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Felipe Bachion de Santana

Soil Spectroscopy Technologist

Research Interests

My Researcher is focused on the development of a fast, robust, precise and accurate methodologies based on the use of MIR (DRIFT), NIR and XRF spectroscopy in tandem with Chemometrics and Machine Learning regression and classification algorithms (Random Forest, Cubistic, Support vector Machine, Artificial neural network, PLS, PCA and PLS-DA) to determine soil attributes used on Soil Science, i.e. Soil particle size (sand, clay and silt) %OM, pH, P, K, Al, CEC, among others.

My research areas are:

  • Chemometrics and Machine learning algorithms
  •  FTIR, NIR, XRF, Raman spectroscopy
  •  NIR, FTIR and Raman Hyperspectral imaging
  •  Handheld and portable spectrometers for field analysis
  •  Benchtop spectrometers
  •  Spectroscopy instrumentation


Current Projects

I am currently working under supervision of Karen Daly in the Terra Soil Project:

The aims of this project is produce new agricultural advice using geological data and soil samples, resulting in smarter agriculture, less environmental impacts and less wasted resources.

In this project I am using several spectral libraries combined with Chemometrics and machine learning algorithms to determine soil Particle Size, Soil Health and Soil Fertility Parameters.


  • 2021 Post-Doctoral,  Soil analysis using Spectroscopy and Chemometrics, Teagasc (Johnstown Castle)
  • 2020 Ph.D. Spectroanalytical & Chemometrics, University of Campinas (Brazil)
  • 2015 M.Sc. Spectroanalytical & Chemometrics, Federal University of Uberlandia (Brazil)  
  • 2013 B.A (Hons) Industrial Chemistry, Federal University of Uberlandia (Brazil)  

Felipe has published many peer reviewed scientific papers.  His full publication list can be found on:

Felipe's Google Scholar account

Felipe's Orcid account

Felipe's Research Gate account

Felipe's Scopus account

de Santana, S. K. Otani, A. M. de Souza, R. J. Poppi, “Comparison of PLS and SVM models for soil organic matter and particle size using vis-NIR spectral libraries”, Geoderma Regional, vol. 27, no. Dec 2021, p. 1-7, doi: 10.1016/j.geodrs.2021.e00436.

 de Santana et al., “Monitoring mineral-associated organic matter in tropical pastures using near infrared spectroscopy”. BRJAC, vol. 8, p. 78-90, doi: 10.30744/brjac.2179-3425.AR-10-2021

F. Gatti, F. B. de Santana, R. J. Poppi, and D. S. Ferreira, “Portable NIR spectrometer for quick identification of fat bloom in chocolates,” Food Chem., vol. 342, no. May 2020, p. 128267, 2021, doi: 10.1016/j.foodchem.2020.128267.

 de Santana et al., “Didactic experiment of Chemometrics for the classification of edible vegetable oils by Fourier Transform Infrared Spectroscopy and Partial Least Squares Discriminant Analysis: a tutorial, part V,” Quim. Nova, vol. 43, no. 3, pp. 371–381, 2020, doi: 10.21577/0100-4042.20170480.

Tasic et al., “Peripheral biomarkers allow differential diagnosis between schizophrenia and bipolar disorder,” J. Psychiatr. Res., vol. 119, pp. 67–75, Dec. 2019, doi: 10.1016/j.jpsychires.2019.09.009.

F. B. de Santana, W. Borges Neto, and R. J. Poppi, “Random forest as one-class classifier and infrared spectroscopy for food adulteration detection,” Food Chem., vol. 293, no. April, pp. 323–332, 2019, doi: 1016/j.foodchem.2019.04.073.

B. de Santana, S. K. Otani, A. M. de Souza, and R. J. Poppi, “Determination of soil organic matter using visible-near infrared spectroscopy and machine learning,” Spectrosc. Eur., vol. 31, no. 4, pp. 14–17, 2019, [Online]. Available: https://www.spectroscopyeurope.com/system/files/pdf/Soil_31%5C-4.pdf.

B. de Santana, A. M. de Souza, and R. J. Poppi, “Green methodology for soil organic matter analysis using a national near infrared spectral library in tandem with learning machine,” Sci. Total Environ., vol. 658, pp. 895–900, 2019, doi: 10.1016/j.scitotenv.2018.12.263.

B. de Santana, L. O. de Giuseppe, A. M. de Souza, and R. J. Poppi, “Removing the moisture effect in soil organic matter determination using NIR spectroscopy and PLSR with external parameter orthogonalization,” Microchem. J., vol. 145, pp. 1094–1101, Mar. 2019, doi: 10.1016/j.microc.2018.12.027.

F. B. de Santana, S. J. Mazivila, L. C. Gontijo, W. B. Neto, and R. J. Poppi, “Rapid Discrimination Between Authentic and Adulterated Andiroba Oil Using FTIR-HATR Spectroscopy and Random Forest,” Food Analytical Methods, Food Analytical Methods, pp. 1–9, 2018.

B. de Santana, A. M. de Souza, and R. J. Poppi, “Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters,” Spectrochim. Acta - Part A Mol. Biomol. Spectrosc., vol. 191, pp. 454–462, 2018, doi: 10.1016/j.saa.2017.10.052.

V. Sitoe, A. D. V. Maquina, F. B. de Santana, L. C. Gontijo, D. Q. Santos, and W. Borges Neto, “Monitoring of biodiesel content and adulterant presence in methyl and ethyl biodiesels of jatropha in blends with mineral diesel using MIR spectrometry and multivariate control charts,” Fuel, vol. 191, pp. 290–299, 2017, doi: 10.1016/j.fuel.2016.11.078.

B. De Santana, L. C. Gontijo, H. Mitsutake, S. J. Mazivila, L. M. De Souza, and W. Borges Neto, “Non-destructive fraud detection in rosehip oil by MIR spectroscopy and chemometrics,” Food Chem., vol. 209, pp. 228–233, 2016, doi: 10.1016/j.foodchem.2016.04.051.