Antony N. Davies

Geographic Classification of Extra Virgin Olive Oils from the Eastern Mediterranean by Chemometric Analysis of Visible and Near-Infrared Spectroscopic Data

Visible and near-infrared reflectance spectra have been examined for their ability to classify extra virgin olive oils from the eastern Mediterranean on the basis of their geographic origin. Classification strategies investigated were partial least-squares regression, factorial discriminant analysis, and k-nearest neighbors analysis. Discriminant models were developed and evaluated using spectral data in the visible (400–750 nm), near-infrared (1100–2498 nm), and combined (400–2498 nm) wavelength ranges. A variety of data pretreatments was applied. Best results were obtained using factorial discriminant analysis on raw spectral data over the combined wavelength range; a correct classification rate of 93.9% was obtained on a prediction sample set. Though the overall sample set was limited in numbers, these results demonstrate the potential of near-infrared spectroscopy to classify extra virgin olive oils on the basis of their geographic origin.

Spectral Variable Selection for Partial Least Squares Calibration Applied to Authentication and Quantification of Extra Virgin Olive Oils Using Fourier Transform Raman Spectroscopy

The limits of quantitative multivariate assays for the analysis of extra virgin olive oil samples from various Greek sites adulterated by sunflower oil have been evaluated based on their Fourier transform (FT) Raman spectra. Different strategies for wavelength selection were tested for calculating optimal partial least squares (PLS) models. Compared to the full spectrum methods previously applied, the optimum standard error of prediction (SEP) for the sunflower oil concentrations in spiked olive oil samples could be significantly reduced. One efficient approach (PMMS, pair-wise minima and maxima selection) used a special variable selection strategy based on a pair-wise consideration of significant respective minima and maxima of PLS regression vectors, calculated for broad spectral intervals and a low number of PLS factors. PMMS provided robust calibration models with a small number of variables. On the other hand, the Tabu search strategy recently published (search process guided by restrictions leading to Tabu list) achieved lower SEP values but at the cost of extensive computing time when searching for a global minimum and less robust calibration models. Robustness was tested by using packages of ten and twenty randomly selected samples within cross-validation for calculating independent prediction values. The best SEP values for a one years harvest with a total number of 66 Cretian samples were obtained by such spectral variable optimized PLS calibration models using leave-20-out cross-validation (values between 0.5 and 0.7% by weight). For the more complex population of olive oil samples from all over Greece (total number of 92 samples), results were between 0.7 and 0.9% by weight with a cross-validation sample package size of 20. Notably, the calibration method with Tabu variable selection has been shown to be a valid chemometric approach by which a single model can be applied with a low SEP of 1.4% for olive oil samples across three different harvest years.

Authentication and Quantification of Extra Virgin Olive Oils by Attenuated Total Reflectance Infrared Spectroscopy Using Silver Halide Fiber Probes and Partial Least-Squares Calibration

Infrared attenuated total reflectance spectroscopy has been assessed for the analysis of extra virgin olive oil samples from various Mediterranean sites and their adulteration by sunflower oil. In this study two different silver halide fiber-optic probes were separately tested for the mid-infrared spectroscopic measurement of pure olive oil samples and these same oils adulterated with sunflower oil. One fiber-optic probe contained an exchangeable U-shaped section of the silver halide fiber, whereas the second probe consisted of a fiber-coupled diamond crystal, which performed slightly less well than the whole fiber probe. The optimum standard error of prediction for the sunflower oil concentrations in spiked olive oil samples, obtained by optimized partial least-squares (PLS) calibration models and leave-one-out cross-validation, was 1.2% by weight with the use of a special variable selection strategy based on a pairwise consideration of significant respective minima and maxima of the optimum PLS regression vector, calculated for broad spectral intervals. Calibration robustness was proven by also using packages of 10 randomly selected samples within a further cross-validation for calculating independent prediction values. The implications for product monitoring are discussed.

JCAMP-DX for NMR

Following the development and publication of the JCAMP-DX protocol 4.24 and its successful implementation in the field of infrared spectroscopy, data exchange without loss of information, between systems of different origin and internal format, has become a reality. The benefits of this system-independent data transfer standard have been recognized by workers in other areas who have expressed a wish for an equivalent, compatible standard in their own fields. This publication details a protocol for the exchange of Nuclear Magnetic Resonance (NMR) spectral data without any loss of information and in a format that is compatible with all storage media and computer systems. The protocol detailed below is designed for spectral data transfer, and its use for NMR imaging data transfer has not as yet been investigated.

JCAMP-DX for Mass Spectrometry

JCAMP-DX has, for several years, been the standard form for the exchange of infrared spectral data. More recently JCAMP-DX protocols have been published for chemical structure data and for nuclear magnetic resonance spectroscopy. This publication presents a new JCAMP-DX data exchange protocol for mass spectrometry, covering the transport of single spectra, spectral series, and raw data files. The protocol can be implemented on any computer system and storage media. It is completely manufacturer independent. As with previous publications in this series, the aim is to provide reliable data transfer without loss of information regardless of the hardware or software involved. A comparison to the work on a binary protocol currently being carried out by the Analytical Instrument Association is also presented.

