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L. Velasco and H. C. Becker, “Estimating the Fatty Acid Composition of the Oil in Intact Seed Rapeseed (Brassica napus L.) by Near Infrared Reflectance Spectroscopy,” Euphytica, Vol. 101, No. 2, 1998, pp. 221-230. doi:10.1023/A:1018358707847

has been cited by the following article:

  • TITLE: Sensing of Moisture Content of In-Shell Peanuts by NIR Reflectance Spectroscopy

    AUTHORS: Jaya Sundaram, Chari V. Kandala, Konda Naganathan Govindarajan, Jeyam Subbiah

    KEYWORDS: In-Shell Peanuts; NIR Spectroscopy; Pretreatments; Partial Least Square; Standard Error of Prediction; Relative Percent Deviation

    JOURNAL NAME: Journal of Sensor Technology, Vol.2 No.1, March 19, 2012

    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 obtained 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 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. 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.