Predicting VOCs content and roasting methods of lamb shashliks using deep learning combined with chemometrics and sensory evaluation

深度学习结合化学计量学和感官评价预测羊肉串的挥发性有机化合物含量和烘烤方法

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作者:Che Shen, Yun Cai, Meiqi Ding, Xinnan Wu, Guanhua Cai, Bo Wang, Shengmei Gai, Dengyong Liu

Abstract

A comparison was made between the traditional charcoal-grilled lamb shashliks (T) and four new methods, namely electric oven heating (D), electric grill heating (L), microwave heating (W), and air fryer treatment (K). Using E-nose, E-tongue, quantitative descriptive analysis (QDA), and HS-GC-IMS and HS-SPME-GC-MS, lamb shashliks prepared using various roasting methods were characterized. Results showed that QDA, E-nose, and E-tongue could differentiate lamb shashliks with different roasting methods. A total of 43 and 79 volatile organic compounds (VOCs) were identified by HS-GC-IMS and HS-SPME-GC-MS, respectively. Unsaturated aldehydes, ketones, and esters were more prevalent in samples treated with the K and L method. As a comparison to the RF, SVM, 5-layer DNN and XGBoost models, the CNN-SVM model performed best in predicting the VOC content of lamb shashliks (accuracy rate all over 0.95) and identifying various roasting methods (accuracy rate all over 0.92).

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