用红外光谱加机器学习，93%准确率识别椰奶是否被掺假。
Detection of Adulteration in Coconut Milk using Infrared Spectroscopy and Machine Learning
- 先去噪，再用LDA提取关键特征，最后用KNN分类
- 在公开数据集上交叉验证准确率达93.33%
- 适合食品检测、质量控制领域研究人员参考
本文提出一种基于红外光谱与机器学习的椰奶掺假检测系统，包含三个阶段：预处理、特征提取与分类。第一阶段去除椰奶光谱信号中的无关数据；第二阶段采用线性判别分析（LDA）提取最具区分性的特征；第三阶段使用K近邻（KNN）模型将样本分类为真品或掺假品。系统在包含纯椰奶与受污染椰奶傅里叶变换红外（FTIR）光谱数据的公开数据集上进行评估，结果显示该方法在交叉验证中准确率达到93.33%。
原文摘要 · Abstract (English)
In this paper, we propose a system for detecting adulteration in coconut milk, utilizing infrared spectroscopy. The machine learning-based proposed system comprises three phases: preprocessing, feature extraction, and classification. The first phase involves removing irrelevant data from coconut milk spectral signals. In the second phase, we employ the Linear Discriminant Analysis (LDA) algorithm for extracting the most discriminating features. In the third phase, we use the K-Nearest Neighbor (KNN) model to classify coconut milk samples into authentic or adulterated. We evaluate the performance of the proposed system using a public dataset comprising Fourier Transform Infrared (FTIR) spectral information of pure and contaminated coconut milk samples. Findings show that the proposed method successfully detects adulteration with a cross-validation accuracy of 93.33%.
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