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芳茗1979銅蟲 (正式寫手)
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[求助]
哪位幫我查一下論文是否被SCI檢索，非常感謝！
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| 哪位蟲友幫我查一下論文檢索了沒有，論文信息如下：論文題目CLASSIFICATION METHOD OF JAPONICA RICE GEOGRAPHICAL ORIGINS IN HEILONGJIANG BASED ON RAMAN SPECTROSCOPY；期刊信息：Oxidation Communications 39, No 4-II, 3273–3283 (2016)，非常感謝！ |
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芳茗1979: 金幣+10, 博學(xué)EPI+1 2017-03-17 07:38:23
芳茗1979: 金幣+10, 博學(xué)EPI+1 2017-03-17 07:38:23
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CLASSIFICATION METHOD OF JAPONICA RICE GEOGRAPHICAL ORIGINS IN HEILONGJIANG BASED ON RAMAN SPECTROSCOPY 作者:Tian, FM (Tian Fang-Ming)[ 1,2 ] ; Yu, HY (Yu Hai-Ye)[ 1 ] ; Tan, F (Tan Feng)[ 2 ] ; Zhao, XY (Zhao Xiao-Yu)[ 2 ] OXIDATION COMMUNICATIONS 卷: 39 期: 4 頁: 3273-3283 子輯: 2 出版年: 2016 查看期刊信息 摘要 A rapid classification method for Japonica rice in Heilongjiang area was established based on the Raman spectroscopy combined with the principal component analysis (PCA) and support vector machine method (SVM) in this paper. There are great differences in the component of Japonica rice due to different varieties and geographical origins. Therefore, it is quite important for the Japonica rice production and trade to build a quick, accurate and effective classification method. 235 samples of Raman spectral lines ranged from 200 to 3400 per centimeter were collected with the Raman spectrometer from the Japonica rice produced in 3 origins of Heilongjiang area. After baseline correction and smooth processing for original Raman data, the Euclidean distance was used to remove the abnormal samples. 20 corresponding values of characteristic peaks were selected by the functional groups analysis for Japonica rice spectrum as the feature vectors. The 2D score chart of the first two principal components was obtained through PCA for 3 types spectrum data of Japonica rice in MATLAB, and a good clustering effect on the 3 different kinds of Japonica rice was shown. For a better classification, a further processing of normalisation needs to be done on the samples. The 2D scores chart for the first two principal components was made based on the normalised data through PCA. The 3 samples were divided into 3 zones and a better clustering effect than that of the former. The original spectrum data were replaced by the score vectors of the first 3 principal components. A C-SVC model based on radial basis kernel function (SVM RBF) was set up after the 100 samples of the three kinds of Japonica rice being trained and the unknown 103 samples being identified. It was shown that the whole accuracy of classification of SVM RBF kernel function for three kinds of Japonica rice is 92.23%, and a good effect was shown by using PCA with SVM method of Raman spectroscopy for Japonica rice classification and identification of different origins in Heilongjiang area. 關(guān)鍵詞 作者關(guān)鍵詞:Raman spectroscopy; Japonica rice; principal component analysis (PCA); classifying; support vector machine (SVM); geographical origin KeyWords Plus:MASS-SPECTROMETRY; HUSKS ASH; COMPOSITES; IDENTIFICATION 作者信息 通訊作者地址: Yu, HY (通訊作者) 顯示增強組織信息的名稱 Jilin Univ, Sch Biol & Agr Engn, Key Lab Bion Engn, Minist Educ, Changchun 130022, Peoples R China. 地址: 顯示增強組織信息的名稱 [ 1 ] Jilin Univ, Sch Biol & Agr Engn, Key Lab Bion Engn, Minist Educ, Changchun 130022, Peoples R China 顯示增強組織信息的名稱 [ 2 ] Heilongjiang Bayi Agr Univ, Coll Informat Technol, Daqing 163319, Peoples R China 電子郵件地址:haiyi2009a@163.com 基金資助致謝 基金資助機構(gòu) 授權(quán)號 National High Technology Research and Developmental Program '863' 2013AA103005-04 National Science and Technology Support Program 2014BAD06B01 Heilongjiang Province Natural Science Foundation F201329 QC2015071 查看基金資助信息 出版商 SCIBULCOM LTD, PO BOX 249, 1113 SOFIA, BULGARIA 類別 / 分類 研究方向:Chemistry Web of Science 類別:Chemistry, Multidisciplinary 文獻信息 文獻類型:Article 語種:English 入藏號: WOS:000392409200004 ISSN: 0209-4541 期刊信息 Impact Factor (影響因子): Journal Citation Reports® 其他信息 IDS 號: EI3RO Web of Science 核心合集中的 "引用的參考文獻": 17 Web of Science 核心合集中的 "被引頻次": 0 |
2樓2017-03-16 20:33:24
芳茗1979
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已經(jīng)檢索了，是嗎？非常感謝！ 3樓2017-03-17 07:38:03
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