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Joint and unique multiblock analysis for integration and calibration transfer of NIR instruments
Umeå University, Faculty of Science and Technology, Department of Chemistry.ORCID iD: 0000-0001-8445-0559
Umeå University, Faculty of Science and Technology, Department of Chemistry.
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2019 (English)In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 91, no 5, p. 3516-3524Article in journal (Refereed) Published
Abstract [en]

In the present paper, we introduce an end-to-end workflow called joint and unique multiblock analysis (JUMBA), which allows multiple sources of data to be analyzed simultaneously to better understand how they complement each other. In near-infrared (NIR) spectroscopy, calibration models between NIR spectra and responses are used to replace wet-chemistry methods, and the models tend to be instrument-specific. Calibration-transfer techniques are used for standardization of NIR-instrumentation, enabling the use of one model on several instruments. The current paper investigates both the similarities and differences among a variety of NIR instruments using JUMBA. We demonstrate JUMBA on both a previously unpublished data set in which five NIR instruments measured mushroom substrate and a publicly available data set measured on corn samples. We found that NIR spectra from different instrumentation largely shared the same underlying structures, an insight we took advantage of to perform calibration transfer. The proposed JUMBA transfer displayed excellent calibration-transfer performance across the two analyzed data sets and outperformed existing methods in terms of both prediction accuracy and stability. When applied to a multi-instrument environment, JUMBA transfer can integrate all instruments in the same model and will ensure higher consistency among them compared with existing calibration-transfer methods.

Place, publisher, year, edition, pages
Washington: American Chemical Society (ACS), 2019. Vol. 91, no 5, p. 3516-3524
Keywords [en]
near-infrared spectroscopy, spent mushroom compost, multivariate calibration, water-content, standardization, regression, vegetation, models, ONPLS
National Category
Analytical Chemistry
Identifiers
URN: urn:nbn:se:umu:diva-156707DOI: 10.1021/acs.analchem.8b05188ISI: 000460709200047PubMedID: 30758178Scopus ID: 2-s2.0-85062418105OAI: oai:DiVA.org:umu-156707DiVA, id: diva2:1291400
Projects
Bio4EnergyAvailable from: 2019-02-25 Created: 2019-02-25 Last updated: 2019-09-06Bibliographically approved
In thesis
1. Multivariate integration and visualization of multiblock data in chemical and biological applications
Open this publication in new window or tab >>Multivariate integration and visualization of multiblock data in chemical and biological applications
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Multivariat integration och visualisering av multiblockdata i kemiska och biologiska applikationer
Abstract [en]

Thanks to improvements in technology more data than ever before is generated in almost all fields of science and industry.

The data is analyzed to hopefully provide valuable information and knowledge about a product or process, such as how to improve the quality of a manufactured product.

Analysis of collected data is often performed on a single dataset or data source at a time. In this thesis, I have focused on multiblock analysis, a concept that includes multiple sources or data blocks.  Analogous to how the human senses combine to let us experience the world around us, multiblock analysis integrates multiple data sources, providing a fuller examination of the product or process under study.

My thesis introduces Joint and Unique Multiblock Analysis, JUMBA, a complete analysis workflow for data integration. I describe each step of JUMBA, including data pre-treatment, model building and validation as well as model interpretation. Special focus is put on several newly developed visualizations for model validation and interpretation to make it as easy as possible to draw conclusions from the analysis.

 

By reading my thesis, the reader will gain a working understanding of the process of performing multiblock analysis, including solutions to common problems that are often encountered.

Abstract [sv]

Tack vare tekniska framsprång genereras det idag stora mängder data inom forskning och industri. Genom att analysera sådan data kan det i slutändan leda till att värdefull kunskap om en produkt eller process erhålls och kvaliteten på de studerade produkterna därmed kan ökas.

Analysen av data sker ofta på en enda datakälla, som då representeras av en matris, även kallat ett datablock. I denna avhandling har jag istället fokuserat på koncept som kan analysera flera datakällor samtidigt och integrera dessa. I likhet med hur människans sinnen låter oss uppleva världen runt omkring medför integrerandet av flera datakällor att undersökningen av en produkt eller process blir mer omfattande.

I min avhandling introduceras arbetsflödet JUMBA (Joint and Unique Multiblock Analysis, eng), som är ämnat för att utföra en fullständig integration av data. Jag beskriver varje enskilt steg av JUMBA, allt från förbehandling av data till byggande och validering av modeller samt deras tolkning. Jag har lagt särskild vikt vid att beskriva flera nyskapade typer av visualiseringar som underlättar att korrekta slutsatser kan dras från analysen.

Jag hoppas att läsaren av min avhandling kommer få förståelse för hur man utför analys av flera datablock och denne hittar även lösningar på problem man normalt sett kan ställas inför vid genomförandet.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2019. p. 62
Keywords
Multivariate analysis, PCA, PLS, OnPLS, JUMBA, Multiblock, calibration transfer
National Category
Analytical Chemistry
Identifiers
urn:nbn:se:umu:diva-158330 (URN)978-91-7855-069-2 (ISBN)
Public defence
2019-05-17, KB.E3.03, KBC - building, Linnaeus väg 6, 90736 Umeå, Umeå, 10:00 (English)
Opponent
Supervisors
Funder
eSSENCE - An eScience Collaboration
Available from: 2019-04-26 Created: 2019-04-25 Last updated: 2019-04-30Bibliographically approved

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Skotare, TomasNilsson, DavidTrygg, Johan

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