What question did this study set out to answer?

The aim is to develop a method that can accurately differentiate genuine wood grains from artificial ones.

May 26, 2026BioResources0 citationsOpen Access

Identifying Genuine vs. Artificial Wood Grain Using PCA and SVM

Identification of genuine and artificial wood grain based on PCA-SVM

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial identifies genuine and artificial wood grains, indicating potential for quality control.

The aim is to develop a method that can accurately differentiate genuine wood grains from artificial ones.
Utilized nine-dimensional gloss data and two surface roughness parameters.
Applied PCA on training data to extract principal components.
Trained an SVM classifier using the components and evaluated performance on test data.
Achieved an accuracy of 96.76%, F1-score of 0.9761, and MCC of 0.9285.
Outperformed other models including standalone SVM, Logistic Regression, and PLS.
Demonstrated robustness and efficiency in distinguishing wood grain types.

Wáng et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark

May 26, 2026BioResources0 citationsOpen Access

Identification of genuine and artificial wood grain based on PCA-SVM

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial identifies genuine and artificial wood grains, indicating potential for quality control.

The aim is to develop a method that can accurately differentiate genuine wood grains from artificial ones.
Utilized nine-dimensional gloss data and two surface roughness parameters.
Applied PCA on training data to extract principal components.
Trained an SVM classifier using the components and evaluated performance on test data.
Achieved an accuracy of 96.76%, F1-score of 0.9761, and MCC of 0.9285.
Outperformed other models including standalone SVM, Logistic Regression, and PLS.
Demonstrated robustness and efficiency in distinguishing wood grain types.

Wáng et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark