Journal of Innovative Optical Health Sciences, Volume. 11, Issue 2, 1750019(2018)

Identification of syrup type using fourier transform-near infrared spectroscopy with multivariate classification methods

Ravipat Lapcharoensuk* and Natrapee Nakawajana
Author Affiliations
  • Department of Agricultural Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand
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    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 6940 cm-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.

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    Ravipat Lapcharoensuk, Natrapee Nakawajana. Identification of syrup type using fourier transform-near infrared spectroscopy with multivariate classification methods[J]. Journal of Innovative Optical Health Sciences, 2018, 11(2): 1750019

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    Paper Information

    Received: Mar. 2, 2017

    Accepted: Jun. 20, 2017

    Published Online: Sep. 18, 2018

    The Author Email: Ravipat Lapcharoensuk (ravipat.la@kmitl.ac.th)

    DOI:10.1142/s1793545817500195

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