文献类型：期刊文献

中文题名：红外光谱-人工神经元网络法测定海藻培养过程中的碳酸盐浓度

英文题名：Determination of Carbonate in Algae Culture Process Using Infrared Spectrum and Processing with Artificial Neural Networks

作者：杭海峰[1];储炬[1];叶勤[1];张嗣良[1]

机构：[1]华东理工大学生物反应器工程国家重点实验室,华东理工大学国家生化工程技术研究中心(上海),上海200237

年份：2005

卷号：31

期号：4

起止页码：521

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;EI(收录号：2005389375494);Scopus;北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

语种：中文

中文关键词：红外光谱;人工神经元网络;海藻培养;碳酸盐

外文关键词：infrared spectroscopy; artificial neural networks; algae culture; carbonate

摘要：用傅里叶变换红外光谱法测定海藻培养液中碳酸盐浓度时,由于pH对碳酸盐特征吸收峰影响严重,本文用人工神经元网络处理红外光谱,用该方法计算得到对标准样品的预测标准误差为NaHCO30.08g/L,pH值为0.12,优于偏最小二乘法的结果。用该方法对实际螺旋藻培养过程中的碳源浓度进行了测定,预测标准误差为0.65g/L。
Fourier transform infrared spectroscopy was applied to the determination of carbonate in an algae culture. Artificial neural networks was used to solve the severe influence of pH on the infrared spectra in the characteristic region. The predicted standard errors of NaHCO3 and pH value in the standard samples are 0.08 g/L and 0. 12, respectively. The results are better than that using partial least square method. This method has been proved to be an effective way to quantify the carbonate and pH value in an algae culture process.