Miguel Vega-Castellote

Honey adulteration remains a major challenge for ensuring food authenticity and sustainable quality control. In this study, near-infrared (NIR) spectroscopy and hyperspectral imaging (HSI) were comparatively evaluated as green, non-destructive analytical techniques for the discrimination of pure and adulterated honey using chemometric modeling. A total of 180 honey samples, including pure and adulterated samples with agave syrup, sucrose syrup, or water at varying concentrations, were analyzed using two NIR platforms (MicroNIR™ 1700 and NIRS™ DS2500) and an HSI system (Micro-Hyperspec^® NIR camera). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied for exploratory analysis and supervised classification, respectively. Both techniques enabled effective discrimination between pure and adulterated honey. The results demonstrated that the two NIR platforms achieved superior classification performance: the MicroNIR™ 1700 yielded overall sensitivities, specificities, and accuracies of 100%, respectively. While the HSI system provided complementary spectral-spatial information, its performance and that of the NIRS™ DS2500 were slightly lower, with an overall accuracy of 93.10%, particularly at low levels of adulteration (≤10%). Overall, these results demonstrate that NIR-based spectroscopy is a reliable, fast, non-destructive, and eco-friendly analytical tool for testing the authenticity of honey. The portable NIR system, in particular, provides a cost-effective and field-deployable solution for in situ quality control. Integrating it into routine quality control practices could help prevent food fraud, protect consumer trust, and promote sustainable industry development.

Watermelons are in high demand for their juicy texture and sweetness, which is linked to their soluble solids content (SSC). Traditionally, watermelons have been sold as whole fruits. However, the decline in the mean size of households and the very large size of the fruits, together with high prices, mainly at the beginning of the season, mean that supermarkets now sell them as half fruits. For consumers, it is important to know in advance that the fruits that they are purchasing are of a high quality, based not only on external flesh colour but also on sweetness. Near-infrared spectroscopy (NIRS) and Raman spectroscopy were used for the in situ determination of SSC in half watermelons while simulating supermarket conditions. A handheld linear variable filter (LVF) device and an all-in-one (AIO) Process Raman analyser were used for the NIRS and Raman analysis, respectively. The excellent results obtained—including residual predictive deviation for prediction (RPD_p) values of 3.06 and 2.90 for NIRS and Raman, respectively—showed the viability of NIRS and Raman spectroscopies for the prediction of sweetness in half watermelons.

Food fraud is a major threat to the integrity of the nut supply chain. Strategies using a wide range of analytical techniques have been developed over the past few years to detect fraud and to assure the quality, safety, and authenticity of nut products. However, most of these techniques present the limitations of being slow and destructive and entailing a high cost per analysis. Nevertheless, near-infrared (NIR) spectroscopy and NIR imaging techniques represent a suitable non-destructive alternative to prevent fraud in the nut industry with the advantages of a high throughput and low cost per analysis. This review collects and includes all major findings of all of the published studies focused on the application of NIR spectroscopy and NIR imaging technologies to detect fraud in the nut supply chain from 2018 onwards. The results suggest that NIR spectroscopy and NIR imaging are suitable technologies to detect the main types of fraud in nuts.

A common fraud in the sweet almond industry is the presence of bitter almonds in commercial batches. The presence of bitter almonds not only causes unpleasant flavours but also problems in the commercialisation and toxicity for consumers. Hyperspectral Imaging (HSI) has been proved to be suitable for the rapid and non-destructive quality evaluation in foods as it integrates the spectral and spatial dimensions. Thus, we aimed to study the feasibility of using an HSI system to identify single bitter almond kernels in commercial sweet almond batches. For this purpose, sweet and bitter almond batches, as well as different mixtures, were analysed in bulk using an HSI system which works in the spectral range 946.6–1648.0 nm. Qualitative models were developed using Partial Least Squares-Discriminant Analysis (PLS-DA) to differentiate between sweet and bitter almonds, obtaining a classification success of over the 99%. Furthermore, data reduction, as a function of the most relevant wavelengths (VIP scores), was applied to evaluate its performance. Then, the pixel-by-pixel validation of the mixtures was carried out, identifying correctly between 61–85% of the adulterations, depending on the group of mixtures and the cultivar analysed. The results confirm that HSI, without VIP scores data reduction, can be considered a promising approach for classifying the bitterness of almonds analysed in bulk, enabling identifying individual bitter almonds inside sweet almond batches. However, a more complex mathematical analysis is necessary before its implementation in the processing lines.

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.