Comparison of various classification techniques for supervision of milk processing

Detecting the types of anomalies that can occur throughout the milk processing process is an important task since it can assist providers in maintaining control over the process. The Raman spectrometer was used in conjunction with several classification approaches-linear discriminant analysis, decision tree, support vector machine, and k nearest neighbor-to establish a viable method for detecting different types of anomalies that may occur during the process-temperature and fat variation and added water or cleaning solution. Milk with 5% fat measured at 10 degrees C was used as the reference milk for this study. Added water, cleaning solution, milk with various fat contents and different temperatures were used to detect abnormal conditions. While decision trees and linear discriminant analysis were unable to accurately categorize the various type of anomalies, the k nearest neighbor and support vector machine provided promising results. The accuracy of the support vector machine test set and the k nearest neighbor test set were 81.4% and 84.8%, respectively. As a result, it is reasonable to conclude that both algorithms are capable of appropriately classifying the various groups of samples. It can assist milk industries in determining what is wrong during milk processing.

Automated summary

The detection of anomalies in the milk processing process is crucial for maintaining control over the process, and the Raman spectrometer combined with various classification approaches proved successful in identifying anomalies such as temperature and fat variations, added water, and cleaning solution. Support vector machine and k nearest neighbor algorithms showed promising results with accuracy rates of 81.4% and 84.8% respectively, indicating their capability in appropriately classifying different groups of samples in the milk processing industry.