A Comparison of Spectral Preprocessing Methods and Their Effects on Nutritional Traits in Cowpea Germplasm

Abstract

ABSTRACT Cowpea (Vigna unguiculata L. (Walp)) is a multipurpose legume, which has good nutritional properties. Nutritional parameters assessed conventionally can be labour intensive, costly and time taking for germplasm screening. Near‐infrared reflectance spectroscopy (NIRS) is a rapid and nondestructive method, which can facilitate high‐throughput germplasm screening. In our study, estimation of amylose and sugars has been done using NIRS. Two preprocessing methods, that is, SNV‐DT (standard normal variate with detrending) and MSC (multiplicative scatter correction), were performed for optimization of the original spectra. Subsequently, MPLS (modified partial least square) regression method was employed to construct the prediction models. In amylose, the best RSQexternal (coefficient of determination) (0.962) was found in SNV‐DT with mathematical treatment 3,8,8,2. The same result was shown in sugar where the best RSQexternal (0.914) was found in SNV‐DT with mathematical treatment 3,4,4,1. Overall, in the case of amylose and sugars, SNV‐DT was found to be a good preprocessing treatment than MSC. Paired t‐test values in all the treatments for both the preprocessing methods were > 0.05 indicating their reliability. High RSQexternal values for both the traits imply the applicability of the prediction models. Thus, these models can facilitate high‐throughput germplasm screening in different national and international crop improvement programmes focusing on quality traits.

Keywords

• legume
• MPLS regression
• multiplicative scatter correction
• nutrition
• standard normal variate with detrending