Monoalgal and mixed algal cultures discrimination by using an artificial neural network

Franco, B.M.; Navas, Luis M.; Gómez-Serrano, Cintia; Sepúlveda, Claudia; Acién, F.G.

doi:10.1016/j.algal.2019.101419

Cited by 42 publications

(12 citation statements)

References 40 publications

(42 reference statements)

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“…Franco et al [65] identified contamination of single-strain microalgal cultivations by other microalgal species using measurements of light absorption in each separate microalgal culture at 31 points between 400 and 700 nm in bandwidths of 10 nm on five consecutive days. Four microalgal strains were used: Nostoc, Scenedesmus, Spirulina and Chlorella.…”

Section: Mixed Culture Discriminationmentioning

confidence: 99%

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On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

et al. 2022

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Microalgae are promising sources of fuels and other chemicals. To operate microalgal cultivations efficiently, process control based on monitoring of process variables is needed. On-line sensing has important advantages over off-line and other analytical and sensing methods in minimizing the measurement delay. Consequently, on-line, in-situ sensors are preferred. In this respect, optical sensors occupy a central position since they are versatile and readily implemented in an on-line format. In biotechnological processes, measurements are performed in three phases (gaseous, liquid and solid (biomass)), and monitored process variables can be classified as physical, chemical and biological. On-line sensing technologies that rely on standard industrial sensors employed in chemical processes are already well-established for monitoring the physical and chemical environment of an algal cultivation. In contrast, on-line sensors for the process variables of the biological phase, whether biomass, intracellular or extracellular products, or the physiological state of living cells, are at an earlier developmental stage and are the focus of this review. On-line monitoring of biological process variables is much more difficult and sometimes impossible and must rely on indirect measurement and extensive data processing. In contrast to other recent reviews, this review concentrates on current methods and technologies for monitoring of biological parameters in microalgal cultivations that are suitable for the on-line and in-situ implementation. These parameters include cell concentration, chlorophyll content, irradiance, and lipid and pigment concentration and are measured using NMR, IR spectrophotometry, dielectric scattering, and multispectral methods. An important part of the review is the computer-aided monitoring of microalgal cultivations in the form of software sensors, the use of multi-parameter measurements in mathematical process models, fuzzy logic and artificial neural networks. In the future, software sensors will play an increasing role in the real-time estimation of biological variables because of their flexibility and extendibility.

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Section: Mixed Culture Discriminationmentioning

confidence: 99%

“…An ANN trained with light absorption data could differentiate between monoalgal and mixed algal cultures and identify contamination of a single strain culture by another species [65] (for more detail, see Section 2.4).…”

Section: Machine Learning Artificial Neural Networkmentioning

confidence: 99%

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

et al. 2022

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“…Optical sensing may be based on spectral absorption (e.g. used with artificial neural network (ANN) to differentiate microalgal species in suspension (Franco et al 2019)), reflectance (e.g. for the detection of diatoms in open ponds, (Reichardt et al 2020)), chlorophyll fluorescence (e.g.…”

Section: Introductionmentioning

confidence: 99%

Image-Based Analysis and Quantification of Biofouling in Cultures of the Red Alga Asparagopsis taxiformis

Dishon

Resetarits

Tsai

et al. 2022

Preprint

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Methane is an extremely potent yet short-lived greenhouse gas and is thus recognized as a promising target for rapid climate change mitigation. About 35% of anthropogenic methane emissions are associated with livestock production, and most of these emissions are the outcome of enteric fermentation in ruminant animals. The red seaweed Asparagopsis is currently considered the most efficient feed additive to suppress methane emissions from enteric fermentation but is not currently available on commercial scale. The ongoing effort to successfully commercialize Asparagopsis requires the development of pest control frameworks which rely on the quantitative assessment of biological contamination in cultures. Here we present a low-cost readily available approach for quantifying biofouling in Asparagopsis taxiformis cultures based on microscopic examination and automated image analysis. The proposed methodology is demonstrated to estimate contamination associated with Asparagopsis biomass with error rates lower than 2% over a wide range of contamination levels and contaminating organisms, while significantly cutting down image processing time and allowing for frequent contamination quantification.

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“…Up to date, several methods have been proposed to allow for increasingly rapid and accurate detection of culture contamination based on 'microbial/biological' composition analysis in the laboratory, in spot samples. To this end, different observational/imaging techniques (up to FlowCAM applications, [17], spectral fingerprints and also in association with artificial neural networks, ANNs, [18], and molecular diagnostics, e.g., quantitative real-time polymerase chain reaction (qPCR) [19,20] and microbiome next generation sequencing (NGS) to unveil predator or pathogen burden (NGS) [21]), were developed. In any case, these procedures focus on detecting/checking culture invasion in culture aliquots and require highly qualified staff involvement, and virtually include infrequent, discontinuous sampling and assays.…”

Section: Introductionmentioning

confidence: 99%

Keeping Track of Phaeodactylum tricornutum (Bacillariophyta) Culture Contamination by Potentiometric E-Tongue

Savio

Natale

Paolesse

et al. 2021

Sensors

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The large-scale cultivation of microalgae provides a wide spectrum of marketable bioproducts, profitably used in many fields, from the preparation of functional health products and feed supplement in aquaculture and animal husbandry to biofuels and green chemistry agents. The commercially successful algal biomass production requires effective strategies to maintain the process at desired productivity and stability levels. Hence, the development of effective early warning methods to timely indicate remedial actions and to undertake countermeasures is extremely important to avoid culture collapse and consequent economic losses. With the aim to develop an early warning method of algal contamination, the potentiometric E-tongue was applied to record the variations in the culture environments, over the whole growth process, of two unialgal cultures, Phaeodactylum tricornutum and a microalgal contaminant, along with those of their mixed culture. The E-tongue system ability to distinguish the cultures and to predict their growth stage, through the application of multivariate data analysis, was shown. A PLS regression method applied to the E-tongue output data allowed a good prediction of culture growth time, expressed as growth days, with R2 values in a range from 0.913 to 0.960 and RMSEP of 1.97–2.38 days. Moreover, the SIMCA and PLS-DA techniques were useful for cultures contamination monitoring. The constructed PLS-DA model properly discriminated 67% of cultures through the analysis of their growth media, i.e., environments, thus proving the potential of the E-tongue system for a real time monitoring of contamination in microalgal intensive cultivation.

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Monoalgal and mixed algal cultures discrimination by using an artificial neural network

Cited by 42 publications

References 40 publications

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

Image-Based Analysis and Quantification of Biofouling in Cultures of the Red Alga Asparagopsis taxiformis

Keeping Track of Phaeodactylum tricornutum (Bacillariophyta) Culture Contamination by Potentiometric E-Tongue

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