Raw data HILIC 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 targeted HILIC-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的鸡肉清汤样本，本研究通过统计多元分析法开展了化学与感官特性表征。通过靶向亲水相互作用色谱-质谱（HILIC-MS）技术获取了非靶向特征谱。本研究采用简洁高效的数据驱动方法，针对添加YP的食品展开研究，以明确分子组成与感官感知之间对风味具有影响的关联关系。同时阐明了构建优质预测模型所需的前提条件及存在的局限性。总体而言，本研究强调了基于食品基质的味觉预测方法，该方法可用于食品的靶向风味设计或质量管控分析。如需获取更多详细信息，请查阅关联出版物的DOI或此处上传的文本文件。