Search Results (281)

Alcoholic fermentation in winemaking is a complex bioprocess governed by physicochemical parameters such as temperature, density, pH, CO₂ and redox potential, which critically affect yeast metabolism and wine quality. This review provides an integrated analysis of fermentative dynamics and emerging sensorization technologies, highlighting how their combined implementation enables real-time monitoring and advanced control in precision enology. Advances in conventional physicochemical sensors, spectroscopic techniques (NIR/MIR/UV-Vis) and non-conventional devices (e-noses, electronic tongues) integrated into IoT platforms enable continuous data acquisition, overcoming traditional manual sampling limitations. Predictive modeling, including kinetic models, machine learning approaches (e.g., Random Forest, XGBoost) and model predictive control (MPC/NMPC), supports anomaly detection, optimization of enological interventions and energy-efficient thermal management, while virtual sensors based on Kalman filters improve the estimation of non-measurable states (e.g., biomass, ethanol kinetics). Despite current challenges in calibration and interoperability, these innovations foster sustainable and reproducible winemaking under climate variability and pave the way for digital twins and semi-autonomous fermentation systems. Full article

Temporal monitoring of bioprocesses is inherently complex because process variables do not evolve independently over time, and their interpretation changes as the reaction progresses. In many existing abnormality detection methods, sensor signals are analyzed at isolated time points or temporal characteristics are only weakly reflected through model structures. As a result, such approaches struggle to explain or detect abnormal behavior that emerges differently across reaction states. This study proposes TPP-TimeNet, a time-aware artificial intelligence framework developed to improve abnormality detection in bioprocess monitoring. Unlike conventional methods, the proposed framework explicitly incorporates reaction time as contextual information. Multivariate process signals are reorganized into sliding windows that reflect reaction-state transitions rather than uniform time segmentation. Temporal behavior inside each window is captured using a sequential encoding model, and reaction-state information is subsequently integrated to form state-dependent representations. Through this design, the model can distinguish between temporal patterns that are similar in shape but occur at different points in the reaction timeline. This capability leads to improved sensitivity to abnormal events that may otherwise remain undetected. Abnormality is evaluated at the window level using a probabilistic scoring scheme with a fixed threshold, enabling consistent and reproducible decision-making. The performance of TPP-TimeNet was evaluated using publicly available process control datasets from Kaggle. The datasets were reinterpreted in a bioprocess context by mapping variables such as temperature, pH, and pressure. Experimental results show that the proposed method outperforms traditional machine learning models as well as deep learning approaches that focus only on temporal features, achieving higher accuracy, sensitivity, and F1-score. These findings suggest that incorporating explicit reaction-state awareness is essential for effective abnormality detection in bioprocess monitoring systems. Full article

Water is fundamental to structural integrity, stability, and functional properties of food systems, biomaterials, and biobased industries. The dynamics of hydration, including hydrogen bonding, hydration shell formation, plasticization, and phase transitions, dictate molecular behavior and exert broad influence on energy consumption, shelf life, biodegradability, and resource efficiency. However, the nonlinear and multiscale characteristics of hydration have constrained the predictive capabilities of conventional empirical methods. This study introduces a comprehensive framework that integrates foundational hydration science with advanced computational intelligence to model, predict, and optimize hydration-driven phenomena across diverse biopolymer classes. Leveraging classical insights into carbohydrate stereochemistry, protein hydrophobic hydration, and phospholipid-bound water, we demonstrate how computational approaches can reduce resource use in bioprocessing by 30–50% and optimize drying curves to lower energy consumption by 25%. By establishing hydration as a strategic design parameter, this work charts a pathway toward a resilient and sustainable economy where predictive error rates for hydration dynamics are significantly minimized through data-driven calibration. Full article

Atopic dermatitis (AD) is a chronic inflammatory skin disorder characterized by epidermal barrier dysfunction and dysregulated immune responses, particularly an imbalance between T helper type 1 (Th1) and type 2 (Th2) cytokines. Natural products with immunomodulatory activity have attracted increasing attention as potential strategies for regulating allergic inflammation. In this study, we investigated the immunomodulatory effects of bioprocessed black rice bran (BRB-F) and bioprocessed balloon flower root (BFR-F). In vitro assays using human B cells, mast cells, and keratinocytes were conducted to evaluate IgE production, mast cell degranulation, and epithelial inflammatory mediator release. The efficacy of the BRB-F:BFR-F mixture was further evaluated in BALB/c mice with 2,4-dinitrochlorobenzene (DNCB)/Dermatophagoides farinae extract (DFE)-induced AD-like dermatitis. BRB-F and BFR-F suppressed IgE production, attenuated mast cell degranulation and thymic stromal lymphopoietin (TSLP) release, and reduced keratinocyte-derived inflammatory mediators (thymus and activation-regulated chemokine (TARC), macrophage-derived chemokine (MDC), and IL-6). In mice, dietary supplementation with the BRB-F:BFR-F mixture (10–80 mg/kg/day) dose-dependently improved clinical skin lesions and histopathological changes, with serum IgE reduced by up to 87.1% at the highest dose. The treatment significantly suppressed Th2 cytokine mRNA expression in ear tissue (IL-4, IL-5, and IL-13) by 37.2%, 32.7%, and 34.0%, respectively, compared with the positive control. In contrast, splenic Th1 cytokine mRNA expression (IL-2, IL-12, and IFN-γ) was partially restored by 37.1%, 22.5%, and 18.7%, respectively. These findings indicate that BRB-F and BFR-F modulate multiple immune pathways and help restore Th1/Th2 immune balance, suggesting their potential as functional materials for regulating immune dysregulation associated with AD. Full article

