Twenty-two years of U.S. meat and poultry product recalls: Implications for food safety and food waste


The U.S. Department of Agriculture, Food Safety and Inspection Service maintains a recall case archive of meat and poultry product recalls from 1994 to the present. In this study, we collected all recall records from 1994 to 2015 and extracted the recall date, meat or poultry species implicated, reason for recall, recall class, and pounds of product recalled and recovered. Of a total of 1,515 records analyzed, the top three reasons for recall were contamination with Listeria, undeclared allergens, and Shiga toxin-producing Escherichia coli. Class I recalls (due to a hazard with a reasonable probability of causing adverse health consequences or death) represented 71% (1,075 of 1,515) of the total recalls. The amounts of product recalled and recovered per event were approximately lognormally distributed. The mean amount of product recalled and recovered was 6,800 and 1,000 lb (3,087 and 454 kg), respectively (standard deviation, 1.23 and 1.56 log lb, respectively). The total amount of product recalled in the 22-year evaluation period was 690 million lb (313 million kg), and the largest single recall involved 140 million lb (64 million kg) (21% of the total). In every data category subset, the largest recall represented .10% of the total product recalled in the set. The amount of product recovered was known for only 944 recalls. In 12% of those recalls (110 of 944), no product was recovered. In the remaining recalls, the median recovery was 29% of the product. The number of recalls per year was 24 to 150. Recall counts and amounts of product recalled over the 22-year evaluation period did not regularly increase by year, in contrast to the regular increase in U.S. meat and poultry production over the same time period. Overall, these data suggest that (i) meat and poultry recalls were heavily skewed toward class I recalls, suggesting recalls were focused on improving food safety, (ii) numbers of products and amounts of each product recalled were highly variable but did not increase over time, and (iii) the direct contribution of recalls to the food waste stream was associated with the largest recalls.

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


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.


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.

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

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.

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