Search

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 preselected 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

The study of volatile compounds obtained by gas chromatography (GC) coupled to mass spectrometry (MS) or ion mobility spectrometry (IMS) may be very useful to protect food quality, especially when using a non-destructive sampling method. In this work, the authentication of the highly appreciated dry-cured Iberian ham by those techniques was studied and compared. The results obtained show the suitability of a non-destructive sampling method coupled to headspace sampling (HS)-GC-IMS or HS-GC-MS to determine volatile markers in the feeding Iberian pig regime. Although both methods were suitable to differentiate the ham categories, HS-GC-IMS was more sensitive detecting a higher number of compounds than HS-GC-MS, which provided accurate qualitative results. The results of principal component analysis showed that ethanol, 2-propanol and 3-methylbutanol, identified by HS-GC-IMS, and 3-methylbutanal and heptane, identified by HS-GC-MS, could be considered potential markers to identify ham from different feeding regimes.

The data obtained with a polar or non-polar gas chromatography (GC) column coupled to ion mobility spectrometry (IMS) has been explored to classify Iberian ham, to detect possible frauds in their labelling. GC-IMS was used to detect the volatile compound profile of dry-cured Iberian ham from pigs fattened on acorn and pasture or on feed. Due to the two-dimensional nature of GC-IMS measurements, great quantities of data are obtained and an exhaustive chemometric processing is required. A first approach was based on the processing of the complete spectral fingerprint, while the second consisted of the selection of individual markers that appeared throughout the spectra. A classification rate of 90% was obtained with the first strategy, and the second approach correctly classified all Iberian ham samples according to the pigs' diet (classification rate of 100%). No significant differences were found between the GC columns tested in terms of classification rate.

Evaluation of dry-cured Iberian ham quality is carried out by means of the sense of smell of experts and a percentage of these hams are discarded due to odour defects. However, hams in cellars cannot be altered by sampling because they would be devaluated, which makes instrumental analysis difficult. Thus, the aim of this work is to assess the potential of headspace gas chromatography coupled to ion mobility spectrometry (HS-GC-IMS) or mass spectrometry (HS-GC-MS) to discriminate defective Iberian hams using a non-destructive sampling. Fifty hams from pigs fed with acorns were sampled in a real industry setting and were classified according to their condition (defective or non-defective). Validated classification rates of 80% and 100% using partial least squares discriminant analysis were obtained with HS-GC-IMS and HS-GC-MS, respectively, demonstrating the potential of the instrumental methods tested, which can be seen as complementary to the traditional olfactory technique. Furthermore, the volatile profile of spoiled Iberian hams was also determined; on average, spoiled pieces showed higher values of nonanal and decanal. Besides that, formic acid was only detected in spoiled pieces. These results might lead to an easier discrimination of spoilage to guarantee the quality of Iberian hams on the market.

The potential of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) to perform non-targeted qualitative analysis of complex samples has led to an unprecedented increase in its popularity in recent years. The operating principle of IMS makes quality control essential to ensure adequate results. Besides this, the suitability of GC-IMS is determined by multiple phenomena that take place before and during IMS detection. The present work discusses a novel GC-IMS quality control protocol for both beginners and experienced users. Likewise, it describes factors that must be taken into account in order to develop a robust GC-IMS qualitative analysis method and, if needed, to achieve the identification of VOCs present in real samples. The developed quality control protocol was successfully employed in our laboratory for the routine analysis of >500 real samples (olive oil and Iberian ham) for 6 months, thus it is recommended for the analysis of a great number of complex samples. Furthermore, the behaviour of the ions produced in the ionisation chamber and the possible reactions between them in GC-IMS qualitative analysis were assessed.