Multi-spectral kernel sorting to reduce aflatoxins and fumonisins in Kenyan maize

Maize, a staple food in many African countries including Kenya, is often contaminated by toxic and carcinogenic fungal secondary metabolites such as aflatoxins and fumonisins. This study evaluated the potential use of a low-cost, multi-spectral sorter in identification and removal of aflatoxin- and fumonisin-contaminated single kernels from a bulk of mature maize kernels. The machine was calibrated by building a mathematical model relating reflectance at nine distinct wavelengths (470–1550\ua0nm) to mycotoxin levels of single kernels collected from small-scale maize traders in open-air markets and from inoculated maize field trials in Eastern Kenya. Due to the expected skewed distribution of mycotoxin contamination, visual assessment of putative risk factors such as discoloration, moldiness, breakage, and fluorescence under ultra-violet light (365\ua0nm), was used to enrich for mycotoxin-positive kernels used for calibration. Discriminant analysis calibration using both infrared and visible spectra achieved 77% sensitivity and 83% specificity to identify kernels with aflatoxin >10\ua0ng\ua0g and fumonisin >1000\ua0ng\ua0g, respectively (measured by ELISA or UHPLC). In subsequent sorting of 46 market maize samples previously tested for mycotoxins, 0–25% of sample mass was rejected from samples that previously tested toxin-positive and 0–1% was rejected for previously toxin-negative samples. In most cases where mycotoxins were detected in sorted maize streams, accepted maize had lower mycotoxin levels than the rejected maize (21/25 accepted maize streams had lower aflatoxin than rejected streams, 25/27 accepted maize streams had lower fumonisin than rejected streams). Reduction was statistically significant (p\ua

Modeling Preharvest Cyclospora cayetanensis Sampling and Testing for Various Water and Produce Sampling Plans

As of August 2023, the two U.S. Food and Drug Administration (FDA) official detection methods for C. cayetanensis are outlined in the FDA Bacteriological Analytical Manual (BAM) Chapters 19b (produce testing) and 19c (agricultural water testing). These newly developed detection methods have been shown to not always detect contamination when present at low levels. Yet, industry and regulators may choose to use these methods as part of their monitoring and verification activities while detection methods continue to be improved. This study uses simulation to better understand the performance of these methods for various produce and water sampling plans. To do so, we used published FDA test validation data to fit a logistic regression model that predicts the methods’ detection rate given the number of oocysts present in a 10-L agricultural water or 25 g produce sample. By doing so, we were able to determine contamination thresholds at which different numbers of samples (n = 1, 2, 4, 8, 16, and 32) would be adequate for detecting contamination. Furthermore, to evaluate sampling plans in use cases, a simulation was developed to represent C. cayetanensis contamination in agricultural water and on cilantro throughout a 45-day growth cycle. The model included uncertainty around the contamination sources, including scenarios of unintentionally contaminated irrigation water or in-field contamination. The results demonstrate that in cases where irrigation water was the contamination source, frequent water testing proved to be more powerful than produce testing. In scenarios where contamination occurred in-field, conducting frequent produce testing or testing produce toward the end of the season more reliably detected contamination. This study models the power of C. cayetanensis detection methods to understand the sampling plan performance and how these methods can be better used to monitor this emerging food safety hazard

Genome analysis of antimicrobial resistance, virulence, and plasmid presence in Turkish Salmonella serovar Infantis isolates

Salmonella enterica subsp. enterica serovar Infantis (S. Infantis) isolates were found to have a multi-drug resistance profile (kanamycin, streptomycin, nalidixic acid, tetracycline, sulfonamide, and sometimes to ampicillin) and high prevalence (91%) in Turkish poultry in our previous studies. To investigate the mechanism behind multi-drug antimicrobial resistance (AMR) and high prevalence in Turkish poultry, 23 of the isolates were sequenced for comparative genomic analyses including: SNP-based comparison to S. Infantis from other countries, comparison of antimicrobial resistance genes (AMGs) with AMR phenotypes, and plasmid identification and annotation. Whole-genome SNP-based phylogenetic analysis found that all 23 Turkish S. Infantis isolates formed a distinct, well-supported Glade, separate from 243 comparison S. Infantis genomes in GenomeTrakr identified as from the US and EU; the isolates most closely related to the cluster of these Turkish isolates were from Israel and Egypt. AMGs identified by bioinformatic analysis, without differentiating chromosomal or plasmid located genes, implied AMR phenotypes with 94% similarity overall to wet lab data, which was performed by phenotypic and conventional PCR methods. Most of the S. Infantis (21/23) isolates had identifiable plasmids, with 76% (16/21) larger than 100 kb and 48% (10/21) larger than 200 kb. A plasmid larger than 200 kb, with the incompatibility type of IncX1, similar to United States S. Infantis plasmid N55391 (99% query coverage and 99% identity overall), which itself is similar to Italian and Hungarian S. Infantis plasmids. Turkish S. Infantis plasmids had different beta-lactam resistance genes (bla(TEM-70), bla(TEM-148) and bla(TEM-198)) than the gene bla(CTX-M-)(65 )found in S. Infantis plasmids from other countries. This is the first observation of these three genes in S. Infantis isolates, The plasmids larger than 200 kb had two distinct regions of interest: Site 1 and Site 2. Site 1 (around 130 kb) had virulence- and bacteriocin- associated genes such as bacteriocin secretion system and type II toxin-antitoxin system genes (vagC, ccdA, ccdB, mchE, cvaB) and an aminoglycoside resistance gene (str). Site 2 (around 75-110 kb) had the antimicrobial resistance genes (aadA, sulI, tetA, tetR) and mercury (mer) resistance gene on tranposons Tn552 and Tn501. Presence of these AMR and virulence genes suggests they may have a role in the emergence of S. Infantis in poultry and support treating this serotype as a an important human health hazard

