Characterization of selected commercially available grilled lamb shashliks based on flavor profiles using GC-MS, GC x GC-TOF-MS, GC-IMS, E-nose and E-tongue combined with chemometrics

HS-SPME-GC-MS, SPME-Arrow-GC x GC-TOF-MS, HS-GC-IMS, Electronic-nose, and Electronic-tongue systems were applied in a feasibility study of the flavor characterization of five commercially available Chinese grilled lamb shashliks. A total of 198 volatile organic compounds (VOCs) were identified (similar to 71% by GC x GC-TOF-MS). Using data fusion strategies, five predictive models were applied to the composition of VOCs and brand identification of the lamb shashliks. Compared with partial least squares regression, support vector machine, deep neural network, and RegBoost modeling, a momentum deep belief network model performed best in predicting VOCs content and identifying shashlik brands (R-2 above 0.96, and RMSE below 0.1). Intelligent sensory technology combined with chemometrics is a promising approach to the flavor characterization of shashliks and other food matrices.

Automated summary

This study investigated the feasibility of using HS-SPME-GC-MS, SPME-Arrow-GC x GC-TOF-MS, HS-GC-IMS, electronic nose, and electronic tongue systems for flavor characterization of Chinese grilled lamb shashliks. A total of 198 VOCs were identified, with GC x GC-TOF-MS accounting for around 71% of the identification. By utilizing data fusion strategies, five predictive models were developed to analyze VOCs composition and brand identification of the lamb shashliks. The momentum deep belief network model outperformed other models in predicting VOCs content and identifying shashlik brands (R-2 above 0.96, and RMSE below 0.1). The combination of intelligent sensory technology and chemometrics shows promise in flavor characterization of shashliks and other food matrices.