太赫兹科学与电子信息学报2024，Vol.22Issue(4)：385-393,9.DOI:10.11805/TKYDA2023284
太赫兹光谱结合机器学习的甜味剂检测
THz spectroscopic detection of sweeteners based on machine learning algorithms
摘要
Abstract
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.关键词
太赫兹/甜味剂/机器学习/优化算法Key words
terahertz/sweeteners/machine learning/optimization algorithms分类
数理科学引用本文复制引用
钟芸襄,张然,熊子仪,邹斌,杨玉平..太赫兹光谱结合机器学习的甜味剂检测[J].太赫兹科学与电子信息学报,2024,22(4):385-393,9.基金项目
国家自然科学基金资助项目(62075248) （62075248）
国家重点基础研究发展计划资助项目(2020YFB2009303 （）
2017YFB00405402) （）
国家外国专家资助项目(G2022184001L) （G2022184001L）
