Kernel principal component analysis and differential non-linear feature extraction of pesticide residues on fruit surface based on surface-enhanced Raman spectroscopy

Shi, Guolong; Shen, Xinyi; Ren, H.; Rao, Yuan James; Weng, Shizhuang; Tang, Xiaoning

doi:10.3389/fpls.2022.956778

Cited by 9 publications

(5 citation statements)

References 36 publications

(38 reference statements)

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“…For example, the residual wax on the fruit surface caused a strong fluorescent signal which interfered with the Raman signal during pesticide detection. 36 Noises in a SERS spectrum affect normalization, baseline correction, and band/peak position determination. Therefore, they need to be removed by preprocessing for improving the interpretability of the data.…”

Section: Chemometric Modeling Of Raman and Sers Spectra For Pesticide...mentioning

confidence: 99%

“…SERS spectra can be corrupted by various types of noises, such as fluorescence, detector noise, calibration errors, cosmic rays, Mie scattering, Gaussian noise, etc. For example, the residual wax on the fruit surface caused a strong fluorescent signal which interfered with the Raman signal during pesticide detection . Noises in a SERS spectrum affect normalization, baseline correction, and band/peak position determination.…”

Section: Chemometric Modeling Of Raman and Sers Spectra For Pesticide...mentioning

confidence: 99%

See 1 more Smart Citation

Raman Spectroscopy-Based Chemometrics for Pesticide Residue Detection: Current Approaches and Future Challenges

Sharma,

Kolašinac,

Jiang

et al. 2024

ACS Agric. Sci. Technol.

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Inappropriate pesticide usage leads to unsustainable agricultural practices and deteriorates the quality of fruits and vegetables by introducing potentially hazardous substances. Raman spectroscopy, specifically surface-enhanced Raman spectroscopy (SERS), offers high-sensitivity in situ monitoring of pesticide residues. This review emphasizes the importance of advanced databases and algorithms in interpreting Raman signals. Various statistical models are introduced for spectral analysis, including self-modeling curve resolution, multivariate curve resolution, and self-modeling mixture analysis. Additionally, this study provides comprehensive information on different SERS substrates and shows great potential in the determination of food pesticide residues. However, a multicomponent analysis is needed for pesticide mixtures. The overlapping of the bands needs to be considered due to the complex matrices of biological samples. Artificial neural networks (ANNs) are applied as nonlinear models when the analytes are in a multicomponent mixture. Further research is needed to establish standardized protocols for SERS-based pesticide quantitative detection, including sample preparation and data analysis.

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Section: Chemometric Modeling Of Raman and Sers Spectra For Pesticide...mentioning

confidence: 99%

Section: Chemometric Modeling Of Raman and Sers Spectra For Pesticide...mentioning

confidence: 99%

Raman Spectroscopy-Based Chemometrics for Pesticide Residue Detection: Current Approaches and Future Challenges

Sharma,

Kolašinac,

Jiang

et al. 2024

ACS Agric. Sci. Technol.

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“…Xinyi Shen applied fuzzy clustering method to transform continuous temporal evolutionary sequences into fuzzy temporal evolutionary sequences [30]. Guolong Shi et al proposed the progressive construction algorithm which can easily and effectively organize the adaptive sequence patterns [31].…”

Section: Related Workmentioning

confidence: 99%

A Novel Text Classification Model Combining Time Correlation Principle and Rough Set Theory

