Multivariate foods

Robust Methods in Analysis of Multivariate Food Chemistry Data... 

Mendez, J. et al.. Color quahty of pigments in cochineals Dactylopius coccus Costa). Geographical characterization using multivariate statistical analysis, J. Agric. Food Chem., 52, 1331, 2004. 

To detect adulteration of wine. Bums et al. (2002) found that the ratios of acetylated to p-coumaroylated conjugates of nine characteristic anthocyanins served as useful parameters to determine grape cultivars for a type of wine. Our laboratory utilized mid-infrared spectroscopy combined with multivariate analysis to provide spectral signature profiles that allowed the chemically based classification of antho-cyanin-containing fruits juices and produced distinctive and reproducible chemical fingerprints, making it possible to discriminate different juices. " This new application of ATR-FTIR to detect adulteration in anthocyanin-containing juices and foods may be an effective and efficient method for manufacturers to assure product quality and authenticity. 

S. Wold, C. Albano, W.J. Dunn III, K. Esbensen, S. Hellberg, E. Johansson and M. Sjostrdm, Pattern recognition finding and using patterns in multivariate data. In Food Research and Data Analysis (H. Martens and H. Russwurm Jr., Eds.). Applied Science, London, 1983, p. 147. 

T. Naes and E. Risvik (Editors), Multivariate Analysis of Data in Sensory Science. Data Handling in Science and Technology Series, Elsevier, Amsterdam, 1996 J.R. Piggott (Editor), Sensory Analysis of Foods. Elsevier, London, 1984. 

Gonzalez-Miret, M. L., Terrab, A., Hernanz, D., Femandez-Recamales, M. A., and Heredia, F. J. (2005). Multivariate correlation between color and mineral composition of honeys and by their botanical origin. /. Agric. Food Chem. 53, 2574-2580. 

Irudayaraj, J., Xu, F., and Tewari, J. (2003). Rapid determination of invert cane sugar adulteration in honey using FTIR spectroscopy and multivariate analysis. /. Food Sci. 68, 2040-2045. 

A. J. Charlton, W. H. Farrington, P. Brereton 2002, (Application of 1H NMR and multivariate statistics for screening complex mixtures quality control and authenticity of instant coffee), J. Agric. Food Chem. 50, 3098-3103. 

Duarte, A. Barros, P. S. Belton, R. Righelato, M. Spraul, E. Humpfer, A. M. Gil 2002, (High-resolution nuclear magnetic resonance spectroscopy and multivariate analysis for the characterisation of beer),/. Agric. Food Chem. 50, 2475-2481. 

The traditional acute, subchronic, and chronic toxicity studies performed in rodents and other species also can be considered to constitute multiple endpoint screens. Although the numerically measured continuous variables (body weight, food consumption, hematology values) generally can be statistically evaluated individually by traditional means, the same concerns of loss of information present in the interrelationship of such variables apply. Generally, traditional multivariate methods are not available, efficient, sensitive, or practical (Young, 1985). 

Because NIR was initially used for food and agriculture products, it has evolved as a technique for complex matrices. Many types of hardware have become available for NIR work interference filters, gratings, interferometers, diode arrays, and acousto-optic tunable filters. And, as it was originally developed for complex mixtures, chemometrics has been an integral part of any NIR analysis for the last few decades. NIR practitioners are quite comfortable with multivariate equations and development of equations for complex matrices. 

In this work, two methods for the determination of caffeine in energy drinks by derivative spectrophotometry and by partial least-squares multivariate spectropho-tometric calibration (PLS-1) are described. Proposed methods involve background correction methods that interferences from vitamins, taurin and food colours were minimized by treating the sample with basic lead acetate and NaHC03 for the arralysis of caffeine in energy drinks. 

Simultaneous Spectrophotometric Determination of Food Additives (Benzoie Aeid, Caffeine, Aspartame and Aeesulfame-K) in Cola Drinks by PLS Multivariate Calibration Method... 

Their increased application in light food and drink products has given a new impetus to develop fast and accurate method for their determination. Among computer-controlled instruments multivariate calibration methods and derivative techniques are playing very important role in the multicomponent analysis of mixtures by UV-VIS molecular absorption spectrophotometry [2]. Both approaches ate useful in the resolution of overlapping band in quantitative analysis [3, 4]. 

Wold, S. Albano, C. Dunn, W. D.,I1I Esbensen, E. Heiberg, S. Johansson, E. Sjostrom, M. "Pattern Recognition Finding and Using Regularities in Multivariate Data in Food Research and Data Analysis," Eds. Martens, H. and H. Russwurm, Jr. Applied Science Publishers, New York,... 

Jorgensen, L.V., Dalgaard, P.H. and Huss, H. (2000). Multiple compound quality index for cold-smoked salmon (Salmo salar) developed by multivariate regression of biogenic amines and pH, J. Agric. Food Chem., 48, 2448. 

Peptide composition can be used to characterize foods and protein hydrolysates by applying multivariate analysis to the peptide pattern [175,182]. Table 19.6 reports basic information on some of the most recent HPLC applications for peptide analysis. 

Vorveille, L., Vercauteren, J., and Rutledge, D.N., Multivariate statistical analysis of two-dimensional NMR data to differentiate grapevine cultivars and clones, Food Chem., 57, 441, 1996. 

Marini, D. and Balestrieri, F., Multivariate analysis of flavanone glycosides in citrus juices, Ital. J. Food Sci., 1, 255, 1995. 

The PLS approach to multivariate linear regression modeling is relatively new and not yet fully investigated from a theoretical point of view. The results with calibrating complex samples in food analysis 122,123) j y jnfj-ared reflectance spectroscopy, suggest that PLS could solve the general calibration problem in analytical chemistry. 

Multivariate calibration is based on the correlation between chemical and physical variables, it is the transformation of a lack of univariate selectivity into a multivariate one. The relationships between chemical variables and those producing food composition (geochemical, variety, alimentary,. . . ) explain the changes caused by factors that cannot be changed one at a time or that have strongly interacting effects. 

The method of audio representation of multivariate analytical data has not been used, to our knowledge, in the representation of food data. However, the large number of properties of sound (pitch, loudness, damping, direction, duration, rest) seems suitable for the recognition of complex information, at least as an alternative. 

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