近红外光谱法定性描述酵母菌的生长过程  

Qualitative Prediction of Yeast Growth Process Based on Near Infrared Spectroscopy

作　　者：王玮 江辉[1] 刘国海[1] 梅从立[1] 吉奕[1] 

机构地区：[1]江苏大学电气信息工程学院,镇江212013

出　　处：《分析化学》2017年第8期1137-1142,共6页Chinese Journal of Analytical Chemistry

基　　金：江苏省自然科学基金项目(No.BK20140538);中国博士后科学基金(No.2016M600381);江苏省高校自然科学研究面上项目(No.16KJB210003);江苏省高校研究生实践创新计划项目(No.SJZZ16_0193)资助

摘　　要：提出了一种基于近红外光谱分析技术的酵母菌生长过程描述方法。利用AntarisⅡ型傅里叶变换近红外光谱仪获取酵母菌培养过程中,发酵物样本在10000~4000 cm^(-1)范围内的光谱数据,同时采用光电比浊法测定各样本的光密度(Optical density,OD)值;运用竞争性自适应重加权采样(Competitive adaptive reweighted sampling,CARS)算法优选特征光谱,再利用极限学习机(Extreme learning machine,ELM)建立酵母菌生长过程4个阶段的分类模型。研究结果显示,参与CARS-ELM模型建立的波长个数为30,其10次运行在训练集和测试集中的平均识别率分别为98.68%和97.37%。研究结果表明,利用近红外光谱分析技术结合适当的化学计量学方法描述酵母菌生长过程是可行的。To improve the yield of industrial fermentation, a method based on near infrared spectroscopy was presented to predict the growth of yeast. The spectral data of fermentation sample were measured by Fourier- transform near-infrared （ FT-NIR） spectrometer in the process of yeast culture. Each spectrum was acquired over the range of 10000 - 4000 cm 1. Meanwhile, the optical density （ OD） of fermentation sample was determined with photoelectric turbidity method. After that, a method based on competitive adaptive reweighted sampling （ CARS） was used to select characteristic wavelength variables of NIR data, and then extreme learning machine （ ELM） algorithm was employed to develop the categorization model about the four growth processes of yeast. Experimental result showed that, only 30 characteristic wavelength variables of NIR data were selected by CRAS algorithms, and the prediction accuracies of training set and test set of the CARS-ELM model were 98.68% and 97.37% , respectively. The research showed that the near infrared spectrum analysis technology was feasible to predict the growth process of yeast.

关 键 词：酵母菌 近红外光谱 竞争性自适应重加权采样法 极限学习机 

分 类 号：O657.33[理学—分析化学] Q93[理学—化学]