Pregled bibliografske jedinice broj: 1282752
Raman spectroscopy for ceritinib solution concentration and slurry density estimation
Raman spectroscopy for ceritinib solution concentration and slurry density estimation // 28 HSKIKI BOOK OF ABSTRACTS / Rogošić, Marko (ur.).
Zagreb, 2023. str. 150-150 (poster, međunarodna recenzija, sažetak, znanstveni)
CROSBI ID: 1282752 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Raman spectroscopy for ceritinib solution
concentration and slurry density estimation
Autori
Gavran, Matea ; Klier, Monika ; Bolf, Nenad ; Šahnić, Damir
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni
Izvornik
28 HSKIKI BOOK OF ABSTRACTS
/ Rogošić, Marko - Zagreb, 2023, 150-150
Skup
28th CROATIAN MEETING OF CHEMISTS & CHEMICAL ENGINEERS
Mjesto i datum
Rovinj, Hrvatska, 28.03.2023. - 31.03.2023
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
crystallization, process analytical technology, Raman spectroscopy, calibration model
Sažetak
The aim of this work is to investigate the use of in-situ Raman spectroscopy for the monitoring of cooling crystallization process. For this purpose, Raman spectroscopy with a focused laser beam was utilized for the estimation of solution concentration and slurry density. The Raman signal depends on many factors, including the composition of the solid and liquid phase, as well as the size and shape of the crystals. [1] Therefore, the use of Raman spectroscopy for quantitative measurements is challenging and requires advanced chemometric methods. The concentration of an Active Pharmaceutical Ingredient (API), ceritinib was determined experimentally in a solution mixture of 90% acetone and 10% water. The goal was to build a calibration model for the estimation of ceritinib concentration during the crystallization process. Methods used to build the calibration models include Partial Least Square Regression (PLSR) and an Artificial Neural Network (ANN). During model development, various methods for spectra preprocessing were evaluated. In addition, different hyper parameters for the models were tested by ANN. The prediction performance used to evaluate the developed models was the root mean square error of prediction (RMSE). The model created with an artificial neural network proved to perform better than the partial least square regression model for the estimation of solution concentration, but were not able to predict slurry density with good accuracy.
Izvorni jezik
Engleski
Znanstvena područja
Kemijsko inženjerstvo
POVEZANOST RADA
Projekti:
EK-EFRR-KK.01.1.1.07.0017 - Napredno vođenje procesa kristalizacije (CrystAPC) (Bolf, Nenad, EK - KK.01.1.1.07) ( CroRIS)
Ustanove:
Fakultet kemijskog inženjerstva i tehnologije, Zagreb,
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