University of Tehran Press Iranian Journal of Biosystem Engineering 2008-4803 55 1 2024 03 20 Detection of Adulteration in cinnamon powder using hyperspectral imaging Detection of Adulteration in cinnamon powder using hyperspectral imaging 19 32 98410 10.22059/ijbse.2024.376855.665550 FA Mohamadhossein Nargesi Biosystem Mechanical Engineering Department, Faculty of Agriculture, Ilam University, Ilam, Iran. 0009-0002-9498-7741 Jafar Amiri Parian Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran 0000-0002-5617-2835 Hossein Bagherpour Department of Biosystem Engineering, Faculty of Agriculture, Bo-Ali Sina University, Hamadan, Iran. 0000-0002-6877-6281 Kamran Kheiralipour Department of Biosystem Engineering, Faculty of Agriculture, Ilam University, Ilam, Iran. 0000-0003-3467-2078 Journal Article 2024 05 25 Cinnamon is one of the most important spices that has medicinal properties. Detecting adulteration in cinnamon powder using laboratory methods is expensive, time-consuming, and requires expert. Hyperspectral imaging is specifically used in the assessment of food safety and quality. The purpose of the present research is to detect adulteration in cinnamon powder using hyperspectral imaging. In the present study, 15 samples of cinnamon were prepared with 0, 5, 15, 30 and 50% adulteration levels. The adulterants were chickpea flour, wheat flour, and sea foam that were used separately. The hyperspectral imaging system received the light emitted from the samples in the visible and near-infrared ranges from 400 to 950 nm wavelength and recorded their hyperspectral images in the computer. After selecting the effective wavelengths and extracting the features from the images, the efficient features were selected and then classified using the support vector machine method. The correct classification rates of the classifier with one-against-one strategy in classification of the efficient features selected from the hyperspectral images related to the light emitted from the visible and infrared ranges to detect adulteration of wheat flour, chickpea flour, and sea foam powder in cinnamon were 95.55, 85.56, and 96.66%, respectively. Its correct classification rates with one-against-all strategy were equal to 78.88, 77.77, and 94.44%, respectively. Cinnamon is one of the most important spices that has medicinal properties. Detecting adulteration in cinnamon powder using laboratory methods is expensive, time-consuming, and requires expert. Hyperspectral imaging is specifically used in the assessment of food safety and quality. The purpose of the present research is to detect adulteration in cinnamon powder using hyperspectral imaging. In the present study, 15 samples of cinnamon were prepared with 0, 5, 15, 30 and 50% adulteration levels. The adulterants were chickpea flour, wheat flour, and sea foam that were used separately. The hyperspectral imaging system received the light emitted from the samples in the visible and near-infrared ranges from 400 to 950 nm wavelength and recorded their hyperspectral images in the computer. After selecting the effective wavelengths and extracting the features from the images, the efficient features were selected and then classified using the support vector machine method. The correct classification rates of the classifier with one-against-one strategy in classification of the efficient features selected from the hyperspectral images related to the light emitted from the visible and infrared ranges to detect adulteration of wheat flour, chickpea flour, and sea foam powder in cinnamon were 95.55, 85.56, and 96.66%, respectively. Its correct classification rates with one-against-all strategy were equal to 78.88, 77.77, and 94.44%, respectively. https://ijbse.ut.ac.ir/article_98410_60a906a727427b86753bf217da5b5af9.pdf