Estratégias quimiométricas para análise de plantas por espectrometria de emissão óptica com plasma induzido por laser

Abstract

A simultaneous optimization strategy based on neuro-genetic approach is proposed for selection of operational parameters for the simultaneous determination of macronutrients (Ca, Mg and P), micronutrients (B, Cu, Fe, Mn and Zn), Al and Si in plants by laser induced breakdown spectroscopy (LIBS). Laser pulse energy, lens-to-sample distance, number of accumulated laser pulses, delay time and integration time gate were optimized. A Q-Switched Nd: YAG laser operating in the fundamental wavelength (1064 nm) with repetition rate of 10 Hz and spectrometer with optical Echelle and ICCD detector was employed. Pellets of spinach leaves (NIST 1570a) were employed as laboratory samples. Measurements of LIBS spectra were based on three replicates and each replicate consisted of an average of ten spectra collected in different sites (i.e. test portions) of the pellet. In order to find a model that could correlate LIBS operational parameters and peak areas of all elements simultaneously a Bayesian Regularized Artificial Neural Network (BRANN) approach was employed. Subsequently, genetic algorithm (GA) was applied to find the optimal parameters for the neural network model. A single LIBS working condition pointed out by genetic algorithm (GA) was obtained with the following optimized parameters: 17.5 cm lens-to-sample distance, 25 accumulated laser pulses, 2.0 μs delay time and 4.5 μs integration time gate using a laser Nd:YAG at 1064 nm with 110 mJ per pulse focused on a pellet surface prepared from ground plant samples. Quantitative determinations were carried out by using chemometric methods, such as PLSR and iPLS. Samples of different cultures were used. For comparative purpose, the laboratory samples were also microwave-assisted digested and further analyzed by ICP OES. In general, results obtained by LIBS did not differ significantly from ICP OES data by applying a t-test at 95% confidence level. It is demonstrated that LIBS is a powerful tool for determination of macro and micronutrients in pellets of plant materials.