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The current quality control of the dry-curing process in Iberian ham is performed with an olfactory evaluation by ham experts. The present study proposes to monitor the dry-curing process of Iberian ham using an objective analytical methodology that involves non-destructive sampling of the subcutaneous fat of the hams and a volatile profile analysis using gas chromatography–ion mobility spectrometry. Thirty-eight 100% Iberian acorn-fed hams were examined in total, with eighteen hams monitored during the post-salting stage and twenty during the drying-maturation stage. A total of 164 markers were detected in the samples monitored during the post-salting stage, whereas 276 were detected in the hams monitored during the drying-maturation stage. The study of the trends observed in the intensities of the markers throughout the curing process enabled the detection of potential indicators of the end of the post-salting stage. Thus, representative intensity thresholds were established for some markers (3-methylbutanal, pentanal, hexanal, (E)-octen-2-al, 2-hexen-1-ol and heptan-1-ol) to determine the optimal point to specify the end of the post-salting process for hams. These findings provide an objective tool to support the traditional sensory evaluation currently performed in the industry.

Paslahepevirus balayani (formerly known as hepatitis E virus) is an emerging cause of foodborne disease in Europe, transmitted mainly by the consumption of raw or undercooked pork. Since little is known about the presence of the virus in several pork products that are eaten uncooked, our aim was to evaluate the prevalence of Paslahepevirus balayani in groups of commercial pork products intended for human consumption subjected to different processing techniques. A total of 1265 samples of pork products from Spain were divided into four groups and tested for the presence of Paslahepevirus balayani RNA: unprocessed pig and wild boar meat frozen at −20 °C (n = 389), dry-cured pork products (n = 391), dry-cured and salted pork products (n = 219), and boiled products (n = 266) (none of these products contained pork liver). Five samples were positive for Paslahepevirus balayani RNA (overall prevalence: 0.4%; 95% CI: 0.17% - 0.92%). All positive samples were from unprocessed meat stored at −20 °C, with a prevalence in this group of 1.3% (95% CI: 0.42–3.44); two samples came from pigs (1.1%; 95% CI: 0.13–3.81) and three from wild boar (1.5%; 95% CI: 0.31–4.28). None of the pork samples in the other groups was positive. In conclusion, Paslahepevirus balayani was found in unprocessed swine products form Spain, but not in processed products intended to be consumed undercooked, demonstrating that transmission of this zoonotic virus by eating these pork products should be more seriously considered.

The number of representative samples to build a calibration model plays a major role in the success of chemometric models for class discrimination; therefore, knowing which samples should be used for the calibration of prediction models is essential. The aim of this work is to design a basic guideline for the training of partial least squares discriminant analysis (PLS-DA) models to classify complex samples analysed by Gas Chromatography (GC) coupled to Ion Mobility Spectrometry (IMS) using dry-cured Iberian ham as an example. The effect of the number, proportion and class of samples for training and validation and the use of two data types (spectral fingerprint or pre-selected markers) has been assessed by analysing with GC-IMS nearly 1000 dry-cured Iberian ham samples obtained from 7 different curing plants. Subsequently, these were classified with PLS-DA according to the pig's feeding regime (acorn-fed vs. feed-fed) and it has been demonstrated that 450 out of 997 samples are enough for model training to achieve a maximum average prediction accuracy rate. Furthermore, the use of pre-selected GC-IMS markers provides slightly better prediction results than the use of the complete spectral fingerprint. In summary, these results represent a tentative guide for the classification of samples in an industrial setting using GC-IMS and PLS-DA. This methodology would allow authorities and producers to ensure the quality of the agri-food products put on the market as is proven in this study.

Gas chromatography—ion mobility spectrometry (GC-IMS) allows the fast, reliable, and inexpensive chemical composition analysis of volatile mixtures. This sensing technology has been successfully employed in food science to determine food origin, freshness and preventing alimentary fraud. However, GC-IMS data is highly dimensional, complex, and suffers from strong non-linearities, baseline problems, misalignments, peak overlaps, long peak tails, etc., all of which must be corrected to properly extract the relevant features from samples. In this work, a pipeline for signal pre-processing, followed by four different approaches for feature extraction in GC-IMS data, is presented. More precisely, these approaches consist of extracting data features from: (1) the total area of the reactant ion peak chromatogram (RIC); (2) the full RIC response; (3) the unfolded sample matrix; and (4) the ion peak volumes. The resulting pipelines for data processing were applied to a dataset consisting of two different quality class Iberian ham samples, based on their feeding regime. The ability to infer chemical information from samples was tested by comparing the classification results obtained from partial least-squares discriminant analysis (PLS-DA) and the samples’ variable importance for projection (VIP) scores. The choice of a feature extraction strategy is a trade-off between the amount of chemical information that is preserved, and the computational effort required to generate the data models.

Dry-cured Iberian ham is officially classified into different commercial categories according to the pig’s breed and feeding regime. These reach very different prices, thus promoting labelling fraud and causing great damage to the food sector. In this work, a method based on Raman spectroscopy was explored as a rapid in situ screening tool for Iberian ham samples. A total of 110 samples were analyzed to assess the potential of this technique to differentiate purebred, crossbred, acorn-fed and feed-fed dry-cured Iberian ham. A continuous signal probably due to sample fluorescence was obtained, which hid the Raman scattering signal. Therefore, chemometric treatment was applied in order to extract non-apparent information. High validated classification rates were obtained for feeding regime (83.3%) and breed (86.7%). In addition, an interlaboratory study was carried out to confirm the applicability of the method with 52 samples, obtaining a validated rate above 80%.