Frontiers in nutrition
Multidimensional characterization of vegetable breads using multisource intelligent sensory technologies and feature-level data fusion
Par Jiang Y, Yang H, Jian C et 5 autres.
Contenu en anglais
Résumé
Background
Food sensory quality is multidimensional, and a single intelligent sensory technique cannot simultaneously characterize color, taste, and odor across multiple dimensions. Multisource intelligent sensory analysis combined with data fusion provides an approach for the objective characterization of multidimensional food sensory characteristics.
Methods
Control bread (CK) and four vegetable breads prepared with pumpkin (PU), Pueraria thomsonii (PG), red pepper (RP), and celery (CE) were analyzed using an electronic eye, an electronic tongue, and an ultrafast gas chromatography electronic nose. These instruments provided color, taste-response, and volatile-compound information, respectively. Principal component analysis (PCA), differential-peak screening, and tentative assignment based on dual-column retention-index (RI) matching were used to compare characteristics among formulations. Principal components from each single-modality dataset were then retained for feature-level data fusion followed by PCA.