Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality

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Pyruvate cultures of C. tepidum exhibited higher CO2 fixation flux and biomass yield (YX/S = 9.2) compared to acetate cultures (YX/S = 6.4), albeit with slower growth rates.

“Pyruvate cultures exhibited higher CO2 fixation flux and biomass yield (YX/S = 9.2) compared to acetate cultures (YX/S = 6.4), albeit with slower growth rates.”

• cites [62] Feng, 2010, Metabolic flux analysis of the mixotrophic metabolisms in the green sulfur bacterium Chlorobaculum tepidum, J. Biol. Chem. sourceSupported

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Feng et al. (2010) metabolic flux analysis of Chlorobaculum tepidum under mixotrophic conditions uncovered an alternative isoleucine synthesis pathway via citramalate synthase and key CO2 fixation routes such as the TCA cycle and pyruvate synthesis pathway.

“Feng et al. (2010) [62] investigated carbon metabolism in Chlorobaculum tepidum under mixotrophic conditions using metabolic flux analysis (MFA), uncovering an alternative isoleucine synthesis pathway via citramalate synthase and key CO2 fixation routes such as the TCA cycle and the pyruvate synthesis pathway.”

• cites [62] Feng, 2010, Metabolic flux analysis of the mixotrophic metabolisms in the green sulfur bacterium Chlorobaculum tepidum, J. Biol. Chem. sourceSupported

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Microalgae can simultaneously yield lipids, proteins, pigments, and bioactive compounds, enhancing economic feasibility.

“Unlike soybeans, microalgae can simultaneously yield lipids, proteins, pigments, and bioactive compounds, significantly enhancing economic feasibility [1,13,14].”

• cites [1] Yang, 2024, Microalgal protein for sustainable and nutritious foods: A joint analysis of environmental impacts, health benefits and consumer’s acceptance, Trends Food Sci. Technol. sourceNot checked — no full text of the source
• cites [13] Oostlander, 2020, Microalgae production cost in aquaculture hatcheries, Aquaculture sourceCould not verify

  The Conclusion (section 4; page unavailable) discusses reducing microalgae biomass production costs through lighting, improved yield and lower labour requirements. Our copy does not address simultaneous production of lipids, proteins, pigments and bioactive compounds, compare microalgae with soybeans, or link those products to economic feasibility. Searches for those products, co-production and soybeans found no supporting passage. (LLM, conf=0.76)

  Experimental — accuracy not yet measured
• cites [14] Blaustein-Rejto, D., Merener, N., and Smith, A. (2024). The potential footprint of alternative meat adoption on corn and soybean producers. Front. Sustain. Food Syst., 8. sourceCould not verify

  The Introduction and Materials and methods (§2.1–2.2; pages unavailable) discuss soybean use in animal feed and plant-based meat, including soybean meal, oil, and protein. Searches for microalgae, pigments, bioactive compounds, and coproducts found no passage addressing whether microalgae yields the listed products or whether doing so improves economic feasibility. (LLM, conf=0.76)

  Experimental — accuracy not yet measured

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A case study on C. vulgaris under varying N:P molar ratios (2-67) demonstrated that nitrogen or phosphorus limitation significantly affected nutrient uptake efficiency, although biomass productivity remained relatively stable.

“Additionally, a case study on C. vulgaris cultivation under varying N:P molar ratios (2–67) demonstrated that nitrogen or phosphorus limitation significantly affected nutrient uptake efficiency, although biomass productivity remained relatively stable.”

• cites [33] Salgado, 2023, Microalgal cultures for the remediation of wastewaters with different nitrogen to phosphorus ratios: Process modelling using artificial neural networks, Environ. Res. sourceSupported

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Nonstationary 13C flux analysis revealed low photorespiration in phototrophic cells and high TCA cycle activity in heterotrophic cells.

“Nonstationary 13C flux analysis revealed low photorespiration in phototrophic cells and high TCA cycle activity in heterotrophic cells.”

