Curve Resolution Technique Research Articles

Using Gas Chromatography–mass Spectrometry in Conjunction with Chemometrics Technique for Characterization of Iranian Essential Oils Myrtus communis L.

Myrtus communis L. essential oils were extracted by simultaneous distillation extraction method and studied with gas chromatography–mass spectrometry (GC–MS). The obtained mass spectra were investigated by searching in NIST library, and 54 components were identified, but due to several problems such as varying baseline due to column bleed, low signal-to-noise ratio of peaks, and co-elution, some of components did not have satisfactory match factors. Chemometric resolution techniques were used to resolve these problems. The number of identified components was expanded to 67 using combination of GC–MS with resolution technique. In addition, the presence of worthful components such as α-Pinene (36.19%), 1,8-Cineole (11.44%), d-Limonene (10.04%), Linalool (7.76%), α-Terpineol (5.51%), linalyl acetate (4.19%) and α-Terpineol acetate (2.61%) in Myrtle essential oils have been verified. The results of this study indicate that joining of hyphenated chromatographic methods and multivariate curve resolution techniques can be useful for comprehensive, rapid, and accurate analysis of complex natural product such as Iranian Myrtle essential oils.

Trilinear self‐modeling curve resolution using Borgen‐Rajkó plot

AbstractModern analytical instruments provide measurement data arrays with full of hidden and redundant information. Multivariate curve resolution (MCR) techniques decompose data set to physic‐chemically meaningful abstract profiles. On the other hand, for such data matrices, Borgen‐Rajkó self‐modeling curve resolution (SMCR) techniques reveal all possible solutions analytically under the minimal assumption. Although Lawton‐Sylvestre (LS) and Borgen methods have been proposed for the non‐negative curve resolution of two‐component and three‐component systems, there is still a great deal of interest to include further restrictions on the Borgen‐Rajkó SMCR. As modern hyphenated analytical instruments produce multiway (eg, three‐way) arrays, multiway analysis (eg, trilinear decomposition) was received much more popularity by chemists.This contribution is decicated to the extension of the Borgen algorithm to the trilinear data sets. The Borgen method incorporating trilinearity constraint is approached in an analogy to the trilinear Lawton‐Sylvester method. The proposed analytical triadic decomposition is applied to the simulated three‐way arrays with full rank and rank overlap (a type of rank deficiency) loadings. Finally, the proposed algorithm is further exemplified with a three‐component spectrofluorimetric study of acid‐base equilibrium of the pyridoxine. Investigating feasible regions of the simulated and experimental three‐component arrays reveal interesting additional information.

A conceptual view to the area correlation constraint in multivariate curve resolution

Multivariate curve resolution (MCR) techniques are widely employed to study chemical systems and unravel underlying physic-chemical information. Different constraints have been introduced to reduce the extent of uncertainty in the results of MCR-ALS. It is important to study the effect of each constraint on the range of feasible solutions in the curve-resolution studies. As each constraint uses specific information, constraints have different effects on the amount of uncertainty.In the present contribution, condition which resulted in uniqueness applying area correlation constraint (ACC) was illustrated. Additionally, the effect of area correlation constraint (relative known-value constraints) on the amount of uncertainty in the resolved profiles was investigated and visualized in detail. Besides, some rules were established for determining the required number and composition of calibration samples. To illustrate how ACC leads to the uniqueness of a MCR result in the presence of validation samples, the extent of rotational ambiguity was evaluated by analyzing different simulated and experimental data sets. The latter involves the quantification of Phenylalanine and Tyrosine in a second-order excitation-emission fluorescence data sets.

