Søren Balling Engelsen

Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy

A new graphically oriented local modeling procedure called interval partial least-squares (iPLS) is presented for use on spectral data. The iPLS method is compared to full-spectrum partial least-squares and the variable selection methods principal variables (PV), forward stepwise selection (FSS), and recursively weighted regression (RWR). The methods are tested on a near-infrared (NIR) spectral data set recorded on 60 beer samples correlated to original extract concentration. The error of the full-spectrum correlation model between NIR and original extract concentration was reduced by a factor of 4 with the use of iPLS (r = 0.998, and root mean square error of prediction equal to 0.17% plato), and the graphic output contributed to the interpretation of the chemical system under observation. The other methods tested gave a comparable reduction in the prediction error but suffered from the interpretation advantage of the graphic interface. The intervals chosen by iPLS cover both the variables found by FSS and all possible combinations as well as the variables found by PV and RWR, and iPLS is still able to utilize the first-order advantage.

icoshift: A versatile tool for the rapid alignment of 1D NMR spectra

The increasing scientific and industrial interest towards metabonomics takes advantage from the high qualitative and quantitative information level of nuclear magnetic resonance (NMR) spectroscopy. However, several chemical and physical factors can affect the absolute and the relative position of an NMR signal and it is not always possible or desirable to eliminate these effects a priori. To remove misalignment of NMR signals a posteriori, several algorithms have been proposed in the literature. The icoshift program presented here is an open source and highly efficient program designed for solving signal alignment problems in metabonomic NMR data analysis. The icoshift algorithm is based on correlation shifting of spectral intervals and employs an FFT engine that aligns all spectra simultaneously. The algorithm is demonstrated to be faster than similar methods found in the literature making full-resolution alignment of large datasets feasible and thus avoiding down-sampling steps such as binning. The algorithm uses missing values as a filling alternative in order to avoid spectral artifacts at the segment boundaries. The algorithm is made open source and the Matlab code including documentation can be downloaded from www.models.life.ku.dk.

Starch phosphorylation: a new front line in starch research

Starch is the primary energy reserve in higher plants and is, after cellulose, the second most abundant carbohydrate in the biosphere. It is also the most important energy source in the human diet and, being a biodegradable polymer with well-defined chemical properties, has an enormous potential as a versatile renewable resource. The only naturally occurring covalent modification of starch is phosphorylation. Starch phosphate esters were discovered a century ago but were long regarded as a curiosity, receiving little attention. Indeed, the mechanism for starch phosphorylation remained completely unknown until recently. The starch-phosphorylating enzyme is an alpha-glucan water dikinase. It is now known that starch phosphorylation plays a central role in starch metabolism.

Prediction of water-holding capacity and composition of porcine meat by comparative spectroscopy

Four spectroscopic instruments, a fibre optical probe (FOP), a visual (VIS) and near infrared (NIR) reflectance spectrophotometer, a reflectance spectrofluorometer and a low-field (1)H nuclear magnetic resonance (LF-NMR) instrument were used to perform measurements on two muscles (longissimus dorsi and semitendinosous) from 39 pigs, 18 of which were carriers of the Halothane gene. Water-holding capacity (drip loss and filter paper wetness) and chemical composition (intramuscular fat and water) of the muscle samples were determined for spectroscopic calibration. Prediction models were established by partial least squares regression to evaluate the potential of using the spectroscopic techniques in an on-line slaughterhouse system. VIS data gave good prediction models, indicating that current industrial colour systems can be advanced into more specific meat evaluation systems by including the entire visible spectral range. The FOP and fluorescence measurements were less successful, and suffered from sampling problems since they measure only a small area. The best regression models were obtained from LF-NMR data for all reference quality measures and yielded a correlation coefficient of 0.75 with drip loss. LF-NMR proved able to distinguish between the two muscles and the results for their longitudinal relaxation times, T(21), were proportional to their average myofibrillar cross-sectional areas reported in the literature.

Vibrational microspectroscopy of food. Raman vs. FT-IR

FT-IR and Raman spectroscopy are complementary techniques for the study of molecular vibrations and structure. The combination with a microscope results in an analytical method that allows spatially resolved investigation of the chemical composition of heterogeneous foods and food ingredients. The high spatial resolution makes it possible to study areas down to approximately 10×10 μm with FT-IR microspectroscopy and approximately 1×1 μm with Raman microspectroscopy. This presentation highlights the advantages and disadvantages of the two microspectroscopic techniques when applied to different heterogeneous food systems. FT-IR and Raman microspectroscopy were applied to a number of different problems related to food analysis: (1) in situ determination of starch and pectin in the potato cell, (2) in situ determination of the distribution of amygdalin in bitter almonds, (3) the composition of blisters found on the surface of bread, (4) the microstructure of high-lysine barley and (5) the composition of white spots in the shell of frozen shrimps.

