Detection Of Microbial Contamination Research Articles

How Often Should Microbial Contamination Be Detected in Aircraft Fuel Systems? An Experimental Test of Aluminum Alloy Corrosion Induced by Sulfate-Reducing Bacteria

Microbial contamination in aircraft fuel-containing systems poses significant threats to flight safety and operational integrity as a result of microbiologically influenced corrosion (MIC). Regular monitoring for microbial contamination in these fuel systems is essential for mitigating MIC risks. However, the frequency of monitoring remains a challenge due to the complex environmental conditions encountered in fuel systems. To investigate the impact of environmental variables such as water content, oxygen levels, and temperature on the MIC of aluminum alloy in aircraft fuel systems, orthogonal experiments with various combinations of these variables were conducted in the presence of sulfate-reducing bacteria. Among these variables, water content in the fuel oil demonstrated the most substantial influence on the corrosion rate of aluminum alloys, surpassing the effects of oxygen and temperature. Notably, the corrosion rate of aluminum alloys was the highest in an environment characterized by a 1:1 water/oil ratio, 0% oxygen, and a temperature of 35 °C. Within this challenging environment, conducive to accelerated corrosion, changes in the corrosion behavior of aluminum alloys over time were analyzed to identify the time point at which MIC intensified. Observations revealed a marked increase in the depth and width of corrosion pits, as well as in the corrosion weight-loss rate, starting from the 7th day. These findings offer valuable insights for determining the optimal frequency of microbial contamination detection in aircraft fuel systems.

Determination of microbial contamination in raw milk, processed milk and yoghurt consumed in Mbarara city, western part of Uganda

he microbial composition of milk serves as a key indicator of its quality. While its nutritional value is significant, the inherent nature of milk also makes it an ideal environment for the proliferation of microorganisms, some of which lead to human illnesses, while others contribute to the spoilage of milk, making it unsafe for consumption. This research therefore aimed to assess the bacterial contamination content in raw milk, UHT and yoghurt, a total of 36 samples were gathered from various retailers, markets and supermarkets across Mbarara city. Contaminated milk and its products are accountable for human dairy related illnesses. This cross-sectional study was conducted to assess micro bacterial contamination and the presence of selected milk-borne zoonotic pathogens. The study involved 36 samples of milk and milk products mainly raw milk, UHT, or processed milk and yoghurt. Laboratory analysis conducted included Total Plate Count (TPC), Yeasts and molds (YM), staphylococcus, salmonella and coliform, as well as detection of Escherichia coli (E coli), biochemical tests including gram staining were employed to isolate and identify bacteria in these samples. The findings indicated the presence of these types of bacteria in raw milk, Staphylococcus spp (25%), Salmonella spp (33.3%), YM (41.6%), TPC (50%), coliforms (33.3%) and E.coli (25%), for UHT milk the percentage was Staphylococcus spp (0%), Salmonella spp (16.6%), YM (8.3%), TPC(16.6%), coliforms (16.6%) and E.coli (8.3%), finally for yoghurt the results were Staphylococcus spp (8.3%), Salmonella spp (8.3%), YM (16.6%), TPC (25%), coliforms (16.6%) and E.coli (0%). In conclusion, the detection of microbial contamination in raw milk and milk products suggests inadequate sanitary practices and poor storage practices. Products that are contaminated by these pathogenic organisms when consumed may result in food poisoning and a proper awareness among the stakeholders and consumers regarding possible outcomes of consuming contaminated food items is necessary. keywords: Milk, Yoghurt, Food poisoning, Microorganisms, Contamination

Beverage Deterioration Monitoring Based on Surface Tension Dynamics and Absorption Spectrum Analysis

Biochemical information sensing has always been one of the challenges in ubiquitous sensing research for mobile computing. Microorganisms will cause undetectable deterioration in drink production, such as wine and beverage, and microbial contamination is highly susceptible during storage like some liquors can be bottled for sometimes over ten years. Microbial culture methods are common for quality monitoring but unsuitable for real-time beverage quality monitoring. As far as we know, we are the first to use ubiquitous sensing for real-time microbial contamination detection. We designed a lightweight monitoring system called Microbe-Radar, which uses light signals to monitor real-time beverage quality. Microbe-Radar uses eight LEDs and a photodiode to detect fine-grained surface tension and absorption spectrum changes caused by microbial metabolites and growth during deterioration. Characteristic offset degree measurement and absorption spectrum dimension expansion are two critical technologies. Moreover, we implemented countermeasures against ambient light noise and sloshing interference. Microbe-Radar's surface tension and absorption spectrum measurement errors are only 0.89 mN/m and 2.4%, respectively, making identifying the contamination duration, microorganism content, and microorganism composition worthwhile. Experiments showed Microbe-Radar could determine potential issues with liquor quality when the liquid becomes health-threatening or even just contaminated, with an accuracy of 97.5%. Microbe-Radar can also be extended to beverage deterioration warning, with deterioration prediction accuracy of more than 90.6% for five beverages (milk, apple juice, etc.).

