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H Husnizar
Gadjah Mada University

Master of Sistems Engineering, Faculty of Engineering

Wahyu Wilopo
Gadjah Mada University

Department of Geological Engineering, Faculty of Engineering

Ahmad Tawfiequrrahman Yuliansyah
Gadjah Mada University

Department of Chemical Engineering, Faculty of Engineering

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The prediction of heavy metals lead (Pb) and zinc (Zn) contents in soil using NIRs technology and PLSR regression method

H Husnizar, Wahyu Wilopo, Ahmad Tawfiequrrahman Yuliansyah
  J. Degrade. Min. Land Manage. , pp. 1153-1159  
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The case of soil contamination by heavy metals in environment shows an increasing amount along with the constructions and development period that has been done. The identification of heavy metal content in the field is extremely hard to be done without a laboratory activity. Therefore, it needs a quick and non-destructive approach or method to identify the metal content of the soil in the field. The application of Near Infrared Reflectance Spectroscopy (NIRs) technology is a method that non-destructively able to detect the heavy metal content in the soil by using Partial Least Square Regression (PLSR). Pretreatment spectrum which is done using the Multiplicative Scatter Correction (MSC) can improve the results of the prediction models of PLSR. The results of MSC pretreatment spectrum can repair and improve the accuracy of the predictions of Lead (Pb) and Zinc (Zn) in the soil. Eight samples were used for analysis of each of Pb and Zn content. The measured data were pre-treated by MSC. It was obtained that value of r = 0.98, R2 = 0.97 and RPD = 6.46 for the Pb content measurement. Meanwhile, the measurement for Zn obtained the value of r = 0.98, R2 = 0.97 and RPD = 6.28. Therefore, it can be inferred that the NIRs is one of technologies which is worth reckoned as the right and quick means to predict the content of heavy metals in soil in a non-destructively and environmentally friendly way.


heavy metals; NIRs; PLSR; solid; standard solution

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Alfia, M., Zulfahrizal, and Munawar, A.A. 2016. Non-destructive determination of cocoa bean fat content by NIRS (comparison between mean normalization and de-trending). Jurnal Ilmiah Mahasiswa Pertanian Unsyiah 1 (1): 1027-1036 (in Indonesian),

Liu, Y., Sun, X. and Ouyang, A. 2010. Nondestructive measurement of soluble solid content of navel orange fruit by visible–NIR spectrometric technique with PLSR and PCA-BPNN. LWT - Food Science and Technology 43 (4): 602–607.

Liu, Y.L., Li, W., Wu, G.F. and Xu, X.G. 2011. Feasibility of estimating heavy metal contam- inations in floodplain soils using laboratory-based hyperspctral data—A case study along Le’an River, China. Geo-spatial Information Science 14 (1): 10–16.

Luce, M.St., Ziadi, N., Gagnon, B. and Karam, A. 2017. Visible near infrared reflectance spectroscopy prediction of soil heavy metal concentrations in paper mill biosolid- and liming by-product-amended agricultural soils. Geoderma 288: 23–36

Magwaza, L.S., Messo Naidoo, S.I., Laurie, S.M., Laing, M.D. and Shimelis, H. 2016. Development of NIRS model for repid quantifcation of protein content in sweetpotato [Ipomoea batatas (L.) LAM.]. LWT – Food Science and Technology 72: 63-70

Moros. J, Llorca. I, Cervera. M.L, Pastor. A, Garrigues. S. and de la Guardia. M. 2008. Chemometric determination of arsenic and lead in untreated powdered red paprika by diffuse reflectance near-infrared spectroscopy. Analytica Chimica Acta 613: 196-206.

Naes, T., Isaksson, T., Fearn, T. and Davies, T. 2004. A User-Friendly Guide to Multivariate Calibration and Classification. Chichester (UK): NIR publications.

Nicolai, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I. and Lamertyn, J. 2007. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biology and Technology 46: 99-118.

Schwartz, G., Eshel, G. and Ben-Dor, E. 2011. Reflectance spectroscopy as a tool for mon- itoring contaminated soils, in: S. Pascucci (Ed.), Soil Contamination, InTech, Rijeka, 2011, pp. 67–90.

Shi, T., Yiyun, C., Yaolin, L. and 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.

Sinelli, N., Spinardi, A., Di Egidio, V., Mignani, I. and Casiraghi, E. 2008. Evaluation of quality and nutraceutical content of blueberries (Vaccinium corymbosum L.) by near and mid-infrared spectroscopy. Postharvest Biology and Technology 50: 31-36.

Wang, J., Lijuan, C., Wenxiu, C., Tiezhu, S., Yiyun, C. and Yin, G. 2014. Prediction of low heavy metal concentrations in agricultural soils using visible and near-infrared reflectance spectroscopy. Geoderma 216: 1-9.

Zhuang, P., McBride, M.B., Xia, H., Li, N. and Li, Z. 2009. Health risk from heavy metals via consumption of food crops in the vicinity of Dabaoshan mine, South China. Science of the Total Environment 407 (5): 1551–1561


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