In this study (purchase required) the authors developed a method using an electronic nose (e-nose) equipped with 8 metal oxide semiconductor (MOS) sensors to detect whey adulteration in powdered milk by analyzing volatile emissions.
They examined pure powdered milk adulterated with whey at six concentration levels (10%, 20%, 30%, 40%, and 50%) in both dry and rehydrated forms. Statistical analyses, including Principal Component Analysis (PCA) and Artificial Neural Network (ANN), were employed to interpret the sensor output responses from the e-nose.
They reported that the ANN analysis demonstrated a total variance of 85%, with only eight out of 180 samples (4.4%) being misclassified in detecting whey adulteration in powdered milk. The model achieved a detection accuracy of 95.6%. Sensors MQ9 and TGS822 exhibited the most robust responses to wet samples, while sensors MQ136 and TGS822 showed the highest reactivity to dry test samples. PCA analysis revealed that the first principal component (PC-1) accounted for 90% of the total variance, whereas PC-2 contributed only 4% to the variance.
They conclude that their study offers insights into the application of an e-nose portable device that enables non-invasive analysis. E-nose technology is a promising tool for rapid quality screening of commercial powdered milk.
Photo by julian mora on Unsplash
Comments