Characterisation of Castor (Ricinus communis L.) Seed Quality Using Fourier Transform Near-Infrared Spectroscopy in Combination with Multivariate Data Analysis

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.