• cites [51] Wu, 2014, Genome-based metabolic mapping and 13C flux analysis reveal systematic properties of an oleaginous microalga Chlorella protothecoides, Plant Physiol. sourceSupported

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This review summarized case studies demonstrating up to 57% improvement in biomass productivity and over 43% enhancement in lipid and high-value compound yields.

“This review highlighted the transformative role of AI in microalgae biotechnology, summarizing case studies that demonstrated up to 57% improvement in biomass productivity and over 43% enhancement in lipid and high-value compound yields.”

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Makaranga et al. (2023) co-culturing Chlorella saccharophila UTEX247 with Exiguobacterium sp. achieved lutein productivity of 298.97 μg L−1 d−1, 1.45 times higher than monocultures.

“Makaranga et al. (2023) [59] optimized lutein production in Chlorella saccharophila UTEX247 through co-culturing with Exiguobacterium sp., thereby achieving a lutein productivity of 298.97 µg L−1 d−1—1.45 times higher than monocultures.”

• cites [59] Makaranga, 2023, Dynamic metabolomic crosstalk between Chlorella saccharophila and its new symbiotic bacteria enhances lutein production in microalga without compromising its biomass, Enzym. Microb. Technol. sourceNot checked — no full text of the source

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Nannochloropsis species can achieve annual biomass production of approximately 127.8 metric ton/acre/year, outpacing traditional oilseed crops.

“Research demonstrated that Nannochloropsis species could achieve an annual biomass production of approximately 127.8 metric ton/acre/year, outpacing traditional oilseed crops [6].”

• cites [6] Mohammady, 2022, Preliminary study on the economic assessment of culturing Nannochloropsis sp. in Egypt for the production of biodiesel and high-value biochemicals, Biomass Convers. Biorefin. sourceNot checked — no full text of the source

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Current AI models have limitations in data availability and species-specific variability.

“Additionally, we discussed the limitations of current AI models, particularly in data availability and species-specific variability, and suggested future research directions to enhance the integration of AI and microalgae systems.”

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Muthuraj et al. (2013) performed flux balance analysis and dynamic FBA on Chlorella sp. FC2 IITG, revealing metabolic shifts including inactivity of pentose phosphate pathway under photoautotrophic growth and elevated glycolytic flux coupled with lipid induction during light phase.

“In addition, Muthuraj et al. (2013) [50] performed flux balance analysis (FBA) and dynamic FBA (dFBA) on Chlorella sp. FC2 IITG to study carbon flux distribution under light–dark cycles under photoautotrophic and heterotrophic conditions. The study revealed metabolic shifts, including the inactivity of the pentose phosphate pathway under photoautotrophic growth and elevated glycolytic flux coupled”

• cites [50] Muthuraj, 2013, Flux balance analysis of Chlorella sp. FC2 IITG under photoautotrophic and heterotrophic growth conditions, Photosynth. Res. sourceNot checked — no full text of the source

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Zhao et al. (2021) melatonin treatment enhanced coproduction of astaxanthin and lipids in H. pluvialis, increasing yields 1.78- and 1.3-fold, respectively.

“Zhao et al. (2021) [60] demonstrated that melatonin (MT) treatment enhanced the coproduction of astaxanthin and lipids in H. pluvialis, increasing yields 1.78- and 1.3-fold, respectively.”

• cites [60] Zhao, Y., Wang, H.-P., Yu, C., Ding, W., Han, B., Geng, S., Ning, D., Ma, T., and Yu, X. (2021). Integration of physiological and metabolomic profiles to elucidate the regulatory mechanisms underlying the stimulatory effect of melatonin on astaxanthin and lipids coproduction in Haematococcus pluvialis under inductive stress conditions. Bioresour. Technol., 319. sourceNot checked — no full text of the source

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Panahi et al. (2019) combined RNA-seq meta-analysis with supervised ML to explore salt stress responses of Dunaliella salina and D. tertiolecta, identifying core meta-genes linked to lipid and nitrogen metabolism, photosynthetic apparatus proteins, autophagy, and ROS-related pathways.

