COMPREHENSIVE ANALYSIS AND CLASSIFICATION OF SIGNALS IN MILK PASTEURIZATION PROCESS USING MACHINE LEARNING ALGORITHMS

Abstract

This article investigates the classification and quality evaluation of process signals in milk pasteurization using machine learning methods. The temperature signal, which is vital for assessing the pasteurization process efficiency, is analyzed in detail. Through statistical feature extraction and intelligent classification, the quality of signals is determined. Algorithms such as Random Forest and Autoencoder are applied for classification and anomaly detection, respectively. Results demonstrate that combining both approaches improves robustness and reliability. The method is implemented using Python and tested with Raspberry Pi sensor inputs, simulating real-time industrial conditions.