Bioprocess and Biosystems Engineering最新文献

{"title":"Limits and benefits of unsupervised transfer learning for Raman spectroscopy-based bioprocess monitoring.","authors":"Bianca Hüpfl, Alexandra Umprecht, Bence Kozma, Peter Ettinger, Oliver Spadiut","doi":"10.1007/s00449-026-03394-8","DOIUrl":"10.1007/s00449-026-03394-8","url":null,"abstract":"<p><p>Reliable analyte prediction from Raman spectra is critical for bioprocess monitoring, yet spectral variability across domains, such as different media or scale-up conditions, challenges model generalization. Models for analyte prediction are typically trained solely on labeled source-domain data, often limiting their performance under domain shifts. Transfer learning has been successful in other fields, but is underexplored for spectroscopic data in upstream bioprocesses. To help bridge this gap we assess whether unsupervised transfer learning methods can enhance model generalization by leveraging unlabeled target-domain spectra. We benchmark four unsupervised transfer learning approaches (CORAL, JDOT, TCA, OPP-MMD) against two bioprocess-relevant baselines: a fixed standard preprocessing workflow (REF) and an optimized source calibration based on systematic preprocessing screening (OPP). All methods are evaluated on two bioprocess Raman datasets: Dataset A comprises five glucose-spiked media and is complemented by in-silico spectral perturbations designed to mimic commonly occurring Raman noise types, such as baseline shifts, scattering artifacts, and spectral shifts. Dataset B evaluates scale-up transfer from pilot to production bioreactors for predicting glucose, biomass, and phosphate. Across the investigated cases, a consistent pattern emerged: JDOT and CORAL were most beneficial when across-domain differences dominated, whereas under high within-domain variability transfer learning provided no benefit and even underperformed the baseline. Because these transfer learning approaches can be integrated with standard Raman preprocessing pipelines, unsupervised transfer learning is best viewed as a complementary PAT tool rather than a universal replacement.</p>","PeriodicalId":9024,"journal":{"name":"Bioprocess and Biosystems Engineering","volume":" ","pages":"2583-2599"},"PeriodicalIF":4.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582229/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148599046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short-term aerobic pretreatment of swine manure enhances short-chain fatty acid production in anaerobic fermentation: performance and mechanisms.","authors":"Bangjie Xiong, Ju Chen, Liangbin Zhao, Mengting Chen, Xiaoxia Hao, Weiping Sang, Zhenzhen Guo, Dongmei Jiang, Shuo Liu, Li Zhu, Lin Bai","doi":"10.1007/s00449-026-03401-y","DOIUrl":"10.1007/s00449-026-03401-y","url":null,"abstract":"<p><p>Facing the dual challenges of swine manure pollution control and carbon resource recovery under intensive farming conditions, conventional anaerobic fermentation technologies are limited in their application for high-value conversion due to low hydrolysis efficiency and high operational costs. To address these limitations, this study proposed a self-heating strategy using short-term aerobic pretreatment, aiming to optimize substrate pretreatment and enhance the synthesis of short-chain fatty acids during subsequent acidogenic fermentation. The results demonstrated that this pretreatment approach rapidly activates aerobic microbial metabolism and significantly improves the substrate's overall properties, as reflected by a decreased pH and increased conductivity, moisture content, total protein, and total organic carbon, and effectively promotes the solubilization and hydrolysis of organic matter, thereby increasing the bioavailability of substrates for anaerobic fermentation. Microbial community analysis further revealed that the pretreatment process selectively enriched hydrolytic-acidogenic bacteria, including Clostridium, Bacillus, Paenibacillus, and Streptomyces, while synergistically enhancing the activities of key metabolic enzymes such as cellulase, protease, and acetate kinase. These effects systematically strengthened the metabolic pathways from substrate degradation to acid synthesis. As a result, the short‑term aerobic pretreatment achieved a significant improvement in SCFAs production and energy efficiency. Specifically, the total SCFAs yield increased by 21.31% ± 4.23%, while energy consumption declined by 94.48% ± 1.48%. In addition, the unit acid production cost amounted to merely 4.37% ± 0.15% of that required for the conventional low‑temperature thermal treatment. Moreover, it significantly reduced the abundance of high-risk antibiotic resistance genes in fermentation residues, highlighting its environmental benefits. This study elucidates the synergistic mechanism of short-term aerobic pretreatment across \"substrate-microorganism-enzyme\" dimensions, confirming it as an efficient, economical, and sustainable pretreatment technology for swine manure resource recovery.