Detection and Identification of Bacillus cereus, Bacillus cytotoxicus, Bacillus thuringiensis, Bacillus mycoides and Bacillus weihenstephanensis via Machine Learning Based FTIR Spectroscopy

Enhancing Food Quality Analysis: The Transformative Role of Artificial Neural Networks in Modern Analytical Techniques

BACKGROUND

Consumers have always anticipated and requested high-quality and safe food items, which is why food quality analyses and mandatory product inspections were introduced. Various novel techniques and analytical breakthroughs have emerged to cater to the problem of food authenticity. It is difficult to interpret and analyze large and complex data with traditional machine learning algorithms. Its effectiveness is constrained when dealing with non-linearity, high-dimension data, and intricate data relationships, and it is frequently faced with struggles to capture the latent patterns and structures within such data. However, using deep learning artificial neural networks has provided solutions to these challenges. Artificial neural networks (ANNs) have propelled breakthroughs across several research areas and have continually been applied in the area of food analysis, quality, and safety. ANNs are pooled along with various analytical techniques for making food quality predictions with fast response time and short computation time.

SCOPE AND APPROACH

Details of the latest ANN models and their contribution in food quality predictions, have been presented in this review. This systematic review has also firmly focused on various types of analytical techniques and recent applications of advanced analytical techniques combining various ANNs in food authentication. Various traditional chemometrics method and their key limitations are also discussed.

KEY FINDINGS AND CONCLUSIONS

ANNs when combined with analytical techniques, provide solutions to the problem of food authenticity and quality analyses can learn representational features from the data-set during the training process and demonstrate stronger ability than traditional chemometric methods. The fusion of different types of ANNs and the development of miniaturized and portable spectrometers are becoming increasingly essential and potential candidates for future research.

Realtime particulate matter and bacteria analysis of peritoneal dialysis fluid using digital inline holography

This study developed a digital inline holography (DIH) system integrated with deep learning algorithms for real-time detection of particulate matter (PM) and bacterial contamination in peritoneal dialysis (PD) fluids. The system comprises a microfluidic sample delivery module and a DIH imaging module that captures holograms using a pulsed laser and a digital camera with a 40 × objective. Our data processing pipeline enhances holograms, reconstructs images, and employs a YOLOv8n-based deep learning model for particle identification and classification, trained on labeled holograms of generic PD particles, Escherichia coli (E. coli), and Pseudomonas aeruginosa (P. aeruginosa). The system effectively detected and classified generic particles in sterile PD fluids, revealing diverse morphologies predominantly sized 1-5 µm with an average concentration of 61 particles/µL. In PD fluid samples spiked with high concentrations of E. coli and P. aeruginosa, our system achieved high sensitivity (>90 %) in detecting and classifying these bacteria at clinically relevant low false positive rates (∼0.5 %). Further validation against standard colony-forming unit (CFU) methods using PD fluid spiked with bacterial concentrations from approximately 100 to 10,000 bacteria/mL demonstrated a clear one-to-one correspondence between our measurements and CFU counts. Our DIH system provides a rapid, accurate alternative to traditional culture-based methods for assessing bacterial contamination in PD fluids. By enabling real-time sterility monitoring, it can significantly improve patient outcomes in PD treatment, facilitate point-of-care fluid production, reduce logistical challenges, and be extended to quality control in pharmaceutical production.

Fast and Accurate Prediction of Antibiotic Susceptibility in Clinical Methicillin-Resistant S. aureus Isolates Using ATR-FTIR Spectroscopy: A Model Validation Study

Diagnosing antimicrobial resistance (AMR) remains critical for improving patient survival rates and treatment outcomes. Current antibiotic susceptibility tests (AST) suffer prolonged turnaround times, necessitating a minimum of 24 h for results. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy emerges as a promising phenotypic testing method in bacteriology due to its rapid chemical characterization capability. Here, we present an innovative approach utilizing ATR-FTIR spectroscopy for rapid AMR assessment, distinguishing between methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-susceptible S. aureus (MSSA). Our approach focuses on detecting early markers of effective antibiotic action and using these to predict resistance profiles. To identify the earliest time for detection, five MSSA and five MRSA strains were subjected to oxacillin exposure for up to 2 h. We observed discernible molecular changes arising in MSSA as early as 1 h after exposure to oxacillin, which were absent in MRSA strains. Bands at 1624 and 1515 cm-1 were identified as markers of positive drug response in MSSA using principal component analysis (PCA) and were associated with peptidoglycan precursor accumulation upon transpeptidation inhibition. To develop predictive models for determining resistance profiles, we implemented ML-based modeling of the spectral data, reflective of the oxacillin-induced chemical composition changes in MSSA and MRSA. Partial least squares discriminant analysis (PLS-DA) and support vector machines classification (SVM-C) algorithms produced the best results, achieving 100% consistency with minimum inhibitory concentration (MIC) classification. Our models were independently validated by blind testing with 35 clinical strains and demonstrated 100% agreement with resistance profiling determined by MIC. Our study underscores the potential of ATR-FTIR spectroscopy for rapid and accurate AMR assessment, with the capacity to revolutionize diagnostics in combating antibiotic resistance.

Natural Products Produced by the Species of Bacillus cereus Group: Recent Updates

Bacillus cereus group produces diverse antimicrobial compounds through different metabolic pathways, including amino acid-based compounds, sugar derivatives, volatile and miscellaneous compounds. These antimicrobial compounds exhibit antibacterial and antifungal activities against various plant pathogens, promoting plant growth and enhancing tolerance to abiotic stresses. They also exhibit nematicidal activities against plant nematodes and antagonistic effects against pathogens in aquatic animals, promoting growth and inducing immune responses. Moreover, B. cereus group bacteria play a significant role in bioremediation by breaking down or neutralizing environmental pollutants, such as plastics, petroleum products, heavy metals, and insecticides. They produce enzymes like laccases, lipases, proteases, and various oxidases, contributing to the degradation of these pollutants. In the food industry, they can cause food poisoning due to their production of enterotoxins. However, they are also utilized in various industrial applications, such as producing environmentally friendly bio-based materials, biofertilizers, and nanoparticles. Notably, B. cereus transforms selenite into selenium nanoparticles, which have health benefits, including cancer prevention. In summary, B. cereus group bacteria have diverse applications in agriculture, bioremediation, industry, and medicine, contributing to sustainable and eco-friendly solutions across multiple fields. In this review, we have revised B. cereus group and the characteristics of every species; we have also highlighted the more important compounds secreted by the species of B. cereus group and the applications of these compounds. The aim is to explain the available secondary metabolites to classify the species from this group, increasing the knowledge about taxonomy of this group.

