Bachinger T, Mårtensson P, Mandenius C F
Department of Physics and Measurement Technology, Linköping University, Sweden.
J Biotechnol. 1998 Feb 5;60(1-2):55-66. doi: 10.1016/s0168-1656(97)00187-9.
A chemical multisensor array is used in combination with an artificial neural network to estimate the biomass concentration and specific growth rate in a recombination Escherichia coli batch cultivation. It is shown that by providing sufficient information to the artificial neural network, an accuracy comparable to that of an established dry weight method can be achieved. The obtained prediction error (1 sigma) of 0.043 g l-1 for biomass compares well with the error of the dry weight method in this low biomass concentration range (0.1-3 g l-1). The prediction for the specific growth rate is accurate during important parts of the cell growth (1 sigma = 0.025 h-1). The results show that this non-invasive method is potentially useful for estimating biomass and specific growth rate on-line in bioprocesses.
将化学多传感器阵列与人工神经网络结合使用，以估计重组大肠杆菌分批培养中的生物量浓度和比生长速率。结果表明，通过向人工神经网络提供足够的信息，可以实现与既定干重法相当的准确度。在这个低生物量浓度范围（0.1 - 3 g l-1）内，所获得的生物量预测误差（1σ）为0.043 g l-1，与干重法的误差相当。在细胞生长的重要阶段，比生长速率的预测是准确的（1σ = 0.025 h-1）。结果表明，这种非侵入性方法在生物过程中在线估计生物量和比生长速率方面具有潜在的应用价值。
