Application of a Commercial Salmonella Real-Time PCR Assay for the Detection and Quantitation of Salmonella enterica in Poultry Ceca

Adaptation of a Commercial Qualitative BAX® Real-Time PCR Assay to Quantify Campylobacter spp. in Whole Bird Carcass Rinses

Poultry is the primary reservoir of Campylobacter, a leading cause of gastroenteritis in the United States. Currently, the selective plating methodology using selective agars, Campy Cefex and Modified Charcoal Cefoperazone Deoxycholate agar, is preferentially used for the quantification of Campylobacter spp. among poultry products. Due to the specific nature of Campylobacter, this methodology is not sensitive, which can lead to skewed detection and quantification results. Therefore, Campylobacter detection and quantification methods are urgently needed. The objective was to develop a shortened enrichment-based quantification method for Campylobacter (CampyQuant™) in post-chill poultry rinsates using the BAX® System Real-Time PCR assay for Campylobacter. The specificity and sensitivity for the detection of C. jejuni, C. coli, and C. lari in pure culture were determined. The BAX® System Real-Time PCR assay consistently detected and identified each species 100% of the time with an enumeration range of 4.00 to 9.00 Log10 CFU/mL. Enrichment time parameters for low-level concentrations (0.00, 1.00, and 2.00 Log10 CFU/mL) of Campylobacter using the BAX® System Real-Time PCR assay were elucidated. It was determined that an enrichment time of 20 h was needed to detect at least 1.00 Log10 CFU/mL of Campylobacter spp. Using the BAX® System Real-Time PCR assay for Campylobacter. As a result, time of detection, detection limits, and enrichment parameters were used to develop the CampyQuant™ linear standard curve using the detected samples from the BAX® System Real-Time PCR assay to quantify the levels in post-chill poultry rinsates. A linear fit equation was generated for each Campylobacter species using the cycle threshold from the BAX® System Real-Time PCR assay to estimate a pre-enrichment of 1.00 to 4.00 Log10 CFU/mL of rinsates detected. The statistical analyses of each equation yielded an R2 of 0.93, 0.76, and 0.94 with a Log10 RMSE of 0.64, 1.09, and 0.81 from C. jejuni, C. coli, and C. lari, respectively. The study suggests that the BAX® System Real-Time PCR assay for Campylobacter is a more rapid, accurate, and efficient alternative method for Campylobacter enumeration.

Bio-Mapping of Microbial Indicators and Pathogen Quantitative Loads in Commercial Broiler Processing Facilities in South America

A bio-mapping study was conducted with the aim of creating a microbiological baseline on indicator organisms and pathogens in commercial broiler processing facilities located in a country in South America. Whole chicken carcass and wing rinses were collected from five stages of the poultry processing line: live receiving (LR), rehanger (R), post-evisceration (PE), post-chilling (PC), and wings (W). Rinses (n = 150) were enumerated using the MicroSnap™ system for total viable counts (TVC) and Enterobacteriaceae (EB), while the BAX®-System-SalQuant® and BAX®-System-CampyQuant™ were used for Salmonella and Campylobacter, respectively. TVC and EB were significantly different between stages at the processing line (p < 0.01). There was a significant reduction from LR to PC for both microbial indicators. TVC and EB counts increased significantly from PC to W. Salmonella counts at PC were significantly different from the other stages at the processing line (p = 0.03). Campylobacter counts were significantly higher than the other stages at PC (p < 0.01). The development of bio-mapping baselines with microbial indicators showed consistent reduction up to the post-chilling stage, followed by an increase at the wings sampling location. The quantification of pathogens demonstrates that prevalence analysis as a sole measurement of food safety is not sufficient to evaluate the performance of processing operations and sanitary dressing procedures in commercial processing facilities.

Data-Mining Poultry Processing Bio-Mapping Counts of Pathogens and Indicator Organisms for Food Safety Management Decision Making

Bio-mapping studies play an important role, as the data collected can be managed and analyzed in multiple ways to look at process trends, find explanations about the effect of process changes, activate a root cause analysis for events, and even compile performance data to demonstrate to inspection authorities or auditors the effect of certain decisions made on a daily basis and their effects over time in commercial settings not only from the food safety perspective but also from the production side. This study presents an alternative analysis of bio-mapping data collected throughout several months in a commercial poultry processing operation as described in the article "Bio-Mapping Indicators and Pathogen Loads in a Commercial Broiler Processing Facility Operating with High and Low Antimicrobial Interventions". The conducted analysis identifies the processing shift effect on microbial loads, attempts to find correlation between microbial indicators data and pathogens loads, and identifies novel visualization approaches and conducts distribution analysis for microbial indicators and pathogens in a commercial poultry processing facility. From the data analyzed, a greater number of locations were statistically different between shifts under reduced levels of chemical interventions with higher means at the second shift for both indicators and pathogens levels. Minimal to negligible correlation was found when comparing aerobic counts and Enterobacteriaceae counts with Salmonella levels, with significant variability between sampling locations. Distribution analysis and visualization as a bio-map of the process resulted in a clear bimodality in reduced chemical conditions for multiple locations mostly explained by shift effect. The development and use of bio-mapping data, including proper data visualization, improves the tools needed for ongoing decision making in food safety systems.

Rapid identification of salmonella serovars by using Raman spectroscopy and machine learning algorithm

A widespread and escalating public health problem worldwide is foodborne illness, and foodborne Salmonella infection is one of the most common causes of human illness.For the three most pathogenic Salmonella serotypes, Raman spectroscopy was employed to acquire spectral data.As machine learning offers high efficiency and accuracy, we have chosen the convolutional neural network(CNN), which is suitable for solving multi-classification problems, to do in-depth mining and analysis of Raman spectral data.To optimize the instrument parameters, we compared three laser wavelengths: 532, 638, and 785 nm.Ultimately, the 532 nm wavelength was chosen as the most effective for detecting Salmonella.A pre-processing step is necessary to remove interference from the background noise of the Raman spectrum.Our study compared the effects of five spectral preprocessing methods, Savitzky-Golay smoothing (SG), Multivariate Scatter Correction (MSC), Standard Normal Variate (SNV), and Hilbert Transform (HT), on the predictive power of CNN models.Accuracy(ACC), Precision, Recall, and F1-score 4 machine learning evaluation indicators are used to evaluate the model performance under different preprocessing methods.In the results, SG combined with SNV was found to be the most accurate spectral pre-processing method for predicting Salmonella serotypes using Raman spectroscopy, achieving an accuracy of 98.7% for the training set and over 98.5% for the test set in CNN model.Pre-processing spectral data using this method yields higher accuracy than other methods.As a conclusion, the results of this study demonstrate that Raman spectroscopy when used in conjunction with a convolutional neural network model enables the rapid identification of three Salmonella serotypes at the single-cell level, and that the model has a great deal of potential for distinguishing between different serotypes of pathogenic bacteria and closely related bacterial species.This is vital to preventing outbreaks of foodborne illness and the spread of foodborne pathogens.