Rotatable central composite design versus artificial neural network for modeling biosorption of Cr(6+) by the immobilized Pseudomonas alcaliphila NEWG-2

Artificial Intelligence-Powered Optimization and Milk Permeate Upcycling for Innovative Sesame Milk with Enhanced Probiotic Viability and Sensory Appeal

Consumer demand for plant-based alternatives drives innovation in nondairy beverages. This study explores the development of a novel sesame milk with enhanced functionality using an artificial neural network (ANN) and milk permeate integration. An ANN model effectively optimized water-based sesame milk (WSM) extraction, maximizing total solids (T.S.) recovery. The ANN model's predicted T.S. yield (99.65%) closely matched the actual value (95.18%), demonstrating its potential for optimizing high-yield production. Furthermore, milk permeate was incorporated (5:1 ratio) to create permeate-based sesame milk (PSM), which supported the growth of lactic acid bacteria, suggesting its potential as a growth medium for future probiotic applications. PSM also displayed superior nutritional value and sensory characteristics compared to WSM. These findings highlight the promise of ANN-powered optimization and milk permeate integration for creating innovative sesame milk alternatives with enhanced probiotic viability and sensory appeal. Future research should focus on ANN optimization of alternative-based-plant milk, including permeate-based sesame milk production, the health benefits of LAB fermentation, and consumer preferences for flavors and textures. Optimizing fermentation and LAB selection remain key for commercial success.

Removing Heavy Metals: Cutting-Edge Strategies and Advancements in Biosorption Technology

This article explores recent advancements and innovative strategies in biosorption technology, with a particular focus on the removal of heavy metals, such as Cu(II), Pb(II), Cr(III), Cr(VI), Zn(II), and Ni(II), and a metalloid, As(V), from various sources. Detailed information on biosorbents, including their composition, structure, and performance metrics in heavy metal sorption, is presented. Specific attention is given to the numerical values of the adsorption capacities for each metal, showcasing the efficacy of biosorbents in removing Cu (up to 96.4%), Pb (up to 95%), Cr (up to 99.9%), Zn (up to 99%), Ni (up to 93.8%), and As (up to 92.9%) from wastewater and industrial effluents. In addition, the issue of biosorbent deactivation and failure over time is highlighted as it is crucial for the successful implementation of adsorption in practical applications. Such phenomena as blockage by other cations or chemical decomposition are reported, and chemical, thermal, and microwave treatments are indicated as effective regeneration techniques. Ongoing research should focus on the development of more resilient biosorbent materials, optimizing regeneration techniques, and exploring innovative approaches to improve the long-term performance and sustainability of biosorption technologies. The analysis showed that biosorption emerges as a promising strategy for alleviating pollutants in wastewater and industrial effluents, offering a sustainable and environmentally friendly approach to addressing water pollution challenges.

Integrating artificial neural networks and response surface methodology for predictive modeling and mechanistic insights into the detoxification of hazardous MB and CV dyes using Saccharum officinarum L. biomass

The presence of dye pollutants in industrial wastewater poses significant environmental and health risks, necessitating effective treatment methods. The optimal adsorption treatment of methylene blue (MB) and crystal violet (CV) dye-simulated wastewater utilising Saccharum officinarum L presents a key challenge in the selection of appropriate modelling approaches. While RSM and ANN models are frequently used, there is a noticeable knowledge gap when it comes to evaluating their relative strengths and weaknesses in this context. The study compared the predictive abilities of response surface methodology (RSM) and artificial neural network (ANN) for the adsorption treatment of MB and CV dye-simulated wastewater using Saccharum officinarum L. The process experimental variables were modelled and predicted using a three-layer artificial neural network trained using the Levenberg-Marquard backpropagation algorithm and 30 central composite designs (CCD). The adsorption study used a specific mechanism, which led to noteworthy maximum removals of 98.3% and 98.2% for dyes (MB and CV), respectively. The RSM model achieved an impressive R2 of 0.9417, while the ANN model achieved 0.9236 in MB. Adsorption is commonly used to remove colour from many different materials. Saccharum officinarum L., a byproduct of sugarcane processing, has shown potential as an efficient and ecological adsorbent in this environment. The purpose of this study is to evaluate sugarcane bagasse's potential as an adsorbent for the removal of dyes MB and CV from industrial wastewater, providing a long-term strategy for reducing dye pollution. Due to its beneficial economic and environmental characteristics, the Saccharum officinarum L. adsorbent has prompted research into sustainable resources with low pollutant indices.

