Determination of Fatty Acid Composition in Peanut Seed by Near Infrared Reflectance Spectroscopy

Abstract

This study was conducted to develop a fast and efficient screening method to determine the quantity of fatty acid in peanut oil for high oleate breeding program. A total of 329 peanut samples were used in this study, 227 of which were considered in the calibration equation development and 102 were utilized for validation, using near infrared reflectance spectroscopy (NIRS). The NIRS equations for all the seven fatty acids had low standard error of calibration (SEC) values, while high R2 values of 0.983 and 0.991 were obtained for oleic and linoleic acids, respectively in the calibration equation. Furthermore, the predicted means of the two main fatty acids in the calibration equation were very similar to the means based on gas chromatography (GC) analysis, ranging from 36.7 to 77.1% for oleic acid and 7.1 to 42.7% for linoleic acid. Based on the standard error of prediction (SEP), bias values, and R 2 statistics, the NIRS fatty acid equations were accurately predicted the concentrations of oleic and linoleic acids of the validation sample set. These results suggest that NIRS equations of oleic and linoleic acid can be used as a rapid mass screening method for fatty acid content analysis in peanut breeding program.

Isolation of High Oleate Recombinants in Peanut by Near Infra‐Red Spectroscopy and Confirmation With Allele Specific Polymerase Chain Reaction Marker

“…NIRS has become a well‐accepted multitrait technology for quantitative analysis of different analytes in pharmaceutical and agro industries (Murphy, ). This technique has been successfully applied for breeding for altered fatty acid profile in soybean (Hurburgh, ; Sato & Kawano, ), sunflower (Vich, Velascob, & Martinez, ), almond (Cuesta, Fernández‐Martínez, Company, & Velasco, ), rapeseed‐mustard (Kim, Park, Choung, & Jang, ; Kumar, Chauhan, & Kumar, ), and peanut (Lee et al, ). Sundaram et al () suggested that reflectance derivative model is better than absorbance‐based model for the prediction of fatty acid profile in peanut seeds.…”

“…Different quantitative predictive models are available for calibration development in NIRS (Rousel, Preys, Chauchard, & Lallemand, ). Of which, PLSR and its modification were used for calibration development for measurement of fatty acid profile and oil content in peanut seeds (Kavera & Hanchinal, ; Lee et al, ). In the present study, calibration model was developed through PLSR method, which represented the most relevant variations in oil, oleic acid, linoleic acid, and palmitic acid and established significant correlations between predictive value and actual value from GC (Tables and ).…”