Abstract
This paper presents a method based on digital imaging and multivariate analysis for the classification of castor seeds with respect to the cultivar type. For this purpose, two seed groups most commonly employed on Brazilian plantations were evaluated: BRS Nordestina and BRS Paraguacu cultivars (group I) and BRS Energia cultivar and CNPA 2009-7 genotype (group II). Images of these two different seed groups were recorded from a webcam and the frequency distribution of color indexes in the red-green-blue (RGB), hue (H), saturation (S), intensity (I), and grayscale channels were obtained. Pattern recognition methods based on partial least squares-discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were applied separately to each seed group. The best results were obtained by using the PLS-DA model, which correctly classified 97.5% and 98.8% of the prediction samples for groups I and II, respectively. The proposed method is simple, fast, non-destructive and non-expensive.
Highlights
The high price of castor oil, insufficient raw materials in the international market and growing demand for biodegradable products obtained from ricinoleic acid, have encouraged public and private companies to develop castor seed cultivars with high oil content
Samples A total of 400 castor seed samples from different cultivars were collected from the Embrapa Algodão localized in Campina Grande, Paraíba, Brazil
The training set was used to calibrate the partial least squares-discriminant analysis (PLS-DA) and linear discriminant analysis (LDA)/successive projections algorithm (SPA) models, whereas test samples were only used in the final stage to evaluate the true predictive ability of the calibrated model
Summary
The castor plant (Ricinnus communis L.) belongs to the Euphorbiáceas family, which includes a large number of plants from tropical regions.[1,2] The oil is the main product extracted from castor seed, which has a high ricinoleic acid content with levels in the range of 78.3-90.0% (m/m).[3,4] This makes the oil soluble in alcohols with low molecular weight.[5,6,7]. A methodology is proposed based on digital imaging data and supervised pattern recognition techniques for the classification of individual castor seeds with respect to four cultivar types: BRS Nordestina, BRS Paraguaçu, BRS Energia and CNPA 2009-7. For this purpose, the frequency distribution of color indexes in the red (R), green (G), blue (B), hue (H), saturation (S), intensity (I), and grayscale channels were obtained from digital images.
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