白酒主体香味成分的含量和比例，是白酒香型风格的“构成要素”。针对白酒的主体香味成分，选择筛选出特异性敏感元件20种，构建5×4比色传感阵列芯片，建立了一种可视化快速鉴别白酒新方法。新阵列芯片检测性能明显优于已报道的通用阵列，可精确识别出不同风味白酒，且能反映出不同白酒中主体香味成分的差异。对15种白酒进行检测，主成分分析结果表明，前三个主成分对识别的贡献率依次为酯、醛、缩醛类物质（34.5%），酸类物质（19.2%）和乙醇（11.9%）。聚类分析发现同一品牌的白酒能正确归类，不同品牌同种香型白酒能率先聚为一类。各白酒间的相似性和差异性在聚类图上有较好的体现，不同香型白酒间的距离远近与白酒的生产原料、糖化发酵剂、酿造工艺密切相关。

The constitution of main flavor components resulted in different types of Chinese liquors. Focused on the main flavor components, a new 5×4 colorimetric sensor array was constructed to differentiate 15 different liquor samples after selective screening of 20 specific sensitive materials. The new sensor showed good accuracy to discriminate all liquor samples which was closely related to the main flavor components in the liquors. Data analysis was performed by principal component analysis (PCA), which suggested that the contribution rate of the main flavor components followed a descending order of esters and aldehydes (34.5%), acids (19.2%) and alcohols (11.9%). Further Hierarchical cluster analysis (HCA) elucidated that analogous liquors sample were correctly classified into one group, and those of the same type from different brands lay closer and were more easier to be clustered. Besides, the clusters indicated good accordance to the similarity of the main flavor components in the liquor, and the distance of different clusters indicated a close relationship between the liquors and raw materials, diastatic fermented agent and brewing process.

国家自然科学基金（30770568；31171684）；四川省科技支撑计划（2013FZ0043；2010NZ0093）；酿酒生物技术及应用四川省重点实验室开放基金（NJ2011-03）并受到重庆大学大型仪器设备开放基金资助