Method to improve discrimination using movingwindow principal component analysis (MWPCA) for origin of products
DC Field | Value | Language |
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dc.contributor.author | 정회일 | - |
dc.date.accessioned | 2021-08-04T00:17:57Z | - |
dc.date.available | 2021-08-04T00:17:57Z | - |
dc.date.created | 2021-06-30 | - |
dc.date.issued | 2008-04-17 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/65170 | - |
dc.description.abstract | A new discrimination method called the moving-window principal component analysis (MW-PCA) has been developed and its performance has been evaluated using several spectroscopic datasets. The main concept of MW-PCA was to combine moving-window system and principal component analysis (PCA), and then using an effective algorithm of error rate to obtain a value of percent of a separation. This algorithm has been using a standard deviation of each groups to make a separation line. To evaluate its discrimination performances, four different spectroscopic datasets were employed: (1) conventional Raman spectra and wide area illumination Raman spectra of a origin of rice, (2) near-infrared(NIR) of a origin of cnidium officinale, (3) near-infrared(NIR) of a origin of carrot, (4) near-infrared(NIR) of a origin of sesame. For each case, results of separation were achieved. Since the method of MW-PCA is different from other algorism and great to separate groups. Combining moving-window and principal component analysis with using newly algorism of error rate provided better result of qualitative analysis. | - |
dc.publisher | The Korean chemical society | - |
dc.title | Method to improve discrimination using movingwindow principal component analysis (MWPCA) for origin of products | - |
dc.type | Conference | - |
dc.contributor.affiliatedAuthor | 정회일 | - |
dc.identifier.bibliographicCitation | 대한화학회 제 101회 총회 및 학술발표회 | - |
dc.relation.isPartOf | 대한화학회 제 101회 총회 및 학술발표회 | - |
dc.citation.title | 대한화학회 제 101회 총회 및 학술발표회 | - |
dc.citation.conferencePlace | 일산 | - |
dc.type.rims | CONF | - |
dc.description.journalClass | 2 | - |
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