Conclusion
Adulteration of high quality and high valued essential oils (for
example, sandalwood oils) is common problem affecting the quality
and commercial value of the product. The overall results of this
study demonstrate the feasibility of utilizing NIR spectroscopy
associated to chemometric techniques to detect sample authenticity
and economic adulteration of sandalwood oils.
In the present research, a spectral data approach ‘full or sequential/
partitioned spectrum’ is used in generating the robust regression
model. The mean centre data pre-processing generates a low
error for full spectrum (1850–1100 nm) approach (RMSEC =
0.00028 and RMSEP = 0.00049) and the highest coefficient of determination
(R2 = 0.99894). Using the sequential/partitioned spectral
approach, a data set containing the 1850–1800 nm wavelength
region with the smoothing (Savitzky–Golay) + mean centre pretreatment
generates the ‘best model’ to determine sample authenticity
and to quantify the adulteration with very much less than
1% error (RMSEC = 0.00009%, RMSEP = 0.00016%, R2 = 0.99989.).
The nonlinear method LWR for multivariate y using the local
PLS algorithm is employed to detect and quantify the adulteration
and to compare with the PLS model. This nonlinear model is also
stable, defined by two principal components with low errors and
high prediction values.
In brief, the results from this study (Figs. 1–3, and Table 1 can be
chosen as a basis for a new exercise) indicate that it is possible to
detect sample authenticity and counterfeits by exploring near
infrared spectral data in the wavelength range 1850–1800 nm with
prominent biomarker peak 1836 nm that are attributed to methylene,
methyl and ethenyl asymmetric stretching (1st overtone C–H
stretch bands) of sandalwood oil or any other essential oil samples.
ConclusionAdulteration of high quality and high valued essential oils (forexample, sandalwood oils) is common problem affecting the qualityand commercial value of the product. The overall results of thisstudy demonstrate the feasibility of utilizing NIR spectroscopyassociated to chemometric techniques to detect sample authenticityand economic adulteration of sandalwood oils.In the present research, a spectral data approach ‘full or sequential/partitioned spectrum’ is used in generating the robust regressionmodel. The mean centre data pre-processing generates a lowerror for full spectrum (1850–1100 nm) approach (RMSEC =0.00028 and RMSEP = 0.00049) and the highest coefficient of determination(R2 = 0.99894). Using the sequential/partitioned spectralapproach, a data set containing the 1850–1800 nm wavelengthregion with the smoothing (Savitzky–Golay) + mean centre pretreatmentgenerates the ‘best model’ to determine sample authenticityand to quantify the adulteration with very much less than1% error (RMSEC = 0.00009%, RMSEP = 0.00016%, R2 = 0.99989.).The nonlinear method LWR for multivariate y using the localPLS algorithm is employed to detect and quantify the adulterationand to compare with the PLS model. This nonlinear model is alsostable, defined by two principal components with low errors andhigh prediction values.In brief, the results from this study (Figs. 1–3, and Table 1 can bechosen as a basis for a new exercise) indicate that it is possible todetect sample authenticity and counterfeits by exploring nearinfrared spectral data in the wavelength range 1850–1800 nm withprominent biomarker peak 1836 nm that are attributed to methylene,methyl and ethenyl asymmetric stretching (1st overtone C–Hstretch bands) of sandalwood oil or any other essential oil samples.
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