“Panahi et al. (2019) [41] combined RNA-seq meta-analysis with supervised ML to explore the salt stress responses of Dunaliella salina and Dunaliella tertiolecta. Using Fisher’s p-value combination for cross-species analysis, the study identified core meta-genes linked to lipid and nitrogen metabolism, photosynthetic apparatus proteins, autophagy, and ROS-related pathways.”

• cites [41] Panahi, B., Frahadian, M., Dums, J.T., and Hejazi, M.A. (2019). Integration of cross species RNA-Seq meta-analysis and machine-learning models identifies the most important salt stress–responsive pathways in microalga Dunaliella. Front. Genet., 10. sourceNot checked yet

  Checked before — its earlier verdict is held back until a new check runs.

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The system was validated using a separate dataset, demonstrating prediction accuracy of 92%.

“The system was validated using a separate dataset, demonstrating a prediction accuracy of 92%, thus showcasing its effectiveness in optimizing environmental parameters for enhanced microalgae growth.”

• cites [36] Kim, 2016, Developing an algae culturing system using a microcontroller platform, Int. J. Biotechnol. Food Sci.Not checked — no full text of the source

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Microalgal cultivation in an outdoor photobioreactor removed 9-17% of industrial CO2 from cement flue gas, with efficiencies reaching 20-60% under optimal conditions and carbon recovery up to 10 g m2 d−1.

“Previous study demonstrated that microalgal cultivation in an outdoor photobioreactor removed 9–17% of industrial CO2 from cement flue gas, with efficiencies reaching 20–60% under optimal conditions and carbon recovery up to 10 g m2 d−1 [7].”

• cites [7] Lindehoff, E., Mattsson, L., Olofsson, M., Svensson, F., Farnelid, H., and Legrand, C. (2024). Biomass performance and stability of 5-year outdoor microalgal cultivation for CO2 removal from cement flue gas. Bioresour. Technol. Rep., 25. sourceNot checked — no full text of the source

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Wang et al. (2012) label-free proteomics identified 896 proteins and quantified 329, while iTRAQ identified 639 proteins and quantified 124 in two C. reinhardtii strains.

“The label-free method identified 896 proteins and quantified 329, while iTRAQ identified 639 proteins and quantified 124, highlighting mechanisms driving lipid production in sta6.”

• cites [56] Wang, 2012, Comprehensive Comparison of iTRAQ and label-free LC-based quantitative proteomics approaches using two Chlamydomonas reinhardtii strains of interest for biofuels engineering, J. Proteome Res. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

Variability among microalgae species and environmental conditions requires development of adaptable AI algorithms capable of generalizing across diverse systems.

“Additionally, the variability among microalgae species and environmental conditions requires the development of adaptable AI algorithms capable of generalizing across diverse systems.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Haematococcus pluvialis produces astaxanthin up to 50 mg g−1 dry biomass under stress conditions.

“For instance, Haematococcus pluvialis, known for its high astaxanthin content, has demonstrated production levels of up to 50 mg g−1 of dry biomass under stress conditions [11].”

• cites [11] Sun, 2015, Enhancement of cell biomass and cell activity of astaxanthin-rich Haematococcus pluvialis, Bioresour. Technol. sourceNot checked — no full text of the source

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A BPNN optimized with GA and PSO predicted harvesting efficiency in ballasted flotation, with GA-BPNN showing best performance (R2 = 0.923, MAE = 0.041).

“A backpropagation neural network (BPNN) was applied to predict harvesting efficiency in a ballasted flotation based on diverse input features, including microalgal and LDM properties. To improve predictive accuracy, the BPNN model was optimized using GA and particle swarm optimization (PSO), with GA-BPNN showing the best performance (R2 = 0.923, MAE = 0.041).”

• cites [47] Xu, K., Zhu, Z., Yu, H., and Zou, X. (2025). Prediction of microalgae harvesting efficiency and identification of important parameters for ballasted flotation using an optimized machine learning model. Algal Res.-Biomass Biofuels Bioprod., 87. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Availability of high-quality, standardized datasets is a major hurdle for training AI models in microalgae technology.

