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ARS Home » Southeast Area » Dawson, Georgia » National Peanut Research Laboratory » Research » Publications at this Location » Publication #285823

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

Location: National Peanut Research Laboratory

Title: Sensing of moisture content in in-shell peanuts by NIR (Near Infra Red) reflectance spectroscopy

item Sundaram, Jaya
item Kandala, Chari
item Govindarajan, Konda - University Of Nebraska
item Subbiah, Jeyam - University Of Nebraska

Submitted to: Journal of Sensor Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/20/2012
Publication Date: 12/1/2012
Citation: Sundaram, J., Kandala, C., Govindarajan, K.N., Subbiah, J. 2012. Sensing of moisture content in in-shell peanuts by NIR (Near Infra Red) reflectance spectroscopy. Journal of Sensor Technology. 2(1):107.

Interpretive Summary: In the work presented here an attempt was made, for the first time, to predict the MC (moisture content) of in-shell peanuts using NIR (Near Infra Red) reflectance method. The primary objectives of this re- search are: 1) To develop calibration models to predict the moisture content of in-shell peanuts using a custom made NIR reflectance spectroscope. 2) To validate the developed calibration models by using them on unknown validation set of samples. 3) To check the suitability of the method, for MC (moisture content) prediction on in-shell peanuts of two different market types. NIR reflectance spectroscopy could be a useful tool for the analysis of moisture concentration of in-shell peanuts that requires minimal sample preparation. The calibrations were obtained using partial least square regression analysis. NIR measurements are procedurally simple and can considerably reduce the time required for measure- ments, compared with the standard air-oven methods or the conventional moisture meters that require the peanut samples to be shelled and cleaned. The use of NIR spectroscopy described in this paper would result in large savings in time and labor during drying, processing and storage of peanuts. By virtue of a low SEP (standard error of prediction) and a high R2 (square of the correlation coefficient R) value, the model obtained with reflectance data subjected to normalization was selected as the preferred calibration model, for MC prediction, for both Valencia and Virginia type in-shell peanuts.

Technical Abstract: It was found earlier that moisture content (MC) of intact kernels of grain and nuts could be determined by Near Infra Red (NIR) reflectance spectrometry. However, if the MC values can be determined while the nuts are in their shells, it would save lot of labor and money spent in shelling and cleaning the nuts. Grain and nuts absorb low levels of NIR, and when NIR radiation is incident on them, a substantial portion of the radiation is reflected back. Thus, studying the NIR reflectance spectra emanating from in-shell peanuts, an attempt is made for the first time to determine the MC of in-shell peanuts. In-shell peanuts of two different market types, Virginia and Valencia, were conditioned to different moisture levels between 6% and 26% (wet basis), and separated into calibration and validation groups. NIR absorption spectral data from 1000 nm to 2500 nm in 1 nm intervals were collected from both groups. Measurements were ob-tained on 30 replicates within each moisture level. Reference MC values for each moisture level in these groups were obtained using standard air-oven method. Partial Least Square (PLS) analysis was performed on the calibration data, and prediction models were developed. The Standard Error of Calibration (SEC), and R2 of the calibration models were computed to select the best calibration model. The selected models were used to predict the moisture content of peanuts in the validation sets. Predicted MC values of the validation samples were compared with their standard air-oven mois-ture 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 val-ues were 0.98 for both peanut types. This work establishes the possibility of sensing MC of intact in-shell peanuts by NIR reflectance method, and would be useful for the peanut and allied industries.