Identification and quantification of single constituents in groundwater with Fourier-transform infrared spectroscopy and Positive Matrix Factorization

Fourier-transform infrared (FTIR) spectra of environmental samples usually comprise superposed pure component spectra of the individual constituents present. Thus, evaluation of band intensities and reconstruction of FTIR spectra by inverse techniques is often prone to ambiguity. Frequently applied multivariate curve resolution techniques (PCA, PCR, PLS) reduce the complexity in the observed spectra by isolating recurring components. However, due to mathematical constraints, such components usually do not resemble the physical spectra of the single constituents in mixed samples, which is, by contrast, principally possible with Positive Matrix Factorization (PMF). In the present study, we tested if PMF is appropriate to conduct quantitative FTIR spectroscopy on environmental samples. We quantified the concentration of humic acid-coated goethite (HA-Goe) in groundwater from two sites, which was sampled before and after HA-Goe was injected into the aquifers. PMF could successfully reconstruct the superposed FTIR spectra of the solids from freeze-dried groundwater, with explained variances of >0.99 and mean residuals of <0.01 (normalized on maximum absorbance). Due to normalization on an internal standard (potassium ferrocyanide), PMF allowed for the quantitative interpretation of compounds that were represented by dedicated PMF components (HA-Goe, kaolinite, montmorillonite, organic matter) even without a priori knowledge on the sample composition. The obtained HA-Goe concentrations agreed very well with those calculated from Fe concentrations determined with ICP-OES (correlation coefficients>0.96). Due to i) the use of objective criteria, which quantified the accuracy and uncertainty of each PMF solution, ii) repetitive PMF runs with randomized initial conditions and iii) physically meaningful PMF-components that resemble the FTIR spectra of single compounds in heterogeneously composed samples, the application of expert knowledge is replicable and less dependent on the operator than in other approaches of quantitative FTIR spectroscopy.

Closure constraint in multivariate curve resolution

AbstractMultivariate curve resolution techniques try to estimate physically and/or chemically meaningful profiles underlying a set of chemical or related measurements. However, the estimation of profiles is not generally unique and it is often complicated by intensity and rotational ambiguities. Constraints as further information of chemical entities can be imposed to reduce the extent of ambiguities. Not only a long list of constraints has been introduced but also some of them can be applied in different ways. Either investigating constraint effects on the extent of rotational ambiguity or how they can be applied during curve resolution can shed light on curve resolution studies. The motivation behind this contribution is to pave the way to a clarification about the closure constraint. Considering simulated equilibrium and kinetic spectrophotometric data sets, different approaches to closure implementation were applied to demonstrate the geometrical explanation of closure constraint and its effect on multivariate curve resolution‐alternating least squares results. Besides, the closure constraint is compared with normalization and it is proved that the closure constraint is a Borgen norm and has the same effect as other Borgen norms in multivariate curve resolution. Finally, to further examine the closure constraint, a real data set was investigated.

Continuous multicomponent quantification during supercritical fluid extraction applied to microalgae using in-line UV/Vis absorption spectroscopy and on-line evaporative light scattering detection

A quantitative methodology based on in-line UV/Vis absorption spectroscopy and on-line evaporative light scattering detection for supercritical fluid extraction is proposed. The method was applied to the extraction of carotenoids, chlorophyll A, ergosterol and total lipids from microalgae. One regression technique and two curve resolution techniques were applied on the absorption spectroscopy data and evaluated, namely classical least squares, multivariate curve resolution by alternating least squares and parallel factor analysis (PARAFAC2). The two former both generated useful models, furthermore multivariate curve resolution also successfully enabled estimation of both spectra and concentration profiles of the analytes. The integrated extraction profiles of each analyte were compared with analysis of the collected fractions using reference analysis methods Precision, in regards to quantification of the analytes in the eluent, was better using in-line measurements compared to off-line measurements by UV/Vis absorption spectroscopy, supercritical fluid chromatography with mass spectrometry and liquid chromatography with UV/Vis detection.

Synthesis and photodegradation studies of analogues of muscle relaxant 1,4-dihydropyridine compounds.

