多光谱食品品质可视化：多光谱成像结合机器学习模型在鉴别特制咖啡和传统咖啡中的应用

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多光谱食品品质可视化：多光谱成像结合机器学习模型在鉴别特制咖啡和传统咖啡中的应用

发表时间：2022-10-08 08:39:02点击：739

来源：北京博普特科技有限公司

多光谱成像结合机器学习模型在鉴别特制咖啡和传统咖啡中的应用

摘要

由机器学习模型辅助的无损检测技术在食品分析中得到了广泛应用。为了区分“特殊”和“传统”类别的绿色咖啡豆，结合四种机器学习算法（SVM、RF、XGBoost和CatBoost），采用了基于反射率和自荧光数据的高级多光谱成像技术。在这四种算法中，SVM对测试数据集显示出较高的精度（0.96）。使用PCA和SVM算法进行的分析表明，405/500 nm激发/发射组合的自荧光数据对区分特种绿咖啡和传统咖啡的贡献最大。与绿色荧光相关的荧光物质，即儿茶素、咖啡因和4-羟基苯甲酸、突触酸和绿原酸，发现其对特制咖啡和传统咖啡的分化有相当大的影响。基于多光谱自荧光成像和SVM模型的分析被证明是一种有价值的工具，可用于未来食品行业对特殊和传统绿咖啡进行无损实时分类。

Application of multispectral imaging combined with machine learning models to discriminate special and traditional green coffee

Abstract

Non-destructive techniques aided by machine learning models are widely implemented in food analysis. To discriminate between 'special' and 'traditional' classes of green coffee beans, an advanced multispectral imaging technique based on reflectance and autofluorescence data was employed in combination with four machine learning algorithms (SVM, RF, XGBoost, and CatBoost). Of the four algorithms, SVM showed superior accuracy (0.96) for the test dataset. Analysis using PCA and SVM algorithms showed that autofluorescence data from excitation/emission combination of 405/500 nm contributed most to the discrimination of special green coffee from the traditional class. Fluorophores that can be linked to green fluorescence, namely catechin, caffeine and 4-hydroxybenzoic, synapic and chlorogenic acids, were found to have a considerable influence on the differentiation of specialty and traditional coffees. Analysis based on multispectral autofluorescence imaging combined with SVM models was proven to be a valuable tool for future applications in the food industry for the non-destructive and real-time classification of special and traditional green coffee.