Study of the Use of Molecular Spectroscopy for the Authentication of Extra Virgin Olive Oils. Part I: Fourier Transform Raman Spectroscopy

Determination of the authenticity of extra virgin olive oils has become more important in recent years following some infamous adulteration and contamination scandals. There is significant economic advantage to be gained by falsely marketing lower quality oils as top-quality products or by adulterating the end product with cheaper alternative oil. The analysis of olive oils is complicated by their complex nature. The conventional analytical techniques involve complicated analyses of individual components in the oil in order to determine their authenticity; however, molecular spectroscopy combined with modern chemometric techniques offers a rapid analytical solution to this complex problem. This paper describes the initial results from the experimental application of Fourier transform Raman spectroscopy in this area.

Data size reduction strategy for the classification of breath and air samples using multicapillary column-ion mobility spectrometry.

Ion mobility spectrometry combined with multicapillary column separation (MCC-IMS) is a well-known technology for detecting volatile organic compounds (VOCs) in gaseous samples. Due to their large data size, processing of MCC-IMS spectra is still the main bottleneck of data analysis, and there is an increasing need for data analysis strategies in which the size of MCC-IMS data is reduced to enable further analysis. In our study, the first untargeted chemometric strategy is developed and employed in the analysis of MCC-IMS spectra from 264 breath and ambient air samples. This strategy does not comprise identification of compounds as a primary step but includes several preprocessing steps and a discriminant analysis. Data size is significantly reduced in three steps. Wavelet transform, mask construction, and sparse-partial least squares-discriminant analysis (s-PLS-DA) allow data size reduction with down to 50 variables relevant to the goal of analysis. The influence and compatibility of the data reduction tools are studied by applying different settings of the developed strategy. Loss of information after preprocessing is evaluated, e.g., by comparing the performance of classification models for different classes of samples. Finally, the interpretability of the classification models is evaluated, and regions of spectra that are related to the identification of potential analytical biomarkers are successfully determined. This work will greatly enable the standardization of analytical procedures across different instrumentation types promoting the adoption of MCC-IMS technology in a wide range of diverse application fields.

JCAMP-DX. A standard format for the exchange of ion mobility spectrometry data (IUPAC Recommendations 2001)

The relatively young field of ion mobility spectrometry has now advanced to the stage where the need to reliably exchange the spectroscopic data obtained worldwide by this technique has become extremely urgent. To assist in the validation of the various new spectrometer designs and to assist in inter-comparisons between different laboratories reference data collections are being established for which an internationally recognized electronic data exchange format is essential. To make the data exchange between users and system administration possible, it is important to define a file format specially made for the requirements of ion mobility spectrometry. The format should be computer readable and flexible enough for extensive comments to be included. In this document, we define a data exchange format, agreed on by a working group of the International Society for Ion Mobility Spectrometry at Hilton Head Island, USA (1998) and Buxton, UK (1999). This definition of this format is based on the IUPAC JCAMP-DX protocols, which were developed for the exchange of infrared spectra [1] and extended to chemical structures [2], nuclear magnetic resonance data [3], and mass spectra [4]. This standard of the Joint Committee on Atomic and Molecular Physical Data is of a flexible design. The International Union of Pure and Applied Chemistry have taken over the support and development of these standards and recently brought out an extension to cover year 2000 compatible date strings and good laboratory practice [5]. The aim of this paper is to adapt JCAMP-DX to the special requirements of ion mobility spectra [6].

Breath analysis: translation into clinical practice

Breath analysis in respiratory disease is a non-invasive technique which has the potential to complement or replace current screening and diagnostic techniques without inconvenience or harm to the patient. Recent advances in ion mobility spectrometry (IMS) have allowed exhaled breath to be analysed rapidly, reliably and robustly thereby facilitating larger studies of exhaled breath profiles in clinical environments. Preliminary studies have demonstrated that volatile organic compound (VOC) breath profiles of people with respiratory disease can be distinguished from healthy control groups but there is a need to validate, standardise and ensure comparability between laboratories before real-time breath analysis becomes a clinical reality. It is also important that breath sampling procedures and methodologies are developed in conjunction with clinicians and the practicalities of working within the clinical setting are considered to allow the full diagnostic potential of these techniques to be realised. A protocol is presented, which has been developed over three years and successfully deployed for quickly and accurately collecting breath samples from 323 respiratory patients recruited from 10 different secondary health care clinics.

Chemometrics for ion mobility spectrometry data: recent advances and future prospects

Historically, advances in the field of ion mobility spectrometry have been hindered by the variation in measured signals between instruments developed by different research laboratories or manufacturers. This has triggered the development and application of chemometric techniques able to reveal and analyze precious information content of ion mobility spectra. Recent advances in multidimensional coupling of ion mobility spectrometry to chromatography and mass spectrometry has created new, unique challenges for data processing, yielding high-dimensional, megavariate datasets. In this paper, a complete overview of available chemometric techniques used in the analysis of ion mobility spectrometry data is given. We describe the current state-of-the-art of ion mobility spectrometry data analysis comprising datasets with different complexities and two different scopes of data analysis, i.e. targeted and non-targeted analyte analyses. Two main steps of data analysis are considered: data preprocessing and pattern recognition. A detailed description of recent advances in chemometric techniques is provided for these steps, together with a list of interesting applications. We demonstrate that chemometric techniques have a significant contribution to the recent and great expansion of ion mobility spectrometry technology into different application fields. We conclude that well-thought out, comprehensive data analysis strategies are currently emerging, including several chemometric techniques and addressing different data challenges. In our opinion, this trend will continue in the near future, stimulating developments in ion mobility spectrometry instrumentation even further.