Geographical origin identification of mandarin fruits by analyzing fingerprint signatures based on multielemental composition.

Given rising traders and consumers concerns, the global food industry is increasingly demanding authentic and traceable products. Consequently, there is a heightened focus on verifying geographical authenticity as food quality assurance. In this work, we assessed pattern recognition approaches based on elemental predictors to discern the provenance of mandarin juices from three distinct citrus-producing zones located in the Northeast region of Argentina. A total of 202 samples originating from two cultivars were prepared through microwave-assisted acid digestion and analyzed by microwave plasma atomic emission spectroscopy (MP-AES). Later, we applied linear discriminant analysis (LDA), k-nearest neighbor (k-NN), support vector machine (SVM), and random forest (RF) to the element data obtained. SVM accomplished the best classification performance with a 95.1% success rate, for which it was selected for citrus samples authentication. The proposed method highlights the capability of mineral profiles in accurately identifying the genuine origin of mandarin juices. By implementing this model in the food supply chain, it can prevent mislabeling fraud, thereby contributing to consumer protection.

Measuring trace element fingerprinting for cereal bar authentication based on type and principal ingredient.

This paper introduces a method for determining the authenticity of commercial cereal bars based on trace element fingerprints. In this regard, 120 cereal bars were prepared using microwave-assisted acid digestion and the concentrations of Al, Ba, Bi, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb, Rb, Se, Sn, Sr, V, and Zn were later measured by ICP-MS. Results confirmed the suitability of the analyzed samples for human consumption. Multielemental data underwent autoscaling preprocessing for then applying PCA, CART, and LDA to input data set. LDA model accomplished the highest classification modeling performance with a success rate of 92%, making it the suitable model for reliable cereal bar prediction. The proposed method demonstrates the potential of trace element fingerprints in distinguishing cereal bar samples according to their type (conventional and gluten-free) and principal ingredient (fruit, yogurt, chocolate), thereby contributing to global efforts for food authentication.

Enhancing Visible Light Photocatalytic Degradation of Bisphenol A Using BiOI/Bi2MoO6 Heterostructures.

With the growing population, access to clean water is one of the 21st-century world's challenges. For this reason, different strategies to reduce pollutants in water using renewable energy sources should be exploited. Photocatalysts with extended visible light harvesting are an interesting route to degrade harmful molecules utilized in plastics, as is the case of Bisphenol A (BPA). This work uses a microwave-assisted route for the synthesis of two photocatalysts (BiOI and Bi2MoO6). Then, BiOI/Bi2MoO6 heterostructures of varied ratios were produced using the same synthetic routes. The BiOI/Bi2MoO6 with a flower-like shape exhibited high photocatalytic activity for BPA degradation compared to the individual BiOI and Bi2MoO6. The high photocatalytic activity was attributed to the matching electronic band structures and the interfacial contact between BiOI and Bi2MoO6, which could enhance the separation of photo-generated charges. Electrochemical, optical, structural, and chemical characterization demonstrated that it forms a BiOI/Bi2MoO6 p-n heterojunction. The free radical scavenging studies showed that superoxide radicals (O2•-) and holes (h+) were the main reactive species, while hydroxyl radical (•OH) generation was negligible during the photocatalytic degradation of BPA. The results can potentiate the application of the microwave synthesis of photocatalytic materials.

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.

Exploring the potential of paper-based analytical sensors for tea geographical origin authentication.

Tea (Camellia sinensis (L.) Kuntze-surname of German origin) is a popular beverage consumed worldwide due to its health benefits. Its quality depends on measuring features that may discriminate teas from distinct provenances. Protected designation of origin (PDO) is therefore a very useful label for tea quality evaluation. In the present work, antioxidant activity profiles obtained from microfluidic paper-based analytical devices (µPADs) were analyzed by chemometrics to determine the tea geographic origin. Based on the existing literature, we constructed a database containing chemical data from 26 samples and evaluated it by principal component analysis (PCA) coupled to linear discriminant analysis (LDA). Antioxidant activity was an effective LDA predictor for sample discrimination accomplishing accuracies from 75 to 82%. Modeling performance was favored by an external validation method. The best classification model was found using the first nine PCs as input variables. Training samples achieved a perfect success rate, while the test ones were predicted with 83% specificity, 100% sensitivity, and 90% overall accuracy. The modeling robustness was verified by integrating AUC (0.943) from ROC curve. The PCA-LDA approach taken here demonstrated that the teas coming from different countries can be correctly authenticated through µPADs, thus contributing to certificate samples PDO. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-022-05440-1.

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.

