da Silva Gilmare A, Maretto Danilo A, Bolini Helena Maria A, Teófilo Reinaldo F, Augusto Fabio, Poppi Ronei J
Department of Chemistry, Federal University of Ouro Preto (UFOP), Morro do Cruzeiro, 35400-000 Ouro Preto, MG, Brazil.
Institute of Chemistry, State University of Campinas (UNICAMP), P. O. Box 6154, 13083-970 Campinas, SP, Brazil.
Food Chem. 2012 Oct 1;134(3):1673-81. doi: 10.1016/j.foodchem.2012.03.080. Epub 2012 Mar 29.
In this study, two important sensorial parameters of beer quality - bitterness and grain taste - were correlated with data obtained after headspace solid phase microextraction - gas chromatography with mass spectrometric detection (HS-SPME-GC-MS) analysis. Sensorial descriptors of 32 samples of Pilsner beers from different brands were previously estimated by conventional quantitative descriptive analyses (QDA). Areas of 54 compounds systematically found in the HS-SPME-GC-MS chromatograms were used as input data. Multivariate calibration models were established between the chromatographic areas and the sensorial parameters. The peaks (compounds) relevant to build each multivariate calibration model were determined by genetic algorithm (GA) and ordered predictors selection (OPS), tools for variable selection. GA selected 11 and 15 chromatographic peak areas, for bitterness and grain taste, respectively; while OPS selected 17 and 16 compounds for the same parameters. It could be noticed that seven variables were commonly pointed out by both variable selection methods to bitterness parameter and 10 variables were commonly selected to grain taste attribute. The peak areas most significant to the evaluation of the parameters found by both variable selection methods fed to the PLS algorithm to find the proper models. The obtained models estimated the sensorial descriptors with good accuracy and precision, showing that the utilised approaches were efficient in finding the evaluated correlations. Certainly, the combination of proper chemometric methodologies and instrumental data can be used as a potential tool for sensorial evaluation of foods and beverages, allowing for fast and secure replication of parameters usually measured by trained panellists.
在本研究中，啤酒品质的两个重要感官参数——苦味和谷物味——与顶空固相微萃取-气相色谱-质谱检测（HS-SPME-GC-MS）分析后获得的数据相关联。不同品牌的32个比尔森啤酒样品的感官描述符先前通过传统的定量描述分析（QDA）进行了评估。在HS-SPME-GC-MS色谱图中系统发现的54种化合物的面积用作输入数据。在色谱面积和感官参数之间建立了多元校准模型。通过遗传算法（GA）和有序预测变量选择（OPS）（变量选择工具）确定了与构建每个多元校准模型相关的峰（化合物）。GA分别为苦味和谷物味选择了11个和15个色谱峰面积；而OPS为相同参数选择了17种和16种化合物。可以注意到，两种变量选择方法都共同指出了7个与苦味参数相关的变量，以及10个与谷物味属性相关的变量。两种变量选择方法找到的对参数评估最具显著性的峰面积被输入到PLS算法中以找到合适的模型。所获得的模型以良好的准确度和精密度估计了感官描述符，表明所采用的方法在发现所评估的相关性方面是有效的。当然，适当的化学计量学方法和仪器数据的结合可以用作食品和饮料感官评估的潜在工具，能够快速且可靠地重现通常由经过培训的评审员测量的参数。
