Hyperspectral Imaging for Color Adulteration Detection in Red Chili

Applied Sciences

Zainab Saleem¹  Hamail Ayaz¹  Muhammad Ahmad¹  MuhammadHussain Khan¹  Ahmed Sohaib¹  Manuel Mazzara² 

[1] Advance Image Processing Research Lab (AIPRL), Department of Computer Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan;Institute of Software Development and Engineering, Innopolis University, 420500 Innopolis, Russia;

关键词: hyperspectral imaging;    red chili;    edible oil;    sudan dye;    PCA;    SVM;   

DOI  :  10.3390/app10175955

来源: DOAJ

【 摘 要 】

The quality of red chili is characterized based on its color and pungency. Several factors like humidity, temperature, light, and storage conditions affect the characteristic qualities of red chili, thus preservation required several measures. Instead of ensuring these measures, traders are using oil and Sudan dye in red chili to increase the value of an inferior product. Thus, this work presents the feasibility of utilizing a hyperspectral camera for the detection of oil and Sudan dye in red chili. This study describes the important wavelengths (500–700 nm) where different adulteration affects the response of the reflected spectrum. These wavelengths are then utilized for training an Support Vector Machine (SVM) algorithm to detect pure, oil-adulterated, and Sudan dye-adulterated red chili. The classification performance achieves $97\%$ with the reduced dimensionality and $100\%$ with complete validation data. The trained algorithm is further tested on separate data with different adulteration levels in comparison to the training data. Results show that the algorithm successfully classifies pure, oil-adulterated, and Sudan-adulterated red chili with an accuracy of 100%.

【 授权许可】

Unknown