Targeted iPLS for the prediction of cheese-making traits from individual milk spectra

This study investigates the potential of the Targeted interval Partial Least Squares (iPLS) models for predicting cheese-making traits from individual milk mid-infrared spectra through high-resolution Fourier-transform infrared spectroscopy. Traditional full-spectrum PLS regression models demonstrated limited predictive ability for all traits, with R 2 for validation (R 2 VAL ) ranging from 0.00 to 0.15, largely due to spectral redundancy and multicollinearity. In contrast, the Targeted iPLS method markedly improved prediction accuracy. The three %CY traits achieved R 2 VAL ranging from 0.59 to 0.91, and %REC reached up to 0.87, with optimal prediction requiring selection of specific spectral regions related to protein, fat, and lactose, and exclusion of water regions. Tailored pre-treatment methods further enhanced trait-specific model performance. These findings underscore the importance of spectral region selection and pre-treatment customization in maximizing predictive performance. The Targeted iPLS framework offers a practical and effective tool for real-time monitoring and optimization in dairy processing. • Targeted iPLS enhances prediction accuracy through focused spectral selection. • Spectral regions for protein, fat, and lactose enhance prediction accuracy of cheese-making traits. • Optimized spectral combinations reduce model complexity without compromising accuracy.