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Abstract: A chemometric challenge was posed at the annual ‘Chimiométrie’ congress organized by the French Chemometrics Society in 2010. The congress was held in Paris on 2–3 December and the data relating to the challenge are available on the congress website (http://www.chimiometrie.fr/chemom2010). The aim of the challenge was to test the ability of congress participants to discriminate Trappist Rochefort 8° beer (one of seven authentic Trappist beers in the world) from other Trappist beers, as well as other special beers. All the beers were measured using three vibrational spectroscopic techniques (NIR, MIR and Raman). Three participants took up the challenge and, as in previous congress challenges, the organizing committee asked them to describe their approaches to face the challenge. This paper summarizes the three approaches put forward by participants, as well as the approach put forward by the organizers. [Copyright &y& Elsevier]

Abstract: Since 2004, a challenge is proposed at the “Chimiométrie” conference organized by the GFC (“Groupe Français de Chimiométrie”). The annual congress was held in Paris 30 November and 1 December 2009. The data are still available on the GFC website (www.chimiometrie.org/). The data for this session were extracted from the CRA-W (Walloon Agricultural Research Centre, www.cra.wallonie.be/) spectral data base. The calibration set and the validation set (the reference values for the latter were not available to the participants) were set up to have an obvious case of extrapolation. The data sets were from whole rapeseed samples and the parameter to be calibrated was the total glucosinolate content. Seven participants reported results and, according to the highest R 2 of prediction, three were asked to present their approach during the conference. These approaches and the ones of the challenge organizers are presented in this paper. [Copyright &y& Elsevier]

Abstract: Following up on the success of previous chemometric challenges arranged during the annual congress organised by the French Chemometrics Society, the organisation committee decided to repeat the idea for the Chimiometrie 2007 event (http://www.chimiometrie.org/) held in Lyon, France (29–30 November) by featuring another dataset on its website. As for the first contest in 2004, this dataset was selected to test the ability of participants to apply regression methods to NIR data. The aim of Challenge 2007 was to perform a calibration model as robust and precise as possible using a data set with only a few reference values available and submitted to different perturbation factors. The committee received nine answers; this paper summarizes the best three approaches, as well as the approach proposed by the organisers. [Copyright &y& Elsevier]

Abstract: For the third consecutive year and due to the success of the chemometric contests organized within the framework of previous congresses, another data set has been proposed for the organisation committee at the ‘Chimiométrie 2006’ meeting (http://www.chimiometrie.org/) held in Paris, France (30th November and 1st December). As in the first contest organized in 2004 this data set was selected in order to test the ability of the participants for using regression methods based on NIR data. The data set consists on three different properties characterizing soils coming from the Walloon region in Belgium. This year, unlike previous contests, the data have been not modified by the authors. Only three participants decided to play with the proposed data and presented their own approaches during the conference. As last year''s, this paper summarizes the approaches presented during the meeting by the participants and the authors. [Copyright &y& Elsevier]

Abstract: Due to the success of the chemometric challenge organized within the framework of the congress ‘Chimiométrie 2004’ [‘A NIR data set is the object of a Chemometric contest at ‘Chimiométrie 2004’’. P. Dardenne, J.A. Fernández Pierna, Accepted for publication in to Chemometrics and Intelligent Laboratory Systems, special issue Chimiométrie (2004)], the organization committee decided to re-launch the idea for the 2005 edition (http://www.chimiometrie.org/) held in Lille, France (30th November and 1st December) by proposing through its website another data set. This data set was selected in order to test the ability of the participants for using discrimination methods based on IR data for the classification of starches according to the type of chemical modification undergone. As the previous edition, the participants were asked to present during the conference their own approaches. The committee received only two answers, which shows the difficulties that the other participants found by using this data set. This paper summarizes the two approaches proposed by the participants and the proposed approach of the authors. [Copyright &y& Elsevier]

Abstract: The organisation committee of the symposium ''Chimiométrie 2004'' (http://www.chimiometrie.org/) held in Paris (30th November and 1st December) proposed on their web site a NIR data set. This data set contained 194 spectra of minced meat taken on an Infratec-Tecator instrument between 850 and 1050 nm. The participants were asked to present during the conference their own approaches to calibrate and predict two independent and blind test sets. The committee received nine answers and this paper summarizes the different ways the data were treated by the participants and the proposed approach of the authors. The conference session was quite interesting and considered valuable for a publication by the committee and the participants. The prediction performances expressed by the RMSEP vary within a ratio of 10 between the extremes values. Due to the non-linearity, methods based on classification gave the best prediction models. A simple ANN model gave the best results. [Copyright &y& Elsevier]

Spectroscopy-based methods are often used to verify the authenticity of food and feed. In the scientific environment, especially non-targeted approaches are becoming increasingly popular here, although their routine applicability, e.g., in official food control, is currently limited. The comparability of spectra acquired by different spectroscopic instruments using the same measurement principle is essential for the application of a common spectral database in laboratories and represents a current challenge in this field. In order to investigate possible approaches towards improved comparability of these spectra, a sample set of rapeseed (Brassica napus) and pumpkin seed (Cucurbita maxima) oils was analyzed using three Fourier-transform-mid-infrared spectrometers following the same procedure for sample preparation and measuring conditions. Depending on the instrument, the obtained spectra exhibited differences in absorbance, but not in wavelengths of bands position and maxima. As a result, the mutual prediction of original data (without mathematical correction) from different instruments using a partial least squares discriminant analysis model built and optimized with spectral data from only one instrument provided a sensitivity of 0% for the two-class model of rapeseed in pumpkin seed oil, indicating no discrimination at all. In order to minimize the spectral differences and thus to enhance the performance parameters of the prediction models, different mathematical correction approaches were investigated: (i) instrument-specific correction factors, (ii) combinations of different pre-processing steps and (iii) piecewise direct standardization (partial least squares-based regression). All investigated approaches achieved classification results with 100% sensitivity for the mathematically corrected data set of the respective instrument. The outcome of this study indicates that the correction approaches can be used to optimize the comparability of spectral data from different instruments, which is a first step towards harmonization in non-targeted analysis. [Display omitted] • Comparison of MIR spectra acquired using three different instruments. • Different approaches to improve the comparability of spectra were tested. • A Calibration transfer approach was investigated. • Assessment of approaches by performance parameters of classification model. • Most promising results based on piecewise direct standardization. [ABSTRACT FROM AUTHOR]

Hydrocolloids such as natural gums and carrageenans are used extensively in the food industry in various mixtures that are difficult to be characterised due to their similar chemical structure. The aim of this study was to develop an analytical framework for the identification and quantification of these compounds in complex mixtures using Near-infrared (NIR) spectroscopy and chemometrics. Partial Least Squares (PLS) regression accompanied by Continuous Locality Preserving Projections (CLPP) dimensionality reduction technique is proposed as chemometric framework. Four different analytical models based on this framework are developed and compared for the analysis of spectral fingerprints of food hydrocolloids mixtures. Classification results showed that this method allowed the discrimination of hydrocolloids in blends with a 100% of correct classification. The same scheme also allows the quantitative determination of the different types of food hydrocolloids (3 types) and/or their individual compounds (8 different compounds) with a relative low root mean square error of prediction (RMSEP) of 0.028 and 0.038 respectively. • An analytical framework for addressing the characterisation of complex food admixtures is proposed. • A double stage hierarchical approach allows the quantification of up to 8 different compounds with a small prediction error. • The framework is showcased in a food hydrocolloid mixtures characterisation problem using NIR data. • Results prove the potential of this framework to be used for compositional analysis problems. [ABSTRACT FROM AUTHOR]