ArticleFrontiers in plant science2022
Kernel principal component analysis and differential non-linear feature extraction of pesticide residues on fruit surface based on surface-enhanced Raman spectroscopy.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Rapid and accessible tumor necrosis factor alpha detection using digital nanoparticle sensors towards point-of-care cytokine monitoring.Sensors and actuators. B, Chemical · 2026Article
- Recent Advances in Nanomaterials for Pesticide Residue Detection: From Spectroscopic Analysis to Electrochemical Sensing.Nanomaterials (Basel, Switzerland) · 2026Review
- Nanomaterial-Enabled Spectroscopic Sensing: Building a New Paradigm for Precision Detection of Pesticide Residues.Nanomaterials (Basel, Switzerland) · 2025Review
- Integrated Metabolomics-KPCA-Machine Learning framework: a solution for geographical traceability of Chinese Jujube.Food chemistry: X · 2025Article
- Spectral Region Optimization and Machine Learning-Based Nonlinear Spectral Analysis for Raman Detection of Cardiac Fibrosis Following Myocardial Infarction.International journal of molecular sciences · 2025Article
- An introductory review on the application of principal component analysis in the data exploration of the chemical analysis of food samples.Food science and biotechnology · 2024Review
- The Integration of the Metabolome and Transcriptome forMolecules (Basel, Switzerland) · 2023Article
- A high performance computing technology powered multimedia fusion model in university English translation.PeerJ. Computer science · 2023Article
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6 authors.
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Abstract
Surface-enhanced Raman spectroscopy (SERS) has attracted much attention because of its high sensitivity, high speed, and simple sample processing, and has great potential for application in the field of pesticide residue detection. However, SERS is susceptible to the influence of a complex detection environment in the detection of pesticide residues on the surface of fruits, facing problems such as interference from the spectral peaks of detected impurities, unclear dimension of effective correlation data, and poor linearity of sensing signals. In this work, the enhanced raw data of the pesticide thiram residues on the fruit surface using gold nanoparticle (Au-NPs) solution are formed into the raw data set of Raman signal in the IoT environment of Raman spectroscopy principal component detection. Considering the non-linear characteristics of sensing data, this work adopts kernel principal component analysis (KPCA) including radial basis function (RBF) to extract the main features for the spectra in the ranges of 653∼683 cm
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