查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of Goa, India, by NewsRx editors, research stated, “Milk adulteration is a significant problem globally, as it is the most widely consumed and essential food product. Due to this, monit oring milk quality is necessary for sustaining human health.” Our news journalists obtained a quote from the research from Goa University, “A Machine Learning (ML) based non-destructive system was developed to identify wat er adulteration in milk using Near Infrared (NIR) Spectroscopy. A database was c reated by mixing water in milk in varying proportions (0 - 40 %) an d capturing spectra using compact TI DLP NIR scan Nano spectroscopy in the 900 - 1700 nm range. The captured spectra were preprocessed with the Savitzky-Golay ( SG) filter, Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV) method. The most informative wavelength points were selected using the wa velength/feature selection technique, and the dimensions of these wavelengths we re reduced using Principal Component Analysis (PCA). Various ML models were empl oyed to predict the water concentration in milk. Both classification and regress ion methods were applied to check the system ‘ s performance. In the regression analysis, the k-Nearest Neighbour (KNN) achieved the best R 2 , Root Mean Square Error (RMSE), Standard Error of Prediction (SEP), Mean Absolute Error (MAE), Ra tio of Performance to Deviation (RPD), Leave One Out Cross-Validation (LOOCV)-R 2 , and LOOCV-RMSE of 0.999, 0.399 mL ( % v/v), 0.096 mL ( % v/v), 0.227 mL ( % v/v), 33.005, 0.999, and 0.353 mL ( % v/v), respectively, while for classification analysis, the Random Forest (RF) ac hieved 100 % accuracy and Matthew ‘ s Correlation Coefficient (MCC ).”
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