Rapid And Simultaneous Prediction Of Soil Quality Attributes Using Near Infrared Technology

The main purpose of this present study is to apply the near infrared (NIR) technology as a rapid and robust method in predicting soil quality parameters in form of potassium (K), Magnesium (Mg) and calcium (Ca) simultaneously. Diffuse reflectance spectra data were acquired for a total of 40 bulk soil samples (60 g per each bulk) in near infrared (NIR) wavelength range from 1000 to 2500 nm. On the other hand, actual reference K, Mg and Ca were measured using standard laboratory procedures. Prediction models, used to predict those three quality parameters were established using principal component regression (PCR) and partial least square regression (PLSR) method. Moreover, prediction accuracy and robustness were evaluated based on correlation coefficient (r) and residual predictive deviation (RPD) index respectively. The result showed that K, Mg and Ca of soil samples can be predicted simultaneously using NIR technology with maximum r coefficient and RPD index were 0.97 and 5.14 for K, 0.98 and 8.34 for Mg, 0.98 and 8.90 for Ca respectively, which categorized as excellent model performance. Thus, it may conclude that NIR technology can be used and applied as rapid and simultaneous method to predict quality parameters of soil samples satisfactory.

REFERENCES

[1] Devianti, Sufardi, Zulfahrizal, and A. A. Munawar, �Rapid and Simultaneous Detection of Hazardous Heavy Metals Contamination in Agricultural Soil Using Infrared Reflectance Spectroscopy,� IOP Conf. Ser. Mater. Sci. Eng., vol. 506, p. 012008, 2019.
[2] M. Knadel et al., �Comparing predictive ability of laser-induced breakdown spectroscopy to visible near-infrared spectroscopy for soil property determination,� Biosyst. Eng., vol. 156, pp. 157�172, 2017.
[3] D. B. Madhavan et al., �Rapid prediction of particulate, humus and resistant fractions of soil organic carbon in reforested lands using infrared spectroscopy,� J. Environ. Manage., vol. 193, pp. 290�299, 2017.
[4] J. Liu, Y. Zhang, H. Wang, and Y. Du, �Study on the prediction of soil heavy metal elements content based on visible near-infrared spectroscopy,� Spectrochim. Acta - Part A Mol. Biomol. Spectrosc., vol. 199, pp. 43�49, 2018.
[5] P. Zhao, F. Yu, R. Wang, Y. Ma, and Y. Wu, �Sodium alginate/graphene oxide hydrogel beads as permeable reactive barrier material for the remediation of ciprofloxacin-contaminated groundwater,� Chemosphere, vol. 200, pp. 612�620, 2018.
[6] M. Zovko et al., �A geostatistical Vis-NIR spectroscopy index to assess the incipient soil salinization in the Neretva River valley, Croatia,� Geoderma, vol. 332, no. April, pp. 60�72, 2018.
[7] A. A. Munawar, D. V. H�rsten, D. M�rlein, E. Pawelzik, and J. K. Wegener, �Rapid and non-destructive prediction of mango sweetness and acidity using near infrared spectroscopy,� in Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI), 2013, vol. P-211.
[8] Y. Deng, Y. Wang, G. Zhong, and X. Yu, �Simultaneous quantitative analysis of protein, carbohydrate and fat in nutritionally complete formulas of medical foods by near-infrared spectroscopy,� Infrared Phys. Technol., vol. 93, no. July, pp. 124�129, 2018.
[9] N. Zhang et al., �Determination of total iron-reactive phenolics, anthocyanins and tannins in wine grapes of skins and seeds based on near-infrared hyperspectral imaging,� Food Chem., vol. 237, pp. 811�817, 2017.
[10] M. Soto-C�mara, A. J. Gait�n-Jurado, and J. Dom�nguez, �Application of near infrared spectroscopy technology for the detection of fungicide treatment on durum wheat samples,� Talanta, vol. 97, pp. 298�302, 2012.
