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ARS Home » Southeast Area » Dawson, Georgia » National Peanut Research Laboratory » Research » Publications at this Location » Publication #256984

Research Project: Systems to Assess, Monitor, and Preserve Peanut Quality and Safety

Location: National Peanut Research Laboratory

Title: Nondestructive analysis of in-shell peanuts for moisture content using a custom built NIR Spectrometer

item Kandala, Chari
item Sundaram, Jaya - University Of Georgia
item Govindarajan, K.n. - University Of Nebraska
item Subbiah, Jeyam - University Of Nebraska

Submitted to: Journal of Food Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/1/2011
Publication Date: 12/1/2011
Citation: Kandala, C., Sundaram, J., Govindarajan, K., Subbiah, J. 2011. Nondestructive analysis of in-shell peanuts for moisture content using a custom built NIR Spectrometer. Journal of Food Engineering. 2:1-7.

Interpretive Summary: Moisture content in peanuts plays important role in the farmer’s stock peanut grading. A device that can measure moisture content rapidly, and without destroying the test samples, would be useful. Presently, capacitance type moisture measuring devices are in commercial use. However, they need the peanut samples to be shelled and cleaned before measurements. Some of the instruments are for use on single kernels and not useful for measurements on bulk samples. Techniques using near infrared (NIR) spectroscopy for food quality measurements are becoming more popular in food processing and quality inspection of agricultural commodities. NIR spectroscopy has several advantages over conventional physical and chemical analytical methods of food quality analysis. It is a rapid and non destructive method, and provides more information about the components present in the food and food product samples. By NIR methods more than one parameter can be measured simultaneously. In this work, a custom made NIR spectroscope was used to measure the moisture content of two different varieties of peanuts, Valencia and Virginia. The moistures of the peanuts tested was between 6% and 26% (wet basis). Before collecting the NIR spectrum of the conditioned samples, moisture contents were determined using standard air-oven method. Reflected energy spectrum from the sample was collected and recorded for each sample. Partial Least Square (PLS) analysis was carried for each of the varieties to build a regression model. Several pretreatments were conducted on the collected data of the calibration and validation groups. From PLS analysis carried on calibration groups prediction models were developed. These models were used with the validation groups to predict the moisture content of the peanuts. Predicted moisture content and the measured moisture content using standard air-oven method were compared to find the best model, based on the values of R2 and standard error of prediction (SEP).

Technical Abstract: A Custom made NIR spectroscope was used to determine the moisture content of in-shell peanuts of two different market types namely Virginia and Valencia. Peanuts were conditioned to different moisture levels between 6 and 26 % (wet basis). Samples from the different moisture levels were separated into two groups such as calibration and validation. NIR absorption spectral data from 1000 nm to 2500 nm were collected from the calibration and validation groups. Measurements were obtained on 30 replicates within each moisture level. Reference moisture data were developed using standard air-oven method. Partial Least Square (PLS) analysis was performed on the calibration set spectral data and models were developed. The Standard Error of Calibration (SEC) and R2 of the calibration models were computed to select the best calibration model. Both Valencia and Virginia types gave R2 of 0.99. The models were used to predict the moisture content of peanuts in the validation set. Predicted moisture contents of the validation samples were compared with their standard air-oven moisture values. Goodness of fit was determined based on the lowest Standard Error of Prediction (SEP) and highest R2 value obtained for the prediction models. The model, with reflectance plus normalization spectral data with an SEP of 0.74 for Valencia and 1.57 for Virginia type in-shell peanuts was selected as the best model. The corresponding R2 values were 0.98 for both peanut types.