1,721,160 research outputs found
Tecniche di analisi per la caratterizzazione dell'oliva Taggiasca in salamoia mediante spettroscopia NIR
No full textClass-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues – A tutorial
No full textQualitative data modelling is a fundamental branch of pattern recognition, with many applications in analytical chemistry, and embraces two main families: discriminant and class-modelling methods. The first strategy is appropriate when at least two classes are meaningfully defined in the problem under study, while the second strategy is the right choice when the focus is on a single class. For this reason, class-modelling methods are also referred to as one-class classifiers.Although, in the food analytical field, most of the issues would be properly addressed by class-modelling strategies, the use of such techniques is rather limited and, in many cases, discriminant methods are forcedly used for one-class problems, introducing a bias in the outcomes.Key aspects related to the development, optimisation and validation of suitable class models for the characterisation of food products are critically analysed and discussed
Multivariate class modeling for the verification of food-authenticity claims
No full textFood authenticity is a challenging analytical problem normally addressed using sophisticated laboratory methods which produce large data sets. Multivariate mathematical methods are required to process such data sets typically to answer a question such as “Is sample X, which claims to be of type A, compatible with type A samples on the basis of its analytical measurements?”. Class-modelling methods are recommended to answer this type of question and the principles, practice and results of several types of such methods are discussed. A comparison, in terms of advantages and short-comings, with the discriminant classification approach is also presented.Food authenticity is a challenging analytical problem normally addressed using sophisticated laboratory methods that produce large data sets. Multivariate mathematical methods are required to process such data sets, typically to answer a question such as " Is sample X, which claims to be of type A, compatible with type-A samples on the basis of its analytical measurements?" .We recommend class-modeling methods to answer this type of question and discuss the principles, the practice and the results of several types of such methods. We also compare them, in terms of advantages and short-comings, with the discriminant-classification approach. © 2012 Elsevier Ltd
Multivariate exploratory strategies for analysis of hyperspectral maps. Applications in the fields of cultural heritage and clinical diagnostics
No full textElectrochemical analysis of Immunoglobulin-Y with ensembles of nanoelectrodes: detection of egg-yolk as binder in tempera paintings
No full textComplete validation for classification and class modeling procedures with selection of variables and/or with additional computed variables
No full textThe evaluation of the predictive ability of a model, is an essential moment of all the chemometrical techniques. So it must be performed very carefully. However, in the case of selection of relevant variables (an essential step in the case of data sets with many, frequently thousands, variables) the selection is generally performed using all the available objects. In some recent classification and class modeling techniques, from the original or from the selected variables the Mahalanobis distances of the leverages from the centroids of the categories in the problem are computed, and then added to the original variables. Also here the Mahalanobis distances are computed with all the objects. The consequence is an overestimate of the prediction ability, very large when the ratio between the number of the objects and that of the variables is rather low, so that the variance-covariance matrix is unstable.In this paper the correct validation procedures are described for the cases of selection of variables and of the addition of Mahalanobis distances computed on the original variables or the selected variables. The estimates of the prediction ability are compared with those obtained with insufficient validation strategies
Use and Abuse of Signal Pre-Processing
No full textRiassunti / absract presentati al XXV Congresso della Divisione di Chimica Analitica della Società Chimica Italian
Classifying aroma of two Italian red wines by headspace-mass spectrometry and chemometrics
No full text
- …
