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

Year of publication

2021

Authors

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

Abstract

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.
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Organizations and authors

Natural Resources Institute Finland

Frondelius Lilli

Pastell Matti Orcid -palvelun logo

VTT Technical Research Centre of Finland Ltd

Hietala Eero

Sumen Juha

Utriainen Mikko Orcid -palvelun logo

Uusitalo Sanna Orcid -palvelun logo

Publication type

Publication format

Article

Parent publication type

Journal

Article type

Original article

Audience

Scientific

Peer-reviewed

Peer-Reviewed

MINEDU's publication type classification code

A1 Journal article (refereed), original research

Publication channel information

Journal/Series

Foods

Publisher

MDPI AG

Volume

10

Issue

11

Article number

2686

Pages

16 p.

​Publication forum

85072

​Publication forum level

1

Open access

Open access in the publisher’s service

Yes

Open access of publication channel

Fully open publication channel

Self-archived

Yes

Other information

Fields of science

Electronic, automation and communications engineering, electronics; Industrial biotechnology; Animal science, dairy science

Keywords

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Internationality of the publisher

International

Language

English

International co-publication

Yes

Co-publication with a company

No

DOI

10.3390/foods10112686

The publication is included in the Ministry of Education and Culture’s Publication data collection

Yes