Visible and Hyperspectral Imaging for Quality Assessment of Milk: Property Characterisation and Identification
arXiv:2602.12313v1 Announce Type: cross 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 betwee
arXiv:2602.12313v1 Announce Type: cross 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.
Executive Summary
The study explores the use of visible and hyperspectral imaging, coupled with machine learning, for rapid and non-destructive assessment of milk quality. It demonstrates high accuracy in distinguishing between fresh and stored milk, antibiotic-treated and untreated samples, and predicting biochemical properties such as polyphenols and antioxidant capacity. The research highlights the potential of imaging technologies as cost-effective and quick-response alternatives to conventional chemical analyses, offering significant advancements in food safety and nutritional assessment.
Key Points
- ▸ Visible and hyperspectral imaging can accurately assess milk quality.
- ▸ Machine learning models achieve high accuracy in predicting milk properties.
- ▸ Imaging technologies offer a non-invasive and rapid assessment method.
Merits
High Accuracy
The study achieves perfect discrimination between fresh and stored milk, as well as antibiotic-treated and untreated samples, demonstrating the reliability of the imaging methods.
Non-Destructive and Rapid
The use of visible and hyperspectral imaging provides a non-destructive and rapid assessment of milk quality, which is crucial for food safety and nutritional value.
Cost-Effective
The methods proposed are cost-effective compared to conventional chemical analyses, making them accessible for widespread use.
Demerits
Sample Size
The study uses a relatively small sample size of 52 milk samples, which may limit the generalizability of the findings.
Technical Complexity
The implementation of hyperspectral imaging and machine learning models requires specialized equipment and expertise, which may be a barrier for some applications.
Limited Scope
The study focuses on specific biochemical properties and does not cover a comprehensive range of milk quality parameters.
Expert Commentary
The study presents a compelling case for the use of visible and hyperspectral imaging in the assessment of milk quality. The high accuracy achieved in distinguishing between different milk samples and predicting biochemical properties underscores the potential of these technologies. However, the relatively small sample size and the technical complexity of the methods warrant further investigation. The study's findings have significant implications for food safety and regulatory standards, highlighting the need for continued research and development in this area. The integration of imaging technologies and machine learning models represents a promising advancement in the field of food quality assessment, offering a non-invasive and rapid alternative to conventional chemical analyses.
Recommendations
- ✓ Conduct further studies with larger and more diverse sample sizes to validate the findings.
- ✓ Explore the integration of these imaging technologies into existing food safety protocols to enhance their practical applicability.