Nontargeted Volatile Metabolite Screening and Microbial Contamination Detection in Fermentation Processes by Headspace GC-IMS.

Gas chromatography combined with ion mobility spectrometry (GC-IMS) is a powerful separation and detection technique for volatile organic compounds (VOC). This combination is characterized by exceptionally low detection limits in the low ppbv range, high 2-dimensional selectivity, and robust operation. These qualities make it an ideal tool for nontarget screening approaches. Fermentation broths contain a substantial number of VOC, either from the medium or produced by microbial metabolism, that are currently not regularly measured for process monitoring. In this study, Escherichia coli, Saccharomyces cerevisiae, Levilactobacillus brevis, and Pseudomonas fluorescens were exemplarily used as model organisms and cultivated, and the headspace was analyzed by GC-IMS. Additionally, mixed cultures for every combination of two of the microorganisms were also characterized. Multivariate data analysis of the GC-IMS data revealed that it is possible to differentiate between the microorganisms using PLS-DA with a prediction accuracy of 0.92. The mixed cultures could be separated from the pure cultures with accuracies between 0.87 and 1.00 depending on the organism. GC-IMS data correlate with the optical density and can be used to follow and model growth curves. The root mean squared errors ranged between 10 and 20% of the maximum value, depending on the organism. Peak identification with reference compounds did not reveal specific marker compounds, rather the pattern was found to be responsible for the model performance.

Rhodamine B functionalized silver nanoparticles paper discs as turn-on fluorescence sensor, coupled with a smartphone for the detection of microbial contamination in drinking water.

The precise detection of pathogenic microorganisms is crucial for the reduction of water-borne diseases. Herein, a filter-paper-based florescent chemosensor was fabricated for the detection of Escherichia coli and Staphylococcus aureus contamination exploiting protein-DNA interaction between the target and a specific probe. The sensing mechanism involved the self-assembly of Rhodamine B (RhB) on silver nanoparticles (AgNPs) surface that was labeled with a single-stranded DNA probe. This causes the fluorescence quenching of RhB by a distant-dependant process. The hybridization between pathogen-specific probe and bacterial surface protein causes the release of fluorescence of RhB, which was observed under UV light. For paper-based bio-surface preparation, the mixture comprising RhB-AgNP-ssDNA was drop-casted on filter paper discs. The conditions were optimized using isolated genomic DNA of the microbes. The method was applied for E.coli detection using an eae gene-based probe targeting intimin protein and S. aureus detection using tuf gene-based probe targeting EF-tuf protein on the microbe's surface. The chemosensor had a notable specificity and selectivity for E.coli, and S. aureus, with detection limits of 0.6 × 108 and 0.37 × 103CFU/mL respectively. Moreover, the sensor was tested on real water samples, whichpresented excellent reproducibility of results (RSD ≤ 0.24%). Furthermore, the gradient change of fluorescence was captured by a smartphone, which allows direct detection of pathogens in a sensitive semi-quantitative way without the need for expensive instruments. The designed chemosensor can serve as a simple, inexpensive, and rapid method for the on-site detection of microbial contamination in drinking water.

B-239 Long-read Nanopore Sequencing is a Practicable Method of Targeted or Untargeted Microbial Contamination Detection

