Paola Corea Ventura

Paola is a first-year Master´s student from El Salvador. She earned her B.S. in Food Science and Technology from Zamorano University in Honduras. She first came to the University of Illinois Urbana-Champaign for an internship in 2021 in Dr. Jaume Amengual´s lab and worked on an animal model for HIV and on an Atherosclerosis regression project. She is now pursuing her M.S. in Food Science and Human Nutrition in Dr. Stasiewicz´s lab.  

She is currently working on a milk spoilage project for K-12 schools to understand how, if at all, the rate of milk spoilage changes as an effect of being exposed to different temperature conditions, and the length of time spent in these conditions. She is also working on testing antimicrobial effectiveness against Salmonella on pork meat.  

She enjoys doing yoga, watching comedy movies, and spending time with her friends. 

Gabby Pinto

Gabby is a first-year PhD student from Illinois. She earned her B.S. in Food Science with a Microbiology Minor from Penn State University.

She is currently working on writing a hazard analysis for share tables in school cafeterias.

She enjoys training for marathons and traveling to new places (sometimes, to run marathons)! She also enjoys trying new places to eat.

Risk Assessment Comparing Alternative Approaches to Regulating Salmonella in Poultry by Public Health Impact Factors

A 1-year and 3 month project funded by US poultry:

The hypotheses of this project are:

  • Most illnesses from Salmonella in poultry are due to consumption of products with relatively high levels of contamination, e.g., > 1 CFU/g, of high-risk serotypes, e.g., Typhimurium.
  • Interventions based on identifying and controlling higher levels of contamination and higher-risk serotypes will result in more targeted effects than prevalence-based interventions, creating
    • Greater protection of public health (fewer predicted illnesses)
    • Greater benefit-cost ratios (less product rework, fewer recalls)

The project will test these hypotheses with the following objectives:

  • Objective 1: Build a farm-to-fork quantitative microbial risk assessment of Salmonella subtypes in poultry products incorporating different production strategies which will allow for assessment of the public heath impact of different interventions, performance standards, and regulations
  • Objective 2: Use the risk assessment to assess

Testing for Enterococcus faceium reduction during corn wet milling dry product production

A 10-week project funded by CRA Corn Refiners Association:Corn Refiners rebrands with new logo, website |

The hypothesis tested in this project is that pilot-scale adaptations of industrially relevant unit operations of steeping, peroxide treatment, and drying, reduce counts of the indicator organism Enterococcus faecium in inoculated challenge studies. The specific objectives to test this hypothesis are:

  1. Conduct an E. faecium inoculated challenge study for production of dry powders at pilot-scale using a corn wet milling unit operations varying steeping, peroxide, and drying parameters.
  2. Count the surviving population of E. faecium at each unit operation

Minho Kim

Minho is a first-year Ph.D. student from South Korea. He earned B.S. in Food Science and Technology from Chung-ang University and an M.S. in Food Science and Technology from the University of Nebraska-Lincoln.

He is currently working on simulating dairy powder products to improve food safety sampling plans. His research goal is to provide industry stakeholders with an open-source app to evaluate existing and candidate plans.

He likes outdoor activities, including playing tennis, hiking, and biking.

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Tina Wu

Tina is a first-year PhD student from China. She earned her B.E. in Food Science and Engineering from Fujian Agriculture and Forestry University and M.S. in Food Science and Technology from National University of Singapore.

She is currently working on developing aggregative sampling for preharvest food safety testing. She also participates in research about building a field-to-facility generic supply chain model of produce safety testing.

She enjoys travelling, skiing, and cooking.

Ruben A. Chavez Viteri

Ruben is a third-year PhD student from Ecuador. He earned his B.S. in Chemistry from The University of Tampa.

He is currently developing a Near Infrared Red/ Visible Light/ Ultraviolet light spectrometer to detect aflatoxin and fumonisin in corn. His research goal is to create a sorting platform with a spectrometer capable of removing mycotoxin contaminated corn kernels and minimize food waste while improving food security.

