Instrumental Techniques to Classify Olive Oils according to Their Quality.

This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.

Usage considerations for headspace-gas chromatography-ion mobility spectrometry as a suitable technique for qualitative analysis in a routine lab.

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.

Quality authentication of virgin olive oils using orthogonal techniques and chemometrics based on individual and high-level data fusion information.

Currently, extra virgin olive oil, virgin olive oil and lampante olive oil are classified using physical-chemical analyses and a sensory analysis of fruitiness and defects, which is carried out by expert panels. This manual analysis is nowadays considered to be controversial and therefore analytical methodologies, which may be automated to classify these samples, are needed. In this work, we propose using an analytical platform based on two orthogonal techniques to determine the flavour components perceived in the mouth and the components contributing to the olive oils (OOs) aroma, respectively. For the former, capillary electrophoresis with ultraviolet detector (CE-UV) and high-performance liquid chromatography with UV or fluorescence detection were explored. The CE-UV analysis provided better results with the developed chemometric models (principal component analysis, linear discriminant analysis and k-nearest neighbors method). While for the latter, headspace (HS) - gas chromatography coupling with ion mobility spectrometry (GC-IMS) was selected due to the easy applicability of this technique to classify OOs. Then both techniques, CE-UV and GC-IMS, were selected to be integrated into one analytical platform. The potential of using both complementary/orthogonal techniques was demonstrated using high-level data fusion of CE-UV and GC-IMS data.

Field Induced Fragmentation (Fif) Spectra of Oxygen Containing Volatile Organic Compounds with Reactive Stage Tandem Ion Mobility Spectrometry and Functional Group Classification by Neural Network Analysis.

Mobility isolated spectra were obtained for protonated monomers of 42 volatile oxygen containing organic compounds at ambient pressure using a tandem ion mobility spectrometer with a reactive stage between drift regions. Fragment ions of protonated monomers of alcohols, acetates, aldehydes, ketones, and ethers were produced in the reactive stage using a 3.3 MHz symmetrical sinusoidal waveform with an amplitude of 1.4 kV and mobility analyzed in a 19 mm long drift region. The resultant field induced fragmentation (FIF) spectra included residual intensities for protonated monomers and fragment ions with characteristic drift times and peak intensities, associated with ion mass and chemical class. High efficiency of fragmentation was observed with single bond cleavage of alcohols and in six-member ring rearrangements of acetates. Fragmentation was not observed, or seen weakly, with aldehydes, ethers, and ketones due to their strained four-member ring transition states. Neural networks were trained to categorize spectra by chemical class and tested with FIF spectra of both familiar and unfamiliar compounds. Rates of categorization were class dependent with best performance for alcohols and acetates, moderate performance for ketones, and worst performance for ethers and aldehydes. Trends in the rates of categorization within a chemical family can be understood as steric influences on the energy of activation for ion fragmentation. Electric fields greater than 129 Td or new designs of reactive stages with improved efficiency of fragmentation will be needed to extend the practice of reactive stage tandem IMS to an expanded selection of volatile organic compounds.

A robustness study of calibration models for olive oil classification: Targeted and non-targeted fingerprint approaches based on GC-IMS.

The dual separation in gas chromatography-ion mobility spectrometry generates complex multi-dimensional data, whose interpretation is a challenge. In this work, two chemometric approaches for olive oil classification are compared to get the most robust model over time: i) an non-targeted fingerprinting analysis, in which the overall GC-IMS data was processed and ii) a targeted approach based on peak-region features (markers). A total of 701 olive samples from two harvests (2014-2015 and 2015-2016) were analysed and processed by both approaches. The models built with data samples of 2014-2015 showed that both approaches were suitable for samples classification (success >74%). However, when these models were applied for classifying samples from 2015-2016, better values were obtained using markers. The combination of data from the two harvests to build the chemometric models improved the percentages of success (>90%). These results confirm the potential of GC-IMS based approaches for olive oil classification.

Deep Learning Techniques to Improve the Performance of Olive Oil Classification.

The olive oil assessment involves the use of a standardized sensory analysis according to the "panel test" method. However, there is an important interest to design novel strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry (MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment for olive oil classification. It is an essential task in an attempt to get the most robust model over time and, both to avoid fraud in the price and to know whether it is suitable for consumption or not. The aim of this paper is to combine chemical techniques and Deep Learning approaches to automatically classify olive oil samples from two different harvests in their three corresponding classes: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). Our Deep Learning model is built with 701 samples, which were obtained from two olive oil campaigns (2014-2015 and 2015-2016). The data from the two harvests are built from the selection of specific olive oil markers from the whole spectral fingerprint obtained with GC-IMS method. In order to obtain the best results we have configured the parameters of our model according to the nature of the data. The results obtained show that a deep learning approach applied to data obtained from chemical instrumental techniques is a good method when classifying oil samples in their corresponding categories, with higher success rates than those obtained in previous works.

Innovative coupling of supercritical fluid extraction with ion mobility spectrometry.

This paper describes a pioneer on-line hyphenation between a supercritical fluid extraction (SFE) and an ion mobility spectrometry (IMS) detector through a Tenax TA sorbent trap as retention interface. By means of a simple design, taking advantage of both techniques, this new coupling allows us to extract and preconcentrate analytes and in a second step to determine them. As result, an increase in the accuracy of the analytical process was achieved by elimination of sample transfer from one device to another. In addition, this new coupling reduces the time needed for the optimization of a new SFE method, since the detector can monitor on-line the efficiency of the extraction. The parameters affecting the coupling and its success have been studied in detail via the extraction of benzene and toluene from soil samples. Finally, the suitability of IMS as on-line detector to monitor compounds of industrial interest extracted by SFE was evaluated taking as a model, the extraction and detection of 1,8-cineole (eucalyptol) in rosemary aromatic plants, which could be extrapolated on an industrial scale.

Stability of proton-bound clusters of alkyl alcohols, aldehydes and ketones in Ion Mobility Spectrometry.

Significant substances in emerging applications of ion mobility spectrometry such as breath analysis for clinical diagnostics and headspace analysis for food purity include low molar mass alcohols, ketones, aldehydes and esters which produce mobility spectra containing protonated monomers and proton-bound dimers. Spectra for all n- alcohols, aldehydes and ketones from carbon number three to eight exhibited protonated monomers and proton-bound dimers with ion drift times of 6.5-13.3 ms at ambient pressure and from 35° to 80 °C in nitrogen. Only n-alcohols from 1-pentanol to 1-octanol produced proton-bound trimers which were sufficiently stable to be observed at these temperatures and drift times of 12.8-16.3 ms. Polar functional groups were protected in compact structures in ab initio models for proton-bound dimers of alcohols, ketones and aldehydes. Only alcohols formed a V-shaped arrangement for proton-bound trimers strengthening ion stability and lifetime. In contrast, models for proton-bound trimers of aldehydes and ketones showed association of the third neutral through weak, non-specific, long-range interactions consistent with ion dissociation in the ion mobility drift tube before arriving at the detector. Collision cross sections derived from reduced mobility coefficients in nitrogen gas atmosphere support the predicted ion structures and approximate degrees of hydration.

Target vs spectral fingerprint data analysis of Iberian ham samples for avoiding labelling fraud using headspace - gas chromatography-ion mobility spectrometry.

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.