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

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...

Full description

Bibliographic Details
Main Authors: Che Shen, Yun Cai, Meiqi Ding, Xinnan Wu, Guanhua Cai, Bo Wang, Shengmei Gai, Dengyong Liu
Format: Article
Language:English
Published: Elsevier 2023-10-01
Series:Food Chemistry: X
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2590157523001980
Description
Summary: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).
ISSN:2590-1575