Chemometric Quantitation of the Active Substance (Containing C(N) in a Pharmaceutical Tablet Using Near-Infrared (NIR) Transmittance and NIR FT-Raman Spectra

In this study, near-infrared (NIR) transmittance and Raman spectroscopy chemometric calibrations of the active substance content of a pharmaceutical tablet were developed using partial least-squares regression (PLS). Although the active substance contained the strongly Raman active C≡N functional group, the best results were obtained with NIR transmittance, which highlights the difference between (microscopic) surface sampling and whole tablet diffuse transmittance sampling. The tablets exist in four dosages with only two different concentrations of active substance (5 mg (5.6% w/w), and 10, 15, and 20 mg (8.0% w/w) active substance per tablet). A calibration on all four dosages resulted in a prediction error expressed as the root mean squared error of cross-validation (RMSECV) of 0.30% w/w for the NIR transmittance calibration. The corresponding error when using Raman spectra was 0.56% w/w. Specially prepared calibration batches covering the range 85–115% of the nominal content for each dosage were added to the first sample set, and NIR transmittance calibrations on this set—containing coated as well as uncoated tablets—gave a further reduction in prediction errors to 0.21–0.289% w/w. This corresponds to relative prediction errors (RMSECV/ynom) of 2.6–3.7%. This is a reasonably low error when compared to the error of the chromatographic reference method, which was estimated to 3.5%.

Chemometrics in food science—a demonstration of the feasibility of a highly exploratory, inductive evaluation strategy of fundamental scientific significance

Abstract At the roots of science lies observation and data collection from the world as is and from which conclusions can be induced after classification. This is far from the present theory-driven, deductive, normative stage of science which depends heavily on modelling discrete functional factors in laboratory experiments and suppresses the aspect of interaction. In spite of its successes, science today has great difficulty in adapting to the changes which technology has created to cope with registering and evaluating real data from the world, such as in food production chains. This paper demonstrates that it is possible and profitable with the help of new technology to reintroduce an explorative, inductive strategy to investigate the chemistry of a complex food process as is with a minimum of a priori assumptions. The food process investigated is a sugar plant and the tools necessary in this strategy include a multivariate screening method (fluorescence spectroscopy), an arsenal of chemometric models (PCA, PLS, principal variables), including multiway models ( parafac , Tucker), and a computer. Not only can chemical criteria and process parameters throughout the process be validly predicted by the screening method, but process irregularities as well as chemical species can also be detected and validated by multiway chemometric techniques. Inspired by examples from the food area, the paper further discusses the nature of the exploration method in the selection of tools and data. The aim is to study complex processes as a whole in order to model interaction of the underlying latent functional factors which may later be defined more precisely by deductive methods. These methods in combination with an appropriate multivariate screening method allow for unique identification of objects—a significant prerequisite for a viable, exploratory, inductive data strategy which is needed as a fundamental complement to prevalent normative research in order to obtain a science on the interdisciplinary level.

icoshift: An effective tool for the alignment of chromatographic data.

The Interval Correlation Optimised Shifting algorithm (icoshift) has recently been introduced for the alignment of nuclear magnetic resonance spectra. The method is based on an insertion/deletion model to shift intervals of spectra/chromatograms and relies on an efficient Fast Fourier Transform based computation core that allows the alignment of large data sets in a few seconds on a standard personal computer. The potential of this programme for the alignment of chromatographic data is outlined with focus on the model used for the correction function. The efficacy of the algorithm is demonstrated on a chromatographic data set with 45 chromatograms of 64,000 data points. Computation time is significantly reduced compared to the Correlation Optimised Warping (COW) algorithm, which is widely used for the alignment of chromatographic signals. Moreover, icoshift proved to perform better than COW in terms of quality of the alignment (viz. of simplicity and peak factor), but without the need for computationally expensive optimisations of the warping meta-parameters required by COW. Principal component analysis (PCA) is used to show how a significant reduction on data complexity was achieved, improving the ability to highlight chemical differences amongst the samples.

Near-Infrared Absorption and Scattering Separated by Extended Inverted Signal Correction (EISC): Analysis of Near-Infrared Transmittance Spectra of Single Wheat Seeds

A new extended method for separating, e.g., scattering from absorbance in spectroscopic measurements, extended inverted signal correction (EISC), is presented and compared to multiplicative signal correction (MSC) and existing modifications of this. EISC preprocessing is applied to near-infrared transmittance (NIT) spectra of single wheat kernels with the aim of improving the multivariate calibration for protein content by partial least-squares regression (PLSR). The primary justification of the EISC method is to facilitate removal of spectral artifacts and interferences that are uncorrelated to target analyte concentration. In this study, EISC is applied in a general form, including additive terms, multiplicative terms, wavelength dependency of the light scatter coefficient, and simple polynomial terms. It is compared to conventional MSC and derivative methods for spectral preprocessing. Performance of the EISC was found to be comparable to a more complex dual-transformation model obtained by first calculating the second derivative NIT spectra followed by MSC. The calibration model based on EISC preprocessing performed better than models based on the raw data, second derivatives, MSC, and MSC followed by second derivatives.

Early prediction of water-holding capacity in meat by multivariate vibrational spectroscopy

This study had the dual purpose of (a) investigating the feasibility of measuring fundamental vibrational information in fresh porcine meat using infrared (IR) absorption and Raman scattering, and (b) investigating if the vibrational spectra obtained within 1 h after slaughter contained information about the water-holding capacity (WHC) of the meat. Preliminary studies performed at a research slaughterhouse revealed a high correlation between WHC and both IR (r=0.89) and Raman spectra using Partial Least Squares Regressions (PLSR). The good results were confirmed under industrial conditions using FT-IR at-line spectroscopy. However, the latter experiment yielded a somewhat lower correlation (r=0.79). This result is, however, promising for the purpose of finding a method for classification of carcasses with regard to WHC at the slaughter line. The IR region 1800-900 cm(-1) contains the best predictive information according to WHC of the porcine meat. This region covers functional group frequencies of water, protein, fat and glycogen, including the carbonyl and amide groups.