[en] Multivariate curve resolution unmixing of hyperspectral imaging data can be challenging when low sources of variance are present in complex samples, as for minor (low-concentrated) chemical compounds in pharmaceutical formulations. In this work, it was shown how the reduction of hyperspectral imaging data matrices through the selection of essential spectra can be crucial for the analysis of complex unknown pharmaceutical formulation applying Multivariate Curve Resolution – Alternating Least Squares (MCR-ALS). Results were obtained on simulated datasets and on real FT-IR and Raman hyperspectral images of both genuine and falsified tablets. When simulating the presence of minor compounds, different situations were investigated considering the presence of single pixels of pure composition as well as binary and ternary mixtures. The comparison of the results obtained applying MCR-ALS on the reduced data matrices with those obtained on the full matrices revealed unequivocal: more accurate decomposition could be achieved when only essential spectra were analyzed. Indeed, when analyzing the full dataset, MCR-ALS failed resolving minor compounds even though pure spectra were provided as initial estimation, as shown for Raman hyperspectral imaging data obtained on a medicine sample containing 7 chemical compounds. In contrast, when considering the reduced dataset, all minor contributions (down to 1 pixel over 17,956) were successfully unmixed. The same conclusion could be drawn from the results obtained analysing FT-IR hyperspectral imaging data of a falsified medicine.
Research Center/Unit :
CIRM - Centre Interdisciplinaire de Recherche sur le Médicament - ULiège
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Bibliography
Bureau, S., Cozzolino, D., Clark, C.J., Contributions of Fourier-transform mid infrared (FT-MIR) spectroscopy to the study of fruit and vegetables: a review. Postharvest Biol. Technol. 148 (2019), 1–14, 10.1016/J.POSTHARVBIO.2018.10.003.
Yaseen, T., Sun, D.W., Cheng, J.H., Raman imaging for food quality and safety evaluation: fundamentals and applications. Trends Food Sci. Technol. 62 (2017), 177–189, 10.1016/j.tifs.2017.01.012.
Qin, J., Kim, M.S., Chao, K., Dhakal, S., Cho, B.-K., Lohumi, S., Mo, C., Peng, Y., Huang, M., Advances in Raman spectroscopy and imaging techniques for quality and safety inspection of horticultural products. Postharvest Biol. Technol. 149 (2019), 101–117, 10.1016/j.postharvbio.2018.11.004.
Gupta, S., Mittal, S., Kajdacsy-Balla, A., Bhargava, R., Bajaj, C., A fully automated, faster noise rejection approach to increasing the analytical capability of chemical imaging for digital histopathology. PLoS One, 14, 2019, e0205219, 10.1371/journal.pone.0205219.
Xu, J.L., Thomas, K.V., Luo, Z., Gowen, A.A., FTIR and Raman imaging for microplastics analysis: state of the art, challenges and prospects. TrAC Trends Anal. Chem. (Reference Ed.), 119, 2019, 10.1016/j.trac.2019.115629.
Castiglione, V., Sacré, P.Y., Cavalier, E., Hubert, P., Gadisseur, R., Ziemons, E., Raman chemical imaging, a new tool in kidney stone structure analysis: case-study and comparison to Fourier Transform Infrared spectroscopy. PLoS One, 13, 2018, 10.1371/journal.pone.0201460.
Rebiere, H., Martin, M., Ghyselinck, C., Bonnet, P.-A., Brenier, C., Raman chemical imaging for spectroscopic screening and direct quantification of falsified drugs. J. Pharm. Biomed. Anal. 148 (2018), 316–323, 10.1016/j.jpba.2017.10.005.
Waffo Tchounga, C.A., Sacre, P.Y., Ciza, P., Ngono, R., Ziemons, E., Hubert, P., Marini, R.D., Composition analysis of falsified chloroquine phosphate samples seized during the COVID-19 pandemic. J. Pharm. Biomed. Anal., 194, 2021, 113761, 10.1016/j.jpba.2020.113761.