A Comparative Study of Cr(VI) Sorption by Aureobasidium pullulans AKW Biomass and Its Extracellular Melanin: Complementary Modeling with Equilibrium Isotherms, Kinetic Studies, and Decision Tree Modeling

Melanin as a natural polymer is found in all living organisms, and plays an important role in protecting the body from harmful UV rays from the sun. The efficiency of fungal biomass (Aureobasidium pullulans) and its extracellular melanin as Cr(VI) biosorbents was comparatively considered. The efficiency of Cr(VI) biosorption by the two sorbents used was augmented up to 240 min. The maximum sorption capacities were 485.747 (fungus biomass) and 595.974 (melanin) mg/g. The practical data were merely fitted to both Langmuir and Freundlich isotherms. The kinetics of the biosorption process obeyed the pseudo-first-order. Melanin was superior in Cr(VI) sorption than fungal biomass. Furthermore, four independent variables (contact time, initial concentration of Cr(VI), biosorbent dosage, and pH,) were modeled by the two decision trees (DTs). Conversely, to equilibrium isotherms and kinetic studies, DT of fungal biomass had lower errors compared to DT of melanin. Lately, the DTs improved the efficacy of the Cr(VI) removal process, thus introducing complementary and alternative solutions to equilibrium isotherms and kinetic studies. The Cr(VI) biosorption onto the biosorbents was confirmed and elucidated through FTIR, SEM, and EDX investigations. Conclusively, this is the first report study attaining the biosorption of Cr(VI) by biomass of A. pullulans and its extracellular melanin among equilibrium isotherms, kinetic study, and algorithmic decision tree modeling.

Application of artificial neural networks for enhancing Aspergillus flavipes lipase synthesis for green biodiesel production

Biodiesel is a sustainable, and renewable alternative to fossil fuels that can be produced from various biological sources with the aid of lipases. This study developed a simple and novel fungal system for lipase biosynthesis to be used for catalyzing the oily residuals into biodiesel, employing the artificial neural network (ANN), and semi-solid-state fermentation (SSSF). Nigella sativa was selected among agro-industrial oily residuals as a substrate for lipase biosynthesis by Aspergillus flavipes MH47297. The effect of cultural humidity (X1), the surfactant; Brij 35 (X2), and inoculum density (X3) on lipase biosynthesis were researched based on the matrix of Box-Behnken design (BBD). The ANN together with a new fungal candidate and SSSF were then applied for the first time to model the biosynthesis process of lipase. The optimum predicted cultural conditions varied according to the model. The optimum predicted conditions were estimated separately by BBD (X1 = 5.8 ml water/g, X2 = 46.6 μl/g, and X3 = 62156610 spore/g) and ANN (X1 = 5.4 ml water/g, X2 = 54.2 μl/g, and X3 = 100000000 spore/g) models. Based on the modeling process, the response of lipase was calculated to be 214.95 (BBD) and 217.72 U (ANN), which revealed high consistency with the experimental lipase yield (209.13 ± 3.27 U for BBD, and 218 ± 2.01 U for ANN). Despite both models showing high accuracy, ANN was more accurate and surpassed the BBD model. Gas chromatography analysis showed that lipase successfully converted corn oil to biodiesel (29.5 mg/l).

A comparative study using response surface methodology and artificial neural network towards optimized production of melanin by Aureobasidium pullulans AKW

The effect of three independent variables (i.e., tyrosine, sucrose, and incubation time) on melanin production by Aureobasidium pullulans AKW was unraveled by two distinctive approaches: response surface methodology (i.e. Box Behnken design (BBD)) and artificial neural network (ANN) in this study for the first time ever using a simple medium. Regarding BBD, sucrose and incubation intervals did impose a significant influence on the output (melanin levels), however, tyrosine did not. The validation process exhibited a high consistency of BBD and ANN paradigms with the experimental melanin production. Concerning ANN, the predicted values of melanin were highly comparable to the experimental values, with minor errors competing with BBD. Highly comparable experimental values of melanin were achieved upon using BBD (9.295 ± 0.556 g/L) and ANN (10.192 ± 0.782 g/L). ANN accurately predicted melanin production and showed more improvement in melanin production by about 9.7% higher than BBD. The purified melanin structure was verified by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction pattern (XRD), and thermogravimetric analysis (TGA). The results verified the hierarchical architecture of the particles as small compasses by SEM analysis, inter-layer spacing in the XRD analysis, maximal atomic % for carbon, and oxygen atoms in the EDX analysis, and the great thermal stability in the TGA analysis of the purified melanin. Interestingly, the current novel endophytic strain was tyrosine-independent, and the uniquely applied ANN paradigm was more efficient in modeling the melanin production with appreciate amount on a simple medium in a relatively short time (168 h), suggesting additional optimization studies for further maximization of melanin production.

