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

Publiceringsår

2021

Upphovspersoner

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

Abstrakt

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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Organisationer och upphovspersoner

Naturresursinstitutet

Frondelius Lilli

Pastell Matti Orcid -palvelun logo

Teknologiska forskningscentralen VTT Ab

Hietala Eero

Sumen Juha

Utriainen Mikko Orcid -palvelun logo

Uusitalo Sanna Orcid -palvelun logo

Publikationstyp

Publikationsform

Artikel

Moderpublikationens typ

Tidning

Artikelstyp

En originalartikel

Målgrupp

Vetenskaplig

Kollegialt utvärderad

Kollegialt utvärderad

UKM:s publikationstyp

A1 Originalartikel i en vetenskaplig tidskrift

Publikationskanalens uppgifter

Journal

Foods

Förläggare

MDPI AG

Volym

10

Nummer

11

Artikelnummer

2686

Sidor

16 p.

Publikationsforum

85072

Publikationsforumsnivå

1

Öppen tillgång

Öppen tillgänglighet i förläggarens tjänst

Ja

Öppen tillgång till publikationskanalen

Helt öppen publikationskanal

Parallellsparad

Ja

Övriga uppgifter

Vetenskapsområden

El-, automations- och telekommunikationsteknik, elektronik; Industriell bioteknologi; Djursvetenskap, mjölkproduktlära

Nyckelord

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Förlagets internationalitet

Internationell

Språk

engelska

Internationell sampublikation

Ja

Sampublikation med ett företag

Nej

DOI

10.3390/foods10112686

Publikationen ingår i undervisnings- och kulturministeriets datainsamling

Ja