Prediction of Mineral Composition in Commercial Extruded Dry Dog Food by Near-Infrared Reflectance Spectroscopy

Arianna Goi; Carmen L Manuelian; Sarah Currò; Massimo De Marchi

doi:10.3390/ani9090640

Prediction of Mineral Composition in Commercial Extruded Dry Dog Food by Near-Infrared Reflectance Spectroscopy

Animals (Basel). 2019 Sep 1;9(9):640. doi: 10.3390/ani9090640.

Authors

Arianna Goi¹, Carmen L Manuelian², Sarah Currò¹, Massimo De Marchi¹

Affiliations

¹ Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
² Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy. carmenloreto.manuelianfuste@unipd.it.

Abstract

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.

Keywords: NIR; dog; dog nutrition; extruded pet food.