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

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

doi:10.3390/foods12193592

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

Foods. 2023 Sep 27;12(19):3592. doi: 10.3390/foods12193592.

Authors

Shengpeng Wang^{1

2}, Lin Feng^{1

2}, Panpan Liu^{1

2}, Anhui Gui^{1

2}, Jing Teng^{1

2}, Fei Ye^{1

2}, Xueping Wang^{1

2}, Jinjin Xue^{1

2}, Shiwei Gao^{1

2}, Pengcheng Zheng^{1

2}

Affiliations

¹ Key Laboratory of Tea Resources Comprehensive Utilization, Ministry of Agriculture and Rural Affairs, Institute of Fruit and Tea, Hubei Academy of Agricultural Sciences, Wuhan 430064, China.
² Hubei Tea Engineering and Technology Research Centre, Wuhan 430064, China.

Abstract

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 (R_p² = 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.

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

Abstract

Grants and funding