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Digital Prediction of the Purchase Price of Fresh Tea Leaves of Enshi Yulu Based on Near-Infrared Spectroscopy Combined with Multivariate Analysis

文献类型： 外文期刊

作者： Wang, Shengpeng 1 ; Feng, Lin 1 ; Liu, Panpan 1 ; Gui, Anhui 1 ; Teng, Jing 1 ; Ye, Fei 1 ; Wang, Xueping 1 ; Xue, Jinjin 1 ; Gao, Shiwei 1 ; Zheng, Pengcheng 1 ;

作者机构： 1.Hubei Acad Agr Sci, Inst Fruit & Tea, Key Lab Tea Resources Comprehens Utilizat, Minist Agr & Rural Affairs, Wuhan 430064, Peoples R China

2.Hubei Tea Engn & Technol Res Ctr, Wuhan 430064, Peoples R China

关键词： fresh tea leaves; price; near-infrared spectroscopy; synergy interval partial least squares; back propagation-artificial neural network; transfer function

期刊名称：FOODS （ 2022影响因子：5.2； 五年影响因子：5.5 ）

ISSN：

年卷期： 2023 年 12 卷 19 期

页码：

收录情况： SCI

摘要： In this study, near-infrared spectroscopy (NIRS) combined with a variety of chemometrics methods was used to establish a fast and non-destructive prediction model for the purchase price of fresh tea leaves. Firstly, a paired t-test was conducted on the quality index (QI) of seven quality grade fresh tea samples, all of which showed statistical significance (p < 0.05). Further, there was a good linear relationship between the QI, quality grades, and purchase price of fresh tea samples, with the determination coefficient being greater than 0.99. Then, the original near-infrared spectra of fresh tea samples were obtained and preprocessed, with the combination (standard normal variable (SNV) + second derivative (SD)) as the optimal preprocessing method. Four spectral intervals closely related to fresh tea prices were screened using the synergy interval partial least squares (si-PLS), namely 4377.62 cm-1-4751.74 cm-1, 4755.63 cm-1-5129.75 cm-1, 6262.70 cm-1-6633.93 cm-1, and 7386 cm-1-7756.32 cm-1, respectively. The genetic algorithm (GA) was applied to accurately extract 70 and 33 feature spectral data points from the whole denoised spectral data (DSD) and the four characteristic spectral intervals data (FSD), respectively. Principal component analysis (PCA) was applied, respectively, on the data points selected, and the cumulative contribution rates of the first three PCs were 99.856% and 99.852%. Finally, the back propagation artificial neural (BP-ANN) model with a 3-5-1 structure was calibrated with the first three PCs. When the transfer function was logistic, the best results were obtained (Rp2 = 0.985, RMSEP = 6.732 RMB/kg) by 33 feature spectral data points. The detection effect of the best BP-ANN model by 14 external samples were R2 = 0.987 and RMSEP = 6.670 RMB/kg. The results of this study have achieved real-time, non-destructive, and accurate evaluation and digital display of purchase prices of fresh tea samples by using NIRS technology.

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