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当前位置: 首页 > 文章 > 基于高光谱成像的干燥胡萝卜片水分及类胡萝卜素含量无损检测和可视化分析 食品科学 2020 (12) 285-291
Position: Home > Articles > Hyperspectral Imaging for Non-destructive Determination and Visualization of Moisture and Carotenoid Contents in Carrot Slices during Drying FOOD SCIENCE 2020 (12) 285-291

基于高光谱成像的干燥胡萝卜片水分及类胡萝卜素含量无损检测和可视化分析

作  者：
杨佳;刘强;赵楠;陈继昆;彭菁;潘磊庆;屠康
单  位：
云南省农产品质量安全中心;南京农业大学食品科学技术学院
关键词：
胡萝卜片;干燥;水分含量;类胡萝卜素含量;高光谱成像;可视化
摘  要：
利用不同波长范围的高光谱成像系统,以热风干燥过程中胡萝卜片水分和类胡萝卜素含量为研究对象,结合多元数据统计分析和化学计量学,分别构建基于偏最小二乘和支持向量机(support?vector?machine,SVM)算法的无损预测模型,并进行可视化分析。结果表明,水分和类胡萝卜素含量预测模型中,基于400～1?000?nm波长范围下多元散射校正的高光谱信息构建的SVM预测模型效果相对最优,对应的预测集决定系数R2P分别为0.984和0.911,预测集均方根误差(root?mean?square?error?of?prediction,RMSEP)分别为0.380?g/g和34.836?mg/100?g。经连续投影算法提取特征波长后,最优模型R2P分别为0.962和0.898,RMSEP分别为0.612?g/g和37.544?mg/100?g,模型剩余预测残差均大于3,精确度和稳定性良好。在最优预测模型的基础上,通过伪彩色成像重现了干燥过程中样品水分及类胡萝卜素的空间分布。实验结果证实高光谱成像技术可以用于胡萝卜片干燥过程水分和类胡萝卜素含量的无损检测,为后续在线检测和胡萝卜片干燥加工提供理论基础和技术支持。
译  名：
Hyperspectral Imaging for Non-destructive Determination and Visualization of Moisture and Carotenoid Contents in Carrot Slices during Drying
作  者：
YANG Jia;LIU Qiang;ZHAO Nan;CHEN Jikun;PENG Jing;PAN Leiqing;TU Kang;College of Food Science and Technology, Nanjing Agricultural University;Center of Agricultural Products Quality and Safety of Yunnan Province;
关键词：
carrot?slices;;drying;;moisture?content;;carotenoid?content;;hyperspectral?imaging;;visualization
摘  要：
In?this?experiment,?hyperspectral?images?in?different?wavelength?ranges?were?acquired?for?carrot?slice?samples?during?hot?air?drying.?Subsequently,?using multivariate?statistical?analysis?combined?with?chemometrics,?a?predictive?model?for?the?non-destructive?determination?of?moisture?content(MC)?and?carotenoid?content(CC)?in?samples?was?developed?separately?based?on?partial?least?squares(PLS)?and?support?vector?machine(SVM)?algorithm.?The?results?showed?that?the?SVM?models?developed?using?multiplicative?scatter?correction(MSC)?in?the?400–1?000?nm?had?the?best?prediction?performance?for?both?MC?and?CC?with?coefficient?of?determination?for?prediction(R2 P)?of?0.984?and?0.911,?and?root?mean?square?error?for?prediction(RMSEP)?of?0.380?g/g?and?34.836?mg/100?g,?respectively.?The?optimal?models?with?the?feature?wavelengths?selected?by?successive?projections?algorithm?showed?R2 P?of?0.962?and?0.898?and?RMSEP?of?0.612?g/g?and?37.544?mg/100?g?for?MC?and?CC,?respectively.?The?residual?predictive?deviation(RPD)?in?the?new?models?was?over?3,?indicating?good?accuracy?and?stability.?Moreover,?the?spatial?distribution?of?moisture?and?carotenoid?during?the?drying?process?were?generated?and?visualized?as?pseudo-color?images.?The?results?indicated?that?the?hyperspectral?imaging?could?be?used?to?effectively?predict?the?MC?and?CC?in?carrot?slices,?demonstrating?the?potential?of?hyperspectral?imaging?as?an?analytical?tool?in?quality?control?of?carrot?slices?during?drying.

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