Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) as a Method of Identifying Contaminants in Sugar Beet Production Process-Case Studies

Abstract

Food safety has received considerable attention in recent years. Methods for rapid identification of a variety contaminants in both the final product and the manufacturing process are constantly developing. This study used Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) spectroscopy to identify various contaminants endangering white sugar production. It was demonstrated that inorganic compounds (calcium carbonate-CaCO₃), plastic contaminants (polypropylene), and oily contaminants (compressor sealing and lubrication lubricant) can be identified with a high degree of precision. FT-MIR spectroscopy was proved to be a useful technique for detecting sugar contaminants rapidly and precisely even without the application of a sophisticated spectra analysis. Commercial databases of reference spectra usage significantly simplify and facilitate the application of this method.

Keywords: food safety; infrared spectroscopy; sugar industry; sugar quality; white sugar contaminants.

Figure 1

Contaminant precipitate (Sample A) after filtering of white sugar water solution.

Figure 2

The statistical analysis of the FT-MIR spectra of contaminants in white sugar (number of samples = 3, total number of scans = 96; for each data point: average—the arithmetic mean of the Y values; variance—the standard deviation of the Y values; range—the margin of Y values).

Figure 3

The MIR spectrum of the white sugar water solution precipitate (Sample A) (a) compared to spectra from a database of HR Aldrich Organometallic, Inorganics and Miscellaneous, Aldrich Catalog No. 20293-2 (b).

Figure 4

The oily contaminants of cossettes mixed with water (Sample B).

Figure 5

The statistical analysis of the FT-MIR spectra of oily contamination in the cossette (number of samples = 3, total number of scans = 96; for each data point: average—the arithmetic mean of the Y values; variance—the standard deviation of the Y values; range—the margin of Y values).

Figure 6

The MIR spectrum of the oily cossette contaminant (Sample B) (a) compared to spectra from a database HR Aldrich Hydrocarbons, Non-Aromatic Hydrocarbons (b).

Figure 7

White sugar dust (a) and the contaminant residues after filtering of sugar dust water solution (Sample C) (b).

Figure 8

The statistical analysis of the FT-MIR spectra of contaminants in sugar dust (number of samples = 3, total number of scans = 96; for each data point: average—the arithmetic mean of the Y values; variance—the standard deviation of the Y values; range—the margin of Y values).

Figure 9

The MIR spectrum of the sugar dust contaminant (Sample C) (a) compared to spectra from a database of reference spectra, HR Nicolet Sampler Library (b).