Terahertz spectra reconstructed using convolutional denoising autoencoder for identification of rice grains infested with Sitophilus oryzae at different growth stages

[Display omitted] •THz-TDS technology was used to identify rice grains infested with Sitophilus oryzae at different growth stages.•CDAE was developed to reconstruct preprocessed THz spectra for eliminating noise.•RFC model was constructed to analysis original and reconstructed THz spectra. Rice grai...

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Vydané v:Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Ročník 311; s. 124015
Hlavní autori: Pu, Hongbin, Yu, Jingxiao, Luo, Jie, Paliwal, Jitendra, Sun, Da-Wen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Elsevier B.V 15.04.2024
Predmet:
Convolutional denoising autoencoder
Data reconstruction
Infested rice grains
Sitophilus oryzae
Terahertz time-domain spectroscopy
SVC
CNN
PS
THz
S-G
HLS
RFC
LLS
Convolutional denoising autoencoder
MC
SNR
CDAE
SNV
2-nd der
MSC
EC
MSE
AE
Terahertz time-domain spectroscopy
HCA
FAC
THz-TDS
Data reconstruction
1-st der
ES
PCA
CS
AS
NIR
Infested rice grains
KNNC
Sitophilus oryzae
ISSN:1386-1425, 1873-3557, 1873-3557
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Shrnutí:[Display omitted] •THz-TDS technology was used to identify rice grains infested with Sitophilus oryzae at different growth stages.•CDAE was developed to reconstruct preprocessed THz spectra for eliminating noise.•RFC model was constructed to analysis original and reconstructed THz spectra. Rice grains are often infected by Sitophilus oryzae due to improper storage, resulting in quality and quantity losses. The efficacy of terahertz time-domain spectroscopy (THz-TDS) technology in detecting Sitophilus oryzae at different stages of infestation in stored rice was employed in the current research. Terahertz (THz) spectra for rice grains infested by Sitophilus oryzae at different growth stages were acquired. Then, the convolutional denoising autoencoder (CDAE) was used to reconstruct THz spectra to reduce the noise-to-signal ratio. Finally, a random forest classification (RFC) model was developed to identify the infestation levels. Results showed that the RFC model based on the reconstructed second-order derivative spectrum with an accuracy of 84.78%, a specificity of 86.75%, a sensitivity of 86.36% and an F1-score of 85.87% performed better than the original first-order derivative THz spectrum with an accuracy of 89.13%, a specificity of 91.38%, a sensitivity of 88.18% and an F1-score of 89.16%. In addition, the convolutional layers inside the CDAE were visualized using feature maps to explain the improvement in results, illustrating that the CDAE can eliminate noise in the spectral data. Overall, THz spectra reconstructed with the CDAE provided a novel method for effective THz detection of infected grains.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1386-1425
1873-3557
1873-3557
DOI:10.1016/j.saa.2024.124015