“A major hurdle is the availability of high-quality, standardized datasets for training AI models.”

May combine two claims.

In the Chlorella saccharophila UTEX247 co-culture, biomass increased 0.84-fold under optimal conditions, and metabolomics identified 75 metabolites, 46 of which were unique.

“Biomass increased 0.84-fold under optimal conditions, with metabolomics identifying 75 metabolites in the co-culture, 46 of which were unique.”

• cites [59] Makaranga, 2023, Dynamic metabolomic crosstalk between Chlorella saccharophila and its new symbiotic bacteria enhances lutein production in microalga without compromising its biomass, Enzym. Microb. Technol. sourceNot checked — no full text of the source

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Hossain et al. (2022a) applied SVR with GA to optimize N:P ratio for Chlorella kessleri in municipal wastewater, identifying optimal N:P 6:1 and achieving nutrient removal efficiencies over 93% at 29.3 °C and continuous light.

“For instance, Hossain et al. (2022a) [32] applied SVR combined with a GA to optimize the nitrogen–phosphorus (N:P) ratio for Chlorella kessleri in municipal wastewater treatment. Their model identified an optimal N:P ratio of 6:1, achieving nutrient removal efficiencies of over 93% under conditions of 29.3 °C and continuous light (24/0 h/h).”

• cites [32] Hossain, 2022, Soft-computing modeling and multiresponse optimization for nutrient removal process from municipal wastewater using microalgae, J. Water Process Eng. sourceNot checked — no full text of the source

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The process resulted in a CO2 sequestration rate of 578.1 ± 23.1 mg L−1 d−1, COD reduction of 95.9 ± 2.4%, and lipid productivity of 106.4 mg L−1 d−1, with biodiesel quality meeting international standards.

“The process resulted in a CO2 sequestration rate of 578.1 ± 23.1 mg L−1 d−1, COD reduction of 95.9 ± 2.4%, and lipid productivity of 106.4 mg L−1 d−1, with biodiesel quality meeting international standards [42].”

• cites [42] Nayak, 2018, Artificial intelligence driven process optimization for cleaner production of biomass with co-valorization of wastewater and flue gas in an algal biorefinery, J. Clean. Prod. sourceNot checked — no full text of the source

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Xu et al. (2020) developed THMI combined with ML, achieving spatial and spectral resolutions of 4 μm and 3 nm, and classifying microalgae species with 94.4% accuracy using PCA and SVM.

“Xu et al. (2020) [37] developed a transmission hyperspectral microscopic imager (THMI) combined with ML for microalgae cultivation optimization. The system achieved high spatial and spectral resolutions (4 µm and 3 nm), using principal component analysis (PCA) and support vector machine (SVM) to classify microalgae species with 94.4% accuracy.”

• cites [37] Xu, 2020, Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning, Opt. Express sourceNot checked — no full text of the source

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Christian Barbosa et al. (2020) developed a cost-effective sensor for real-time monitoring of microalgae biomass concentration, showing strong linearity with dry weight in the range 40-800 mg L−1.

“Christian Barbosa et al. (2020) [43] developed a cost-effective sensor for real-time monitoring of microalgae biomass concentration, addressing challenges in optimizing growth conditions. The sensor, utilizing ultraviolet and near-infrared wavelengths, showed strong linearity with dry weight (DW) measurements in the range of 40–800 mg L−1, closely matching spectrophotometer readings.”

• cites [43] Soares, 2020, Low-cost and versatile sensor based on multi-wavelengths for real-time estimation of microalgal biomass concentration in open and closed cultivation systems, Comput. Electron. Agric. sourceNot checked — no full text of the source

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The DNN model achieved prediction accuracy of 95% with error within ±1 °C.