Simulation Evaluation of Power of Sampling Plans to Detect Cronobacter in Powdered Infant Formula Production

Cronobacter is a hazard in Powdered Infant Formula (PIF) products that is hard to detect due to localized and low-level contamination. We adapted a previously published sampling simulation to PIF sampling and benchmarked industry-relevant sampling plans across different numbers of grabs, total sample mass, and sampling patterns. We evaluated performance to detect published Cronobacter contamination profiles for a recalled PIF batch [42% prevalence, −1.8 ± 0.7 log(CFU/g)] and a reference, nonrecalled, PIF batch [1% prevalence, −2.4 ± 0.8 log(CFU/g)]. Simulating a range of numbers of grabs [n = 1–22,000 (representing testing every finished package)] with 300 g total composite mass showed that taking 30 or more grabs detected contamination reliably (50% median probability of acceptance, all plans). Overall, (i) systematic or stratified random sampling patterns are equal to or more powerful than random sampling of the same sample size and total sampled mass, and, (ii) taking more samples, even if smaller, can increase the power to detect contamination

Non-Destructive Luminescence-Based Screening Tool for Listeria monocytogenes Growth on Ham

Listeria monocytogenes is a food-borne pathogen often associated with ready-to-eat (RTE) food products. Many antimicrobial compounds have been evaluated in RTE meats. However, the search for optimum antimicrobial treatments is ongoing. The present study developed a rapid, non-destructive preliminary screening tool for large-scale evaluation of antimicrobials utilizing a bioluminescent L. monocytogenes with a model meat system. Miniature hams were produced, surface treated with antimicrobials nisin (at 0–100 ppm) and potassium lactate sodium diacetate (at 0–3.5%) and inoculated with bioluminescent L. monocytogenes. A strong correlation (r = 0.91) was found between log scale relative light units (log RLU, ranging from 0.00 to 3.35) read directly from the ham surface and endpoint enumeration on selective agar (log colony forming units (CFU)/g, ranging from 4.7 to 8.3) when the hams were inoculated with 6 log CFU/g, treated with antimicrobials, and L. monocytogenes were allowed to grow over a 12 d refrigerated shelf life at 4 °C. Then, a threshold of 1 log RLU emitted from a ham surface was determined to separate antimicrobial treatments that allowed more than 2 log CFU/g growth of L. monocytogenes (from 6 log CFU/g inoculation to 8 log CFU/g after 12 d). The proposed threshold was utilized in a luminescent screening of antimicrobials with days-to-detect growth monitoring of luminescent L. monocytogenes. Significantly different (p < 0.05) plate counts were found in antimicrobial treated hams that had reached a 1 log RLU increase (8.1–8.5 log(CFU/g)) and the hams that did not reach the proposed light threshold (5.3–7.5 log(CFU/g)). This confirms the potential use of the proposed light threshold as a qualitative tool to screen antimicrobials with less than or greater than a 2 log CFU/g increase. This screening tool can be used to prioritize novel antimicrobials targeting L. monocytogenes, alone or in combination, for future validation

Aggregative Soil Sampling Using Boot Covers Compared to Soil Grabs From Commercial Romaine Fields Shows Similar Indicator Organism and Microbial Community Recoveries

