近红外光谱分析技术在监测L-异亮氨酸发酵过程中的应用

以L-异亮氨酸发酵过程的发酵液为样品, 用偏最小二乘法考察2种光谱采集方式、 7种光谱预处理方法及不同光谱波段选择对建立5种氨基酸光谱预测模型精度的影响. 通过建立L-异亮氨酸发酵过程中主副产物最佳光谱预测模型, 确定最佳光谱信息采集方式、 光谱预处理方法、 光谱波长范围及模型因子数. 结果表明: 反射扫描优于透射扫描获取光谱所建最佳预测模型; 反射光谱采集L- 异亮氨酸、 L-丙氨酸、 L-谷氨酸、 L-亮氨酸和L-苏氨酸5种氨基酸最佳校正模型相关系数均大于0.96, 其交互验证均方差分别为1.760,0.462,0.430,0.259,0.199, 相对分析误差分别为7.8,6.8,6.3,5.0,6.4， 表明所提出的近红外光谱分析法快速检测氨基酸发酵液中各成分稳定可行.

Application of Near Infrared Spectroscopy inMonitoring Fermentation Process of L-Isoleucine

Taking fermentation broth of fermentation process of L-isoleucine as the samples, using partial leastsquare method, we investigated effects of two spectral acquisition methods, 7 spectral pretreatment methods and different spectral band selection on the accuracy of prediction models of 5 amino acids. By establishing the optimal spectral prediction model of the main and by products in thefermentation process of L-isoleucine, we determined the optimal spectral information acquisition method, spectral pretreatment method, spectral wavelength range and the number of factors of the model. The results show that the optimal prediction model of the spectrum obtained by reflection scanning is superior to that of transmission scanning. The correlation coefficients (Rc) of the optimal correction model ofL-Ile, L-Ala, L-Glu, L-Leu and L-Thr are all greater than 0.96, the root mean square error of cross validation (RMSECV) are 1.760,0.462,0.430,0.259,0.199, and the residual predictive deviation (RPD) are 7.8,6.8,6.3,5.0,6.4, respectively. Therefore, the proposed near infrared spectroscopy is stable and feasible for rapid detection of various components in amino acid fermentation broth.