This paper describes the synthesis of 1,4-dihydropyridine compounds (DHPs) endowed with good muscle relaxant activity and stability to light. Six new condensed DHPs were synthesized by the microwave irradiation method. A long-chain ester moiety [2-(methacryloyloxy)ethyl] and various substituents on the phenyl ring were demonstrated to affect the muscle relaxant activity occurring in isolated rabbit gastric fundus smooth muscle strips. Forced photodegradation conditions were applied to the molecules according to the ICH rules. The degradation profile of the drugs was monitored by spectrophotometry coupled with the multivariate curve resolution technique. Formation of the oxidized pyridine derivative was observed for all the studied DHPs, except for one compound, which showed very fast degradation and formation of a second photo-product. Pharmacological tests on the molecules showed a good muscle relaxing effect, with a mechanism similar to that of nifedipine, however, proving to be more stable to light.

Random Initialisation of the Spectral Variables: an Alternate Approach for Initiating Multivariate Curve Resolution Alternating Least Square (MCR-ALS) Analysis.

Multivariate curve resolution alternating least square (MCR-ALS) analysis is the most commonly used curve resolution technique. The MCR-ALS model is fitted using the alternate least square (ALS) algorithm that needs initialisation of either contribution profiles or spectral profiles of each of the factor. The contribution profiles can be initialised using the evolve factor analysis; however, in principle, this approach requires that data must belong to the sequential process. The initialisation of the spectral profiles are usually carried out using the pure variable approach such as SIMPLISMA algorithm, this approach demands that each factor must have the pure variables in the data sets. Despite these limitations, the existing approaches have been quite a successful for initiating the MCR-ALS analysis. However, the present work proposes an alternate approach for the initialisation of the spectral variables by generating the random variables in the limits spanned by the maxima and minima of each spectral variable of the data set. The proposed approach does not require that there must be pure variables for each component of the multicomponent system or the concentration direction must follow the sequential process. The proposed approach is successfully validated using the excitation-emission matrix fluorescence data sets acquired for certain fluorophores with significant spectral overlap. The calculated contribution and spectral profiles of these fluorophores are found to correlate well with the experimental results. In summary, the present work proposes an alternate way to initiate the MCR-ALS analysis.

On rotational ambiguity in parallel profiles with linear dependencies

Several curve resolution techniques have been developed to analyze higher‐order data structures, among which one can name parallel profiles with linear dependencies (PARALIND). The PARALIND is a variant of the Parallel Factor Analysis (PARAFAC) to cope with linear dependencies. Applying a PARAFAC model with different random initializations to a noisy three‐way data with linearly dependent loadings always converges to a best‐fitted noisy solution. Since PARAFAC maximally fits the noise part of the data, rank‐deficiency is broken, and it causes a noise‐induced unique decomposition. The uniqueness due to the presence of noise in rank‐deficient cases is an artificial phenomenon called “surface uniqueness.” The PARALIND model causes non‐uniqueness to be revealed, ie, it leads to a different set of profiles in each run. However, it might not give us all the possible solutions. Therefore, there is still a demand for determining all and every possible solution. The aim of this paper is 3‐fold: (1) A method is developed for determining feasible regions using the PARALIND model. (2) The surface uniqueness phenomenon is clarified. (3) The effect of interaction matrices in PARALIND as an active constraint is emphasized and discussed in detail using simulated noisy and noise‐free two‐ and three‐component data arrays.

