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Investigation on the effectiveness of mid-infrared spectroscopy to predict detailed mineral composition of bulk milk

Published online by Cambridge University Press:  22 February 2018

Massimo Malacarne ,
Giulio Visentin ,
Andrea Summer ,
Martino Cassandro ,
Mauro Penasa ,
Giuseppe Bolzoni ,
Giorgio Zanardi  and
Massimo De Marchi
Massimo Malacarne
Affiliation:
Department of Veterinary Science, University of Parma, Via del Taglio 10, 43126 Parma, Italy
Giulio Visentin*
Affiliation:
Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
Andrea Summer
Affiliation:
Department of Veterinary Science, University of Parma, Via del Taglio 10, 43126 Parma, Italy
Martino Cassandro
Affiliation:
Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
Mauro Penasa
Affiliation:
Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
Giuseppe Bolzoni
Affiliation:
Centro Referenza Nazionale Qualità Latte Bovino, IZSLER, Via Bianchi 9, 25124 Brescia, Italy
Giorgio Zanardi
Affiliation:
Centro Referenza Nazionale Qualità Latte Bovino, IZSLER, Via Bianchi 9, 25124 Brescia, Italy
Massimo De Marchi
Affiliation:
Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
*
*For correspondence; e-mail: [email protected]
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Abstract

This Research Communication investigated the potential of mid-infrared spectroscopy to predict detailed mineral composition of bovine milk. A total of 153 bulk milk samples were analysed for contents of Ca, Cl, Cu, Fe, K, Mg, Na, P and Zn. Also, soluble and colloidal fractions of Ca, Mg and P were quantified. For each milk sample the mid-infrared spectrum was captured and stored. Prediction models were developed using partial least squares regression and the accuracy of prediction was evaluated using both cross- and external validation. The proportion of variance explained by the prediction models in cross-validation ranged from 34% (Na) to 77% (total P), and it ranged from 13% (soluble Mg) to 54% (Cl−) in external validation. The ratio of the standard deviation of each trait to the standard error of prediction in external validation, which is an indicator of the practical utility of the prediction model, was low and never greater than 2. Results from the current study supported the limited usefulness of mid-infrared spectroscopy to predict minerals present in low concentration in bulk milk. For major mineral components, results from the present research did not match previous findings demonstrating the need for further studies using larger reference datasets.

Keywords

Fourier transform infrared spectrometrymilk mineralhuman healthdairy processing
Type
Research Article
Information
Journal of Dairy Research , Volume 85 , Issue 1 , February 2018 , pp. 83 - 86
Copyright
Copyright © Hannah Dairy Research Foundation 2018 

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