English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Food safety and quality are paramount concerns worldwide, especiallyconcerning nutritional quality and its impact on human health. Ensuring theaccuracy and efficiency of milk quality assessment is vital for maintaining thequality 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 milkquality analyses have overlooked the scaling nature, known as stochasticsimilarity in different scales, inherent in milk spectral data. Wavelettransforms are among the tools used in these analyses, although they areprimarily used as data pre-processing techniques without fully realizing theirpotential in extracting valuable insights. The primary purpose of this study isto demonstrate the importance of accounting for scaling properties in assessingmilk quality. A set of 12 descriptors is computed to characterize scalingproperties in milk spectral data within the wavelet domain. These descriptorsare then assessed for their effectiveness in milk quality assessments utilizing18 different milk quality parameters. They notably demonstrated comparableperformance to existing methods while utilizing fewer features when applied toan MIRS dataset. This innovative approach holds substantial promise foradvancing the field of milk quality assessment, offering a means to achievemore accurate and efficient evaluations while shedding light on previouslyunexplored aspects of milk spectral data.

Utilizing Wavelet Transform in the Analysis of Scaling Dynamics for Milk Quality Evaluation