The Germination and Growth of Two Strains of Bacillus cereus in Selected Hot Dishes After Cooking

The aim of this study was to assess the germination and growth of two strains of Bacillus cereus following the artificial inoculation of six selected hot dishes with spores which were then stored at temperatures of 40, 50, and 60 °C for 0.5, 1.0, 2.0, 2.5, 3.0, and 4.0 h. The water activity of the prepared meals varied between 0.967 and 0.973 and the salt content between 0.74 and 1.40%. The pH value of four dishes exceeded 6.0, but for two (tomato sauce and ratatouille) it was 4.6. The tested strain DSM 4312 showed good growth abilities and attained a population exceeding 6.0 log CFU/g within 4 h at 40 °C in foods with pH values > 6.0. The study demonstrated that a drop in food temperatures to 40 °C is risky, while no growth of B. cereus was detected within 4 h at 50 and 60 °C. The growth rate of B. cereus is conditioned not merely by environmental conditions (temperature, pH values, food composition), but also by the bacterial strain.

Genomic Insights into the Bactericidal and Fungicidal Potential of Bacillus mycoides b12.3 Isolated in the Soil of Olkhon Island in Lake Baikal, Russia

The dispersal of plant pathogens is a threat to the global economy and food industry which necessitates the need to discover efficient biocontrol agents such as bacteria, fungi, etc., inhibiting them. Here, we describe the Bacillus mycoides strain b12.3 isolated from the soil of Olkhon Island in Lake Baikal, Russia. By applying the co-cultivation technique, we found that the strain inhibits the growth of plant pathogens, such as the bacteria Xanthomonas campestris, Clavibacter michiganensis, and Pectobacterium atrospecticum, as well as the fungus Alternaria solani. To elucidate the genomic fundament explaining these activities, we leveraged next-generation whole-genome sequencing and obtained a high-quality assembly based on short reads. The isolate bore seven known BGCs (biosynthetic gene clusters), including those responsible for producing bacillibactin, fengycin, and petrobactin. Moreover, the genome contained insecticidal genes encoding for App4Aa1, Tpp78Ba1, and Spp1Aa1 toxins, thus implicating possible pesticidal potential. We compared the genome with the 50 closest assemblies and found that b12.3 is enriched with BGCs. The genomic analysis also revealed that genomic architecture corresponds to the experimentally observed activity spectrum implying that the combination of produced secondary metabolites delineates the range of inhibited phytopathogens Therefore, this study deepens our knowledge of the biology and ecology of B. mycoides residing in the Lake Baikal region.

Prevalence, Characteristics, and Selection of Bacillus cereus Subgroups from Dairy Products for Challenge Testing and Predictive Model Development

Prevalence, toxin gene profiles, lactose fermentation, and growth responses of B. cereus sensu lato subgroups in various dairy and dairy alternative products and ingredients were studied to identify relevant isolates for challenge testing and model development to predict and manage growth responses. Out of 71 examined products or ingredients, 51 B. cereus s.l. isolates were obtained from 35 positive samples (49% prevalence). These 51 isolates along with 18 additional dairy isolates and 12 B. cereus s.l. reference strains were identified using MALDI-TOF. The 81 isolates were further characterized by panC sequencing, testing for cold shock and toxin genes (cspA; hbl, nhe, CytK and ces), lactose fermentation, and study of growth rates (µmax) under various conditions (45 °C, 10 °C, 6% NaCl, pH 5.1), resulting in 298 µmax-values. These conditions were selected to differentiate mesophilic and psychrotolerant strains and to identify tolerant isolates. Dairy powders (83%), pasteurized upconcentrated cheese whey (43%), and cheeses (42%) had the highest prevalences of B. cereus s.l. and the highest concentrations in positive samples (5-100 CFU/g or ml). The panC groups II, III, IV, VI, and VIII were detected among the dairy isolates, with 97% harboring one or more toxin genes. Lactose fermentation was observed in 42% of isolates, with lactose-fermenting B. cereus s.l. isolates of panC groups III and IV dominant in dairy powders. Growth rates of B. cereus s.l. varied considerably, among and within panC groups of the dairy isolates. Based on the highest growth rates at 45 °C, 10 °C, 6% NaCl, pH 5.1, panC group membership, toxin genes profiles, and lactose fermentation ability, two cocktails of "mesophilic" or "psychrotolerant" isolates were selected. These strain cocktails can be used in future challenge testing and predictive food microbiology studies to evaluate and manage the growth of B. cereus s.l. in dairy products and ingredients.

The megaplasmid pCER270 of Bacillus cereus emetic strain affects the timing of the sporulation process, spore resistance properties, and germination

The Bacillus cereus group includes closely related spore-forming Gram-positive bacteria. In this group, plasmids play a crucial role in species differentiation and are essential for pathogenesis and adaptation to ecological niches. The B. cereus emetic strains are characterized by the presence of the pCER270 megaplasmid, which encodes the non-ribosomal peptide synthetase for the production of cereulide, the emetic toxin. This plasmid carries several genes that may be involved in the sporulation process. Furthermore, a transcriptomic analysis has revealed that pCER270 influences the expression of chromosome genes, particularly under sporulation conditions. In this study, we investigated the role of pCER270 on spore properties in different species of the B. cereus group. We showed that pCER270 plays a role in spore wet heat resistance and germination, with varying degrees of impact depending on the genetic background. In addition, pCER270 ensures that sporulation occurs at the appropriate time by delaying the expression of sporulation genes. This regulation of sporulation timing is controlled by the pCER270-borne Rap-Phr system, which likely regulates the phosphorylation state of Spo0A. Acquisition of the pCER270 plasmid by new strains could give them an advantage in adapting to new environments and lead to the emergence of new pathogenic strains.

