Abstract This study reports method for rapid and nondestructive identification of single-grain rice produced in neighboring areas. Four types of representative rice from different areas were selected as experimental samples. A total of 284 single-grain rice Raman spectra were acquired and spectral information in the 400–1700 cm−1 spectral area was extracted for analysis. First, the samples were divided into 190 calibration sets and 94 validation sets by the Kennard–Stone method, and the raw spectra were pretreated using smoothing and differential methods. Next, principal component analysis (PCA) and a successive projections algorithm (SPA) were used to extract the optimal principal components and effective wavelengths, which were used as the input variables for k-NearestNeighbor (KNN) and least-squares support vector machine (LS-SVM) algorithms. The PCA-KNN,SPA-KNN,PCA-LS-SVM and SPA-LS-SVM models were established based on information from the rice spectra. Finally, the model was applied to classify the validation set samples. The recognition accuracies of the PCA-KNN,SPA-KNN, PCA-LS-SVM and SPA-LS-SVM models for the validation set are 91.43 %,93.62 %,91.49 and 94.68 %, respectively. These results indicate that single-grain rice from different producing areas can be identified by using Raman spectroscopy combined with KNN and LS-SVM. This method can achieve rapid and completely non-destructive testing.

中文翻译：

---

基于拉曼光谱和支持向量机的水稻产区快速无损检测方法

摘要 本研究报告了对邻近地区生产的单粒稻进行快速无损识别的方法。选取了来自不同地区的四种代表性水稻作为试验样品。共采集了 284 个单粒大米拉曼光谱，并提取了 400-1700 cm-1 光谱区域的光谱信息进行分析。首先，通过 Kennard-Stone 方法将样品分为 190 个校准集和 94 个验证集，并使用平滑和差分方法对原始光谱进行预处理。接下来，使用主成分分析 (PCA) 和逐次投影算法 (SPA) 提取最佳主成分和有效波长，将其用作 k-NearestNeighbor (KNN) 和最小二乘支持向量机 ( LS-SVM）算法。基于水稻光谱信息建立了PCA-KNN、SPA-KNN、PCA-LS-SVM和SPA-LS-SVM模型。最后，应用该模型对验证集样本进行分类。PCA-KNN、SPA-KNN、PCA-LS-SVM和SPA-LS-SVM模型对验证集的识别准确率分别为91.43%、93.62%、91.49和94.68%。这些结果表明，使用拉曼光谱结合 KNN 和 LS-SVM 可以识别来自不同产区的单粒稻。这种方法可以实现快速、完全无损的检测。验证集的 PCA-LS-SVM 和 SPA-LS-SVM 模型分别为 91.43%、93.62%、91.49 和 94.68%。这些结果表明，使用拉曼光谱结合 KNN 和 LS-SVM 可以识别来自不同产区的单粒稻。这种方法可以实现快速、完全无损的检测。验证集的 PCA-LS-SVM 和 SPA-LS-SVM 模型分别为 91.43%、93.62%、91.49 和 94.68%。这些结果表明，使用拉曼光谱结合 KNN 和 LS-SVM 可以识别来自不同产区的单粒稻。这种方法可以实现快速、完全无损的检测。