Prodigiosin, a red pigment with diverse biotechnological applications, is produced as a secondary metabolite by Gram-negative bacilli Serratia marcescens. In this study, we implemented an AI-guided hybrid optimization framework combining Response Surface Methodology (RSM) using a Circumscribed Central Composite Design (CCCD) and Artificial Neural Network (ANN) modeling to enhance prodigiosin pigment production. Across 34 experimental runs, we optimized sucrose and peptone concentrations along with inoculum size. The RSM-derived model exhibited a strong correlation (R² = 0.953), while the ANN, trained using a backpropagation algorithm, demonstrated superior predictive power (R² = 0.998; MSE = 0.000414), underscoring the potential of artificial intelligence in modeling complex bioprocesses. Beyond statistical optimization, an induction strategy using 1% of various natural additives (vegetable oils and egg components) identified egg white, rich in albumin, as the most effective enhancer, tripling prodigiosin yield. Further investigation revealed that a 2% egg white concentration maximized production to 1070 mg L⁻¹, a substantial increase compared to the optimized yield of 359.2 ± 12 mg L⁻¹ and predicted value of 391.86 mg L⁻¹. These results highlight the value of integrating machine learning with experimental design and protein-rich inducers to strengthen sustainable microbial pigment production in a cost-effective and scalable manner. Full article

This work proposes an integral-enhanced nonlinear $P{I}^2$ state observer for the robust estimation of unmeasured states in nonlinear dynamic systems, with experimental validation on a flat-panel photobioreactor. The observer is designed as a virtual sensor to reconstruct key biological variables using a reduced set of online measurements and known operating conditions. Compared with a conventional extended Luenberger observer, the proposed structure improves estimation accuracy and robustness against constant disturbances and model mismatch, which are common in bioprocess applications. The experimental results show a clear performance advantage during transient growth phases while highlighting that the method relies on a locally valid model structure and appropriate gain tuning. Overall, the proposed observer provides a practical and scalable monitoring tool for nonlinear systems where the direct measurement of critical state is not feasible. Full article

In this work, we present the scale-up of a batch anaerobic fermentation system for the production of succinic acid from glycerol using A. succinogenes. The system has been successfully scaled up from an initial bioreactor working volume of 1 L (laboratory scale) to a working volume of 100 L (pilot scale). At the same time, we have developed a hybrid model, combining the intrinsic kinetics of the microbial growth, with a computational fluid dynamics model (CFD) of the bioreactor. The proposed model is able to predict the productivity drop, usually observed while scaling up a bioprocess. In our process, this is a result of the limitations on the mass transfer of CO₂ between the gas and the liquid phase of the system. The model is successfully used to predict the amount of aeration needed in order to achieve increased succinic acid productivity. Using the model, the final succinic acid increased by 4.3%, and the succinic acid productivity increased by 8.5%, while the fermentation by-products decreased by approxiamtely 3% each. Full article

The tomato processing industry is among the most widespread food industries worldwide, generating residues that pose an important issue due to their abundance and the rising negative environmental impacts associated with waste. This paper summarizes potential products that can be obtained from these sources, with a focus on the production of a specific biopolymer, cutin, which has great potential as a food packaging material. It emphasizes the development of an integration proposal model for the biorefinery process of tomato pomace, in line with the zero-waste concept, by performing comparative techno-economic analysis (TEA) of two processing scenarios: (1) a biorefinery pathway that valorizes tomato pomace utilization by producing cutin and (2) an integrated process designed for the simultaneous production of cutin and phenolic antioxidants. The study identifies current research gaps and outlines strategic directions for potential integration pathways that can enhance not only the economic viability and profitability of the process but also its environmental benefits through more complete. The techno-economic analysis model for cutin extraction showed an internal rate of return (IRR) of only 2%, which is five times lower than the IRR achieved in our integrated model for cutin and phenolic compounds. Additionally, the payback time in the integrated approach improved significantly from 9.56 to 5.7 years. This paper assesses the potential of tomato pomace as a sustainable source for the production of high-value bioproducts that can economically justify investments in sustainable bioprocessing technologies and reduce waste through an integrated approach. Full article

