Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice - John, Racheal; Bhardwaj, Rakesh; Jeyaseelan, Christine; Bollinedi, Haritha; Singh, Neha; Harish, G. D.; Singh, Rakesh; Nath, Dhrub Jyoti; Arya, Mamta; Sharma, Deepak; Singh, Satyapal; John K, Joseph; et al. (Frontiers in Nutrition, 2022)
Rice is a major staple food across the world in which wide variations in nutrient composition are reported. Rice improvement programs need germplasm accessions with extreme values for any nutritional trait. Near Infrared Reflectance Spectroscopy (NIRS) uses electromagnetic radiations in the NIR region to rapidly measure the biochemical composition of food and agricultural products. NIRS prediction models provide rapid assessment tool but their applicability is limited by the sample diversity used for developing them. NIRS spectral variability was used to select a diverse sample set of 180 accessions and reference data was generated using AOAC and standard methods. Different spectral pre-processing (up to fourth order derivatization), scatter corrections (SNV-DT, MSC) and regression methods (PLS, MPLS and PCR) were employed for each trait. Best fit models for total protein, starch, amylose, dietary fibre and oil content were selected on the basis of high RSQ, RPD with low SEP(C) in external validation. All the prediction models had RPD >2 amongst which the best models were obtained for dietary fibre and protein with R2=0.945 and 0.917, SEP(C) = 0.069 and 0.329, RPD= 3.62 and 3.46, respectively. A paired sample t-test at 95% confidence interval was performed to ensure that the difference in predicted and laboratory values were non-significant.