IMPORTANCE

The acquisition of new mobile genetic elements, such as plasmids, is essential for the pathogenesis and adaptation of bacteria belonging to the Bacillus cereus group. This can confer new phenotypic traits and beneficial functions that enable bacteria to adapt to changing environments and colonize new ecological niches. Emetic B. cereus strains cause food poisoning linked to the production of cereulide, the emetic toxin whose synthesis is due to the presence of plasmid pCER270. In the environment, cereulide provides a competitive advantage in producing bacteria against various competitors or predators. This study demonstrates that pCER270 also regulates the sporulation process, resulting in spores with improved heat resistance and germination capacity. The transfer of plasmid pCER270 among different strains of the B. cereus group may enhance their adaptation to new environments. This raises the question of the emergence of new pathogenic strains, which could pose a serious threat to human health.

New insights into Bacillus cytotoxicus sources, screening, toxicity, and persistence in food production facilities

Bacillus cytotoxicus is a thermotolerant member of the Bacillus cereus group. It has been linked to rare, but at times fatal cases of diarrheal disease and might be missed at routine diagnostic screening temperatures commonly used for the B. cereus group. The pathogen is mostly found on dehydrated foods containing potato starch or insects. How it enters the food chain or whether it persists in food producing environments is largely unknown. Increased consumption of insects and convenience foods in Europe and the lack of information on the persistence of B. cytotoxicus in food environments and its virulence demand for further characterization. In this study, we aimed to obtain a better understanding of i) the food sources of B. cytotoxicus, ii) screening temperatures needed for its isolation from food matrices, iii) cytotoxicity of the organism, and iv) its ecological niche and potential epidemiological links. To this end, 112 food samples were collected, with a focus on foods exhibiting low water activity. The samples were screened for B. cytotoxicus at 42 °C and at 50 °C. Presumptive isolates were characterized by cytK-1 toxin gene PCR for differentiation of B. cytotoxicus from other B. cereus group members. Vero cell cytotoxicity assays were performed, and selected isolates were sequenced. Our results show that screening at 42 °C might be insufficient for detecting B. cytotoxicus in foods that harbor other less thermophilic Bacillus species. When screening at 50 °C, B. cytotoxicus was detected in 23% of the food samples (n = 26 isolates). The highest prevalence was detected in mashed potato products (82%) and potato flakes (67%). In contrast, a wide range of products not containing any potato ingredients did not yield B. cytotoxicus isolates. All B. cytotoxicus isolates exhibited either low or no detectable cytotoxicity. WGS analysis revealed that a highly toxic isolate is closely related to the French outbreak strain NVH 391-98. In addition, we could show that two isolates sampled 5 years apart from the same production facility only differed by seven SNPs, making it likely that B. cytotoxicus is able to persist in production facilities over a long time. Interestingly, the reoccurring strain possessed an additional plasmid and did not show cytotoxic potential when re-isolated after 5 years.

Advances in machine learning-based bacteria analysis for forensic identification: identity, ethnicity, and site of occurrence

When faced with an unidentified body, identifying the victim can be challenging, particularly if physical characteristics are obscured or masked. In recent years, microbiological analysis in forensic science has emerged as a cutting-edge technology. It not only exhibits individual specificity, distinguishing different human biotraces from various sites of occurrence (e.g., gastrointestinal, oral, skin, respiratory, and genitourinary tracts), each hosting distinct bacterial species, but also offers insights into the accident's location and the surrounding environment. The integration of machine learning with microbiomics provides a substantial improvement in classifying bacterial species compares to traditional sequencing techniques. This review discusses the use of machine learning algorithms such as RF, SVM, ANN, DNN, regression, and BN for the detection and identification of various bacteria, including Bacillus anthracis, Acetobacter aceti, Staphylococcus aureus, and Streptococcus, among others. Deep leaning techniques, such as Convolutional Neural Networks (CNN) models and derivatives, are also employed to predict the victim's age, gender, lifestyle, and racial characteristics. It is anticipated that big data analytics and artificial intelligence will play a pivotal role in advancing forensic microbiology in the future.

The toxicological spectrum of the Bacillus cereus toxin cereulide points towards niche-specific specialisation

Most microbes share their environmental niches with very different forms of life thereby engaging in specialised relationships to enable their persistence. The bacterium Bacillus cereus occurs ubiquitously in the environment with certain strain backgrounds causing foodborne and opportunistic infections in humans. The emetic lineage of B. cereus is capable of producing the toxin cereulide, which evokes emetic illnesses. Although food products favouring the accumulation of cereulide are known, the ecological role of cereulide and the environmental niche of emetic B. cereus remain elusive. To better understand the ecology of cereulide-producing B. cereus, we systematically assayed the toxicological spectrum of cereulide on a variety of organisms belonging to different kingdoms. As cereulide is a potassium ionophore, we further tested the effect of environmental potassium levels on the action of cereulide. We found that adverse effects of cereulide exposure are species-specific, which can be exacerbated with increased environmental potassium. Additionally, we demonstrate that cereulide is produced within an insect cadaver indicating its potential ecological function for a saprophytic lifestyle. Collectively, distinct cereulide susceptibilities of other organisms may reflect its role in enabling competitive niche specialization of emetic B. cereus.

Characterization of Bacillus thuringiensis isolated from soils in the Jazan region of Saudi Arabia, and their efficacy against Spodoptera littoralis and Aedes aegypti larvae

Pest control in Saudi Arabia depends on applying chemical insecticides, which have many undesirable considerations and impacts on the environment. Therefore, the aim of this study was to isolate Bacillus thuringiensis from different rhizosphere soil samples in the Jazan region for the biological control of Spodoptera littoralis and Aedes aegypti larvae. The samples were collected from the rhizosphere of different plants located in eight agricultural areas in Jazan, Saudi Arabia. Out of 100 bacterial isolates, four bacterial isolates belonging to Bacillus species were selected namely JZ1, JZ2, JZ3, and JZ4, and identified using classical bacteriological and molecular identification using 16S rRNA. JZ1 and JZ2 isolates were identified as Bacillus thuringiensis. SDS-PAGE analysis and the detection of the Cry1 gene were used to describe the two isolates JZ1 and JZ2 in comparison to Bacillus thuringiensis reference strain Kurstaki HD1 (BTSK) were revealed that slightly different from each other due to the place of their isolation and namely Khlab JZ1 and Ayash JZ2. The EC₅₀ of JZ1 and JZ2 isolates, BTSK, and the commercial biopesticide DiPEL 6.4 DF against the second-instar larvae of Aedes aegypti were 207, 932, 400, and 500 ppm respectively, while EC₅₀ against first-instar larvae of Spodoptera littoralis were 193.93, 589.7, 265.108, and 342.9, ppm respectively. Isolate JZ1 recorded the highest mortality while JZ2 isolate gave the lowest mortality. It can be concluded that the local isolate of JZ1 and JZ2 can be developed for bio formulations to be used in Spodoptera littoralis and Aedes aegypti biological control programs.

