Abstract：
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.
摘要：
为了模拟嗅觉的功能，gustation,愿景,和口头接触，智能感官技术已经发展起来。电子鼻顶空固相微萃取气相色谱-质谱(HS-SPME-GC/MS),电子舌头计算机视觉（CV），和质地分析仪（TA）用于各种烘烤方法的羔羊shashliks（LS）的感官表征。通过HS-SPME/GC-MS鉴定了5种烘烤方法的羔羊shashliks中的56种VOCs，基于OAV(>1)，21种VOCs被确定为关键化合物。还提出了跨通道感觉转换（CCST），并将其用于预测19种感觉属性及其不同烘烤方法的羔羊shashlik得分。该模型在预测集中取得了令人满意的结果(R2=0.964)。这项研究表明，多模态深度学习模型可以用来模拟评估者，指导和正确的感官评价是可行的。