</p>","PeriodicalId":9024,"journal":{"name":"Bioprocess and Biosystems Engineering","volume":" ","pages":"2637-2649"},"PeriodicalIF":4.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148787875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modulation of mycelial formation in Streptomyces toxytricini and enhancement of lipstatin using light.","authors":"Deepanshi Rajput, Kashyap Kumar Dubey","doi":"10.1007/s00449-026-03411-w","DOIUrl":"10.1007/s00449-026-03411-w","url":null,"abstract":"<p><p>Scanty reports exist on Streptomyces toxytricini, a natural producer of the anti-obesity pro-drug lipstatin. For the first time, a study has been conducted on the regulation of its secondary metabolite synthesis using light. The present study investigates the impact of multiple light conditions on mycelial hyphal development and lipstatin production in S. toxytricini. Upon examining the mycelial, hyphal, and pellet morphogenesis under various light conditions, the differential pattern from dense pellet to loose mycelial hyphae was observed in the submerged fermentation condition. Biomass accumulation, as indicated by dry cell weight (DCW), showed a significant increase under red (4.2 g/L), indigo (4 g/L), green (4.13 g/L), yellow (3.37 g/L), and violet light (3.53 g/L) conditions. The maximum biomass was observed under red light, showing a four-fold increase compared with the dark condition (1 g/L). Along with the biomass and mycelial arrangements, red light produced significantly smaller pellet sizes (81.4 ± 13 μm) than the dark (784.7 ± 46 μm) and white-light (332.5 ± 59 μm) conditions. The HPLC analysis confirmed the significant effect of the red-light condition on lipstatin yield, with ~ 6 g/L, five-fold higher than ~ 1.2 g/L in white light and 300-fold higher than ~ 0.02 g/L in dark conditions. Besides that, in silico analysis of the photoreceptors identified two putative bacteriophytochrome (BphP) containing proteins in S. toxytricini, suggesting a potential sensory framework of red-light perception. Collectively, this work highlights light as a promising and easily implementable process parameter for enhancing secondary metabolite production, providing a foundation for future mechanistic and omics-based investigations.</p>","PeriodicalId":9024,"journal":{"name":"Bioprocess and Biosystems Engineering","volume":" ","pages":"2761-2775"},"PeriodicalIF":4.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148787841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Raman-guided sample subset selection for cost-efficient offline calibration in bioprocesses.","authors":"Terrance Wilms, Fabian Schwenke, Rudibert King, Steffi Knorn","doi":"10.1007/s00449-026-03402-x","DOIUrl":"10.1007/s00449-026-03402-x","url":null,"abstract":"<p><p>In bioprocess engineering, model-based methods play a vital role in understanding complex dynamics of novel species or strains. However, the model development is often hampered by prohibitive costs associated with redundant reference analyses and poorly informed sampling schedules due to insufficient prior knowledge about the process dynamics. We propose a model-free PAT strategy, termed Raman-guided sample subset selection (RGSS) to prioritise informative offline reference assays from inline Raman spectra before nonlinear model calibration. RGSS is demonstrated with Saccharomyces cerevisiae fed-batch fermentations and evaluated by auto- and cross-validation (CV), as well as with practical identifiability analysis of an unstructured mechanistic model. The approach is implemented using constrained vector quantization (CVQ) with full-spectrum and analyte-specific wavenumber-selection and benchmarked against uniform-in-time sampling, random subsampling, and Kennard-Stone (KS) selection. The best RGSS scenarios, using only five selected reference assays per analyte, retained offline CV accuracy (NRMSE = 3.49) close to the full-data reference (NRMSE = 3.29) using all 22 samples. KS selection was also competitive for ten selected samples (NRMSE = 3.79), whereas uniform-in-time sampling resulted in higher CV errors for five and ten samples (8.47 and 65.57). Random subsampling occasionally produced competitive subsets, but showed broad variability over 20 random runs (NRMSE median [IQR]: 6.14 [4.84-47.76] and 15.48 [5.07-44.34]) for five and ten randomly selected samples. These results support RGSS as a cost-efficient, spectrally informed sample subset selection framework for prioritising offline assays in model-based bioprocess development.