrogen-containing groups. The proposed device is capable of non-destructive detection of tea polyphenol content in fresh tea leaves, which can provide effective technical support for tea breeding and tea leaf quality control.
引用
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页数:14
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