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. 2021 May 28;10(6):1221.
doi: 10.3390/foods10061221.

Fraud Detection in Batches of Sweet Almonds by Portable Near-Infrared Spectral Devices

Irina Torres  1 , María-Teresa Sánchez  1 , Miguel Vega-Castellote  1 , Dolores Pérez-Marín  2
Affiliations

Fraud Detection in Batches of Sweet Almonds by Portable Near-Infrared Spectral Devices

Irina Torres et al. Foods. .

Abstract

One of the key challenges for the almond industry is how to detect the presence of bitter almonds in commercial batches of sweet almonds. The main aim of this research is to assess the potential of near-infrared spectroscopy (NIRS) by means of using portable instruments in the industry to detect batches of sweet almonds which have been adulterated with bitter almonds. To achieve this, sweet almonds and non-sweet almonds (bitter almonds and mixtures of sweet almonds with different percentages (from 5% to 20%) of bitter almonds) were analysed using a new generation of portable spectrophotometers. Three strategies (only bitter almonds, bitter almonds and mixtures, and only mixtures) were used to optimise the construction of the non-sweet almond training set. Models developed using partial least squares-discriminant analysis (PLS-DA) correctly classified 86-100% of samples, depending on the instrument used and the strategy followed for constructing the non-sweet almond training set. These results confirm that NIR spectroscopy provides a reliable, accurate method for detecting the presence of bitter almonds in batches of sweet almonds, with up to 5% adulteration levels (lower levels should be tested in future studies), and that this technology can be readily used at the main steps of the production chain.

Keywords: almond batches; authentication; in situ NIR spectroscopy; non-destructive assessment; non-targeted fraud detection.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper in any way.

Figures

Figure 1
Figure 1
Mean raw (a) and second derivative (b) spectra of the almond samples analysed using the Aurora and MicroNIRTM Pro 1700 instruments.
Figure 2
Figure 2
Score plot (a) and loading weights (b) for the second (PC2) and third (PC3) principal components of the different groups of almonds using the Aurora instrument and the second derivative.
Figure 3
Figure 3
Values of the discriminatory variable for each sample of the validation set in the “non-sweet” category, obtained for Strategies II and III using the Aurora instrument.
Figure 3
Figure 3
Values of the discriminatory variable for each sample of the validation set in the “non-sweet” category, obtained for Strategies II and III using the Aurora instrument.
See this image and copyright information in PMC

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