Repositori URV

Identificador: imarina:5130937

Handle: https://hdl.handle.net/20.500.11797/imarina5130937

Autores:
Gondim, Carina de SouzaJunqueira, Roberto GoncalvesCarvalho de Souza, Scheilla VitorinoRuisanchez, ItziarPilar Callao, M

Resumen:
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.