Evaluation of MEMS NIR Spectrometers for On-Farm Analysis of Raw Milk Composition

Sanna Uusitalo (Corresponding Author), José Diaz-Olivares, Juha Sumen, Eero Hietala, Ines Adriaens, Wouter Saeys, Mikko Utriainen, Lilli Frondelius, Matti Pastell, Ben Aernouts

Research output: Contribution to journalArticleScientificpeer-review

15 Citations (Scopus)
133 Downloads (Pure)


Today, measurement of raw milk quality and composition relies on Fourier transform infrared spectroscopy to monitor and improve dairy production and cow health. However, these laboratory analyzers are bulky, expensive and can only be used by experts. Moreover, the sample logistics and data transfer delay the information on product quality, and the measures taken to optimize the care and feeding of the cattle render them less suitable for real-time monitoring. An on-farm spectrometer with compact size and affordable cost could bring a solution for this discrepancy. This paper evaluates the performance of microelectromechanical system (MEMS)-based near-infrared (NIR) spectrometers as on-farm milk analyzers. These spectrometers use Fabry–Pérot interferometers for wavelength tuning, giving them the advantage of very compact size and affordable price. This study discusses the ability of MEMS spectrometers to reach the accuracy limits set by the International Committee for Animal Recording (ICAR) for at-line analyzers of the milk content regarding fat, protein and lactose. According to the achieved results, the transmission measurements with the NIRONE 2.5 spectrometer perform best, with an acceptable root mean squared error of prediction (RMSEP = 0.21% w/w) for the measurement of milk fat and excellent performance (RMSEP ≤ 0.11% w/w) for protein and lactose. In addition, the transmission measurements using the NIRONE 2.0 module give similar results for fat and lactose (RMSEP of 0.21 and 0.10% w/w respectively), while the prediction of protein is slightly deteriorated (RMSEP = 0.15% w/w). These results show that the MEMS spectrometers can reach sufficient prediction accuracy compared to ICAR standard values for at-line and in-line fat, protein and lactose prediction.
Original languageEnglish
Article number2686
Issue number11
Publication statusPublished - 3 Nov 2021
MoE publication typeA1 Journal article-refereed


  • Analysis
  • At-line
  • Fat
  • FPI
  • Lactose
  • MEMS
  • Milk
  • NIR
  • On-farm
  • Protein


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