Rapid and nondestructive identification of rice storage year using hyperspectral technology

文献类型： 外文期刊

第一作者： Sun, Xiaorong

作者： Sun, Xiaorong;Zhou, Xinpeng;Liu, Cuiling;Zhang, Shanzhe;Zheng, Dongyu;Liu, Cuiling;Li, Chunlin;Sun, Xiaorong;Zhou, Xinpeng;Liu, Cuiling;Zhang, Shanzhe;Zheng, Dongyu

作者机构：

关键词： Rice; Hyperspectral imaging; Feature dimensionality reduction; Storage; WOA-SVM; Data fusion

期刊名称：FOOD CONTROL （ 影响因子：6.3； 五年影响因子：6.1 ）

ISSN： 0956-7135

年卷期： 2025 年 168 卷

页码：

收录情况： SCI

摘要： Rice is the main staple food for more than half of the world's population. Consumption of long-stored rice will have adverse effects on the human body. Here, we proposed the near-infrared (NIR) hyperspectral imaging (HSI) technique to distinguish rice from different storage years. Multiplicative Scatter Correction (MSC), Standard Normalize Variate (SNV) and 1st Derivative (1st) were used for the pretreatment of HSI data. In order to reduce dimensional spectral features, Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (tSNE) were used for data visualization. Besides, spectral characteristic wavelengths were extracted by competitive adaptive reweighted sampling (CARS) and least absolute shrinkage and selection operator (Lasso). Simultaneously, the textural features of rice were analyzed by Local Binary Pattern (LBP), Gray-Level Co- occurrence Matrix (GLCM) and Tamura algorithms. In order to realize feature fusion of spectra and texture, Support Vector Machine (SVM) model was optimized using the Whale optimization algorithm (WOA) and Extreme Gradient Boosting (XGBoost) model were established based on spectral features and textural features. Compared to other models, feature fusion model showed an excellent result with an accuracy of 98.89%. Experimental results suggested that HSI technology can be served as an effective method for rice detection.

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