烘烤
感觉系统
气相色谱-质谱法
化学
感官分析
人工智能
固相微萃取
质谱法
计算机科学
色谱法
食品科学
心理学
物理化学
认知心理学
作者
Che Shen,Guoqiang Cai,Jingwen Tian,Xinnan Wu,Meiqi Ding,Bo Wang,Dengyong Liu
标识
DOI:10.1016/j.foodchem.2023.138265
摘要
To simulate the functions of olfaction, gustation, vision, and oral touch, intelligent sensory technologies have been developed. Headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) with electronic noses (E-noses), electronic tongues (E-tongues), computer vision (CVs), and texture analyzers (TAs) was applied for sensory characterization of lamb shashliks (LSs) with various roasting methods. A total of 56 VOCs in lamb shashliks with five roasting methods were identified by HS-SPME/GC-MS, and 21 VOCs were identified as key compounds based on OAV (>1). Cross-channel sensory Transformer (CCST) was also proposed and used to predict 19 sensory attributes and their lamb shashlik scores with different roasting methods. The model achieved satisfactory results in the prediction set (R2 = 0.964). This study shows that a multimodal deep learning model can be used to simulate assessor, and it is feasible to guide and correct sensory evaluation.
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