Department of Biotechnology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India.
Bioprocess Biosyst Eng. 2018 Jul;41(7):917-929. doi: 10.1007/s00449-018-1923-2. Epub 2018 Mar 21.
Direct measurement of cell biomass is difficult in a solid-state fermentation (SSF) process involving filamentous fungi since the mycelium and the solid substrate are often inseparable. However, respiratory data are rich in information for real-time monitoring of microbial biomass production. In this regard, a correlation was obtained between oxygen uptake rate (OUR) and biomass concentration (X) of Rhizopus oryzae MTCC 1987, during phytase production, in an intermittently mixed novel SSF bioreactor. To obtain the correlation, various models describing sigmoidal growth were tested, namely the logistic, Gompertz, Stannard, and Schnute models. Regression analysis of experimental results, at different operating conditions of inlet air flow rate and relative humidity suggested that OUR and X were correlated well by the logistic model (R > 0.90). To corroborate the use of respiratory data for on-line measurement of metabolic activity, OUR was related to metabolic heat generation rate (R), and the logistic model was found to satisfactorily correlate R and X as well. The model parameter, Y, when substituted into a heat transfer design equation, along with the values of other parameters and operating variables, gave reliable estimates of bed temperature. The correlations developed in the present study, between respiratory activity and biomass concentration may be extended on to other SSF processes for further validation and real-time monitoring of cell biomass and bed temperature.
直接测量固态发酵(SSF)过程中丝状真菌的细胞生物量是困难的,因为菌丝体和固体基质通常是不可分离的。然而,呼吸数据为实时监测微生物生物量的生产提供了丰富的信息。在这方面,我们在间歇式混合新型 SSF 生物反应器中获得了植酸酶生产过程中米根霉 MTCC 1987 的耗氧速率(OUR)和生物量浓度(X)之间的相关性。为了获得相关性,我们测试了各种描述 S 形生长的模型,即 logistic、Gompertz、Stannard 和 Schnute 模型。在不同的进气流量和相对湿度操作条件下对实验结果进行回归分析表明,OUR 和 X 通过 logistic 模型很好地相关(R > 0.90)。为了证实使用呼吸数据在线测量代谢活性,我们将 OUR 与代谢热生成率(R)相关联,发现 logistic 模型也很好地将 R 和 X 相关联。当模型参数 Y 代入传热设计方程,并结合其他参数和操作变量的值,可以可靠地估计床层温度。本研究中在呼吸活性和生物量浓度之间建立的相关性,可以扩展到其他 SSF 工艺,以进一步验证和实时监测细胞生物量和床层温度。