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Review

Commentary on the review articles of spectroscopy technology combined with chemometrics in the last three years

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References

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  • Arroyo-Cerezo, A.; Jimenez-Carvelo, A. M.; González-Casado, A.; Koidis, A.; Cuadros-Rodríguez, L. Deep (Offset) Non-Invasive Raman Spectroscopy for the Evaluation of Food and Beverages – A Review. LWT 2021, 149, 111822. doi: 10.1016/j.lwt.2021.111822.
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  • Fernandes Andrade, D.; Pereira-Filho, E. R.; Amarasiriwardena, D. Current Trends in Laser-Induced Breakdown Spectroscopy: A Tutorial Review. Appl. Spectrosc. Rev. 2021, 56, 98–114. doi: 10.1080/05704928.2020.1739063.
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  • Harmon, R. S.; Senesi, G. S. Laser-Induced Breakdown Spectroscopy–a Geochemical Tool for the 21st Century. Appl. Geochem. 2021, 128, 104929. doi: 10.1016/j.apgeochem.2021.104929.
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  • Chen, T.; Zhang, T.; Li, H. Applications of Laser-Induced Breakdown Spectroscopy (LIBS) Combined with Machine Learning in Geochemical and Environmental Resources Exploration. TrAC, Trends Anal. Chem. 2020, 133, 116113. doi: 10.1016/j.trac.2020.116113.
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  • Limbeck, A.; Brunnbauer, L.; Lohninger, H.; Pořízka, P.; Modlitbová, P.; Kaiser, J.; Janovszky, P.; Kéri, A.; Galbács, G. Methodology and Applications of Elemental Mapping by Laser Induced Breakdown Spectroscopy. Anal. Chim. Acta. 2021, 1147, 72–98. doi: 10.1016/j.aca.2020.12.054.
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  • Wang, H.-P.; Chen, P.; Dai, J.-W.; Liu, D.; Li, J.-Y.; Xu, Y.-P.; Chu, X.-L. Recent Advances of Chemometric Calibration Methods in Modern Spectroscopy: Algorithms, Strategy, and Related Issues. TrAC, Trends Anal. Chem. 2022, 153, 116648. doi: 10.1016/j.trac.2022.116648.
  • Houhou, R.; Bocklitz, T. Trends in Artificial Intelligence, Machine Learning, and Chemometrics Applied to Chemical Data. Anal. Sci. Adv. 2021, 2, 128–141. doi: 10.1002/ansa.202000162.
  • Zhang, D.; Zhang, H.; Zhao, Y.; Chen, Y.; Ke, C.; Xu, T.; He, Y. A Brief Review of New Data Analysis Methods of Laser-Induced Breakdown Spectroscopy: Machine Learning. Appl. Spectrosc. Rev. 2022, 57, 89–111. doi: 10.1080/05704928.2020.1843175.
  • Costa, V.; Babos, D.; Castro, J.; Andrade, D.; Gamela, R.; Machado, R.; Speranca, M.; Araujo, A.; Garcia, J.; Pereira-Filho, E. Calibration Strategies Applied to Laser-Induced Breakdown Spectroscopy: A Critical Review of Advances and Challenges. J. Braz. Chem. Soc. 2021, 31, 2439–2451. doi: 10.21577/0103-5053.20200175.
  • Peris-Díaz, M. D.; Krężel, A. A Guide to Good Practice in Chemometric Methods for Vibrational Spectroscopy, Electrochemistry, and Hyphenated Mass Spectrometry. TrAC, Trends Anal. Chem. 2021, 135, 116157. doi: 10.1016/j.trac.2020.116157.
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  • Meza Ramirez, C. A.; Greenop, M.; Ashton, L.; Rehman, I. u Applications of Machine Learning in Spectroscopy. Appl. Spectrosc. Rev. 2021, 56, 733–763. doi: 10.1080/05704928.2020.1859525.
  • Oliveira, L. G.; Araújo, K. C.; Barreto, M. C.; Bastos, M. E. P. A.; Lemos, S. G.; Fragoso, W. D. Applications of Chemometrics in Oil Spill Studies. Microchem. J. 2021, 166, 106216. doi: 10.1016/j.microc.2021.106216.
  • Aleixandre-Tudo, J. L.; Castello-Cogollos, L.; Aleixandre, J. L.; Aleixandre-Benavent, R. Chemometrics in Food Science and Technology: A Bibliometric Study. Chemometr. Intellig. Lab. Syst. 2022, 222, 104514. doi: 10.1016/j.chemolab.2022.104514.
  • Rocha, W. F. C.; Prado, C. B. D.; Blonder, N. Comparison of Chemometric Problems in Food Analysis Using Nonlinear Methods. Molecules 2020, 25, 3025. doi: 10.3390/molecules25133025.
  • Buve, C.; Saeys, W.; Rasmussen, M. A.; Neckebroeck, B.; Hendrickx, M.; Grauwet, T.; Van Loey, A. Application of Multivariate Data Analysis for Food Quality Investigations: An Example-Based Review. Food Res. Int. 2022, 151, 110878. doi: 10.1016/j.foodres.2021.110878.
  • Ma, P.; Zhang, Z.; Jia, X.; Peng, X.; Zhang, Z.; Tarwa, K.; Wei, C.-I.; Liu, F.; Wang, Q. Neural Network in Food Analytics. Crit. Rev. Food Sci. Nutr. 2022, 2, 1–19. doi: 10.1080/10408398.2022.2139217.
