[目的]本研究旨在探索利用微型近红外光谱技术检测特色乳中掺假荷斯坦牛乳的可行性。[方法]通过微型透射型和反射型近红外光谱仪获取了骆驼乳、牦牛乳、水牛乳、羊乳及其掺假荷斯坦牛乳样品的光谱数据,并采用偏最小二乘回归、支持向量机和线性判别分析分别建立了定量和定性模型。[结果]定量模型展现出良好的预测能力,透射型预测模型决定系数为0.78~0.99,均方根误差为2.31~13.68,而反射型预测模型决定系数为0.89~0.98,均方根误差为4.68~9.77。在定性分析中,支持向量机模型的分类性能优于线性判别分析模型,其中透射型光谱的支持向量机模型分类准确率为90.91%~100.00%,反射型光谱的支持向量机分类准确率为80.00%~100.00%。[结论]本研究表明,微型近红外光谱仪作为一种快速、便捷的检测工具,具有显著的应用潜力,可实现乳品掺假的现场化和实时化检测,为乳品质量控制和市场监管提供了有力的技术支持。
[Objective] This study explored using miniaturized near-infrared （NIR） spectroscopy to detect Holstein milk adulteration in specialty milks. [Method] Spectral data of camel,yak,buffalo,goat milk,and their adulterated samples with Holstein milk were collected via miniaturized transmissive and reflective NIR spectrometers. Then,quantitative models were built with partial least squares regression,while qualitative models were developed using support vector machine （SVM） and linear discriminant analysis （LDA）. [Result] Quantitative models showed good predictive ability. Transmissive predictive models achieved a coefficient of determination（R²） of 0.78~0.99 and a root mean square error （RMSE） of 2.31~13.68,while reflective ones got 0.89~0.98 and 4.68~9.77,respectively. In qualitative analysis,SVM outperformed LDA in classification. Transmissive-spectrum SVM reached 90.91%~100.00% accuracy,and reflective-spectrum SVM 80.00%~100.00%. [Conclusion] This study indicated that miniaturized NIR spectrometers,as fast and convenient detection tools,hold great application potential. The devices enable on-site and real-time detection of milk adulteration,offering strong technical support for dairy quality control and market regulation.
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