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Author: search.filters.author.Boonmung, Suwanee

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    Item type:Publication,
    A preliminary study on classification of mango maturity by compression test
    (2008-01-01)
    Sirisomboon, Panmanas
    ;
    Boonmung, Suwanee
    ;
    Pornchaloempong, Pimpen
    ;
    Pithuncharurnlap, Manat
    A preliminary study toward the design of a firmness tester suitable for mango maturity classification was conducted through an experiment consisting of two parts: (1) probe selection, followed by (2) evaluation of the selected probe in mango maturity classification using a texture analyzer. This resulted in a technique based on the firmness of the fruit measured by a compression test with a maximum force of 3N using a 5mm diameter spherical stainless steel probe. This technique demonstrated the possibility of classifying the maturity of mangoes into two different stages, i.e., 60% and 80% of full ripeness. However, it could not detect the difference between 60% and 70% of full ripeness or between 70% and 80% of full ripeness.
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    Evaluation of artificial neural networks for pineapple grading
    (2006-10-01)
    Boonmung, Suwanee
    ;
    Chomtee, Boonorm
    ;
    Kanlayasiri, Kannachai
    The objective of this study is to evaluate resonant frequency, firmness and soluble solids for pineapple classification using artificial neural networks (ANNs) as the analytical tool. A sample of 149 pineapples was classified based on their internal qualities into five classes: unripe, partially ripe, ripe, partially overripe and completely overripe. The developed ANN model successfully classified pineapples into merely three classes as unripe, ripe and completely overripe. The most effective model was obtained when both resonant frequency and soluble solids were included in the model. The classification accuracy was more than 83% for all three classes. © 2006, Blackwell Publishing.