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ARS Home » Southeast Area » Dawson, Georgia » National Peanut Research Laboratory » Research » Publications at this Location » Publication #240117
Title: Application of NIR Reflectance Spectroscopy for the Determination of Moisture Content of In-shell Peanuts: A Non Destructive Analysis

Author
item Sundaram, Jaya
item Kandala, Chari
item Butts, Christopher - Chris
item Windham, William

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/15/2010
Publication Date: 3/1/2010
Citation: Sundaram, J., Kandala, C., Butts, C.L., Windham, W.R. 2010. Application of NIR Reflectance Spectroscopy on Determination of Moisture Content of Peanuts: A Non Destructive Analysis Method. Transactions of the ASABE. 53(1): 183-189.

Interpretive Summary: Moisture content of the peanut plays important role in the farmer’s stock peanut grading. A device which can measure moisture content rapidly with non destructive samplings is very much useful. Capacitance type moisture measuring devices are in use now, however they need the peanut to be shelled before the measurements. Some of them use single kernel method which does not give possible precision in moisture reading for the large farmer’s stock and some are destructive methods. 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 very rapid and non destructive method and provides more information about the components and its structure present in the food and food products. It also measures more than one parameter simultaneously. Foss NIR spectroscopy was used to measure the moisture content of the peanuts. Two different varieties of peanuts (Valencia and Florida) were selected. Initially peanuts were conditioned to different moisture content levels varying from 6 to 26 percent (wet basis). Before collecting the NIR spectrum of the conditioned samples, moisture contents were determined using standard air oven method, which was conducted at 130oC with three replicated. Then these samples were separated into two different groups such as calibration group and validation group. To collect the NIR spectrum peanut samples from each moisture levels and groups were placed individually in a rectangular sample cup having transparent glass bottom. Light was allowed to pass on the sample through bottom of the sample cup and the spectrum of light reflected from the samples was collected. This was repeated 30 times for each moisture content levels. Then 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 from calibration and validation groups. After that PLS analysis was carried on calibration groups to develop model for the individual pretreatments. These models were used on 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 pretreatment and the model based on the R2 and standard error of prediction (SEP).

Technical Abstract: NIR spectroscopy was used to measure the moisture content of virginia and valencia type in-shell peanuts. Peanuts were conditioned to various moisture levels between 7 and 26 % (wet basis) and the moisture content was verified using a standard oven method. Sample from the various moisture levels were separated into two groups, as calibration and validation. NIR absorption spectral data from 400 nm to 2500 nm were collected using peanuts within the calibration and validation sample sets. Measurements were obtained on 30 replicates within each moisture level. Partial Least Square (PLS) analysis was performed on the calibration set and models were developed using the raw spectral data and its derivative function data. The Standard Error of Calibration (SEC) and R2 of the calibration models were calculated to select the best calibration model for each peanut market type. Both valencia and virginia types gave R2 of 0.99 for the derivative spectral data treatment as well as for the raw data. The selected models were used to predict the moisture content of peanuts in the validation sample set. Predicted and reference moisture contents were compared. Relative Percent Deviation (RPD) and Standard Error of Prediction (SEP) were calculated to validate goodness of fit of the prediction model. Raw reflectance spectra model gave the RPD of 5.55 with corresponding SEP of 0.97 for valencia type peanuts, which is a good number for quality control and analysis. For virginia type peanuts derivative reflectance spectra model gave the highest RPD value of 5.75 and the lowest SEP of 0.771. Thus, these two models were selected for the respective peanut types as the best models for prediction of moisture content.