Comparison of multivariate curve resolution strategies in quantitative LCxLC: Application to the quantification of furanocoumarins in apiaceous vegetables

Comprehensive two-dimensional liquid chromatography (LC × LC) has been gaining popularity for the analysis of complex samples in a wide range of fields including metabolomics, environmental analysis, and food analysis. While LC × LC can provide greater chromatographic resolution than one-dimensional LC (1D-LC), overlapping peaks are often still present in separations of complex samples, a problem that can be alleviated by chemometric curve resolution techniques such as multivariate curve resolution-alternating least squares (MCR-ALS). MCR-ALS has also been previously shown to assist in the quantitative analysis of LC x LC data by isolating pure analyte signals from background signals which are often present at higher levels in LC x LC compared to 1D-LC. In this work we present the analysis of a dataset from the LC × LC analyses of parsley, parsnip and celery samples for the presence and concentrations of 14 furanocoumarins. Several MCR-ALS implementations are compared for the analysis of LC × LC data. These implementations include analyzing the LC x LC chromatogram alone, analyzing the one-dimensional chromatogram alone, as well as two hybrid approaches that make use of both the first and second dimension chromatograms. Furthermore, we compared manual integration of resolved chromatograms versus a simple summation approach, using the resolved chromatographic peaks in both cases. It is found that manual integration of the resolved LC × LC chromatograms provides the best quantification as measured by the consistency between replicate injections. If the summation approach is desired for automation, the choice of MCR-ALS implementation has a large effect on the precision of the analysis. Based on these results, the concentrations of the 14 furanocoumarins are determined in the three apiaceous vegetable types by analyzing the LC × LC chromatograms with MCR-ALS and manual integration for peak area determination. The concentrations of the analytes are found to vary greatly between samples, even within a single vegetable type.

Mechanisms of Chromate, Selenate, and Sulfate Adsorption on Al-Substituted Ferrihydrite: Implications for Ferrihydrite Surface Structure and Reactivity

Ferrihydrite is a nanocrystalline Fe (hydr)oxide and important sink for environmental contaminants. Although Fe (hydr)oxides are rarely pure in natural systems, little is known about the effects of structural impurities such as Al on the surface properties and reactivity of ferrihydrite. In this study, we characterized the adsorption mechanisms of chromate, selenate, and sulfate on Al-substituted ferrihydrite (0, 6, 12, 18, and 24 mol % Al) using in situ attenuated total reflection Fourier transform infrared spectroscopy. Spectral data sets recorded as a function of pH were processed using a multivariate curve resolution technique to identify which types of surface species form and to generate their concentration profiles as a function of pH and Al content. Results show a significant increase in relative fraction of outer-sphere complexes for all three oxyanions with increasing Al substitution. In addition, the effect of Al substitution is found to be mechanism-specific in the case of chromate, with bidentate complexes disproportionately suppressed over monodentate complexes at higher Al contents. Overall, our findings have important implications for the fate of chromate, selenate, and sulfate in subsurface environments and offer new insight into the surface reactivity of Al-ferrihydrite.

The spectral resolution of unknown mixture based on THz spectroscopy with self-modeling technique

This study aims at resolving the spectral profiles of pure components from the mixture THz data. A method based on simple-to-use interactive self-modeling mixture analysis (SIMPLISMA), one of self-modeling curve resolution (SMCR) techniques, was put forward to deal with two-way THz spectral data of unknown mixtures for spectral resolution of single species. SMCR requires pure variables which have intensity contributions from only one component for extracting the content information. However, for the spectra of many materials in THz region (about 0.1–3THz), pure variables are usually unavailable for the overlap of absorption peaks or the presence of baseline. To settle this problem, inverted second derivative was employed as an intermediate step to optimize the determination of pure variable. To test the effect, the proposed method was performed on simulated and experimental THz spectra of multicomponent systems. It is demonstrated that SIMPLISMA combined with inverted second derivative spectra is a promising tool to deconvolve multicomponent systems contributing to the identification of ingredients in unknown mixture in THz region.

