Rapid quantification of alkaloids, sugar and yield of tobacco (Nicotiana tabacum L.) varieties by using Vis-NIR-SWIR spectroradiometry.

Tobacco genetic improvement programs, as well as the tobacco industry, require techniques that allow the estimation of its attributes in a fast and cheap way. The use of remote sensing through visible, near infrared and short-wave spectroscopy (Vis-NIR-SWIR) has been studied aiming to meet such demand. Thus, the aim of this work was to evaluate the use of Vis-NIR-SWIR spectroradiometer as a rapid tool to estimate alkaloids, sugars and yield of tobacco varieties. For that purpose, a study was carried out in a greenhouse with plants grown in pots (18 dm-3) containing nutrient solutions. The experimental design was completely randomized, with 30 treatments (tobacco varieties) and 10 repetitions. Tobacco leaf reflectance was collected at 13, 34 and 68 days after transplantation (DAT) with a plant-probe device connected to the spectroradiometer by an optical fiber. Subsequently, leaf analysis of alkaloids, sugars and yield were performed, and such attributes were estimated by using the Partial Least Squares Regression (PLSR), combined with the following pre-processing (PP) techniques: multiplicative scatter correction (MSC), Savitzky-Golay (SG) and standard normal variate (SNV). The results showed presence of typical inflections of chemical and structural components of the plants, which allowed obtaining PLSR models with R2p and RPDp superior to 0.71 and 2.27, respectively, for all PP techniques and attributes evaluated. The most important wavelengths were well distributed within the three operating ranges of the spectroradiometer (Vis-NIR-SWIR). Thus, the methodology proposed by this research was able to simultaneously determine all the three attributes (alkaloids, sugars and yield) with excellent predictive capacity. This is a promising result for genetic improvement and processing of tobacco (as well as other crops), since it is necessary to evaluate a large number of samples within a short period and at a low cost.

High resolution leaf spectral signature as a tool for foliar pigment estimation displaying potential for species differentiation.

Optical leaf profiles depend on foliar pigment type and content, as well as anatomical aspects and cellular ultrastructure, whose effects are shown in several species. Monocotyledon and Dicotyledon plants presenting natural pigment content variations and anatomical alterations were analyzed. Each plant species displays its own spectral signatures, which are, in turn, influenced by foliar pigment class (composition) and concentration, as well as anatomical and ultrastructural plant cell characteristics. Plants with no anthocyanin displayed increased reflectance and transmittance in the green spectral region (501-565 nm), while values decreased in the presence of anthocyanin. At wavelengths below 500 nm (350-500 nm), strong overlapping signatures of phenolics, carotenoids, chlorophylls, flavonoids and anthocyanins were observed. Using a partial least squares regression applied to 350-700 nm spectral data allowed for accurate estimations of different foliar pigment levels. In addition, a PCA and discriminant analysis were able to efficiently discriminate different species displaying spectra overlapping. The use of absorbance spectra only was able to discriminate species with 100 % confidence. Finally, a discussion on how different wavelengths are absorbed and on anatomical interference of light interaction in leaf profiles is presented.