DETECTION OF MUSSELS CONTAMINATED WITH CADMIUM BASED ON NEAR-INFRARED SPECTROSCOPY AND LSPTSVM

ZENG Shaogeng; LIU Yao; LIU Zhongyan

doi:10.13205/j.hjgc.202401030

Volume 42 Issue 1

Jan. 2024

Turn off MathJax

Article Contents

Article Navigation > ENVIRONMENTAL ENGINEERING > 2024 > 42(1): 235-242

ZENG Shaogeng, LIU Yao, LIU Zhongyan. DETECTION OF MUSSELS CONTAMINATED WITH CADMIUM BASED ON NEAR-INFRARED SPECTROSCOPY AND LSPTSVM[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(1): 235-242. doi: 10.13205/j.hjgc.202401030

Citation:

ZENG Shaogeng, LIU Yao, LIU Zhongyan. DETECTION OF MUSSELS CONTAMINATED WITH CADMIUM BASED ON NEAR-INFRARED SPECTROSCOPY AND LSPTSVM[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(1): 235-242. doi: 10.13205/j.hjgc.202401030

Citation:

PDF( 4670 KB)

DETECTION OF MUSSELS CONTAMINATED WITH CADMIUM BASED ON NEAR-INFRARED SPECTROSCOPY AND LSPTSVM

doi: 10.13205/j.hjgc.202401030

1. School of Computer and Intelligence Education, Lingnan Normal University, Zhanjiang 524048, China;
2. School of Electronic and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China

Received Date: 2023-02-07
Available Online: 2024-04-29

Abstract

Abstract

Heavy metal contamination of shellfish has become an urgent problem of marine food safety, among which cadmium is one of the important contamination sources. The consumption of mussels contaminated with heavy metal cadmium is a serious health hazard. A non-destructive and rapid detection method for mussels contaminated with cadmium based on near-infrared reflectance spectroscopy was researched in this study. By collecting spectral data of normal and cadmium-contaminated mussels in the range of 950~1700 nm, a detection model based on the least squares projection twin support vector machine(LSPTSVM) was constructed. The parameters of the model and the number of orthogonal projection axes were optimized to obtain the best detection performance. The accuracy of the proposed LSPTSVM model achieved 99.50% for detecting cadmium-contaminated mussels, which was superior to other twin support vector machine models. The LSPTSVM model was suitable for the datasets with small samples. In the case that it was difficult to obtain many cadmium-contaminated training samples, the LSPTSVM model had better robustness than other models. The results showed that near-infrared spectroscopy combined with the LSPTSVM model can realize the detection of cadmium-contaminated mussels, which provides a new method for quality evaluation and safety detection of shellfish.
- near-infrared spectroscopy,
- cadmium contamination,
- least squares projection twin support vector machine,
- mussels,
- heavy metal detection

FullText(HTML)

References(41)

