Accurate control of the specific growth rate (µ) of microorganisms is dependent on the ability to quantify the evolution of biomass reliably in real time. Biomass concentration can be monitored online using various tools and methods, but the obtained signal is often very noisy and unstable, leading to inaccuracies in the estimation of μ. Furthermore, controlling the growth rate is challenging as the process evolves nonlinearly and is subject to unpredictable disturbances originating from the culture’s metabolism. In this work, a novel feedforward-feedback controller logic is presented to counter the problem of noise and oscillations in the control variable and to address the exponential growth dynamics more effectively. The controller was tested on fed-batch cultures of Kluyveromyces marxianus, during which μ was estimated in real time from online biomass concentration measurements obtained with dielectric spectroscopy. It is shown that the specific growth rate can be maintained at different setpoint values with an average root mean square control error of 23 ± 6%.

A global literature search identified only 123 formal, peer reviewed journal articles relating to chain management in the agrifood industry. The papers, published from 1987 to August 2000, were distributed unevenly across industry sectors and parts of the world. These quantitative results suggest that many of the opportunities for the agrifood industry to benefit from chain management research are yet to be explored. Information published in fora other than scholarly journals represents the bulk of the agrifood chain management literature. Acknowledges and discusses the relevance of this research and commentary.

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