Food safety and quality are paramount concerns worldwide, especially concerning nutritional quality and its impact on human health. Ensuring the accuracy and efficiency of milk quality assessment is vital for maintaining the quality of dairy farm produce. Milk spectral data, Mid-infrared spectra (MIRS) of milk samples, are frequently employed for milk quality evaluations, encompassing various milk quality parameters. However, conventional milk quality analyses have overlooked the scaling nature, known as stochastic similarity in different scales, inherent in milk spectral data. Wavelet transforms are among the tools used in these analyses, although they are primarily used as data pre-processing techniques without fully realizing their potential in extracting valuable insights. The primary purpose of this study is to demonstrate the importance of accounting for scaling properties in assessing milk quality. A set of 12 descriptors is computed to characterize scaling properties in milk spectral data within the wavelet domain. These descriptors are then assessed for their effectiveness in milk quality assessments utilizing 18 different milk quality parameters. They notably demonstrated comparable performance to existing methods while utilizing fewer features when applied to an MIRS dataset. This innovative approach holds substantial promise for advancing the field of milk quality assessment, offering a means to achieve more accurate and efficient evaluations while shedding light on previously unexplored aspects of milk spectral data.

翻译：食品安全与质量是全球关注的核心问题，尤其关乎营养品质及其对人类健康的影响。确保牛奶质量评估的准确性和效率，对于维持乳制品生产质量至关重要。牛奶光谱数据，即牛奶样本的中红外光谱，常被用于牛奶质量评估，涵盖多种牛奶质量参数。然而，传统牛奶质量分析忽略了牛奶光谱数据中固有的尺度特性，即不同尺度下的随机相似性。小波变换是此类分析中使用的工具之一，但主要被用作数据预处理技术，未能充分发挥其提取有价值信息的潜力。本研究的主要目的在于证明在牛奶质量评估中考虑尺度特性的重要性。我们通过计算12个描述符来表征牛奶光谱数据在小波域中的尺度特性，并利用这些描述符评估其在18种不同牛奶质量参数下的有效性。值得注意的是，在应用于中红外光谱数据集时，这些描述符在利用更少特征的情况下，展现出与现有方法相当的性能。这一创新方法为推进牛奶质量评估领域提供了重要潜力，不仅能够实现更准确、高效的评估，还能揭示牛奶光谱数据以往未被探索的方面。