Raw data untargeted LCMS underlying the research of a data-driven approach to link GCMS and LCMS with sensory attributes of chicken bouillon with added yeast-derived flavor products in a combined prediction mode

Chicken bouillon samples containing diverse YP were chemically and sensorially characterized by using statistical multivariate analyses. Untargeted profiles were obtained using RPLC-MS. This study was used for a straight-forward data-driven approach for studying foods with added YP to identify flavor-impacting correlations between molecular composition and sensory perception. It also highlights the limitations and preconditions for good prediction models. Overall, this study emphasises a matrix-based approach for the prediction of food taste, which can be used to analyse foods for targeted flavor design or quality control.For more information use the DOI for the linked publication or the textfile uploaded here.

针对含有多种YP的鸡肉高汤样品，本研究借助统计多元分析方法完成了化学特性与感官特性的表征工作。本研究采用反相液相色谱-质谱联用（RPLC-MS）技术获取了样品的非靶向分子谱图。本研究采用简洁直观的数据驱动方法，针对添加了YP的食品开展研究，以厘清分子组成与感官感知之间影响风味的关联机制；同时阐明了构建优质预测模型所需的前置条件及其存在的局限性。总体而言，本研究着重提出了一种基于食品基质的食品风味预测方法，该方法可用于开展靶向风味设计或质量管控相关的食品分析工作。如需获取更多详细信息，请查阅关联出版物的数字对象唯一标识符（DOI）或此处上传的文本文件。