基于HS-SPME-GC-MS的肉粉挥发性物质分析与新鲜度评价

Analysis of Volatile Compounds and Freshness Evaluation of Meat Powder Based on HS-SPME-GC-MS

摘要:

为明晰不同储存条件下肉粉挥发性物质与新鲜度的变化规律，揭示肉粉挥发性物质与新鲜度指标的相关性，采用顶空固相微萃取-气相色谱-质谱（HS-SPME-GC-MS）联用技术对不同条件下鸡肉粉中挥发性物质（VOCs）进行测定分析，并结合相关国家标准测定挥发性盐基氮（TVB-N）、酸价（AV）、pH值和霉菌总数（TVC）新鲜度指标，建立基于VOCs的新鲜度指标预测模型。方差分析（ANOVA）结果表明，温度和相对湿度是影响TVB-N、AV、pH值和TVC指标的重要因素;鸡肉粉中VOCs测定分析结果表明，不同储存条件下VOCs种类和含量显著不同，其中醛酮类物质变化最为显著，且与新鲜度指标相关性分析结果表明共有225种VOCs与新鲜度指标显著相关（|r|>0.5、P<0.05）;基于相关VOCs建立随机森林（RF），支持向量回归（SVM）和极端梯度提升（XGBoost）新鲜度指标预测模型，其中XGBoost对新鲜度指标预测模型准确率较高，XGBoost新鲜度指标预测模型结果表明，TVB-N、AV、pH值和TVC预测模型决定系数R2分别为0.953、0.80、0.78和0.77，均方根误差分别为8.87 mg/(100 g)、0.91 mg/g、0.19和0.81 lg CFU/g;采用SHAP值对模型中显著影响新鲜度指标的前10种VOCs进行分析表明，己醛、壬醛、6-甲基-3-庚酮等是影响新鲜度的关键VOCs。研究结果可为肉粉储存及品质控制提供技术参考，并为开发检测肉粉新鲜度的气敏传感器的选择提供理论依据和技术支撑。

Abstract:

Aiming to clarify the variation patterns of volatile compounds and freshness indicators of meat meal under different storage conditions, and reveal the correlation between volatile compounds and freshness, focusing on chicken meat powder, headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS) was employed to analyze volatile organic compounds (VOCs) in chicken meat powder stored under various conditions. In parallel, freshness indicators, including total volatile base nitrogen (TVB-N), acid value (AV), pH value, and total viable count (TVC) were determined in accordance with relevant national standards. A predictive model for freshness indicators based on VOCs was then established. Results showed that storage temperature, humidity, and duration had significant effects on the freshness indicators, particularly under high-temperature and high-humidity conditions, where all indicators increased markedly. Analysis of variance (ANOVA) revealed that temperature and humidity were the dominant factors affecting freshness. VOCs profiling demonstrated that both the types and concentrations of VOCs varied significantly across different storage conditions, with aldehydes and ketones showing the most pronounced changes. Correlation analysis identified 225 VOCs significantly associated with freshness indicators (|r|>0.5, P<0.05). Freshness indicator prediction models were developed based on the relevant VOCs using random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). Among these models, XGBoost exhibited higher prediction accuracy. The prediction results of the XGBoost model showed TVB-N with R2=0.953 and RMSE=8.87 mg/(100 g), AV with R2=0.80 and RMSE=0.91 mg/g, pH value with R2=0.78 and RMSE=0.19, and TVC with R2=0.77 and RMSE=0.81 lg CFU/g, indicating the feasibility of freshness prediction based on VOCs. Furthermore, the Shapley additive explanations (SHAP) method was used to interpret the top 10 VOCs contributing to freshness prediction, identifying hexanal, nonanal, and 6-methyl-3-heptanone as key contributors. These findings can provide technical insights for meat powder storage and quality control, and offer a theoretical and technological foundation for the development of gas-sensitive sensors for freshness detection.