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Chemometric Authentication of Brazilian Coffees Based on Chemical Profiling

机译:基于化学分析的巴西咖啡的化学计量认证

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摘要

In this work, different chemometric tools were compared to classify n = 26 conventional (CONV) and n = 19 organic (ORG) coffees from the main Brazilian producing regions based on the chemical composition, physicochemical properties, and antioxidant activity. Principal component analysis separated ORG and CONV coffees but the distinction among the producing regions of Brazilian coffee was not possible. Partial least squares discriminant analysis classified all ORG and CONV coffees in the external validation. Similarly, linear discriminant analysis was able to discriminate 100% and 81% of ORG and CONV coffees in the external validation, respectively, in which total phenolic content (TPC), ferric reducing antioxidant activity, and caffeic acid were the main discriminant variables. Overall 100% of samples from Parana, Minas Gerais, and blended samples were correctly classified, where TPC, flavonoids, inhibition of lipid peroxidation, caffeic acid, pH, and soluble solids were the main discriminant variables. Support vector machines classified 95% ORG and 88% CONV, 100% Coffea arabica, and 88% and 78% coffees produced in Sao Paulo and Minas Gerais. k-Nearest neighbors was effective in distinguishing 100% CONV, 89% ORG, 100% coffees from Sao Paulo, and 100% C. arabica coffees. Overall, HPLC data and simple physicochemical parameters allied to chemometrics were effective in authenticating the cultivation system and the botanical origin of Brazilian coffees.
机译:在这项工作中,将不同的化学计量工具进行比较,以基于化学成分,物理化学性质和抗氧化活性来分类n = 26常规(CONV)和N = 19个有机(ORG)咖啡。主要成分分析分开组织和经常咖啡,但不可能区分巴西咖啡的产区。偏最小二乘判别分析分类为所有组织和Conv咖啡在外部验证中。类似地,线性判别分析能够分别歧视100%和81%的ORG,并分别在外部验证中进行咖啡,其中总酚类含量(TPC),还原抗氧化活性和咖啡酸是主要的判别变量。总共100%来自ParaNa,Minas Gerais和混合样品的样品被正确分类,其中TPC,黄酮类化合物,脂质过氧化抑制,咖啡酸,pH和可溶性固体是主要判别变量。支持向量机分类为95%ORG和88%CONVEA,100%COFFEA阿拉比卡,88%和78%的咖啡在圣保罗和MINAS Gerais生产。 K-Collow Neighbors有效地区分了100%Conv,89%Org,100%来自圣保罗的咖啡,100%C.Ascica Coffees。总体而言,HPLC数据和简单的物理化学参数均毗邻化学计量学有效地验证培养系统和巴西咖啡的植物来源。

著录项

  • 来源
    《Journal of Food Science》 |2019年第12期|3099-3108|共10页
  • 作者单位

    Graduation Program in Food Science and Technology State Univ. of Ponta Grossa Ponta Grossa Parana 84030—900 Brazil;

    Graduation Program in Food Science and Technology State Univ. of Ponta Grossa Ponta Grossa Parana 84030—900 Brazil;

    Graduation Program in Food Science and Technology State Univ. of Ponta Grossa Ponta Grossa Parana 84030—900 Brazil Semcnov Inst of Chemical Physics Russian Academy of Sciences Moscow 119991 Russia;

    Branch oi Inst oj Natural and Technical Systems Russian Academy of Sciences Sochi 354024 Russia;

    Dept. of Chemistry Federal Univ. of Santa Catarina Florianopolis Santa Caiarina 88040-900 Brazil;

    Branch oi Inst oj Natural and Technical Systems Russian Academy of Sciences Sochi 354024 Russia;

    Graduation Program in Food Science and Technology State Univ. of Ponta Grossa Ponta Grossa Parana 84030—900 Brazil Food Processing and Quality Innovative Food System Production Systems Unit—Natural Resources Inst. Finland (Luke) Espoo FI-02150 Finland;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    discriminant analysis; multivariate statistics; one-class classifiers; organic farming; phenolic compounds;

    机译:判别分析;多元统计;单级分类器;有机耕作;酚类化合物;

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