Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Turin, Italy.
Soremartec Italia S.r.l. (Ferrero Group), P.le P. Ferrero 1, 12051 Alba, CN, Italy.
Food Res Int. 2023 Oct;172:113199. doi: 10.1016/j.foodres.2023.113199. Epub 2023 Jun 28.
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 cross-validation of the results, affording to discriminate between the origins with effective classification models.
在这项研究中，HS-SPME-GC-MS 与机器学习工具相结合，应用于一组不同来源的可可样品的鉴定。对非靶向指纹图谱和分析方法进行了测试，以评估其对原始豆和工厂内进口原浆挥发物的信息性、区分性和分类能力，旨在寻找可用于长期研究的稳健工具。由于豆的加工步骤，区分豆和原浆的原产国的能力并不明显，但这种能力对巧克力行业特别感兴趣，因为豆和原浆都可以进入巧克力的加工过程。指纹图谱（非靶向）和分析策略（靶向）都能够解读复杂数据集所包含的信息，并且可以对结果进行交叉验证，从而使用有效的分类模型来区分起源地。
