Assessment the quality of apricots in the process of drying with neural networks and support vector machines

The paper presents an analysis of the assessment the quality of apricots during the drying process using two types of classifires: ANNs and SVMs. The quality of apricots is categorized in three classes according to the color and b-carotene content through the process of drying. The classification is...

Full description

Saved in:

Bibliographic Details
Published in:	MATEC web of conferences Vol. 292; p. 3019
Main Authors:	Dejanov, Mаrtin, Ilieva-Stefanova, Darinka, Chelik, Iva
Format:	Journal Article Conference Proceeding
Language:	English
Published:	Les Ulis EDP Sciences 2019
Subjects:	Apricots Carotene Classification Classifiers Color Drying Kernels Neural networks Quality assessment Spectra Support vector machines
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	The paper presents an analysis of the assessment the quality of apricots during the drying process using two types of classifires: ANNs and SVMs. The quality of apricots is categorized in three classes according to the color and b-carotene content through the process of drying. The classification is made by using ‘CIE Lab’ color model and spectral characteristics in the VIS range. Neural networks are BPN and PNN, and classifiers are kernel and linear SVM. The spectral characteristics are pre-processed with SNV, MSC, First derivative and PCA. According to the results for color features, BPN and SVM with “rbf” kernel have the best performance while PNN has the worst performance. When using spectral characteristics the BPN network performs well: e avg = 4.1% and e max = 12.1% but the SVM linear (e avg = 3.4%, e max =5.3%) and SVM with “rbf” kernel (e avg = 2.4%, e max =5.2%) classifiers have better results. As a conclusion, it could be said that classifiers using spectral features perform well with errors at about 2-5%. Classification with color features is an alternative method, which is less complex, cheaper and with acceptable errors.
ISSN:	2261-236X 2274-7214 2261-236X
DOI:	10.1051/matecconf/201929203019