Conventional and advanced detection techniques of foodborne pathogens: A comprehensive review

Foodborne pathogens are a major public health concern and have a significant economic impact globally. From harvesting to consumption stages, food is generally contaminated by viruses, parasites, and bacteria, which causes foodborne diseases such as hemorrhagic colitis, hemolytic uremic syndrome (HUS), typhoid, acute, gastroenteritis, diarrhea, and thrombotic thrombocytopenic purpura (TTP). Hence, early detection of foodborne pathogenic microbes is essential to ensure a safe food supply and to prevent foodborne diseases. The identification of foodborne pathogens is associated with conventional (e.g., culture-based, biochemical test-based, immunological-based, and nucleic acid-based methods) and advances (e.g., hybridization-based, array-based, spectroscopy-based, and biosensor-based process) techniques. For industrial food applications, detection methods could meet parameters such as accuracy level, efficiency, quickness, specificity, sensitivity, and non-labor intensive. This review provides an overview of conventional and advanced techniques used to detect foodborne pathogens over the years. Therefore, the scientific community, policymakers, and food and agriculture industries can choose an appropriate method for better results.

Optical Methods for Label-Free Detection of Bacteria

Pathogenic bacteria are the leading causes of food-borne and water-borne infections, and one of the most serious public threats. Traditional bacterial detection techniques, including plate culture, polymerase chain reaction, and enzyme-linked immunosorbent assay are time-consuming, while hindering precise therapy initiation. Thus, rapid detection of bacteria is of vital clinical importance in reducing the misuse of antibiotics. Among the most recently developed methods, the label-free optical approach is one of the most promising methods that is able to address this challenge due to its rapidity, simplicity, and relatively low-cost. This paper reviews optical methods such as surface-enhanced Raman scattering spectroscopy, surface plasmon resonance, and dark-field microscopic imaging techniques for the rapid detection of pathogenic bacteria in a label-free manner. The advantages and disadvantages of these label-free technologies for bacterial detection are summarized in order to promote their application for rapid bacterial detection in source-limited environments and for drug resistance assessments.

Influence of Emulsifying Salts on the Growth of Bacillus thuringiensis CFBP 3476 and Clostridium perfringens ATCC 13124 in Processed Cheese

Processed cheese is a dairy product with multiple end-use applications, where emulsifying salts play a fundamental role in physicochemical changes during production. Moreover, some of these salts may be a strategy to control spoilage and pathogenic microorganisms, contributing to safety and shelf life extension. This study aimed to evaluate the in vitro inhibitory activity of two emulsifying salts (ESSP = short polyP and BSLP = long polyP) against Bacillus thuringiensis CFBP 3476 and Clostridium perfringens ATCC 13124, and to compare the in situ effects of two emulsifying salts treatments (T1 = 1.5% ESSP and T2 = 1.0% ESSP + 0.5% BSLP) in processed cheeses obtained by two different methods (laboratory- and pilot-scales), during 45-day storage at 6 °C. C. perfringens ATCC 13124 growth was not affected in vitro or in situ (p > 0.05), but both of the treatments reduced B. thuringiensis CFBP 4376 counts in the tested condition. Counts of the treatments with B. thuringiensis CFBP 3476 presented a higher and faster reduction in cheeses produced by the laboratory-scale method (1.6 log cfu/g) when compared to the pilot-scale method (1.8 log cfu/g) (p < 0.05). For the first time, the inhibitory effect of emulsifying salts in processed cheeses obtained by two different methods was confirmed, and changes promoted by laboratory-scale equipment influenced important interactions between the processed cheese matrix and emulsifying salts, resulting in B. thuringiensis CFBP 4376 growth reduction.

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.

Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-supervised Learning

The past decade witnessed rapid development in the measurement and monitoring technologies for food science. Among these technologies, spectroscopy has been widely used for the analysis of food quality, safety, and nutritional properties. Due to the complexity of food systems and the lack of comprehensive predictive models, rapid and simple measurements to predict complex properties in food systems are largely missing. Machine Learning (ML) has shown great potential to improve the classification and prediction of these properties. However, the barriers to collecting large datasets for ML applications still persists. In this paper, we explore different approaches of data annotation and model training to improve data efficiency for ML applications. Specifically, we leverage Active Learning (AL) and Semi-Supervised Learning (SSL) and investigate four approaches: baseline passive learning, AL, SSL, and a hybrid of AL and SSL. To evaluate these approaches, we collect two spectroscopy datasets: predicting plasma dosage and detecting foodborne pathogen. Our experimental results show that, compared to the de facto passive learning approach, advanced approaches (AL, SSL, and the hybrid) can greatly reduce the number of labeled samples, with some cases decreasing the number of labeled samples by more than half.

Impact and prospect of the fourth industrial revolution in food safety: Mini-review

The fourth industrial revolution represented by big data and artificial intelligence (AI), already had a significant impact on the food industry. In this review, the impacts and prospects of the 4th industrial revolution in food safety were discussed. First, the general process and characteristics of AI application from data collection to visualization are covered. Additionally, various data collection and analysis methods are discussed, with emphasis on the collection of high variety, volume, and velocity data and visualization. Available literature presents examples of machine learning applications in food samples that are mostly associated with the classification of agricultural food items through convolutional neural networks. Based on these examples, the prospects of the 4th industrial revolution in food safety are categorized as follows: prediction of food safety risk, detection of foodborne pathogens, and food safety management. This mini-review will help understand the relationship between the 4th industrial revolution and food safety.

