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Determination of maize hardness by biospeckle and fuzzy granularity
Author(s) -
Weber Christian,
Dai Pra Ana L.,
Passoni Lucía I.,
Rabal Héctor J.,
Trivi Marcelo,
Poggio Aguerre Guillermo J.
Publication year - 2014
Publication title -
food science and nutrition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.614
H-Index - 27
ISSN - 2048-7177
DOI - 10.1002/fsn3.130
Subject(s) - granularity , fuzzy logic , mathematics , computer science , agricultural engineering , data mining , artificial intelligence , engineering , operating system
In recent years there has been renewed interest in the development of novel grain classification methods that could complement traditional empirical tests. A speckle pattern occurs when a laser beam illuminates an optically rough surface that flickers when the object is active and is called biospeckle. In this work, we use laser biospeckle to classify maize ( Zea mays L.) kernel hardness. A series of grains of three types of maize were cut and illuminated by a laser. A series of images were then registered, stored, and processed. These were compared with results obtained by floating test. The laser speckle technique was effective in discriminating the grains based on the presence of floury or vitreous endosperm and could be considered a feasible alternative to traditional floating methods. The results indicate that this methodology can distinguish floury and vitreous grains. Moreover, the assay showed higher discrimination capability than traditional tests. It could be potentially useful for maize classification and to increase the efficiency of processing dry milling corn.

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