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United States Department of Agriculture

Agricultural Research Service

Related Topics

Research Project: Cerealscan: Advancement of Spectral Imaging Technologies for Enhancement of Cereal Quality and Safety

Location: Food Quality Laboratory

Project Number: 8042-44000-009-02
Project Type: Trust

Start Date: May 01, 2013
End Date: Apr 30, 2016

Objective:
The overall goal of the project is to develop and validate on-line, non-destructive optical imaging technologies to rapidly assess safety and quality of cereals at critical processing stages. This will reduce food safety risks and result in economic benefit to the cereal industry. This work will focus primarily on cereals of major economic importance, such as wheat, barley and corn. The specific objectives are as follows: 1) Development of combined Vis/NIR reflectance, fluorescence and Raman hyperspectral imaging technologies for cereal safety and quality control. 2) Development of super-resolution algorithms to achieve sub-pixel detection of contaminants at trace levels. This research will build on the Cooperator’s current technical and research competencies in the areas of chemometrics, sensor design and development by applying new skills to agri-food safety and quality control in the production of cereals will further validate the techniques developed using cereals of economic importance to the EU and will promote the developed techniques to the major European cereal stakeholders.

Approach:
There are two major efforts under Objective 1. The first is to develop and refine procedures and algorithms for detecting and quantifying mycotoxins, and assessing quality parameters of interest on the whole-surface of samples and the extent of damage caused by Fusarium, heat, frost, black point and insect invasion. The second is to build data fusion algorithms that can enhance the synergy of multiple detection systems using the technologies previously developed at BARC that would facilitate the development of a system for simultaneous acquisition of reflectance, fluorescence and Raman images. This will involve the development of image processing routines that identify the infected/damaged kernels in representative samples of intact and damaged kernels. It will be necessary to identify multispectral wavebands and develop detection algorithms and image segmentation procedures for whole-surface inspection of cereal kernels which can be utilized for multiple detection screening for safety and quality concerns. Objective 2 focuses on rapid evaluation of the extent of damage and presence of contaminants at trace levels in cereals. This addresses the need of the cereal industry for evaluation or inspection of cereal safety risks using tools for rapid detection of contaminants at sub-pixel resolution. It also expands sensing capabilities to trace levels. Suitability of algorithms for multi-frame image super-resolution will be tested using data acquired with the optical spectral systems at BARC. These algorithms gain additional information from the sub-pixel spatial shift in the multiple images of the sample. The increased resolution will be investigated for improvement of accuracy and limit of detection of preliminary models. Combination of “superresolution” and data fusion of the different optical spectral techniques will be carried out for further enhancement. Quantitative assessment of spatial and spectral quality will be performed on superresolved fused image.

Last Modified: 10/24/2014
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