Development of a headspace-solid phase microextraction gas chromatography-high resolution mass spectrometry method for analyzing volatile organic compounds in urine: Application in breast cancer biomarker discovery

BACKGROUND AND AIM

Breast cancer (BC) is the leading cause of cancer-related death in females. The development of non-invasive methods for the early diagnosis of BC still remains challenge. Here, we aimed to discover the urinary volatile organic compounds (VOCs) pattern of BC patients and identify potential VOC biomarkers for BC diagnosis.

METHODS

Urine samples were analyzed by headspace-solid phase microextraction (HS-SPME) combined with gas chromatography-high resolution mass spectrometry (GC-HRMS). To assure reliable analysis, the factors influencing HS-SPME extraction efficiency were comprehensively investigated and optimized by combing the Plackett-Burman design (PBD) with the central composite design (CCD). The established HS-SPME/GC-HRMS method was validated and applied to analyze urine samples from BC patients (n = 80) and healthy controls (n = 88).

RESULTS

A total number of 134 VOCs belonging to distinct chemical classes were identified by GC-HRMS. BC patients demonstrated unique urinary VOCs pattern. Orthogonal partial least squares-discriminant analysis (OPLS-DA) showed a clear separation between BC patients and healthy controls. Eight potential VOC biomarkers were identified using multivariate and univariate statistical analysis. The predictive ability of candidate VOC biomarkers was further investigated by the random forest (RF) algorithm. The candidate VOC biomarkers yielded 76.3% sensitivity and 85.4% specificity on the training set, and achieved 76.0% sensitivity and 92.3% specificity on the validation set.

CONCLUSIONS

Overall, this work not only established a standardized HS-SPME/GC-HRMS approach for urinary VOCs analysis, but also highlighted the value of urinary VOCs for BC diagnosis. The knowledge gained from this study paves the way for early diagnosis of BC using urine in a non-invasive manner.

Innovative biosynthesis, artificial intelligence-based optimization, and characterization of chitosan nanoparticles by Streptomyces microflavus and their inhibitory potential against Pectobacterium carotovorum

Microbial-based strategy in nanotechnology offers economic, eco-friendly, and biosafety advantages over traditional chemical and physical protocols. The current study describes a novel biosynthesis protocol for chitosan nanoparticles (CNPs), employing a pioneer Streptomyces sp. strain NEAE-83, which exhibited a significant potential for CNPs biosynthesis. It was identified as Streptomyces microflavus strain NEAE-83 based on morphological, and physiological properties as well as the 16S rRNA sequence (GenBank accession number: MG384964). CNPs were characterized by SEM, TEM, EDXS, zeta potential, FTIR, XRD, TGA, and DSC. CNPs biosynthesis was maximized using a mathematical model, face-centered central composite design (CCFCD). The highest yield of CNPs (9.41 mg/mL) was obtained in run no. 27, using an initial pH of 5.5, 1% chitosan, 40 °C, and a 12 h incubation period. Innovatively, the artificial neural network (ANN), was used for validating and predicting CNPs biosynthesis based on the trials data of CCFCD. Despite the high precision degree of both models, ANN was supreme in the prediction of CNPs biosynthesis compared to CCFCD. ANN had a higher prediction efficacy and, lower error values (RMSE, MDA, and SSE). CNPs biosynthesized by Streptomyces microflavus strain NEAE-83 showed in-vitro antibacterial activity against Pectobacterium carotovorum, which causes the potato soft rot. These results suggested its potential application for controlling the destructive potato soft rot diseases. This is the first report on the biosynthesis of CNPs using a newly isolated; Streptomyces microflavus strain NEAE-83 as an eco-friendly approach and optimization of the biosynthesis process by artificial intelligence.