Research Progress of Applying Infrared Spectroscopy Technology for Detection of Toxic and Harmful Substances in Food

In recent years, food safety incidents have been frequently reported. Food or raw materials themselves contain substances that may endanger human health and are called toxic and harmful substances in food, which can be divided into endogenous, exogenous toxic, and harmful substances and biological toxins. Therefore, realizing the rapid, efficient, and nondestructive testing of toxic and harmful substances in food is of great significance to ensure food safety and improve the ability of food safety supervision. Among the nondestructive detection methods, infrared spectroscopy technology has become a powerful solution for detecting toxic and harmful substances in food with its high efficiency, speed, easy operation, and low costs, while requiring less sample size and is nondestructive, and has been widely used in many fields. In this review, the concept and principle of IR spectroscopy in food are briefly introduced, including NIR and FTIR. Then, the main progress and contribution of IR spectroscopy are summarized, including the model’s establishment, technical application, and spectral optimization in grain, fruits, vegetables, and beverages. Moreover, the limitations and development prospects of detection are discussed. It is anticipated that infrared spectroscopy technology, in combination with other advanced technologies, will be widely used in the whole food safety field.

Performance Testing of Bacillus cereus Chromogenic Agar Media for Improved Detection in Milk and Other Food Samples

In this study, the performance of four alternative selective chromogenic B. cereus agar was compared to the reference mannitol-yolk polymyxin (MYP) agar (ISO 7932) using inclusion and exclusion test strains (n = 110) and by analyzing naturally contaminated milk and other food samples (n = 64). Subsequently, the panC group affiliation and toxin gene profile of Bacillus cereus senso lato (s.l.) isolates were determined. Our results corroborate that the overall best performing media CHROMagar™ B. cereus (93.6% inclusivity; 82.7% exclusivity) and BACARA^® (98.2% inclusivity, 62.7% exclusivity) are more sensitive and specific compared to Brilliance™ B. cereus, MYP and ChromoSelect Bacillus Agar. Both media allow unequivocal detection of B. cereus with low risks of misidentification. Media containing ß-D-glucosidase for the detection of presumptive B. cereus may form atypical colony morphologies resulting in a false negative evaluation of the sample. Naturally contaminated samples presented high numbers of background flora, while numbers of presumptive B. cereus were below the detection limit (<10 CFU g⁻¹ or mL⁻¹). Recovery after freezing resulted in the highest detection of B. cereus s.l. on BACARA^® (57.8%), CHROMagar™ B. cereus (56.3%) and MYP agar (54.7%). The panC/toxin profile combination IV/A was the most abundant (33.0%), followed by III/F (21.7%) and VI/C (10.4%). More panC and toxin combinations were present in 15.6% of samples when reanalyzed after freezing. In order to improve detection and confirmation of B. cereus s.l. in food samples, we recommend the parallel use of two complementary selective media followed by molecular characterization (e.g., panC typing combined with toxin gene profiling). When determining psychrotolerant or thermophilic members of the B. cereus group, the selective agar media should additionally be incubated at appropriate temperatures (5 °C, ≥45 °C). If high-risk toxin genes (e.g., ces or cytK-1) are detected, the strain-specific ability to produce toxin should be examined to decisively assess risk.

Whole Genome Sequencing Reveals Biopesticidal Origin of Bacillus thuringiensis in Foods

Bacillus thuringiensis is a microbial insecticide widely used to control agricultural pests. Although generally regarded as safe, B. thuringiensis is phylogenetically intermingled with the foodborne pathogen B. cereus sensu stricto and has been linked to foodborne outbreaks. Limited data on the pathogenicity potential of B. thuringiensis and the occurrence of biopesticide residues in food compromise a robust consumer risk assessment. In this study, we analyzed whole-genome sequences of 33 B. thuringiensis isolates from biopesticides, food, and human fecal samples linked to outbreaks. All food and outbreak-associated isolates genomically matched (≤ 6 wgSNPs; ≤ 2 cgSNPs) with one of six biopesticide strains, suggesting biopesticide products as their source. Long-read sequencing revealed a more diverse virulence gene profile than previously assumed, including a transposase-mediated disruption of the promoter region of the non-hemolytic enterotoxin gene nhe and a bacteriophage-mediated disruption of the sphingomyelinase gene sph in some biopesticide strains. Furthermore, we provide high-quality genome assemblies of seven widely used B. thuringiensis biopesticide strains, which will facilitate improved microbial source tracking and risk assessment of B. thuringiensis-based biopesticides in the future.

Possible impacts of the predominant Bacillus bacteria on the Ophiocordyceps unilateralis s. l. in its infected ant cadavers

Animal hosts infected and killed by parasitoid fungi become nutrient-rich cadavers for saprophytes. Bacteria adapted to colonization of parasitoid fungi can be selected and can predominate in the cadavers, actions that consequently impact the fitness of the parasitoid fungi. In Taiwan, the zombie fungus, Ophiocordyceps unilateralis sensu lato (Clavicipitaceae: Hypocreales), was found to parasitize eight ant species, with preference for a principal host, Polyrhachis moesta. In this study, ant cadavers grew a fungal stroma that was predominated by Bacillus cereus/thuringiensis. The bacterial diversity in the principal ant host was found to be lower than the bacterial diversity in alternative hosts, a situation that might enhance the impact of B. cereus/thuringiensis on the sympatric fungus. The B. cereus/thuringiensis isolates from fungal stroma displayed higher resistance to a specific naphthoquinone (plumbagin) than sympatric bacteria from the environment. Naphthoquinones are known to be produced by O. unilateralis s. l., and hence the resistance displayed by B. cereus/thuringiensis isolates to these compounds suggests an advantage to B. cereus/thuringiensis to grow in the ant cadaver. Bacteria proliferating in the ant cadaver inevitably compete for resources with the fungus. However, the B. cereus/thuringiensis isolates displayed in vitro capabilities of hemolysis, production of hydrolytic enzymes, and antagonistic effects to co-cultured nematodes and entomopathogenic fungi. Thus, co-infection with B. cereus/thuringiensis offers potential benefits to the zombie fungus in killing the host under favorable conditions for reproduction, digesting the host tissue, and protecting the cadaver from being taken over by other consumers. With these potential benefits, the synergistic effect of B. cereus/thuringiensis on O. unilateralis infection is noteworthy given the competitive relationship of these two organisms sharing the same resource.

Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging

This work investigates the application of reflectance Fourier transform infrared (FTIR) microscopic imaging for rapid, and non-invasive detection and classification between Bacillus subtilis and Escherichia coli cell suspensions dried onto metallic substrates (stainless steel (STS) and aluminium (Al) slides) in the optical density (OD) concentration range of 0.001 to 10. Results showed that reflectance FTIR of samples with OD lower than 0.1 did not present an acceptable spectral signal to enable classification. Two modelling strategies were devised to evaluate model performance, transferability and consistency among concentration levels. Modelling strategy 1 involves training the model with half of the sample set, consisting of all concentrations, and applying it to the remaining half. Using this approach, for the STS substrate, the best model was achieved using support vector machine (SVM) classification, providing an accuracy of 96% and Matthews correlation coefficient (MCC) of 0.93 for the independent test set. For the Al substrate, the best SVM model produced an accuracy and MCC of 91% and 0.82, respectively. Furthermore, the aforementioned best model built from one substrate was transferred to predict the bacterial samples deposited on the other substrate. Results revealed an acceptable predictive ability when transferring the STS model to samples on Al (accuracy = 82%). However, the Al model could not be adapted to bacterial samples deposited on STS (accuracy = 57%). For modelling strategy 2, models were developed using one concentration level and tested on the other concentrations for each substrate. Results proved that models built from samples with moderate (1 OD) concentration can be adapted to other concentrations with good model generalization. Prediction maps revealed the heterogeneous distribution of biomolecules due to the coffee ring effect. This work demonstrated the feasibility of applying FTIR to characterise spectroscopic fingerprints of dry bacterial cells on substrates of relevance for food processing.

A robust metabolomics approach for the evaluation of human embryos from in vitro fertilization

The identification of the most competent embryos for transfer to the uterus constitutes the main challenge of in vitro fertilization (IVF). We established a metabolomic-based approach by applying Fourier transform infrared (FTIR) spectroscopy on 130 samples of 3-day embryo culture supernatants from 26 embryos that implanted and 104 embryos that failed. On examining the internal structure of the data by unsupervised multivariate analysis, we found that the supernatant spectra of nonimplanted embryos constituted a highly heterogeneous group. Whereas ∼40% of these supernatants were spectroscopically indistinguishable from those of successfully implanted embryos, ∼60% exhibited diverse, heterogeneous metabolic fingerprints. This observation proved to be the direct result of pregnancy's multifactorial nature, involving both intrinsic embryonic traits and external characteristics. Our data analysis strategy thus involved one-class modelling techniques employing soft independent modelling of class analogy that identified deviant fingerprints as unsuitable for implantation. From these findings, we could develop a noninvasive Fourier-transform-infrared-spectroscopy-based approach that represents a shift in the fundamental paradigm for data modelling applied in assisted-fertilization technologies.

Culture and genome-based analysis of four soil Clostridium isolates reveal their potential for antimicrobial production

BACKGROUND

Soil bacteria are a major source of specialized metabolites including antimicrobial compounds. Yet, one of the most diverse genera of bacteria ubiquitously present in soil, Clostridium, has been largely overlooked in bioactive compound discovery. As Clostridium spp. thrive in extreme environments with their metabolic mechanisms adapted to the harsh conditions, they are likely to synthesize molecules with unknown structures, properties, and functions. Therefore, their potential to synthesize small molecules with biological activities should be of great interest in the search for novel antimicrobial compounds. The current study focused on investigating the antimicrobial potential of four soil Clostridium isolates, FS01, FS2.2 FS03, and FS04, using a genome-led approach, validated by culture-based methods.

RESULTS

Conditioned/spent media from all four Clostridium isolates showed varying levels of antimicrobial activity against indicator microorganism; all four isolates significantly inhibited the growth of Pseudomonas aeruginosa. FS01, FS2.2, and FS04 were active against Bacillus mycoides and FS03 reduced the growth of Bacillus cereus. Phylogenetic analysis together with DNA-DNA hybridization (dDDH), average nucleotide identity (ANI), and functional genome distribution (FGD) analyses confirmed that FS01, FS2.2, and FS04 belong to the species Paraclostridium bifermentans, Clostridium cadaveris, and Clostridium senegalense respectively, while FS03 may represent a novel species of the genus Clostridium. Bioinformatics analysis using antiSMASH 5.0 predicted the presence of eight biosynthetic gene clusters (BGCs) encoding for the synthesis of ribosomally synthesized post-translationally modified peptides (RiPPs) and non-ribosomal peptides (NRPs) in four genomes. All predicted BGCs showed no similarity with any known BGCs suggesting novelty of the molecules from those predicted gene clusters. In addition, the analysis of genomes for putative virulence factors revealed the presence of four putative Clostridium toxin related genes in FS01 and FS2.2 genomes. No genes associated with the main Clostridium toxins were identified in the FS03 and FS04 genomes.

CONCLUSIONS

The presence of BGCs encoding for uncharacterized RiPPs and NRPSs in the genomes of antagonistic Clostridium spp. isolated from farm soil indicated their potential to produce novel secondary metabolites. This study serves as a basis for the identification and characterization of potent antimicrobials from these soil Clostridium spp. and expands the current knowledge base, encouraging future research into bioactive compound production in members of the genus Clostridium.

Bacillus cytotoxicus-A potentially virulent food-associated microbe

Bacillus cytotoxicus is a member of the Bacillus cereus group with the ability to grow at high temperatures (up to 52℃) and to synthesize cytotoxin K-1, a diarrhoeagenic cytotoxin, which appears to be unique to this species and more cytotoxic than the cytotoxin K-2 produced by other members of this group. Only a few isolates of this species have been characterized with regard to their cytotoxic effects, and the role of cytotoxin K-1 as a causative agent of food poisoning remains largely unclear. Bacillus cytotoxicus was initially isolated from a food-borne outbreak, which led to three deaths, and the organism has since been linked to other outbreaks all involving plant-based food matrices. Other studies, as well as food-borne incidents reported to the UK Food Standards Agency, detected B. cytotoxicus in insect-related products and in dried food products. With insect-related food becoming increasingly popular, the association with this pathogen is concerning, requiring further investigation and evidence to protect public health. This review summarizes the current knowledge around B. cytotoxicus and highlights gaps in the literature from a food safety perspective.

