Flavor profile analysis of grilled lamb seasoned with classic salt, chili pepper, and cumin (Cuminum cyminum) through HS-SPME-GC-MS, HS-GC-IMS, E-nose techniques, and sensory evaluation on Sonit sheep.

In this study, the volatile flavor profiles of grilled lamb seasoned with salt, chili pepper, and cumin were analyzed employing HS-SPME-GC-MS, HS-GC-IMS, E-nose, and sensory evaluation techniques. The E-nose was found effective in differentiating the samples seasoned variously. A total of 67 volatile compounds were identified by HS-SPME-GC-MS, and 59 by HS-GC-IMS. The PCA demonstrated a correlation between the seasonings and the volatile compounds, with five principal components accounting for 99.54% of the total variance. 1-octen-3-ol, 3-furanmethanol, acetic acid, and heptanal were introduced by salt; compounds like propyl acetate were correlated with chili pepper; a broader range, including ethyl 3-methylbutanoate and high concentrations of alpha-pinene, was associated with cumin. Samples seasoned with all three ingredients showed similarities to those associated with cumin, alongside unique compounds such as gamma-octalactone and alpha-pinene. Sensory evaluations by consumers indicated that the combination of these seasonings significantly enhanced the overall acceptability of the grilled lamb. PRACTICAL APPLICATION: Utilizing modern analytical techniques, this study has successfully revealed the distinct impacts of seasonings-salt, chili pepper, and cumin-on the flavor profile of grilled lamb. By providing experimental data on how each seasonings influence the flavor profile of grilled lamb prepared with Sonit sheep. The research offers theoretical foundation for the development of grilled lamb products. By conducting a thorough comparison between GC-MS and GC-IMS, this study has expanded the understanding of the distinct characteristics of these two technologies. It has also provided a clearer analysis of some flavor compounds dimers produced in GC-IMS system.

Relationship between phospholipid molecules species and volatile compounds in grilled lambs during the heating process.

The present study used a comprehensive analysis combining headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) and ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS) to investigate changes in volatile compounds and phospholipid molecules in grilled lambs. The results revealed 19 key volatile compounds (OAV > 1) involved in the grilling process of lambs. Additionally, UPLC-ESI-MS/MS analysis detected 142 phospholipid molecules in grilled lamb, with phosphatidylcholine exhibiting the highest content (36.62 %), followed by phosphatidyl ethanolamine, phosphatidylinositol, phosphatidylserine, phosphatidyl glycerol, and phosphatidic acid. Through partial least squares analysis, 63 key differential phospholipids were identified. Principal component analysis of the key differential phospholipids and volatile compounds indicated that phosphatidylcholine and phosphatidyl ethanolamine phospholipids are the key substrates in forming volatile compounds in grilled lambs. This information is essential for precisely regulating the flavor profile, enhancing the grilling process, and minimizing the production of harmful compounds in grilled meat products.

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

HS-SPME-GC-MS, SPME-Arrow-GC × 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 (∼71% by GC × 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 (R2 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.