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Food Qual Safet
Food Qual Safet
    
Quantification of browning in apples using colour and textural features by image analysis
Srinivasagan N. Subhashree,* S. Sunoj,* Jun Xue** and Ganesh C. Bora***
*Agricultural and Biosystems Engineering, North Dakota State University, Fargo, North Dakota, USA, *Guelph Food Research Center, Agriculture and Agri-Food Canada, Ontario, Canada, and ***Agricultural and Biological Engineering, Mississippi State University, Mississippi, USA
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Abstract  This study analyses the effect of browning through image analysis based on colour and textural features in fresh-cut apple slices. A computer vision system (CVS) was developed for image acquisition, which consisted of a digital camera and a florescent lamp source for illumination with a contrasting background. The CVS was calibrated using standard colour values and a model was developed by artificial neural network technique. Three varieties of apples such as Honey crisp, Granny Smith, and Golden Delicious were used for the analysis. The apples were freshly cut and subjected to image acquisition. Normalized colour features (L*, browning index, hue, and colour change) and textural features (entropy, contrast, and homogeneity) were analysed from the acquired images. The varieties Honey Crisp and Granny Smith did undergo browning within 120 min, whereas Golden delicious did not brown significantly. The study concluded that colour and textural features were important decision features for detecting browning in apples through image analysis.

Key words： apple      browning      colour calibration      image analysis      textural features     
Received: 13 April 2017      Published: 01 September 2017
Corresponding Authors: Ganesh C. Bora, Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS 39762, USA.     E-mail: gcbora@abe.msstate.edu
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Srinivasagan N. Subhashree
S. Sunoj
Jun Xue
Ganesh C. Bora
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Srinivasagan N. Subhashree, S. Sunoj, Jun Xue, Ganesh C. Bora. Quantification of browning in apples using colour and textural features by image analysis. Food Qual Safet, 2017, 1(3): 221-226.

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http://www.zjujournals.com/fqs/10.1093/fqsafe/fyx021     OR     http://www.zjujournals.com/fqs/Y2017/V1/I3/221


图像分析颜色和纹理特征量化苹果褐变

本研究通过图像分析颜色和纹理特征来研究鲜切苹果片褐变的影响。开发了用于图像采集的计算机视觉系统（CVS），其由数码相机和具有对比背景的照明荧光灯源组成。头通过标准色度值和人工神经网络技术开发模型来校准CVS。三个苹果品种包括Honey crisp，Granny Smith和Golden Delicious作为研究对象，对其鲜切后进行图像采集。从获得的图像中分析标准颜色特征（L*、褐变指数、色调和色泽变化）和纹理特征（熵，对比度和均匀性）。实验发现品种Honey Crisp和Granny Smith在120分钟内进行了褐变，而Golden Delicious没有发生显著褐变。该研究得出通过图像分析颜色和纹理特征是检测苹果褐变的重要决定特征。

关键词： 苹果,  褐变,  色彩校正,  图像分析,  纹理特征 
[1] Ganesh C Bora, Rohit Pathak, Mojtaba Ahmadi, Purbasha Mistry. Image processing analysis to track colour changes on apple and correlate to moisture content in drying stages[J]. Food Qual Safet, 2018, 2(2): 105-110.