Classification of potato chips using pattern recognition

Pedreschi, F.; Mery, D; Mendoza, F; aguilera, jm

Abstract

An approach to classify potato chips using pattern recognition from color digital images consists of 5 steps: (1) image acquisition, (2) preprocessing, (3) segmentation, (4) feature extraction, and (5) classification. Ten chips prepared for each of the following 6 conditions were examined: 2 pretreatments (blanched and unblanched) at 3 temperatures (120°C, 150°C, and 180°C). More than 1500 features were extracted from each of the 60 images. Finally, 11 features were selected according to their classification attributes. Seven different classification cases (for example, classification of the 6 classes or distinction between blanched and unblanched samples) were analyzed using the selected features. Although samples were highly heterogeneous, using a simple classifier and a small number of features, it was possible to obtain a good performance value in all cases: classification of the 6 classes was in the confidence interval between 78% and 89% with a probability of 95%.

Más información

Título según WOS: Classification of potato chips using pattern recognition
Título según SCOPUS: Classification of potato chips using pattern recognition
Título de la Revista: JOURNAL OF FOOD SCIENCE
Volumen: 69
Número: 6
Editorial: WILEY-BLACKWELL
Fecha de publicación: 2004
Página de inicio: E264
Página final: E270
Idioma: English
Notas: ISI, SCOPUS