Aggregative Sampling Performs Similar to Composite Produce Samples to Recover Quality and Safety Indicators Throughout Romaine Lettuce Production

Overall experimental sampling design during preharvest, in-harvest and postharvest stages of commercial romaine lettuce. This sampling process was performed in two replicates. For each replicate, commercial romaine grown on eight beds was sampled preharvest, during harvest and postharvest. For preharvest sampling (1 d before harvest), aggregative, composite produce (60 grabs, 150 g/sample) and high-resolution produce samples were collected (60 grabs, 25 g/sample). During harvest, romaine was sampled by collecting aggregative gloves worn by the harvesting crew, composite produce samples from leftover romaine leaf trims on the ground (60 grabs, 150 g/sample), aggregative swabs of harvested romaine heads as they exited the harvester chute, and aggregative swabs and composite produce samples (60 grabs, 150 g/sample) of romaine heads from the top of transportation bins. For postharvest sampling (∼6 hrs after harvest), ∼800 lb of romaine was sampled by using aggregative gloves to manipulate and place individual heads on a moving conveyor belt; as the heads exited the conveyor belt, they were sampled by an aggregative swab placed at the exit chute, before they fell onto smaller bins. Once there, composite romaine samples (60 grabs, 150 g/sample) were collected from the exterior of the heads. All sampled heads were then chopped in half lengthwise and were sampled by rubbing their interior against an aggregative swab placed on a cartridge and collecting composite romaine samples (60 grabs, 150 g/sample) from the interior of the heads.

Highlights

Aggregative and tissue sampling were compared pre-, during-, and postharvest.
Aggregative glove and swab sampling recovered similar aerobic bacteria to tissue grabs.
Aggregative gloves recovered similar coliforms to tissue grabs.
Aggregative swab recovery could be improved for coliforms and generic E. coli.

Abstract

Aggregative sampling using polymer cloth swabs is a nondestructive, potentially more representative food safety sampling alternative for leafy greens. This study compared aggregative and produce tissue grab sampling to recover aerobic bacteria, total coliforms, and generic Escherichia coli, from commercial romaine grown in 120 m fields, with 5–36 samples at various stages. Aggregative swabs and grab samples were collected preharvest. During harvest, aggregative swabs were collected from romaine exiting the harvester chute, transport bin tops, and as gloves worn by harvesters. Romaine grabs were collected from transport bin tops and trim leftover on the ground. During postharvest, gloves, swabs, and grabs were collected from romaine exteriors, and swabs and grabs from head interiors. In preharvest, swabs had 1.2 log(CFU/g) higher means of aerobic bacteria than romaine tissue grab samples (p < 0.001), but 1.7 log(CFU/g) lower coliforms (p < 0.001). In-harvest, aerobic bacteria means from gloves worn by harvesters and swabs from harvester chute were 0.5 log(CFU/g) higher than romaine samples from leftover trims (p < 0.001) and bin tops (p = 0.01), respectively. Coliform recovery means from gloves was not significantly different from romaine leftover trims (p = 0.99). Swabs from harvester chute and bin tops recovered 1.6 and 1.4 log(CFU/g) lower coliforms means (p < 0.001) than romaine from bin tops, respectively. Generic E. coli was only recovered from one romaine leftover trim grab sample. During postharvest processing, aerobic bacteria (p = 0.25) and total coliforms (p = 0.16) recovery from the exterior of heads was not significantly different between gloves and romaine samples, nor was aerobic bacteria (p = 0.17) and total coliform (p = 0.86) recovery from head interiors. These results suggest that aggregative sampling performs similar to produce grab sampling to recover quality and safety indicators and justifies testing these methods for pathogen sampling in leafy greens.

DOI

https://doi.org/10.1016/j.jfp.2025.100481

Aggregative Swab Sampling Method for Romaine Lettuce Show Similar Quality and Safety Indicators and Microbial Profiles Compared to Composite Produce Leaf Samples in a Pilot Study

Abstract

Composite produce leaf samples from commercial production rarely test positive for pathogens, potentially due to low pathogen prevalence or the relatively small number of plants sampled. Aggregative sampling may offer a more representative alternative. This pilot study investigated whether aggregative swab samples performed similarly to produce leaf samples in their ability to recover quality indicators (APCs and coliforms), detect Escherichia coli, and recover representative microbial profiles. Aggregative swabs of the outer leaves of romaine plants (n = 12) and composite samples consisting of various grabs of produce leaves (n = 14) were collected from 60 by 28 ft sections of a one-acre commercial romaine lettuce field. Aerobic plate counts were 9.17 ± 0.43 and 9.21 ± 0.42 log(CFU/g) for produce leaf samples and swabs, respectively. Means and variance were not significantly different (p = 0.38 and p = 0.92, respectively). Coliform recoveries were 3.80 ± 0.76 and 4.19 ± 1.15 log(CFU/g) for produce leaf and swabs, respectively. Means and variances were not significantly different (p = 0.30 and p = 0.16, respectively). Swabs detected generic E. coli in 8 of 12 samples, more often than produce leaf samples (3 of 14 positive, Fisher’s p = 0.045). Full-length 16S rRNA microbial profiling revealed that swab and produce leaf samples did not show significantly different alpha diversities (p = 0.75) and had many of the most prevalent bacterial taxa in common and in similar abundances. These data suggest that aggregative swabs perform similarly to, if not better than, produce leaf samples in recovering indicators of quality (aerobic and coliform bacteria) and food safety (E. coli), justifying further method development and validation.

