Visible and near-infrared hyperspectral imaging to describe properties of conventionally and organically grown carrots

• Published in: Journal of elementology Vol. 24; no. 2/2019; pp. 421 - 435
• Main Authors: Cesoniene, Laima, Masaitis, Gediminas, Mozgeris, Gintautas, Gadal, Sébastien, Sileikiene, Daiva, Karkleliene, Rasa
• Format: Journal Article
• Language: English
• Published: Polish Society for Magnesium Research and University of Warmia and Mazury in Olsztyn 01.01.2019
• Subjects: Chemical Sciences; Computer Science; Food and Nutrition; Humanities and Social Sciences; Image Processing; Life Sciences; Methods and statistics; Organic and conventional farming; Carrots; Chemical and electro-chemical properties; Lithuania; Hyperspectral imaging
• ISSN: 1644-2296
• DOI: 10.5601/jelem.2018.23.4.1724

This paper discusses the potential of visible and near-infrared hyperspectral imaging to describe properties of conventionally and organically grown carrots. 140 samples of four Lithuanian carrot cultivars were scanned using a VNIR400H hyperspectral camera, capable of covering the spectral range of 400-1000 nm with a sampling interval of 0.6 nm. Half of the samples were grown under organic farming conditions and the remainder under conventional conditions. Chemical and electro-chemical properties, i.e. nitrate content, acidity, reduction potential and electrical conductivity, were determined for the carrot root samples using conventional methods of chemical investigations. The ability to separate organically and conventionally grown samples on the basis of spectral data was examined by applying estimations of Jeffries-Matusita distances and linear discriminant analysis. Opportunities to predict the chemical and electro-chemical properties of samples applying the partial least squares regression and the spectral data as predictors were also investigated. The overall classification accuracy of samples of organically and conventionally grown carrot cultivars when applying linear discriminant analysis was in the range of 94.4-100% and the Jeffries-Matusita distances were in the range of 1.98-2.00. There was good prediction potential using the partial least squares regression for electrical conductivity (R 2 = 0.88) and reduction potential (R 2 = 0.81), better than moderate for nitrate content (R 2 = 0.77) and moderate for acidity (R 2 = 0.68) using hyperspectral reflectance data of carrot captured under laboratory conditions. Both the separation ability and prediction potential were higher if taking into account the cultivar.