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 µs (FIDPK herein), FIDPK + 20 µs (FID40) and spin-echo at 2000 µs (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 = 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.