He loves to practice sports, especially soccer, and play videogames with his friends. He is a food lover, and in his leisure, he likes to improve his culinary skills.

Cristina Resendiz-Moctezuma

Cristina is a second-year PhD student from Mexico. She earned her B.S. in Food Science from the Autonomous University of Queretaro. She first came to the lab for a summer internship in 2018, then started her grad school journey in Fall 2019.

She uses a high-throughput system to test the effectiveness of novel antimicrobials, such as essential oils, against Listeria monocytogenes on deli-ham. She also focuses her research on understanding the action mechanisms of these compounds using RNA-seq.

She enjoys cooking and watching series (Especially, Grey’s Anatomy on repeat).

Single kernel aflatoxin and fumonisin contamination distribution and spectral classification in commercial corn


Aflatoxin and fumonisin contamination distribution in corn is non-homogeneous. Therefore, bulk sample testing may not accurately represent the levels of contamination. Single kernel analysis could provide a solution to these problems and lead to remediation strategies such as sorting. Our study uses extensive single kernel aflatoxin (AF) and fumonisin (FM) measurements to (i) demonstrate skewness, calculate weighted sums of toxin contamination for a sample, and compare those values to bulk measurements, and (ii) improve single kernel classification algorithm performance. Corn kernels with natural contamination of aflatoxin and fumonisin (n = 864, from 9 bulk samples) were scanned individually twice for reflectance between the ultraviolet–visible–near infrared spectrum (304 nm–1086 nm), then ground and measured for aflatoxin and fumonisin using ELISA. Single kernel contamination distribution was non-homogeneous with 1.0% (n = 7) of kernels with ≥20 ppb aflatoxin (range 0 – 4.2×10^5 ppb), and 5.0% (n = 45) kernels with ≥2 ppm fumonisin (range 0 – 7.0×10^2 ppm). A single kernel weighted sum was calculated and compared to bulk measurements. Average difference in mycotoxin levels (AF = 0.0 log(ppb), FM = 0.0 log(ppm), weighted sum – measured bulk levels) calculated no systematic bias between the two methods, though with considerable range of −1.4 to 0.7 log(ppb) for AF and −0.6 to 0.8 log(ppm) for FM. Algorithms were trained on 70% of the kernels to classify aflatoxin (≥20ppb) and fumonisin (≥2ppm), while the remaining 30% of kernels were used for testing. For aflatoxin, the best performing algorithm was stochastic gradient boosting model with an accuracy of 0.83 (Sensitivity (Sn) = 0.75, Specificity (Sp) = 0.83), for both training and testing set. For fumonisin, the penalized discriminant analysis outperformed the rest of the algorithms, with a training accuracy of 0.89 (Sn = 0.87, Sp = 0.88), and testing accuracy of 0.86 (Sn = 0.78, Sp = 0.87). The present study improves the foundations for single kernel classification of aflatoxin and fumonisin in corn, and can be applied to high throughput screening. This study demonstrates the heterogeneous distribution of aflatoxin and fumonisin contamination at single kernel level, comparing bulk levels calculated from those data to traditional bulk tests, and utilizing a UV–Vis–NIR spectroscopy system to classify single corn kernels by aflatoxin and fumonisin level.


Simulating Powdered Product Sampling to Improve Food Safety Sampling Plans

A 1-year and 3 month project funded by IAFNS: 

The overall objective of this project is to build a simulation for powdered product testing.This work would provide not just comprehensive guidance on generic powder plans, but a tool for industry to assess their specific concerns when working to improve their food safety testing plans. The objectives of the project are:

  • Leverage an existing bulk product simulation model developed by our lab to model
    powder sampling for microbiological safety and validate against academic data..
  • Benchmark the ability of existing industry sampling plans to detect food safety
    and quality hazards at relevant prevalence and levels.
  • Develop a web-based graphical user interface so that producers could assess
    sampling plans for their own processes and suppliers could develop science-based requirements to manage specific risks

More information can be found on the funder’s website.