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Yıl 2023, Cilt: 23, 93 - 99, 30.09.2023
https://doi.org/10.55549/epstem.1361727

Öz

Kaynakça

  • Azcarate S. M. et al. (2015). Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety. Food Chem, 184, 214–219.
  • Gomidze N., Jabnidze I., Makharadze K., Khajishvili M., Shashikadze Z., Surmanidze Z., Surmanidze I. (2012). Numerical analyses of fluorescence characteristics of watery media via laser spectroscopy Method. Journal of Advanced Materials Research, 590, 206-211.
  • Gomidze N.K., Makharadze K.A., Khajishvili M.R., Jabnidze I.N., & Shashikadze Z.K. (2014). Some issues of fluorescence characteristics aqueous media via diagnosis of laser spectroscopy method. International Journal of Engineering, Science and Innovative Technology, 3(3), 142-152.

On the Development of the Fluorescence Excitation-Emission Etalon Matrix Algorithm of Wine

Yıl 2023, Cilt: 23, 93 - 99, 30.09.2023
https://doi.org/10.55549/epstem.1361727

Öz

Our research provides for the analysis of different types of Georgian wine based on 3D fluorescence spectroscopy (3DF) using the Black Comet (200-950 nm) spectrometer manufactured by StellarNet. In this method, the 3D fluorescence signal is divided into a fixed number of statistical components. For each type of wine, a 3D database is strictly defined, which we conventionally call references. The etalon describe the excitation/emission spectra in detail. The advantage of the 3DF method compared to other statistical methods, such as peak component analysis (PCA), lies in the uniqueness of the unfolding of the spectra. The fluorescence spectra of the wine will be further analyzed by peak component analysis (PCA). After performing the PCA analysis, in order to reduce the number of tolerant etalon, we used the tolerant etalon sample (TES) comparison analysis, thus determining how tolerant the researched wine sample is to this or that specific etalon.

Kaynakça

  • Azcarate S. M. et al. (2015). Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety. Food Chem, 184, 214–219.
  • Gomidze N., Jabnidze I., Makharadze K., Khajishvili M., Shashikadze Z., Surmanidze Z., Surmanidze I. (2012). Numerical analyses of fluorescence characteristics of watery media via laser spectroscopy Method. Journal of Advanced Materials Research, 590, 206-211.
  • Gomidze N.K., Makharadze K.A., Khajishvili M.R., Jabnidze I.N., & Shashikadze Z.K. (2014). Some issues of fluorescence characteristics aqueous media via diagnosis of laser spectroscopy method. International Journal of Engineering, Science and Innovative Technology, 3(3), 142-152.
Toplam 3 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Kimya Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Miranda Khajıshvılı

Jaba Shaınıdze

Kakha Makharadze

Nugzar Gomıdze

Erken Görünüm Tarihi 17 Eylül 2023
Yayımlanma Tarihi 30 Eylül 2023
Yayımlandığı Sayı Yıl 2023Cilt: 23

Kaynak Göster

APA Khajıshvılı, M., Shaınıdze, J., Makharadze, K., Gomıdze, N. (2023). On the Development of the Fluorescence Excitation-Emission Etalon Matrix Algorithm of Wine. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 23, 93-99. https://doi.org/10.55549/epstem.1361727