Growing global energy demand and concerns over climate change and fossil fuel depletion have increased interest in sustainable bioproducts such as ethanol. Unlike first-generation (1G) ethanol derived from food crops (e.g., corn), second-generation (2G) ethanol is produced from lignocellulosic biomass, an abundant non-food resource that addresses key sustainability concerns. Consolidated bioprocessing (CBP) integrates enzyme production, hydrolysis, and fermentation into a single step, using either microbial consortia or engineered microorganisms, thereby simplifying the process and potentially reducing costs compared with separate hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF). However, CBP systems are complex due to dynamic interactions among microbial communities, metabolic pathways, and process conditions. Addressing this complexity requires modeling approaches that capture nonlinear relationships and support robust process optimization. Machine learning (ML)-based models offer data-driven tools to represent complex bioprocess dynamics, improve predictive accuracy, and optimize bioproduct formation, thereby supporting progress toward commercial viability. Although CBP can be applied to a range of bioproducts, this review primarily focuses on lignocellulosic ethanol and closely related biofuels. The review provides a comprehensive overview of key CBP processes, the current state of CBP modeling, major limitations, and the emerging role of ML in addressing modeling challenges. It summarizes recent modeling techniques for CBP, including polynomial models and response surface methodologies, and discusses regression and neural network approaches in detail. Both first-principles and data-driven modeling strategies are considered, highlighting advances that can improve the scalability and efficiency of CBP for bioproduction. Overall, this review offers perspectives on modeling-enabled pathways for utilizing low-cost lignocellulosic biomass in sustainable bioprocessing. Full article

Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is increasingly transforming the study, manufacturing, and clinical translation of adipose-derived stem/stromal cells (ADSCs). ADSC-based therapies face persistent challenges related to donor variability, heterogeneous cell populations, limited standardization of culture protocols, and the need for robust quality control (QC) and potency assessment under Good Manufacturing Practice (GMP) conditions. This review discusses how AI-driven approaches can support the ADSC pipeline from donor and tissue pre-screening, through isolation and expansion, to differentiation and batch release decisions. We highlight major methodological advances in computer vision and label-free imaging for monitoring morphology, confluency, proliferation, senescence, and contamination, as well as AI-assisted optimization strategies for culture parameters and differentiation protocols. In addition, we examine the growing role of multi-omics integration (transcriptomics, proteomics, metabolomics, and secretomics) combined with ML to predict functional potency, stratify donors, and identify biomarkers associated with therapeutic efficacy. Finally, we address current limitations, including data scarcity, inter-laboratory variability, model interpretability, and regulatory requirements, and outline future perspectives such as closed-loop bioprocess control, foundation models, and federated learning frameworks. Overall, AI offers a powerful toolkit to improve the reproducibility, safety, and scalability of ADSC manufacturing and to accelerate the development of standardized, data-driven regenerative medicine products. Full article

The revalorization of food processing by-products represents a critical strategy for enhancing resource efficiency and advancing circularity within the food system. This review examines the potential of three major plant-based agro-industrial by-products—fruit and vegetable residues, brewer’s spent grain, and spent coffee grounds—as sources of high-value functional ingredients. These by-products contain bioactive compounds, including dietary fibers, polyphenols, proteins, peptides, oils, and antioxidants, that can be recovered using emerging green extraction and bioprocessing technologies. Conventional extraction methods are progressively being replaced or hybridized with enzyme-assisted, ultrasound-assisted, microwave-assisted, and deep eutectic solvent techniques to improve yield, reduce solvent consumption, and preserve bioactivity. The recovered compounds have demonstrated promising applications as gelling agents (pectin), natural colorants and antioxidants, protein-enriched flours, prebiotic fibers, and bioactive extracts for functional food and nutraceutical formulations. However, challenges persist in standardizing feedstock composition, scaling continuous extraction processes, ensuring safety and regulatory compliance, and generating robust techno-economic and life-cycle assessments to validate sustainability claims. This review synthesizes biochemical composition data, processing pathways, food applications, and regulatory considerations, and identifies research priorities for developing integrated, scalable biorefinery models that valorize food by-products into market-ready functional ingredients. Full article

