一个低成本的图像直方图和机器学习方法来检测牛奶伪造
Madhav Kumar1, Rakesh Kumar1, Abir Chakravorty1
1Agricultural & Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.
Journal of food science
|January 23, 2026
概括
一种新的机器学习方法使用简单的牛奶图像来检测水和洗剂等常见的 adulterants. 这种负担得起的方法为在资源有限的环境中进行食品安全测试提供了实际解决方案.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 计算机科学 计算机科学
背景情况:
- 牛奶造对食品安全和公共卫生构成重大风险.
- 对于快速,廉价的检测方法的有限访问,阻碍了检测,特别是在某些地区.
- 传统的实验室分析通常是昂贵和耗时的.
研究的目的:
- 开发一种简单,负担得起和可访问的方法来检测常见的牛奶 adulterants.
- 用图像分析和机器学习来评估牛奶质量.
- 为昂贵的光谱技术提供实用的替代方案.
主要方法:
- 收集纯牛奶样本和用水,洗剂,粉和合成牛奶伪造的样本的数字图像.
- 提取图像统计数据,包括亮度,方差,斜率和曲率,以分析光散射和度.
- 使用主要组件分析 (PCA) 来减少数据的维度.
- 使用支持矢量机器 (SVM) 来分类伪造与纯牛奶样本.
主要成果:
- 开发的模型在识别各种类型和数量的改中达到约85%的准确性.
- 特定的伪造剂诱导了图像统计学中的独特变化:水增加了平均强度,而洗剂,粉和合成牛奶改变了斜度和形.
- 该方法在基于光学特性来区分纯牛奶和造牛奶方面表现出有效性.
结论:
- 图像分析与机器学习相结合,提供了一种可行的,经济的方法来检测牛奶造.
- 这种方法更快,不需要化学物质,而且比传统的光谱方法更容易获得.
- 该技术适用于在销售点或偏远地区进行快速质量控制检查.
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