Innovative binary sorption of Cobalt(II) and methylene blue by Sargassum latifolium using Taguchi and hybrid artificial neural network paradigms

The present investigation has been designed by Taguchi and hybrid artificial neural network (ANN) paradigms to improve and optimize the binary sorption of Cobalt(II) and methylene blue (MB) from an aqueous solution, depending on modifying physicochemical conditions to generate an appropriate constitution for a highly efficient biosorption by the alga; Sargassum latifolium. Concerning Taguchi's design, the predicted values of the two responses were comparable to actual ones. The biosorption of Cobalt(II) ions was more efficient than MB, the supreme biosorption of Cobalt(II) was verified in run L21 (93.28%), with the highest S/N ratio being 39.40. The highest biosorption of MB was reached in run L22 (74.04%), with a S/N ratio of 37.39. The R2 and adjusted R2 were in reasonable values, indicating the validity of the model. The hybrid ANN model has exclusively emerged herein to optimize the biosorption of both Cobalt(II) and MB simultaneously, therefore, the ANN model was better than the Taguchi design. The predicted values of Cobalt(II) and MB biosorption were more obedience to the ANN model. The SEM analysis of the surface of S. latifolium showed mosaic form with massive particles, as crosslinking of biomolecules of the algal surface in the presence of Cobalt(II) and MB. Viewing FTIR analysis showed active groups e.g., hydroxyl, α, β-unsaturated ester, α, β-unsaturated ketone, N-O, and aromatic amine. To the best of our knowledge, there are no reports deeming the binary sorption of Cobalt(II) and MB ions by S. latifolium during Taguchi orthogonal arrays and hybrid ANN.

Optimization of Heavy Metals Biosorption via Artificial Neural Network: A Case Study of Cobalt (II) Sorption by Pseudomonas alcaliphila NEWG-2

The definitive screening design (DSD) and artificial neural network (ANN) were conducted for modeling the biosorption of Co(II) by Pseudomonas alcaliphila NEWG-2. Factors such as peptone, incubation time, pH, glycerol, glucose, K2HPO4, and initial cobalt had a significant effect on the biosorption process. MgSO4 was the only insignificant factor. The DSD model was invalid and could not forecast the prediction of Co(II) removal, owing to the significant lack-of-fit (P < 0.0001). Decisively, the prediction ability of ANN was accurate with a prominent response for training (R2 = 0.9779) and validation (R2 = 0.9773) and lower errors. Applying the optimal levels of the tested variables obtained by the ANN model led to 96.32 ± 2.1% of cobalt bioremoval. During the biosorption process, Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy, and scanning electron microscopy confirmed the sorption of Co(II) ions by P. alcaliphila. FTIR indicated the appearance of a new stretching vibration band formed with Co(II) ions at wavenumbers of 562, 530, and 531 cm-1. The symmetric amino (NH2) binding was also formed due to Co(II) sorption. Interestingly, throughout the revision of publications so far, no attempt has been conducted to optimize the biosorption of Co(II) by P. alcaliphila via DSD or ANN paradigm.

Natural Melanin: Current Trends, and Future Approaches, with Especial Reference to Microbial Source

Melanin is a universal natural dark polymeric pigment, arising in microorganisms, animals, and plants. There is a couple of pieces of literature on melanin, each focusing on a different issue, the goal of the present review is to focus on microbial melanin. It has numerous benefits with very few drawbacks. The current situation and expected trends are discussed. Intriguing, numerous studies have provoked a serious necessity for a comprehensive assessment of microbial melanin pigments. So that, such review would help scholars from diverse backgrounds to realize the importance of melanin pigments isolated from microorganisms, with this aim in mind, information, and hypothesis from this review could be the paradigm for studies on melanin in the next era.

Innovative Artificial-Intelligence- Based Approach for the Biodegradation of Feather Keratin by Bacillus paramycoides, and Cytotoxicity of the Resulting Amino Acids

The current study reported a new keratinolytic bacterium, which was characterized as Bacillus paramycoides and identified by 16S rRNA, and the sequence was then deposited in the GenBank (MW876249). The bacterium was able to degrade the insoluble chicken feather keratin (CFK) into amino acids (AA) through the keratinase system. The statistical optimization of the biodegradation process into AA was performed based on the Plackett–Burman design and rotatable central composite design (RCCD) on a simple solid-state fermentation medium. The optimum conditions were temperature, 37°C, 0.547 mg KH₂PO₄, 1.438 mg NH₄Cl, and 11.61 days of incubation. Innovatively, the degradation of the CFK process was modeled using the artificial neural network (ANN), which was better than RCCD in modeling the biodegradation process. Differentiation of the AA by high-performance liquid chromatography (HPLC) revealed the presence of 14 AA including essential and non-essential ones; proline and aspartic acids were the most dominant. The toxicity test of AA on the HepG2 cell line did not show any negative effect either on the cell line or on the morphological alteration. B. paramycoides ZW-5 is a new eco-friendly tool for CFK degradation that could be optimized by ANN. However, additional nutritional trials are encouraged on animal models.

