The purpose of this study was to highlight the use of multispectral imaging in seed quality testing of castor seeds. Visually, 120 seeds were divided into three classes: yellow, grey and black seeds. Thereafter, images at 19 different wavelengths ranging from 375–970 nm were captured of all the seeds. Mean intensity for each single seed was extracted from the images, and a significant difference between the three colour classes was observed, with the best separation in the near-infrared wavelengths. A specified feature (RegionMSI mean) based on normalized canonical discriminant analysis, were employed and viable seeds were distinguished from dead seeds with 92% accuracy. The same model was tested on a validation set of seeds. These seeds were divided into two groups depending on germination ability, 241 were predicted as viable and expected to germinate and 59 were predicted as dead or non-germinated seeds. This validation of the model resulted in 96% correct classification of the seeds. The results illustrate how multispectral imaging technology can be employed for prediction of viable castor seeds, based on seed coat colour.

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Dog rose (Rosa canina L.) is a wild native species in Iran, with a significant genetic diversity. This plant serves as a rich source of vitamin C, anthocyanins, phenolic contents and carotenoids. Rose hips have been used in several food products, as well as perfumery and cosmetics industries. In this research, we investigate biochemical characteristics of five dog rose ecotypes (Kopehjamshid, Zarneh, Miyankish, Aghcheh and Sadeghiyeh), that were collected from the central part of Iran (Isfahan province). Amounts of vitamin C, total carotenoids, total phenolic contents, total anthocyanins, macro and micro minerals were measured. Seed oil are extracted by soxhlet method and analysed by gas chromatography. The macro and micro minerals levels in the fruit vary significantly among these regions. The results of this study demonstrate that dog rose have great diversity and can be used in breeding programmes in order to increase nutrient values as a food resource additive.

Abstract:The potential of single-seed near-infrared (NIR) spectroscopy was investigated to characterise castor seeds based on their seed viability and seed oil content. Distinct differences between viable and non-viable seeds were observed in the principal component analysis (PCA) analysis. Furthermore, the PCA compared heavy and medium seeds with light seeds, which were comparable to the clusters of viable and non-viable seeds, respectively. Prediction accuracies of 98.7% and 99.6% were obtained with the partial least squares discriminant analysis (PLS-DA) model with a classification error rate of 0.8% and 1.1% for the training set and test set, respectively. The NIR spectral regions having chemical information from the oil in castor seeds were found to be vital for determination of seed viability.

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