Application of KPCA combined with SVM in Raman spectral discrimination

Sun, Haotong; Lv, Guodong; Mo, Jiaqing; Lv, Xiaoyi; Du, Guoli; Liu, Yajun

doi:10.1016/j.ijleo.2019.02.126

Cited by 43 publications

(16 citation statements)

References 16 publications

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“…A brief description of the basic idea of the SVM classication algorithm is provided here. [23][24][25] First, a known training set (T) can be described using the formula…”

Section: Discussionmentioning

confidence: 99%

The statistical fusion identification of dairy products based on extracted Raman spectroscopy

Zhang

2020

RSC Adv.

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“…A brief description of the basic idea of the SVM classication algorithm is provided here. [23][24][25] First, a known training set (T) can be described using the formula…”

Section: Discussionmentioning

confidence: 99%

The statistical fusion identification of dairy products based on extracted Raman spectroscopy

Zhang

2020

RSC Adv.

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“…Xin et al (2020) used a kernel function to non-linearly map the calibrated samples to a high-dimensional space, evaluated the Raman spectral reconstruction accuracy based on the relative root mean square error, and reduced bad data and non-performing samples in the sample. Sun et al (2019) proposed a model combining KPCA and support vector machine, which effectively eliminated the influence of noise in the spectrum. Wang et al (2021) used the synthetic minority oversampling technique (SMOTE) to predict protein-protein interaction sites and applied KPCA to remove redundant features.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

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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−1, 705∼728 cm−1, and 847∼872 cm−1, and discusses the effects of different kernel function widths (σ) to construct a qualitative analysis of pesticide residues based on SERS spectral data model, so that the SERS spectral data produce more useful dimensionality reduction with minimal loss, higher mean squared error for cross-validation in non-linear scenarios, and effectively weaken the interference features of detecting impurity spectral peaks, unclear dimensionality of effective correlation data, and poor linearity of sensing signals, reflecting better extraction effects than conventional principal component analysis (PCA) models.

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“…Since the spectra have high dimensionality, dimension reduction is a frequent preprocessing step ( Figure 12 ). PCA is again popularly used as a dimension reduction or feature extraction step [ 60 , 61 , 64 , 65 , 68 , 70 , 71 , 73 ], or for exploratory analysis [ 62 , 72 , 157 ]. Once the spectra are remapped using PCA, a classifier or regression model is employed such as an extreme learning machine (ELM) [ 71 ], LDA [ 68 ], SVM [ 60 , 64 , 73 ], PLSR [ 65 ], or ANN [ 70 ].…”

Section: Optical Bioreceptor-free Biosensorsmentioning

confidence: 99%

“…PCA is again popularly used as a dimension reduction or feature extraction step [ 60 , 61 , 64 , 65 , 68 , 70 , 71 , 73 ], or for exploratory analysis [ 62 , 72 , 157 ]. Once the spectra are remapped using PCA, a classifier or regression model is employed such as an extreme learning machine (ELM) [ 71 ], LDA [ 68 ], SVM [ 60 , 64 , 73 ], PLSR [ 65 ], or ANN [ 70 ]. An alternative to dimension reduction is utilizing the high dimensionality spectral data directly with a node-based algorithm such as ANN [ 72 , 158 , 159 ] and CNN [ 160 , 161 ].…”

Section: Optical Bioreceptor-free Biosensorsmentioning

confidence: 99%

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Schackart

Yoon

2021

Sensors

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Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor’s signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.

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Application of KPCA combined with SVM in Raman spectral discrimination

Cited by 43 publications

References 16 publications

The statistical fusion identification of dairy products based on extracted Raman spectroscopy

The statistical fusion identification of dairy products based on extracted Raman spectroscopy

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

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

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