Raman Spectroscopy Coupled with Multivariate Statistical Process Control for Detecting Anomalies During Milk Coagulation

This study explores the potential of Raman spectroscopy as a screening tool for fault detection in dairy processing, focusing its application on milk rennet coagulation. Multivariate Statistical Process Control techniques were employed to analyze spectral data collected under both nominal and failure conditions, with the aim of identifying deviations from normal operating conditions. Both global and local Principal Component Analysis-based algorithms were employed to detect two types of fault conditions, namely, rennet concentration and temperature control failures. High performance was obtained by each algorithm, reaching up to an accuracy of 99.8% and a minimum detection time of 7 min after rennet addition, which is earlier than the milk phase transition, meaning that the fault can be detected before it affects the product’s quality. The fault diagnosis revealed consistent fault-related Raman shifts, below 900 cm−1 and between 1100 and 1600 cm−1, suggesting that these spectral features may serve as reliable indicators of process failure sources. The results supported the reliability of Raman spectroscopy as a Process Analytical Technology tool for monitoring dairy processes.