Skip to main content

Nondestructive moisture content determination of three different market type in-shell peanuts using near infrared reflectance spectroscopy

Journal of Food Measurement and Characterization volume 8, pages 132–141 (2014)Cite this article

Abstract

A near infrared reflectance (NIR) method is presented here by which the average moisture content (MC) of about 100 g of in-shell peanuts could be determined rapidly and nondestructively. MCs of three market type peanuts, Runners, Valencia and Virginia were determined by this method while the peanuts were in their shells (in-shell peanuts). The MC range of the peanuts tested was between 6 and 26 %. NIR reflectance measurements were made at 1 nm intervals in the wavelength range of 1,000–1,800 nm and the spectral data was modeled using partial least squares regression analysis. Eight different models were developed by utilizing different data preprocessing methods such as, Norris-Gap first derivative with a gap size of 3, peak normalization with 1,680 nm (which is the no absorbance wavelength for water), and transformation from reflectance to absorption. Applying model fitness measures, a suitable model was selected out of these. Predicted values of the samples tested were compared with the values determined by the standard air-oven method. The predicted values agreed well with the air-oven values with an R 2 value better than 0.93 for all three types of in-shell peanuts. This method being rapid, nondestructive, and non contact, may be suitable for measuring and monitoring MCs of different types of peanuts, while they are in their shells itself, in the peanut industry.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Notes

  1. Moisture content is expressed in % wet basis throughout this paper.

  2. Mention of company or trade names is for the purpose of description only and does not imply endorsement by the US Department of Agriculture.

  3. SEC = \( \left( {\frac{1}{n - p - 1}\sum\limits_{i = 1}^{n} {{\text{e}}_{i}^{2} } } \right)^{\frac{1}{2}} \) where n is the number of observations, p is the number of variables in the regression equation with which the calibration is performed, and ei is the difference between the observed and reference value for the ith observation.

  4. SEP = \( \left( {\frac{1}{n - 1}\sum\limits_{i = 1}^{n} {({\text{e}}_{i} - {\bar{\text{e}}})^{2} } } \right)^{\frac{1}{2}} \) where n is the number of observations, ei is the difference in the moisture content predicted and that determined by the reference method for the ith sample, and \( {\bar{\text{e}}} \) is the mean of ei for all of the samples.

References

  1. USDA, AMS Farmers Stock Peanuts Inspection Instructions. Updated 2000 (USDA, Washington, 2000)

    Google Scholar 

  2. C.L. Butts, Incremental cost of over-drying farmers’ stock peanuts. Appl. Eng. Agric. 11(5), 671–675 (1995)

    Article  Google Scholar 

  3. M. Iwamoto, S. Kawano, Advantages and disadvantages of NIR applications for the food industry, in Making Light Work: Advances in Near Infrared Spectroscopy, ed. by I. Murray, I.A. Cowe (Wiley, Weinheim, 1992), pp. 367–375

    Google Scholar 

  4. L.A. Mohan, C. Karunakaran, D.S. Jayas, N.D.G. White, Classification of bulk cereals using visible and NIR reflectance characteristics. Can. Biosyst. Eng. 47(7), 7–14 (2005)

    Google Scholar 

  5. J.B. Mishra, R.S. Mathur, D.M. Bhatt, Near-infrared transmittance spectroscopy: a potential tool for non-destructive determination of oil content in groundnuts. J. Sci. Food Agric. 80, 237–240 (2000)

    Article  Google Scholar 

  6. C.G. Blazquez, D.C. O’Donnell, D. O’Callaghan, V. Howard, Prediction of moisture, fat and inorganic salts in processed cheese by near infrared reflectance spectroscopy and multivariate data analysis. J. Near Infrared Spectrosc. 12(3), 149 (2004)

    Article  CAS  Google Scholar 

  7. G. Fox, A. Cruickshank, Near infrared reflectance as a rapid and inexpensive surrogate measurement of composition and oil content of peanuts (Arachis hypogaea L.). J. Near Infrared Spectrosc. 13(5), 287 (2005)

    Article  CAS  Google Scholar 

  8. J. Sundaram, C.V.K. Kandala, K.N. Govindarajan, J. Subbiah, Sensing of moisture content of in-shell peanuts by NIR reflectance spectroscopy. J. Sens. Technol. 2(1), 1–7 (2012). doi:10.4236/jst.2012.21001

    Article  CAS  Google Scholar 

  9. ASAE Standards, S410.1: Moisture Measurements: Peanuts (ASAE, St. Joseph, 2000)

    Google Scholar 

  10. K.N. Govindarajan, C.V.K. Kandala, J. Subbiah, NIR reflectance spectroscopy for nondestructive moisture content determination in peanut kernels. Trans. ASABE. 52(5), 1661–1665 (2009)

    Article  Google Scholar 

  11. H. Martens, T. Naes, Multivariate calibration by data compression, in Near-infrared Technology in the Agricultural and Food Industries, 2nd edn., ed. by P. Williams, K. Norris (American Association of Cereal Chemists, St. Paul, 2001), pp. 59–99

    Google Scholar 

  12. R.P. Marvin, M. Singh, Calibration of a near infrared transmission grain analyzer for extractable starch in maize. Biosyst. Eng. 89(1), 79–83 (2004)

    Article  Google Scholar 

  13. A. Inoue, K. Kojima, Y. Taniguchi, K. Suzuki, Near-infrared spectra of water and aqueous electrolyte solutions at high pressures. J. Solut. Chem. 13(11), 811–823 (1984)

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Peanut Research Laboratory, ARS, USDA, Dawson, GA, 39842, USA

    Chari V. Kandala & Jaya Sundaram

Authors
  1. Chari V. Kandala
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jaya Sundaram
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chari V. Kandala.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Kandala, C.V., Sundaram, J. Nondestructive moisture content determination of three different market type in-shell peanuts using near infrared reflectance spectroscopy. Food Measure 8, 132–141 (2014). https://doi.org/10.1007/s11694-014-9173-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11694-014-9173-8

Keywords

  • In-shell peanuts
  • Near infrared spectroscopy
  • Moisture content
  • Runners
  • Valencia
  • Virginia
  • Multiple linear regression