Abstract Background Sequencing-based methods for microbe detection are poised to replace both in vivo and PCR-based detection methods due to advantages in speed, sensitivity, specificity, and multiplexing. Here we describe pilot experiments to assess the practicability of nanopore sequencing for microbe detection. Methods Quantitative genomic Human Adenovirus 5 or Mycoplasma hominis DNA was spiked into remnant human serum and nucleic acids were extracted. 16S rRNA PCR amplification and barcoding was performed for Mycoplasma hominis identification. Sequencing was performed with a R9.4.1 flowcell on the MinION sequencer. Basecalling and genome alignment was performed with Guppy v.6.4.2. Results The performance of untargeted sequencing was evaluated by spiking quantitative Adenovirus DNA at 104 genome copies per microliter. Illustrative data from a single run is presented. Replicates yielded similar results. 1 520 991 reads between 31 bases and 970 kilobases were generated. 162 reads between 700 bases and 11.3 kilobases aligned to the Adenovirus genome (Image 1). To evaluate the performance of PCR amplified sequencing, we spiked Mycoplasma genomic DNA into serum or water. The entire 16S rRNA gene (1.5 kb) was PCR amplified and genomic DNA was spiked in at 104, 102, 10, and 0 (control) copies per microliter. A 10% signal: noise ratio cutoff yielded 100% specificity between spiked samples and negative controls. Conclusion Short read sequencing techniques are adequate for microbial detection but are capital intensive and prone to false positive results. We demonstrate that nanopore sequencing can generate long reads (>1.5 kb) with high-confidence microbe genome alignment. Our methods were sensitive to 104 and 10 copies per microliter utilizing untargeted and targeted methods, respectively. Further method optimization and validation will need to be performed, but these results demonstrate that nanopore sequencing is a practicable approach for microbe detection.

Penurunan waktu proses cleaning in place (CIP) dengan cara wet wash pada produksi susu bubuk di PT XYZ

The diversity of products in a company makes the process of cleaning production areas important to prevent cross contamination. Cleaning in Place (CIP) is a method to clean product residues from production equipment automatically without the need to disassemble the machine. This study aims to determine the required minimum time for the CIP drying process to dry the production equipment. The experiment was done by testing the treatment based on changes in compressed air, drying, and cooling process time. Cleaning verification was done by examining visual inspection and test for detection of microbial contamination, allergens and ATP. The results showed that reducing the CIP processing time can be done by minimizing the drying time. The best treatment resulted in the treatment with compressed air 300 s, drying 900 s and cooling 900 s. This can be seen from the 3 repetitions, the results obtained by examining the overall appearance of the surface in the tote bin are clean and dry. In addition, changes in the time parameter did not affect the cleaning process of residues on the surface of the tote bin, which was shown in the results of testing for detection of microbial contamination, allergens and ATP.

Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing.

Assuring that cell therapy products are safe before releasing them for use in patients is critical. Currently, compendial sterility testing for bacteria and fungi can take 7-14 days. The goal of this work was to develop a rapid untargeted approach for the sensitive detection of microbial contaminants at low abundance from low volume samples during the manufacturing process of cell therapies. We developed a long-read sequencing methodology using Oxford Nanopore Technologies MinION platform with 16S and 18S amplicon sequencing to detect USP <71> organisms and other microbial species. Reads are classified metagenomically to predict the microbial species. We used an extreme gradient boosting machine learning algorithm (XGBoost) to first assess if a sample is contaminated, and second, determine whether the predicted contaminant is correctly classified or misclassified. The model was used to make a final decision on the sterility status of the input sample. An optimized experimental and bioinformatics pipeline starting from spiked species through to sequenced reads allowed for the detection of microbial samples at 10 colony-forming units (CFU)/mL using metagenomic classification. Machine learning can be coupled with long-read sequencing to detect and identify sample sterility status and microbial species present in T-cell cultures, including the USP <71> organisms to 10 CFU/mL. IMPORTANCE This research presents a novel method for rapidly and accurately detecting microbial contaminants in cell therapy products, which is essential for ensuring patient safety. Traditional testing methods are time-consuming, taking 7-14 days, while our approach can significantly reduce this time. By combining advanced long-read nanopore sequencing techniques and machine learning, we can effectively identify the presence and types of microbial contaminants at low abundance levels. This breakthrough has the potential to improve the safety and efficiency of cell therapy manufacturing, leading to better patient outcomes and a more streamlined production process.

Recent Advances in Optical Sensing for the Detection of Microbial Contaminants.

Microbial contaminants are responsible for several infectious diseases, and they have been introduced as important potential food- and water-borne risk factors. They become a global burden due to their health and safety threats. In addition, their tendency to undergo mutations that result in antimicrobial resistance makes them difficult to treat. In this respect, rapid and reliable detection of microbial contaminants carries great significance, and this research area is explored as a rich subject within a dynamic state. Optical sensing serving as analytical devices enables simple usage, low-cost, rapid, and sensitive detection with the advantage of their miniaturization. From the point of view of microbial contaminants, on-site detection plays a crucial role, and portable, easy-applicable, and effective point-of-care (POC) devices offer high specificity and sensitivity. They serve as advanced on-site detection tools and are pioneers in next-generation sensing platforms. In this review, recent trends and advances in optical sensing to detect microbial contaminants were mainly discussed. The most innovative and popular optical sensing approaches were highlighted, and different optical sensing methodologies were explained by emphasizing their advantages and limitations. Consequently, the challenges and future perspectives were considered.