Bacillus cereus food intoxication and toxicoinfection

Bacillus cereus is one of the leading etiological agents of toxin-induced foodborne diseases. Its omnipresence in different environments, spore formation, and its ability to adapt to varying conditions and produce harmful toxins make this pathogen a health hazard that should not be underestimated. Food poisoning by B. cereus can manifest itself as an emetic or diarrheal syndrome. The former is caused by the release of the potent peptide toxin cereulide, whereas the latter is the result of proteinaceous enterotoxins (e.g., hemolysin BL, nonhemolytic enterotoxin, and cytotoxin K). The final harmful effect is not only toxin and strain dependent, but is also affected by the stress responses, accessory virulence factors, and phenotypic properties under extrinsic, intrinsic, and explicit food conditions and host-related environment. Infamous portrait of B. cereus as a foodborne pathogen, as well as a causative agent of nongastrointestinal infections and even nosocomial complications, has inspired vast volumes of multidisciplinary research in food and clinical domains. As a result, extensive original data became available asking for a new, both broad and deep, multifaceted look into the current state-of-the art regarding the role of B. cereus in food safety. In this review, we first provide an overview of the latest knowledge on B. cereus toxins and accessory virulence factors. Second, we describe the novel taxonomy and some of the most pertinent phenotypic characteristics of B. cereus related to food safety. We link these aspects to toxin production, overall pathogenesis, and interactions with its human host. Then we reflect on the prevalence of different toxinotypes in foods opening the scene for epidemiological aspects of B. cereus foodborne diseases and methods available to prevent food poisoning including overview of the different available methods to detect B. cereus and its toxins.

Fourier transform infrared spectroscopy coupled with machine learning classification for identification of oxidative damage in freeze-dried heart valves

Freeze-drying can be used to ensure off-the-shelf availability of decellularized heart valves for cardiovascular surgery. In this study, decellularized porcine aortic heart valves were analyzed by nitroblue tetrazolium (NBT) staining and Fourier transform infrared spectroscopy (FTIR) to identify oxidative damage during freeze-drying and subsequent storage as well as after treatment with H₂O₂ and FeCl₃. NBT staining revealed that sucrose at a concentration of at least 40% (w/v) is needed to prevent oxidative damage during freeze-drying. Dried specimens that were stored at 4 °C depict little to no oxidative damage during storage for up to 2 months. FTIR analysis shows that fresh control, freeze-dried and stored heart valve specimens cannot be distinguished from one another, whereas H₂O₂- and FeCl₃-treated samples could be distinguished in some tissue section. A feed forward artificial neural network model could accurately classify H₂O₂ and FeCl₃ treated samples. However, fresh control, freeze-dried and stored samples could not be distinguished from one another, which implies that these groups are very similar in terms of their biomolecular fingerprints. Taken together, we conclude that sucrose can minimize oxidative damage caused by freeze-drying, and that subsequent dried storage has little effects on the overall biochemical composition of heart valve scaffolds.

Bioremediation of hexavalent chromium from wastewater using bacteria-a green technology

Hexavalent chromium has high toxic effect on the ecological system. The aim of the present study is to isolate and characterize the bacteria that can reduce the toxicity of hexavalent chromium from liquid effluent. The bacterial isolate was identified as Bacillus sp. ltds1 after 16 S rRNA gene sequencing, and annotation has been submitted in National Center for Biotechnology Information (NCBI) GenBank. The bacterial strain was found able to grow in Luria Broth medium at 100 mg/L Cr⁶⁺ concentration. A maximum Cr⁶⁺ bioremediation (95.24 ± 2.08 %) could be achieved using the said isolate at 40 mg/L, pH 7, and inoculum concentration 4 % at 24 h. The residual chromium was found in the form of less toxic trivalent chromium (Cr³⁺), which confirms that the bacterial isolate can transform toxic Cr⁶⁺ to non-toxic Cr³⁺. Fourier Transform Infra-Red (FTIR) study was performed to analyze the functional groups and overall nature of chemical bonds involved in the remediation process, whereas, Energy-Dispersive Spectroscopy (EDS) studies of native and treated cells showed the changes in elemental composition in response to metal stress. Artificial Neural Network (ANN) based prediction model is developed based on experimental points. The developed model was found to predict the bioremediation of Cr⁶⁺ at various operating conditions. Particle Swarm Optimization (PSO) is used to optimize the variables like the initial concentration of metal, pH, temperature, and inoculum concentration for the said bacterial strain. The results showed that the isolate could be applied as a potential bioremediation agent for Cr⁶⁺ removal.

Keeping up with the Bacillus cereus group: taxonomy through the genomics era and beyond

The Bacillus cereus group, also known as B. cereus sensu lato (s.l.), is a species complex that contains numerous closely related lineages, which vary in their ability to cause illness in humans and animals. The classification of B. cereus s.l. isolates into species-level taxonomic units is thus essential for informing public health and food safety efforts. However, taxonomic classification of these organisms is challenging. Numerous-often conflicting-taxonomic changes to the group have been proposed over the past two decades, making it difficult to remain up to date. In this review, we discuss the major nomenclatural changes that have accumulated in the B. cereus s.l. taxonomic space prior to 2020, particularly in the genomic sequencing era, and outline the resulting problems. We discuss several contemporary taxonomic frameworks as applied to B. cereus s.l., including (i) phenotypic, (ii) genomic, and (iii) hybrid nomenclatural frameworks, and we discuss the advantages and disadvantages of each. We offer suggestions as to how readers can avoid B. cereus s.l. taxonomic ambiguities, regardless of the nomenclatural framework(s) they choose to employ. Finally, we discuss future directions and open problems in the B. cereus s.l. taxonomic realm, including those that cannot be solved by genomic approaches alone.

The Food Poisoning Toxins of Bacillus cereus

Bacillus cereus is a ubiquitous soil bacterium responsible for two types of food-associated gastrointestinal diseases. While the emetic type, a food intoxication, manifests in nausea and vomiting, food infections with enteropathogenic strains cause diarrhea and abdominal pain. Causative toxins are the cyclic dodecadepsipeptide cereulide, and the proteinaceous enterotoxins hemolysin BL (Hbl), nonhemolytic enterotoxin (Nhe) and cytotoxin K (CytK), respectively. This review covers the current knowledge on distribution and genetic organization of the toxin genes, as well as mechanisms of enterotoxin gene regulation and toxin secretion. In this context, the exceptionally high variability of toxin production between single strains is highlighted. In addition, the mode of action of the pore-forming enterotoxins and their effect on target cells is described in detail. The main focus of this review are the two tripartite enterotoxin complexes Hbl and Nhe, but the latest findings on cereulide and CytK are also presented, as well as methods for toxin detection, and the contribution of further putative virulence factors to the diarrheal disease.

