Author, as appears in the article.: Gondim, Carina de Souza; Junqueira, Roberto Goncalves; Carvalho de Souza, Scheilla Vitorino; Ruisanchez, Itziar; Pilar Callao, M
Department: Química Analítica i Química Orgànica
URV's Author/s: Callao Lasmarias, María Pilar / Ruisánchez Capelastegui, María Iciar
Keywords: Sucrose (pubchem cid: 5988). Starch (pubchem cid: 24836924) Starch Spectrophotometry, infrared Sodium hypochlorite (pubchem cid: 23665760) Sodium hydroxide (pubchem cid: 14798) Sodium citrate (pubchem cid: 23666341) Sodium chloride (pubchem cid: 5234) Sodium carbonate (pubchem cid: 10340) Sodium bicarbonate (pubchem cid: 516892) One-class modelling Multivariate simca screening Multi-class modelling Milk adulteration Milk Hydrogen peroxide (pubchem cid: 784) Hydrogen peroxide Formaldehyde (pubchem cid: 712) Formaldehyde Food contamination Citric acid Animals Adulterant detection starch (pubchem cid: 24836924) sodium hypochlorite (pubchem cid: 23665760) sodium hydroxide (pubchem cid: 14798) sodium citrate (pubchem cid: 23666341) sodium chloride (pubchem cid: 5234) sodium carbonate (pubchem cid: 10340) sodium bicarbonate (pubchem cid: 516892) one-class modelling multivariate simca screening multi-class modelling milk adulteration hydrogen peroxide (pubchem cid: 784) formaldehyde (pubchem cid: 712) 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.
Thematic Areas: Zootecnia / recursos pesqueiros Saúde coletiva Química Odontología Nutrition & dietetics Nutrição Medicine (miscellaneous) Medicina veterinaria Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Interdisciplinar Geociências Food science & technology Food science Farmacia Ensino Engenharias iv Engenharias iii Engenharias ii Engenharias i Enfermagem Educação física Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Chemistry, applied Biotecnología Biodiversidade Astronomia / física Analytical chemistry Administração pública e de empresas, ciências contábeis e turismo
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: mariapilar.callao@urv.cat itziar.ruisanchez@urv.cat
Author identifier: 0000-0003-2691-329X 0000-0002-7097-3583
Record's date: 2024-10-12
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.sciencedirect.com/science/article/pii/S0308814617303874
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Food Chemistry. 230 68-75
APA: Gondim, Carina de Souza; Junqueira, Roberto Goncalves; Carvalho de Souza, Scheilla Vitorino; Ruisanchez, Itziar; Pilar Callao, M (2017). Detection of several common adulterants in raw milk by MID-infrared spectroscopy and one-class and multi-class multivariate strategies. Food Chemistry, 230(), 68-75. DOI: 10.1016/j.foodchem.2017.03.022
Article's DOI: 10.1016/j.foodchem.2017.03.022
Entity: Universitat Rovira i Virgili
Journal publication year: 2017
Publication Type: Journal Publications
Repositori URV