Determination of Iron in Vegetable Oil by Fourier Transform Mid-Infrared Spectroscopy

  •  Pedro S. Panero    
  •  Francisco S. Panero    
  •  Jose C. S. Oliveira    
  •  João S. Panero    
  •  Anderson L. Ramos    
  •  Fernando S. E. D. V. Faria    
  •  Anselmo F. R. Rodriguez    


The potential of Fourier transform mid-infrared spectroscopy with attenuated total reflection (FTIR-ATR) for the quantification of iron (Fe) in vegetable oil extracted from the fruit of the moriche palm (buriti) [Mauritia flexuosa] was evaluated. This green method enables direct measurements without previous sample handling. Twenty-five buriti samples were collected in Roraima (Brazil). The statistical models were developed using the technique of partial least squares (PLS) analysis and the data set was divided into two parts: one used for calibration (n = 20) and one used for testing (n = 5). First, the model was calibrated and cross-validated with the calibration data set so that the model was validated with the test data set to verify its prediction ability. To obtain reference data, the samples were analyzed by X-ray fluorescence (EDXRF). The coefficient of determination (R2) was 0.9965 and the mean square error of prediction (RMSEP) obtained for iron (Fe) was 0.8067 (in ppm). The results showed that the prediction ability can be considered good for large quantification of iron intervals in vegetable oil, and the mean relative errors were less than ±7%. This indicated that the green method for the determination of iron (Fe) in vegetable oil by Fourier transform mid-infrared spectroscopy with attenuated total reflection can be used as an alternative method to the classic methods of analysis, because it does not use reagents harmful to the environment or operator, does not generate harmful waste, uses a fast technique, and there is minimal manipulation of the sample.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • Issn(Print): 1916-9752
  • Issn(Onlne): 1916-9760
  • Started: 2009
  • Frequency: monthly

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