Korean Journal of Agricultural Science (농업과학연구)

Institute of Agricultural Science, CNU (충남대학교 농업과학연구소)
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A comparison of ATR-FTIR and Raman spectroscopy for the non-destructive examination of terpenoids in medicinal plants essential oils

  • Rahul Joshi (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Sushma Kholiya (Department of Chemistry, Moti Ram Babu Ram Government Post Graduate College Haldwani, Kumaun University) ;
  • Himanshu Pandey (Department of Chemistry, Moti Ram Babu Ram Government Post Graduate College Haldwani, Kumaun University) ;
  • Ritu Joshi (Department of Biochemistry and Biophysics, Texas A&M University) ;
  • Omia Emmanuel (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Ameeta Tewari (Department of Chemistry, Moti Ram Babu Ram Government Post Graduate College Haldwani, Kumaun University) ;
  • Taehyun Kim (Department of Agriculture Engineering, National Institute of Agricultural Science, Rural Development Administration) ;
  • Byoung-Kwan Cho (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University)
  • Received : 2023.08.14
  • Accepted : 2023.09.26
  • Published : 2023.12.01
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Abstract

Terpenoids, also referred to as terpenes, are a large family of naturally occurring chemical compounds present in the essential oils extracted from medicinal plants. In this study, a nondestructive methodology was created by combining ATR-FT-IR (attenuated total reflectance-Fourier transform infrared), and Raman spectroscopy for the terpenoids assessment in medicinal plants essential oils from ten different geographical locations. Partial least squares regression (PLSR) and support vector regression (SVR) were used as machine learning methodologies. However, a deep learning based model called as one-dimensional convolutional neural network (1D CNN) were also developed for models comparison. With a correlation coefficient (R2) of 0.999 and a lowest RMSEP (root mean squared error of prediction) of 0.006% for the prediction datasets, the SVR model created for FT-IR spectral data outperformed both the PLSR and 1 D CNN models. On the other hand, for the classification of essential oils derived from plants collected from various geographical regions, the created SVM (support vector machine) classification model for Raman spectroscopic data obtained an overall classification accuracy of 0.997% which was superior than the FT-IR (0.986%) data. Based on the results we propose that FT-IR spectroscopy, when coupled with the SVR model, has a significant potential for the non-destructive identification of terpenoids in essential oils compared with destructive chemical analysis methods.

Keywords

Acknowledgement

This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through Smart Farm Innovation Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (421005-04). Further, we also give deep thanks for Department of Chemistry, M.B.G.P.G College Haldwani, Kumaun University Nainital-263139, Uttarakhand, India for their collaboration during plants sample collection and their essential oils extraction and later terpenoids determination using GC-FID chemical analysis methods.

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