食品科学2026，Vol.47Issue(1)：309-316,8.DOI:10.7506/spkx1002-6630-20250724-189
基于近红外光谱技术结合机器学习的咖啡粉掺假检测分析
Coffee Powder Adulteration Detection Based on Near-Infrared Spectroscopy Combined with Machine Learning
摘要
Abstract
This study aims to develop a rapid and non-destructive method based on near-infrared(NIR)spectroscopy combined with machine learning modeling for the quantitative detection of soybean-adulterated coffee powder.A hierarchical modeling strategy was adopted to improve prediction accuracy.Support vector regression(SVR)combined with three spectral preprocessing methods was used to construct prediction models.A total of 30 characteristic wavelengths were selected by comparing competitive adaptive reweighted sampling(CARS)and iteratively retains informative variables(IRIV).Furthermore,three optimization algorithms:dung beetle optimization(DBO),particle swarm optimization(PSO),and grey wolf optimizer(GWO)were tested to find the most effective algorithm.The CARS-DBO-SVR model exhibited coefficients of determination(R2)of 0.978 4 and 0.966 9,root mean square error(RMSE)of 0.015 7 and 0.022 8,and residual prediction deviation(RPD)of 6.809 6 and 5.499 8 for the calibration and test sets,respectively.This study demonstrates that NIR spectroscopy provides an effective technical means for detecting soybean powder adulteration in coffee.关键词
近红外光谱技术/咖啡掺假/支持向量回归Key words
near-infrared spectroscopy/coffee adulteration/support vector regression分类
化学化工引用本文复制引用
张付杰,曾庆宇,孔丹丹,余小宁,胡伟明,陈申奥,岳啸先,梁嘉雯..基于近红外光谱技术结合机器学习的咖啡粉掺假检测分析[J].食品科学,2026,47(1):309-316,8.基金项目
云南省基础研究专项重点项目(202301AS070030) （202301AS070030）
云南省科技计划项目(202502AS100016) （202502AS100016）
