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中国精品科技期刊2020
周颀伟,宋燕如,张展硕,等. 基于温控近红外光谱快速检测泥蚶重金属污染[J]. 食品工业科技,2022,43(19):326−330. doi: 10.13386/j.issn1002-0306.2021120125.
引用本文: 周颀伟,宋燕如,张展硕,等. 基于温控近红外光谱快速检测泥蚶重金属污染[J]. 食品工业科技,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.

基于温控近红外光谱快速检测泥蚶重金属污染

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

  • 摘要: 目的:探索一种基于蛋白酶解、温控近红外光谱表征技术的贝类重金属污染快速检测方法。方法:以人工饲养的贝类泥蚶为研究对象,以铜(Cu)、镉(Cd)、铅(Pb)三种重金属分别胁迫感染泥蚶;利用酶解和离心等预处理分别提取健康泥蚶和各重金属污染泥蚶的全蛋白上清液样品;控制样品处于一个25~60 ℃的升温过程中,以傅里叶变换近红外光谱,每间隔5 ℃采集各样品光谱,并构建判别模型对泥蚶污染样本进行区分。结果:通过偏最小二乘-判别模型识别不同温度下的泥蚶重金属污染类别,其准确率随温度先升高后降低;当样品升温至40 ℃时,判别模型的准确率达到92%;通过变量筛选优化,能够将判别模型的准确率提高至98%。结论:借助化学计量学、近红外光谱技术和酶解技术,可以快速鉴别泥蚶中的重金属污染问题,丰富贝类重金属污染的检测手段。

     

    Abstract: Objective: To explore a rapid determination method of heavy metal contaminated shellfish based on enzymatic hydrolysis and temperature-dependent near infrared spectroscopy. Methods: Shellfish of Tegillarca granosa was reared artificially and used in this paper. Three heavy metals, including copper (Cu), cadmium (Cd) and lead (Pb), were used to contaminate Tegillarca granosa. The full protein supernatant samples of heavy metal-contaminated and healthy Tegillarca granosa were extracted by enzymolysis and centrifugation pretreatments. Fourier transform near-infrared spectroscopy (FT-NIR) was employed to collect spectra of each sample under the process of elevating temperature with each 5 ℃ interval in the range of 25~60 ℃, and a discriminant model was constructed to distinguish these samples contaminated with heavy metals. Results: Partial least square-discrimination analysis (PLS-DA) was used to identify the heavy metals contaminated Tegillarca granosa under each temperature. The accuracy rate was influenced by the temperature first increasing and then decreasing with raising the temperature. When the temperature of sample was kept at 40 ℃, the accuracy of discriminant model was 92%, and it can be improved up to 98% by the optimized parameters through variable selection methods. Conclusion: With the help of chemo-metrics, near infrared spectroscopy and enzymatic hydrolysis, the heavy metals-contaminated Tegillarca granosa can be quickly identified and the detection methods of heavy metal pollution of shellfish can be enriched.

     

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