食品与机械2025，Vol.41Issue(6)：81-87,7.DOI:10.13652/j.spjx.1003.5788.2024.80959
基于可见近红外反射光谱的柑橘糖度在线检测
Online detection of citrus sugar content based on visible near-infrared reflectance spectroscopy
摘要
Abstract
[Objective]To lower the complexity,improve the accuracy and reduce the damage of the detection process of citrus sugar content.[Methods]An online detection device for citrus sugar content is designed based on visible near-infrared reflectance spectroscopy.With Jinniu Citrus as the research object,the modeling set and verification set are divided by sample set partitioning based on the joint x-y distance(SPXY)classification method.The partial least square regression(PLS)modeling and detection effects after pretreatment are respectively compared and analyzed by methods including multiple scattering correction(MSC),standard normal variation(SNV),and SG-smoothing(SG)to determine the optimal pretreatment method.At the same time,a comparative study is conducted on the extraction of feature bands from pretreatment spectral data using the successive projections algorithm(SPA),the competitive adaptive reweighted sampling(CARS),and the random frog(RF)algorithm.Suitable feature wavelength points are screened out and the PLS prediction models are established.[Results]The PLS model established by screening out the 95 feature wavelength points using SG+MSC+CARS has the best prediction performance.Its correlation coefficient of calibration(Rc)and correlation coefficient of prediction(Rp)are 0.913 and 0.881,respectively,root mean square error of the calibration set(RMSEC)and root mean square error of the prediction set(RMSEP)are 0.274 and 0.207,respectively,and residual predictive deviation(RPD)is 2.114.[Conclusion]This method effectively lowers the complexity of the citrus sugar content detection process,improves the detection accuracy,and reduces the detection damage.关键词
柑橘/可见近红外光谱/在线检测/糖度Key words
citrus/visible near-infrared spectroscopy/online detection/sugar content引用本文复制引用
李利桥,高宗余,时如意,聂晶,柴建敏,刘伟..基于可见近红外反射光谱的柑橘糖度在线检测[J].食品与机械,2025,41(6):81-87,7.基金项目
国家自然科学基金项目(编号:52265038) （编号:52265038）
新疆维吾尔自治区2023人才发展基金—天池英才创新领军人才项目(编号:CZ002507) （编号:CZ002507）
