Inspection of quince slice dehydration stages based on extractable image features.

Jafari A., Bakhshipour A. (2014): Inspection of quince slice dehydration stages based on extractable image features. Czech J. Food Sci., 32: 456–463. The relation between the moisture content of the fruit and image-based characteristics was investigated. Quince sam ples were dried in an oven dryer at three different temperatures (40, 50, and 60°C). Several shape, texture, and colour features of the quince slices were extracted from the images. Gradual reduction was observed in all morphological features when the moisture content of the samples decreased. Regression equations between the extracted features and moisture content of the quince slices were investigated. The moisture content prediction equations based on morpho logical features were more precise than the textural features while colour information did not yield any satisfactory result. To exploit the morphological and textural features simultaneously, several artificial neural network models were developed to predict the drying behaviour of quince. R 2 and RMSE values were determined as 0.998, 0.008%. It was concluded that the combination of the neural networks and image processing technique has the potential to determine the moisture variations.

[1]  E. Šárka,et al.  Image vision technology for the characterisation of shape and geometrical properties of two varieties of lentil grown in Turkey , 2018 .

[2]  Mehdi Jahangiri,et al.  Shrinkage of potato slice during drying , 2009 .

[3]  F. Cubillos,et al.  Drying of Carrots in a Fluidized Bed. II. Design of a Model Based on a Modular Neural Network Approach , 2003 .

[4]  H. Ney,et al.  Local Features for Image Classification , .

[5]  Kit L. Yam,et al.  A simple digital imaging method for measuring and analyzing color of food surfaces , 2004 .

[6]  Cristina L. M. Silva,et al.  Quantification of microstructural changes during first stage air drying of grape tissue , 2004 .

[7]  Digvir S. Jayas,et al.  Multi-layer neural networks for image analysis of agricultural products , 2000 .

[8]  Yong He,et al.  Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine , 2008 .

[9]  Bosoon Park,et al.  Co-occurrence matrix texture features of multi-spectral images on poultry carcasses , 2001 .

[10]  Tayfun Menlik,et al.  Determination of freeze-drying behaviors of apples by artificial neural network , 2010, Expert Syst. Appl..

[11]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[12]  I. Farkas,et al.  A neural network topology for modelling grain drying , 2000 .

[13]  Iman Golpour,et al.  Identification and Classification of Bulk Paddy, Brown, and White Rice Cultivars with Colour Features Extraction using Image Analysis and Neural Network , 2018 .

[14]  José Miguel Aguilera,et al.  Description of the Convective Air-Drying of a Food Model by Means of the Fractal Theory , 2003 .

[15]  A. Nasirahmadi,et al.  Identification of bean varieties according to color features using artificial neural network , 2013 .

[16]  José Miguel Aguilera,et al.  An application of image analysis to dehydration of apple discs , 2005 .

[17]  Chris T. Kiranoudis,et al.  Pareto design of conveyor-belt dryers , 2000 .

[18]  Da-Wen Sun,et al.  Recent developments in the applications of image processing techniques for food quality evaluation , 2004 .