Microbial testing in food safety: effect of specificity and sensitivity on sampling plans—how does the OC curve move

Comparison of Statistical Methods for Identifying Risk Factors for Salmonella Contamination of Whole Chicken Carcasses

ABSTRACT

The complexity of the food system makes analyzing microbiological data from food studies challenging because many of the assumptions (e.g., linear relationship between independent and dependent variable and independence of observations) associated with common analytical approaches (e.g., analysis of variance) are violated. Repeated sampling within an establishment introduces longitudinal correlation that must be accounted for during analyses. In this study, statistical methods for clustered or correlated data were used to determine how correlation impacts conclusions and to compare how assumptions associated with statistical methods impact the appropriateness of these methods within the context of food safety. Risk factor analyses for Salmonella contamination of whole chicken carcasses were conducted as a case study with regulatory data collected by the U.S. Department of Agriculture Food Safety and Inspection Service between May 2015 and December 2019 from 203 regulated establishments. Three models, generalized estimating equation, random effects, and logistic, were fit to Salmonella presence or absence data with establishment demographics and inspection history included as potential covariates. Beta parameter estimates and their standard errors and odds ratios and their 95% confidence intervals were compared across models. Conclusions drawn from the three models differed with respect to geographic region, whether the chicken establishment also slaughters turkeys, and establishment noncompliance with 9 CFR §417.4 (hazard analysis critical control point system validation, verification, and reassessment) in the 84 days leading up to sample collection. The results of this study reveal the need to consider clustering and correlation when analyzing food microbiological data, provide context for selecting a statistical method, and suggest that generalized estimating equation and random effects models are preferrable over logistic regression when analyzing correlated food data. These results support a renewed focus on statistical methodology in food safety.

HIGHLIGHTS

Guidance on the requirements for the development of microbiological criteria

The European Food Safety Authority asked the Panel on Biological Hazards (BIOHAZ) to deliver a scientific opinion providing: (i) a review of the approaches used by the BIOHAZ Panel to address requests from risk managers to suggest the establishment of microbiological criteria; (ii) guidance on the required scientific evidence, data and methods/tools necessary for considering the development of microbiological criteria for pathogenic microorganisms and indicator microorganisms; (iii) recommendations on methods/tools to design microbiological criteria and (iv) guidelines for the requirements and tasks of risk assessors, compared to risk managers, in relation to microbiological criteria. This document provides guidance on approaches when: (i) a quantitative microbial risk assessment (QMRA) is available, (ii) prevalence and concentration data are available, but not a QMRA model, and (iii) neither a QMRA nor prevalence and/or concentration data are available. The role of risk assessors should be focused on assessing the impact of different microbiological criteria on public health and on product compliance. It is the task of the risk managers to: (1) formulate unambiguous questions, preferably in consultation with risk assessors, (2) decide on the establishment of a microbiological criterion, or target in primary production sectors, and to formulate the specific intended purpose for using such criteria, (3) consider the uncertainties in impact assessments on public health and on product compliance and (4) decide the point in the food chain where the microbiological criteria are intended to be applied and decide on the actions which should be taken in case of non‐compliance. It is the task of the risk assessors to support risk managers to ensure that questions are formulated in a way that a precise answer can be given, if sufficient information is available, and to ensure clear and unambiguous answers, including the assessment of uncertainties, based on available scientific evidence.

The Use of Electrochemical Biosensors in Food Analysis

Rapid and accurate analysis of food produce is essential to screen for species that may cause significant health risks like bacteria, pesticides and other toxins. Considerable developments in analytical techniques and instrumentation, for example chromatography, have enabled the analyses and quantitation of these contaminants. However, these traditional technologies are constrained by high cost, delayed analysis times, expensive and laborious sample preparation stages and the need for highly-trained personnel. Therefore, emerging, alternative technologies, for example biosensors may provide viable alternatives. Rapid advances in electrochemical biosensors have enabled significant gains in quantitative detection and screening and show incredible potential as a means of countering such limitations. Apart from demonstrating high specificity towards the analyte, these biosensors also address the challenge of the multifactorial food industry of providing high analytical accuracy amidst complex food matrices, while also overcoming differing densities, pH and temperatures. This (public and Industry) demand for faster, reliable and cost-efficient analysis of food samples, has driven investment into biosensor design. Here, we discuss some of the recent work in this area and critique the role and contributions biosensors play in the food industry. We also appraise the challenges we believe biosensors need to overcome to become the industry standard.