Discrimination of bacteria using whole organism fingerprinting: the utility of modern physicochemical techniques for bacterial typing

This review compares and contrasts MALDI-MS, FT-IR spectroscopy and Raman spectroscopy for whole organism fingerprinting and bacterial typing.

Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food

The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.

Advances in Optical Detection of Human-Associated Pathogenic Bacteria

Bacterial infection is a global burden that results in numerous hospital visits and deaths annually. The rise of multi-drug resistant bacteria has dramatically increased this burden. Therefore, there is a clinical need to detect and identify bacteria rapidly and accurately in their native state or a culture-free environment. Current diagnostic techniques lack speed and effectiveness in detecting bacteria that are culture-negative, as well as options for in vivo detection. The optical detection of bacteria offers the potential to overcome these obstacles by providing various platforms that can detect bacteria rapidly, with minimum sample preparation, and, in some cases, culture-free directly from patient fluids or even in vivo. These modalities include infrared, Raman, and fluorescence spectroscopy, along with optical coherence tomography, interference, polarization, and laser speckle. However, these techniques are not without their own set of limitations. This review summarizes the strengths and weaknesses of utilizing each of these optical tools for rapid bacteria detection and identification.

Evaluation of MALDI-ToF Mass Spectrometry for Rapid Detection of Cereulide From Bacillus cereus Cultures

Bacillus cereus plays an often unrecognized role in food borne diseases. Food poisoning caused by this pathogen is manifested by either diarrhea or emesis. Due to the relatively high prevalence of emetic toxin cereulide associated food poisoning, methods for simple and reliable detection of cereulide producing strains are of utmost importance. Recently, two different studies reported on the application of MALDI-ToF MS for either the differentiation of emetic and non-emetic strains of B. cereus or for direct detection of cereulide from bacterial colony smears. However, for implementation of cereulide detection using MALDI-ToF MS in routine microbiological diagnostics additional investigations on the sensitivity and specificity as well as on the fitting into common workflows for bacterial identification are needed. These aspects prompted us to investigate open issues and to test sample preparation methods, commonly used for microbial identification for their suitability to detect the emetic toxin from bacteria. Based on our experimental findings we propose a workflow that allows identification of B. cereus and sensitive detection of cereulide in parallel, using linear-mode MALDI-ToF MS equipment. The protocol was validated in a blinded study and is based on the well-established ethanol/formic acid extraction method. Cereulide is detected in the ethanol wash solution of samples identified as B. cereus as peaks at m/z 1175 and 1191. Peak position difference of 16 Th (Thomson) indicates detection of the sodium and potassium adducts of cereulide. This sample treatment offers possibilities for further characterization by more sophisticated LC-MS-based methods. In summary, the ease of use and the achieved level of analytical sensitivity as well as the wide-spread availability of MALDI-ToF MS equipment in clinical microbiological laboratories provides a promising tool to improve and to facilitate routine diagnostics of B. cereus associated food intoxications.

Machine Learning-Empowered FTIR Spectroscopy Serum Analysis Stratifies Healthy, Allergic, and SIT-Treated Mice and Humans

The unabated global increase of allergic patients leads to an unmet need for rapid and inexpensive tools for the diagnosis of allergies and for monitoring the outcome of allergen-specific immunotherapy (SIT). In this proof-of-concept study, we investigated the potential of Fourier-Transform Infrared (FTIR) spectroscopy, a high-resolution and cost-efficient biophotonic method with high throughput capacities, to detect characteristic alterations in serum samples of healthy, allergic, and SIT-treated mice and humans. To this end, we used experimental models of ovalbumin (OVA)-induced allergic airway inflammation and allergen-specific tolerance induction in BALB/c mice. Serum collected before and at the end of the experiment was subjected to FTIR spectroscopy. As shown by our study, FTIR spectroscopy, combined with deep learning, can discriminate serum from healthy, allergic, and tolerized mice, which correlated with immunological data. Furthermore, to test the suitability of this biophotonic method for clinical diagnostics, serum samples from human patients were analyzed by FTIR spectroscopy. In line with the results from the mouse models, machine learning-assisted FTIR spectroscopy allowed to discriminate sera obtained from healthy, allergic, and SIT-treated humans, thereby demonstrating its potential for rapid diagnosis of allergy and clinical therapeutic monitoring of allergic patients.

Extended multiplicative signal correction for FTIR spectral quality test and pre-processing of infrared imaging data

Spectral quality control is an important step in the analysis of infrared spectral data, however, often neglected in scientific literature. A frequently used quality test that was originally developed for infrared spectra of bacteria is provided by OPUS software from Bruker Optik GmbH. In this study, the OPUS quality test is applied to a large number of spectra of bacteria, yeasts and moulds and hyperspectral images of microorganisms. It is shown that the use of strict thresholds for parameters of the OPUS quality test leads to discarding too many spectra. A strategy for optimizing parameters thresholds of the OPUS quality test is provided and a novel approach for spectral quality testing based on extended multiplicative signal correction (EMSC) is suggested. For all the data sets considered in our study, the EMSC quality test is shown to be the best among different alternatives of OPUS quality test provided.

Feasibility of using RFLP of PCR-amplified 16S rRNA gene(s) for rapid differentiation of isolates of aerobic spore-forming bacteria from honey

This study aimed to assess the feasibility of using RFLP of PCR-amplified 16S rRNA gene (s) by using universal primers 27f/1492r and a combination of three restriction enzymes, AluI, CfoI, and TaqI, for a low-cost, rapid screen for a primarily differentiation of isolates of the complex of aerobic spore-forming bacteria commonly found in honey samples. The described method produced unique and distinguishable patterns to differentiate among 80 isolates belonging to 26 different species of Bacillus, Brevibacillus, Lysinibacillus, Rummeliibacillus, and Paenibacillus reported in honey and other apiarian sources.