**Author details**

*Microalgae - From Physiology to Application*

affiliation.

**6. Conclusion**

or 3 nearest ones (see **Figure 7**). Therefore the ANN cannot make a correct decision. And the false result of ANN classificator in classification of 1763 *Nostoc* may due to the incorrect initial dataset or false a priori information about 1763 strain

To validate the correctness of the neural network operation, the results of the ANN classification were compared with the results of the LDA. The neural network-based classification agrees well with the expected results and with the results of LDA. The identification performance of the network for cyanobacterial strains from the same species is slightly less than for the cells from different species,

The automatization of the cyanobacterial species differentiation is a key problem in both industrial biomass production and environmental monitoring. Unfortunately, all presently utilized methods cannot be implemented in online monitoring procedures due to various reasons. In this work, an example of the use of LDA and ANN technologies for online differentiation of cyanobacterial strains according to their in vivo single-cell fluorescence spectra is presented. The novel discrimination technique demonstrated here includes a strict procedure for recording and processing single-cell fluorescence emission spectra, which eliminates most of usual data processing difficulties and, as a result, has a quite high classification accuracy. And the initial information is obtained via fluorescent spectroscopy; the experimental data can be processed automatically. Moreover, due to the use of CLSM microscopic spectroscopy instead of conventional fluorimetry, the initial data have less variations and can be accurately sorted. Any objectionable and unpredictable impact is eliminated at the first step of obtaining fluorescence spectra. Since noninvasive and nondistructive method is used, the information about vital cell operation (e.g., light harvesting) can be additionally taken into account, to

The universality of the considered technique makes it possible to use it for investigation of any phytoplankton species irrespective of their habitat or cultivation. Utilizing data from several fluorescence spectra, instead of one, results in more fingerprint information which leads to the taxonomic differentiation on a finer scale. Differentiation procedure, presented here, was carried out by means of statistical analysis on the base of mathematical characteristics of intrinsic fluorescence spectra of living single cells; therefore it is free from usual subjectivity, which can occur while using methods of direct optical microscopy. Moreover, formalization of data processing gives a wide opportunity for automating of the classification procedure of cyanobacterial strains in field samples, while online monitoring of

Undoubtedly, the data set should be expanded to include more species and phytoplankton classes/divisions, grown under different nutrient and light conditions. However, this work already demonstrates the potential of the discrimination of phytoplankton classes by means of fluorescence microscopic spectroscopy. Combining the knowledge of phytoplankton structure along with taxon-specific measurements of photosynthetic activity and biochemical cell composition can lead

to new models which increase the reliability of online monitoring.

but anyway they can also be distinguished perfectly well.

obtain the desirable precision of discrimination.

water bodies is conducted.

**20**

Natalia Grigoryeva St. Petersburg Scientific Research Centre for Ecological Safety of RAS, Saint-Petersburg, Russia

\*Address all correspondence to: renes3@mail.ru

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
