Seeing good and bad: Optical sensing of microalgal culture condition

Solovchenko, Alexei

doi:10.1016/j.algal.2023.103071

Cited by 13 publications

(2 citation statements)

References 54 publications

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“…spectral data, image data, color features) and the target variable (i.e. concentration of fucoxanthin) that may not easily discernible through conventional methods [ 95 ].…”

Section: Digitalised Perspectives On the Quantification Of Organic Pi...mentioning

confidence: 99%

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Chong

Tang

Leong³

et al. 2023

Bioengineered

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Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R 2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R 2 accuracy ranging from 66.0% − 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.

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“…spectral data, image data, color features) and the target variable (i.e. concentration of fucoxanthin) that may not easily discernible through conventional methods [ 95 ].…”

Section: Digitalised Perspectives On the Quantification Of Organic Pi...mentioning

confidence: 99%

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Chong

Tang

Leong³

et al. 2023

Bioengineered

View full text Add to dashboard Cite

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“…CNNs are widely used to classify phytoplankton from images (Henrichs et al 2021; Kraft et al 2022) but have also been used to establish calibrations between reflectance spectra and Chl a (Aptoula and Ariman 2021) or phycocyanin (Pyo et al 2019). The combination of a spectral imager and machine learning are becoming the direction of development in monitoring cultivated microalgae (Solovchenko 2023); however, this approach could be beneficial for environmental motoring, too.…”

mentioning

confidence: 99%

Resolving phytoplankton pigments from spectral images using convolutional neural networks

Salmi,

Pölönen,

Beckmann

et al. 2023

Limnology & Ocean Methods

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Motivated by the need for rapid and robust monitoring of phytoplankton in inland waters, this article introduces a protocol based on a mobile spectral imager for assessing phytoplankton pigments from water samples. The protocol includes (1) sample concentrating; (2) spectral imaging; and (3) convolutional neural networks (CNNs) to resolve concentrations of chlorophyll a (Chl a), carotenoids, and phycocyanin. The protocol was demonstrated with samples from 20 lakes across Scotland, with special emphasis on Loch Leven where blooms of cyanobacteria are frequent. In parallel, samples were prepared for reference observations of Chl a and carotenoids by high‐performance liquid chromatography and of phycocyanin by spectrophotometry. Robustness of the CNNs were investigated by excluding each lake from model trainings one at a time and using the excluded data as independent test data. For Loch Leven, median absolute percentage difference (MAPD) was 15% for Chl a and 36% for carotenoids. MAPD in estimated phycocyanin concentration was high (102%); however, the system was able to indicate the possibility of a cyanobacteria bloom. In the leave‐one‐out tests with the other lakes, MAPD was 26% for Chl a, 27% for carotenoids, and 75% for phycocyanin. The higher error for phycocyanin was likely due to variation in the data distribution and reference observations. It was concluded that this protocol could support phytoplankton monitoring by using Chl a and carotenoids as proxies for biomass. Greater focus on the distribution and volume of the training data would improve the phycocyanin estimates.

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Non-invasive monitoring of microalgae cultivations using hyperspectral imager

Pääkkönen,

Pölönen,

Raita-Hakola

et al. 2024

J Appl Phycol

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High expectations are placed on microalgae as a sustainable source of valuable biomolecules. Robust methods to control microalgae cultivation processes are needed to enhance their efficiency and, thereafter, increase the profitability of microalgae-based products. To meet this need, a non-invasive monitoring method based on a hyperspectral imager was developed for laboratory scale and afterwards tested on industrial scale cultivations. In the laboratory experiments, reference data for microalgal biomass concentration was gathered to construct 1) a vegetation index-based linear regression model and 2) a one-dimensional convolutional neural network model to resolve microalgae biomass concentration from the spectral images. The two modelling approaches were compared. The mean absolute percentage error (MAPE) for the index-based model was 15–24%, with the standard deviation (SD) of 13-18 for the different species. MAPE for the convolutional neural network was 11–26% (SD = 10–22). Both models predicted the biomass well. The convolutional neural network could also classify the monocultures of green algae by species (accuracy of 97–99%). The index-based model was fast to construct and easy to interpret. The index-based monitoring was also tested in an industrial setup demonstrating a promising ability to retrieve microalgae-biomass-based signals in different cultivation systems.

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Seeing good and bad: Optical sensing of microalgal culture condition

Cited by 13 publications

References 54 publications

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Resolving phytoplankton pigments from spectral images using convolutional neural networks

Non-invasive monitoring of microalgae cultivations using hyperspectral imager

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