Rapid evaluation of Ziziphi Spinosae Semen and its adulterants based on the combination of FT-NIR and multivariate algorithms

Ming-Xuan Li; Ya-Bo Shi; Jiu-Ba Zhang; Xin Wan; Jun Fang; Yi Wu; Rao Fu; Yu Li; Lin Li; Lian-Lin Su; De Ji; Tu-Lin Lu; Zhen-Hua Bian

doi:10.1016/j.fochx.2023.101022

Rapid evaluation of Ziziphi Spinosae Semen and its adulterants based on the combination of FT-NIR and multivariate algorithms

Food Chem X. 2023 Nov 20:20:101022. doi: 10.1016/j.fochx.2023.101022. eCollection 2023 Dec 30.

Authors

Ming-Xuan Li¹, Ya-Bo Shi¹, Jiu-Ba Zhang¹, Xin Wan¹, Jun Fang¹, Yi Wu¹, Rao Fu¹, Yu Li¹, Lin Li¹, Lian-Lin Su¹, De Ji¹, Tu-Lin Lu¹, Zhen-Hua Bian^{2

3}

Affiliations

¹ College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
² Department of Pharmacy, Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi, 214071, China.
³ Jiangsu CM Clinical Innovation Center of Degenerative Bone & Joint Disease, Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi, 214071, China.

Abstract

Ziziphi Spinosae Semen (ZSS) is a valued seed renowned for its sedative and sleep-enhancing properties. However, the price increase has been accompanied by adulteration. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) combined with multivariate algorithms were employed to identify the adulteration and quantitatively predict the adulteration ratio. The findings suggested that the utilization of chromaticity extractor was insufficient for identification of adulteration ratio. The raw spectrum of ZMS and HAS adulterants extracted by FT-NIR was processed by SNV + CARS and 1d + SG + ICO respectively, the average accuracy of machine learning classification model was improved from 77.06 % to 97.58 %. Furthermore, the R² values of the calibration and prediction set of the two quantitative prediction regression models of adulteration ratio are greater than 0.99, demonstrating excellent linearity and predictive accuracy. Overall, this study demonstrated that FT-NIR combined with multivariate algorithms provided a significant approach to addressing the growing issue of ZSS adulteration.

Keywords: Adulteration; Chromaticity; FT-NIR; Machine learning algorithm; Partial least squares regression model; Spectra preprocessing; Wavelength selection; Ziziphi Spinosae Semen.