DOI

https://doi.org/10.3390/foods13193080

Perspective: Challenges with product testing in powdered infant formula

Abstract

Right now, many concerned parents are probably thinking, how could Cronobacter sakazakii get through the powdered infant formula (PIF) production system? Shouldn’t a food safety monitoring system find such a serious problem? Aren’t those products tested before arriving on shelves? And the very challenging answer is yes, these products were likely tested with a standardized sampling and testing method consistent with regulatory guidance. In fact, in an official statement, the company said all finished products are tested for Salmonella spp. and Cronobacter sakazakii before they are released, and the product samples the company retained tested negative for the implicated microorganism related to the complaints (Abbott, 2022). This begs the questions, how powerful can the current sampling and testing program be, and is there an end-product testing strategy that would guarantee that this would not happen again? The short answers are that current sampling and testing are not that powerful, and no, testing will not ever guarantee complete safety.

DOI

https://doi.org/10.3168/jds.2022-22374

A Review of the Methodology of Analyzing Aflatoxin and Fumonisin in Single Corn Kernels and the Potential Impacts of These Methods on Food Security

Abstract

Current detection methods for contamination of aflatoxin and fumonisin used in the corn industry are based on bulk level. However, literature demonstrates that contamination of these mycotoxins is highly skewed and bulk samples do not always represent accurately the overall contamination in a batch of corn. Single kernel analysis can provide an insightful level of analysis of the contamination of aflatoxin and fumonisin, as well as suggest a possible remediation to the skewness present in bulk detection. Current literature describes analytical methods capable of detecting aflatoxin and fumonisin at a single kernel level, such as liquid chromatography, fluorescence imaging, and reflectance imaging. These methods could provide tools to classify mycotoxin contaminated kernels and study potential co-occurrence of aflatoxin and fumonisin. Analysis at a single kernel level could provide a solution to the skewness present in mycotoxin contamination detection and offer improved remediation methods through sorting that could impact food security and management of food waste.

DOI

https://doi.org/10.3390/foods9030297

(This article belongs to the Special Issue Safeguarding the Global Food Supply: Advances in Mycotoxin Prevention, Surveillance and Mitigation)

Classification of aflatoxin contaminated single corn kernels by ultraviolet to near infrared spectroscopy

Highlights

Novel UV–Vis–NIR spectroscopy system built to scan single corn kernels in motion.
Random forest model classifies single kernels by aflatoxin level with high accuracy.
BGYF, discoloration, brokenness associated with aflatoxin contamination.

Abstract

Aflatoxin contamination in corn poses threats to consumer food safety and grower economic stability. Current industrial methods for aflatoxin management in corn focus on the bulk aflatoxin level, which can lead to either acceptance of lots with contaminated corn kernels (consumer food safety risk) or rejection of lots with mostly harmless corn kernels (grower economic loss). This dilemma may be resolved by utilizing spectroscopy to classify single corn kernels. Hence, our research aims to investigate the potential of using a custom-built UltraViolet-Visible-Near InfraRed spectroscopy system (UV–Vis–NIR) to classify single corn kernels by aflatoxin level. Single kernels from cobs inoculated with aflatoxin-producing Aspergillus flavus (240 kernels) and uninoculated cobs (240 kernels) were i) scanned individually for reflectance from 304 nm to 1086 nm by an increment of 0.5 nm; ii) ground; iii) measured for aflatoxin by ELISA. Using the spectra and the aflatoxin concentration, a random forest model was trained on 80% of the kernels to classify single corn kernels above or below 20 ppb of aflatoxin and was tested on the remaining 20% of the kernels. Among 480 kernels, 374 kernels had <20 ppb of aflatoxin and 106 kernels had ≥20 ppb of aflatoxin. The random forest model had a sensitivity of 87.1% and specificity of 97.7% in the training set and a sensitivity of 85.7% and specificity of 97.3% in the test set, which is higher than previous models where kernels were in motion and comparable to models where kernels were stationary. Spectral regions around 390, 540, and 1050 nm are found to be important for classification. This study demonstrated the custom-built UV–Vis–NIR spectroscopy system showed considerable potential in classifying single corn kernels by aflatoxin level while the kernels are in motion.

DOI

https://doi.org/10.1016/j.foodcont.2018.11.037