Coic, L., Sacré, P.-Y., Dispas, A., Sakira, A.K., Fillet, M., Marini, R.D., Hubert, P., Ziemons, E., Comparison of hyperspectral imaging techniques for the elucidation of falsified medicines composition. Talanta 198 (2019), 457–463, 10.1016/j.talanta.2019.02.032.
Coic, L., Sacré, P.Y., Dispas, A., De Bleye, C., Fillet, M., Ruckebusch, C., Hubert, P., Ziemons, E., Pixel-based Raman hyperspectral identification of complex pharmaceutical formulations. Anal. Chim. Acta, 1155, 2021, 338361, 10.1016/j.aca.2021.338361.
Cailletaud, J., De Bleye, C., Dumont, E., Sacré, P.-Y., Gut, Y., Bultel, L., Ginot, Y.-M., Hubert, P., Ziemons, E., Towards a spray-coating method for the detection of low-dose compounds in pharmaceutical tablets using surface-enhanced Raman chemical imaging (SER-CI). Talanta 188 (2018), 584–592, 10.1016/j.talanta.2018.06.037.
EDQM - European Directorate for the Quality of Medicines |, (n.d.). https://www.edqm.eu/(accessed December 4, 2020).
R. Spectroscopy, P.-Y. Sacré, L. Netchacovitch, E. Dumont, J. Cailletaud, C. De Bleye, M. Boiret, P. Hubert, E. Ziemons, Raman Hyperspectral Imaging: an Essential Tool in the Pharmaceutical Field Application Note Pharmaceutical RA-66, (n.d.).
Nardecchia, A., Fabre, C., Cauzid, J., Pelascini, F., Motto-Ros, V., Duponchel, L., Detection of minor compounds in complex mineral samples from millions of spectra: a new data analysis strategy in LIBS imaging. Anal. Chim. Acta 1114 (2020), 66–73, 10.1016/j.aca.2020.04.005.
Boiret, M., Gorretta, N., Ginot, Y.M., Roger, J.M., An iterative approach for compound detection in an unknown pharmaceutical drug product: application on Raman microscopy. J. Pharm. Biomed. Anal. 120 (2016), 342–351, 10.1016/j.jpba.2015.12.038.
Boiret, M., de Juan, A., Gorretta, N., Ginot, Y.M., Roger, J.M., Distribution of a low dose compound within pharmaceutical tablet by using multivariate curve resolution on Raman hyperspectral images. J. Pharm. Biomed. Anal. 103 (2015), 35–43, 10.1016/j.jpba.2014.10.024.
Duponchel, L., Exploring hyperspectral imaging data sets with topological data analysis. Anal. Chim. Acta 1000 (2018), 123–131, 10.1016/j.aca.2017.11.029.
Moncayo, S., Duponchel, L., Mousavipak, N., Panczer, G., Trichard, F., Bousquet, B., Pelascini, F., Motto-Ros, V., Exploration of megapixel hyperspectral LIBS images using principal component analysis. J. Anal. At. Spectrom. 33 (2018), 210–220, 10.1039/c7ja00398f.
Boiret, M., Gorretta, N., Ginot, Y.M., Roger, J.M., An iterative approach for compound detection in an unknown pharmaceutical drug product: application on Raman microscopy. J. Pharm. Biomed. Anal. 120 (2016), 342–351, 10.1016/j.jpba.2015.12.038.
Ruckebusch, C., De Juan, A., Duponchel, L., Huvenne, J.P., Matrix augmentation for breaking rank-deficiency: a case study. Chemometr. Intell. Lab. Syst. 80 (2006), 209–214, 10.1016/J.CHEMOLAB.2005.06.009.
Badaró, A.T., Amigo, J.M., Blasco, J., Aleixos, N., Ferreira, A.R., Clerici, M.T.P.S., Barbin, D.F., Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta. Food Chem., 343, 2021, 128517, 10.1016/J.FOODCHEM.2020.128517.
