Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine

Wu, Di; Yang, Haiqing; Chen, Xiaojing; He, Yong; Liu, Yuanyuan

doi:10.1016/j.jfoodeng.2008.03.005

Cited by 79 publications

(29 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…LS-SVM was an optimized version based on the standard support vector machine (Wu et al, 2008d). The RBF (radial basis function) kernel was adopted here.…”

Section: Multivariate Modelingmentioning

confidence: 99%

Application of visible and near infrared spectroscopy for rapid and non-invasive quantification of common adulterants in Spirulina powder

Nie

Cuello

et al. 2011

Journal of Food Engineering

Self Cite

View full text Add to dashboard Cite

“…LS-SVM was an optimized version based on the standard support vector machine (Wu et al, 2008d). The RBF (radial basis function) kernel was adopted here.…”

Section: Multivariate Modelingmentioning

confidence: 99%

Application of visible and near infrared spectroscopy for rapid and non-invasive quantification of common adulterants in Spirulina powder

Nie

Cuello

et al. 2011

Journal of Food Engineering

Self Cite

View full text Add to dashboard Cite

“…For each combination of c and r 2 parameters, the root mean square error of cross-validation (RMSECV) was calculated and the optimum parameters were selected when produced smaller RMSECV. The details of LS-SVM description could be found in the literature [25].…”

Section: Simulated Algorithm-based Uninformative Variable Eliminationmentioning

confidence: 99%

Application of a hybrid variable selection method for the classification of rapeseed oils based on 1H NMR spectral analysis

Chen

Han

et al. 2010

Eur Food Res Technol

Self Cite

View full text Add to dashboard Cite

1 H nuclear magnetic resonance (NMR) spectroscopy was utilized to distinguish the brands of rapeseed oils. As there are more than four hundreds of NMR variables which can cause the discrimination model redundancy, it is necessary to do effective variable selection. Successive projections algorithm (SPA) executed on the full spectrum only improved a few correct answer rate (CAR) and Cohen's kappa coefficient (K) compared to full spectrum-least-square support vector machine (LS-SVM) model. The better results of uninformative variable elimination (UVE)-based SPA calculation show that it is necessary to do UVE before SPA. Because the cutoff threshold selection in UVE algorithm using an artificial random noise cannot obtain the optimal results, we applied simulated annealing (SA) algorithm to estimate the optimal cutoff threshold. The discrimination results show that UVE-SA did better works than conventional UVE. Only 13 variables were obtained by UVE-SA-SPA while the conventional UVE-based SPA selected 77 variables. The best 97.5% CAR and K of 0.967 result of UVE-SPA-LS-SVM model show that it is feasible to distinguish different brands of rapeseed oils using 1 H NMR spectra. It shows that a combination of SA, UVE, and SPA is effective method for the classification of rapeseed oils. Final result shows that all acyl chains, linolenyl and linoleyl chains, and triglycerides were most important for the classification.

show abstract

“…Feature selection algorithms, such as principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares (PLS) and genetic algorithm (GA), have been utilized in food analysis [7][8][9][24][25][26]. In these algorithms, PCA and LDA are the two most used algorithms in quality classification of tea [9,10,20,23,25]. However, considering the nonlinear data acquired by E-nose, the linear algorithms (PCA and LDA) may not be the most suitable ways for feature selection.…”

Section: Introductionmentioning

confidence: 99%

Longjing tea quality classification by fusion of features collected from E-nose

Dai

Zhi

Zhao

et al. 2015

Chemometrics and Intelligent Laboratory Systems

View full text Add to dashboard Cite

Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine

Cited by 79 publications

References 26 publications

Application of visible and near infrared spectroscopy for rapid and non-invasive quantification of common adulterants in Spirulina powder

Application of visible and near infrared spectroscopy for rapid and non-invasive quantification of common adulterants in Spirulina powder

Application of a hybrid variable selection method for the classification of rapeseed oils based on 1H NMR spectral analysis

Longjing tea quality classification by fusion of features collected from E-nose

Contact Info

Product

Resources

About