Photo-Fenton-Like Treatment of Municipal Wastewater

In this work, the photochemical treatment of a real municipal wastewater using a persulfate-driven photo-Fenton-like process was studied. The wastewater treatment efficiency was evaluated in terms of total carbon (TC), total organic carbon (TOC) and total nitrogen (TN) removal. Response surface methodology (RSM) in conjunction Box-Behnken design (BBD) and multilayer artificial neural network (ANN) have been utilized for the optimization of the treatment process. The effects of four independent factors such as reaction time, pH, K2S2O8 concentration and K2S2O8/Fe2+ molar ratio on the TC, TOC and TN removal have been investigated. The process significant factors have been determined implementing Analysis of Variance (ANOVA). Both RSM and ANN accurately found the optimum conditions for the maximum removal of TOC (100% and 98.7%, theoretically), which resulted in complete mineralization of TOC at the reaction time of 106.06 min, pH of 7.7, persulfate concentration of 30 mM and K2S2O8/Fe2+ molar ratio of 7.5 for RSM and at the reaction time of 104.93 min, pH of 7.7, persulfate concentration of 30 mM and K2S2O8/Fe2+ molar ratio of 9.57 for ANN. On the contrary, the attempts to find the optimal conditions for the maximum TC and TN removal using statistical, and neural network models were not successful.

Definitive Screening Design and Artificial Neural Network for Modeling a Rapid Biodegradation of Date Palm Fronds by a New Trichoderma sp. PWN6 into Citric Acid

Generally, the bioconversion of lignocellulolytics into a new biomolecule is carried out through two or more steps. The current study used one-step bioprocessing of date palm fronds (DPF) into citric acid as a natural product, using a pioneer strain of Trichodermaharzianum (PWN6) that has been selected from six tested isolates based on the highest organic acid (OA) productivity (195.41 µmol/g), with the lowest amount of the released glucose. Trichoderma sp. PWN6 was morphologically and molecularly identified, and the GenBank accession number was MW78912.1. Both definitive screening design (DSD) and artificial neural network (ANN) were applied, for the first time, for modeling the bioconversion process of DPF. Although both models are capable of making accurate predictions, the ANN model outperforms the DSD model in terms of OA production, as ANN is characterized by a higher value of R² (0.963) and validation R² (0.967), and lower values of the RMSE (13.44), MDA (11.06), and SSE (9749.5). Citric acid was the only identified OA as was confirmed by GC-MS and UPLC, with a total of 1.5%. In conclusion, DPF together with T. harzianum PWN6 is considered an excellent new combination for citric acid biosynthesis, after modeling with artificial intelligence procedure.

Microbial Sensing and Removal of Heavy Metals: Bioelectrochemical Detection and Removal of Chromium(VI) and Cadmium(II)

The presence of inorganic pollutants such as Cadmium(II) and Chromium(VI) could destroy our environment and ecosystem. To overcome this problem, much attention was directed to microbial technology, whereas some microorganisms could resist the toxic effects and decrease pollutants concentration while the microbial viability is sustained. Therefore, we built up a complementary strategy to study the biofilm formation of isolated strains under the stress of heavy metals. As target resistive organisms, Rhizobium-MAP7 and Rhodotorula ALT72 were identified. However, Pontoea agglumerans strains were exploited as the susceptible organism to the heavy metal exposure. Among the methods of sensing and analysis, bioelectrochemical measurements showed the most effective tools to study the susceptibility and resistivity to the heavy metals. The tested Rhizobium strain showed higher ability of removal of heavy metals and more resistive to metals ions since its cell viability was not strongly inhibited by the toxic metal ions over various concentrations. On the other hand, electrochemically active biofilm exhibited higher bioelectrochemical signals in presence of heavy metals ions. So by using the two strains, especially Rhizobium-MAP7, the detection and removal of heavy metals Cr(VI) and Cd(II) is highly supported and recommended.