Rapid and non-destructive spatially offset Raman spectroscopic analysis of packaged margarines and fat-spread products

Spatially offset Raman spectroscopy (SORS) is a novel technique capable of measuring samples through the original packaging and recovering the spectra without the contribution of surface layers. Here, a portable SORS equipment was used to measure 62 samples of margarines and fat spreads through the original plastic container. Chemometric tools were used to analyse the data obtained. A total of 25 classification models were developed based on: (i) geographical origin, (ii) vegetable oils and (iii) some significant minor constituents present in the samples. Partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and soft independent modelling of class analogy (SIMCA) were used for model classification. Quantitative analysis using the partial least squares regression (PLSR) method was also performed to determine the total fat content. In parallel, a benchtop conventional Raman spectrometer was used to analyse the same samples, develop the models with the same training and validation sets in order to compare the results. The calculated classification performance metrics showed better classification models from SORS data than conventional Raman spectroscopy (CRS), highlighting the one-class SIMCA models for margarines containing phytosterols, olive oil or linseed oil. These models exhibited very high predictability (performance parameters with values equal to or higuer than 0.8, 0.9 and 1, respectively). The quantitation model developed from SORS exhibited a higher R2 than from CRS data, and prediction errors below 5% from SORS versus errors between 5 and 13% from CRS data. These results reveal the ability of SORS to avoid the influence of fluorescence, a major drawback when analysing Raman spectra, but also the potential of the technique as a fast, non-destructive and non-invasive analytical technique in the field of food analysis. In conclusion, the tandem 'SORS-chemometrics' has been shown to be a potential tool in the food quality and food authentication fields. Thus, it is necessary to perform further investigations in this field in order to advance the knowledge of this technique and to be able to develop new methods of rapid analysis.

Automated summary

Spatially offset Raman spectroscopy (SORS) is a novel technique that allows measurement of samples through packaging and recovery of spectra without surface layer interference. This study used a portable SORS equipment to measure margarines and fat spreads, and chemometric tools were used for data analysis. The results showed that SORS data outperformed conventional Raman spectroscopy (CRS) in classification models and quantitative analysis, demonstrating the potential of SORS as a fast, non-destructive, and non-invasive analytical technique in food analysis.