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Interpretation of matrix chromatographic-spectral data modeling with parallel factor analysis 2 and multivariate curve resolution

Anzardi, Maria Betania et al · Elsevier Science · 2019

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The use of edible films to release antimicrobial constituents in food packaging is a form of active packaging that contributes to extend the shelf-life of a product and provides microbial safety for consumers. A number of plant and animal proteins have been investigated for the production of edible films such as corn zein, wheat gluten, soy and peanut proteins, gelatin, collagen, casein, and whey proteins. Several antimicrobial agents such as organic acids, enzymes, fungicides and natural antimicrobial compounds (spices and essential oils) can be incorporated into edible films. Potassium sorbate (PS) have a long history as a generally recognized as safe food preservative, being widely used to inhibit or retard the growing of a number of recognized food pathogens. Shiga toxin-producing Escherichia coli (STEC) O157 and non-O157 strains have been associated with human disease, ranging from uncomplicated diarrhea to hemorrhagic colitis and hemolytic uremic syndrome. STEC is transmitted to humans through contaminated food, water, and direct contact with infected persons or animals. Several outbreaks caused by non-O157 STEC were described although data implicating these STEC were scanty and the source of infection was not always known. Therefore, the objective of the present study was to incorporate PS into whey protein concentrate (WPC) films and to determine the inhibitory effects of these films against eight non-O157 STEC strains isolated from readyto-eat food samples. Fil: Anzardi, Maria Betania. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina Fil: Arancibia, Juan Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina

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APA 7

Anzardi, M. B. E. A. (2019). Interpretation of matrix chromatographic-spectral data modeling with parallel factor analysis 2 and multivariate curve resolution. http://hdl.handle.net/11336/111697

MLA

Anzardi, Maria Betania et al. "Interpretation of matrix chromatographic-spectral data modeling with parallel factor analysis 2 and multivariate curve resolution." 2019. http://hdl.handle.net/11336/111697.

Chicago

Anzardi, Maria Betania et al. 2019. "Interpretation of matrix chromatographic-spectral data modeling with parallel factor analysis 2 and multivariate curve resolution.". http://hdl.handle.net/11336/111697.

Harvard

Anzardi, M. B. E. A. 2019, Interpretation of matrix chromatographic-spectral data modeling with parallel factor analysis 2 and multivariate curve resolution, Elsevier Science, available at: http://hdl.handle.net/11336/111697 [Accessed 28 Jun. 2026].

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Título
Interpretation of matrix chromatographic-spectral data modeling with parallel factor analysis 2 and multivariate curve resolution
Autor / colaboradores
Anzardi, Maria Betania et al
Editorial
Elsevier Science
Año de publicación
2019
ISSN
0021-9673
ISSN
0021-9673
Idioma
eng

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