Detection of several common adulterants in raw milk by mid-infrared spectroscopy and one-class and multi-class multivariate strategies
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...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | Brasil |
| Institución: | Universidade Federal de Minas Gerais (UFMG) |
| Repositorio: | Repositório Institucional da UFMG |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.ufmg.br:1843/40513 |
| Acceso en línea: | http://hdl.handle.net/1843/40513 |
| Access Level: | acceso abierto |
| Palabra clave: | Milk adulteration One-class modelling Adulterant detection Multi-class modelling Multivariate SIMCA screening Tecnologia de alimentos Leite |
| Sumario: | 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. |
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