Prediction of mineral composition in commercial extruded dry dog food by near-infrared reflectance spectroscopy
Goi, Arianna (Università di Padova)
Manuelian, Carmen L (Università di Padova. Dipartimento di Scienze Animali e degli Alimenti)
Currò, Sarah (Università di Padova)
De Marchi, Massimo (Università di Padova)

Fecha:	2019
Resumen:	The pet food industry is interested in performing fast analyses to control the nutritional quality of their products. This study assessed the feasibility of near-infrared spectroscopy to predict mineral content in extruded dry dog food. Mineral content in commercial dry dog food samples (n = 119) was quantified by inductively coupled plasma optical emission spectrometry and reflectance spectra (850-2500 nm) captured with FOSS NIRS DS2500 spectrometer. Calibration models were built using modified partial least square regression and leave-one-out cross-validation. The best prediction models were obtained for S (coefficient of determination; R2 = 0. 89), K (R2 = 0. 85), and Li (R2 = 0. 74), followed by P, B, and Sr (R2 = 0. 72 each). Only prediction models for S and K were adequate for screening purposes. This study supports that minerals are difficult to determine with NIRS if they are not associated with organic molecules.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Dog ; Dog nutrition ; Extruded pet food ; NIR
Publicado en:	Animals, Vol. 9 Núm. 9 (2019) , p. 640, ISSN 2076-2615

DOI: 10.3390/ani9090640
PMID: 31480585

11 p, 1.1 MB

El registro aparece en las colecciones:
Artículos > Artículos de investigación
Artículos > Artículos publicados