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            引用本文:   南迪娜, 劉衛衛, 傅文翔, 李寶強, 孔景臨. 基于數據融合的生物毒素及生物調節劑快速識別研究. 分析化學, 2020, 48(10): 1343-1350. doi:  10.19756/j.issn.0253-3820.201137 [復制]

            Citation:   NAN Di-Na , LIU Wei-Wei , FU Wen-Xiang , LI Bao-Qiang , KONG Jing-Lin . Study on Fast Recognition of Biotoxins and Biological Modifiers Using Data Fusion Algorithm. Chinese Journal of Analytical Chemistry, 2020, 48(10): 1343-1350. doi: 10.19756/j.issn.0253-3820.201137 [復制]

            基于數據融合的生物毒素及生物調節劑快速識別研究

            通訊作者:  孔景臨, jlkong@sina.com

            收稿日期: 2020-03-17

            基金項目: 本文系國家自然科學基金項目(No.61775017)資助

            Study on Fast Recognition of Biotoxins and Biological Modifiers Using Data Fusion Algorithm

            Corresponding author:  KONG Jing-Lin , jlkong@sina.com

            Received Date:  2020-03-17

            Fund Project:  This work was supported by the National Natural Science Foundation of China (No. 61775017).

            建立了多種生物毒素和生物調節劑的電噴霧離子遷移譜和拉曼光譜分析方法,構建了幾類代表性生物毒素、生物調節劑及常見無毒白色粉末共26種物質的離子遷移譜和拉曼光譜數據庫,其中包括烏頭堿、河豚毒素等生物堿,緩激肽、P物質等生物調節劑及其同分異構體等結構類似物,芋螺毒素、α-銀環蛇毒素、蓖麻毒素等多肽和蛋白質毒素和食鹽、面粉、蛋白粉等常見無毒物。在此基礎上,利用線性判別分析、二次判別分析、k近鄰、樸素貝葉斯模型、分類決策樹、Sigmoid核函數支持向量機共6種模式識別算法對單一和融合數據進行識別歸類,不同方法的識別準確率在76.0%~97.2%范圍內,并分析了方法識別準確率差異原因。研究發現,以支持向量機模型建立的融合識別算法準確率高達97.2%。本方法能區分兩種生物調節劑的多個結構類似物,可用于不明白色粉末的識別歸類。

            關鍵詞:   數據融合, 模式識別, 拉曼光譜, 離子遷移譜, 生物毒素
            Key words:   Data fusion, Pattern recognition, Raman spectroscopy, Ion mobility spectrometry, Biotoxin
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            目錄

            基于數據融合的生物毒素及生物調節劑快速識別研究

            南迪娜, 劉衛衛, 傅文翔, 李寶強, 孔景臨

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