Zhang

2023

IEEE Access

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This research aims to design a literary text feature classification and information extraction model based on the principle of temporal association and rough set theory. We put forward a new text classification method through the in-depth study of time series correlation principle algorithm and text classification technology based on rough set theory. First, we propose to use the lexical space feature vector as the input channel of the rough set model to extract literary sentence-level features according to the spatial relationship between words. Secondly, aiming at the problem of low efficiency of KNN text classification algorithm, we propose a KNN literature text classification algorithm based on rough set approximation set, which significantly improves classification efficiency while ensuring classification accuracy. The effectiveness of the algorithm is proved by experiments, and it promotes the progress of rough set theory in practical application research. In addition, we propose an improved attribute reduction algorithm in the process of literary text classification by combining feature selection, information extraction, and the correlation of feature items generated by text description and the evaluation criteria of rough set itself. This algorithm makes the reduced attribute set more important, and then improves the text recognition rate. Through comparative experiments, it is proved that our improved method increases the number of applicable texts by 12.86%, and the improvement effect is good. In summary, our model combines the temporal correlation principle with rough set theory, and provides a new method for feature classification and information extraction of literary texts. Our results demonstrate that the method is able to achieve better classification results when applied to collections of literary texts.

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“…These public database sources included KNAPSAcK [54], METLIN [55], MassBank [56], MoToDB [57], and HMDB [58]. Potential biomarker variables were identified by a PCA [59] and OPLS-DA [60]. In addition, differential metabolites could also be further screened by combining the p-values or fold changes from the univariate analysis.…”

Section: Characterization and Quantification Of Metabolitesmentioning

confidence: 99%

The Integration of the Metabolome and Transcriptome for Dendrobium nobile Lindl. in Response to Methyl Jasmonate

Gong,

Li,

et al. 2023

Molecules

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Dendrobium nobile Lindl., as an endangered medicinal plant within the genus Dendrobium, is widely distributed in southwestern China and has important ecological and economic value. There are a variety of metabolites with pharmacological activity in D. nobile. The alkaloids and polysaccharides contained within D. nobile are very important active components, which mainly have antiviral, anti-tumor, and immunity improvement effects. However, the changes in the compounds and functional genes of D. nobile induced by methyl jasmonate (MeJA) are not clearly understood. In this study, the metabolome and transcriptome of D. nobile were analyzed after exposure to MeJA. A total of 377 differential metabolites were obtained through data analysis, of which 15 were related to polysaccharide pathways and 35 were related to terpenoids and alkaloids pathways. Additionally, the transcriptome sequencing results identified 3256 differentially expressed genes that were discovered in 11 groups. Compared with the control group, 1346 unigenes were differentially expressed in the samples treated with MeJA for 14 days (TF14). Moreover, the expression levels of differentially expressed genes were also significant at different growth and development stages. According to GO and KEGG annotations, 189 and 99 candidate genes were identified as being involved in terpenoid biosynthesis and polysaccharide biosynthesis, respectively. In addition, the co-expression analysis indicated that 238 and 313 transcription factors (TFs) may contribute to the regulation of terpenoid and polysaccharide biosynthesis, respectively. Through a heat map analysis, fourteen terpenoid synthetase genes, twenty-three cytochrome P450 oxidase genes, eight methyltransferase genes, and six aminotransferase genes were identified that may be related to dendrobine biosynthesis. Among them, one sesquiterpene synthase gene was found to be highly expressed after the treatment with MeJA and was positively correlated with the content of dendrobine. This study provides important and valuable metabolomics and transcriptomic information for the further understanding of D. nobile at the metabolic and molecular levels and provides candidate genes and possible intermediate compounds for the dendrobine biosynthesis pathway, which lays a certain foundation for further research on and application of Dendrobium.

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Kernel principal component analysis and differential non-linear feature extraction of pesticide residues on fruit surface based on surface-enhanced Raman spectroscopy

Cited by 9 publications

References 36 publications

Raman Spectroscopy-Based Chemometrics for Pesticide Residue Detection: Current Approaches and Future Challenges

Raman Spectroscopy-Based Chemometrics for Pesticide Residue Detection: Current Approaches and Future Challenges

A Novel Text Classification Model Combining Time Correlation Principle and Rough Set Theory

The Integration of the Metabolome and Transcriptome for Dendrobium nobile Lindl. in Response to Methyl Jasmonate

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