Understanding the effect of calibration set design for the application of MCR-ALS analysis on excitation–emission matrix fluorescence (EEMF) data sets under commonly used non-negativity constraints

In this work, by analysing excitation–emission matrix fluorescence (EEMF) spectroscopic data sets with multivariate curve-resolution alternating least square (MCR-ALS) analysis under the commonly used mathematical constraint of non-negativity, an attempt was made to understand how the outcome of curve resolution technique can be influenced by the design of calibration sets. MCR-ALS analysis with commonly used mathematical constraints has been successfully used to analyse multi-component systems having significant spectral overlap. However, the present work showed that even for a simple system where the components do not have spectral overlap with each other, the MCR-ALS technique can resolve the spectra only if the calibration set is suitably designed. To carry out this study, three fluorophores—benzo[a]pyrene, perylene, and pyrene—having no spectral overlap with each other, were chosen. Selection of such fluorophores would enable the observation of fluorescence signatures of one fluorophore if it appears in the MCR-ALS retrieved profile of other fluorophores. Ten calibration sets with different approaches were created. EEMF data sets acquired for these calibration sets were subjected to MCR-ALS analysis. With most of the calibration sets, it was difficult to retrieve the pure EEMF spectra of all three fluorophores even if they did not have spectral overlap with each other. This work also evolved certain criterion for creating the calibration set which would enable the retrieval of pure EEMF spectra of all fluorophores of a multifluorophoric mixture using curve resolution technique. The present work clearly shows that pure spectral profiles of all the fluorophores under commonly used mathematical constraints can only be retrieved provided curve resolution analysis is performed on a suitably designed calibration set.

A multiresolution approach for the convergence acceleration of multivariate curve resolution methods

Modern computerized spectroscopic instrumentation can result in high volumes of spectroscopic data. Such accurate measurements rise special computational challenges for multivariate curve resolution techniques since pure component factorizations are often solved via constrained minimization problems. The computational costs for these calculations rapidly grow with an increased time or frequency resolution of the spectral measurements.The key idea of this paper is to define for the given high-dimensional spectroscopic data a sequence of coarsened subproblems with reduced resolutions. The multiresolution algorithm first computes a pure component factorization for the coarsest problem with the lowest resolution. Then the factorization results are used as initial values for the next problem with a higher resolution. Good initial values result in a fast solution on the next refined level. This procedure is repeated and finally a factorization is determined for the highest level of resolution. The described multiresolution approach allows a considerable convergence acceleration. The computational procedure is analyzed and is tested for experimental spectroscopic data from the rhodium-catalyzed hydroformylation together with various soft and hard models.

Fitting-free curve resolution of spectroscopic data: Chemometric and physical chemical viewpoints

In this paper the authors have investigated spectroscopic data analysis according to a recent development, i.e. the Direct Inversion in the Spectral Subspace (DISS) procedure.DISS is a supervised curve resolution technique, consequently it can be used once the spectra of the potential pure components are known and the experimental spectrum of a chemical mixture is also presented; hence the task is to determine the composition of the unknown chemical mixture. In this paper, the original algorithm of DISS is re-examined and some further critical reasoning and essential developments are provided, including the detailed explanations of the constrained minimization task based on Lagrange multiplier regularization approach.The main conclusion is that the regularization used for DISS is needed because of the possible shifted spectra effect instead of collinearity; and this new property, i.e. treating the mild shifted spectra effect, of DISS can be considered as its main scientific advantage.

Elimination of chromatographic and mass spectrometric problems in GC-MS analysis of Lavender essential oil by multivariate curve resolution techniques: Improving the peak purity assessment by variable size moving window-evolving factor analysis.

In analysis of complex natural matrices by gas chromatography-mass spectrometry (GC-MS), many disturbing factors such as baseline drift, spectral background, homoscedastic and heteroscedastic noise, peak shape deformation (non-Gaussian peaks), low S/N ratio and co-elution (overlapped and/or embedded peaks) lead the researchers to handle them to serve time, money and experimental efforts. This study aimed to improve the GC-MS analysis of complex natural matrices utilizing multivariate curve resolution (MCR) methods. In addition, to assess the peak purity of the two-dimensional data, a method called variable size moving window-evolving factor analysis (VSMW-EFA) is introduced and examined. The proposed methodology was applied to the GC-MS analysis of Iranian Lavender essential oil, which resulted in extending the number of identified constituents from 56 to 143 components. It was found that the most abundant constituents of the Iranian Lavender essential oil are α-pinene (16.51%), camphor (10.20%), 1,8-cineole (9.50%), bornyl acetate (8.11%) and camphene (6.50%). This indicates that the Iranian type Lavender contains a relatively high percentage of α-pinene. Comparison of different types of Lavender essential oils showed the composition similarity between Iranian and Italian (Sardinia Island) Lavenders.

