Cocoa quality: Chemical relationship of cocoa beans and liquors in origin identitation

被引：8

作者：

Bagnulo, Eloisa ^{[1
]}

Scavarda, Camilla ^{[1
]}

Bortolini, Cristian ^{[2
]}

Cordero, Chiara ^{[1
]}

Bicchi, Carlo ^{[1
]}

Liberto, Erica ^{[1
]}

机构：

[1] Univ Torino, Dipartimento Sci & Tecnol Farmaco, Turin, Italy

[2] Sremartec Italia Srl, Ferrero Grp, Ple P Ferrero 1, I-12051 Alba, CN, Italy

来源：

FOOD RESEARCH INTERNATIONAL | 2023年 / 172卷

关键词：

Quality; Cocoa beans and liquors; Origin identitation; Machine learning; Fingerprinting; Profiling; OF-THE-ART; FOOD AUTHENTICITY; CHOCOLATE; FLAVOR; AROMA; TECHNOLOGIES; FINGERPRINTS; CHALLENGES;

D O I：

10.1016/j.foodres.2023.113199

中图分类号：

TS2 [食品工业];

学科分类号：

0832 ;

摘要：

In this study, HS-SPME-GC-MS was applied in combination with machine learning tools to the identitation of a set of cocoa samples of different origins. Untargeted fingerprinting and profiling approaches were tested for their informative, discriminative and classification ability provided by the volatilome of the raw beans and liquors inbound at the factory in search of robust tools exploitable for long-time studies. The ability to distinguish the country of origin on both beans and liquors is not so obvious due to processing steps accompanying the transformation of the beans, but this capacity is of particular interest to the chocolate industry as both beans and liquors can enter indifferently into the processing of chocolate. Both fingerprinting (untargeted) and profiling (targeted) strategies enable to decipher of the information contained in the complex dataset and the crossvalidation of the results, affording to discriminate between the origins with effective classification models.

引用

页数：8

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