Predicting peanut maturity with magnetic resonance

Knowledge of peanut (Arachis hypogaea) maturity is crucial in harvest timing for minimizing aflatoxin and maximizing harvest yield. Low resolution pulse nuclear magnetic resonance (NMR) was explored as an alternative to current maturity evaluation methods which are based on pod color as determined using the hull-scrape approach. For the 1992 through 1995 seasons, peanuts (cv. Florunner) were sampled weekly over a 3 to 5 week period. Nearly 200 kernels per week were analyzed by hull-scrape, gravimetric and NMR methods. The NMR data consisted of the Free Induction Decay peak as observed at 20 mu s (FIDPK herein), FIDPK + 20 mu s (FID40) and spin-echo at 2000 mu s (ECHO). The FIDPK and the FID40 each strongly increased nonlinearly with maturity class as did ECHO; but to a lesser extent. Data from 1992-94 were processed to select randomly an equal number of peanuts in each of six maturity levels. This data set was then divided into a discriminant classifier model building set (2/3) and a validation set (1/3). Chi square values based on predicted versus observed maturity distributions exceeded the P less than or equal to 0.05 value of 12.8; however the days to harvest from the classifier validation data set were nearly identical To that estimated by the hull-scrape method. The maturity prediction model based solely on the above mentioned NMR parameters predicted identical days to harvest as obtained from corresponding hull scrape data for a validation sample from the 1995 season.