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.

Detection and quantification of adulterants in honey by LIBS.

The present work proposes methods for detection and quantification of honey adulterants using laser-induced breakdown spectroscopy (LIBS). The sample set consisted of 6 pure honey from different botanical sources, 2 sweetener syrups and 228 fortified samples. The spectra acquired using a spark discharge coupled to the LIBS system were used for the development of the PLS-DA (classification) and PLS (calibration) models. Several data preprocessing and variable selection methods were evaluated to obtain the best fit. The detection of adulterants was performed with 100% of accuracy. The quantification of adulterants was possible through a PLS model with the variables selected by iPLS. The PLS model was validated with external samples and presented good accuracy, selectivity, sensitivity, and linearity. The proposed methods highlighted the potential of the LIBS technique for honey authenticity certification, providing fast, simple, and clean determinations since no sample pretreatment was required.

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.

Evaluation of alternate lines of Fe for sequential multi-element determination of Cu, Fe, Mn and Zn in soil extracts by high-resolution continuum source flame atomic absorption spectrometry.

The usefulness of the secondary line at 252.744nm and the approach of side pixel registration were evaluated for the development of a method for sequential multi-element determination of Cu, Fe, Mn and Zn in soil extracts by high-resolution continuum source flame atomic absorption spectrometry (HR-CS FAAS). The influence of side pixel registration on the sensitivity and linearity was investigated by measuring at wings (248.325, 248.323, 248.321, 248.329, and 248.332nm) of the main line for Fe at 248.327nm. For the secondary line at 252.744nm or side pixel registration at 248.325nm, main lines for Cu (324.754nm), Mn (279.482nm) and Zn (213.875nm), sample flow-rate of 5.0mLmin(-1) and calibration by matrix matching, analytical curves in the 0.2-1.0mgL(-1) Cu, 1.0-20.0mgL(-1) Fe, 0.2-2.0mgL(-1) Mn, 0.1-1.0mgL(-1) Zn ranges were obtained with linear correlations better than 0.998. The proposed method was applied to seven soil samples and two soil reference materials (IAC 277; IAC 280). Results were in agreement at a 95% confidence level (paired t-test) with reference values. Recoveries of analytes added to soil extracts containing 0.15 and 0.30mgL(-1) Cu, 7.0 and 14mgL(-1) Fe, 0.60 and 1.20mgL(-1) Mn, 0.07 and 0.15mgL(-1) Zn, varied within the 94-99, 92-98, 93-101, and 93-103% intervals, respectively. The relative standard deviations (n=12) were 2.7% (Cu), 1.4% (Fe - 252.744nm), 5.7% (Fe - 248.325nm), 3.2% (Mn) and 2.8% (Zn) for an extract containing 0.35mgL(-1) Cu, 14mgL(-1) Fe, 1.1mgL(-1) Mn and 0.12mgL(-1) Zn. Detection limits were 5.4microgL(-1) Cu, 55microgL(-1) Fe (252.744nm), 147microgL(-1) Fe (248.325nm), 3.0microgL(-1) Mn and 4.2microgL(-1) Zn.