LÜ Hongxia, WANG Yongrui. Changes of Volatile Flavor Compounds in Chicken Soup during Stewing Process of Yellow-feathered Chicken Using Multiple Technologies Combined with PCA-BP Neural NetworkJ. Science and Technology of Food Industry, 2026, 47(18): 1−10. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2025070092.
Citation: LÜ Hongxia, WANG Yongrui. Changes of Volatile Flavor Compounds in Chicken Soup during Stewing Process of Yellow-feathered Chicken Using Multiple Technologies Combined with PCA-BP Neural NetworkJ. Science and Technology of Food Industry, 2026, 47(18): 1−10. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2025070092.

Changes of Volatile Flavor Compounds in Chicken Soup during Stewing Process of Yellow-feathered Chicken Using Multiple Technologies Combined with PCA-BP Neural Network

  • To investigate the changes in volatile flavor compounds in chicken soup during the stewing process of yellow-feathered chicken, headspace solid phase microextraction (HS-SPME)-gas chromatography-mass spectrometry (GC-MS), electronic nose, and electronic tongue combined with PCA-BP neural network were used to study chicken soup samples at different cooking times (0.5, 1.0, 1.5, 2.0 and 2.5 h). The results showed that as the stewing time increased, the color of the chicken soup became darker. A total of 35 volatile compounds were identified through GC-MS, primarily consisting of aldehydes and alcohols. The total content of volatile flavor compounds increased first and then decreased, and reached a peak after stewing for 2.0 h. GC-MS combined with OAVs value method identified 13 key volatile compounds, including 1-hexanol, 1-octene-3-ol, (E,E)-2,4-decadiena,l (E,E)-2,4-nonadienal, (Z)-2-heptenal, (E)-2-nonenal, (E)-2-octenal, (E)-2-decenal, 2-pentylfuran, heptanal, hexanal, nonanal and octanal. Based on VIP values, 19 characteristic volatile compounds were screened, which could be used to distinguish 5 chicken soup samples. Significant differences in the electronic nose and electronic tongue of chicken soup samples with different stewing times, and these samples could be effectively distinguished by PCA and cluster analysis. The electronic tongue PCA-BP neural network model could accurately predict the stewing time of chicken soup samples and the linear relationship between the true value and the predicted value was good (R2=0.9998). The chicken soup stewed for 2.0 h showed significantly high scores for flavor, color, appearance, and overall acceptability. In conclusion, the optimal stewing time for chicken soup was 2.0 h. The results can be used as the basis for the flavor of the original chicken soup and pave the way for subsequent high-quality Chinese herbal chicken soup research.
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