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.

Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion.

The present work proposes to detect adulteration in rice flour using mineral profiles. Eighty-seven flour samples from two rice kinds (Indica and Japonica) plus thirty adulterated flour samples were analyzed by ICP OES. After obtaining the quantitative elemental fingerprint of the samples, PCA and LDA were applied. Binary and multiclass associations were considered to assess rice flour authenticity through fraud identification. Models based on element predictors showed accuracies ranging from 72 to 88% to distinguish adulterated and unadulterated samples. The fusion of the mineral features with the principal components (PCs) obtained from PCA provided classification rates of 100% in training samples, and 91-100% in test samples. The proposed method proved to be a useful tool for quality control in the rice industry since a perfect success rate was achieved for rice flour fraud detection.

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.

An overview of the main foodstuff sample preparation technologies for tetracycline residue determination.

Tetracyclines are widely used for both the treatment and prevention of diseases in animals as well as for the promotion of rapid animal growth and weight gain. This practice may result in trace amounts of these drugs in products of animal origin, such as milk and eggs, posing serious risks to human health. The presence of tetracycline residues in foods can lead to the transmission of antibiotic-resistant pathogenic bacteria through the food chain. In order to ensure food safety and avoid exposure to these substances, national and international regulatory agencies have established tolerance levels for authorized veterinary drugs, including tetracycline antimicrobials. In view of that, numerous sensitive and specific methods have been developed for the quantification of these compounds in different food matrices. One will note, however, that the determination of trace residues in foods such as milk and eggs often requires extensive sample extraction and preparation prior to conducting instrumental analysis. Sample pretreatment is usually the most complicated step in the analytical process and covers both cleaning and pre-concentration. Optimal sample preparation can reduce analysis time and sources of error, enhance sensitivity, apart from enabling unequivocal identification, confirmation and quantification of target analytes. The development and implementation of more environmentally friendly analytical procedures, which involve the use of less hazardous solvents and smaller sample sizes compared to traditional methods, is a rapidly increasing trend in analytical chemistry. This review seeks to provide an updated overview of the main trends in sample preparation for the determination of tetracycline residues in foodstuffs. The applicability of several extraction and clean-up techniques employed in the analysis of foodstuffs, especially milk and egg samples, is also thoroughly discussed.

Classification of Argentinean Sauvignon blanc wines by UV spectroscopy and chemometric methods.

UNLABELLED: Argentina is an important worldwide wine producer. In this country, there are several recognizable provinces that produce Sauvignon blanc wines: Neuquén, Río Negro, Mendoza, and San Juan. The analysis of the provenance of these white wines is complex and requires the use of expensive and time-consuming techniques. For this reason, this work discusses the determination of the provenance of Argentinean Sauvignon blanc wines by the use of UV spectroscopy and chemometric methods, such as principal component analysis (PCA), cluster analysis (CA), linear discriminant analysis (LDA), and partial least square discriminant analysis (PLS-DA). The proposed method requires low-cost equipment and short-time analysis in comparison with other techniques. The results are in very good agreement with results based on the geographical origin of Sauvignon blanc wines. PRACTICAL APPLICATION: This manuscript describes a method to determine the geographical origin of Sauvignon wines from Argentina. The main advantage of this method is the use of nonexpensive techniques, such as UV-Vis spectroscopy.