Vegard H. Segtnan

Bio: Vegard H. Segtnan is an academic researcher from Norwegian Food Research Institute. The author has contributed to research in topics: Partial least squares regression & Regression analysis. The author has an hindex of 17, co-authored 26 publications receiving 913 citations.

Raman Spectra of Biological Samples: A Study of Preprocessing Methods

Abstract: In this study preprocessing of Raman spectra of different biological samples has been studied, and their effect on the ability to extract robust and quantitative information has been evaluated. Four data sets of Raman spectra were chosen in order to cover different aspects of biological Raman spectra, and the samples constituted salmon oils, juice samples, salmon meat, and mixtures of fat, protein, and water. A range of frequently used preprocessing methods, as well as combinations of different methods, was evaluated. Different aspects of regression results obtained from partial least squares regression (PLSR) were used as indicators for comparing the effect of different preprocessing methods. The results, as expected, suggest that baseline correction methods should be performed in advance of normalization methods. By performing total intensity normalization after adequate baseline correction, robust calibration models were obtained for all data sets. Combination methods like standard normal variate (SNV), multiplicative signal correction (MSC), and extended multiplicative signal correction (EMSC) in their basic form were not able to handle the baseline features present in several of the data sets, and these methods thus provide no additional benefits compared to the approach of baseline correction in advance of total intensity normalization. EMSC provides additional possibilities that require further investigation.

Non-Contact Transflectance near Infrared Imaging for Representative on-Line Sampling of Dried Salted Coalfish (Bacalao):

Abstract: This paper describes a multi-spectral imaging near infrared (NIR) transflectance system developed for on-line determination of crude chemical composition of highly heterogeneous foods and other bio-materials. The system was evaluated for moisture determination in 70 dried salted coalfish (bacalao), an extremely heterogeneous product. A spectral image cube was obtained for each fish and different sub-sampling approaches for spectral extraction and partial least squares calibration were evaluated. The best prediction models obtained correlation R2 values around 0.92 and root mean square error of cross-validation of 0.70%, which is much more accurate than today's traditional manual grading. The combination of non-contact NIR transflectance measurements with spectral imaging allows rather deep penetrating optical sampling as well as large flexibility in spatial sampling patterns and calibration approaches. The technique works well for moisture determination in heterogeneous foods and should, in principle, wor...

Ensemble methods and partial least squares regression

Abstract: Recently, there has been increased attention in the literature on the use of ensemble methods in multivariate regression and classification. These methods have been shown to have interesting properties for both regression and classification. In particular, they can improve the accuracy of unstable predictors. Ensemble methods have so far been little studied in situations that are common for calibration and prediction in chemistry, i.e. situations with a large number of collinear x-variables and few samples. These situations are often approached by data compression methods such as principal component regression (PCR) or partial least squares regression (PLSR). The present paper is an investigation of the properties of different types of ensemble methods used with PLSR in situations with highly collinear x-data. Bagging and data augmentation by simulated noise are studied. The focus is on the robustness of the calibrations. Real and simulated data are used. The results show that ensembles trained on data with added noise can make PLSR robust against the type of noise added. In particular, the effects of sample temperature variations can be eliminated. Bagging does not seem to give any improvement over PLSR for small and intermediate numbers of components. It is, however, less sensitive to overfitting. Copyright © 2005 John Wiley & Sons, Ltd.

Raman and near-infrared spectroscopy for quantification of fat composition in a complex food model system.

Abstract: Raman and near-infrared (NIR) spectroscopy have been evaluated for determining fatty acid composition and contents of main constituents in a complex food model system. A model system consisting of 70 different mixtures of protein, water, and oil blends was developed in order to create a rough chemical imitation of typical fish and meat samples, showing variation both in fatty acid composition and in contents of main constituents. The model samples as well as the pure oil mixtures were measured using Raman and NIR techniques. Partial least squares regression was utilized for prediction, and fatty acid features were expressed in terms of the iodine value and as contents of saturated, monounsaturated, and polyunsaturated fatty acids. Raman spectroscopy provided the best results for predicting iodine values of the model samples, giving validated estimation errors accounting for 2.8% of the total iodine value range. Both techniques provided good results for predicting the content of saturated, monounsaturated, and polyunsaturated fatty acids in the model samples, yielding validated estimation errors in the range of 2.4–6.1% of the total range of fatty acid content. Prediction results for determining fatty acid features of the pure oil mixtures were similar for the two techniques. NIR was clearly the best technique for modeling content of main constituents in the model samples.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Principal component analysis

Abstract: Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how to understand, use, and interpret principal component analysis. The paper focuses on the use of principal component analysis in typical chemometric areas but the results are generally applicable.

On-line application of near infrared (NIR) spectroscopy in food production

Abstract: Near infrared (NIR) spectroscopy represents an emerging analytical technique, which is enjoying increasing popularity in the food processing industry due to its low running costs, and since it does not require sample preparation. Moreover, it is a non-destructive, environmental friendly, rapid technique capable for on-line application. Therefore, this technique is predestined for implementation as an analytical tool in industrial processing. The different fields of application of NIR spectroscopy reported in the present review highlight its enormous versatility. Quantitative analyses of chemical constituents using this methodology are widespread. Moreover, a wide range of qualitative determinations, e.g. for authenticity control, sample discrimination, the assessment of sensory, rheological or technological properties, and physical attributes have been reported. Both animal- and plant-derived foodstuffs have been evaluated in this context. Highly diverse matrices such as intact solid samples, free-flowing solids, pasty, and fluid samples can by analysed by NIR spectroscopy. Sophisticated conditions for the application in industrial scale comprise among others measurements on moving conveyor belts, in continuous flows in tubes, and monitoring of fermentation processes. For such purposes, different construction designs of NIR spectrometers for hyperspectral imaging, portable devices, fibre optical and direct contact probes as well as tube integrated probes measuring through windows, and automated sample cell loading have been developed. In the present review, emphasis was put on studies dealing with on-line application of NIR spectroscopy for industrial processes in the food industry, which were categorised according to their application conditions into semi-industrial scale and industrial scale.