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

By Arianna Goi, Carmen L. Manuelian, Sarah Currò, Massimo De Marchi

Category Journal Articles
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.

Submitter

Marcy Wilhelm-South

Purdue University

Date 2019
Publication Title Animals
Volume 9
Issue 9
Pages 11
Publisher MDPI
DOI 10.3390/ani9090640
URL https://www.mdpi.com/2076-2615/9/9/640
Language English
Additional Language English
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Tags
  1. Animal nutrition
  2. Animal welfare
  3. Dogs
  4. Mammals
  5. Nutrition
  6. open access
  7. pet foods
  8. Pet ownership
Badges
  1. open access