## 5 Results and Discussion
### Prospects for the Development of the Methodology and Areas of Its Applicability  
Considering the prospects for topic development, including alternative research methods, the approach for determining the key parameter of the biosynthesis process developed in the current paper might represent the core for determination (and prediction) of the required characteristic based on classical computer vision methods, neural network algorithms and their possible superposition. The principal idea of handling the biosynthesis process key parameters in virtue of the demonstrated approach is based on the connectivity of a wide spectrum of fermentation process (occurring in a bioreactor) parameters at the physical level, where a combination of certain characteristics allows one to calculate parameters which cannot be explicitly observed. These characteristics may include degree of gas substrate utilization, crude protein yield, methane, air, titrate consumption, etc. Thus, regarding the problem of determining the phase contact area in gas-liquid flows, a special regression model can be developed, although the problem can be formulated in the form of classification of time series [53] for the case of discretization of the target variable state scale. In general, this approach corresponds to sensorless measurement, which is currently being actively developed [54], especially in the field of electrical engineering. The advantage of this approach lies in utilization of only those process characteristics that are measured using basic sensors with additional investments in equipment, while the disadvantages include the facts that, firstly, it is necessary to prepare a labeled dataset of sufficient size and, secondly, the development of the algorithm itself can be quite laborious, since there is no guarantee of a good connection between the measured parameters and the target  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Estimate** & **20 Hz** & **30 Hz** & **40 Hz** & **50 Hz** \\ \hline \multirow{2}{*}{CCV} & Mean total area of bubbles, 10\({}^{5}\) p.d.u. & 0.57 \(\pm\) 0.15 & 1.5 \(\pm\) 0.3 & 1.59 \(\pm\) 0.16 & 0.96 \(\pm\) 0.16 \\  & Mean number of bubbles & 160 \(\pm\) 39 & 334 \(\pm\) 66 & 330 \(\pm\) 45 & 204 \(\pm\) 26 \\ \hline \multirow{2}{*}{NN} & Mean total area of bubbles, 10\({}^{5}\) p.d.u. & 1.48 \(\pm\) 0.27 & 3.3 \(\pm\) 0.3 & 3.1 \(\pm\) 0.4 & 2.01 \(\pm\) 0.19 \\  & Mean number of bubbles & 186 \(\pm\) 34 & 340 \(\pm\) 29 & 326 \(\pm\) 30 & 253 \(\pm\) 13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected quantitative estimates of the applied methods: the metrics were considered due to their direct correspondence to project goals. One should note that the absolute values should be calibrated; thus, the standard deviations are more representative.  
value. In other words it might turn out that in order to solve the problem it is necessary to add a set of new measurements or change the sensor parameters. Thus, it would be necessary to conduct a joint analysis of measurement data, extract additional features from them using data mining techniques [55], and possibly reduce the dimension or get embeddings (for neural networks). Furthermore, it is necessary to choose and fine-tune the deep learning method to determine the target value, controlling possible overfitting. Good examples of this approach are known [56] when the only operational data from oil wells collected initially for other process purposes made it possible to develop an algorithm for determining lost circulations, diagnostics for which by traditional methods are known to be slow and pricey. In either event the demonstrated approach lays the foundation for modern method development for bioreactor bubble flow analysis, which is the core for understanding and controlling both its productivity and safety, and ultimately economic efficiency when applied on an industrial scale of biotechnological production.