Visible and Hyperspectral Imaging for Quality Assessment of Milk: Property Characterisation and Identification

TL;DR

This study demonstrates that visible and hyperspectral imaging combined with machine learning can rapidly and non-invasively assess milk quality, accurately predicting biochemical properties and distinguishing storage and treatment conditions.

Contribution

The paper introduces a comprehensive imaging and machine learning framework for non-destructive milk quality assessment, achieving high accuracy in property prediction and classification.

Findings

Visible imaging distinguished fresh and stored milk with 100% accuracy.

Hyperspectral imaging classified fatty acids with over 95% accuracy.

Imaging-based models accurately predicted biochemical properties like polyphenols.

Abstract

Rapid and non-destructive assessment of milk quality is crucial to ensuring both nutritional value and food safety. In this study, we investigated the potential of visible and hyperspectral imaging as cost-effective and quick-response alternatives to conventional chemical analyses for characterizing key properties of cow\'s milk. A total of 52 milk samples were analysed to determine their biochemical composition (polyphenols, antioxidant capacity, and fatty acids) using spectrophotometer methods and standard gas-liquid and high-performance liquid chromatography (GLC/HPLC). Concurrently, visible (RGB) images were captured using a standard smartphone, and hyperspectral data were acquired in the near-infrared range. A comprehensive analytical framework, including eleven different machine learning algorithms, was employed to correlate imaging features with biochemical measurements. Analysis of visible images accurately distinguished between fresh samples and those stored for 12 days (100 percent accuracy) and achieved perfect discrimination between antibiotic-treated and untreated groups (100 percent accuracy). Moreover, image-derived features enabled perfect prediction of the polyphenols content and the antioxidant capacity using an XGBoost model. Hyperspectral imaging further achieved classification accuracies exceeding 95 percent for several individual fatty acids and 94.8 percent for treatment groups using a Random Forest model. These findings demonstrate that both visible and hyperspectral imaging, when coupled with machine learning, are powerful, non-invasive tools for the rapid assessment of milk\'s chemical and nutritional profiles, highlighting the strong potential of imaging-based approaches for milk quality assessment.