Towards the non-invasive assessment of staling in bovine hides with hyperspectral imaging

Microbial spoilage or staling of bovine hides during storage leads to poor leather quality and increased chemical consumption during processing. Conventional microbiological examinations of hide samples which require time-consuming microbe culture cannot be employed as a practical staling detection approach for leather production. Hyperspectral imaging (HSI), featuring fast data acquisition and implementation flexibility has been considered ideal for in-line detection of microbial contamination in Agri- food products. In this study, a linescan hyperspectral imaging system working in a spectral range of 550 nm to 1700 nm was utilized as a rapid and non-destructive technique for predicting the aerobic plate counts (APC) on raw hide samples during storage. Fresh bovine hide samples were stored at 4 °C and 20 °C for 3 days. Every day, hyperspectral images were acquired on both sides for each sample. The APCs were determined simultaneously by conventional microbiological plating method. Leather quality was evaluated by microscopic inspection of grain surfaces, which indicate the acceptable threshold of microbe load on hide samples for leather processing. Partial least squares regression (PLSR) was applied to fit the spectral information extracted from the samples to the logarithmic values of APC to develop microbe load prediction models. All models showed good prediction accuracy, yielding a Rcv2 in the range of 0.74–0.92 and standard error of cross validation (SECV) in the range of 0.61–0.76 %. The prediction capability of the HSI was explored using the model developed with SNV + smoothened pre-processing to spatially predict plate count in the samples. Models established in this study successfully predicted the staling states characterised by bacterial loads on hide samples with low prediction errors. Models, visually, showed the differences in microbial load across the storage time and temperatures. Results illustrate that HSI can be potentially implemented as a non-invasive tool to predict microbe loads in bovine hides before leather processing, so that real-time grading of hides based on staling states can be achieved. This will reduce the cost of leather production and waste management and pave the way for allocating material supply for different production purposes.

De novo identification of microbial contaminants in low microbial biomass microbiomes with Squeegee

Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low-biomass environments. Contamination from DNA extraction kits or sampling lab environments leaves taxonomic "bread crumbs" across multiple distinct sample types. Here we describe Squeegee, a de novo contamination detection tool that is based upon this principle, allowing the detection of microbial contaminants when negative controls are unavailable. On the low-biomass samples, we compare Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers putative contaminants. We analyze samples of varying biomass from the Human Microbiome Project and identify likely, previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision and thus represents a computational approach for contaminant detection when negative controls are unavailable.

Rapid Detection of Escherichia coli: Optimized Peptide-Polythiophene Interactions Help Reduce Assay Time and Improve Naked-Eye Detection.

Recent improvements in methods for rapid detection of microbial contamination in food and water samples have aided in the development of on-site and point-of-care testing. Early detection, made possible via on-site testing, can help limit the spread of food and waterborne illnesses. Recently, we reported a novel fluorescence-based Omptin-Polythiophene assay (the assay) to detect Escherichia coli in contaminated water samples. The assay targets OmpT─an E. coli outer membrane protease─and exploits the protease's ability to cleave at dibasic sites within a peptide. By combining a peptide substrate optimized for OmpT with a conjugated polythiophene reporter molecule whose optical properties vary upon interaction with the intact or cleaved peptide, we demonstrated the detection of 1-10 CFU/mL and 105 CFU/mL E. coli in 5.5 and 1 h, respectively. In comparison, most microbial detection methods that rely on culturing and plating techniques take anywhere between 8 and 24 h to report their results. Herein we report significant improvements in the assay which include reducing the assay time from an already short 1 h to a mere 10 min for detecting E. coli in highly contaminated samples and augmenting the assay with colorimetric sensing capability for naked eye discernment under normal visible light or under UV-A light. These improvements were made possible by characterizing the optical changes resulting from the interaction of the peptide with five carboxylate-functionalized polythiophene variants carrying different 3- side chain carboxylic acids and by identifying preferential peptide substrates via the screening of ten peptide sequence variants for OmpT activity.

