【摘要】 淀粉作为一种主要的储存碳水化合物，是人类饮食中的主要能量来源，提供了人体50%以上的能量需求。同时，淀粉及其深加工行业是关乎国计民生的基础产业。然而，鉴于淀粉种类的多样化，并且它们在外观上相似度较高，直接对它们进行区分比较困难，一些不法商家往往会将价格较低的淀粉包装成价格较高的淀粉来抬高价格。因此，对淀粉品种进行分类对我国食品加工和工业生产具有重要的实际应用意义。太赫兹(Terahertz, THz)技术作为一种高效的非破坏性、非接触和无标签的光学方法，在与物质作用时不会发生有害的电离辐射，可同时获得样品的吸收系数等光学参数，具有较高的信噪比和检测灵敏度，已被众多学者应用于农产品品质检测方面。为实现对淀粉品种的快速无损鉴别，从禾谷类淀粉与根茎类淀粉中选取了五种最为常见的淀粉样品作为样本，利用太赫兹时域光谱(THz-TDS)技术获取其光谱信息，并根据实验数据计算了不同品种淀粉在0.2～1.2 THz波段的吸收系数；之后结合Savitzky-Golay(S-G)平滑、多元散射校正(MSC)、标准正态变换(SNV)三种预处理方法对原始光谱进行处理。采用主成分分析(PCA)根据累计贡献率超过95%提取特征数据，选取了前3个主成分，随后应用支持向量机(SVM)方法建立多分类模型。选取了三种核函数(linear, radial basis functions, polynomial)对不同品种淀粉类型进行了识别。结果显示：PCA-SVM-polynomial结合SG平滑对淀粉品种分类建模效果最好，其中测试集平均准确率为0.941 9, Kappa系数为0.933, F1得分为0.941 7。此外，还将该方法与逻辑回归(LR)、决策树(DT)、随机森林(RF)进行了比较，研究结果表明PCA-SVM优于其他方法，也证明了太赫兹技术对淀粉品种鉴别的可行性，对食品加工业的现代化及淀粉基产品的开发具有重要的实际应用价值。更多还原

【Abstract】 As a major stored carbohydrate, starch is a major source of energy in the human diet and provides more than 50% of the energy needs of the human body. Meanwhile, the starch and its deep-processing industry are fundamental to the national economy and people’s livelihoods. However, due to the diversity of starch types and their high similarity in appearance, it is relatively challenging to distinguish amongthem directly. Some illegal merchants often package lower-priced starches as higher-priced starches to increase profits. Consequently, the classification of starch types has significant practical relevance for food processing and industrial production in China. Terahertz(THz) technology, as an effective non-destructive, non-contact, and label-free optical approach, does not produce harmful ionizing radiation during interactions with materials, and can obtain optical parameters such as the absorption coefficient of samples simultaneously. It has a high signal-to-noise ratio and detection sensitivity, and many scholars have applied it to the quality detection of agricultural products. Five of the most common starch samples were selected from cereal starch and rhizome starch to achieve rapid and non-destructive identification of starch. The spectral information was obtained using Terahertz time-domain spectroscopy(THz-TDS) technology, and the absorption coefficient of different starch varieties in the range of 0.2～1.2 THz was calculated based on the experimental data. Subsequently, the original spectra were processed using three preprocessing methods: Savitzky-Golay(S-G) smoothing, multiplicative scatter correction(MSC), and standard normal variate(SNV). Principal component analysis(PCA) was employed to extract feature data based on a cumulative contribution rate exceeding 95%, resulting in the selection of the first three principal components. A multi-classification model was established using the support vector machine(SVM) method. Three types of kernels(linear, polynomial, and radial basis functions) were selected to identify different varieties of starch. The results showed that the PCA-SVM-polynomial combined with SG smoothing achieved the best modeling performance for starch variety classification, with an average accuracy of 0.941 9 on the test set, a Kappa of 0.933, and an F1 score of 0.941 7. Furthermore, this method was compared with logistic regression(LR), decision tree(DT), and random forest(RF). The research results indicated that PCA-SVM was superior to other methods, proving the feasibility of THz technology for starch variety identification and demonstrating important practical application value for the modernization of the food processing industry and the development of starch-based products.更多还原