Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy.

Rice is an important source of nutrition and energy consumed around the world. Thus, quality inspection is crucial for protecting consumers and increasing the rice's value in the productive chain. Currently, methods for rice labeling depending on grain quality features are based on image and/or visual inspection. These methods have shown subjectivity and inefficiency for large-scale analyses. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique showing attractive features due to how quick the analysis can be carried out and its capability of providing spectra that are true fingerprints of the sample's elemental composition. In this work, LIBS performance was evaluated for labeling rice according to grain quality features. The LIBS spectra of samples with their grain quality numerically described as Type 1, 2, and 3 were measured. Several spectral processing methods were evaluated when modeling a k-nearest neighbors (k-NN) classifier. Variable selection was also carried out by principal component analysis (PCA), and then the optimal k-value was selected. The best result was obtained by applying spectrum smoothing followed by normalization by using the first fifteen principal components (PCs) as input variables and k = 9. Under these conditions, the method showed excellent performance, achieving sample classification with 94% overall prediction accuracy. The sensitivities ranged from 90 to 100%, and specificities were in the range of 92-100%. The proposed method has remarkable characteristics, e.g., analytical speed and analysis guided by chemical responses; therefore, the method is not susceptible to subjectivity errors.

Characterization and predictive modeling potential of aging time of roasted coffee using infrared spectroscopy.

Repackaging and tampering with labels of foods to extend their shelf life is an illegal practice, increasingly common in some Brazilian coffee retail markets. Fast, easy-to-use, and low-cost analytical techniques for the large-scale screening of aging time have been demanded lately to fight the growth of these frauds in retail coffee markets. In this work, Fourier transform infrared spectroscopy was evaluated as a provider of relevant regressors, chemically explainable, aiming for predictive models for estimating the aging of roasted and packaged coffees during their shelf life. Spectra of two Coffea arabica varieties (Bourbon and Obatã) were periodically acquired during eleven months of storage. The most relevant absorption bands were selected, which showed a moderate correlation with the storage time. They were identified as responses from lipids, phenolic compounds, and carbohydrates. From those responsive bands, logistic regression (sigmoid functions) models were fitted for each coffee variety, as well as for both together. Predictive models for Bourbon and Obatã showed high performances in validation data, with r (Pearson correlation) above 0.92 and root mean square error (RMSE) below 43 days. For both varieties, the logistic model showed r greater than 0.83 and RMSE equal to 56 days. Results corroborate the methodological approach efficacy towards agile technological innovations in the coffee value chain, as well as opening new application fronts for estimating the aging of other foods.

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.

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.

Effect of different precursors on generation of reference spectra for structural molecular background correction by solid sampling high-resolution continuum source graphite furnace atomic absorption spectrometry: Determination of antimony in cosmetics.

Different precursors were evaluated for the generation of reference spectra and correction of the background caused by SiO molecules in the determination of Sb in facial cosmetics by high-resolution continuum source graphite furnace atomic absorption spectrometry employing direct solid sample analysis. Zeolite and mica were the most effective precursors for background correction during Sb determination using the 217.581nm and 231.147nm lines. Full 23 factorial design and central composite design were used to optimize the atomizer temperature program. The optimum pyrolysis and atomization temperatures were 1500 and 2100°C, respectively. A Pd(NO3)2/Mg(NO3)2 mixture was employed as the chemical modifier, and calibration was performed at 217.581nm with aqueous standards containing Sb in the range 0.5-2.25ng, resulting in a correlation coefficient of 0.9995 and a slope of 0.1548s ng-1. The sample mass was in the range 0.15-0.25mg. The accuracy of the method was determined by analysis of Montana Soil (II) certified reference material, together with addition/recovery tests. The Sb concentration found was in agreement with the certified value, at a 95% confidence level (paired t-test). Recoveries of Sb added to the samples were in the range 82-108%. The limit of quantification was 0.9mgkg-1 and the relative standard deviation (n=3) ranged from 0.5% to 7.1%. From thirteen analyzed samples, Sb was not detected in ten samples (blush, eye shadow and compact powder); three samples (two blush and one eye shadow) presented Sb concentration in the 9.1-14.5mgkg-1 range.

Cobalt internal standard for Ni to assist the simultaneous determination of Mo and Ni in plant materials by high-resolution continuum source graphite furnace atomic absorption spectrometry employing direct solid sample analysis.

A new method is proposed for the simultaneous determination of Mo and Ni in plant materials by high-resolution continuum source graphite furnace atomic absorption spectrometry (HR-CS GFAAS), employing direct solid sample analysis (DSS) and internal standardization (IS). Cobalt was used as internal standard to minimize matrix effects during Ni determinations, enabling the use of aqueous standards for calibration. Correlation coefficients for the calibration curves were typically better than 0.9937. The performance of the method was checked by analysis of six plant certified reference materials, and the results for Mo and Ni were in agreement with the certified values (95% confidence level, t-test). Analysis was made of different types of plant materials used as renewable sources of energy, including sugarcane leaves, banana tree fiber, soybean straw, coffee pods, orange bagasse, peanut hulls, and sugarcane bagasse. The concentrations found for Mo and Ni ranged from 0.08 to 0.63 ng mg(-1) and from 0.41 to 6.92 ng mg(-1), respectively. Precision (RSD) varied from 2.1% to 11% for Mo and from 3.7% to 10% for Ni. Limits of quantification of 0.055 and 0.074 ng were obtained for Mo and Ni, respectively.

Evaluation of laser induced breakdown spectroscopy for multielemental determination in soils under sewage sludge application.

Laser induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy technique for simple, direct and clean analysis, with great application potential in environmental sustainability studies. In a single LIBS spectrum it is possible to obtain qualitative information on the sample composition. However, quantitative analysis requires a reliable model for analytical calibration. Multilayer perceptron (MLP), an artificial neural network, is a multivariate technique that is capable of learning to recognize features from examples. Therefore MLP can be used as a calibration model for analytical determinations. Accordingly, the present study proposes to evaluate the traditional linear fit and MLP models for LIBS calibration, in order to attain a quantitative multielemental method for contaminant determination in soil under sewage sludge application. Two sets of samples, both composed of two kinds of soils were used for calibration and validation, respectively. The analyte concentrations in these samples, used as reference, were determined by a reference analytical method using inductively coupled plasma optical emission spectrometry (ICP OES). The LIBS-MLP was compared to a LIBS-linear fit method. The values determined by LIBS-MLP showed lower prediction errors, correlation above 98% with values determined by ICP OES, higher accuracy and precision, lower limits of detection and great application potential in the analysis of different kinds of soils.