Effect of organic load on the efficacy of activated persulfate in inactivating Escherichia coli O157:H7 and the production of halogenated by-products

Oversampling methods for machine learning model data training to improve model capabilities to predict the presence of Escherichia coli MG1655 in spinach wash water

We assessed the efficacy of oversampling techniques to enhance machine learning model performance in predicting Escherichia coli MG1655 presence in spinach wash water. Three oversampling methods were applied to balance two datasets, forming the basis for training random forest (RF), support vector machines (SVMs), and binomial logistic regression (BLR) models. Data underwent method-specific centering and standardization, with outliers replaced by feature-specific means in training datasets. Testing occurred without these preprocessing steps. Model hyperparameters were optimized using a subset of testing data via 10-fold cross-validation. Models were trained on full datasets and tested on newly acquired spinach wash water samples. Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling approach (ADASYN) achieved strong results, with SMOTE RF reaching an accuracy of 90.0%, sensitivity of 93.8%, specificity of 87.5%, and an area under the curve (AUC) of 98.2% (without data preprocessing) and ADASYN achieving 86.55% accuracy, 87.5% sensitivity, 83.3% specificity, and a 92.4% AUC. SMOTE and ADASYN significantly improved (p < 0.05) SVM and RF models, compared to their non-oversampled counterparts without preprocessing. Data preprocessing had a mixed impact, improving (p < 0.05) the accuracy and specificity of the BLR model but decreasing the accuracy and specificity (p < 0.05) of the SVM and RF models. The most influential physiochemical feature for E. coli detection in wash water was water conductivity, ranging from 7.9 to 196.2 µS. Following closely was water turbidity, ranging from 2.97 to 72.35 NTU within this study.

Microbiological hazards associated with the use of water in the post-harvest handling and processing operations of fresh and frozen fruits, vegetables and herbs (ffFVHs). Part 1 (outbreak data analysis, literature review and stakeholder questionnaire)

The contamination of water used in post-harvest handling and processing operations of fresh and frozen fruit, vegetables and herbs (ffFVHs) is a global concern. The most relevant microbial hazards associated with this water are: Listeria monocytogenes, Salmonella spp., human pathogenic Escherichia coli and enteric viruses, which have been linked to multiple outbreaks associated with ffFVHs in the European Union (EU). Contamination (i.e. the accumulation of microbiological hazards) of the process water during post-harvest handling and processing operations is affected by several factors including: the type and contamination of the FVHs being processed, duration of the operation and transfer of microorganisms from the product to the water and vice versa, etc. For food business operators (FBOp), it is important to maintain the microbiological quality of the process water to assure the safety of ffFVHs. Good manufacturing practices (GMP) and good hygienic practices (GHP) related to a water management plan and the implementation of a water management system are critical to maintain the microbiological quality of the process water. Identified hygienic practices include technical maintenance of infrastructure, training of staff and cooling of post-harvest process water. Intervention strategies (e.g. use of water disinfection treatments and water replenishment) have been suggested to maintain the microbiological quality of process water. Chlorine-based disinfectants and peroxyacetic acid have been reported as common water disinfection treatments. However, given current practices in the EU, evidence of their efficacy under industrial conditions is only available for chlorine-based disinfectants. The use of water disinfection treatments must be undertaken following an appropriate water management strategy including validation, operational monitoring and verification. During operational monitoring, real-time information on process parameters related to the process and product, as well as the water and water disinfection treatment(s) are necessary. More specific guidance for FBOp on the validation, operational monitoring and verification is needed.

Chlorine inactivation of Escherichia coli O157:H7 in fresh produce wash process: Effectiveness and modeling

Inactivation rate constant or inactivation coefficient (specific lethality) quantifies the rate at which a chemical sanitizer inactivates a microorganism. This study presents a modified disinfection kinetics model to evaluate the potential effect of organic content on the chlorine inactivation coefficient of Escherichia coli O157:H7 in fresh produce wash processes. Results show a significant decrease in the bactericidal efficacy of free chlorine (FC) in the presence of organic load compared to its absence. While the chlorine inactivation coefficient of Escherichia coli O157:H7 is 70.39 ± 3.19 L/mg/min in the absence of organic content, it drops by 73% for a chemical oxygen demand (COD) level of 600-800 mg/L. Results also indicate that the initial chlorine concentration and bacterial load have no effect on the chlorine inactivation coefficient. A second-order chemical reaction model for FC decay, which utilizes a proportion of COD as an indicator of organic content in fresh produce wash was employed, yielding an apparent reaction rate of (9.45 ± 0.22) × 10^-4 /μM/min. This model was validated by predicting FC concentration in multi-run continuous wash cycles with periodic replenishment of chlorine.

Iron, thermal stratification, Eucalyptus sp., and hypoxia: drivers to water blackening in southern China reservoirs

The management of black water depends primarily on the knowledge of the dynamics of organic matter (OM), iron (Fe), sulfide (S), and manganese (Mn), at the water-sediment boundary (WSB). However, the mechanistic path of these substances leading to black water remains unsettled. In this study, a 35-day field study was conducted using the thin-film diffusion gradient technology (DGT) and the planar optrode to address the unknown combined effects of Fe, Mn, OM, S, and tannins from Eucalyptus species on Tianbao reservoir.Our results indicated that the hypolimnion was hypoxic due to thermal stratification, which caused the reduction of insoluble Fe and Mn from sediments to bottom water. Correlation analysis (Fe:S (r:0.5-0.9); Mn:S (r:0.2-0.8)) and elevated fluxes (Fe²⁺, Mn²⁺, S^2-) connoted that these parameters interacted chemically to give black matter. The content of OM, Fe²⁺, and tannic acid in the benthic region diminished remarkably (p < 0.05) from day 1 (strong stratification) to day 35 (weak stratification), connoting that these parameters also interacted chemically to give black matter. The turbidity (clarity of the water) increased from day 1 to 35 with a significant difference (p < 0.05) recorded on day 14 confirming that black water was formed on this day when the thermal structure of the reservoir was annihilated. Correlation analysis supported the assertion that the variability in oxygen and redox conditions caused changes in Fe, Mn, and OM content at the WSB.The finding from the field research provides useful information to stakeholders on how to improve the quality of freshwater management designs.