Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice

Abstract

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 a 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 were generated using association of analytical chemists and standard methods. Different spectral pre-processing (up to fourth-order derivatization), scatter corrections (SNV-DT, MSC), and regression methods (partial least square, modified partial least square, and principle component regression) were employed for each trait. Best-fit models for total protein, starch, amylose, dietary fiber, and oil content were selected based on high RSQ, RPD with low SEP(C) in external validation. All the prediction models had ratio of prediction to deviation (RPD) > 2 amongst which the best models were obtained for dietary fiber and protein with R2 = 0.945 and 0.917, SEP(C) = 0.069 and 0.329, and RPD = 3.62 and 3.46. A paired sample t-test at a 95% confidence interval was performed to ensure that the difference in predicted and laboratory values was non-significant.

Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm

“…For development of calibration and validation set, the accessions were arranged in ascending order and every second value was taken out to make the calibration set. Therefore, calibration and validation sets were obtained in the ratio of 2:1, which ensured uniform variability in both the sets (32). Thus, the accessions were divided into two sets for modeling, i.e., 81 accessions in the training set and 40 accessions in the validation set for all the traits.…”

Prediction of Mineral Composition in Wheat Flours Fortified with Lentil Flour Using NIR Technology

“…These differences could be related to the scattering behavior of samples with different particle sizes. Scattering is the main source of spectral variability when analyzing milled products such as flour although the use of pre-treatment for the original spectra allows for the elimination of particle size effects [ 47 , 64 ]. Considering the above, it is necessary to evaluate different spectral pre-treatments [ 65 ].…”

Comparative Assessment of WINISI and Unscrambler for NIRS Modelling of Phytate and Antioxidants in Brown Rice

“…For calibrating WIN ISI, the laboratory reference data of the samples was arranged in ascending order and every third sample was selected to form the calibration (training) set (John et al, 2022) [12]. This way the calibration set and the validation sets constituted of equally variable data sets for all three traits.…”

“…As a result these methods become intricate and would increase the possibility of errors in wet chemistry evaluations. NIRS prediction modeling has been proved to be rapid, fast, non-destructive, accurate, and used in the prediction of large germplasm for nutritional and/or anti-nutritional attributes (Bartwal et 2023;John et al, 2022;Tomar et al, 2021) [11][12][13]. NIRS technique covers the absorbance, re ection or transmittance of infrared rays in 750-2500 nm due to the vibrations of molecular hydrogen bonds.…”