Effect of disinfectants on preventing the cross-contamination of pathogens in fresh produce washing water

The potential cross-contamination of pathogens between clean and contaminated produce in the washing tank is highly dependent on the water quality. Process wash water disinfectants are applied to maintain the water quality during processing. The review examines the efficacy of process wash water disinfectants during produce processing with the aim to prevent cross-contamination of pathogens. Process wash water disinfection requires short contact times so microorganisms are rapidly inactivated. Free chlorine, chlorine dioxide, ozone, and peracetic acid were considered suitable disinfectants. A disinfectant's reactivity with the organic matter will determine the disinfectant residual, which is of paramount importance for microbial inactivation and should be monitored in situ. Furthermore, the chemical and worker safety, and the legislative framework will determine the suitability of a disinfection technique. Current research often focuses on produce decontamination and to a lesser extent on preventing cross-contamination. Further research on a sanitizer's efficacy in the washing water is recommended at the laboratory scale, in particular with experimental designs reflecting industrial conditions. Validation on the industrial scale is warranted to better understand the overall effects of a sanitizer

Willingness to adopt microbial applications in arable farming

Identify and understand the technological, organisational and behavioural drivers and barriers for the uptake of the novel microbial technologies by arable farmer

Farm innovation and technical efficiency of Dutch arable farms: An innovation index and DEA approach

In this article, we analysed the relationship between farm innovation and farm efficiency. We computed an innovation index based on Dutch Innovation Monitor data and ratings from an expert elicitation. The innovation index is an adaptation and extension of an existing innovation index for Irish dairy farms. We computed technical efficiency scores with a Data Envelopment Analysis (DEA). The DEA scores are computed with Farm Accountancy Data Network (FADN) data. We investigated the relationship with pre-registered ordinary least square (OLS) regression analyses in quadratic form and additional Chi-square tests. Unanimously, we reject the first hypothesis that farm innovation and farm efficiency can be described by an inverse parabolic relationship. Early adopters and innovators are not necessarily less efficient than the early and late majority of innovation adopters. We also reject the second hypothesis that innovation front-runners become more efficient. These are preliminary findings

Optimization of Sampling for Monitoring Chemicals in the Food Supply Chain Using a Risk-Based Approach : The Case of Aflatoxins and Dioxins in the Dutch Dairy Chain

Food safety monitoring faces the challenge of tackling multiple chemicals along the various stages of the food supply chain. Our study developed a methodology for optimizing sampling for monitoring multiple chemicals along the dairy supply chain. We used a mixed integer nonlinear programming approach to maximize the performance of the sampling in terms of reducing the risk of the potential disability adjusted life years (DALYs) in the population. Decision variables are the number of samples collected and analyzed at each stage of the food chain (feed mills, dairy farms, milk trucks, and dairy processing plants) for each chemical, given a predefined budget. The model was applied to the case of monitoring for aflatoxin B1/M1(AFB1/M1) and dioxins in a hypothetical Dutch dairy supply chain, and results were calculated for various contamination scenarios defined in terms of contamination fraction and concentrations. Considering various monitoring budgets for both chemicals, monitoring for AFB1/M1 showed to be more effective than for dioxins in most of the considered scenarios, because AFB1/M1 could result into more DALYs than dioxins when both chemicals are in same contamination fraction, and costs for analyzing one AFB1/M1 sample are lower than for one dioxins sample. The results suggest that relatively more resources be spent on monitoring AFB1/M1 when both chemicals’ contamination fractions are low; when both contamination fractions are higher, relatively more budget should be addressed to monitoring dioxins.</p

Dataset underlying the publication: Optimization of Sampling for Monitoring Chemicals in the Food Supply Chain Using a Risk-Based Approach: The Case of Aflatoxins and Dioxins in the Dutch Dairy Chain

Dioxins and aflatoxins contamination along the dairy supply chain. We used a mixed integer nonlinear programming approach to maximize the performance of the sampling in terms of reducing the risk of the potential disability adjusted life years (DALYs) in the population. Decision variables are the number of samples collected and analyzed at each stage of the food chain (feed mills, dairy farms, milk trucks, and dairy processing plants) for each chemical, given a predefined budget. The model was applied to the case of monitoring for aflatoxin B1/M1(AFB1/M1) and dioxins in a hypothetical Dutch dairy supply chain, and results were calculated for various contamination scenarios defined in terms of contamination fraction and concentrations

Incentives to stimulate European wheat farmers to adapt their Fusarium species mycotoxin management

A questionnaire was distributed amongst wheat farmers from Italy, the Netherlands, Serbia, and the United Kingdom. Using the respondents’ data, Bayesian Network modelling was applied to estimate the probability that farmers would adapt their current agronomic management under eight different incentives given the conditions set by their farm and farmer characteristic

