PREDICTING PEANUT MATURITY WITH MAGNETIC RESONANCE

Abstract

Knowledge of peanut (Arachis hypogaea) maturity is crucial in harvest timing for minimizing aflatoxin andmaximizing harvest yield. Low resolution pulse nuclear magnetic resonance (NMR) was explored as an alternative tocurrent maturity evaluation methods which are based on pod color as determined using the hull-scrape approach. For the1992 through 1995 seasons, peanuts (cv. Florunner) were sampled weekly over a 3 to 5 week period. Nearly 200 kernelsper week were analyzed by hull-scrape, gravimetric and NMR methods. The NMR data consisted of the Free InductionDecay peak as observed at 20 s (FIDPK herein), FIDPK + 20 s (FID40) and spin-echo at 2000 s (ECHO). TheFIDPK and the FID40 each strongly increased nonlinearly with maturity class as did ECHO, but to a lesser extent. Datafrom 1992-94 were processed to select randomly an equal number of peanuts in each of six maturity levels. This data setwas then divided into a discriminant classifier model building set (2/3) and a validation set (1/3). Chi square values basedon predicted versus observed maturity distributions exceeded the P = 0.05 value of 12.8; however, the days to harvest fromthe classifier validation data set were nearly identical to that estimated by the hull-scrape method. The maturityprediction model based solely on the above mentioned NMR parameters predicted identical days to harvest as obtainedfrom corresponding hull scrape data for a validation sample from the 1995 season.