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

Che Shen; Yun Cai; Meiqi Ding; Xinnan Wu; Guanhua Cai; Bo Wang; Shengmei Gai; Dengyong Liu

doi:10.1016/j.fochx.2023.100755

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

Food Chem X. 2023 Jun 14:19:100755. doi: 10.1016/j.fochx.2023.100755. eCollection 2023 Oct 30.

Authors

Che Shen¹, Yun Cai¹, Meiqi Ding¹, Xinnan Wu¹, Guanhua Cai¹, Bo Wang^{1

2}, Shengmei Gai¹, Dengyong Liu¹

Affiliations

¹ College of Food Science and Technology, Bohai University, Jinzhou 121013, China.
² Key Laboratory of Meat Processing and Quality Control, MOE, Key Laboratory of Meat Processing, MARA, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.

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).

Keywords: Deep learning; HS-GC-IMS; Lamb shashliks; Roasting methods; SPME-GC–MS; Sensory evaluation.