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Near-infrared (NIR) spectroscopy is a proven tool for the determination of food authenticity, mainly because of good classification results and the possibility of industrial use due to its easy and fast application. Since water shows broad absorption bands, the water content of a sample should be as low as possible. Freeze-drying is a commonly used preparatory step for this to reduce the water content in the sample. However, freeze-drying, also known as lyophilization, is very time-consuming impeding the widespread usage of NIR analysis as a rapid method for incoming goods inspections. We used a sample set of 72 almond samples from six economically relevant almond-producing countries to investigate the question of how important lyophilization is to obtain a well-performing classification model. For this approach, the samples were ground and lyophilized for 3 h, 24 h, and 48 h and compared to non-freeze-dried samples. Karl-Fischer titration of non-lyophilized samples showed that water contents ranged from 3.0 to 10.5% and remained constant at 0.36 ± 0.13% after a freeze-drying period of 24 h. The non-freeze-dried samples showed a classification accuracy of 93.9 ± 6.4%, which was in the same range as the samples which were freeze-dried for 3 h (94.2 ± 7.8%), 24 h (92.5 ± 8.7%), and 48 h (95.0 ± 9.0%). Feature selection was performed using the Boruta algorithm, which showed that signals from lipids and proteins are relevant for the origin determination. The presented study showed that samples with low water content, especially nuts, can be analyzed without the time-consuming preparation step of freeze-drying to obtain robust and fast results, which are especially required for incoming goods inspection.

The aim of the present study was the prediction of the geographical origin of almonds (Prunus dulcis Mill.) via Fourier transform near-infrared (FT-NIR) spectroscopy. For this purpose, 250 almond samples from six different countries were analyzed. As the year of harvest has a major impact on the metabolome, three different crop years (2017–2019) were considered. In order to predict the geographical origin, a support vector machine (SVM) model was trained. The SVM achieved a mean classification accuracy of 80.3% (± 1.5%). In particular one of the economically relevant questions – the distinction between Mediterranean almonds and American almonds – can be answered with this model. Combining the Spanish and Italian almonds to one Mediterranean class the overall classification accuracy is increased to up to 88.2% ± 1.0%. These results confirmed the suitability of NIR screening for the determination of the geographical origin of almonds and may pave the way for future analytical applications. With regard to potential future applications, the transferability of the developed NIR method to blanched almonds was discussed and evaluated: Even if the classification accuracy of unblanched almonds is higher than the prediction based on blanched almonds, the determination of the geographical origin still seems to be possible with this type of processed almonds.

The aim of the present study was to conduct a systematic and in-depth comparison of four different sample preparation techniques for near-infrared (NIR) spectroscopy analysis of almonds and evaluation of their suitability for the geographical origin determination. Although it is generally known that the sample preparation has an impact on the NIR screening, there is no scientific consensus on a commonly accepted procedure. In this work, 64 almond samples from six countries were analyzed as whole and bisected nuts as well as in a ground and freeze-dried state (after grinding). In order to assess the suitability for future applications, both the labor effort for sample preparation and the classification accuracy were evaluated. Using support vector machine (SVM) classification we obtained a classification accuracy of 80.2% (±1.9%) on the validation set for the determination of origin of freeze-dried almonds. The other three sample preparations result in at least 8.3 percentage points lower classification accuracies. Nonetheless, the analysis of whole and bisected almonds is more suitable for an initial rapid screening due to a lower overall required work effort. The results confirm the influence of the sample preparation techniques in NIR screening and pave the way for future widespread analytical applications.

The extraction of metabolites turns out to be one of the most important key factors for nontargeted metabolomics approaches as this step can significantly affects the informative value of the successive measurements. Compared to metabolomics experiments of various matrices of bacterial or mammalian origins, there are only few studies, which focus on different extraction methods for plant metabolomics analyses. In this study, various solvent extraction compositions were compared and assessed using an UPLC-ESI-QTOF-MS strategy. Exemplary, white asparagus ( Asparagus officinalis) were employed as a low-fat-, low-protein-, high-water-content model commodity with the objective of designing an optimal nontargeted extraction protocol for polar and nonpolar metabolites. Furthermore, the influence of acid addition, mechanical cell disruption methods (ball mill, ultrasonic bath, vortex mixer), and extract stability have been systematically scrutinized too. The different extraction protocols were compared based on sum of features, sum of peak intensities, sum of peak areas, as well as by analyzing individual signals of as many different substance groups as possible to obtain a maximum overview.