| Title: | Predicting VOCs content and roasting methods of lamb shashliks using deep learning combined with chemometrics and sensory evaluation |
| Author(s): | Shen C; Cai Y; Ding M; Wu X; Cai G; Wang B; Gai S; Liu D; |
| Address: | "College of Food Science and Technology, Bohai University, Jinzhou 121013, China. 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" |
| DOI: | 10.1016/j.fochx.2023.100755 |
| ISSN/ISBN: | 2590-1575 (Electronic) 2590-1575 (Linking) |
| 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; |
| Notes: | "PubMed-not-MEDLINEShen, Che Cai, Yun Ding, Meiqi Wu, Xinnan Cai, Guanhua Wang, Bo Gai, Shengmei Liu, Dengyong eng Netherlands 2023/06/30 Food Chem X. 2023 Jun 14; 19:100755. doi: 10.1016/j.fochx.2023.100755. eCollection 2023 Oct 30" |