  • Wang, H.; Chu, X.; Chen, P.; Liu, D.; Li, J.; Xu, Y. Research and Application Progress of Algorithms for Spectral Baseline Correction. Chin. J. Anal. Chemist 2021, 49, 1270–1281. doi: 10.19756/j.issn.0253-3820.201679.
  • Mishra, P.; Biancolillo, A.; Roger, J. M.; Marini, F.; Rutledge, D. N. New Data Preprocessing Trends Based on Ensemble of Multiple Preprocessing Techniques. TrAC, Trends Anal. Chem. 2020, 132, 116045. doi: 10.1016/j.trac.2020.116045.
  • Knadel, M.; Castaldi, F.; Barbetti, R.; Ben-Dor, E.; Gholizadeh, A.; Lorenzetti, R. Mathematical Techniques to Remove Moisture Effects from Visible–near-Infrared–Shortwave-Infrared Soil Spectra. Appl. Spectrosc. Rev. 2022, 1–34. doi: 10.1080/05704928.2022.2128365.
  • Fu, J.; Yu, H.-D.; Chen, Z.; Yun, Y.-H. A Review on Hybrid Strategy-Based Wavelength Selection Methods in Analysis of near-Infrared Spectral Data. Infrared Phys. Technol. 2022, 125, 104231. doi: 10.1016/j.infrared.2022.104231.
  • de Araujo Gomes, A.; Azcarate, S. M.; Diniz, P.; de Sousa Fernandes, D. D.; Veras, G. Variable Selection in the Chemometric Treatment of Food Data: A Tutorial Review. Food Chem. 2022, 370, 131072. doi: 10.1016/j.foodchem.2021.131072.
  • Wu, H.-L.; Long, W.-J.; Wang, T.; Dong, M.-Y.; Yu, R.-Q. Recent Applications of Multiway Calibration Methods in Environmental Analytical Chemistry: A Review. Microchem. J. 2020, 159, 105575. doi: 10.1016/j.microc.2020.105575.
  • Wu, H.-L.; Wang, T.; Yu, R.-Q. Recent Advances in Chemical Multiway Calibration with Second-Order or Higher-Order Advantages: Multilinear Models, Algorithms, Related Issues and Applications. TrAC, Trends Anal. Chem. 2020, 130, 115954. doi: 10.1016/j.trac.2020.115954.
  • Sun, W.; Braatz, R. D. Opportunities in Tensorial Data Analytics for Chemical and Biological Manufacturing Processes. Comput. Chem. Eng. 2020, 143, 107099. doi: 10.1016/j.compchemeng.2020.107099.
  • Vignaduzzo, S. E.; Maggio, R. M.; Olivieri, A. C. Why Should the Pharmaceutical Industry Claim for the Implementation of Second-Order Chemometric models-A Critical Review. J. Pharm. Biomed. Anal. 2020, 179, 112965. doi: 10.1016/j.jpba.2019.112965.
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  • de Juan, A.; Tauler, R. Multivariate Curve Resolution: 50 Years Addressing the Mixture Analysis problem - A Review. Anal. Chim. Acta. 2021, 1145, 59–78. doi: 10.1016/j.aca.2020.10.051.
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  • Park, Y.; Jin, S.; Noda, I.; Jung, Y. M. Emerging Developments in Two-Dimensional Correlation Spectroscopy (2D-COS). J. Mol. Struct. 2020, 1217, 128405. doi: 10.1016/j.molstruc.2020.128405.
  • Yang, R.-J.; Liu, C.-Y.; Yang, Y.-R.; Wu, H.-Y.; Jin, H.; Shan, H.-Y.; Liu, H. Two-Trace Two-Dimensional (2T2D) Correlation Spectroscopy Application in Food Safety: A Review. J. Mol. Struct. 2020, 1214, 128219. doi: 10.1016/j.molstruc.2020.128219.
  • Liu, L. I. Y.; Yang, R.-J.; Zhang, J.; Gong, G.-M.; Yang, Y.-R. Recent Progress in Two-Dimensional Correlation Spectroscopy for the Environmental Detection and Analysis. J. Mol. Struct. 2020, 1214, 128263. doi: 10.1016/j.molstruc.2020.128263.
  • Rutherford, S. H.; Nordon, A.; Hunt, N. T.; Baker, M. J. Biofluid Analysis and Classification Using IR and 2D-IR Spectroscopy. Chemometr. Intelligent Lab. Syst. 2021, 217, 104408. doi: 10.1016/j.chemolab.2021.104408.
  • Calvini, R.; Pigani, L. Toward the Development of Combined Artificial Sensing Systems for Food Quality Evaluation: A Review on the Application of Data Fusion of Electronic Noses, Electronic Tongues and Electronic Eyes. Sensors 2022, 22, 577. doi: 10.3390/s22020577.
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  • Debus, B.; Parastar, H.; Harrington, P.; Kirsanov, D. Deep Learning in Analytical Chemistry. TrAC, Trends Anal. Chem. 2021, 145, 116459. doi: 10.1016/j.trac.2021.116459.
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  • Li, L.-N.; Liu, X.-F.; Yang, F.; Xu, W.-M.; Wang, J.-Y.; Shu, R. A Review of Artificial Neural Network Based Chemometrics Applied in Laser-Induced Breakdown Spectroscopy Analysis. Spectrochim. Acta, Part B 2021, 180, 106183. doi: 10.1016/j.sab.2021.106183.
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  • Ozdemir, A.; Polat, K, School of Natural Sciences, Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University, 14280, Bolu, Turkey Deep Learning Applications for Hyperspectral Imaging: A Systematic Review. JIEC. 2020, 2, 39–56. doi: 10.33969/jiec.2020.21004.
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