Mesoporous ZnO@CO3O4 nanosphere for sensitive detection of 3-hydroxy-2-butanone

It is important to detect foodborne pathogens in food and water, such as Listeria monocytogenes (L.monocytrogenes), which can cause foodborne illness in people with compromised immune systems. Volatile organic compounds (VOC) and 3‑hydroxy-2-butanone (3H-2B) produced by L.monocytrogenes can be used as biomarkers for indirect detection of microbial contamination. In this study, the mesoporous ZnO@CO3O4 nanosphere was prepared through the simple hydrothermal synthesis method of ZIF-l-Zn@Co, the cage construction of mesoporous ZnO@CO3O4 nanosphere was remained unchanged after the calcination of the ZIF-l-Zn@Co at 600 °C for 2 h in air. The mesoporous ZnO@CO3O4 nanosphere was selected as the gas sensor showed the strongest response to 3H-2B, and 7–12 times higher than the response of other test gases. In addition, compared with ZnO and Co3O4 single metal oxide gas sensor, the ZnO@CO3O4 sensor possessed better repeatability and stability after repeated repeatability tests under the same conditions.

Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial, fungal, and viral contaminants

Hyperspectral imaging (HSI) is a robust and nondestructive method that can detect foreign particles such as microbial, chemical, and physical contamination in food. This review summarizes the work done in the last two decades in this field with a highlight on challenges, risks, and research gaps. Considering the challenges of using HSI on complex matrices like food (e.g., the confounding and masking effects of background signals), application of machine learning and modeling approaches that have been successful in achieving better accuracy as well as increasing the detection limit have also been discussed here. Foodborne microbial contaminants such as bacteria, fungi, viruses, yeast, and protozoa are of interest and concern to food manufacturers due to the potential risk of either food poisoning or food spoilage. Detection of these contaminants using fast and efficient methods would not only prevent outbreaks and recalls but will also increase consumer acceptance and demand for shelf-stable food products. The conventional culture-based methods for microbial detection are time and labor-intensive, whereas hyperspectral imaging (HSI) is robust, nondestructive with minimum sample preparation, and has gained significant attention due to its rapid approach to detection of microbial contaminants. This review is a comprehensive summary of the detection of bacterial, viral, and fungal contaminants in food with detailed emphasis on the specific modeling and datamining approaches used to overcome the specific challenges associated with background and data complexity.

Molecular Diagnostic Tools Applied for Assessing Microbial Water Quality

Microbial water quality is of vital importance for human, animal, and environmental health. Notably, pathogenically contaminated water can result in serious health problems, such as waterborne outbreaks, which have caused huge economic and social losses. In this context, the prompt detection of microbial contamination becomes essential to enable early warning and timely reaction with proper interventions. Recently, molecular diagnostics have been increasingly employed for the rapid and robust assessment of microbial water quality implicated by various microbial pollutants, e.g., waterborne pathogens and antibiotic-resistance genes (ARGs), imposing the most critical health threats to humans and the environment. Continuous technological advances have led to constant improvements and expansions of molecular methods, such as conventional end-point PCR, DNA microarray, real-time quantitative PCR (qPCR), multiplex qPCR (mqPCR), loop-mediated isothermal amplification (LAMP), digital droplet PCR (ddPCR), and high-throughput next-generation DNA sequencing (HT-NGS). These state-of-the-art molecular approaches largely facilitate the surveillance of microbial water quality in diverse aquatic systems and wastewater. This review provides an up-to-date overview of the advancement of the key molecular tools frequently employed for microbial water quality assessment, with future perspectives on their applications.

Fast detection of bacterial contamination in fresh produce using FTIR and spectral classification

Screening for microbial contaminants in fresh produce is a lengthy process relative to their short shelf-life. The aim of this study is to develop a comprehensive assay which employs FTIR and spectral classification algorithm for detection of bacterial contamination of fresh produce.The procedure starts by soaking a sample of the fresh produce in broth for 5 h. Then, magnetic nanoparticles are added to capture bacteria which are then collected and prepared for FTIR scanning. The generated FTIR spectra are compared against an in-house database of different bacterial species (n = 6). The ability of the database to discriminate contaminated and uncontaminated samples and to identify the bacterial species was assessed. The compatibility of the FTIR procedures with subsequent DNA extraction and PCR was tested. The developed procedure was applied for assessment of bacterial contamination in fresh produce samples from the market (n = 78). Results were compared to the conventional culture methods.The generated FTIR database coupled to spectral classification was able to detect bacterial contamination with overall accuracy exceeding 90%. The sample processing did not alter the integrity of the bacterial DNA which was suitable for PCR. On application to fresh produce samples collected from the market, the developed method was able to detect bacterial contamination with 94% concordance with the culture method.In conclusion, the developed procedure can be applied for fast detection of microbial contamination in fresh produce with comparable accuracy to conventional microbiological assays and is compatible with subsequent molecular assays.

