Submitted to: American Peanut Research and Education Society Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: March 25, 2009
Publication Date: July 17, 2009
Citation: Sundaram, J., Kandala, C., Butts, C.L., Windham, W.R., Lamb, M.C. 2009. Identification of inferior quality peanuts without shelling during peanut grading. American Peanut Research and Education Society Abstracts. Interpretive Summary: none required.
Technical Abstract: Peanuts produced in United States are considered as high quality peanuts. To continue this quality, grading of farmers stock peanuts should be improved further. When the peanuts are picked from the farmers they are unshelled peanuts. There are some peanuts that contain kernels with damages, immature, discolored kernels etc, which are simply called as damages. Traditional way of grading and counting these types of damages is slow, labor intensive and sometimes inaccurate too. A device which can identify the inferior quality peanut kernels rapidly without shelling them is very useful. Techniques using near infrared (NIR) spectroscopy for food quality measurements are becoming more popular in food processing and quality inspection of agricultural commodities. It is a very rapid and non destructive method. Foss NIR spectroscopy was used to identify the inferior quality kernels without shelling the peanuts. Reflectance spectra were collected for individual peanut pods in the wavelength ranges from 400 to 2500 nm. 300 peanuts without shelling were scanned. They were separated in to two groups such as calibration, which contained 200 pods and validation, which had 100 pods for scanning. After each scan the corresponding peanut was shelled and the quality was noted as ‘bad’ for inferior quality kernels and ‘good’ for high quality kernels. Partial Least Square (PLS) analysis was carried on calibration set and a model was developed to predict the quality using validation set. Using this method the quality of the peanut was predicted with a Standard Error of Prediction (SEP) of 0.401 and Bias of -0.109. Predicted quality character was compared with the actual quality and it was found that 83.3% of the peanuts were predicted well. This method is rapid, non labor intensive and it has the promising application in peanut grading.