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comparison of the laser backscattering and digital imaging techniques on detection of αsolanine in potatoes
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نویسنده
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babazadeh s ,ahmadi moghaddam p ,sabatyan a ,sharifian f
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منبع
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ماشين هاي كشاورزي - 2020 - دوره : 10 - شماره : 1 - صفحه:49 -58
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چکیده
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The overall objective of this research is to check the abilities of two nondestructive techniques, the digital imaging (di) and laser light backscattering imaging (llbi), on detection of αsolanine toxicant in potatoes. potato samples were classified in healthy and toxic categories based on the amount of αsolanine. for quantifying αsolanine in potato tubers, highperformance liquid chromatography (hplc) has been used. the results of classification showed that single layer perceptron neural networks can classify potatoes with the accuracies of 94.28% and 98.66% by di and llbi systems (donald cultivar), respectively. it can be said that llbi systems might take precedent over di systems due to their high accuracy, rapidity, and industrial capability.
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کلیدواژه
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backscattering imaging ,digital imaging ,glycoalkaloids ,liquid chromatography ,quality inspection
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آدرس
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urmia university, faculty of agriculture, department of mechanical engineering of biosystems, iran, urmia university, faculty of agriculture, department of mechanical engineering of biosystems, iran, urmia university, faculty of science, department of physics, iran, urmia university, faculty of agriculture, department of mechanical engineering of biosystems, iran
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Comparison of the Laser Backscattering and Digital Imaging Techniques on Detection of αSolanine in Potatoes
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Authors
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Babazadeh S ,Ahmadi Moghaddam P ,Sabatyan A ,Sharifian F
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Abstract
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The overall objective of this research is to check the abilities of two nondestructive techniques, the digital imaging (DI) and laser light backscattering imaging (LLBI), on detection of αsolanine toxicant in potatoes. Potato samples were classified in healthy and toxic categories based on the amount of αsolanine. For quantifying αsolanine in potato tubers, highperformance liquid chromatography (HPLC) has been used. The results of classification showed that single layer perceptron neural networks can classify potatoes with the accuracies of 94.28% and 98.66% by DI and LLBI systems (Donald cultivar), respectively. It can be said that LLBI systems might take precedent over DI systems due to their high accuracy, rapidity, and industrial capability.
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Keywords
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Backscattering imaging ,Digital imaging ,Glycoalkaloids ,Liquid chromatography ,Quality inspection
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