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ARS Home » Plains Area » Manhattan, Kansas » Center for Grain and Animal Health Research » Grain Quality and Structure Research » Research » Publications at this Location » Publication #210602
Title: Digital Image Analysis of Cereals

Author
item Wilson, Jeff

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 6/1/2007
Publication Date: 10/7/2007
Citation: Wilson, J.D. 2007. Digital Image Analysis of Cereals [abstract]. Cereal Foods World. 52:A10.

Interpretive Summary:

Technical Abstract: Image analysis is the extraction of meaningful information from images, mainly digital images by means of digital processing techniques. The field was established in the 1950s and coincides with the advent of computer technology, as image analysis is profoundly reliant on computer processing. As the computer sciences has expanded with respect to data storage and processing speed, the applications of digital image analysis has also expanded into all areas of science and industry. The cereal sciences industry has also expanded the use of image analysis to include: classification and morphological identification of cereal grains, phytopathological identification of diseases, milling yield and quality of various cereals, starch size distribution as related to quality, bread volume and crumb grain scores, noodle quality as well as numerous other aspects of cereal processing and research. This presentation will review some of the more recent developments and applications of image analysis in cereal research. Some of our own research concerning wheat starch size distribution as it relates to quality will also be discussed. Starch constitutes the greatest weight portion of the wheat endosperm (65-75%) and contributes its own unique functional qualities such as texture, volume, consistency, aesthetics, moisture and shelf stability to various baked products. Particle size, distribution and shape have long been recognized as an important variable in the efficiency of a range of processes including predicting rheology and flow behavior. Digital image analysis coupled to light microscopy offers the ability to have physical parameters recorded for each individual particle and be able to distinguish among individual granules, agglomerated granules, and non-starch particles.