Rapid, high-throughput, and quantitative determination of orange juice adulteration by Fourier-transform infrared spectroscopy†
Abstract
Orange juice is a hugely popular, widely consumed, and high price commodity typically traded in a concentrate form making it highly susceptible to adulteration. It has been consistently shown to be one of the leading food categories of reported cases of food fraud. One of the many forms of adulteration is dilution which can then be disguised with sugar solutions, or juices from other fruits or vegetables, which mimic the natural fruit sugars in this juice. Here, we demonstrate Fourier transform infrared (FT-IR) spectroscopy as a rapid, high-throughput and quantitative method for the determination of orange juice adulteration. Initial experiments involved the simple adulteration of pure orange juice with 0.5–20.0% water disguised with glucose, fructose or sucrose individually. This was followed by more complex mixtures of these three sugars at appropriate concentrations found in freshly prepared orange juice established using GC-MS; a total of 41 samples were prepared and all experiments undertaken in triplicate. Principal components-discriminant function analysis (PC-DFA) was undertaken on raw spectral data followed by partial least squares regression (PLSR) for quantification of the level of adulteration. Results from these chemometric analyses showed that infrared spectra contained information allowing for the discrimination and quantification between the three naturally occurring sugars in orange juice to disguise adulteration via dilution. Furthermore, it was clearly demonstrated that FT-IR in combination with PLSR is able to predict the levels of adulteration with excellent accuracy; the typical error on these predictions for test samples was 1.7%. We believe that the further development of these and other rapid methods could have an important role to play in the area of food authenticity and integrity, and food analysis in general.
- This article is part of the themed collection: Detecting food authenticity and integrity