“The DNN model achieved a prediction accuracy of 95% with a prediction error within ±1 °C, demonstrating its precision in temperature optimization, leading to enhanced CO2 capture efficiency and biomass productivity, in turn emphasizing the critical role of precise temperature control in microalgae cultivation systems.”

• cites [30] Janjua, M.Y., Azfar, A., Asghar, Z., and Shehzad Quraishi, K. (2024). Modeling and optimization of biomass productivity of Chlorella vulgaris using response surface methodology, analysis of variance and machine learning for carbon dioxide capture. Bioresour. Technol., 400. sourceNot checked — no full text of the source

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Janjua et al. (2024) identified optimal temperature of 29.55 °C for C. vulgaris CO2 capture using DNN, outperforming RSM prediction of 28.74 °C.

“Their study identified an optimal temperature of 29.55 °C with DNN, outperforming RSM’s prediction of 28.74 °C.”

• cites [30] Janjua, M.Y., Azfar, A., Asghar, Z., and Shehzad Quraishi, K. (2024). Modeling and optimization of biomass productivity of Chlorella vulgaris using response surface methodology, analysis of variance and machine learning for carbon dioxide capture. Bioresour. Technol., 400. sourceNot checked — no full text of the source

May combine two claims.

The ANN model by Yu et al. was validated using a separate dataset, demonstrating prediction error less than 5%.

“The model was validated using a separate dataset, demonstrating a prediction error of less than 5%, which highlights its robustness and reliability in optimizing microalgae growth, thereby demonstrating its effectiveness in optimizing microalgae growth.”

• cites [21] Yu, T., Fan, F., Huang, L., Wang, W., Wan, M., and Li, Y. (2024). Artificial neural networks prediction and optimization based on four light regions for light utilization from Synechocystis sp. PCC 6803. Bioresour. Technol., 394. sourceNot checked — no full text of the source

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Yu et al. (2024) developed an ANN growth model for Synechocystis sp. PCC 6803 based on four light regions, achieving validation R2 of 0.97, 76.36% more accurate than traditional light-dark models.

“For example, Yu et al. (2024) [21] developed an artificial neural network (ANN) growth model for Synechocystis sp. PCC 6803 based on four light regions (FLRs), achieving a validation R2 value of 0.97, 76.36% more accurate than traditional light–dark models.”

• cites [21] Yu, T., Fan, F., Huang, L., Wang, W., Wan, M., and Li, Y. (2024). Artificial neural networks prediction and optimization based on four light regions for light utilization from Synechocystis sp. PCC 6803. Bioresour. Technol., 394. sourceNot checked — no full text of the source

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Fernández Izquierdo et al. (2024) used ANN-GA to optimize light conditions for polyphenol production in Parachlorella kessleri, identifying white light at 1000 lx and 12:12 light-dark cycle as optimal, yielding 28 mg GAE/g DW.

“For instance, Fernández Izquierdo et al. (2024) [28] employed an ANN coupled with a genetic algorithm (ANN-GA) to optimize light conditions for polyphenol production in Parachlorella kessleri. The model identified white light at an intensity of 1000 lx and a 12:12 light–dark cycle as optimal, contributing to a polyphenol yield of 28 mg gallic acid equivalents per gram dry weight (GAE/g DW),”

• cites [28] 2024, Application of an artificial neural network coupled to a genetic algorithm for the production of polyphenols in Parachlorella kessleri grown under mixotrophic conditions, Algal Res. sourceNot checked — no full text of the source

May combine two claims. The quote only partly states this claim.

The ANN model demonstrated superior computational efficiency and better generalization across light intensities and photoperiods than mechanistic and hybrid models.

“Furthermore, the ANN model demonstrated superior computational efficiency, requiring significantly less time for training and prediction compared to the mechanistic and hybrid models.”

• Also stated in: § 2.1. Optimizing Cultivation Conditions · plain

  “Furthermore, the ANN model showed better generalization capabilities, performing well across a range of light intensities and photoperiods.”

