Quality Assessment of Reconstructed Cow, Camel and Mare Milk Powders by Near-Infrared Spectroscopy and Chemometrics

Author:

Majadi Mariem1,Barkó Annamária2,Varga-Tóth Adrienn2,Maukenovna Zhulduz Suleimenova3,Batirkhanovna Dossimova Zhanna3ORCID,Dilora Senkebayeva4,Lukacs Matyas1,Kaszab Timea1ORCID,Mednyánszky Zsuzsanna5ORCID,Kovacs Zoltan1ORCID

Affiliation:

1. Department of Food Measurements and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary

2. Department of Livestock Product and Food Preservation Technology, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary

3. Reference Laboratory of Dairy Products, Kazakh National Agrarian Research University, 050010 Almaty, Kazakhstan

4. Department of Technology and Processing of Livestock Production, S. Seifullin Kazakh Agro-Technical Research University, 010011 Astana, Kazakhstan

5. Department of Nutrition, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary

Abstract

Milk powders are becoming a major attraction for many industrial applications due to their nutritional and functional properties. Different types of powdered milk, each with their own distinct chemical compositions, can have different functionalities. Consequently, the development of rapid monitoring methods is becoming an urgent task to explore and expand their applicability. Lately, there is growing emphasis on the potential of near-infrared spectroscopy (NIRS) as a rapid technique for the quality assessment of dairy products. In the present work, we explored the potential of NIRS coupled with chemometrics for the prediction of the main functional and chemical properties of three types of milk powders, as well as their important processing parameters. Mare, camel and cow milk powders were prepared at different concentrations (5%, 10% and 12%) and temperatures (25 °C, 40 °C and 65 °C), and then their main physicochemical attributes and NIRS spectra were analyzed. Overall, high accuracy in both recognition and prediction based on type, concentration and temperature was achieved by NIRS-based models, and the quantification of quality attributes (pH, viscosity, dry matter content, fat content, conductivity and individual amino acid content) also resulted in high accuracy in the models. R2CV and R2pr values ranging from 0.8 to 0.99 and 0.7 to 0.98, respectively, were obtained by using PLSR models. However, SVR models achieved higher R2CV and R2pr values, ranging from 0.91 to 0.99 and 0.80 to 0.99, respectively.

Publisher

MDPI AG

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