Summary
Milk is a nutrient-rich food, and water adulteration in milk can reduce its quality and increase food safety risks. Nondestructive and efficient detection of milk adulteration levels is crucial to addressing this issue. This study employed a portable near-infrared spectrometer to measure and analyze the absorbance of milk samples within the wavelength range of 900 to 1,800 nm. Based on the original spectra, models such as soft independent modeling of class analogy (SIMCA), naive bayes, k-nearest neighbors, and support vector machine were constructed for the discrimination of water-adulterated milk. Preprocessing methods including Savitzky-Golay convolutional smoothing, Savitzky-Golay filtered derivative, multiplicative scatter correction, standard normal variate, vector normalization (VN),
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