利用高光谱图像技术结合模式识别方法，研究了镇江香醋固态发酵中产酸芽孢杆菌的快速识别方法。筛选3种芽孢杆菌为标准菌,以标准菌生长12 h的菌落为研究对象，利用高光谱成像系统采集图像：提取感兴趣区域（20×20）单菌落平均光谱共120条，并SNV预处理，采用主成分分析（PCA）从每幅图像优选3幅特征图像，并从每幅特征图像提取4个基于灰度共生矩阵的纹理特征变量；对光谱和图像纹理的特征变量均进行PCA，分别提取合适的主成分构建BP-ANN和KNN识别模型。其中，光谱模型识别效果优于图像模型，且BP-ANN光谱模型识别效果最优，对校正集和预测集样本的识别率分别为98.70%和97.78%，主成分因子数为5。研究表明，菌落内部特征是识别菌种属的关键，且利用高光谱图像技术识别细菌具有可行性，且快速简便。

Hyperspectral imaging technology combined with pattern recognition methods were used to rapidly identify the types of acid-producing Bacillus in the solid-state fermentation for the production of Zhenjiang balsamic vinegar. First, three species of Bacillus were screened as standard bacteria. After 12 h of growth, the standard bacterial colonies were used as study objects and images were collected using a hyperspectral imaging system. Next, a total of 120 average spectra of an area of interest (20 × 20) in a single colony were extracted and processed by standard normal variate transform. Principal component analysis (PCA) was used to select three images with a characteristic wavelength from each image, and four texture characteristic variables were extracted from each image with a characteristic wavelength based on a gray level co-occurrence matrix. Principal component analysis (PCA) was conducted on the characteristic variables of the spectra and image texture, and appropriate principle components were extracted to construct k-nearest neighbor and back propagation-artificial neural network identification models. Among them, the identification results for the spectral models were better than those of the image models, and the optimal result was obtained from the back propagation-artificial neural network spectral model, whose identification rates of calibration set and prediction set were 98.70% and 97.78%, respectively, and the number of principal component factors was five. The study shows that the internal characteristics of the bacterial colony are important for identifying the species of a colony, and hyperspectral image technology can be used for rapid and convenient bacterial identification.

高新技术发展计划国家863项目（2011AA100807）；全国优秀博士基金资助项目（200968）；国家自然科学基金（61301239）；新世纪优秀人才项目（NCET-11-00986）；江苏省杰出青年基金（BK20130010）；江苏省研究生创新基金（KYLX_1070）