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Evaluation of Gualouzi quality using hyperspectral imaging technology and explainable artificial intelligence

文献类型： 外文期刊

作者： Xiao, Dan 1 ; Bai, Ruibin 1 ; Wang, Siman 1 ; Wang, Youyou 1 ; Hao, Qingxiu 1 ; Dong, Ling 2 ; Li, Weiwen 2 ; Yang, Jian 1 ;

作者机构： 1.China Acad Chinese Med Sci, Natl Resource Ctr Chinese Mat Med, State Key Lab Qual Ensurance & Sustainable Use Dao, Beijing 100700, Peoples R China

2.Anhui Acad Agr Sci, Inst Hort, Key Lab Hort Crop Germplasm Innovat & Utilizat Coc, Hefei 230001, Peoples R China

3.Res Ctr Qual Evaluat Dao di Herbs, Ganjiang 330000, Peoples R China

关键词： Explainable artificial intelligence; Food quality; Gualouzi; Hyperspectral imaging; Shapley additive explanations

期刊名称：LWT-FOOD SCIENCE AND TECHNOLOGY （ 影响因子：6.6； 五年影响因子：6.9 ）

ISSN： 0023-6438

年卷期： 2025 年 231 卷

页码：

收录情况： SCI

摘要： The adulteration of aged and fresh Gualouzi (GLZ) poses risks to product quality and consumer health. This study aimed to develop a rapid, nondestructive, and high-throughput method for evaluating GLZ quality by integrating hyperspectral imaging (HSI) with explainable artificial intelligence (XAI). First, spectral data were collected from two sets of samples, Wanlou No. 20 (GLZ-20) and Wanlou No. 9 (GLZ-9), stored for varying durations. The preprocessed full-wavelength spectral data were used to construct classification models. Shapley additive explanations (SHAP) was then applied to the models for global interpretability analysis to select feature wavelengths. The results demonstrated that SHAP-based feature selection outperformed conventional methods. Models using the top 16 and 23 SHAP-selected wavelengths achieved prediction accuracies of 97.2 % and 98.3 % for GLZ-20 and GLZ-9, respectively, in the prediction sets. In validation set I, the accuracies reached 97.7 % and 98.9 %. Both varieties showed prediction errors below 3.3 % in validation set II. Furthermore, SHAP-based model interpretation revealed the intrinsic relationship between feature wavelengths and sample characteristics, thereby enhancing the reliability of discrimination results. This study confirms the feasibility of integrating HSI and XAI for GLZ quality evaluation and provides a reference framework for assessing seed-type nuts.

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