食品与机械2024，Vol.40Issue(8)：49-57,9.DOI:10.13652/j.spjx.1003.5788.2024.80001
基于近红外光谱的翡翠贻贝重金属铅污染识别
Identification of heavy metal Pb pollution in Perna viridis based on near-infrared spectroscopy
摘要
Abstract
[Objective]Addressing the heavy metal lead pollution in oysters using near-infrared spectroscopy technology.[Methods]This study proposed the use of near-infrared reflectance spectroscopy combined with pattern recognition for detecting Pb contamination.Initially,spectral data of healthy mussels and Pb-contaminated mussels in the range of 950～1 700 nm were collected.The wavelength selection algorithm of variable importance analysis based on the random variable combination(VIAVC)was utilized to reduce the dimensionality,and selected the optimal subset of wavelengths.Considering the detection of healthy mussels and Pb-contaminated mussels as an imbalanced classification problem,the gravitational fixed radius nearest neighbor(GFRNN)method based on universal gravity was explored for identifying Pb contamination in mussels.[Results]The experimental results demonstrated that the proposed VIAVC-GFRNN method outperformed traditional algorithms such as K-nearest neighbor,fixed radius nearest neighbor,and support vector machine algorithms in detecting Pb contamination,while remaining unaffected by the imbalance ratio.The area under the receiver operation curve value of the VIAVC-GFRNN model reached 0.988 6,with a detection accuracy and geometric mean of 99.17%.[Conclusion]Near-infrared spectroscopy combined with pattern recognition methods has great potential for detecting Pd pollution in mussels.关键词
近红外光谱/贻贝/重金属检测/不平衡分类Key words
near-infrared spectroscopy/mussels/heavy metal detection/unbalanced classification引用本文复制引用
姜微,刘忠艳,刘瑶,熊建芳,曾绍庚..基于近红外光谱的翡翠贻贝重金属铅污染识别[J].食品与机械,2024,40(8):49-57,9.基金项目
国家自然科学基金青年科学基金项目(编号:62005109) （编号:62005109）
广东省科技创新战略专项资金竞争性项目(编号:2023A01025) （编号:2023A01025）
岭南师范学院红树林生态系统智能监测创新团队项目 （）
