title: Partial least squares density modeling (PLS-DM) - a new class-modeling strategy applied to the authentication of olives in brine by near-infrared spectroscopy.
abstract: A new class-modeling method, referred to as partial least squares density modeling (PLS-DM), is presented. The method is based on partial least squares (PLS), using a distance-based sample density measurement as the response variable. Potential function probability density is subsequently calculated on PLS scores and used, jointly with residual Q statistics, to develop efficient class models. The influence of adjustable model parameters on the resulting performances has been critically studied by means of cross-validation and application of the Pareto optimality criterion. The method has been applied to verify the authenticity of olives in brine from cultivar Taggiasca, based on near-infrared (NIR) spectra recorded on homogenized solid samples. Two independent test sets were used for model validation. The final optimal model was characterized by high efficiency and equilibrate balance between sensitivity and specificity values, if compared with those obtained by application of well-established class-modeling methods, such as soft independent modeling of class analogy (SIMCA) and unequal dispersed classes (UNEQ).
authors.affiliation: Department of Pharmacy, University of Genoa, Via Brigata Salerno, 13, I-16147 Genoa, Italy. Electronic address: oliveri@dictfa.unige.it.
authors.forename: Paolo
authors.initials: P
authors.lastname: Oliveri
authors.affiliation: Chemometrics, Qualimetric and Nanosensors Group, Department of Analytical and Organic Chemistry, Rovira i Virgili University, Marcellí Domingo s/n, 43007 Tarragona, Spain.
authors.forename: M Isabel
authors.initials: MI
authors.lastname: López
authors.affiliation: Department of Pharmacy, University of Genoa, Via Brigata Salerno, 13, I-16147 Genoa, Italy.
authors.forename: M Chiara
authors.initials: MC
authors.lastname: Casolino
authors.affiliation: Chemometrics, Qualimetric and Nanosensors Group, Department of Analytical and Organic Chemistry, Rovira i Virgili University, Marcellí Domingo s/n, 43007 Tarragona, Spain.
authors.forename: Itziar
authors.initials: I
authors.lastname: Ruisánchez
authors.affiliation: Chemometrics, Qualimetric and Nanosensors Group, Department of Analytical and Organic Chemistry, Rovira i Virgili University, Marcellí Domingo s/n, 43007 Tarragona, Spain.
authors.forename: M Pilar
authors.initials: MP
authors.lastname: Callao
authors.affiliation: Special Company for Professional Training and Technological and Commercial Promotion of the Chamber of Commerce of Savona, Regione Rollo, 98, I-17031 Albenga, SV, Italy.
authors.forename: Luca
authors.initials: L
authors.lastname: Medini
authors.affiliation: Department of Pharmacy, University of Genoa, Via Brigata Salerno, 13, I-16147 Genoa, Italy.
authors.forename: Silvia
authors.initials: S
authors.lastname: Lanteri
mesh_terms: D005607:Fraud / Q000517:prevention & control*; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D031658:Olea / Q000737:chemistry*; D012492:Salts / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared
chemical_list: D012492:Salts; C017082:brine
keywords: Class-modeling; Density estimation; One-class classifier; Partial least squares (PLS); Potential functions