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Modelling and optimization of physical characteristics based on UV-VIS/NIR spectra of aqueous extracts of lavender, mint and melissa

Plants belonging to Lamiaceae family contain a large number of naturally occurring compounds, with important antioxidant activities. They are often used in traditional and modern medicine, food industry, cosmetics and pharmaceutical industry. UV-VIS/NIR spectroscopy, in combination with chemometrics, is often applied to build models for qualification and quantification of the major compounds in many agricultural products and plant materials. The aim of this work was to investigate the applicability of UV-VIS/NIR spectroscopy for prediction of electrical conductivity (G) and total dissolved solids (TDS) of lavender (Lavandula x hybrida L.), melissa (Melissa officinalis L.) and mint (Mentha piperita L.) aqueous extracts. Plant extracts were prepared by conventional aqueous extraction at T = 40, 60, 80 oC, rpm = 500 min-1 and sampled at regular time intervals (0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 70, 80, 90 min). Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS) were used for qualitative and quantitative analysis of the recorded UV- VIS/NIR spectra of each extract. PLS models provided very good prediction of G and TDS for mint extracts (T = 40 oC) using UV-VIS/NIR spectra in the spectral range of λ = 400 – 1699 nm. Determination coefficients were higher than R2 = 0.9, for both model prediction and model validation. The ratio of standard error of performance to standard deviation (RPD) was greater than 3 indicating good quantitative application of developed models. The results show that UV-VIS/NIR spectroscopy, in combination with multivariate analysis, has a promising potential to qualitatively and quantitatively predict the physical characteristics of plant materials.

medicinal plants ; UV-VIS/NIR spectroscopy ; Principal Component Analysis (PCA) ; Partial Least Square regression (PLS)

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