Identification of Geographical Origin for Hawthorn Based on Hyperspectral Imaging Technology

LIU Zijian; GU Jiacheng; ZHOU Cong; WANG Youyou; YANG Jian; HUANG Jun; WANG Hongpeng; BAI Ruibin

doi:10.13386/j.issn1002-0306.2023090074

Volume 45 Issue 10

May 2024

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Science and Technology of Food Industry > 2024 > 45(10): 282-291. > DOI: 10.13386/j.issn1002-0306.2023090074

LIU Zijian, GU Jiacheng, ZHOU Cong, et al. Identification of Geographical Origin for Hawthorn Based on Hyperspectral Imaging Technology[J]. Science and Technology of Food Industry, 2024, 45(10): 282−291. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023090074.

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Identification of Geographical Origin for Hawthorn Based on Hyperspectral Imaging Technology

LIU Zijian^1,,
GU Jiacheng¹,
ZHOU Cong^{2, 3},
WANG Youyou²,
YANG Jian^{2, 3},
HUANG Jun¹,
WANG Hongpeng^1, ,,
BAI Ruibin^{2, 3, ,}

1.
School of Biological and Chemical Engineering, Zhejiang University of Science and Technology,Hangzhou 310023, China
2.
Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
3.
Evaluation and Research Center of Daodi Herbs of Jiangxi Province, Nanchang 330000, China

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Received Date: September 10, 2023
Available Online: March 12, 2024

Graphical Abstract

Abstract

Abstract

The geographical origin was one of the important factors affecting the quality of hawthorn. To discriminate the geographical origin of hawthorn rapidly and nondestructively, hawthorns from five different provincial production areas were used as samples, and visible-shortwave infrared (410~2500 nm) band hyperspectral data were obtained for the pedicel face (G), side (C), and bottom (D) of each sample by using a near-infrared hyperspectral imaging system. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and random forests (RF) classification models were built by multivariate scattering correction (MSC), first derivative (D1), SG smoothing (Savitzky-Golay, SG), and standard normal transform (SNV) four preprocessing methods. The results showed that the D-D1-SVM model discriminated optimally, with 100% accuracy in both the training and prediction sets. To simplify the model, successive projections algorithm (SPA) and competitive adaptive reweighted sampling algorithm (CARS) were applied to select feature wavelength. The multivariate data analysis revealed that the D-SPA-SVM model had the best performance, with an accuracy of 95.2% and 93% for the training and prediction sets, respectively. This study could provide technical support for rapid and non-destructive identification of hawthorn origin.
- hyperspectral imaging technology,
- hawthorn,
- origin identification,
- nondestructive testing,
- machine learning

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References (33)

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