Author, as appears in the article.: Ruisánchez, I.; Gondim, C.D.S.; Junqueira, R.G.; Souza, S.V.C.D.; Callao, M.P.
Department: Química Analítica i Química Orgànica
URV's Author/s: RUISANCHEZ CAPELASTEGUI, MARÍA ICIAR; Gondim, C.D.S.; Junqueira, R.G.; Souza, S.V.C.D.; CALLAO LASMARIAS, MARÍA PILAR
Keywords: Milk adulteration Adulterant detection
Abstract: A sequential strategy was proposed to detect adulterants in milk using a mid-infrared spectroscopy and soft independent modelling of class analogy technique. Models were set with low target levels of adulterations including formaldehyde (0.074 g.L−1), hydrogen peroxide (21.0 g.L−1), bicarbonate (4.0 g.L−1), carbonate (4.0 g.L−1), chloride (5.0 g.L−1), citrate (6.5 g.L−1), hydroxide (4.0 g.L−1), hypochlorite (0.2 g.L−1), starch (5.0 g.L−1), sucrose (5.4 g.L−1) and water (150 g.L−1). In the first step, a one-class model was developed with unadulterated samples, providing 93.1% sensitivity. Four poorly assigned adulterants were discarded for the following step (multi-class modelling). Then, in the second step, a multi-class model, which considered unadulterated and formaldehyde-, hydrogen peroxide-, citrate-, hydroxide- and starch-adulterated samples was implemented, providing 82% correct classifications, 17% inconclusive classifications and 1% misclassifications. The proposed strategy was considered efficient as a screening approach since it would reduce the number of samples subjected to confirmatory analysis, time, costs and errors.
Research group: Grup de Quimiometria, Qualimetria i Nanosensors
Thematic Areas: Química Química Chemistry
licence for use: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 0308-8146
Author identifier: ; 0000-0003-0889-6596; n/a; n/a;
Record's date: 2017-04-20
Last page: 75
Journal volume: 230
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: http://www.sciencedirect.com/science/article/pii/S0308814617303874
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Article's DOI: 10.1016/j.foodchem.2017.03.022
Entity: Universitat Rovira i Virgili
Journal publication year: 2017
First page: 68
Publication Type: Article Artículo Article
Repositori URV