Arctangent normalization and principal-component analyses merge method to classify characteristics utilizing time-dependent material data.
Characteristic classification
Fourier transform infrared spectroscopy
Head space–gas chromatograph/mass spectrometry
Inductively coupled plasma mass spectrometry
Normalization
Principal-component analysis
Journal
Analytical sciences : the international journal of the Japan Society for Analytical Chemistry
ISSN: 1348-2246
Titre abrégé: Anal Sci
Pays: Switzerland
ID NLM: 8511078
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
07
03
2023
accepted:
01
08
2023
medline:
19
8
2023
pubmed:
19
8
2023
entrez:
18
8
2023
Statut:
ppublish
Résumé
We propose a technique for classifying paints with time-dependent properties using a new method of merging principal-component analyses (the "PCA-merge" method) that utilizes shifting of the barycenter of the PCA score plot. To understand the molecular structure, elemental concentrations, and the concentrations in the evolved gaseous component of various paints, we performed comprehensive characterizations using Fourier transform infrared spectroscopy, inductively coupled plasma mass spectrometry, and head-space-gas chromatograph/mass spectrometry while drying the paint films for 1-48 h. As various detected intensity- and time-axis variables have different dimensions that cannot be handled equally, we normalized those data as an angle parameter (θ) using arctangent to reduce the influence of high/low intensity data and the various analytical instrument. We could classify the paints into suitable categories by applying multivariate analysis to this arctangent-normalized data set. In addition, we developed a new PCA-merge method to analyze data groups that include different time components. This method merges the PCA data groups of each time-component axis into that of specific-component axes and distinguishes each sample by utilizing the shift in the barycenter of the PCA score plot. The proposed method enables the simultaneous utilization of various data groups that contain information about static and dynamic properties. This provides further insight into the characteristics of the paint materials via shifts in the barycenter of the PCA scores without requiring numerous peak identifications.
Identifiants
pubmed: 37596373
doi: 10.1007/s44211-023-00403-8
pii: 10.1007/s44211-023-00403-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1957-1966Informations de copyright
© 2023. The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry.
Références
Y. Nishimoto, H. Eguchi, E. Shimoda, T. Suzuki, Anal. Sci. (2015). https://doi.org/10.2116/analsci.31.929
doi: 10.2116/analsci.31.929
pubmed: 26353960
JIS K 5500:2000 Glossary of terms for coating materials. https://kikakurui.com/k5/K5500-2000-01.html
T. Suzuki, K. Takahashi, H. Uehara, T. Yamanobe, J. Therm. Anal. Calorim. (2013). https://doi.org/10.1007/s10973-013-3098-z
doi: 10.1007/s10973-013-3098-z
R. Bro, A.K. Smilde, Anal. Methods. (2014). https://doi.org/10.1039/C3AY41907J
doi: 10.1039/C3AY41907J
M. Isshiki, S. Nakamura, Y. Suzuki, Nippon Shokuhin Kagaku Kogaku Kaishi (2015). https://doi.org/10.3136/nskkk.62.257
doi: 10.3136/nskkk.62.257
M.J. Latorre, Food Chem. (1999). https://doi.org/10.1016/S0308-8146(98)00217-9
doi: 10.1016/S0308-8146(98)00217-9
M.J.J. Baxter, H.M. Crews, M. John-Dennis, I. Goodall, D. Anderson, Food Chem. (1997). https://doi.org/10.1016/S0308-8146(96)00365-2
doi: 10.1016/S0308-8146(96)00365-2
Y. Murakami, H. Iwabuchi, Y. Ohba, H. Fukami, J. Oleo Sci. (2019). https://doi.org/10.5650/jos.ess19155
doi: 10.5650/jos.ess19155
pubmed: 31787673
S.D. Rodríguez, M. Gagneten, A.E. Farroni, N.M. Percibaldi, M.P. Buera, Food Cont. (2019). https://doi.org/10.1016/j.foodcont.2019.05.025
doi: 10.1016/j.foodcont.2019.05.025
G. Squeo, S. Grassi, V.M. Paradiso, C. Alamprese, F. Caponio, Food Cont. (2019). https://doi.org/10.1016/j.foodcont.2019.03.027
doi: 10.1016/j.foodcont.2019.03.027
D. Granato, J.S. Santos, G.B. Escher, B.L. Ferreira, R.M. Maggio, Trends Food Sci. Technol. (2018). https://doi.org/10.1016/j.tifs.2017.12.006
doi: 10.1016/j.tifs.2017.12.006
M. Maric, W. van Bronswijk, S.W. Lewis, K. Pitts, D.E. Martin, Forensic Sci. Int. (2013). https://doi.org/10.1016/j.forsciint.2013.01.032
doi: 10.1016/j.forsciint.2013.01.032
pubmed: 23462650
R. Chophi, S. Sharma, R. Singh, Forensic Chem. (2020). https://doi.org/10.1016/j.forc.2019.100209
doi: 10.1016/j.forc.2019.100209
N.Z. Shafii, A.S.M. Saudi, J.C. Pang, I.F. Abu, N. Sapawe, M.K.A. Kamarudin, H.F.M. Saudi, Heliyon. (2019). https://doi.org/10.1016/j.heliyon.2019.e02534
doi: 10.1016/j.heliyon.2019.e02534
pubmed: 31667387
pmcid: 6812457
M. Quinn, T. Brettell, M. Joshi, J. Bonetti, L. Quarino, Forensic Sci. Int. (2020). https://doi.org/10.1016/j.forsciint.2019.110135
doi: 10.1016/j.forsciint.2019.110135
pubmed: 31923853
X. Ran, Y. Xi, Y. Lu, X. Wang, Z. Lu, Artif. Intell. Rev. (2022). https://doi.org/10.1007/s10462-022-10366-3
doi: 10.1007/s10462-022-10366-3
T. Tanji, M. Furukawa, S. Taguma, K. Fujimoto, H. Sato, N. Shibasaki, Y. Takagai, ACS ES&T Water (2023). https://doi.org/10.1021/acsestwater.2c00455
doi: 10.1021/acsestwater.2c00455
K. Kobayashi, Y. Niida, M. Furukawa, O. Shikino, M. Furuishi, T. Suzuki, Eng. Mater. (THE NIKKAN KOGYO SHIMBUN Japan 67(9), 50–51 (2019)
T. Suzuki, N. Sokutei, (2020) https://doi.org/10.11311/jscta.47.4_148
G.E.P. Box, G.M. Jenkins, G.C. Reinsel, Time series analysis: forecasting and control (Wiley, Hoboken, 2016). (978-1-118-63434-9)
B.P. Geurts, A.H. Neerincx, S. Bertrand, M.A.A.P. Leemans, G.J. Postma, J.L. Wolfender, S.M. Cristescu, L.M.C. Buydens, J.J. Jansen, Anal. Chim. Acta. (2017). https://doi.org/10.1016/j.aca.2017.01.064
doi: 10.1016/j.aca.2017.01.064
pubmed: 28335962
T. Morishita, J. Chem. Phys. (2021). https://doi.org/10.1063/5.0061874
doi: 10.1063/5.0061874
pubmed: 34624975