品质至上，客户至上，您的满意就是我们的目标
当前位置: 首页 > 新闻动态
科学家利用Videometer多光谱成像系统发表咖啡研究文章
发表时间： 点击：748
来自巴西的科学家，利用Videometer多光谱成像系统在期刊Computers and Electronics in Agriculture 发表了题为“Application of multispectral imaging combined with machine learning models to discriminate special and traditional green coffee”的文章。目前利用Videometer多光谱成像系统发表的文章已经接近400篇。
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
DOI:10.1016/j.compag.2022.107097
项目：种子质量无损评价方法
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
June 2022
Computers and Electronics in Agriculture 198(12):107097
DOI:10.1016/j.compag.2022.107097
Project: Non-destructive methods for seed quality evalsuation
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.
