Kim Hyeon-Jae, Hong Dong-Lee, Yu Jung-Wan, Lee Seung-Min, Lee Yang-Bong
Department of Food Science and Technology, Pukyong National University, Busan 48513, Korea.
Food R&D Health Supplement, CJ Blossom Park, Gyeonggi 16495, Korea.
Prev Nutr Food Sci. 2019 Jun;24(2):217-223. doi: 10.3746/pnf.2019.24.2.217. Epub 2019 Jun 30.
Coffee can be blended to create a variety of products to meet consumer's needs. In order to uncover the blending effect of coffee beans, we performed an experiment using principal component analysis (PCA). Twelve varieties of green beans were tested in 11 experimental groups, and the volatile compounds of the beans were analyzed. A total of 41 volatile compounds were identified. PCA was performed on 13 compounds that had a low odor threshold value or a high concentration among the identified compounds. PCA of total volatile compounds showed that principal component (PC) 1 and PC2 were extracted within 80% cumulative dispersion level. In PC1 and PC2, furfuryl alcohol and formic acid ethyl ester showed the greatest positive correlation coefficients among all the volatile compounds. The largest negative correlation coefficients in PC1 and PC2 were 4-hydroxy-2-butanone and 3-(ethylthio)propanal, respectively. Using PCA of the major volatile compounds in coffee, propanal and 1-methylpyrrole were found to have the largest positive correlation coefficients in PC1 and PC2, respectively. In the score plot of the major volatile components, 4 kinds of blended coffee were closely grouped, therefore showing similar aroma qualities. However, 5 kinds of other blended coffees showed a positive correlation with PC2. This is probably due to 3-(ethylthio)propanal acting as a specific value. The application of statistical methods to blended coffee allows for logical and systematic data analysis of data and may be used as a basis for quality evaluation.
咖啡可以进行调配，以创造出满足消费者需求的各种产品。为了揭示咖啡豆的调配效果，我们使用主成分分析（PCA）进行了一项实验。对12个品种的生豆在11个实验组中进行了测试，并对咖啡豆的挥发性化合物进行了分析。共鉴定出41种挥发性化合物。对鉴定出的化合物中气味阈值低或浓度高的13种化合物进行了主成分分析。总挥发性化合物的主成分分析表明，在累积方差贡献率达到80%的水平内提取了主成分（PC）1和PC2。在PC1和PC2中，糠醇和甲酸乙酯在所有挥发性化合物中显示出最大的正相关系数。PC1和PC2中最大的负相关系数分别是4-羟基-2-丁酮和3-（乙硫基）丙醛。通过对咖啡中主要挥发性化合物的主成分分析发现，丙醛和1-甲基吡咯在PC1和PC2中分别具有最大的正相关系数。在主要挥发性成分的得分图中，4种混合咖啡紧密聚类，因此显示出相似的香气品质。然而，其他5种混合咖啡与PC2呈正相关。这可能是由于3-（乙硫基）丙醛作为一个特定值所致。将统计方法应用于混合咖啡可以对数据进行逻辑和系统的数据分析，并可作为质量评估的依据。