Real-time monitoring, diagnosis, and time-course analysis of microalgae Scenedesmus AMDD cultivation using dual excitation wavelength fluorometry

Dual excitation wavelength fluorometry was used for real-time monitoring and diagnosis of the growth of the microalgae Scenedesmus AMDD in a 300-L continuous photobioreactor (PBR). Emission spectra were acquired at 1-min intervals using excitation lights at 365 and 540 nm. Real-time dry weight estimations were achieved using linear regression with the chlorophyll peak, while protein estimations required a more complex Principal Component Regression model, which takes advantage of the full emission spectrum. The resulting regression coefficients were 0.95 and 0.80, respectively. Furthermore, the spectra were analyzed using multivariate curve resolution technique. The proposed approach for fluorescence-based, real-time measurements of key algae cultivation parameters and culture state diagnosis was successfully demonstrated in a 42-day PBR validation test.

Headspace Adsorptive Microextraction Analysis of Oregano Fragrance Using Polyaniline-Nylon-6 Nanocomposite, GC-MS, and Multivariate Curve Resolution

Volatile components of oregano (Origanum vulgare L.) were extracted using headspace adsorptive microextraction and were analyzed by gas chromatography-mass spectrometry. Novel polyaniline-nylon-6 nanocomposite was used for headspace adsorptive microextraction due to its suitable characteristics. Multivariate curve resolution-alternating least square was applied for resolving the co-eluted gas chromatography-mass spectrometry peak clusters into pure chromatograms and mass spectra. Problems such as low signal-to-noise ratio of peaks and noisy data were handled with the aim of chemometrics. Chemicals of γ-terpinene, p-cymene, thymol, and carvacrol are the main components among 48 identified components of oregano fragrance. The volatile components of the fragrance and the essential oil of oregano were compared in term of chemical components and relative percentages points of view. The reproducibility in peak responses was investigated using three replicate experiments for the fragrance and the essential oil samples. The results proved that headspace adsorptive microextraction, gas chromatography-mass spectrometry, and multivariate curve resolution techniques could be useful to extract and analyze natural aromas and fragrances.

How To Apply the Complementarity and Coupling Theorems in MCR Methods: Practical Implementation and Application to the Rhodium-Catalyzed Hydroformylation

Multivariate curve resolution techniques can be used in order to extract from spectroscopic data of chemical mixtures the contributions from the pure components, namely, their concentration profiles and their spectra. The curve resolution problem is by nature a matrix factorization problem, which suffers from the difficulty that the pure component factors are not unique. In chemometrics the so-called rotational ambiguity paraphrases the existence of numerous, feasible solutions. However, most of these solutions are not chemically meaningful. The rotational ambiguity can be reduced by adding additional information on the pure factors such as known pure component spectra or measured concentration profiles of the components. The complementarity and coupling theory (as developed in J. Chemometrics 2013 27, 106–116) provides a theoretical basis for exploiting such adscititious information in order to reduce the ambiguity. In this work the practical application of the complementarity and coupling theory is expl...