[11] D. Cozzolino, �An overview of the use of infrared spectroscopy and chemometrics in authenticity and traceability of cereals,� FRIN, vol. 60, pp. 262�265, 2014.
[12] B. M. Nicola� et al., �Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review,� Postharvest Biol. Technol., vol. 46, no. 2, pp. 99�118, 2007.
[13] E. Arendse, O. A. Fawole, L. S. Magwaza, and U. L. Opara, �Non-destructive prediction of internal and external quality attributes of fruit with thick rind: A review,� J. Food Eng., vol. 217, pp. 11�23, 2018.
[14] C. Pasquini, �Near infrared spectroscopy: A mature analytical technique with new perspectives � A review,� Anal. Chim. Acta, vol. 1026, pp. 8�36, 2018.
[15] V. Cort�s et al., �Integration of simultaneous tactile sensing and visible and near-infrared reflectance spectroscopy in a robot gripper for mango quality assessment,� Biosyst. Eng., vol. 162, pp. 112�123, 2017.
[16] K. Song, H. Cha, S. H. Park, and Y. I. Lee, �Determination of trace cobalt in fruit samples by resonance ionization mass spectrometry,� Microchem. J., vol. 75, no. 2, pp. 87�96, 2003.
[17] N. Reis, A. S. Franca, and L. S. Oliveira, �Discrimination between roasted coffee, roasted corn and coffee husks by Diffuse Reflectance Infrared Fourier Transform Spectroscopy,� LWT - Food Sci. Technol., vol. 50, no. 2, pp. 715�722, 2013.
[18] C. Li, H. Guo, B. Zong, P. He, F. Fan, and S. Gong, �Rapid and non-destructive discrimination of special-grade flat green tea using Near-infrared spectroscopy,� Spectrochim. Acta - Part A Mol. Biomol. Spectrosc., vol. 206, pp. 254�262, 2019.
[19] N. Prieto, �. L�pez-Campos, J. L. Aalhus, M. E. R. Dugan, M. Ju�rez, and B. Uttaro, �Use of near infrared spectroscopy for estimating meat chemical composition, quality traits and fatty acid content from cattle fed sunflower or flaxseed,� Meat Sci., vol. 98, no. 2, pp. 279�288, 2014.
[20] H. T. Zhao, Y. Z. Feng, W. Chen, and G. F. Jia, �Application of invasive weed optimization and least square support vector machine for prediction of beef adulteration with spoiled beef based on visible near-infrared (Vis-NIR) hyperspectral imaging,� Meat Sci., vol. 151, no. January, pp. 75�81, 2019.
[21] N. Meyer, H. Meyer, G. Welp, and W. Amelung, �Soil respiration and its temperature sensitivity (Q10): Rapid acquisition using mid-infrared spectroscopy,� Geoderma, vol. 323, no. February, pp. 31�40, 2018.
[22] J. Liu, X. Luan, and F. Liu, �Adaptive JIT-Lasso modeling for online application of near infrared spectroscopy,� Chemom. Intell. Lab. Syst., vol. 183, no. October, pp. 90�95, 2018.
[23] R. Matamala et al., �Predicting the decomposability of arctic tundra soil organic matter with mid infrared spectroscopy,� Soil Biol. Biochem., vol. 129, no. July 2018, pp. 1�12, 2018.
[24] Samadi, S. Wajizah, and A. A. Munawar, �Fast and simultaneous prediction of animal feed nutritive values using near infrared reflectance spectroscopy,� IOP Conf. Ser. Earth Environ. Sci., vol. 122, no. 1, 2018.
[25] A. A. Munawar, D. von H�rsten, J. K. Wegener, E. Pawelzik, and D. M�rlein, �Rapid and non-destructive prediction of mango quality attributes using Fourier transform near infrared spectroscopy and chemometrics,� Eng. Agric. Environ. Food, vol. 9, no. 3, 2016.
[26] Yusmanizar et al., �Fast and Non-Destructive Prediction of Moisture Content and Chologenic Acid of Intact Coffee Beans Using Near Infrared Reflectance Spectroscopy,� IOP Conf. Ser. Mater. Sci. Eng., vol. 506, p. 012033, 2019.