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Traditional fermented foods are appreciated worldwide for their cultural significance and health-promoting properties. However, traditional fermentation production suffers from many obstacles such as microbial variability, varying quality, and lack of scalability. The implementation of smart fermentation technologies, including biosensors, the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), hold the key to the optimization of microbial process control, enhance product consistency, and improve production efficiency. This review summarizes modern developments in real-time microbial monitoring, IoT, AI, and ML tailored to traditional fermented foods. Despite significant technical advancements, challenges related to high costs, the absence of standardized frameworks, and access restrictions for small producers remain substantial limitations. This review proposed a future direction prioritizing modular, scalable solutions, open-source innovation, and environmental sustainability. In alignment with Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure), smart fermentation technologies advance sustainable industry through innovation and serve as a critical bridge between traditional craftsmanship and Industry 4.0, fostering inclusive development while preserving microbial biodiversity and cultural heritage.

Fungi are ubiquitous in outdoor air, and their concentration, aerodynamic diameters and taxonomic composition have potentially important implications for human health. Although exposure to fungal allergens is considered a strong risk factor for asthma prevalence and severity, limitations in tracking fungal diversity in air have thus far prevented a clear understanding of their human pathogenic properties. This study used a cascade impactor for sampling, and quantitative real-time PCR plus 454 pyrosequencing for analysis to investigate seasonal, size-resolved fungal communities in outdoor air in an urban setting in the northeastern United States. From the 20 libraries produced with an average of ∼800 internal transcribed spacer (ITS) sequences (total 15 326 reads), 12 864 and 11 280 sequences were determined to the genus and species levels, respectively, and 558 different genera and 1172 different species were identified, including allergens and infectious pathogens. These analyses revealed strong relationships between fungal aerodynamic diameters and features of taxonomic compositions. The relative abundance of airborne allergenic fungi ranged from 2.8% to 10.7% of total airborne fungal taxa, peaked in the fall, and increased with increasing aerodynamic diameter. Fungi that can cause invasive fungal infections peaked in the spring, comprised 0.1–1.6% of fungal taxa and typically increased in relative abundance with decreasing aerodynamic diameter. Atmospheric fungal ecology is a strong function of aerodynamic diameter, whereby through physical processes, the size influences the diversity of airborne fungi that deposit in human airways and the efficiencies with which specific groups of fungi partition from outdoor air to indoor environments.

Background Monitoring and control of both growth media and microbial biomass is extremely important for the development of economical bioprocesses. Unfortunately, process monitoring is still dependent on a limited number of standard parameters (pH, temperature, gasses etc.), while the critical process parameters, such as biomass, product and substrate concentrations, are rarely assessable in-line. Bioprocess optimization and monitoring will greatly benefit from advanced spectroscopy-based sensors that enable real-time monitoring and control. Here, Fourier transform (FT) Raman spectroscopy measurement via flow cell in a recirculatory loop, in combination with predictive data modeling, was assessed as a fast, low-cost, and highly sensitive process analytical technology (PAT) system for online monitoring of critical process parameters. To show the general applicability of the method, submerged fermentation was monitored using two different oleaginous and carotenogenic microorganisms grown on two different carbon substrates: glucose fermentation by yeast Rhodotorula toruloides and glycerol fermentation by marine thraustochytrid Schizochytrium sp. Additionally, the online FT-Raman spectroscopy approach was compared with two at-line spectroscopic methods, namely FT-Raman and FT-infrared spectroscopies in high throughput screening (HTS) setups. Results The system can provide real-time concentration data on carbon substrate (glucose and glycerol) utilization, and production of biomass, carotenoid pigments, and lipids (triglycerides and free fatty acids). Robust multivariate regression models were developed and showed high level of correlation between the online FT-Raman spectral data and reference measurements, with coefficients of determination (R 2 ) in the 0.94–0.99 and 0.89–0.99 range for all concentration parameters of Rhodotorula and Schizochytrium fermentation, respectively. The online FT-Raman spectroscopy approach was superior to the at-line methods since the obtained information was more comprehensive, timely and provided more precise concentration profiles. Conclusions The FT-Raman spectroscopy system with a flow measurement cell in a recirculatory loop, in combination with prediction models, can simultaneously provide real-time concentration data on carbon substrate utilization, and production of biomass, carotenoid pigments, and lipids. This data enables monitoring of dynamic behaviour of oleaginous and carotenogenic microorganisms, and thus can provide critical process parameters for process optimization and control. Overall, this study demonstrated the feasibility of using FT-Raman spectroscopy for online monitoring of fermentation processes.

