TY - JOUR
T1 - Non-destructive Prediction of Nicotine Content in Tobacco Using Hyperspectral Image–Derived Spectra and Machine Learning
AU - Divyanth, L. G.
AU - Chakraborty, Somsubhra
AU - Li, Bin
AU - Weindorf, David C.
AU - Deb, Prithwiraj
AU - Gem, Carol Jacob
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to The Korean Society for Agricultural Machinery.
PY - 2022/6
Y1 - 2022/6
N2 - Purpose: Rapid prediction of tobacco nicotine content in tobacco industries has become essential to maintain a stable and reliable cigarette quality. This research deals with combining hyperspectral images (HSI) and chemometric models to predict nicotine content in powdered tobacco samples. Methods: Fifty-seven dried powdered tobacco leaf samples were scanned using a hyperspectral camera followed by image processing. The region of interest (ROI) was selected for calculating average spectra. The average spectra and the destructive measurements of nicotine concentration in the samples were used to develop four regression models based on partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and PLSR–variable importance in projection (PLSR–VIP). The models were evaluated using leave-one-out cross-validation (LOOCV) and on 15% test dataset. Results: The PLSR outperformed (R2=0.93, RMSE= 0.21%) SVR- and RF-based nicotine prediction models using the entire 970–1700-nm range. Five bands centred at 976.15 nm, 1452 nm, 1575.5 nm, 1592.3 nm, and 1698.9 nm were identified as effective wavelengths for nicotine content prediction and used by the PLSR–variable importance in projection (PLSR–VIP) model to produce satisfactory validation performance (R2=0.91, RMSE= 0.30%). The LOOCV yielded R2 values ranging between 0.89 and 0.93 for the evaluated models. Conclusion: The PLSR-VIP model with 96% fewer wavelengths than the full range PLSR highlighted its potential for a more simplistic nicotine prediction mechanism. The HSI plus chemometric model approach has shown the potential to predict tobacco nicotine content rapidly.
AB - Purpose: Rapid prediction of tobacco nicotine content in tobacco industries has become essential to maintain a stable and reliable cigarette quality. This research deals with combining hyperspectral images (HSI) and chemometric models to predict nicotine content in powdered tobacco samples. Methods: Fifty-seven dried powdered tobacco leaf samples were scanned using a hyperspectral camera followed by image processing. The region of interest (ROI) was selected for calculating average spectra. The average spectra and the destructive measurements of nicotine concentration in the samples were used to develop four regression models based on partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and PLSR–variable importance in projection (PLSR–VIP). The models were evaluated using leave-one-out cross-validation (LOOCV) and on 15% test dataset. Results: The PLSR outperformed (R2=0.93, RMSE= 0.21%) SVR- and RF-based nicotine prediction models using the entire 970–1700-nm range. Five bands centred at 976.15 nm, 1452 nm, 1575.5 nm, 1592.3 nm, and 1698.9 nm were identified as effective wavelengths for nicotine content prediction and used by the PLSR–variable importance in projection (PLSR–VIP) model to produce satisfactory validation performance (R2=0.91, RMSE= 0.30%). The LOOCV yielded R2 values ranging between 0.89 and 0.93 for the evaluated models. Conclusion: The PLSR-VIP model with 96% fewer wavelengths than the full range PLSR highlighted its potential for a more simplistic nicotine prediction mechanism. The HSI plus chemometric model approach has shown the potential to predict tobacco nicotine content rapidly.
KW - Chemometrics
KW - Partial least squares regression
KW - Random forest
KW - Support vector regression
KW - Variable importance in projection
UR - http://www.scopus.com/inward/record.url?scp=85126549504&partnerID=8YFLogxK
U2 - 10.1007/s42853-022-00134-0
DO - 10.1007/s42853-022-00134-0
M3 - Article
AN - SCOPUS:85126549504
SN - 1738-1266
VL - 47
SP - 106
EP - 117
JO - Journal of Biosystems Engineering
JF - Journal of Biosystems Engineering
IS - 2
ER -