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Cured Iberian ham is a worldwide known product due to its high quality. Nowadays, there is a lack of official analytical methods to differentiate geographical origin (Protected Designation of Origin (PDO)), the curing plant where hams are processed, as well as the commercial categories in each industry. In this work, volatile organic compounds (VOCs) extracted from 998 Iberian hams were analyzed by Gas Chromatography coupled to Ion Mobility Spectrometry (GC-IMS), using the subsequent information to design discriminant models. High classification percentages were obtained for the three objectives of the study: 98,5% for geographical origin, 93,5% and 100% for curing plant discrimination, and an average rate of 84,5% for commercial category discrimination in the seven curing plants subject to study. Differences obtained in discriminant models are probably due to the complexity of Iberian ham manufacturing process. In this sense, the results obtained in the present study suggest slight differences between geographical areas and industries evaluated, even covered by the same PDO. Also, those differences may be related to the existing variability in terms of breed purity and feeding regime of Iberian pigs, which are two of the main determining factors of ham aroma.

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

With globalization and complex distribution systems, the increase of food adulteration has become a significant concern for manufacturers, researchers, governments, and consumers. A collective approach is needed to identify all stakeholders in the food supply chain, to verify their qualification, and to exclude those who do not meet the required standards through all levels of food supply and production. Food authentication is a broad range of efficient activities, which is rapidly growing due to increasing public awareness concerning food quality and safety. Food products can be verified based on their chemical composition, botanical sources, specified geographical origin, or possible adulterations by modern analytical methods. This chapter represents a detailed explanation about critical concepts including “authentication,” “traceability,” and “labeling” followed by a systematic discrimination of authentication on different food products. Classification of different food products is based on authenticity indicators providing insight into future developments. The most common adulteration methods have been explained for different categories of food products. This chapter also presents critically the analytical techniques that are used for authenticity assessment, their operation, together with their advantages and drawbacks, in order to show different strategies for food authentication. A wide range of the currently available multivariate pattern recognition tools for detecting food adulteration are also represented.

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.

The analysis of the quality of olive oil is a task that is hav-ing a lot of impact nowadays due to the large frauds that have been observed in the olive oil market. To solve this problem we have trained a Convolutional Neural Network (CNN) to classify 701 images obtained using GC-IMS methodology (gas chromatography coupled to ion mobil-ity spectrometry). The aim of this study is to show that Deep Learn-ing techniques can be a great alternative to traditional oil classification methods based on the subjectivity of the standardized sensory analy-sis according to the panel test method, and also to novel techniques provided by the chemical field, such as chemometric markers. This tech-nique is quite expensive since the markers are manually extracted by an expert. The analyzed data includes instances belonging to two different crops, the first covers the years 2014–2015 and the second 2015–2016. Both har-vests have instances classified in the three categories of existing oil, extra virgin olive oil (EVOO), virgin olive oil (VOO) and lampante olive oil (LOO). The aim of this study is to demonstrate that Deep Learning techniques in combination with chemical techniques are a good alterna-tive to the panel test method, implying even better accuracy than results obtained in previous work

Iberian cured ham from purebred pigs fattened on grazing acorns is a highly appreciated product. There are several analytical methods to avoid its labelling fraud; however, these require opening the ham. The aim of this work is testing a non-destructive sampling method which does not spoil the physical integrity of the ham. It consists of a puncture with a needle imitating a traditional olfactory system. After removing the needle impregnated with fat, its volatiles are analyzed with a Gas Chromatography (GC) coupled to Ion Mobility Spectrometry (IMS). The potential of this methodology was studied analyzing 156 Iberian hams from pigs under two different feeding regimes (acorns versus feed) and from different breed (Iberian versus Duroc crossed). Intensity of GC-IMS plot features was extracted and chemometric differentiation models were obtained; one for feeding regime and another for breed, providing validated classification rates of 91.7% and 100%, respectively. In addition, 29 features used for construction of both models were tentatively identified using chemical standards. The suitability of the method for quality control analysis was characterized by means of a precision study. As a conclusion, GC-IMS becomes a useful tool to guarantee dry-cured Iberian ham authenticity and detect labelling fraud.

In recent years, Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) has been successfully employed in food science as a control technique for the prevention of fraud according to food and labeling regulations. In this work, we propose the use of GC-IMS technique to assess the quality of Iberian ham with regard to the Iberian Pig's diet (either nourished with feed or with acorns). For this purpose, we have acquired a dataset composed of 53 samples of Iberian ham from different food providers using a commercial GC-IMS (FlavourSpec, from G.A.S. Dortmund, Germany). Intensive signal pre-processing for GC-IMS was applied to the raw data. This dataset was employed to create four Partial Least Squares Discriminant Analysis (PLSDA) models corresponding to different train/test partitions of the dataset. Nearly perfect classification rates (above 91 %) were obtained for each partition of the dataset, denoting the high power of GC-IMS to characterize food samples.