Real-time electrochemical monitoring in bioprocesses is an improvement over existing systems because it is versatile and provides more information to the user than periodic measurements of cell density or metabolic activity. Real-time electrochemical monitoring provides the ability to monitor the physiological status of actively growing cells related to electron transfer activity and potential changes in the proton gradient of the cells. Voltammetric and amperometric techniques offer opportunities to monitor electron transfer reactions when electrogenic microbes are used in microbial fuel cells or bioelectrochemical synthesis. Impedance techniques provide the ability to monitor the physiological status of a wide range of microorganisms in conventional bioprocesses. Impedance techniques involve scanning a range of frequencies to define physiological activity in terms of equivalent electrical circuits, thereby enabling the use of computer modeling to evaluate specific growth parameters. Electrochemical monitoring of microbial activity has applications throughout the biotechnology industry for generating real-time data and offers the potential for automated process controls for specific bioprocesses.

The current strong reliance on synthetic chemicals, namely pesticides, is far from environmentally sustainable. These xenobiotics contribute significantly to global change and to the current biodiversity crisis, but have been overlooked when compared to other agents (e.g., climate change). Aquatic ecosystems are particularly vulnerable to pesticides, making monitoring programs essential to preserve ecosystem health, safeguard biodiversity, ensure water quality, and mitigate potential human health risks associated with contaminated water sources. Biosensors show great potential as time/cost-effective and disposable systems for the high-throughput detection (and quantification) of these pollutants. In this mini-review, we provide an overview of biosensors specifically developed for environmental water monitoring, covering different pesticide classes (and active ingredients), and types of biosensors (according to the bio-recognition element) and transducers, as well as the nature of sample matrices analyzed. We highlight the variety of biosensors that have been developed and successfully applied to detection of pesticides in aqueous samples, including enzymatic biosensors, immunosensors, aptasensors, and whole cell–based biosensors. While most biosensors have been designed to detect insecticides, expanding their compound target range could significantly streamline monitoring of environmental contaminants. Despite limitations related to stability, reproducibility, and interference from environmental factors, biosensors represent a promising and sustainable technology for pesticide monitoring in the aquatic environments, offering sensitivity and specificity, as well as portability and real-time results. We propose that biosensors would be most effective as an initial screening step in a tiered assessment, complementing conventional methods. Key points • Pesticides harm aquatic ecosystems and biodiversity, requiring better monitoring • Biosensors offer cost-effective solutions to detect pesticides in water samples • Biosensors complement conventional methods as a sustainable tool for initial screens

The resistance of biofilms to antibiotics is a key factor that makes bacterial infections unsusceptible to antimicrobial therapy. The results of classical tests of cell sensitivity to antibiotics cannot be used to predict therapeutic success in infections associated with biofilm formation. We describe a simple and rapid method for the real-time evaluation of bacterial biofilm sensitivity to antibiotics, with Pseudomonas putida and ampicillin as examples. The method uses an electric biosensor to detect the difference between changes in the biofilm electric polarizability, thereby evaluating antibiotic sensitivity. The electric signals showed that P. putida biofilms were susceptible to ampicillin and that at high antibiotic concentrations, the biofilms differed markedly in their susceptibility (dose-dependent effect). The sensor also detected differences between biofilms before and after ampicillin treatment. The electric-signal changes enabled us to describe the physical picture of the processes occurring in bacterial biofilms in the presence of ampicillin. The approach used in this study is promising for evaluating the activity of various compounds against biofilms, because it permits a conclusion about the antibiotic sensitivity of biofilm bacteria to be made in real time and in a short period (analysis time, not longer than 20 min). An added strong point is that analysis can be done directly in liquid, without preliminary sample preparation. Key points • Sensor system to analyze biofilm antimicrobial susceptibility is described. • The signal change depended on the ampicillin concentration (dose-dependent effect). • The sensor allows real-time determination of the antibiofilm effect of ampicillin.

The dominant filamentous bacteria associated with bulking incidents in Japanese activated sludge plants with nutrient removal were identified and their quantitative correlations with sludge settleability were assessed, with the aim of controlling bulking incidents by specifically suppressing bacterial growth. Fluorescence in situ hybridization (FISH) analyses using existing oligonucleotide FISH probes indicated that the presence of Eikelboom type 1851 filamentous bacteria belonging to the phylum Chloroflexi is correlated with biomass settleability in the municipal wastewater treatment plants examined. Real-time quantitative PCR (qPCR) assays developed in this study also showed a linear correlation between type 1851 filament members and sludge settleability, with the exception of some winter samples. The real-time qPCR assays and 16S ribosomal RNA gene amplicon sequencing to reveal the microbial community of activated sludge showed that the abundance of type 1851 at 200 mL g −1 of sludge volume index was estimated to be about 1.9% of the total microbial cells. The abundance of type 1851 served as a bulking indicator in plants where type 1851 was dominant.