Characterisation of Intervertebral Discs Using MID-IR Spectroscopic Imaging

Introduction Lower back pain affects millions of people worldwide, and has been linked to degenerative changes in the intervertebral disc (IVD) of the spine. In the “NPmimetic” project, a multidisciplinary team has come together to create a biomimetic nanopolymer based implant and develop a minimally invasive therapy to reconstruct and regenerate diseased nucleus pulposus (NP). The biodegradable nanofibers of the implant can also be designed to carry anti-inflammatory drugs, which can be released in situ promoting healing and preventing inflammation (http://npmimetic.com/). An IVD consists primarily of a proteoglycan-water gel embedded in a randomly arranged collagen network in the NP, and highly ordered collagen lamella in the annulus fibrosus (AF). Disc cells in the AF are elongated parallel to the collagen fibers and produce predominantly collagen I in the outer AF in response to deformation. NP cells are responsive to hydrostatic pressure and synthesize mostly proteoglycan and collagen II. In adults, the cell density in an IVD is very low and cell phenotypes can change in response to altered matrix and stress distribution.1 For successful regeneration, tissue integrity together with the right mechanical environment is essential for normal cell function. To develop suitable biomimetic implants a thorough characterization profile, that can be used as an aspirational target is important. In this study, we use Fourier transform infrared (FTIR) microscopic imaging to obtain chemical maps of control and in-vivo CABC-degenerated goat IVDs.2,3 Materials and Methods Goat IVDs were kindly provided from VU University Medical Centre (VUMC), Amsterdam. The IVDs were formalin fixed (10%, overnight), paraffin embedded, and 4 µm sections were mounted on custom- made reflective steel slides. FTIR microscopy in transflection mode was used to generate chemical distribution maps from unstained paraffin sections. One microscopic image covers only a very small area (350 × 350 µm) of an IVD sample (∼2.8 × 2.3 cm, transverse section). Bigger areas are measured by sequential sample movement and image acquisition covering a user-defined mosaic image area. In this example, an area of 80 × 64 images was measured resulting in a total of 10,720 IR spectra per IVD section. FTIR mosaic imaging generally generates many thousands of data points. A major challenge is handling and analyzing such large and chemically complex datasets to extract meaningful information. However, using iterative multivariate curve resolution (MCR) techniques on the reduced data matrix from principal component analysis analysis of second derivative spectra it is possible to deconvolute highly overlapping infrared peaks into single contributions of different molecular species. Results The chemical identity of the extracted component using an iterative MCR algorithm is determined by comparing the extracted spectral profiles with the spectral profiles of reference materials for proteoglycan and collagen. Spectral features matching typical proteoglycan and collagen spectral characteristics are observed. The distribution of the extracted MCR factors for collagen and proteoglycan are compared with consecutive IVD sections, which were stained using traditional histological methods; Masson trichrome staining for collagen and Alcian blue staining for proteoglycan. A good match can be observed between the distribution maps of collagen and proteoglycans derived from FTIR microscopic imaging measurements and the traditionally stained sections. Conclusion FTIR microscopic imaging in transflection mode has been successfully used to generate molecular images of the collagen and proteoglycan distribution of natural IVD material without the use of contrast-enhancing agents. The use of second derivative spectra together with the application of iterative MCR algorithms shows great potential to enhance the chemical specificity of FTIR mosaic imaging and opens up the possibility to distinguish between different proteclycans and proteins as well as their secondary structure, which usually are expressed only through minor peak shifts. Acknowledgments We thank the European Union (NPMIMETIC ref 246351) for 7th Framework Program funding. Disclosure of Interest None declared References Adams MA, Roughley PJ. What is intervertebral disc degeneration, and what causes it? Spine 2006;31(18):2151–2161 Hoogendoorn RJW, Wuisman PIJM, Smit TH, Everts VE, Helder MN. Experimental intervertebral disc degeneration induced by chondroitinase ABC in the goat. Spine 2007;32(17):1816–1825 Hoogendoorn RJW, Helder MN, Kroeze RJ, Bank RA, Smit TH, Wuisman PI. Reproducible long-term disc degeneration in a large animal model. Spine 2008;33(9):949–954