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中國精品科技期刊2020

基于溫控近紅外光譜快速檢測泥蚶重金屬污染

周頎偉 宋燕如 張展碩 袁雷明 孫一葉

周頎偉,宋燕如,張展碩,等. 基于溫控近紅外光譜快速檢測泥蚶重金屬污染[J]. 食品工業科技,2022,43(19):326?330. doi:  10.13386/j.issn1002-0306.2021120125
引用本文: 周頎偉,宋燕如,張展碩,等. 基于溫控近紅外光譜快速檢測泥蚶重金屬污染[J]. 食品工業科技,2022,43(19):326?330. doi:  10.13386/j.issn1002-0306.2021120125
ZHOU Qiwei, SONG Yanru, ZHANG Zhanshuo, et al. Rapid Detection of Heavy Metal Contaminated Tegillarca granosa by Temperature-dependent Near-infrared Spectroscopy[J]. Science and Technology of Food Industry, 2022, 43(19): 326?330. (in Chinese with English abstract). doi:  10.13386/j.issn1002-0306.2021120125
Citation: ZHOU Qiwei, SONG Yanru, ZHANG Zhanshuo, et al. Rapid Detection of Heavy Metal Contaminated Tegillarca granosa by Temperature-dependent Near-infrared Spectroscopy[J]. Science and Technology of Food Industry, 2022, 43(19): 326?330. (in Chinese with English abstract). doi:  10.13386/j.issn1002-0306.2021120125

基于溫控近紅外光譜快速檢測泥蚶重金屬污染

doi: 10.13386/j.issn1002-0306.2021120125
基金項目: 浙江省大學生新苗人才計劃項目(2020R434018,2021R429050);國家自然科學基金(61705168)。
詳細信息
    作者簡介:

    周頎偉(1998?),男,本科,研究方向:水產品品質快速檢測,E-mail:470476769@qq.com

    通訊作者:

    孫一葉(1985?),女,碩士,講師,研究方向:光電傳感與信息分析等方面的研究,E-mail:syy@wzu.edu.cn

  • 中圖分類號: TS254.7

Rapid Detection of Heavy Metal Contaminated Tegillarca granosa by Temperature-dependent Near-infrared Spectroscopy

  • 摘要: 目的:探索一種基于蛋白酶解、溫控近紅外光譜表征技術的貝類重金屬污染快速檢測方法。方法:以人工飼養的貝類泥蚶為研究對象,以銅(Cu)、鎘(Cd)、鉛(Pb)三種重金屬分別脅迫感染泥蚶;利用酶解和離心等預處理分別提取健康泥蚶和各重金屬污染泥蚶的全蛋白上清液樣品;控制樣品處于一個25~60 ℃的升溫過程中,以傅里葉變換近紅外光譜,每間隔5 ℃采集各樣品光譜,并構建判別模型對泥蚶污染樣本進行區分。結果:通過偏最小二乘-判別模型識別不同溫度下的泥蚶重金屬污染類別,其準確率隨溫度先升高后降低;當樣品升溫至40 ℃時,判別模型的準確率達到92%;通過變量篩選優化,能夠將判別模型的準確率提高至98%。結論:借助化學計量學、近紅外光譜技術和酶解技術,可以快速鑒別泥蚶中的重金屬污染問題,豐富貝類重金屬污染的檢測手段。
  • 圖  1  四類泥蚶樣本25 ℃時的近紅外平均透射光譜

    Figure  1.  The averaged transmittance of NIR at termperature of 25 ℃ for four classes of Tegillarca granosa samples

    圖  2  不同溫度下的泥蚶樣本平均透射光譜

    Figure  2.  The averaged transmitted spectra of Tegillarca granosa samples at different temperatures

    圖  3  PLS-DA各溫度下的分類準確性

    Figure  3.  Classification accuracy of PLS-DA at different temperatures

    表  1  40 ℃ PLS-DA分類結果

    Table  1.   Classsification results of 40 ℃ PLS-DA

    類別特異性(%)敏感性(%)精準性(%)
    健康泥蚶全蛋白948973
    Cd污染全蛋白100100100
    Cu污染全蛋白9710094
    Pb污染全蛋白10081100
    整體989292
    下載: 導出CSV

    表  2  40 ℃溫度下光譜變量篩選后建模分類結果

    Table  2.   Modeling classification results by variable selection at temperature of 40 ℃

    波長篩選無處理CARSUVEGA
    光譜數量2074986674
    準確率(%)92989788
    下載: 導出CSV
    人妻AV无码系列一区二区三区
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  • 收稿日期:  2021-12-13
  • 網絡出版日期:  2022-08-18
  • 刊出日期:  2022-09-23

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