• cites [28] 2024, Application of an artificial neural network coupled to a genetic algorithm for the production of polyphenols in Parachlorella kessleri grown under mixotrophic conditions, Algal Res. sourceNot checked — no full text of the source

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Hossain et al. (2022b) used SVR with Bayesian optimization to enhance C. vulgaris biomass productivity and CO2 biofixation, achieving R2 of 0.911 and optimal conditions of 40 °C, N:P 1:1, 12/12 light-dark, yielding 0.0979 g L−1 d−1 BP and 0.1408 g L−1 d−1 RCO2.

“For instance, Hossain et al. (2022b) [25] used SVR with Bayesian optimization to enhance C. vulgaris biomass productivity (BP) and CO2 biofixation (RCO2), achieving a high R2 of 0.911 and optimizing conditions to 40 °C, an N:P ratio of 1:1, and a 12/12 light–dark cycle, yielding 0.0979 g L−1 d−1 BP and 0.1408 g L−1 d−1 RCO2.”

• cites [25] Hossain, 2022, Modeling and multi-objective optimization of microalgae biomass production and CO2 biofixation using hybrid intelligence approaches, Renew. Sustain. Energy Rev. sourceNot checked — no full text of the source

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In the mixed culture, H. pluvialis benefited from pyruvate and CO2 excreted by P. rhodozyma, reducing carbon flux from the Calvin cycle to catabolic pathways by 33% while increasing flux to glyceraldehyde-3-phosphate by 25%.

“Conversely, H. pluvialis benefited from pyruvate and CO2 excreted by P. rhodozyma, reducing carbon flux from the Calvin cycle to catabolic pathways by 33% while increasing flux to glyceraldehyde-3-phosphate by 25%.”

• cites [63] Dong, 2006, Metabolic flux analysis of the two astaxanthin-producing microorganisms Haematococcus pluvialis and Phaffia rhodozyma in the pure and mixed cultures, Biotechnol. J. sourceNot checked — no full text of the source

May combine two claims.

Heidari Baladehi et al. (2021) established a reference ramanome database from over 9000 cells of 27 microalgal species and classified species and growth states with 97% accuracy using ensemble ML.

“Heidari Baladehi et al. (2021) [39] established a reference ramanome database comprising single-cell Raman spectra (SCRS) from over 9000 cells of 27 diverse microalgal species in stationary and exponential phases. By combining pre-quenching “pigment spectrum” (PS) and post-quenching “whole spectrum” (WS) signals, species and growth states were classified with 97% accuracy using ensemble ML,”

• cites [39] Hekmatara, 2021, Culture-free identification and metabolic profiling of microalgal single cells via ensemble learning of Ramanomes, Anal. Chem. sourceNot checked — no full text of the source

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Coşgun et al. (2021) analyzed a dataset of 4670 instances from 102 published studies using ML and identified 11 combinations linked to high biomass productivity and 13 for high lipid content.

“Coşgun et al. (2021) [49] analyzed a dataset of 4670 instances from 102 published studies, using ML to evaluate the impact of cultivation and processing parameters on microalgal biomass and lipid production. By applying decision trees and association rule mining, they identified 11 combinations linked to high biomass productivity and 13 for high lipid content, demonstrating that ML can effectively”

• cites [49] Cosgun, 2021, Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning, Renew. Energy sourceNot checked — no full text of the source

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Kim et al. (2016) developed a microcontroller-based system with MG-811 CO2 sensor that maintained CO2 concentrations above 350 ppm, triggering at 2.75 V.

“Kim et al. (2016) [36] developed a microcontroller-based system incorporating an MG-811 CO2 sensor to manage CO2 levels in microalgae cultivation. The sensor, highly sensitive to CO2 and equipped with a heating circuit, maintained concentrations above 350 ppm, thereby triggering responses at a voltage of 2.75 V.”

• cites [36] Kim, 2016, Developing an algae culturing system using a microcontroller platform, Int. J. Biotechnol. Food Sci.Not checked — no full text of the source

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The incorporation of AI in the production process enhances the economic viability of high-value microalgae products by improving yields, reducing production costs, and accelerating time-to-market.

