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高质量可可在加工早期的综合化学指纹图谱:联合非靶向和靶向方法用于分类和鉴别分析的有效性。

Comprehensive Chemical Fingerprinting of High-Quality Cocoa at Early Stages of Processing: Effectiveness of Combined Untargeted and Targeted Approaches for Classification and Discrimination.

机构信息

Dipartimento di Scienza e Tecnologia del Farmaco, Università di Torino , I-10125 Turin, Italy.

Department of Computer Science and Engineering, University of Nebraska-Lincoln , Lincoln, Nebraska 68588-0115, United States.

出版信息

J Agric Food Chem. 2017 Aug 2;65(30):6329-6341. doi: 10.1021/acs.jafc.7b02167. Epub 2017 Jul 18.

Abstract

This study investigates chemical information of volatile fractions of high-quality cocoa (Theobroma cacao L. Malvaceae) from different origins (Mexico, Ecuador, Venezuela, Columbia, Java, Trinidad, and Sao Tomè) produced for fine chocolate. This study explores the evolution of the entire pattern of volatiles in relation to cocoa processing (raw, roasted, steamed, and ground beans). Advanced chemical fingerprinting (e.g., combined untargeted and targeted fingerprinting) with comprehensive two-dimensional gas chromatography coupled with mass spectrometry allows advanced pattern recognition for classification, discrimination, and sensory-quality characterization. The entire data set is analyzed for 595 reliable two-dimensional peak regions, including 130 known analytes and 13 potent odorants. Multivariate analysis with unsupervised exploration (principal component analysis) and simple supervised discrimination methods (Fisher ratios and linear regression trees) reveal informative patterns of similarities and differences and identify characteristic compounds related to sample origin and manufacturing step.

摘要

本研究调查了来自不同产地(墨西哥、厄瓜多尔、委内瑞拉、哥伦比亚、爪哇、特立尼达和圣多美)的优质可可(可可树属豆科)挥发物部分的化学信息,这些可可都用于生产优质巧克力。本研究探讨了整个挥发性物质图谱随可可加工(生豆、烘焙豆、蒸制豆和研磨豆)的演变。先进的化学指纹图谱(例如,非靶向和靶向指纹图谱相结合)与全面二维气相色谱-质谱联用技术相结合,可实现先进的模式识别,用于分类、区分和感官质量特征描述。对包括 130 种已知分析物和 13 种有效气味物质在内的 595 个可靠二维峰区的整个数据集进行了分析。采用无监督探索(主成分分析)和简单监督判别方法(费舍尔比和线性回归树)进行多元分析,揭示了相似性和差异性的信息模式,并确定了与样品产地和生产步骤相关的特征化合物。

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