摘要
Near infrared reflectance spectra (NIRS) was collected from Arachis hypogaea seed samples and used in predictive models to rapidly identify varieties with high oleic acid. The method was developed for shelled peanut seeds with intact testa. Spectra was evaluated initially by principal component analysis (PCA) followed by partial least squares (PLS). PCA performed with full spectra and reduced spectra with one principal component accounted for 97% to 99% variability, respectively. The PLS model generated from first derivative spectra provided a standard error of prediction (SEP) of 7.7204808. This technique provides a non-destructive method to rapidly identify high oleic peanut seeds to support the selection and cultivation of high oleic acid peanut varieties. The method can also be useful at peanut processing facilities for screening and quality assessments.
Near infrared reflectance spectra (NIRS) was collected from Arachis hypogaea seed samples and used in predictive models to rapidly identify varieties with high oleic acid. The method was developed for shelled peanut seeds with intact testa. Spectra was evaluated initially by principal component analysis (PCA) followed by partial least squares (PLS). PCA performed with full spectra and reduced spectra with one principal component accounted for 97% to 99% variability, respectively. The PLS model generated from first derivative spectra provided a standard error of prediction (SEP) of 7.7204808. This technique provides a non-destructive method to rapidly identify high oleic peanut seeds to support the selection and cultivation of high oleic acid peanut varieties. The method can also be useful at peanut processing facilities for screening and quality assessments.