Factores de riesgo relacionado con la mortalidad en la cirugía multivalvular / Fatores de risco relacionados à mortalidade na cirurgia multivalvar / Risk factors related to multivalve surgey mortality

Las enfermedades cardiovasculares son las que afectan en mayor grado a los países desarrollados y a los países en vía de desarrollo. Cuba no está exenta de esto, y las enfermedades cardiovasculares son la primera causa de muerte en nuestro medio y, dentro de ellas, las lesiones valvulares siguensiendo una enfermedad cardiaca frecuente. Se realizó un estudio descriptivo y retrospectivo en 128 pacientes operados de 2 o más válvulas cardíacas en el Servicio de Cirugía Cardiovascular del Hospital Hermanos Ameijeiras, de enero del 2006 a diciembre del 2010. Se evaluaron las variables preoperatorias, transoperatorias y postoperatorias y su relación con la mortalidad. Predominóel sexo femenino y las edades entre 40 y 59 años. Todos los pacientes se encontraban en clase funcional III y IV. El 50% de los casos presentó el antecedente de fiebre reumática. La afectación valvular predominante fue mitro-aórtica, así como la doble sustitución valvular el procedimientoquirúrgico más realizado. La mortalidad global en este quinquenio de los pacientes operados de válvulas en nuestro servicio fue del 5,9%. Sin embargo en el subgrupo específico de operaciones realizadas sobre varias válvulas fue del 14,8 %, constatándose una asociación significativa de esta variable con: la presencia de fiebre reumática, el hecho de pertenecer a la clase funcional IV, tiempos de CEC por encima de 211 minutos y tiempo de paro anóxico superior a 150 minutos. La presencia de complicaciones como arritmias, anemia y trastornos de la coagulación, del tipo respiratorias, las complicaciones renales y la reintervención quirúrgica mostraron asociación significativa con la mortalidad...

As doenças cardiovasculares são as mais comuns nos países desenvolvidos e nos países em via de desenvolvimento. Cuba também não está isenta e as doenças cardiovasculares são a primeiracausa de morte em nosso meio e, como sabemos, as lesões valvares continuam sendo uma doença cardíaca frequente. Realizou-se um estudo descritivo e retrospectivo em 128 pacientes operados de 2 ou mais valvas cardíacas no Serviço de Cirurgia Cardiovascular do Hospital Hermanos Ameijeiras,de janeiro de 2006 a dezembro de 2010. Avaliaram-se as variáveis pré operatórias, transoperatóriase pós operatórias e sua relação com a mortalidade. O predomínio foi do sexo feminino e as idades predominantes foram de 40 a 59 anos. Todos os pacientes se encontravam em classe funcional III e IV. 50% dos casos apresentou febre reumática como antecedente. A valva mais afetada foi amitro-aórtica, e a dupla troca valvar foi o procedimento cirúrgico mais realizado.A mortalidade global dos pacientes operados de válvulas em nosso serviço neste quinquênio, foi de 5,9%, entretanto, no subgrupo específico de operações realizadas sobre várias válvulas, foi de 14,8%, constatando-se uma associação significativa desta variável com a presença de febrereumática, o fato de pertenecer à classe funcional IV, tempos de CEC acima de 211 minutos e tempo de parada anóxica superior a 150 minutos. A presença de complicações como arritmias, anemia e trastornos de coagulação, de tiporespiratórias, as complicações renais e a reintervenção cirúrgica, mostraram associação significativa com a mortalidade...

Cardiovascular diseases affect both developed and developing countries. Cuba is not an exception and cardiovascular diseases are the first cause of death in our country and, in them, valve lesions still continue being a frequent heart disease. A descriptive and retrospective analysis of 128 patients operated of two or more cardiac valves was carried out at the Department of Cardiovascular Surgery at the Hermanos Ameijeiras Hospital from January 2006 to December 2010. Preoperative, intraoperative and postoperative variables were assessed and how they related to mortality. There was a prevalence of the female gender and ages ranged from 40 to 59 years. Allpatients were in functional Class III and IV. 50% of the cases had preexisting rheumatic fever. The predominant valve disease was mitral aortic, and the procedure mostly carried out was double valve replacement. Overall mortality of patients undergoing valve surgery in our Department during these five years was 5.9%. Nevertheless, in the specific subgroup of surgeries of several valves mortality was 14.8%, thus reporting a significant relationship of this variable with: presence of rheumatic fever, belonging to functional Class IV, by-pass time over 211 minutes and time of anoxic arrest over 150minutes. The presence of complications such as arrhythmia, anemia, coagulation, respiratory and renaldisorders and surgical re-interventions were statistically significantly related to mortality...