ENGINEERING

A comparison of ATR-FTIR and Raman spectroscopy for the non-destructive examination of terpenoids in medicinal plants essential oils

Rahul Joshi1, Sushma Kholiya2, Himanshu Pandey2, Ritu Joshi3,4, Omia Emmanuel1, Ameeta Tewari2, Taehyun Kim5, Byoung-Kwan Cho1,6,*

1Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea
2Department of Chemistry, Moti Ram Babu Ram Government Post Graduate College Haldwani, Kumaun University, Uttarakhand 263139, India
3Department of Biochemistry and Biophysics, Texas A&M University, College Station, Texas 77843, United States
4Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, United States
5Department of Agriculture Engineering, National Institute of Agricultural Science, Rural Development Administration, Wanju 55365, Korea
6Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea

*Corresponding author: chobk@cnu.ac.kr

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 reflectanceFourier 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

ATR−FT-IR (attenuated total reflectance-Fourier transform infrared) spectroscopy, essential oils, Raman spectroscopy, spectral data analysis, terpenoids

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