骆驼肉在不同温度下的感官变化及基于感官差异的货架期模型的建立与验证

Sensory changes of camel meat at different temperatures and the establishment and validation of a shelf-life model based on sensory differences

中文关键词:  骆驼肉  感官指标  理化指标  差别度  货架期

英文关键词:camel meat  sensory indicators  physicochemical indicators  divergence degree  shelf-life

中文摘要:

      目的 探究骆驼肉在不同贮藏温度(4、15、25 ℃)下的感官变化, 并建立基于感官差异的货架期预测模型。方法 通过模拟实际贮藏条件, 分析骆驼肉的颜色、电子舌味觉指标(鲜味、咸味、酸味、甜味等)以及电子鼻嗅觉指标(氮氧化物、甲烷、硫化物等)的变化。采用零级反应动力学模型和Arrhenius方程对数据进行拟合, 建立以颜色、电子舌和电子鼻为基准的货架期预测模型, 并通过pH、菌落总数和挥发性盐基氮(Total volatile basic nitrogen, TVB-N)对模型进行验证。结果 4 ℃条件下, 骆驼肉亮度值(L*)和红度值(a*)无显著变化(P>0.05), 而15 ℃和25 ℃下的a*显著下降(P<0.05), 黄度值(b*)在所有温度下均呈现上升趋势。味觉分析结果显示, 鲜味(AEE)、咸味(CT0)、酸味(CA0)和涩味(AE1)强度随时间和温度增加, 而甜味(GL1)强度减弱。电子鼻检测发现, 氮氧化物(W5S)、甲烷(W1S)、硫化物和萜烯(W1W)、醇类化合物(W2S)的浓度随贮藏时间显著增加。通过模型计算得出, 以颜色、电子舌和电子鼻为基准的货架期分别为7.23、6.24和6.12 d。用6 ℃下的骆驼肉进行验证, 结果显示, 电子舌模型的预测准确度较高, 但不同理化指标(如菌落总数、pH和TVB-N)反映的货架期存在显著差异。结论 本研究成功建立了基于感官的货架期预测模型。基于电子舌和电子鼻建立的预测模型与pH的验证结果吻合度较高, 相对误差分别为–2.80%和–4.67%)。不同理化指标对肉品质的要求严格程度不同, 菌落总数最为严格, 而TVB-N相对宽松。本研究为冷鲜骆驼肉的品质监控提供了参考依据。

英文摘要:

      Objective To investigate the sensory changes of camel meat stored at different temperatures (4, 15, and 25 ℃) and establish a shelf-life prediction model based on sensory differences. Methods By simulating actual storage conditions, the changes in color, electronic tongue taste indices (umami, saltiness, sourness, sweetness, etc.), and electronic nose olfactory indicators (nitrogen oxides, methane, sulfides, etc.) of camel meat were analyzed. Zero-order reaction kinetics models and the Arrhenius equation were used to fit the data, establishing a shelf-life prediction model based on color, electronic tongue and electronic nose metrics. The model was validated using pH, total bacterial count and total volatile basic nitrogen (TVB-N). Results At 4 ℃, there was no significant change in the lightness (L*) and redness (a*) of camel meat (P>0.05), while the a* value significantly decreased at 15 ℃ and 25 ℃ (P<0.05), and yellowness (b*) showed an upward trend across all temperatures. Taste analysis revealed that umami (AEE), saltiness (CT0), sourness (CA0) and astringency (AE1) intensities increased with time and temperature, whereas sweetness (GL1) intensity decreased. Electronic nose detection found that concentrations of nitrogen oxides (W5S), methane (W1S), sulfides and terpenes (W1W) and alcohol compounds (W2S) significantly increased over storage time. The calculated shelf lives based on color, electronic tongue and electronic nose were 7.23, 6.24 and 6.12 d, respectively. Verification using camel meat stored at 6 ℃ showed high prediction accuracy of the electronic tongue model, but significant discrepancies were observed among shelf lives predicted by different physicochemical indicators (e.g., total bacterial count, pH, TVB-N). Conclusion This study successfully establish a shelf-life prediction model based on sensory characteristics. The prediction models established based on the electronic tongue and electronic nose show good agreement with validation results based on pH, with relative errors of –2.80% and –4.67%, respectively. Different physicochemical indicators have varying degrees of strictness towards meat quality requirements, with total bacterial count being the most stringent and TVB-N relatively more lenient. This study provides a reference for the quality monitoring of fresh camel meat.