Identification of sampling points suitable for the detection of microbial contamination in fresh-cut processing lines

Unveiling Fresh-Cut Lettuce Processing in Argentine Industries: Evaluating Salmonella Levels Using Predictive Microbiology Models

A survey was performed to gather information on the processing steps, conditions, and practices employed by industries processing ready-to-eat (RTE) leafy vegetables in Argentina. A total of seven industries participated in the survey. A cluster analysis of the data obtained was performed to identify homogeneous groups among the participating industries. The data collected were used as inputs of two predictive microbiology models to estimate Salmonella concentrations after chlorine washing, during storage and distribution of final products, and to rank the different practices according to the final estimated Salmonella levels. Six different clusters were identified by evaluating the parameters, methods, and controls applied in each processing step, evidencing a great variability among industries. The disinfectant agent applied by all participating industries was sodium hypochlorite, though concentrations and application times differed among industries from 50 to 200 ppm for 30 to 110 s. Simulations using predictive models indicated that the reductions in Salmonella in RTE leafy vegetables would vary in the range of 1.70-2.95 log CFU/g during chlorine-washing depending on chlorine concentrations applied, washing times, and vegetable cutting size, which varied from 9 to 16 cm2 among industries. Moreover, Salmonella would be able to grow in RTE leafy vegetables during storage and distribution, achieving levels of up to 2 log CFU/g, considering the storage and transportation temperatures and times reported by the industries, which vary from 4 to 14 °C and from 18 to 30 h. These results could be used to prioritize risk-based sampling programs by Food Official Control or determine more adequate process parameters to mitigate Salmonella in RTE leafy vegetables. Additionally, the information gathered in this study is useful for microbiological risk assessments.

Efficacy of commercial overhead washing and waxing systems on the microbiological quality of fresh peaches

Overhead spray washing and waxing systems (WWS) are used commercially to reduce the risk of microbial contamination and improve the quality of fresh produce during packing. This study evaluated the microbiological quality of overhead spray water and spent peach wash water, as well as fresh peaches before and after they pass the WWS. Pre- and post-washed/waxed peach samples (n = 192) and overhead spray water and spent peach wash water samples (n = 54) were collected several times over the course of a processing day in three packing facilities located in the state of Georgia. Populations of total aerobes (TA), yeasts and molds (YM), and coliforms (TC) and the presence of thermotolerant coliforms (TTC) and enterococci (EC) were measured in collected samples. The average TA and TC counts and the incidences of TTC and EC were significantly higher (P < 0.05) on peach samples collected after the WWS compared to those collected before the WWS. Counts and incidences of TA, YM, and TC in spent peach wash water were significantly higher than in the overhead spray water where neither TTC nor EC was detected. Results suggest that the commercial washing and waxing systems had little effect in improving the microbiological quality of fresh peaches.

Suitability of centrifuge water for detecting the presence of Escherichia coli versus finished fresh-cut lettuce testing

Fresh produce causes most foodborne outbreaks in the USA, and it is also considered a hazardous food product in other areas of the world such as Europe. The outbreaks attributed to fresh produce increase the focus of producers on hygiene to minimize exposure to food hazards. The fresh produce industry has the urgent need to detect if there are production lots contaminated with pathogenic microorganisms before distribution. Although the industry is mostly using end-product testing for the detection of target microorganisms, previous studies have evaluated the suitability of different sampling points within the production line of a fresh-cut processing plant. In the present study, the centrifuge effluent water was assessed as an alternative sampling point to end-product testing. E. coli was selected as an index microorganism of the presence of pathogens. The presence of E. coli was assessed in centrifuge effluent water, and fresh-cut lettuce from a commercial fresh-cut produce processing line (n = 95). The rate of false positives and negatives, as well as the specificity, sensitivity, and efficiency of the alternative method were calculated. The mean population of E. coli in positive water samples was 0.86 log cfu/100 mL, while the mean population of E. coli in positive fresh-cut lettuce samples was 0.23 log cfu/g. The proportion of positive samples in centrifuge effluent water and lettuce was similar (≈20%), and most of the results in both matrices were coincident (81.1%). However, the alternative method was not reliable due to its low sensitivity, as only 47.6% of the lettuce samples positive for E. coli could be matched with positive water samples.

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.