- Qianyi Wu
- Targeted detection of allergens in quinoa flour
- Agriculture & Biological Engineering
Describe
your research
experience
My project explores the potential of a visible near-infrared (VNIR) hyperspectral imaging system for allergen (i.e. sesame, peanut, and wheat) detection in quinoa flour.
Food allergens are extremely dangerous to certain groups of people; they may cause severe illnesses or even deaths to people regardless of gender and age.Cross-contamination between grains/grain flours during processing is a major issue for the food industry. Due to cross-contact with allergen foods/gluten-rich grains, an allergen can get into the food that is free from allergen and may be unconsciously ingested by people who are allergic to the component. The main goal of this study was to explore the potential of a visible near-infrared (VNIR) hyperspectral imaging system (400-1000 nm) for allergen (sesame, peanut, and wheat) detection in quinoa flour. Quinoa samples were adulterated with sesame, peanut, and wheat in the range of 2-100% at 2% intervals. Their spectral data were extracted, and initial calibration models were developed using PLSR. The effect of different machine learning calibrations, important variable selection, and spectral pre-treatment will be applied to improve the model accuracy. The final calibration model will be transferred to each pixel in the images for visualization.
As one of the recipient's of OUR's Research Support Grant, the results of Qianyi's research can be found here.
