Detection of mussels contaminated with cadmium by near-infrared reflectance spectroscopy based on RELS-TSVM.

IF 3.2 2区 农林科学 Q2 FOOD SCIENCE & TECHNOLOGY
Yao Liu, Fu Qiao, Zhen Xu
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引用次数: 0

Abstract

Eating mussels contaminated with cadmium (Cd) can seriously harm health. In this study, a non-destructive and rapid detection method for Cd-contaminated mussels based on near-infrared reflectance spectroscopy was studied. The spectral data of Cd-contaminated and non-contaminated mussels were collected in the range of 950-1700 nm. The model based on a robust energy-based least squares twin support vector machine (RELS-TSVM) was established to detect Cd-contaminated mussels. The influence of parameters on the RELS-TSVM model was analyzed, and the most suitable parameters were determined. The average accuracy of the proposed RELS-TSVM model in detecting Cd-contaminated mussels reached 99.92%, which was better than other twin support vector machine-derived models. For test datasets with different kinds of spectral noises (Gaussian noise, baseline shift, stray light, and wavelength shift), the RELS-TSVM model had a high robustness for noise disturbance. The results show that near-infrared spectroscopy combined with the RELS-TSVM model can realize the detection of Cd-contaminated mussels, which can provide technical support for the monitoring of heavy metals in shellfish. PRACTICAL APPLICATION: The method of detecting Cd-contaminated mussels by the NIRS has important practical significance for ensuring the safety of consumers. It provides a new way for the quality assessment and safety detection of shellfish and provides a technical basis for the marine environment assessment and management.

基于 RELS-TSVM 的近红外反射光谱法检测受镉污染的贻贝。
食用受镉(Cd)污染的贻贝会严重危害健康。本研究采用近红外反射光谱法,对受镉污染的贻贝进行非破坏性的快速检测。采集了受镉污染和未受镉污染贻贝在 950-1700 nm 范围内的光谱数据。建立了基于稳健能量最小二乘双支持向量机(RELS-TSVM)的镉污染贻贝检测模型。分析了参数对 RELS-TSVM 模型的影响,并确定了最合适的参数。所提出的 RELS-TSVM 模型检测镉污染贻贝的平均准确率达到 99.92%,优于其他双支持向量机衍生模型。对于不同光谱噪声(高斯噪声、基线偏移、杂散光和波长偏移)的测试数据集,RELS-TSVM 模型对噪声干扰具有较高的鲁棒性。结果表明,近红外光谱仪结合 RELS-TSVM 模型可实现镉污染贻贝的检测,为贝类中重金属的监测提供了技术支持。实际应用:利用近红外光谱检测受镉污染的贝类的方法对确保消费者的安全具有重要的现实意义。它为贝类的质量评估和安全检测提供了新的途径,为海洋环境评估和管理提供了技术依据。
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来源期刊
Journal of Food Science
Journal of Food Science 工程技术-食品科技
CiteScore
7.10
自引率
2.60%
发文量
412
审稿时长
3.1 months
期刊介绍: The goal of the Journal of Food Science is to offer scientists, researchers, and other food professionals the opportunity to share knowledge of scientific advancements in the myriad disciplines affecting their work, through a respected peer-reviewed publication. The Journal of Food Science serves as an international forum for vital research and developments in food science. The range of topics covered in the journal include: -Concise Reviews and Hypotheses in Food Science -New Horizons in Food Research -Integrated Food Science -Food Chemistry -Food Engineering, Materials Science, and Nanotechnology -Food Microbiology and Safety -Sensory and Consumer Sciences -Health, Nutrition, and Food -Toxicology and Chemical Food Safety The Journal of Food Science publishes peer-reviewed articles that cover all aspects of food science, including safety and nutrition. Reviews should be 15 to 50 typewritten pages (including tables, figures, and references), should provide in-depth coverage of a narrowly defined topic, and should embody careful evaluation (weaknesses, strengths, explanation of discrepancies in results among similar studies) of all pertinent studies, so that insightful interpretations and conclusions can be presented. Hypothesis papers are especially appropriate in pioneering areas of research or important areas that are afflicted by scientific controversy.
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