Ghaffari, M., Omidikia, N., Ruckebusch, C., Essential spectral pixels for multivariate curve resolution of chemical images. Anal. Chem. 91 (2019), 10943–10948, 10.1021/acs.analchem.9b02890.
Ghaffari, M., Omidikia, N., Ruckebusch, C., Joint selection of essential pixels and essential variables across hyperspectral images. Anal. Chim. Acta 1141 (2021), 36–46, 10.1016/J.ACA.2020.10.040.
de Juan, A., Multivariate curve resolution for hyperspectral image analysis. Data Handl. Sci. Technol., 2020, Elsevier Ltd, 115–150, 10.1016/B978-0-444-63977-6.00007-9.
de Juan, A., Tauler, R., Multivariate Curve Resolution: 50 years addressing the mixture analysis problem – a review. Anal. Chim. Acta 1145 (2021), 59–78, 10.1016/j.aca.2020.10.051.
Ghaffari, M., Hugelier, S., Duponchel, L., Abdollahi, H., Ruckebusch, C., Effect of image processing constraints on the extent of rotational ambiguity in MCR-ALS of hyperspectral images. Anal. Chim. Acta 1052 (2019), 27–36, 10.1016/j.aca.2018.11.054.
de Juan, A., Tauler, R., Multivariate curve resolution-alternating least squares for spectroscopic data. Data Handl. Sci. Technol., 2016, Elsevier Ltd, 5–51, 10.1016/B978-0-444-63638-6.00002-4.
Ruckebusch, C., Vitale, R., Ghaffari, M., Hugelier, S., Omidikia, N., Perspective on essential information in multivariate curve resolution. TrAC Trends Anal. Chem. (Reference Ed.), 132, 2020, 10.1016/j.trac.2020.116044.
Hugelier, S., Devos, O., Ruckebusch, C., A smoothness constraint in multivariate curve resolution-alternating least squares of spectroscopy data. Data Handl. Sci. Technol., 2016, Elsevier Ltd, 453–476, 10.1016/B978-0-444-63638-6.00014-0.
Malik, A., Tauler, R., Ambiguities in multivariate curve resolution. Data Handl. Sci. Technol., 2016, Elsevier Ltd, 101–133, 10.1016/B978-0-444-63638-6.00004-8.
Hugelier, S., Devos, O., Ruckebusch, C., On the implementation of spatial constraints in multivariate curve resolution alternating least squares for hyperspectral image analysis. J. Chemom. 29 (2015), 557–561, 10.1002/cem.2742.
Paul, H.F.M.B., Eilers, H.C., Baseline Correction with Asymmetric Least Squares Smoothing - PDF Free Download. 2005 https://technodocbox.com/3D_Graphics/76845483-Baseline-correction-with-asymmetric-least-squares-smoothing.html. (Accessed 18 August 2021)
Hecht, H.G., The interpretation of diffuse reflectance spectra. J. Res. Natl. Bur. Stand. Chem., 80, 1976.
Savitzky, A., Golay, M.J.E., Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36 (2002), 1627–1639, 10.1021/AC60214A047.
de Juan, A., Tauler, R., Multivariate Curve Resolution: 50 years addressing the mixture analysis problem – a review. Anal. Chim. Acta 1145 (2021), 59–78, 10.1016/J.ACA.2020.10.051.
de Juan, A., Tauler, R., Multivariate Curve Resolution (MCR) from 2000: Progress in Concepts and Applications. Http://Dx.Doi.Org/10.1080/10408340600970005. 36, 2007, 163–176, 10.1080/10408340600970005.
Bogomolov, A., Hachey, M., Application of SIMPLISMA purity function for variable selection in multivariate regression analysis: a case study of protein secondary structure determination from infrared spectra. Chemometr. Intell. Lab. Syst. 88 (2007), 132–142, 10.1016/J.CHEMOLAB.2006.07.006.
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