Tormod Næs

The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

Near-infrared (NIR) reflectance spectra of five different food products were measured. The spectra were transformed by multiplicative scatter correction (MSC). Principal component regression (PCR) was performed, on both scatter-corrected and uncorrected spectra. Calibration and prediction were performed for four food constituents: protein, fat, water, and carbohydrates. All regressions gave lower prediction errors (7–68% improvement) by the use of MSC spectra than by the use of uncorrected absorbance spectra. One of these data sets was studied in more detail to clarify the effects of the MSC, by using PCR score, residual, and leverage plots. The improvement by using nonlinear regression methods is indicated.

Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data

Abstract Various multiplicative transformations of spectral variables have been used with some success as preprocessing methods for diffuse near-infrared spectroscopy data. We discuss first which additive/multiplicative transformations conserve the area under the spectral curve. Next we look at the relationship between the Multiplicative Scatter and the Standard Normal Variate transformations, and use a minimizing norm formulation to isolate three specific transformations that seem to be singled out as particularly interesting. In an empirical investigation using 8 different data sets, each with several constituents, the different transformation methods are compared.

Comparison of prediction methods for multicollinear data

In this paper we discuss the partial least squares (PLS) prediction method. The method is compared to the predictor based on principal component regression (PCR). Both theoretical considerations and computations on artificial and real data are presented.

Consumer preference mapping of dry fermented lamb sausages

Abstract This paper focuses on using and comparing preference mapping (both internal and external) and partial least squares (PLS) regression in relating consumer preference data of six different dry fermented lamb sausages to conventional sensory profiling (quantitative descriptive analysis—QDA). For segmenting the consumers into smaller, more homogenous consumer groups, two different methods were compared, namely cluster-analysis and visual inspection of plots. The consumer groups were related to demographic and sociological variables. The alternative approaches to preference mapping provided the same general conclusions. For all the three approaches, preference was positively influenced by juiciness, acidic flavour and odour, greasiness and lamb flavour. The cluster analysis identified four subgroups with different preference patterns and indicated that there were market segments for each of the six dry fermented lamb sausages.

Prediction of sensory characteristics of beef by near-infrared spectroscopy

Sensory hardness, tenderness and juiciness of M. Longissimus dorsi muscles from 10 beef carcasses at three ageing stages were predicted by near-infrared (NIR) spectroscopic analysis in the reflection (NIRR) and transmission modes (NIRT) during 14 days ageing at 2°C. Predicting the sensory variables hardness and tenderness from NIRR measurements using principal component regression (PCR), yielded correlation coefficients in the range 0·80-0·90. The root mean square errors of prediction for the predictions of hardness and tenderness were in the range 0·5-0·7, given in sensory assessment units. Juiciness was not well predicted. Prediction of sensory variables from NIRT measurements did not give satisfactory results. Including samples from all carcasses, cows and young bulls in the models resulted in good predictions from NIRR measurements of frozen and thawed samples. However, the best prediction results were generally obtained from separate calibrations of the samples from the bulls. The potential of NIR spectroscopy in the prediction of sensory variables in whole meat needs to be further investigated on a larger number of samples with different breeds, animals and process treatments included.

Artificial neural networks in multivariate calibration

This paper is about the use of artificial neural networks for multivariate calibration. We discuss network architecture and estimation as well as the relationship between neural networks and related linear and non-linear techniques. A feed-forward network is tested on two applications of near infrared spectroscopy, both of which have been treated previously and which have indicated non-linear features. In both cases, the network gives more precise prediction results than the linear calibration method of PCR.

Effect of contextual factors on liking for wine—use of robust design methodology

Abstract This research investigated the effects of context on the acceptability of Chardonnay wines using the robust design methodology. Robust design methods distinguish between two types of design variables: control factors and noise factors. The control factors in this study were enological variables used to make the wines. The noise factors were the contexts in which the wines were evaluated. Eight Chardonnay wines were produced according to an experimental design with or without (1) malolactic fermentation, (2) oak contact, and (3) sugar addition to the finished wine. The wines were served in a laboratory and in a reception room with or without food, and rated for degree of liking on the nine-point hedonic scale by 55 wine consumers. Analyses of variance showed that the control factors and the noise factors had significant, and similar in size, effects on liking. The robust design methodology affords the product designer the ability to better understand the effects of product variation and context variation on product acceptability.

Locally Weighted Regression in Diffuse Near-Infrared Transmittance Spectroscopy

This paper presents an application of locally weighted regression (LWR) in diffuse near-infrared transmittance spectroscopy. The data are from beef and pork samples. The LWR method is based on the idea that a nonlinearity can be approximated by local linear equations. Different weight functions (for the samples) as well as different distance measures for “closeness” are tested. The LWR is compared to principal component regression and partial least-squares regression. The LWR with weighted principal components is shown to give the best results. The improvements with respect to linear regression are up to 15% of the prediction errors.

Characterization of selected South African young cultivar wines using FTMIR Spectroscopy, Gas chromatography, and multivariate data analysis

The powerful combination of analytical chemistry and chemometrics and its application to wine analysis provide a way to gain knowledge and insight into the inherent chemical composition of wine and to objectively distinguish between wines. Extensive research programs are focused on the chemical characterization of wine to establish industry benchmarks and authentication systems. The aim of this study was to investigate the volatile composition and mid-infrared spectroscopic profiles of South African young cultivar wines with chemometrics to identify compositional trends and to distinguish between the different cultivars. Data were generated by gas chromatography and FTMIR spectroscopy and investigated by using analysis of variance (ANOVA), principal component analysis (PCA), and linear discriminant analysis (LDA). Significant differences were found in the volatile composition of the cultivar wines, with marked similarities in the composition of Pinotage wines and white wines, specifically for 2-phenylethanol, butyric acid, ethyl acetate, isoamyl acetate, isoamyl alcohol, and isobutyric acid. Of the 26 compounds that were analyzed, 14 had odor activity values of >1. The volatile composition and FTMIR spectra both contributed to the differentiation between the cultivar wines. The best discrimination model between the white wines was based on FTMIR spectra (98.3% correct classification), whereas a combination of spectra and volatile compounds (86.8% correct classification) was best to discriminate between the red wine cultivars.

Multivariate calibration. II: Chemometric methods

Abstract In this outline of new approaches to multivariate calibration in chemistry the following topics are treated: Advantages of multivariate calibration over conventional univariate calibration: detect and eliminate selectivity problems. Multivariate calibration methods based on selection of some variables vs. methods based on data compression of all the variables. Direct vs. indirect calibration: pure constituents or known samples for calibration? Calibration methods based on data compression by physical modelling: Beers law. Use of Beers law in controlled and natural calibration: the generalized least-squares fit and the best linear predictor. Extending Beers law to handle unknown selectivity problems. Calibration methods based on data compression by factor modelling: the principal component regression and partial least-squares regression. Methods for detecting abnormal samples (outliers). Pre-treatments to linearize data.