食品科学2025，Vol.46Issue(13)：54-62,9.DOI:10.7506/spkx1002-6630-20241214-115
基于反向传播神经网络分析的田菁胶添加对萨拉米发酵香肠品质的影响
Impact of Sesbania Gum Addition on the Quality of Salami Based on Backpropagation-Artificial Neural Network Analysis
摘要
Abstract
This study explored the impact of adding sesbania gum on the quality of salami using backpropagation-artificial neural network(BP-ANN)analysis.Four treatment groups were designed:blank control(CK),inoculation of a mixed culture(CG),addition of sesbania gum(SE),and sesbania gum addition combined with mixed culture inoculation(SE-CG).The quality of salami was evaluated in terms of its pH,water activity(aw),color difference,texture,sensory evaluation,and electronic nose analysis.It was demonstrated that the combined treatment rapidly decreased the pH and aw of the product,thereby contributing to the formation of the final quality of salami.Compared with the CK and CG groups,the SE-CG group exhibited significantly improved a* value(4.64±0.38)and hardness((60.95±1.48)N).Furthermore,the electronic nose analysis revealed that the SE-CG treatment significantly increased the contents of sulfur-containing compounds,alcohols,and aromatic compounds in the product.The developed BP-ANN model had good classification accuracy and predictive ability with a 96%accuracy.Additionally,the Shapley additive explanations(SHAP)method was employed to interpret the BP-ANN model,highlighting the significance of various quality indicators in the prediction.Notably,the signal of electronic nose sensor S12,hardness,and chewiness were identified as the most important features for the model prediction.关键词
萨拉米发酵香肠/田菁胶/反向传播神经网络/品质分析/沙普利加和解释方法Key words
salami/sesbania gum/backpropagation artificial neural networks/quality analysis/Shapley additive explanations method分类
轻工业引用本文复制引用
卢慧,宋艾颖,凌峰,蔡玉玲,黄启亮,刘云国,康大成..基于反向传播神经网络分析的田菁胶添加对萨拉米发酵香肠品质的影响[J].食品科学,2025,46(13):54-62,9.基金项目
国家自然科学基金青年科学基金项目(32001723) （32001723）
山东省高等学校大学生创新创业训练计划项目(X202410452615) （X202410452615）