Data analyses and modelling for risk based monitoring of mycotoxins in animal feed

Following legislation, European Member States should have multi-annual control programs for contaminants, such as for mycotoxins, in feed and food. These programs need to be risk based implying the checks are regular and proportional to the estimated risk for animal and human health. This study aimed to prioritize feed products in the Netherlands for deoxynivalenol and aflatoxin B1 monitoring. Historical mycotoxin monitoring results from the period 2007–2016 were combined with data from other sources. Based on occurrence, groundnuts had high priority for aflatoxin B1 monitoring; some feed materials (maize and maize products and several oil seed products) and complete/complementary feed excluding dairy cattle and young animals had medium priority; and all other animal feeds and feed materials had low priority. For deoxynivalenol, maize by-products had a high priority, complete and complementary feed for pigs had a medium priority and all other feed and feed materials a low priority. Also including health consequence estimations showed that feed materials that ranked highest for aflatoxin B1 included sunflower seed and palmkernel expeller/extracts and maize. For deoxynivalenol, maize products were ranked highest, followed by various small grain cereals (products); all other feed materials were of lower concern. Results of this study have proven to be useful in setting up the annual risk based control program for mycotoxins in animal feed and feed materials

Methods to perform risk-based inspections of food companies

Abstract: Risk-based monitoring programs are increasingly applied for cost-effective monitoring of food safety. Such programs ideally consist of three steps: risk-ranking, risk-based inspections, and cost-effective monitoring. Various methods have been described to perform the first step of risk-based monitoring. However, once the risk-ranking has been completed, identifying the hazard-food combinations to monitor, the frequency of inspection needs to be established based on a prioritization of food business operators (FBOs). The aim of this paper is to provide an overview of methods available for risk-based inspections. Literature shows that FBO's food safety compliance can be assessed based on company size, historical monitoring data, and socio-economic factors influencing compliance behavior. Non-compliance can either be intentional or unintentional. The latter can be assessed by evaluating the food safety culture of a company. Various models—ranging from qualitative (e.g., focus groups) to quantitative (e.g., scoring)—can be used for this purpose. These models usually include an evaluation of the organizational structure (e.g., management control, communication, commitment), the technical food safety environment (e.g., hygienic design, zoning), and employee characteristics (e.g., knowledge, risk awareness). Intentional non-compliance can be assessed using food fraud vulnerability tools. These tools incorporate factors influencing the likelihood of food fraud at the company, that is, opportunity, motivation, and (lack of) control measures. The literature indicates that either self-assessment tools or risk matrices are applied. There is no global consensus on the methods to apply for risk-based inspections. Depending on time and budget available as well as preferred output, one of the presented methods may be applied for prioritizing FBOs

Tolerance and Excretion of the Mycotoxins Aflatoxin B1, Zearalenone, Deoxynivalenol, and Ochratoxin A by Alphitobius diaperinus and Hermetia illucens from Contaminated Substrates

This study aimed to investigate the potential accumulation of mycotoxins in the lesser mealworm (Alphitobius diaperinus, LMW) and black soldier fly (Hermetia illucens, BSF) larvae. Feed was spiked with aflatoxin B1, deoxynivalenol (DON), ochratoxin A or zearalenone, and as a mixture of mycotoxins, to concentrations of 1, 10, and 25 times the maximum limits set by the European Commission for complete feed. This maximum limit is 0.02 mg/kg for aflatoxin B1, 5 mg/kg for DON, 0.5 mg/kg for zearalenone and 0.1 mg/kg for ochratoxin A. The mycotoxins and some of their metabolites were analysed in the larvae and residual material using a validated and accredited LC-MS/MS-based method. Metabolites considered were aflatoxicol, aflatoxin P1, aflatoxin Q1, and aflatoxin M1, 3-acetyl-DON, 15-acetyl-DON and DON-3-glycoside, and α- and β-zearalenol. No differences were observed between larvae reared on mycotoxins individually or as a mixture with regards to both larvae development and mycotoxin accumulation/excretion. None of the mycotoxins accumulated in the larvae and were only detected in BSF larvae several orders of magnitude lower than the concentration in feed. Mass balance calculations showed that BSF and LMW larvae metabolized the four mycotoxins to different extents. Metabolites accounted for minimal amounts of the mass balance, except for zearalenone metabolites in the BSF treatments, which accounted for an average maximum of 86% of the overall mass balance. Both insect species showed to excrete or metabolize the four mycotoxins present in their feed. Hence, safe limits for these mycotoxins in substrates to be used for these two insect species possibly could be higher than for production animals. However, additional analytical and toxicological research to fully understand the safe limits of mycotoxins in insect feed, and thus the safety of the insects, is required