基于透射式多光谱成像技术对牛乳中尿素掺假的无损定量检测
Non-Destructive Quantification of Urea Adulteration in Bovine Milk Using Transmittance Multispectral Imaging
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中文总结 AI 辅助
本研究开发了基于透射式多光谱成像（MSI）的方法，结合多元线性回归和前馈神经网络，可在受控密度条件下无损定量检测牛乳中的尿素掺假，验证集R²分别达0.9599和0.9773，具备快速筛查潜力。
中文摘要 AI 辅助
牛乳中尿素掺假仍是食品质量与健康领域的重大问题，推动了快速定量筛查工具的开发。传统方法包括实验室分析方法和光谱技术，已用于尿素检测，但许多方法因需要专用仪器、样品制备、化学试剂或实验室操作，难以满足快速、低成本的常规筛查需求。本研究提出一种实用、低成本、准确且经实验室验证的基于多光谱成像（MSI）的方法，用于在受控密度条件下定量估算尿素，采用基于多光谱成像的回归框架。使用自行构建的多光谱成像系统（工作于12个离散光谱波段，365至940纳米）采集添加受控量尿素并加水调节密度的牛乳样品的多光谱图像。新鲜牛乳在图像采集当日获取，用比重计验证其在20℃时的比重为1.032。多元线性回归提供了初始映射，验证集决定系数R²达0.9599，而前馈神经网络进一步提升了预测性能，验证集R²达0.9773。这些结果表明，在受控密度平衡条件下，透射式多光谱成像技术可实现准确、无损的尿素定量检测，支持其作为牛乳质量评估快速筛查方法的潜力。
英文摘要
Adulteration of bovine milk using urea remains a major food quality and health concern, motivating the development of rapid and quantitative screening tools. Conventional approaches, including laboratory-based analytical methods and spectroscopic techniques, have been used for urea detection; however, many remain less suitable for rapid, low-cost routine screening due to requirements such as specialized instrumentation, sample preparation, chemical reagents, or laboratory operation. This study introduces a pragmatic, cost-effective, accurate, and laboratory-validated MSI-based method for quantitative urea estimation under controlled density conditions using a multispectral-imaging-based regression framework. An in-house-built multispectral imaging system operating in twelve discrete spectral bands (365--940~nm) was used to acquire multispectral images of milk samples prepared with controlled urea addition and water for density balancing. Fresh milk was obtained on the day of image acquisition, and the specific gravity of the milk was verified to be 1.032 at 20°C using a hydrometer. Multiple linear regression provided an initial mapping with a high validation $R^2$ of 0.9599, while a feed-forward neural network further improved predictive performance with a validation $R^2$ of 0.9773. These results demonstrate the feasibility of transmittance multispectral imaging for accurate, non-destructive urea quantification under controlled density-balanced conditions, supporting its potential as a rapid screening approach for milk-quality assessment.
