用多光谱成像无损检测牛奶中尿素含量，快速准确。
Non-Destructive Quantification of Urea Adulteration in Bovine Milk Using Transmittance Multispectral Imaging
- 基于12个波段的透射多光谱成像，结合回归模型定量分析
- 验证集决定系数达0.9773，检测精度高
- 无需试剂、适合现场筛查，适合食品质检人员
使用尿素对牛乳进行掺假仍是食品安全的重大隐患，亟需快速、定量的筛查工具。传统方法如实验室分析和光谱技术虽已应用，但因需专用设备、样品前处理或化学试剂，难以实现低成本、快速的日常筛查。本研究提出一种实用、低成本、高精度且经实验室验证的多光谱成像（MSI）方法，在密度平衡条件下实现尿素定量。自建多光谱成像系统在365–940 nm范围内采集12个离散波段图像，样本通过添加尿素与水调节密度，新鲜牛奶在20℃时比重为1.032（用乳稠计验证）。多元线性回归初始建模验证$R^2$达0.9599，前馈神经网络进一步提升至0.9773。结果表明，在控制密度条件下，透射多光谱成像可实现精准、无损的尿素定量，具备作为牛奶质量快速筛查技术的潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。