The increasing demand for recombinant proteins has driven innovation in bioprocessing strategies using Komagataella phaffii as a host organism. Conventional fed-batch cultivation with pure methanol induction remains widely used but presents challenges including high methanol consumption, extended downtime, and elevated operational costs. This study evaluates alternative strategies combining mixed induction (methanol/sorbitol) with continuous cultivation to enhance productivity, sustainability, and improved economic outcome. Using KEX2 protease as a model industrial recombinant protein, we compared four cultivation modes: fed-batch with methanol (benchmark), fed-batch with mixed induction, continuous with methanol, and continuous with mixed induction. Cell growth, volumetric yield, and specific productivity were evaluated at 5L scale and then modelled to simulate industrial scales (40 L and 400 L). Results demonstrate that continuous cultivation with mixed induction significantly improves yield up to 9-fold compared to conventional fed-batch and reduces methanol usage and oxygen demand. Techno-economic simulations reveal that a 40 L continuous process can match or exceed the output of two 400 L fed-batch runs, while lowering capital and operating costs and minimizing environmental footprint. This integrated strategy offers a scalable, low-cost, and safer method for recombinant protein production, supporting compact and sustainable manufacturing solutions. Full article

Due to the increasing demand for greater safety and ease of scale bioprocessing, fault detection and diagnosis (FDD) is becoming an effective method to avoid breakdowns and disasters. Therefore, this work focuses on developing a dedicated observer-based fault diagnosis for nonlinear systems. To solve this, the FDD scheme is needed to make it perform satisfactorily even in a faulty situation. A case study on bioethanol production is proposed to illustrate and demonstrate the proposed techniques in real time. Single faults and different sensor faults are considered. The effectiveness of the proposed model is proved by comparing its performance obtained by simulation with the experimental data. In order to supervise the change of the possible faulty parameter, robust adaptive full-order observers that focus not only on the state estimation but also on the parameter change are applied to the considered bioreactor. In order to achieve the desired outcome of sensor fault detection, we propose a residual evaluation function, given by the root-mean-square (RMS) value of the residual and a practical threshold for the bioreactor. Experimental results show that sensor faults can be well diagnosed by the proposed observer-based FDD method. The precision, recall rate, and overall accuracy of three diagnostic metrics for abrupt failures were compared. The diagnostic approach was successful, achieving an overall accuracy rate of over $90\%$ for each of the three abrupt failure scenarios in every sensor. Finally, even if the biomass or $C{O}_2$ sensors fail, the FDD system can reconstruct the substrate and ethanol dynamics that are typically quantified offline in bioprocesses in real time. Full article

This manuscript provides a comprehensive review of the enzymatic hydrolysis of lignocellulosic biomass, emphasizing how chemical composition, structural features, inhibitory compounds, and process configurations collectively influence the conversion of structural polysaccharides into fermentable sugars. Variability among herbaceous, woody, and residual biomasses results in differences in cellulose, hemicellulose, lignin content, and crystallinity, which strongly affect enzyme accessibility. The review discusses key inhibitory mechanisms, including nonproductive cellulase adsorption onto lignin, interference from phenolic derivatives and pretreatment by-products, and inhibition caused by accumulating mono- and oligosaccharides. Process configurations such as SHF, SSF, PSSF, and consolidated bioprocessing are compared, with SSF often achieving superior performance by mitigating end-product inhibition. The manuscript also highlights the growing relevance of computational modeling and simulation tools, which support kinetic prediction, the evaluation of transport limitations, and the optimization of operating conditions in high-solids systems. Additionally, recent advances in artificial intelligence are presented as powerful approaches for modeling nonlinear hydrolysis behavior, estimating kinetic parameters, identifying rate-limiting steps, and improving predictive accuracy in complex bioprocesses. Overall, the integration of experimental insights with advanced modeling, simulation, and AI-based strategies is essential for overcoming current limitations and enhancing the technical feasibility and industrial competitiveness of lignocellulosic bioconversion. Full article

Gas–liquid–solid (G-L-S) mechanically agitated reactors are commonly used in chemical, pharmaceutical and bioprocessing applications due to their low operating costs and controlled and effective mixing. Computational Fluid Dynamics (CFD) is a powerful tool that enhances the understanding of flow dynamics, phase interactions and reactor performance. However, the CFD modeling of G-L-S mechanically agitated reactors is not extensively studied in the literature due to complex multiphase interactions, along with reactor design variations. This paper provides a critical synthesis of the literature, offering an overview not only of G-L-S stirred tank CFD modeling approaches but also of practical guidance on their selection and validation. Emerging high-resolution experimental techniques such as Electrical Resistance Tomography (ERT) coupled with pressure transducers, and Machine Learning (ML) models combined with experimental data, look promising to overcome current three-phase validation limitations. Future work to enhance predictive capabilities and reactor design and operation includes developing real-time digital twins, physics-based ML models and/or hybrid CFD-ML models. Full article