利用近红外光谱快速监测甜茶（甜茶栲，Lithocarpus litseifolius (Hance) Chun）叶片中的黄酮含量。

Rapid monitoring of flavonoid content in sweet tea (Lithocarpus litseifolius (Hance) Chun) leaves using NIR spectroscopy.

作者信息

Tian Zhaoxia, Tan Zifeng, Li Yanjie, Yang Zhiling

机构信息

Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou, 311400, Zhejiang Province, China.

College of Forestry, Nanjing Forestry University, Nanjing, People's Republic of China.

出版信息

Plant Methods. 2022 Apr 2;18(1):44. doi: 10.1186/s13007-022-00878-y.

DOI:10.1186/s13007-022-00878-y

PMID:35366929

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8977023/

Abstract

BACKGROUND

Sweet tea, which functions as tea, sugar and medicine, was listed as a new food resource in 2017. Flavonoids are the main medicinal components in sweet tea and have significant pharmacological activities. Therefore, the quality of sweet tea is related to the content of flavonoids. Flavonoid content in plants is normally determined by time-consuming and expensive chemical analyses. The aim of this study was to develop a methodology to measure three constituents of flavonoids, namely, total flavonoids, phloridin and trilobatin, in sweet tea leaves using near-infrared spectroscopy (NIR).

RESULTS

In this study, we demonstrated that the combination of principal component analysis (PCA) and NIR spectroscopy can distinguish sweet tea from different locations. In addition, different spectral preprocessing methods are used to establish partial least squares (PLS) models between spectral information and the content of the three constituents. The best total flavonoid prediction model was obtained with NIR spectra preprocessed with Savitzky-Golay combined with second derivatives (SG + D2) (R = 0.893, and RMSEP = 0.131). For trilobatin, the model with the best performance was developed with raw NIR spectra (R = 0.902, and RMSEP = 2.993), and for phloridin, the best model was obtained with NIR spectra preprocessed with standard normal variate (SNV) (R = 0.818, and RMSEP = 1.085). The coefficients of determination for all calibration sets, validation sets and prediction sets of the best PLS models were higher than 0.967, 0.858 and 0.818, respectively.

CONCLUSIONS

The conclusion indicated that NIR spectroscopy has the ability to determine the flavonoid content of sweet tea quickly and conveniently.

摘要

背景

甜茶兼具茶、糖和药的功能，于2017年被列为新食品原料。黄酮类化合物是甜茶中的主要药用成分，具有显著的药理活性。因此，甜茶的品质与黄酮类化合物的含量有关。植物中黄酮类化合物的含量通常通过耗时且昂贵的化学分析来测定。本研究的目的是开发一种利用近红外光谱（NIR）测定甜茶叶中三种黄酮类成分（即总黄酮、根皮苷和三叶苷）的方法。

结果

在本研究中，我们证明了主成分分析（PCA）和近红外光谱相结合能够区分不同产地的甜茶。此外，采用不同的光谱预处理方法在光谱信息与三种成分的含量之间建立偏最小二乘法（PLS）模型。用Savitzky-Golay结合二阶导数（SG + D2）预处理近红外光谱得到了最佳的总黄酮预测模型（R = 0.893，RMSEP = 0.131）。对于三叶苷，用原始近红外光谱建立了性能最佳的模型（R = 0.902，RMSEP = 2.993），对于根皮苷，用标准正态变量（SNV）预处理近红外光谱得到了最佳模型（R = 0.818，RMSEP = 1.085）。最佳PLS模型的所有校正集、验证集和预测集的决定系数分别高于0.967、0.858和0.818。