1. Introduction
Microorganisms are industrially used to produce either generic biomass or specific substances such as enzymes, amino acids or antibiotics. When biomass production is performed, usually a high yield is targeted. ForSaccharomyces cerevisiae (S. cerevisiae ) this is achieved by purely oxidative metabolism (Madigan, Bender et al. 2017). S. cerevisiae can use glucose as energy and carbon source for aerobic respiration. When a critical glucose concentration is exceeded, this microorganism produces ethanol. This is known as Crabtree effect and results in a lower biomass yield which might be, depending on the goal of the cultivation, undesirable (De Deken 1966). Therefore, in order to achieve high productivity of biomass continues real-time monitoring of ethanol concentration is required.
Bioprocess variables are of a chemical, physical, or biological nature and can be measured in the gas, liquid, and on solid phases of a bioprocess. On-line measurements of these variables make great demands on the sensing device. It is easier to meet these demands in the gas phase environment than in the liquid phase for various reasons: in the gas phase, the number of interfering substances is smaller and the mechanical stress on the sensor membrane is lower than in the stirred bioreactor liquid. In addition, prevention of so-called sensor fouling by cell adhesion is not necessary and a sterile barrier in the form of a mass filter can be easily introduced into the gas stream (Wild, Citterio et al. 1996).
Numerous methods attempt to measure the concentration of volatile organic compounds (VOCs) from the vapor phase. In recent years, particularly due to recent technological developments in sensor technology and computing power, gas sensor arrays (electronic nose techniques) have become valuable tools for VOC measurements. Generally, the sensor array technique is attractive for a number of significant features, such as the relatively fast assessment of headspace, a quantitative representation or qualitative identification of a gas and cheap chemical sensors which can be easily integrated in current production processes, thus becoming particularly suitable for the continuous monitoring of microbial fermentation processes (Jiang, Zhang et al. 2015). Recent applications of gas sensor arrays for monitoring fermentation process are reported in literature (Buratti and Benedetti 2016; Hidayat, Nuringtyas et al. 2018; Tan, Xie et al. 2018; Ghosh, Tudu et al. 2017; Li, Yuan et al. 2019; Tan, Balasubramanian et al. 2019). However, only a few works in literature demonstrate the application of gas sensor arrays for monitoring ethanol concentration during S.cerevisiae cultivation (Mandenius, Eklöv et al. 1997; Lidén, Mandenius et al. 1998; Bachinger and Mandenius 2001). In order to predict a specific volatile compound with a gas sensor array, chemometric modeling techniques are required. In the previous studies, the calibration methods for the chemometric models are limited to data-driven calibration methods. The main disadvantage of data-driven calibration methods is the huge amount of off-line data necessary to calculate a reliable model.
An alternative to data driven calibration method, which is a time consuming task, is the model-based calibration method. A statistical model-based approach for developing calibration models does not require the time expensive collection of samples for off-line measurements. Furthermore, this approach addresses some of the shortcomings of traditional calibration methods to study the entire system response which results in robust calibration. Lin et al. (Lin and Recke 2007) give a systematic approach for development of data-driven soft sensors. Model-based calibration approaches have been implemented on spectroscopy-based monitoring systems. Solle et. al (Solle, Geissler et al. 2003), as well as Paquet-Durand et al. (Paquet‐Durand, Assawarajuwan et al. 2017) had used this evaluation technique for the prediction of biomass, glucose, and ethanol during a S. cerevisiae cultivation. Furthermore, Paquet-Durand et al. (Paquet‐Durand, Ladner et al. 2017 a) applied this method for evaluation of fluorescence measurements during several parallel cultivations of H. polymorpha in a microtiter plate.
Based on fluorescence measurements Ödman et. al (Ödman, Johansen et al. 2009) and Solle et. al (Solle, Geissler et al. 2003) have evaluated yeast cultivations using glucose as substrate and developed chemometric models, one for the glucose consumption phase with concomitant ethanol production and one separate for the ethanol consumption phase (after glucose depletion). They have stated that it was difficult to use one and the same model for both phases. Paquet-Durand et al. (Paquet‐Durand, Assawarajuwan et al. 2017) examined artificial neural networks for the correlation between the fluorescence spectra with glucose, biomass and ethanol concentrations. They implemented a model-based training approach for training the neural network with only using a single model. They have reported an accurate prediction of glucose and biomass (error of prediction below 5%) however the prediction error of ethanol was 10 %. This is due to ethanol not being fluorescent and it could only be determined indirectly from the spectra. Therefore fluorescence-based monitoring methods are not the most accurate methods for predicting ethanol concentrations during S. cerevisiae cultivation process. In this contribution, ethanol concentration during yeast cultivation was predicted using a gas sensor array and chemometric modeling. The main contribution of this paper can be summarized as follow:
The results of the proposed calibration method are compared with a classical calibration method which the parameters of the model are acquired by least squares fitting to off-line measurements.
The remaining paper is organized as follows. Section 2 provides the materials and methods which were applied in this study. Section 3 provides the results and Section 4 concludes this paper.