TY - GEN
T1 - Determinación de la adulteración de sémola mediante espectroscopia NIR
AU - Oblitas-Cruz, Jimy
AU - Cieza-Rimarachin, Yuleyci
AU - Castro-Silupu, Wilson
N1 - Publisher Copyright:
© 2022 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2022
Y1 - 2022
N2 - The objective was to implement a semolina percentage recognition system using near-infrared spectroscopy (NIR) and multivariate data analysis. For this purpose, 6 samples were analyzed with different percentages of semolina(20, 40, 60, 80 and 100 %). Samples were repeated 20 times. The observed NIR spect rum was absorbance in the range of 1100 and 2500 nm. In order to reduce the data, the analysis of main components was used by testing 24 classification models, from whichthe one that reached the highest level of precision was the Linear Support Vector Machine (SVM) algorithm, reaching 98.8%, achieving fairly satisfactorydiscrimination with values of PC1 (99.7%), PC2 (0.3%) and PC3 (0.1%), reaching a total cumulative variation of the contribution of the first 3 P Cs of 99.9%. Partial Least Regression (PLS) models applied to NIR-spectra showed R2 between 0.9388. These values demonstrated that NIR spectroscopy can be used for the identification and quantification of fiber added to semolina.
AB - The objective was to implement a semolina percentage recognition system using near-infrared spectroscopy (NIR) and multivariate data analysis. For this purpose, 6 samples were analyzed with different percentages of semolina(20, 40, 60, 80 and 100 %). Samples were repeated 20 times. The observed NIR spect rum was absorbance in the range of 1100 and 2500 nm. In order to reduce the data, the analysis of main components was used by testing 24 classification models, from whichthe one that reached the highest level of precision was the Linear Support Vector Machine (SVM) algorithm, reaching 98.8%, achieving fairly satisfactorydiscrimination with values of PC1 (99.7%), PC2 (0.3%) and PC3 (0.1%), reaching a total cumulative variation of the contribution of the first 3 P Cs of 99.9%. Partial Least Regression (PLS) models applied to NIR-spectra showed R2 between 0.9388. These values demonstrated that NIR spectroscopy can be used for the identification and quantification of fiber added to semolina.
KW - Fiber
KW - Near infrared spectroscopy
KW - Semolina
U2 - 10.18687/LACCEI2022.1.1.69
DO - 10.18687/LACCEI2022.1.1.69
M3 - Contribución a la conferencia
AN - SCOPUS:85140015199
T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
BT - 20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
A2 - Larrondo Petrie, Maria M.
A2 - Texier, Jose
A2 - Pena, Andrea
A2 - Viloria, Jose Angel Sanchez
PB - Latin American and Caribbean Consortium of Engineering Institutions
T2 - 20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2022
Y2 - 18 July 2022 through 22 July 2022
ER -