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Volume 30
Issue 17
10.3390/molecules30173565
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Novel Pattern Recognition Method for Non-Destructive and Accurate Origin Identification of Food and Medicine Homologous Substances with Portable Near-Infrared Spectroscopy

by
Wei Liu
1,
Ziqin Zhang
1,
Yang Liu
1,
Liwen Jiang
1,
Pao Li
1,2,* and
Wei Fan
1,*
1
College of Food Science and Technology, Hunan Agricultural University, Changsha 410128, China
2
Guangdong Provincial Key Laboratory of Utilization and Conservation of Food and Medicinal Resources in Northern Region, Shaoguan University, Shaoguan 512005, China
*
Authors to whom correspondence should be addressed.
Molecules 2025, 30(17), 3565; https://doi.org/10.3390/molecules30173565 (registering DOI)
Submission received: 12 August 2025 / Revised: 27 August 2025 / Accepted: 29 August 2025 / Published: 30 August 2025
(This article belongs to the Special Issue Application of Spectroscopy for Drugs)
Versions Notes

Abstract

In this study, a novel pattern recognition method named boosting–partial least squares–discriminant analysis (Boosting-PLS-DA) was developed for the non-destructive and accurate origin identification of food and medicine homologous substances (FMHSs). Taking Gastrodia elata, Aurantii Fructus Immaturus, and Angelica dahurica as examples, spectra of FMHSs from different origins were obtained by portable near-infrared (NIR) spectroscopy without destroying the samples. The identification models were developed with Boosting-PLS-DA, compared with principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA) models. The model performances were evaluated using the validation set and an external validation set obtained one month later. The results showed that the Boosting-PLS-DA method can obtain the best results. For the analysis of Aurantii Fructus Immaturus and Angelica dahurica, 100% accuracies of the validation sets and external validation sets were obtained using Boosting-PLS-DA models. For the analysis of Gastrodia elata, Boosting-PLS-DA models showed significant improvements in external validation set accuracies compared to PLS-DA, reducing the risk of overfitting. Boosting-PLS-DA method combines the high robustness of ensemble learning with the strong discriminative capability of discriminant analysis. The generalizability will be further validated with a sufficiently large external validation set and more types of FMHSs.
Keywords: food and medicine homologous substances; origin identification; near-infrared spectroscopy; boosting–partial least squares–discriminant analysis; partial least squares–discriminant analysis food and medicine homologous substances; origin identification; near-infrared spectroscopy; boosting–partial least squares–discriminant analysis; partial least squares–discriminant analysis

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MDPI and ACS Style

Liu, W.; Zhang, Z.; Liu, Y.; Jiang, L.; Li, P.; Fan, W. A Novel Pattern Recognition Method for Non-Destructive and Accurate Origin Identification of Food and Medicine Homologous Substances with Portable Near-Infrared Spectroscopy. Molecules 2025, 30, 3565. https://doi.org/10.3390/molecules30173565

AMA Style

Liu W, Zhang Z, Liu Y, Jiang L, Li P, Fan W. A Novel Pattern Recognition Method for Non-Destructive and Accurate Origin Identification of Food and Medicine Homologous Substances with Portable Near-Infrared Spectroscopy. Molecules. 2025; 30(17):3565. https://doi.org/10.3390/molecules30173565

Chicago/Turabian Style

Liu, Wei, Ziqin Zhang, Yang Liu, Liwen Jiang, Pao Li, and Wei Fan. 2025. "A Novel Pattern Recognition Method for Non-Destructive and Accurate Origin Identification of Food and Medicine Homologous Substances with Portable Near-Infrared Spectroscopy" Molecules 30, no. 17: 3565. https://doi.org/10.3390/molecules30173565

APA Style

Liu, W., Zhang, Z., Liu, Y., Jiang, L., Li, P., & Fan, W. (2025). A Novel Pattern Recognition Method for Non-Destructive and Accurate Origin Identification of Food and Medicine Homologous Substances with Portable Near-Infrared Spectroscopy. Molecules, 30(17), 3565. https://doi.org/10.3390/molecules30173565

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