Visible-near infrared spectral analysis for identification of physiological and genetic features in rice.

Visible-near infrared hyperspectral analysis is widely used for plant characterization and evaluation of agricultural products and food quality. On the other hand, it has remained un-certain whether this technique has a sufficient potential for evaluation of biological complexity during the growth of crop plants. In the present study, using a spectrometer and hyperspectral camera placed in a laboratory environment, we carried out continuous hyperspectral profiling of leaves derived from four rice cultivars grown under two field conditions. Combined analysis with transcriptome data revealed that the hyperspectral profile had potential to predict the degree of expression of developmentally regulated genes. In addition, principal component analysis of hyperspectral imaging data made it possible to detect growth-stage dependent dynamics and to distinguish differences between subspecies as well as field conditions by selecting an adequate pretreatment method. Furthermore, we obtained hyperspectral data for brown rice grains of recombinant inbred lines derived from a cultivar with high temperature tolerance during the ripening stage and with a good grain appearance. We then performed quantitative trait locus analysis using the extracted principal component scores and trait values related to grain appearance to explore the possibility of using spectral analysis for genetic studies.