Continuous wavelet transform-based feature selection applied to near-infrared spectral diagnosis of cancer

Chen, Hui; Lin, Zan; Mo, Lin; Wu, Hegang; Wu, Tong; Tan, Chao

doi:10.1016/j.saa.2015.06.109

Cited by 11 publications

(3 citation statements)

References 20 publications

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“…Mallat algorithm was used to decompose the original signal into the low frequency signal and high frequency signal, respectively (Chang & Li, ). Further, the low frequency signal represents the approximations of the original signal and the high frequency signal represents the details of the original signal.…”

Section: Theory and Algorithmsmentioning

confidence: 99%

“…It has been widely used for the study of nondestructive testing of fruits and vegetables (Carlotta, Giorgia, Rosalba, & Alessandro, ; Deng, Xu, Li, & He, ; Mo et al, ; Pan et al, ). Moreover, wavelet transform (WT) method is a strongly effective tool to analyze hyperspectral signal and it is a partial analysis of time (space) frequencies used to gain the gradual multi‐scale zooming of signals through dilation and translation operations, in order to finally achieve the time subdivision at the high‐frequency point, and the frequency subdivision at the low‐frequency poin (Chen, Cui, et al, ). Moreover, discrete wavelet transform (DWT) algorithm often used to feature extraction processing data on a computer (Chen, Chen, Li, & Ni, ; Chen, Lin, et al, ; Lark, ).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, wavelet transform (WT) method is a strongly effective tool to analyze hyperspectral signal and it is a partial analysis of time (space) frequencies used to gain the gradual multi‐scale zooming of signals through dilation and translation operations, in order to finally achieve the time subdivision at the high‐frequency point, and the frequency subdivision at the low‐frequency poin (Chen, Cui, et al, ). Moreover, discrete wavelet transform (DWT) algorithm often used to feature extraction processing data on a computer (Chen, Chen, Li, & Ni, ; Chen, Lin, et al, ; Lark, ). However, hyperspectral data processing time will be greatly reduced with the determination of characteristic wave bands.…”

Section: Introductionmentioning

confidence: 99%

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Discrimination of pesticide residues in lettuce based on chemical molecular structure coupled with wavelet transform and near infrared hyperspectra

Sun

Zhou

Mao³

et al. 2016

J Food Process Engineering

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To facilitate more quickly and effectively detect the types of pesticide residues on the surface of lettuce, a method involving the chemical molecular structure coupled with wavelet transform (CMS‐WT) was proposed to extract the characteristic wavelength. Five different kinds of pesticide residues were sprayed on the surface of lettuce, respectively, dimethoate, acephate, phoxim, dichlorvos, avermectin (the ratio of pesticides and water were 1:1000). In addition, the near infrared hyperspectral image information of 200 samples in five different kinds of pesticides residue in lettuce were achieved by the NIR hyperspectral imaging system (870–1780 nm). The region of interest (ROI) in hyperspectral image of samples was selected to get the near infrared spectral data by the software of ENVI. Furthermore, CMS‐WT was used to extract the most influential wavelengths. Four characteristic intervals were extracted by comparing the different of pesticides in chemical molecular structures, respectively, 875—1050 nm, 1050—1250 nm, 1350—1550 nm, 1650—1780 nm. Further, the best combination of eight features were selected according to the reorder of the size of the singular value by wavelet transform algorithm using db6 as wavelet basis function, respectively, 919.18, 944.25, 972.25, 1194.20, 1363.81, 1426.69, 1673.29, 1680.74 nm. Finally, SVM model was established according to the extracted characteristic spectral data. The results showed that the calibration and prediction accuracy of SVM model established by the best combination of eight features were all achieved 100%. It confirms that the CMS‐WT feature extraction algorithm is feasible and effective for building models of different pesticide residues in lettuce. Practical applications Well understanding the effect of pesticide residues to biological structure is very important for revelation of novel biological function and mechanism of action of the protein. To facilitate more quickly and effectively detect the types of pesticide residues on the surface of lettuce, a method involving the chemical molecular structure coupled with wavelet transform (CMS‐WT) was proposed to extract the characteristic wavelength in this article. It confirms that the CMS‐WT feature extraction algorithm is feasible and effective for building models of different pesticide residues in lettuce.

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Section: Theory and Algorithmsmentioning

confidence: 99%