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首页> 外文期刊>Food analytical methods >Optimization of a HS-SPME-GC-MS Procedure for Beer Volatile Profiling Using Response Surface Methodology: Application to Follow Aroma Stability of Beers Under Different Storage Conditions
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Optimization of a HS-SPME-GC-MS Procedure for Beer Volatile Profiling Using Response Surface Methodology: Application to Follow Aroma Stability of Beers Under Different Storage Conditions

机译:使用响应面法优化啤酒挥发性分析的HS-SPME-GC-MS程序:在不同储存条件下跟踪啤酒香气稳定性的应用

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摘要

The optimization of the main experimental variables, such as extraction temperature, volume of sample and the extraction time of an HS-SPME/GC-MS procedure, for profiling beer volatile analysis was evaluated using response surface methodology. A central composite circumscribed design was employed to study the effect of the experimental variables on the extraction of 28 representative volatile compounds of beer flavour profile. The parameters of the models were estimated by multiple linear regressions. The strongest influence in the volatile extraction yield was the volume of the sample (V) and the extraction temperature (T), with a positive and a negative effect, respectively. The performance characteristics of the optimised method were also determined, showing adequate linear ranges, repeatability, detection and quantification limits. The optimised methodology was applied to the same beer sample stored during 5 months at three different temperature conditions (4, 20 and 40 °C). Sampling was performed monthly, and the results showed that the concentration of most volatile compounds decreased during beer storage, although the rate of decrease was clearly higher at room temperature (20 °C) compared with refrigeration conditions (4 °C). Accelerated ageing conditions (40 °C) showed the most different volatile profile. Sensory analysis also revealed large differences in the overall quality of the samples, showing that even at room temperature the aroma profile of beer is greatly modified during its shelf life.
机译:使用响应面分析法评估了用于分析啤酒挥发物分析的主要实验变量(例如提取温度,样品量和HS-SPME / GC-MS方法的提取时间)的优化。采用中央复合外接设计来研究实验变量对提取啤酒风味特征的28种代表性挥发性化合物的影响。模型的参数通过多元线性回归估计。对挥发物提取率的影响最大的是样品的体积(V)和提取温度(T),分别具有正效应和负效应。还确定了优化方法的性能特征,显示出足够的线性范围,可重复性,检测和定量限。将优化的方法应用于在三个不同温度条件(4、20和40°C)下保存5个月的同一啤酒样品。每月进行一次采样,结果表明,尽管在冷藏(4°C)下,室温(20°C)的降低速率明显更高,但在啤酒储存期间,大多数挥发性化合物的浓度均降低了。加速老化条件(40°C)表现出最不同的挥发性曲线。感官分析还显示出样品总体质量的巨大差异,表明即使在室温下,啤酒的香气在保存期内也会大大改变。

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