Karst aquifers are important resources for drinking water supply and are very vulnerable to contamination. Microbial concentrations at karst springs, in particular, often vary quickly over a short period of time. In this study, the response of microbial water quality and particle-size distribution of two alpine karst springs to rainfall events was investigated to test and validate parameters that can be used as early-warning systems for fecal contamination. At both investigated karst springs, total organic carbon, particle-size distribution (especially small particle fractions), and particle load show a good correlation to the fecal indicator bacteria E. coli and can therefore be used as a real-time indicator of fecal contamination at the investigated springs. In addition to conventional bacterial determination methods, the β-D-glucuronidase activity, which can be measured in near real-time, was used as a novel indicator parameter for fecal contamination. At the event scale, the β-D-glucuronidase (GLUC) activity shows a good correlation to E. coli and can be used as an additional real-time indicator of fecal contamination. For the studied springs, when they show two peaks in turbidity and small particles, these two parameters are suitable for an early warning system because the bacterial contamination occurs during the secondary peak of these parameters. These results highlight the vulnerability of karst aquifers and demonstrate the applicability of advanced measurement techniques in detecting fecal contamination in real-time, which is especially important given the time-consuming nature of conventional bacterial detection methods.

The monitoring of microbial cultivation in real time and controlling their cultivation aid in increasing the production yield of useful material in a jar fermenter. Common sensors such as dissolved oxygen (DO) and pH can easily provide general-purpose indexes but do not reveal the physiological states of microbes because of the complexity of measuring them in culture conditions. It is well known from microscopic observations that the microbial morphology changes in response to the intracellular state or extracellular environment. Recently, studies have focused on rapid and quantitative image analysis techniques using machine learning or deep learning for gleaning insights into the morphological, physiological or gene expression information in microbes. During image analysis, it is necessary to retrieve high-definition images to analyze the microbial morphology in detail. In this study, we have developed a microfluidic device with a high-speed camera for the microscopic observation of yeast, and have constructed a system capable of generating their morphological information in real-time and at high definition. This system was connected to a jar fermenter, which enabled the automatic sampling for monitoring the cultivation. We successfully acquired high-definition images of over 10,000 yeast cells in about 2.2 s during ethanol fermentation automatically for over 168 h. We recorded 33,600 captures containing over 1,680,000 cell images. By analyzing these images, the morphological changes of yeast cells through ethanol fermentation could be captured, suggesting the expansion of the application of this system in controlling microbial fermentation using the morphological information generated. Key points • Enables real-time visualization of microbes in a jar fermenter using microscopy. • Microfluidic device for acquiring high-definition images. • Generates a large amount of image data by using a high-speed camera. Graphical abstract

Successful antimicrobial therapy depends on achieving optimal drug concentrations within individual patients. Inter-patient variability in pharmacokinetics (PK) and differences in pathogen susceptibility (reflected in the minimum inhibitory concentration, [MIC]) necessitate personalised approaches. Dose individualisation strategies aim to address this challenge, improving treatment outcomes and minimising the risk of toxicity and antimicrobial resistance. Therapeutic drug monitoring (TDM), with the application of population pharmacokinetic (popPK) models, enables model-informed precision dosing (MIPD). PopPK models mathematically describe drug behaviour across populations and can be combined with patient-specific TDM data to optimise dosing regimens. The integration of machine learning (ML) techniques promises to further enhance dose individualisation by identifying complex patterns within extensive datasets. Implementing these approaches involves challenges, including rigorous model selection and validation to ensure suitability for target populations. Understanding the relationship between drug exposure and clinical outcomes is crucial, as is striking a balance between model complexity and clinical usability. Additionally, regulatory compliance, outcome measurement, and practical considerations for software implementation will be addressed. Emerging technologies, such as real-time biosensors, hold the potential for revolutionising TDM by enabling continuous monitoring, immediate and frequent dose adjustments, and near patient testing. The ongoing integration of TDM, advanced modelling techniques, and ML within the evolving digital health care landscape offers a potential for enhancing antimicrobial therapy. Careful attention to model development, validation, and ethical considerations of the applied techniques is paramount for successfully optimising antimicrobial treatment for the individual patient.