Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination.

A simple, fast, and efficient spark discharge-laser-induced breakdown spectroscopy (SD-LIBS) method was developed for determining rice botanic origin using predictive modeling based on support vector machine (SVM). Seventy-two samples from four rice varieties (Guri, Irga 424, Puitá, and Taim) were analyzed by SD-LIBS. Spectral lines of C, Ca, Fe, Mg, N and Na were selected as input variables for prediction model fitting. The SVM algorithm parameters were optimized using a central composite design (CCD) to find the better classification performance. The optimum model for discriminating rice samples according to their botanical variety was obtained using C = 5.25 and γ = 0.119. This model achieved 96.4% of correct predictions in test samples and showed sensitivities and specificities per class within the range of 92-100%. The developed method is robust and eco-friendly for rice botanic identification since its prediction results are consistent and reproducible and its application does not generate chemical waste.

Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy.

Rice is the most consumed food worldwide, therefore its designation of origin (PDO) is very useful. Laser-induced breakdown spectroscopy (LIBS) is an interesting analytical technique for PDO certification, since it provides fast multielemental analysis requiring minimal sample treatment. In this work LIBS spectral data from rice analysis were evaluated for PDO certification of Argentine brown rice. Samples from two PDOs were analyzed by LIBS coupled to spark discharge. The selection of spectral data was accomplished by extreme gradient boosting (XGBoost), an algorithm currently used in machine learning, but rarely applied in chemical issues. Emission lines of C, Ca, Fe, Mg and Na were selected, and the best performance of classification were obtained using k-nearest neighbor (k-NN) algorithm. The developed method provided 84% of accuracy, 100% of sensitivity and 78% of specificity in classification of test samples. Furthermore, it is simple, clean and can be easily applied for rice certification.

Prediction of black, immature and sour defective beans in coffee blends by using Laser-Induced Breakdown Spectroscopy.

One of the most important factors that interfere negatively in coffee global quality has been blends with defective beans, especially those called Black, Immature and Sour (BIS). The methods based on visual-manual estimation of defective beans have shown their inefficiency in coffee value chain for large-scale analysis. The lack of fast, accurate and robust analytical methods for BIS determination is still a research gap. Laser-Induced Breakdown Spectroscopy (LIBS) is a fast, low-cost and residue-free technique capable of performing multielemental determination and investigating organic composition of samples. In the present work, LIBS together with spectral processing and variable selection were evaluated to fit linear regression models for predicting BIS in blends. Models showed high capacity of prediction with RMSEP smaller than 3.8% and R2 higher than 80%. Most importantly, measurements are guided by chemical responses, which make LIBS-based methods less susceptible to the visual indistinguishability that occurs in manual inspections.

Laser-induced breakdown spectroscopy determination of K in biochar-based fertilizers in the presence of easily ionizable element.

Laser-induced breakdown spectroscopy is an optical emission technique quite suitable for the analysis of recalcitrant materials as it eliminates complex procedures of sample preparation. However, for some simple LIBS instrumentation the detection limits are still higher compared to those of consolidated spectroscopic techniques. The aim of the present work was to develop a method for the determination of K in new biochar-based fertilizer samples using a simple single pulse LIBS arrangement. Due to the low K detectability, which made impossible to obtain calibration curves, an exploratory qualitative study was performed aiming to evaluate the influence of the addition of easily ionizable elements (EIE) on the sensitivity. To this aim different salts containing EIE (K, Li and Na) and other cations (Cu and Mg) have been evaluated. Results obtained showed that salts containing EIE cations increased the spectral emission signals of some elements in samples previously submitted to charring. In particular, the strategy of using Li+ was applied to the determination of K in biochar-based fertilizers. The addition of Li+ allowed to develop an analytical method for K determination featuring a linear dynamic range from 0.8% to 21.56% K, and limits of detection and quantification of 0.2% and 0.8%, respectively.