An integral approach to validation of analytical fingerprinting methods in combination with chemometric modelling for food quality assurance

Analytical fingerprinting in combination with chemometric modelling provides a powerful approach in the framework of food quality assurance. This recently emerging approach allows verifying claims regarding a whole range of food quality characteristics that were previously difficult or impossible to determine. These include claims regarding the nutritional composition, product typicality and method of production as well as the geographical origin of food. As such, the fingerprinting approach can for example be used to verify claims of fish being from a wild origin, or of eggs being organic. Since the fingerprinting approach differs from the classical analytical approach for food quality assurance, the existing protocols for validation of classical analytical methods are not directly applicable. To further standardize and harmonize the fingerprinting approach on an international level, an extension to existing validation protocols is required. This chapter provides a first step towards a further standardization by providing a tentative strategy for integral validation of the fingerprinting approach.

[1]  David T. Morse,et al.  Minsize2: a Computer Program for Determining Effect Size and Minimum Sample Size for Statistical Significance for Univariate, Multivariate, and Nonparametric Tests , 1999 .

[2]  A. Garrido-Varo,et al.  Authentication of organic feed by near-infrared spectroscopy combined with chemometrics: a feasibility study. , 2012, Journal of agricultural and food chemistry.

[3]  K. Héberger,et al.  Supervised pattern recognition in food analysis. , 2007, Journal of chromatography. A.

[4]  S. Uhlig,et al.  Assessment of detection methods in trace analysis by means of a statistically based in-house validation concept , 1998 .

[5]  Knut Baumann,et al.  Cross-validation as the objective function for variable-selection techniques , 2003 .

[6]  Sayan Mukherjee,et al.  Permutation Tests for Classification , 2005, COLT.

[7]  B. Efron Bootstrap Methods: Another Look at the Jackknife , 1979 .

[8]  R. Simon,et al.  Sample size planning for developing classifiers using high-dimensional DNA microarray data. , 2007, Biostatistics.

[9]  Gemma C. Garriga,et al.  Permutation Tests for Studying Classifier Performance , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[10]  Tom Fawcett,et al.  ROC graphs with instance-varying costs , 2006, Pattern Recognit. Lett..

[11]  Jean-Philippe Antignac,et al.  Validation of analytical methods based on mass spectrometric detection according to the “2002/657/EC” European decision: guideline and application , 2003 .

[12]  N. Dovichi,et al.  Nanomolar determination of aminated sugars by capillary electrophoresis , 1995 .

[13]  R. Brereton Consequences of sample size, variable selection, and model validation and optimisation, for predicting classification ability from analytical data , 2006 .

[14]  Olejnik,et al.  Measures of Effect Size for Comparative Studies: Applications, Interpretations, and Limitations. , 2000, Contemporary educational psychology.

[15]  S. Uhlig,et al.  Multi-residue method for non-steroidal anti-inflammatory drugs in plasma using high-performance liquid chromatography-photodiode-array detection. Method description and comprehensive in-house validation. , 1998, Journal of chromatography. B, Biomedical sciences and applications.

[16]  Giulia Papotti,et al.  Detection of honey adulteration by sugar syrups using one-dimensional and two-dimensional high-resolution nuclear magnetic resonance. , 2010, Journal of agricultural and food chemistry.

[17]  Paul Brereton,et al.  Application of (1)h NMR and multivariate statistics for screening complex mixtures: quality control and authenticity of instant coffee. , 2002, Journal of agricultural and food chemistry.

[18]  B. Villegas,et al.  Prediction of the identity of fats and oils by their fatty acid, triacylglycerol and volatile compositions using PLS-DA , 2010 .

[19]  Andrew R. Webb,et al.  Statistical Pattern Recognition: Webb/Statistical Pattern Recognition , 2011 .

[20]  Yu Guo,et al.  Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms , 2010, BMC Bioinformatics.

[21]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .

[22]  M. Eberlin,et al.  Electrospray ionization mass spectrometry fingerprinting of whisky: immediate proof of origin and authenticity. , 2005, The Analyst.