</p>","PeriodicalId":9024,"journal":{"name":"Bioprocess and Biosystems Engineering","volume":" ","pages":"2651-2671"},"PeriodicalIF":4.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582237/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond conventional imaging: curvelet-AI convergence for non-invasive microalgal lipid quantification and biofuel optimization.","authors":"Soumya Singh, Nirbikalpa Mukherjee, Sunil Kumar Singh, Ranjan Singh","doi":"10.1007/s00449-026-03423-6","DOIUrl":"https://doi.org/10.1007/s00449-026-03423-6","url":null,"abstract":"<p><p>The transition toward sustainable bioenergy systems necessitates robust, scalable, and non-invasive strategies for monitoring microalgal biomass and lipid accumulation. Despite the superior productivity and carbon sequestration potential of microalgae, large-scale biofuel commercialization remains constrained by the lack of efficient real-time quantification techniques and the inherent complexity of microalgal imaging data. While existing reviews on AI-based microalgal monitoring focus primarily on model architectures, the novelty of this review lies in identifying multi-resolution image representation rather than network architecture alone as the principal determinant of analytical accuracy. Consequently, this review evaluates the image representation layer itself, assessing the Fast Discrete Curvelet Transform (FDCT) as a key preprocessing framework for deep-learning-based lipid quantification. Unlike conventional Fourier- and wavelet-based approaches, the curvelet transform provides near-optimal sparse representation of anisotropic and curved biological structures, enabling superior edge preservation and noise suppression in microscopy images. When coupled with machine learning and deep learning architectures, including Support Vector Machines, Random Forests, Convolutional Neural Networks, U-Net, and ResNet, curvelet-enhanced preprocessing significantly improves segmentation accuracy and lipid droplet detection at the pixel level. Furthermore, the review examines emerging hybrid curvelet-AI frameworks, where high accuracies (> 98%) reported in analogous imaging tasks and strong correlations with analytical standards (e.g., GC-MS) underscore their potential as reliable, non-destructive proxies for lipid quantification. Additionally, the integration of these frameworks with IoT-enabled photobioreactor systems is discussed as a pathway toward real-time, automated process optimization. However, challenges related to data scarcity, computational complexity, and imaging variability remain critical barriers. Future directions emphasize the need for standardized datasets, lightweight architectures, and multimodal data fusion to enhance scalability and industrial applicability. Overall, the convergence of curvelet-based image processing and AI offers a promising route toward scalable microalgal biofuel technologies.</p>","PeriodicalId":9024,"journal":{"name":"Bioprocess and Biosystems Engineering","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148982139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of enrofloxacin and sulfamethoxazole on Haematococcus lacustris.","authors":"Vijay Rayamajhi, Yunji An, Huijeong Byeon, Jihyun Lee, Taesoo Kim, Mudasir Hussain, AhJung Choi, JongDae Lee, KwangSoo Lee, ChulHyun Kim, HyunWoung Shin, SangMok Jung","doi":"10.1007/s00449-025-03264-9","DOIUrl":"10.1007/s00449-025-03264-9","url":null,"abstract":"","PeriodicalId":9024,"journal":{"name":"Bioprocess and Biosystems Engineering","volume":" ","pages":"2535-2550"},"PeriodicalIF":4.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582304/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145686556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enzymatic hydrolysis of Chlorella sorokiniana biomass using fungal enzyme mixtures: screening, optimization, and proteomic analysis for enhanced microalgal biomass valorization.","authors":"Timo Dräger, Annika Krimmel, Felix Melcher, Michael Paper, Melania Pilz, Daniel Garbe, Norbert Mehlmer, Corinna Dawid, Thomas Brück","doi":"10.1007/s00449-026-03356-0","DOIUrl":"10.1007/s00449-026-03356-0","url":null,"abstract":"<p><p>The sustainable production of microalgal biomass for food and feed applications requires efficient downstream processing methods, particularly for the disruption of recalcitrant microalgal cell walls. This study aimed to develop an enzymatic hydrolysis method using fungal enzyme mixtures for effective degradation of the cell wall of the green microalga Chlorella