“The incorporation of AI in the production process enhances the economic viability of these products by improving yields, reducing production costs, and accelerating time-to-market.”

• AssumptionRests on an assumption: The global market for high-value products derived from microalgae is rapidly expanding, driven by increasing consumer demand for natural and sustainable products.

Probably not a substantial claim: may not pass S3 (load-bearing).

Genome-scale reconstructions (GEMs) are developed using databases such as KEGG and MetaCyc, which provide comprehensive information on metabolic pathways and enzymes from various organisms.

“Genome-scale reconstructions (GEMs) are developed using databases such as KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc, which provide comprehensive information on metabolic pathways and enzymes from various organisms [54].”

• cites [54] Sun, 2021, Powerful tools for productivity improvements in microalgal production, Renew. Sustain. Energy Rev. sourceNot checked — no full text of the source

May combine two claims.

Wu et al. (2014) reconstructed metabolic network of C. protothecoides included 272 reactions, enabling simulations of phototrophic and heterotrophic growth.

“The reconstructed metabolic network included 272 reactions, enabling simulations of phototrophic and heterotrophic growth.”

• cites [51] Wu, 2014, Genome-based metabolic mapping and 13C flux analysis reveal systematic properties of an oleaginous microalga Chlorella protothecoides, Plant Physiol. sourceNot checked yet

  Checked before — its earlier verdict is held back until a new check runs.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Data and model limitations pose significant challenges to implementation and optimization of AI-driven solutions in microalgae technologies.

“In the research and development of microalgae technologies, data and model limitations pose significant challenges to the implementation and optimization of AI-driven solutions.”

May combine two claims.

Many microalgae experiments are conducted on a small scale, resulting in datasets that may not be sufficiently comprehensive to represent a broader range of environmental and operational conditions.

“Many microalgae experiments are conducted on a small scale, resulting in datasets that may not be sufficiently comprehensive to represent a broader range of environmental and operational conditions.”

May combine two claims.

While challenges such as data quality and model generalization remain, the integration of AI offers a promising pathway to achieve scalable, efficient, and economically viable microalgae-based solutions for biofuel and high-value product production.

“While challenges such as data quality and model generalization remain, the integration of AI offers a promising pathway to achieve scalable, efficient, and economically viable microalgae-based solutions for biofuel and high-value product production.”

May combine two claims.

Through real-time monitoring, predictive modeling, and automated control systems, AI has proven effective in optimizing cultivation parameters, enhancing CO2 sequestration, and streamlining harvesting and processing.

“Through real-time monitoring, predictive modeling, and automated control systems, AI has proven effective in optimizing cultivation parameters, enhancing CO2 sequestration, and streamlining harvesting and processing.”

May combine two claims.

Some studies utilized different metrics (such as photosynthetic efficiency and carbon dioxide absorption rates) to evaluate microalgal performance, making it challenging to combine diverse datasets.

“For example, some studies utilized different metrics (such as photosynthetic efficiency and carbon dioxide absorption rates) to evaluate microalgal performance [65], making it challenging to combine diverse datasets.”

• cites [65] Helmy, 2023, Perspective: Multiomics and machine learning help unleash the alternative food potential of microalgae, Adv. Nutr. sourceNot checked yet

  Checked before — its earlier verdict is held back until a new check runs.

May combine two claims.

At the current stage of development, AI can be effectively applied to optimize specific cultivation parameters, monitor growth dynamics, predict yields, and support decision-making in laboratory and pilot-scale operations.

“At the current stage of development, AI can be effectively applied to optimize specific cultivation parameters, monitor growth dynamics, predict yields, and support decision-making in laboratory and pilot-scale operations.”

May combine two claims.

The integration of AI in production of high-value products from microalgae has the potential to optimize yield, enhance extraction processes, and accelerate product development.

“The integration of AI in the production of high-value products from microalgae has the potential to optimize yield, enhance extraction processes, and accelerate product development.”