柠檬酸发酵过程种子罐培养液质量参数的近红外预测模型  

Near-Infrared Prediction Models for Quality Parameters of Culture Broth in Seed Tank During Citric Acid Fermentation

作　　者：穆良银 赵忠盖 金赛 孙福新 刘飞 MU Liang-yin;ZHAO Zhong-gai;JIN Sai;SUN Fu-xin;LIU Fei(Key Laboratory for Advanced Process Control of Light Industry of Ministry of Education,Jiangnan University,Wuxi 214122,China;Jiangsu Guoxin Union Energy Co.,Ltd.,Wuxi 214122,China)

机构地区：[1]江南大学轻工过程先进控制教育部重点实验室,江苏无锡214122 [2]江苏国信协联能源有限公司,江苏无锡214122

出　　处：《光谱学与光谱分析》2024年第10期2819-2826,共8页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(61833007)资助。

摘　　要：在柠檬酸发酵过程中,种子罐中菌种培养的好坏将直接关系到发酵的水平,因此快速准确检测种子罐中培养液质量参数非常重要。柠檬酸发酵过程中种子罐培养液质量参数目前大多采用人工测量,无法满足实时监控和精确控制的需求。基于近红外光谱,针对种子罐培养液中总酸(TA)和还原糖(RS)的测量,构建了化学计量模型。首先,对原始光谱进行分析,为消除随机噪声以及减少批次差异性对于样本光谱的影响,依次采用平滑处理(SG)和去趋势处理(DT)的SG-DT方法进行光谱预处理。然后,利用间隔偏最小二乘(iPLS)方法对光谱进行特征波长选择,讨论了不同划分区间数对选择结果的影响,并确定了目标质量参数为总酸(TA)时的最佳划分区间数为21,特征波长个数为495,目标质量参数为还原糖(RS)时的最佳划分区间数为20,特征波长个数为361。分析光谱变量和质量参数变量之间的相关程度,引入BP网络建立总酸(TA)的校正模型,分别用PLSR和BP网络建立还原糖(RS)的校正模型,比较模型预测效果以确定最优模型。得到基于BP网络的总酸(TA)的最优预测模型的R_(p)^(2)为0.8085,RMSEP为0.1234;基于BP网络的还原糖(RS)的模型预测效果优于PLSR模型,最优模型的R_(p)^(2)为0.9647,RMSEP为0.1739。在复杂的柠檬酸发酵系统中实现了发酵过程中菌种培养过程多质量参数在线预测,为发酵过程的实时智能控制提供了依据。The quality of the bacterial strain cultivation in the seed tank during the citric acid fermentation process directly affects the fermentation level.Hence,it is crucial to accurately and rapidly detect the quality parameters of the culture solution in the seed tank.However,these parameters are currently largely measured manually,which does not meet real-time monitoring and precise control requirements.This paper builds a chemometric model for measuring the total acidity(TA)and reducing sugars(RS)in the seed tank's culture solution,based on near-infrared spectroscopy.Initially,the original spectra were analyzed,and to eliminate random noise and reduce batch variability effects on the sample spectra,the SG-DT method of smoothing(SG)and detrending(DT)were sequentially used for spectral preprocessing.Then,the Interval Partial Least Squares(iPLS)method was used for feature wavelength selection,the effect of different division intervals on the selection result was discussed,and the optimal division interval number for the target quality parameter of TA was determined to be 21,with 495 feature wavelengths.For RS,it was 20,with 361 feature wavelengths.Subsequently,the correlation between spectral variables and quality parameter variables was analyzed.A BP network was introduced to establish the calibration model for TA,and both PLSR and BP networks were used to establish the calibration model for RS,and model prediction effects were compared to determine the optimal model.Finally,the optimal prediction model for TA based on the BP network had an R_(p)^(2) of 0.8085 and an RMSEP of 0.1234.The model prediction effect of RS based on the BP network was superior to the PLSR model,with an R_(p)^(2) of 0.9647 and RMSEP of 0.1739.This paper has realized online prediction of multiple quality parameters during the bacterial strain cultivation process in the complex citric acid fermentation system,providing a basis for real-time intelligent control of the fermentation process.

关 键 词：柠檬酸发酵 种子罐培养液 质量参数 近红外光谱 预测模型 

分 类 号：O657.3[理学—分析化学]