SOS-HPTLC: second-order spectral high-performance thin-layer chromatography as a sustainable approach for wine discrimination

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MATLAB codes and data for the article "SOS-HPTLC: SECOND ORDER SPECTRAL HIGH-PERFORMANCE THIN-LAYER CHROMATOGRAPHY AS A SUSTAINABLE APPROACH FOR WINE DISCRIMINATION." Experimental data were measured with a chromatograph (HPTLC or HPLC) with spectral detection, and saved as ASCII data. Steps to reproduce the obtained results: A) SOS-HPTLC DATA: The data corresponding to the 52 training samples are in "Xcal_SOS_HPTLC" (Size: 416 x 160, [52 samples x 8 RFs = 416] x 160 wavelngths). The data corresponding to the 19 validation samples are in "Xval_SOS_HPTLC" (Size: 19x 1280, 19 samples x [8 RFs x 160 wavlengths = 1280] ). 1) First, run the "pca_training_SOS_HPTLC.m" script. You will obtain the training samples distribution as shown in Figure 2-A of the paper. 2) Now run the "pca_validation_SOS_HPTLC.m" script. You will obtain the validation samples distribution as shown in Figure 2-B of the paper. 3) Finally, run the script "pca_clusters_SOS_HPTLC.m" and follow the instructions provided. Depending on your selection, you should obtain a figure similar to Figure 3 in the paper. Important: For the routine to work, you must also have the auxiliary function "heat_maps.m", which is part of the supplied package. B) HPLC-DAD DATA: The data corresponding to the 52 training samples are in "Xcal_HPLC_DAD" (Size: 170000 x 52, [1000 times x 170 wavelengths = 170000] x 52 samples). The data corresponding to the 19 validation samples are in "Xval_HPLC_DAD" (Size: 170000 x 19, [1000 times x 170 wavelengths = 170000] x 19 samples). Run the "pca_HPLC_DAD.m" script. You will obtain the training sample distribution first, as shown in Figure 5-A of the paper, and later the validation sample distribution, as shown in Figure 5-B of the paper.

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Funders

• Consejo Nacional de Investigaciones Científicas y Técnicas

  Ministerio de Ciencia, Tecnología e Innovación

  Buenos Aires

  Grant ID: PIP 11220200102423CO
• Agencia Nacional de Promoción Científica y Tecnológica

  Grant ID: PICT 2020-1372
• Agencia Nacional de Promoción Científica y Tecnológica

  Grant ID: PICT-2021-1034
• National University of Rosario

  Rosario

  Grant ID: 80020220600144UR

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