Non-destructive detection of milk nutritional components based on hyperspectral imaging.

As consumers increasingly prioritize food safety and nutritional value, the dairy industry faces a pressing need for rapid and accurate methods to detect essential nutritional components in milk, such as fat, protein, and lactose. Hyperspectral imaging (HSI) technology, known for its non-destructive, fast, and precise nature, shows great promise in food quality assessment. However, the high dimensionality of HSI data poses challenges for effective band selection and model optimization. Additionally, prior studies primarily focus on predicting single nutritional components without addressing simultaneous multi-component detection. To overcome these challenges, this study presents a comprehensive approach that integrates moving average smoothing and first derivative (MA-FD) preprocessing, the improved coati optimization algorithm (ICOA), and the CatBoost model for multi-target regression. ICOA incorporates the good point set strategy, dynamic opposition-based learning, and the golden sine algorithm, which significantly enhance its global search capability and convergence speed in band selection. Combined with CatBoost's multi-target prediction capability, this method enables accurate detection of fat, protein, and lactose levels in milk. Experimental results demonstrate high prediction accuracy, with the calibration set achieving an multi-target coefficient of determination (MultiR) of 0.9992 and multi-target root mean square error (MultiRMSE) of 0.0240, while the prediction set yielded an MultiR of 0.9797 and MultiRMSE of 0.1181. Prediction set R values for fat, protein, and lactose were 0.9658, 0.9910, and 0.9825, respectively. The proposed method demonstrates robust predictive accuracy and reliability in milk quality assessment, and its potential for application in broader food quality assessments is substantial. PRACTICAL APPLICATION: This study provides a rapid, non-destructive method for assessing milk quality by detecting key nutritional components through hyperspectral imaging, combined with MA-FD preprocessing, ICOA for band selection, and CatBoost for multi-target regression. This approach offers the dairy industry a reliable, non-invasive solution that supports quality control and helps safeguard consumer health.