THz spectroscopic detection of sweeteners based on machine learning algorithms
Three artificial sweeteners,sucralose,erythritol and xylitol,are qualitatively and quantitatively studied based on Terahertz time-domain spectroscopy combined with machine learning algorithms and optimization algorithms.The results show that the Sparrow Search Algorithm-Support Vector Machines/Support Vector Regression(SSA-SVM/SVR)model is optimal for qualitative and quantitative analysis of the mixture.The accuracy of classification prediction is up to 95.56%,and the optimal regression coefficient for quantitative regression prediction is 0.999 8,so that a high-precision classification and quantitative analysis of three sweetener-flour mixtures is achieved.This provides an effective and reliable method for the rapid detection of artificial sweeteners.
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