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dc.contributor.authorSakunna Wongsaipunen_US
dc.contributor.authorParichat Theanjumpolen_US
dc.contributor.authorNadthawat Muenmaneeen_US
dc.contributor.authorDanai Boonyakiaten_US
dc.contributor.authorSujitra Funsueben_US
dc.contributor.authorSila Kittiwachanaen_US
dc.identifier.citationChiang Mai Journal of Science 48, 1 (January 2021), 163-175en_US
dc.descriptionThe Chiang Mai Journal of Science is an international English language peer-reviewed journal which is published in open access electronic format 6 times a year in January, March, May, July, September and November by the Faculty of Science, Chiang Mai University. Manuscripts in most areas of science are welcomed except in areas such as agriculture, engineering and medical science which are outside the scope of the Journal. Currently, we focus on manuscripts in biology, chemistry, physics, materials science and environmental science. Papers in mathematics statistics and computer science are also included but should be of an applied nature rather than purely theoretical. Manuscripts describing experiments on humans or animals are required to provide proof that all experiments have been carried out according to the ethical regulations of the respective institutional and/or governmental authorities and this should be clearly stated in the manuscript itself. The Editor reserves the right to reject manuscripts that fail to do so.en_US
dc.description.abstractThe aim of this research study was to investigate the difference among coffee bean from different plantation areas in the northern of Thailand. Near infrared (NIR) spectra were recorded from Arabica coffee samples which were collected from Chiang Mai, Lampang and Mae Hong Son provinces in Thailand. In addition, color parameters and moisture content were analyzed. The data were exploratorily analyzed based on the uses of principal component analysis (PCA) and an artificial neural network (ANN) called self-organizing map (SOM). To identify the significant parameters of the spectroscopic data, a variable selection called self-organizing map discrimination index (SOMDI) was applied. As a result, SOM could overcome the PCA technique where the samples from the three different origins could be separated. Additionally, based on the SOMDI results, the coffee samples from Chiang Mai could be well discriminated using the NIR spectral regions of 880-1182, 1254-1326, 1896-2180 and 2260-2498 nm. This research demonstrated that using NIR spectroscopy coupled with the ANN algorithm allowed an efficient tracing method to differentiate the coffee bean samples in the northern of Thailand.en_US
dc.publisherFaculty of Science, Chiang Mai Universityen_US
dc.subjectgeographical origin tracingen_US
dc.subjectnear infrared (NIR) spectroscopyen_US
dc.subjectartificial neural network (ANN)en_US
dc.subjectself-organizing map (SOM)en_US
dc.titleApplication of Artificial Neural Network for Tracing the Geographical Origins of Coffee Bean in Northern Areas of Thailand Using Near Infrared Spectroscopyen_US
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