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Article

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

Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell’Università 16, 35020 Legnaro (PD), Italy
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Animals 2019, 9(9), 640; https://doi.org/10.3390/ani9090640
Received: 12 July 2019 / Revised: 22 August 2019 / Accepted: 29 August 2019 / Published: 1 September 2019
(This article belongs to the Section Companion Animals)
Mineral content in dog food is essential to ensure animals’ adequate development and health status, but its analysis is time-consuming and companies are not always equipped with the technology to perform it. Near-infrared spectroscopy (NIRS) is a rapid, objective, easy to manage, chemical-free, and non-destructive method that is already available in the food industry for the prediction of gross composition (e.g., moisture, protein, fat, etc.). However, this technological approach is not yet used for the prediction of minerals because there is scarce information regarding the feasibility of NIRS to predict minerals in pet food. Results of this study revealed that, among all minerals analyzed, adequate NIRS prediction models were obtained for S and K for extruded dry dog food. The development of prediction models for mineral content in dry dog food opens the possibility of on-line and at-line analyses of minerals in the products during the manufacturing process, which could help the manufacturing decision support system in the pet food industry.
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. View Full-Text
Keywords: dog; NIR; dog nutrition; extruded pet food dog; NIR; dog nutrition; extruded pet food
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MDPI and ACS Style

Goi, A.; Manuelian, C.L.; Currò, S.; De Marchi, M. Prediction of Mineral Composition in Commercial Extruded Dry Dog Food by Near-Infrared Reflectance Spectroscopy. Animals 2019, 9, 640. https://doi.org/10.3390/ani9090640

AMA Style

Goi A, Manuelian CL, Currò S, De Marchi M. Prediction of Mineral Composition in Commercial Extruded Dry Dog Food by Near-Infrared Reflectance Spectroscopy. Animals. 2019; 9(9):640. https://doi.org/10.3390/ani9090640

Chicago/Turabian Style

Goi, Arianna, Carmen L. Manuelian, Sarah Currò, and Massimo De Marchi. 2019. "Prediction of Mineral Composition in Commercial Extruded Dry Dog Food by Near-Infrared Reflectance Spectroscopy" Animals 9, no. 9: 640. https://doi.org/10.3390/ani9090640

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