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Prediction of essential oil content in spearmint (Mentha spicata) via near-infrared hyperspectral imaging and chemometrics

Sam Van Haute (UGent) , Amin Nikkhah (UGent) , Derick Malavi (UGent) and Sajad Kiani
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Abstract
Spearmint (Mentha spicata L.) is grown for its essential oil (EO), which find use in food, beverage, fragrance and other industries. The current study explores the ability of near infrared hyperspectral imaging (HSI) (935 to 1720 nm) to predict, in a rapid, nondestructive manner, the essential oil content of dried spearmint (0.2 to 2.6% EO). Spectral values of spearmint samples varied considerably with spatial coordinates, and so the use of averaging the spectral values of a surface scan was warranted. Data preprocessing was done with Multiplicative Scatter Correction (MSC) or Standard Normal Variate (SNV). Selection of spectral input variables was done with Least Absolute Shrinkage and Selection Operator (LASSO), Principal Component Analysis (PCA) or Partial Least Squares (PLS). Regression was executed with linear regression (LASSO, PLS regression, PCA regression), Support Vector Machine (SVM) regression, and Multilayer Perceptron (MLP). The best prediction of EO concentration was achieved with the combination of MSC or SNV preprocessing, PLS dimension reduction, and MLP regression (1 hidden layer with 6 nodes), achieving a good prediction with a ratio of performance to deviation (RPD) of 2.84 ± 0.07, an R2 of prediction of 0.863 ± 0.008, and a RMSE of prediction of 0.219 ± 0.005% EO. These results show that NIR-HSI is a viable method for rapid, nondestructive analysis of EO concentration. Future work should explore the use of NIR in the visible spectrum, the use of HSI for determining EO in other plant materials and the potential of HSI to determine individual compounds in these solid plant/food matrices.
Keywords
CHITOSAN COATINGS, NIR SPECTROSCOPY, PLS-REGRESSION, OLIVE OILS, IDENTIFICATION, ANTIOXIDANT, PLANTS, BLACK, TOOL

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MLA
Van Haute, Sam, et al. “Prediction of Essential Oil Content in Spearmint (Mentha Spicata) via near-Infrared Hyperspectral Imaging and Chemometrics.” SCIENTIFIC REPORTS, vol. 13, no. 1, 2023, doi:10.1038/s41598-023-31517-8.
APA
Van Haute, S., Nikkhah, A., Malavi, D., & Kiani, S. (2023). Prediction of essential oil content in spearmint (Mentha spicata) via near-infrared hyperspectral imaging and chemometrics. SCIENTIFIC REPORTS, 13(1). https://doi.org/10.1038/s41598-023-31517-8
Chicago author-date
Van Haute, Sam, Amin Nikkhah, Derick Malavi, and Sajad Kiani. 2023. “Prediction of Essential Oil Content in Spearmint (Mentha Spicata) via near-Infrared Hyperspectral Imaging and Chemometrics.” SCIENTIFIC REPORTS 13 (1). https://doi.org/10.1038/s41598-023-31517-8.
Chicago author-date (all authors)
Van Haute, Sam, Amin Nikkhah, Derick Malavi, and Sajad Kiani. 2023. “Prediction of Essential Oil Content in Spearmint (Mentha Spicata) via near-Infrared Hyperspectral Imaging and Chemometrics.” SCIENTIFIC REPORTS 13 (1). doi:10.1038/s41598-023-31517-8.
Vancouver
1.
Van Haute S, Nikkhah A, Malavi D, Kiani S. Prediction of essential oil content in spearmint (Mentha spicata) via near-infrared hyperspectral imaging and chemometrics. SCIENTIFIC REPORTS. 2023;13(1).
IEEE
[1]
S. Van Haute, A. Nikkhah, D. Malavi, and S. Kiani, “Prediction of essential oil content in spearmint (Mentha spicata) via near-infrared hyperspectral imaging and chemometrics,” SCIENTIFIC REPORTS, vol. 13, no. 1, 2023.
@article{01GWGPP3GREJDWAM7B07WQTAQP,
  abstract     = {{Spearmint (Mentha spicata L.) is grown for its essential oil (EO), which find use in food, beverage, fragrance and other industries. The current study explores the ability of near infrared hyperspectral imaging (HSI) (935 to 1720 nm) to predict, in a rapid, nondestructive manner, the essential oil content of dried spearmint (0.2 to 2.6% EO). Spectral values of spearmint samples varied considerably with spatial coordinates, and so the use of averaging the spectral values of a surface scan was warranted. Data preprocessing was done with Multiplicative Scatter Correction (MSC) or Standard Normal Variate (SNV). Selection of spectral input variables was done with Least Absolute Shrinkage and Selection Operator (LASSO), Principal Component Analysis (PCA) or Partial Least Squares (PLS). Regression was executed with linear regression (LASSO, PLS regression, PCA regression), Support Vector Machine (SVM) regression, and Multilayer Perceptron (MLP). The best prediction of EO concentration was achieved with the combination of MSC or SNV preprocessing, PLS dimension reduction, and MLP regression (1 hidden layer with 6 nodes), achieving a good prediction with a ratio of performance to deviation (RPD) of 2.84 ± 0.07, an R2 of prediction of 0.863 ± 0.008, and a RMSE of prediction of 0.219 ± 0.005% EO. These results show that NIR-HSI is a viable method for rapid, nondestructive analysis of EO concentration. Future work should explore the use of NIR in the visible spectrum, the use of HSI for determining EO in other plant materials and the potential of HSI to determine individual compounds in these solid plant/food matrices.}},
  articleno    = {{4261}},
  author       = {{Van Haute, Sam and Nikkhah, Amin and Malavi, Derick and Kiani, Sajad}},
  issn         = {{2045-2322}},
  journal      = {{SCIENTIFIC REPORTS}},
  keywords     = {{CHITOSAN COATINGS,NIR SPECTROSCOPY,PLS-REGRESSION,OLIVE OILS,IDENTIFICATION,ANTIOXIDANT,PLANTS,BLACK,TOOL}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{13}},
  title        = {{Prediction of essential oil content in spearmint (Mentha spicata) via near-infrared hyperspectral imaging and chemometrics}},
  url          = {{http://doi.org/10.1038/s41598-023-31517-8}},
  volume       = {{13}},
  year         = {{2023}},
}

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