The Detection of Microbial Contamination on Goat Meat from Traditional Market in Bandar Lampung City

The research aims to evaluate the microbial contamination of goat meat sold from the traditional market in Bandar Lampung. The survey method was used in this research, and the data were analyzed descriptively. 250 gr goat meat sample was taken from 5 traditional market in Bandar Lampung (Koga Market, Bambu Kuning Market, Way Kandis Market, Tamin Market and Tugu Market). Samples were taken at 07.00 PM. The research was conducted in the Disease Investigation Center of Lampung. The parameter of this research was Total Plate Count (TPC), coliform dan Escherichia coli. Total Plate Count (TPC) analysis in goat meat samples showed the highest contamination was from Tugu Market at 2.90 × 105 cfu/gr. Followed by Tamin Market at 1.30 × 105 cfu/gr and the lowest was from Koga Market, Bambu Kuning Market and Way Kandis Market at 1.00 × 101 cfu/gr respectively. Meanwhile, the highest coliform contamination was found from the goat meat sample from Tugu Market and Tamin Market at 11.00 × 102 cfu/gr. Followed by Way Kandis Market at 2.4 × 102 mpn/gr, and the lowest coliform contamination was found in goat meat sample from Koga Market and Bambu Kuning Market at 9.30 × 101 cfu/gr and 0.36 × 101 cfu/gr respectively. Furthermore, the content of Escherichia coli contamination from all traditional markets was below <101.

Application Progress of Hyperspectral Imaging Technology in Rapid Detection of Microbial Contamination in Animal Derived Food

Application Progress of Hyperspectral Imaging Technology in Rapid Detection of Microbial Contamination in Animal Derived Food

MOF-templated synthesis of cobalt-doped zinc oxide superparticles for detection of the 3-hydroxy-2-butanone microbial biomarker

It is important to detect the foodborne pathogens, such as Listeria monocytogenes (LMs), in food and water since the bacteria often induced serious illnesses in the global world. The bacteria generate the microbial volatile organic compound (VOC), 3-hydroxy-2-butanone (3H-2B), which could be used as a biomarker to indirectly detect the microbial contamination. Herein, ordered cobalt-doped zinc oxide superparticles (Co3O4/ZnO SPs) have been constructed by using MOFs as templates and have been employed as a sensing material to develop gas sensors for highly sensitive and selective detection of LMs. The 1% Co3O4/ZnO SPs based gas sensor showed a fast response, highest sensitivity and selectivity towards the 3 H-2B when compared to other dosage of Co doping. The proposed gas sensing mechanism is discussed. By using the Co3O4/ZnO SPs based sensors, a rapid bacteria detection platform was developed, with a high sensitivity and selectivity to trace the LMs at low concentrations. The gas sensing performance of the Co3O4/ZnO SPs based sensors has great potential applications for fast and sensitive detection of microbial contaminations. The study also introduces a way to construct MOF derived superstructure nanomaterials for gas sensing and other applications.

Electrochemical Detection of Waterborne Bacteria Using Bi-Functional Magnetic Nanoparticle Conjugates.

Detection of microbial contamination in water is imperative to ensure water quality. We have developed an electrochemical method for the detection of E. coli using bi-functional magnetic nanoparticle (MNP) conjugates. The bi-functional MNP conjugates were prepared by terminal-specific conjugation of anti-E. coli IgG antibody and the electroactive marker ferrocene. The bi-functional MNP conjugate possesses both E. coli-specific binding and electroactive properties, which were studied in detail. The conjugation efficiency of ferrocene and IgG antibodies with amine-functionalized MNPs was investigated. Square-wave voltammetry enabled the detection of E. coli concentrations ranging from 101–107 cells/mL in a dose-dependent manner, as ferrocene-specific current signals were inversely dependent on E. coli concentrations, completely suppressed at concentrations higher than 107 cells/mL. The developed electrochemical method is highly sensitive (10 cells/mL) and, coupled to magnetic separation, provides specific signals within 1h. Overall, the bi-functional conjugates serve as ideal candidates for electrochemical detection of waterborne bacteria. This approach can be applied for the detection of other bacteria and viruses.