APPLICATION OF NEAR INFRARED SPECTROSCOPY COMBINED WITH MULTIVARIATE ANALYSIS FOR SCREENING FOLIAR MAIN ESSENTIAL OIL COMPONENTS IN BAY LAUREL

Abstract

Ground bay laurel leaf samples (10–15 g) were scanned using Fourier-transform nearinfrared (FT-NIR) spectrometer with reflectance mode in the 1000–2500 nm wavelength range. According to the wet chemical analyses, the essential oil content of the samples from different locations varied between 1.77 and 5.30%. The major component of essential oil was 1-8 cineole with a concentration of 43.4–58.1%. The regression coefficients of calibration (R2 CAL) and validation (R2 VAL) for essential oil and 1-8 cineol content with partial least square regression (PLSR) actualized as 0.96–0.98 and 0.98– 0.98, respectively. The prediction accuracy of the final NIRS model was reasonable, with acceptable root mean standard errors of prediction (RMSEP) of 0.18% and 0.45%. According to the residual predictive deviation (RPD) index (3.58 and 8.41), the accuracy of the NIRS models was regarded as the best. The PLSR model differentiated bay laurel genotypes very well on the first principal component (PC1), based on the related properties.