Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data

Mid-infrared spectroscopy and UV−vis spectroscopy combined with multivariate data analysis have been applied for the discrimination of Austrian red wines, including the cultivars Cabernet Sauvignon, Merlot, Pinot Noir, Blaufrankisch (Lemberger), St. Laurent, and Zweigelt. Both authentic wines and their phenolic extracts were investigated by attenuated total reflectance (ATR)−mid-infrared spectroscopy. Phenolic extracts were also investigated by UV−vis spectroscopy. The wine extracts were obtained by solid-phase extraction with C-18 columns and elution by methanol containing 0.01% hydrochloric acid. Hierarchical cluster analysis was performed with mid-infrared spectra of both wines and extracts, as well as with UV−vis spectra of the phenolic extracts. Data processing involved vector normalization and derivation of the spectra. Due to varying concentrations of main components including sugar and organic acids, satisfactory classification of untreated wines was not achieved. However, when using mid-infrared ...

Rapid Method for the Discrimination of Red Wine Cultivars Based on Mid-Infrared Spectroscopy of Phenolic Wine Extracts

Food authenticity is an issue of concern to food processors, retailers, regulatory authorities and consumers alike. Near infrared (NIR) spectroscopy has many potential advantages as an authenticity testing tool and has already been applied to a number of authentication problems using a range of sample presentation and chemometric techniques. This review outlines the principles of the statistical procedures used so far, and summarises the work reported to-date on a range of foods and food ingredients.

Review: Authentication of food and food ingredients by near infrared spectroscopy

Non-destructive prediction of selected quality attributes of beef by near-infrared reflectance spectroscopy between 750 and 1098 nm.

Classification of bread wheat flours in different quality categories by a wavelet-based feature selection/classification algorithm on NIR spectra

This paper reports an application of qualitative analysis in the flour milling industry based on near infrared spectroscopy and a factorial discriminant procedure. Samples of different commercial flour types were collected from a number of mills and a discriminant model developed; evaluation of this model on a different set of 99 samples produced a correct classification rate of 97%.

G. Downey

Papers

Commercial Wheatflour Authentication by Discriminant Analysis of near Infrared Reflectance Spectra