Aggregative boot cover sampling may be a more representative, practical, and powerful method for preharvest produce soil testing than grab sampling because boot covers aggregate soil from larger areas. Our study tests if boot cover sampling results reflect quality and safety indicator organisms and community diversity of grab sampling. We collected soil samples from commercial romaine lettuce fields spanning 5060 m2 using boot covers (n = 28, m = 1.1 ± 0.4 g; wearing boot covers and walking along the path), composite grabs (n = 28, m = 231 ± 24 g; consisting of 60 grabs of 3–5 g each), and high-resolution grabs (n = 72, m = 56 ± 4 g; taking one sample per stratum). Means and standard deviations of log-transformed aerobic plate counts (APCs) were 7.0 ± 0.3, 7.1 ± 0.2, and 7.3 ± 0.2 log(CFU/g) for boot covers, composite grabs, and high-resolution grabs, respectively. APCs did not show biologically meaningful differences between sample types. Boot covers recovered on average 0.6 log(CFU/g) more total coliforms than both grabs (p < 0.001) where means and standard deviations of log-transformed counts were 3.2 ± 1.0, 2.6 ± 0.6, and 2.6 ± 1.0 log(CFU/g) for boot covers, composite grabs, and high-resolution grabs, respectively. There were no generic E. coli detected in any sample by enumeration methods with LODs of 1.3–2.1 log(CFU/g) for boot covers and 0.5 log(CFU/g) for both grabs. By 16S rRNA sequencing, community species diversity (alpha diversity) was not significantly different within collection methods. While communities differed (p < 0.001) between soil sampling methods (beta diversity), variance in microbial communities was not significantly different. Of the 28 phyla and 297 genera detected, 25 phyla (89%) and 258 genera (87%) were found by all methods. Overall, aggregative boot cover sampling is similar to both grab methods for recovering quality and safety indicator organisms and representative microbiomes. This justifies future work testing aggregative soil sampling for foodborne pathogen detection

Multispectral Sorting Based on Visibly High-Risk Kernels Sourced from Another Country Reduces Fumonisin and Toxigenic Fusarium on Maize Kernels

Fusarium species infect maize crops leading to the production of fumonisin by their toxigenic members. Elimination of microbes is critical in mitigating further postharvest spoilage and toxin accumulation. The current study investigates the efficacy of a previously described multispectral sorting technique to analyze the reduction of fumonisin and toxigenic Fusarium species found contaminating maize kernels in Kenya. Maize samples (n = 99) were collected from six mycotoxin hotspot counties in Kenya (Embu, Meru, Tharaka Nithi, Machakos, Makueni, and Kitui County) and analyzed for aflatoxin and fumonisin using commercial ELISA kits. Aflatoxin levels in majority (91%) of the samples were below the 10 ng/g threshold set by the Kenya Bureau of Standards and therefore not studied further. The 23/99 samples that had >2,000 ng/g of fumonisin were selected for sorting. The sorter was calibrated using kernels sourced from Ghana to reject visibly high-risk kernels for fumonisin contamination using reflectance at nine distinct wavelengths (470–1,550 nm). Accepted and rejected streams were tested for fumonisin using ELISA, and the presence of toxigenic Fusarium using qPCR. After sorting, there was a significant (p < 0.001) reduction of fumonisin, by an average of 1.8 log ng/g (98%) and ranging between 0.14 and 2.7 log ng/g reduction (28–99.8%) with a median mass rejection rate of 1.9% (ranged 0% to 48%). The fumonisin rejection rate ranged between 0 and 99.8% with a median of 77%. There was also a significant reduction (p = 0.005) in the proportion of DNA represented by toxigenic Fusarium, from a mean of 30–1.4%. This study demonstrates the use of multispectral sorting as a potential postharvest intervention tool for the reduction of Fusarium species and preformed fumonisin. The spectral sorting approach of this study suggests that classification algorithms based on high-risk visual features associated with mycotoxin can be applied across different sources of maize to reduce fumonisin

Machine Learning and Taguchi DOE Combined Approach for Modeling Dynamic Ultrasound-Assisted Fresh-Cut Leafy Green Sanitation

Chlorine-based fresh produce sanitation is a dynamic process, and sanitation efficiency is limited due to chlorine degradation. Here, ultrasound was coupled with a benchtop sanitation system to enhance chlorine sanitizer efficiency in fresh-cut leafy green sanitation. Taguchi design of experiments (DOE) and machine learning (ML) were combined to model the relationship between sanitation condition parameters and sanitation outcomes. Multiple ML algorithms were fitted, tuned, and compared for performance using 127 experimental trials (training-to-validation ratio = 3:1). Gaussian process regression (GPR) models showed the best performance in predicting sanitation outcomes of chemical oxygen demand (COD, R2 = 0.73), remaining Escherichia coli O157:H7 on the leaf surface (“Surface Microbe”, R2 = 0.88), and E. coli O157:H7 concentration in sanitation water (“Water Microbe”, R2 = 1.00). Cut size and agitation speed were identified as the most critical input parameters. An initial free chlorine concentration over 20 mg/L was recommended to minimize the E. coli O157:H7 concentration in sanitation water. This work showcases the combined approach of ML and DOE in optimizing fresh-cut produce sanitation. Moreover, it provides a solution for overcoming the difficulties of modeling multiple controllable and uncontrollable factors with reduced experimental runs