References

[1]	农业部渔业局编制.中国渔业统计年鉴[M].北京:中国农业出版社,2022.
[2]	翟毓秀,郭萌萌,江艳华,等.贝类产品质量安全风险分析[J].中国渔业质量与标准,2020,10(4):1-25.
[3]	SATARUG S,BAKER J R,REILLY P E B,et al.Evidence for a synergistic interaction between cadmium and endotoxin toxicity and for nitric oxide and cadmium displacement of metals in the kidney[J].Nitric Oxide,2000,4(4):431-440.
[4]	王倩茹,安文佳,蒋宁,等.市售水产品中镉污染状况评价及健康风险评估[J].中国食品卫生杂志,2020,32(2):201-206.
[5]	梁辉,刘志婷,周少君,等.广东省居民贝类水产品中镉暴露的风险评估[J].中国食品卫生杂志,2017,29(4):492-495.
[6]	林功师.厦门市售贝类中重金属镉含量分析与评价[J].中国渔业质量与标准,2022,12(2):54-59.
[7]	黄国清,陈国薇.青岛市场上常见贝类Cd污染情况调查研究[J].青岛农业大学学报:自然科学版,2014,31(1):41-44.
[8]	杜睿贤,贾雪峰,林艺佳,等.海洋贝类重金属富集特征及影响因素研究进展[J].中国农学通报,2019,35(11):155-159.
[9]	陈海刚,林钦,蔡文贵,等.3种常见海洋贝类对重金属HgPb和Cd的积累与释放特征比较[J].农业环境科学学报,2008,27(3):1163-1167.
[10]	ALELUIA A C M,DE SANTANA F A,BRANDAO G C,et al.Sequential determination of cadmium and lead in organic pharmaceutical formulations using high-resolution continuum source graphite furnace atomic absorption spectrometry[J].Microchemical Journal,2017,130:157-161.
[11]	方玲,马海霞,李来好,等.贝类中重金属镉的研究进展[J].核农学报,2019,33(7):1408-1414.
[12]	ALEXANDER D,ELLERBY R,HERNANDEZ A,et al.Investigation of simultaneous adsorption properties of Cd,Cu,Pb and Zn by pristine rice husks using ICP-AES and LA-ICP-MS analysis[J].Microchemical Journal,2017,135:129-139.
[13]	赵艳芳,尚德荣,宁劲松,等.体积排阻高效液相色谱-电感耦合等离子体质谱法测定海产贝类中镉的形态[J].分析化学,2012,40(5):681-686.
[14]	刘爽,柴春祥.近红外光谱技术在水产品检测中的应用进展[J].食品安全质量检测学报,2021,12(21):8590-8596.
[15]	占可,陈季旺,徐言,等.树脂吸附结合近红外光谱同时检测小龙虾中铅,镉模型的建立[J].食品安全质量检测学报,2022,13(15):4858-486.
[16]	李玉环.贝类体内重金属镉的富集和消除规律及食用安全性的研究[D].青岛:中国海洋大学,2005.
[17]	林冬秀,刘科,陈孝敬.基于近红外光谱的重金属污染泥蚶的快速检测[J].中国食品学报,2015,15(4):189-195.
[18]	LIU Y,XU L,WANG R,et al.Study on the detection of heavy metal lead (Pb) in mussels based on near-infrared spectroscopy technology and a REELM classifier[J].Microchemical Journal,2022,178:107394.
[19]	CHEN X,LIU K,CAI J,et al.Identification of heavy metal-contaminated Tegillarca granosa using infrared spectroscopy[J].Analytical Methods,2015,7(5):2172-2181.
[20]	熊建芳,乔付,刘忠艳,等.基于高光谱图像的贝类重金属污染快速无损检测方法研究[J].环境工程,2022,40(10):141-149.
[21]	DENG N,TIAN Y,ZHANG C.Support vector machines:optimization based theory,algorithms,and extensions[M].CRC Press,2012.
[22]	JAYADEVA,KHEMCHANDANI R,CHANDRA S.Twin support vector machines for pattern classification[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(5):905-910.
[23]	CHEN X,JIAN Y,YE Q,et al.Recursive projection twin support vector machine via within-class variance minimization[J].Pattern Recognition,2011,44(10):2643-2655.
[24]	KUMAR M A,GOPAL M.Least squares twin support vector machines for pattern classification[J].Expert Systems with Applications,2009,36(4):7535-7543.
[25]	SHAO Y H,DENG N Y,YANG Z M.Least squares recursive projection twin support vector machine for classification[J].Pattern Recognition,2012,45(6):2299-2307.
[26]	马藏允,刘海,姚波,等.几种大型底栖生物对Cd,Zn,Cu的积累实验研究[J].中国环境科学,1997,17(2):56-60.
[27]	马胜伟,林钦,陈海刚,等.混合重金属对翡翠贻贝的积累与排放规律研究[J].南方水产,2008,4(6):78-82.
[28]	彭玲,曾江宁,陈全震,等.镉对厚壳贻贝急性毒性及对其鳃抗氧化酶活性的影响[J].环境科学与技术,2015,38(2):13-18 ,24.
[29]	王晓玲.紫贻贝体内重金属镉的富集代谢及脱除方法研究[D].舟山:浙江海洋学院,2015.
[30]	张少娜,孙耀,宋云利,于志刚.紫贻贝(Mytilus edulis)对4种重金属的生物富集动力学特性研究[J].海洋与湖沼,2004,35(5):438-445.
[31]	刘宏明,刘玉娟,仲志成,等.一种油田原油含水率的近红外光谱检测与分析方法[J].光谱学与光谱分析,2021,41(2):505-510.
[32]	中华人民共和国农业部.NY 5073—2006,无公害食品水产品中有毒有害物质限量[S].
[33]	张中卫,温志渝,曾甜玲,等.微型近红外光纤光谱仪用于奶粉中蛋白质脂肪的定量检测研究[J].光谱学与光谱分析,2013,33(7):1796-1800.
[34]	李瑞,李博,王学文,等.基于XGBoost与可见-近红外光谱的煤矸识别方法[J].光谱学与光谱分析,2022,42(9):2947-2955.
[35]	周燕萍,业巧林.基于L₁-范数距离的最小二乘对支持向量机[J].计算机科学,2018,45(4):100-105 ,130.
[36]	姜涛,王长江,陈厚合,等.基于正则化投影孪生支持向量机的电力系统暂态稳定评估[J].电力系统自动化,2019,43(1):141-148.
[37]	王动民,纪俊敏,高洪智.多元散射校正预处理波段对近红外光谱定标模型的影响[J].光谱学与光谱分析,2014,34(9):2387-2390.
[38]	HSU C W,LIN C J.A comparison of methods for multiclass support vector machines[J].IEEE Transactions on Neural Networks,2002,13(2):415-425.
[39]	DING S,HUA X.Recursive least squares projection twin support vector machines for nonlinear classification[J].Neurocomputing,2014,130:3-9.
[40]	LI C N,HUANG Y F,WU H J,et al.Multiple recursive projection twin support vector machine for multi-class classification[J].International Journal of Machine Learning and Cybernetics,2016,7(5):729-740.
[41]	SHAO Y H,WANG Z,CHEN W J,et al.A regularization for the projection twin support vector machine[J].Knowledge-Based Systems,2013,37:203-210.