Article

Multivariate prototype approach for authentication of food products

UMR Sciences pour l'Oenologie, INRA, 34060, Montpellier, France; Unité de Sensométrie et de Chimiométrie, ENITIAA/INRA, 44322, Nantes Cedex, France
Chemometrics and Intelligent Laboratory Systems (Impact Factor: 2.38). 03/2007; 87(2). DOI: 10.1016/j.chemolab.2007.01.003

ABSTRACT 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.

0 Bookmarks
 · 
294 Views
  • Source
    [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.
    International Journal of Pharmaceutics 07/2014; · 3.79 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Seventy-three Southern Italian red wines were characterized according to their content in total polyphenols, trans- and cis-resveratrol and biogenic amines. These quality parameters were used in multivariate statistical analysis to discriminate the wines according to their specific geographical origin. The results indicated that total polyphenols, resveratrol isomers and biogenic amines provide a good prospect for discriminating wines by regions. The discrimination was also possible to a lesser extent by cultivar. In particular, canonical variate analysis suggested that the discrimination of wines according to their provenance is based on the following parameters: cis-resveratrol, total polyphenols, spermidine and tryptamine for Basilicata region; agmatine and trans-resveratrol for Calabria and Campania regions; cadaverine, ethanolamine, histamine, putrescine and tyramine for Puglia region. KeywordsBiogenic amines–Multivariate analysis–Red wine–Resveratrol–Total polyphenols–Wine authentication
    European Food Research and Technology 05/2011; 232(5):889-897. · 1.39 Impact Factor
  • [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.
    Journal of Agricultural and Food Chemistry 03/2009; 57(5):1903-7. · 3.11 Impact Factor

Full-text (2 Sources)

Download
136 Downloads
Available from
Jun 5, 2014