  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.
