Investigation of chloride diffusion in cement mortar via statistical learning theory

Liao, Kuo‐Wei; Chen, Chun-Tao; Wu, Bang-Ho; Chen, Wei‐Lun; Yeh, Chih-Ming

doi:10.1680/jmacr.14.00413

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…Therefore, the SVM was introduced and trained to develop a robust model for predicting the different stage of the T. aurantialba fermentation. Support vector machine (SVM) is a kind of statistical learning theory based on the statistical learning theory (STL; Liao, Chen, Chen, Wu, & Yeh, 2016). Furthermore, STL can effectively minimize the sample error and structural risk adopting the criterion of structural risk minimization (Garg, Garg, Sreedeep, & Tai, 2014).…”

Section: Classification Results By Svmmentioning

confidence: 99%

Analysis of volatile compounds of Tremella aurantialba fermentation via electronic nose and HS‐SPME‐GC‐MS

Dai

Huang

et al. 2018

Journal of Food Safety

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To detect the odor changes of the volatile compounds during the Tremella aurantialba (T. aurantialba) fermentation associated with its degree, electronic nose (E‐nose) technique, and headspace solid phase microextraction combined with gas chromatography‐mass spectrometry (HS‐SPME‐GC‐MS) analyses were performed each day. E‐nose and GC‐MS were used to recognize the comprehensive aroma characterization and volatile compounds of T. aurantialba fermentation, respectively. Principal component analysis (PCA) were used to analyze the data detected by E‐nose technique and GC‐MS. Support vector machine (SVM) was established to analyze E‐nose date for predicting the different stage of the T. aurantialba fermentation. The main volatile compounds were identified by GC‐MS analysis as alcohols, esters, aldehydes, ketones, heterocyclic, and aromatic compounds. The results of PCA by E‐nose were more similar with that of GC‐MS. The discrimination rates of the SVM model were 100 and 97.14% in the training and testing sets. It can be concluded that E‐nose technique coupled with SVM classification tool has high potential to monitor the fermentation process. These may sufficiently demonstrate excellent promise for the E‐nose technique and GC‐MS method to be applied in the liquid state industry. Practical applications E‐nose combined with GC‐MS could detect the odor changes of the volatile compounds during the T. aurantialba fermentation. Both analytical techniques showed good performances in the classification of the fermentation degree of T. aurantialba. These may provide a theoretical basis for the design of the portable electronic nose, which would achieve the real‐time smell monitoring of liquid state fermentation of T. aurantialba associated with its degree of fermentation. This equipment can be used to determine the state of fermentation instead of workers. The E‐nose technique and HS‐SPME‐GC‐MS method could be applied in the liquid state fermentation industry in the future.

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Section: Classification Results By Svmmentioning

confidence: 99%

Analysis of volatile compounds of Tremella aurantialba fermentation via electronic nose and HS‐SPME‐GC‐MS

Dai

Huang

et al. 2018

Journal of Food Safety

View full text Add to dashboard Cite

show abstract

“…Support vector machine (SVM) is a kind of statistical learning theory based on the statistical learning theory (STL) (Liao, Chen, Chen, Wu, & Yeh, 2016). Furthermore, STL can effectively minimize the sample error and structural risk adopting the criterion of structural risk minimization (SRM) (Garg, Garg, Sreedeep, & Tai, 2014).…”

Section: Support Vector Machinementioning

confidence: 99%

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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Application of machine learning technique for predicting and evaluating chloride ingress in concrete

Tran

Giap

et al. 2022

Front. Struct. Civ. Eng.

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Investigation of chloride diffusion in cement mortar via statistical learning theory

Cited by 7 publications

References 17 publications

Analysis of volatile compounds of Tremella aurantialba fermentation via electronic nose and HS‐SPME‐GC‐MS

Analysis of volatile compounds of Tremella aurantialba fermentation via electronic nose and HS‐SPME‐GC‐MS

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

Application of machine learning technique for predicting and evaluating chloride ingress in concrete

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