Rapid and non-destructive prediction of C-organic in agricultural soil using near infrared reflectance spectroscopy (NIRS)

Darusman Darusman, Zulfahrizal Zulfahrizal, Agus Arip Munawar


Soil organic carbon (C-organic) is one of main of soil quality which affects the assortment of organic materials and mixtures properties of soils. This C-organic also have a practical value and importance in agriculture. To determine C-organic, normally, conventional and laborious procedures were employed. Yet, this method is expensive, time consuming, involve chemical materials and may cause pollution. Thus, alternative fast and environmental friendly method is required to determine C-organic in soil. The near infrared reflectance spectroscopy (NIRS) technique can be considered to be applied, since this method is fast, nondestructive, simple preparation and pollution free. Therefore, the main objective of this present study is apply NIRS technique in predicting C-organics and classifying soils based on geographical characteristics. Soil samples from 4 different site locations were taken spectra data of these samples were acquired in wavenumbers range of 4000-10 000 cm -1 . C-organic prediction model was developed using NIR spectra data and partial least square regression (PLS), while classification model was established using principal component analysis (PCA). The results showed that Soil characteristics from 4 different locations can be classified with total explained variance of PCA was 99% (PC1 = 88% and PC2 = 11%). Moreover, NIRS technique was able to predict C-organic with maximum correlation coefficient (r) was 0.93 and residual predictive deviation (RPD) index was 3.22 which categorized as excellent prediction model performance. It may conclude that NIRS technique can be applied as a rapid and non-destructive method in predicting C-organic and classifying soil characteristics.

Teks Lengkap:



Knadel, M., Gilsum, R., Hermansen, C., Peng, Y., Moldrup, P., de Jonge, L. W., & Greve, M. H.

(2017). Comparing predictive ability of laser-induced breakdown spectroscopy to visible nearinfrared








, 157-172.

Mouazen, A., Kuang, B., De Baerdemaeker, J., Ramon, H. (2010). Comparison among principal

component, partial least squares and back propagation neural network analyses for accuracy of

measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma,158,


Moros, J., Vallejuelo, S.F.O., Gredilla, A., Diego, A., Madariaga, J.M., Garrigues, S., & Guardia,

M. (2009). Use of reflectance infrared spectroscopy for monitoring the metal content of the estuarine

sediments of the Nerbioi–Ibaizabal River (Metropolitan Bilbao, Bay of Biscay, Basque Country).

Environ. Sci. Technol. 43 (24), 9314–9320.

Ren, H.Y., Zhuang, D.F., Singh, A.N., Pan, J.J., Qiu, D.S., Shi, R.H. (2009). Estimation of As and

Cu contamination in agricultural soils around a mining area by reflectance spectroscopy: a case

study. Pedosphere. 19, 719–726.

Munawar, A. A., Hörsten, D. v., Mörlein, D., Pawelzik, E., & Wegener, J. K. (2016). Rapid and nondestructive

prediction of mango quality attributes using Fourier transform near infrared

spectroscopy and chemometrics, Engineering in Agriculture, Environment and Food, 9(1).

Munawar, A.A., D.v Hoersten., J.K Wegener, E.Pawelzik & D, Moerlein. (2013). Rapid and nondestructive



mango sweetness and acidity using near infrared spectroscopy, Lectures

note in Informatics GIL jahrestagung, Potsdam.

Shi, T., Yiyun, C., Yaolin, L., & Guofeng, L. (2014). Visible and near-infrared reflectance

spectroscopy: An alternative for monitoring soil contamination by heavy metals. Journal of

Hazardous Materials, 265, 166–176.

Salazar, M., J.H. Rodriguez, G.L. Nieto, M.L. Pignata, Effects of heavy metal concentrations (Cd,

Zn and Pb) in agricultural soils near different emission sources on quality, accumulation and food

safety in soybean, J. Hazard. Mater. (2012) 244–253.

Li, Q.S., Y.Y. Chen, H.B. Fu, Z.H. Cui, L. Shi, L.L. Wang, & Z.F. Liu, (2012). Health risk of heavy

metals in food crops grown on reclaimed tidal flat soil in the Pearl River Estuary, China, J. Hazard.

Mater. 22, 148–154.

Wang, J., Lijuan, C., Wenxiu, C., Tiezhu, S., Yiyun, C., & Yin, G. (2014). Prediction of low heavy

metal concentrations in agricultural soils using visible and near-infrared reflectance spectroscopy.

Geoderma, 216, 1-9.

Senesi, G., & Senesi. N. (2016). Laser-induced breakdown spectroscopy to measure quantitatively

soil carbon with emphasis on soil organic carbon. A review. Analytica Chimica Acta, 938, 7 – 17.


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