Multivariate prototype approach for authentication of food products

UMR Sciences pour l'Oenologie, INRA, 34060, Montpellier, France
Chemometrics and Intelligent Laboratory Systems (Impact Factor: 2.32). 03/2007; 87(2). DOI: 10.1016/j.chemolab.2007.01.003


Authentication basically consists in deciding if a given unknown product belongs or not to a group of interest, defined by producers or regulators. More often, in order to demonstrate the authentication ability of a given instrumental analysis, several other groups are arbitrarily chosen. Then a Factorial or Linear Discriminant Analysis (FDA or LDA) or a Partial Least Squares Discriminant Analysis (PLS-DA) is usually performed; the model therefore depends on the nature of all observed groups of the study. The aim of this paper was to investigate an approach, named "prototype approach", based on a model built up only using the group of products of interest. Such an approach has the advantage not to depend on the whole complementary data of the study. Prototype approach is inspired by Multivariate Statistical Process Control and Hotelling T 2 statistic and consists in buiding up the assignment model according to the group of interest. Then, authentication step of new data is performed. Prototype approach and FDA were compared on a case study (authentication of Beaujolais red wines using their polyphenolic composition). False negative (#FN) and false positive (#FP) numbers were estimated by bootstrapping procedures for both methods. Compared to FDA, the prototype approach gave higher #FP with larger variability and lower #FN with lower variability. Wines produced with the same grape variety as AOC Beaujolais but in other regions were poorly authenticated. The prototype approach appears to be more flexible than FDA. The user can adjust the theoretical α risk in relation to its strategy, making that decision tool an alternative to discriminant analyses for authentication.

Download full-text


Available from: Dominique Bertrand
  • Source
    • "Qualitative blend monitoring and control (Prototype) Prototype is inspired by Multivariate Statistical Process Control and Hotelling's T 2 statistics, and belongs to the same family of qualitative methods as SIMCA. The Prototype approach was described by Preys et al., 2007 "
    [Show abstract] [Hide abstract]
    ABSTRACT: The implementation of a blend monitoring and control method based on a process analytical technology such as near infrared spectroscopy requires the selection and optimization of numerous criteria that will affect the monitoring outputs and expected blend end-point. Using a five component formulation, the present article contrasts the modeling strategies and end-point determination of a traditional quantitative method based on the prediction of the blend parameters employing partial least-squares regression with a qualitative strategy based on principal component analysis and Hotelling's T(2) and residual distance to the model, called Prototype. The possibility to monitor and control blend homogeneity with multivariate curve resolution was also assessed. The implementation of the above methods in the presence of designed experiments (with variation of the amount of active ingredient and excipients) and with normal operating condition samples (nominal concentrations of the active ingredient and excipients) was tested. The impact of criteria used to stop the blends (related to precision and/or accuracy) was assessed. Results demonstrated that while all methods showed similarities in their outputs, some approaches were preferred for decision making. The selectivity of regression based methods was also contrasted with the capacity of qualitative methods to determine the homogeneity of the entire formulation.
    Full-text · Article · Jul 2014 · International Journal of Pharmaceutics
  • [Show abstract] [Hide abstract]
    ABSTRACT: Identification of mushrooms that have been physically damaged and the measurement of time elapsed from harvest are very important quality issues in industry. The purpose of this study was to assess whether the chemical changes induced by physical damage and the aging of mushrooms can: (a) be detected in the visible and near infrared absorption spectrum and (b) be modeled using multivariate data analysis. The effect of pre-treatment and the use of different spectral ranges to build PLS models were studied. A model that can identify damaged mushrooms with high sensitivity (0.98) and specificity (1.00), and models that allow estimation of the age (1.0-1.4 days root mean square error of cross-validation) were developed. Changes in water matrix and alterations caused by enzymatic browning were the factors that most influenced the models. The results reveal the possibility of developing an automated system for grading mushrooms based on reflectance in the visible and near infrared wavelength ranges.
    No preview · Article · Mar 2009 · Journal of Agricultural and Food Chemistry
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We propose a very simple and fast method for detecting Sudan dyes (I, II, III and IV) in commercial spices, based on characterizing samples through their UV-visible spectra and using multivariate classification techniques to establish classification rules. We applied three classification techniques: K-Nearest Neighbour (KNN), Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA). A total of 27 commercial spice samples (turmeric, curry, hot paprika and mild paprika) were analysed by chromatography (HPLC-DAD) to check that they were free of Sudan dyes. These samples were then spiked with Sudan dyes (I, II, III and IV) up to a concentration of 5 mg L(-1). Our final data set consisted of 135 samples distributed in five classes: samples without Sudan dyes, samples spiked with Sudan I, samples spiked with Sudan II, samples spiked with Sudan III and samples spiked with Sudan IV. Classification results were good and satisfactory using the classification techniques mentioned above: 99.3%, 96.3% and 90.4% of correct classification with PLS-DA, KNN and SIMCA, respectively. It should be pointed out that with SIMCA, there are no real classification errors as no samples were assigned to the wrong class: they were just not assigned to any of the pre-defined classes.
    Full-text · Article · Sep 2009 · Talanta
Show more