No abstract

No abstract

No abstract

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 • 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.

The important quality parameters of cow’s milk for barista applications are frothability and foam stability. In the past, quality assessment was very time-consuming and could only be carried out after milk treatment had been completed. Since spectroscopy is already established in dairies, it could be advantageous to develop a spectrometer-based measurement method for quality control for barista applications. By integrating online spectroscopy to the processing of UHT (ultra-high temperature processing) milk before filling, it can be checked whether the currently processed product is suitable for barista applications. To test this hypothesis, a feasibility study was conducted. For this purpose, seasonal UHT whole milk samples were measured every 2 months over a period of more than 1 year, resulting in a total of 269 milk samples that were foamed. Samples were frothed using a self-designed laboratory frother. Frothability at the beginning and foam loss after 15 min describe the frothing characteristics of the milk and are predicted from the spectra. Near-infrared, Raman, and fluorescence spectra were recorded from each milk sample. These spectra were preprocessed using 15 different mathematical methods. For each spectrometer, 85% of the resulting spectral dataset was analyzed using partial least squares (PLS) regression and nine different variable selection (VS) algorithms. Using the remaining 15% of the spectral dataset, a prediction error was determined for each model and used to compare the models. Using spectroscopy and PLS modeling, the best results show a prediction error for milk frothability of 3% and foam stability of 2%.