Near-infrared spectra dataset of milk composition in transmittance mode

Jose A Diaz-Olivares; Arnout van Nuenen; Martin J Gote; Valeria Fonseca Díaz; Wouter Saeys; Ines Adriaens; Ben Aernouts

doi:10.1016/j.dib.2023.109767

Near-infrared spectra dataset of milk composition in transmittance mode

Data Brief. 2023 Nov 4:51:109767. doi: 10.1016/j.dib.2023.109767. eCollection 2023 Dec.

Authors

Jose A Diaz-Olivares¹, Arnout van Nuenen¹, Martin J Gote¹, Valeria Fonseca Díaz¹, Wouter Saeys², Ines Adriaens¹, Ben Aernouts¹

Affiliations

¹ Department of Biosystems, Division of Animal and Human Health Engineering, KU Leuven, Geel Campus, Kleinhoefstraat 4, 2440 Geel, Belgium.
² Department of Biosystems, Division of Mechatronics Biostatistics and Sensors, KU Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium.

Abstract

Monitoring of milk composition can support several dimensions of dairy management such as identification of the health status of individual dairy cows and the safeguarding of dairy quality. The quantification of milk composition has been traditionally executed employing destructive chemical or laboratory Fourier-transform infrared (FTIR) spectroscopy analyses which can incur high costs and prolonged waiting times for continuous monitoring. Therefore, modern technology for milk composition quantification relies on non-destructive near-infrared (NIR) spectroscopy which is not invasive and can be performed on-farm, in real-time. The current dataset contains NIR spectral measurements in transmittance mode in the wavelength range from 960 nm to 1690 nm of 1224 individual raw milk samples, collected on-farm over an eight-week span in 2017, at the experimental dairy farm of the province of Antwerp, 'Hooibeekhoeve' (Geel, Belgium). For these spectral measurements, laboratory reference values corresponding to the three main components of raw milk (fat, protein and lactose), urea and somatic cell count (SCC) are included. This data has been used to build multivariate calibration models to predict the three milk compounds, as well as develop strategies to monitor the prediction performance of the calibration models.

Keywords: Calibration monitoring; Food quality control; Milk; Multivariate calibration; Near-infrared spectroscopy; Real-time prediction; Transmittance.