Identification of syrup type using fourier transform-near infrared spectroscopy with multivariate classification methods
| dc.contributor.author | Lapcharoensuk, Ravipat | |
| dc.contributor.author | Nakawajana, Natrapee | |
| dc.date.accessioned | 2026-08-06T10:19:38Z | |
| dc.date.available | 2026-08-06T10:19:38Z | |
| dc.date.issued | 2018-03-01 | |
| dc.description.abstract | This research aimed to establish near infrared (NIR) spectroscopy models for identification of syrup types in which the maple syrup was discriminated from other syrup types. Thirty syrup types were used in this research; the NIR spectra of each type were recorded with 10 replicates. The repeatability and reproducibility of NIR scanning were performed, and the absorbance at 6940cm-1 was used for calculation. Principal component analysis was used to group the syrup type. Identification models were developed by soft independent modeling by class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA). The SIMCA models of all syrup types exhibited accuracy percentage of 93.3-100% for identifying syrup types, whereas maple syrup discrimination models showed percentage of accuracy between 83.2% and 100%. The PLS-DA technique gave the accuracy of syrup types classification between 96.6% and 100% and presented ability on discrimination of maple syrup form other types of syrup with accuracy of 100%. The finding presented the potential of NIR spectroscopy for the syrup type identification. | |
| dc.identifier.citation | Journal of Innovative Optical Health Sciences, 11(2), 2018 | |
| dc.identifier.doi | 10.1142/S1793545817500195 | |
| dc.identifier.issn | 17935458 | |
| dc.identifier.other | 2-s2.0-85025434141 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/8484 | |
| dc.source | Journal of Innovative Optical Health Sciences | |
| dc.subject | Identification | |
| dc.subject | multivariate classification | |
| dc.subject | NIR spectroscopy | |
| dc.subject | syrup | |
| dc.title | Identification of syrup type using fourier transform-near infrared spectroscopy with multivariate classification methods | |
| dc.type | Article |