Application of short-wave pocket-sized near-infrared spectrophotometer to predict milk quality traits

Alberto Guerra; Massimo De Marchi; Giovanni Niero; Elena Chiarin; Carmen L Manuelian

doi:10.3168/jds.2023-24302

Application of short-wave pocket-sized near-infrared spectrophotometer to predict milk quality traits

J Dairy Sci. 2024 Jan 19:S0022-0302(24)00031-6. doi: 10.3168/jds.2023-24302. Online ahead of print.

Authors

Alberto Guerra¹, Massimo De Marchi¹, Giovanni Niero², Elena Chiarin¹, Carmen L Manuelian³

Affiliations

¹ Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
² Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy. Electronic address: g.niero@unipd.it.
³ Group of Ruminant Research (G2R), Department of Animal and Food Sciences, Universitat Autònoma de Barcelona (UAB), 08193, Bellaterra, Spain.

PMID: 38246541
DOI: 10.3168/jds.2023-24302

Abstract

Portable-hand-held devices based on near-infrared (NIR) technology have improved and are gaining popularity, even if their implementation in milk has been barely evaluated. Thus, the aim of the present study was to assess short-wave pocket-sized NIR devices' feasibility to predict milk quality. A total of 331 individual milk samples from different cow breeds and herds were collected in 2 consecutive days for chemical determination and spectral collection by using 2 pocket-sized NIR spectrophotometers working in the range of 740-1070 nm. The reference data was matched with the corresponding spectrum and modified-partial least-squares regression models were developed. A 5-fold cross-validation was applied to evaluate individual devices' performance and an external-validation with 25% of the data set as the validation set was applied for the final models. Results revealed that both devices' absorbance was highly correlated but greater for instrument A than B. Thus, the final models were built by averaging the spectra from both devices for each sample. The fat content prediction model was adequate for quality control with a coefficient of determination (R²_ExV) and a residual predictive deviation (RPD_ExV) in external validation of 0.93 and 3.73, respectively. Protein and casein content as well as fat-to-protein ratio prediction models might be used for a rough screening (R²_ExV > 0.70; RPD_ExV > 1.73). However, poor prediction models were obtained for all the other traits with an R²_ExV between 0.43 (urea) and 0.03 (somatic cell count), and a RPD_ExV between 1.18 (urea) and 0.22 (somatic cell count). In conclusion, short-wave portable-hand-held NIR devices accurately predicted milk fat content, and protein, casein, and fat-to-protein ratio might be applied for rough screening. It seems that there is not enough information in this NIR region to develop adequate prediction models for lactose, somatic cell count, urea, and freezing point.

Keywords: cow; milk composition; near-infrared spectroscopy; pocket.

© 2024, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).