sorokiniana. Eight fungal strains were screened for their enzyme production and hydrolysis efficiency, with Aspergillus awamori, Aspergillus niger, and Ceratocystis paradoxa showing the highest performance and selected for detailed analysis. A Design of Experiment approach optimized hydrolysis parameters, revealing that all enzyme mixtures showed maximal sugar release at 60 °C and an enzyme-to-biomass ratio of 5% (v/w). Among these, A. awamori enzyme mixtures exhibited the highest sugar-to-protein ratio, indicating superior catalytic efficiency. Proteomic analysis highlighted distinct enzymatic profiles between A. awamori and a commercial reference enzyme preparation (Cellic CTec3), with A. awamori showing higher glucosidase, mannosidase, and chitinase activities, which correlated with enhanced hydrolysis of chitin-like polysaccharides in the microalgal cell wall. These findings suggest that enzyme mixtures tailored to the specific carbohydrate composition of microalgae, which combine cellulolytic, mannosidic, and chitinolytic activities, can enhance microalgal biomass hydrolysis efficiency. Such enzyme mixtures represent promising candidates for sustainable bioconversion and valorization of C. sorokiniana biomass in industrial bioprocesses.</p>","PeriodicalId":9024,"journal":{"name":"Bioprocess and Biosystems Engineering","volume":" ","pages":"2507-2522"},"PeriodicalIF":4.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148175280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fermentative routes of microbial biosurfactants production: emerging trends, innovations and multifaceted applications.","authors":"Debraj Maji, Dheepak Ravichandran, Garima Dalal, Kanhaiya Kumar","doi":"10.1007/s00449-026-03321-x","DOIUrl":"10.1007/s00449-026-03321-x","url":null,"abstract":"","PeriodicalId":9024,"journal":{"name":"Bioprocess and Biosystems Engineering","volume":" ","pages":"2333-2352"},"PeriodicalIF":4.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147761284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential application of acidogenic effluent from dark fermentation process as a phosphate solubilizing organic liquid fertilizer.","authors":"Sulogna Chatterjee, Omprakash Sarkar, S Venkata Mohan","doi":"10.1007/s00449-026-03323-9","DOIUrl":"10.1007/s00449-026-03323-9","url":null,"abstract":"","PeriodicalId":9024,"journal":{"name":"Bioprocess and Biosystems Engineering","volume":" ","pages":"2523-2533"},"PeriodicalIF":4.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147589720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soybean molasses: a promising substrate for the production of microbial oil by Phaffia rhodozyma.","authors":"Shirley Denisse Ccori Ponce, Luiz Henrique Han, Luiz Antonio de Almeida Pinto, Carlos André Veiga Burkert, Janaina Fernandes de Medeiros Burkert","doi":"10.1007/s00449-026-03312-y","DOIUrl":"10.1007/s00449-026-03312-y","url":null,"abstract":"<p><p>Soybean molasses represents a promising substrate for the production of high-value bioproducts, particularly in the cultivation of Phaffia rhodozyma, a yeast classified as GRAS (Generally Recognized as Safe). Although this yeast is well known for carotenoid synthesis, its potential for lipid accumulation remains largely unexplored. In this study, oil production by P. rhodozyma was evaluated using soybean molasses as the sole carbon source. The yeast was cultured in a 5 L bench-scale bioreactor (100 g·L⁻¹ substrate, 300 rpm, 1 vvm, 25°C) and the biomass was subsequently spouted bed drying. Lipids were extracted following the Bligh and Dyer method. After 144 h, 12.59 g.L⁻¹ of biomass was obtained, containing 154.23 µg·g⁻¹ of astaxanthin and 85.91 µg·g⁻¹ of β-carotene. Lipid yield reached 34.9% (w.w^- 1), corresponding to a C/N ratio of 44.39 in the cultivation medium. Fatty acid analysis revealed a predominance of oleic acid (66.98%), with a saturated/unsaturated ratio of 0.24, iodine value of 74 g.I₂.100 g⁻¹, saponification value of 161 mg KOH.g⁻¹, acidity of 5.02 mg KOH.g⁻¹, and peroxide value of 3.19 mEq O₂.kg⁻¹. These results demonstrate the potential of P. rhodozyma cultivated on soybean molasses to produce microbial oil with high nutritional and functional value, favorable lipid composition, and oxidative stability, highlighting the need for optimization strategies in the oil recovery step to enable future applications in the food industry.</p>","PeriodicalId":9024,"journal":{"name":"Bioprocess and Biosystems Engineering","volume":" ","pages":"2471-2486"},"PeriodicalIF":4.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582171/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147688111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}