Search

Tea (The online version contains supplementary material available at 10.1007/s13197-022-05440-1.

Multielemental compositions (Ag, As, Ba, Be, Cd, Cs, Co, Cr, Cu, Mo, Ni, Pb, Sb, Se, Sn, Sr, Tl, Rb, V, and Zn) of 106 cowpea bean samples belonging to different varieties collected from the province of Corrientes in Argentina were determined using inductively coupled plasma mass spectrometry (ICP-MS). Based on the multielemental data, five supervised learning techniques, namely, linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k nearest neighbors (k-NN), random forest (RF), and support vector machine (SVM) with radial basis function Kernel, were computed aiming at building classification models that allow one to predict the botanical variety of the samples based on their element profiles. The best classification performance was obtained by SVM with 93% accuracy rate. The model developed through this method enabled the correct separation of the samples into the five cowpea varieties investigated, where 100% sensitivity was achieved for most of the predicted classes. Thus, SVM was the algorithm selected for the classification of the cowpea beans according to their botanical variety. Multielemental determination coupled with supervised pattern recognition techniques have proved to be an interesting approach for differentiating a diverse range of cowpea genotypes. This study has contributed toward generalizing the use of multielemental fingerprinting as a promising tool for testing the authenticity of cowpea beans on a global scale. Fil: Pérez Rodríguez, Michael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina Fil: Gaiad, José Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina Fil: Hidalgo, Melisa Jazmin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina Fil: Avanza, María Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina Fil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina

Nowadays, coffee has a broad highlight in the international commerce and its qualities are certified according to the Global Quality Program developed by specialized coffee agencies. Expert tasters provide a score classifying the coffees as Gourmet, Superior, Traditional and Inferior. However, due to relatively high-cost, possibility of misclassification, difficulty of large-scale analysis, and mainly to the subjectivity generated by the tasters, the international commerce requires a fast and reliable analysis. Fourier Transform Infrared Spectroscopy (FTIR) is a widely disseminated technique in laboratories routine for direct analysis besides successfully applied to measure the coffee quality. In the present work, coffee FTIR spectra were assisted by chemometric tools based on pattern recognition such as principal component analysis (PCA) and linear discriminant analysis (LDA) to find a suitable classification model for authentication purposes. Spectral predictors did not favor the discrimination of coffee samples. LDA performance was greatly improved using the PCs obtained from PCA as input variables. Several models were fitted from these PCs, so that the optimal classifier achieved 95 % accuracy in the overall predictions. This model also showed sensitivities in the range 83–100 % and specificities ranged from 93 to 100 %, indicating that the tuned model is trustworthy to successfully evaluate the commercial coffee authenticity. The developed method is simple, rapid, and environmentally friendly, since not demanding any sample preparation and does not producing waste after analysis. Its application can help to protect coffee market and companies by product quality certification.

The development of microfluidic devices for point-of-care (POC) testing has been widely applied for food quality control, environmental monitoring, and clinical diagnoses. Regarding the latter, microfluidic paper-based analytical devices (µPADs) have a great potential in developing portable and disposable platforms for fast disease-relevant biomarker detection in remote areas. In this work, k-nearest neighbors (k-NN) classifiers were applied to demonstrate the reliability of using chemical data obtained from µPADs for diagnosis of diseases like diabetes mellitus and hyperuricemia. Predictors based on glucose, lactate, and uric acid biomarkers were evaluated to reach this goal. Healthy adults were differentiated from the diagnosed patients with a perfect success rate. Metabolic disorders were suitably predicted with 95.0% overall accuracy. This model also showed sensitivities and specificities in the ranges 75–100% and 93–100%, respectively, indicating that the classification protocol is robust. The k-NN modeling proved the potential of the µPADs to be applied in telemedicine for reliable off-site diagnoses.

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.

Made available in DSpace on 2018-12-11T17:28:53Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-09-01 A simple, rapid, and efficient ultrasound-assisted dispersive liquid–liquid microextraction (US-DLLME) method was developed for extraction of tetracycline residues from egg supplement samples, with subsequent determination by flow injection analysis (FIA) coupled to a liquid waveguide capillary cell (LWCC) and a controlled temperature heating bath. Tetracyclines react with diazotized p-sulfanilic acid, in a slightly alkaline medium, to form azo compounds that can be measured at 435 nm. The reaction sensitivity improved substantially (5.12-fold) using an in-line heating temperature of 45 °C. Multivariate methodology was used to optimize the factors affecting the extraction efficiency, considering the volumes of extraction and disperser solvents, sonication time, extraction time, and centrifugation time. Good linearity in the range 30–600 μg L−1 was obtained for all the tetracyclines, with regression coefficients (r) higher than 0.9974. The limits of detection ranged from 6.4 to 11.1 μg L−1, and the recoveries were in the range 85.7–96.4 %, with relative standard deviation lower than 9.8 %. Analyte recovery was improved by approximately 6 % when the microextraction was assisted by ultrasound. The results obtained with the proposed US-DLLME-FIA method were confirmed by a reference HPLC method and showed that the egg supplement samples analyzed were suitable for human consumption. Institute of Chemistry State University of São Paulo—UNESP, R. Prof. Francisco Degni 55, P.O. Box 355 Institute of Chemistry State University of São Paulo—UNESP, R. Prof. Francisco Degni 55, P.O. Box 355

An easy and greener flow injection method was developed for the spectrophotometric screening of tetracycline antibiotic residues in bovine milk. The combined benefits of the use of a detection system based on a liquid waveguide capillary cell (LWCC) and stop-flow (60 s) conditions significantly enhanced (45.6-fold) the analytical sensitivity. The determination is based on the formation of tetracycline azo derivatives (λmax = 435 nm) from diazotized p-sulfanilic acid, in a slightly alkaline medium. The analytical conditions were optimized by multivariate analysis, using a fractional factorial design. The linear range was 50–400 μg L−1, and the limits of detection and quantification were in the ranges 8.6–12.2 μg L−1 and 28.8–40.7 μg L−1, respectively. The applicability of the LWCC-based method was successfully tested using fortified milk samples that were only submitted to a prior acid treatment. The screening provided a quick response, enabling the determination of whether the tetracyclines were above or below the limit established by legislation (100 μg L−1). The positive samples were also analyzed by an AOAC Official Method, and the results obtained with the two methods were in good agreement. Additionally, the presence of tetracycline residues was confirmed using an HPLC-MS/MS reference analytical procedure. The excellent performance of the new screening method makes it suitable for use in the quality control of bovine milk in the food industry, aiming at consumer protection.

An environmentally reliable analytical methodology was developed for direct quantification of tetracycline (TC) and oxytetracycline (OTC) using continuous flow injection analysis with spectrophotometric detection. The method is based on the diazo coupling reaction between the tetracyclines and diazotized sulfanilic acid in a basic medium, resulting in the formation of an intense orange azo compound that presents maximum absorption at 434 nm. Experimental design was used to optimize the analytical conditions. The proposed technique was validated over the concentration range of 1 to 40 μg mL(-1), and was successfully applied to samples of commercial veterinary pharmaceuticals. The detection (LOD) and quantification (LOQ) limits were 0.40 and 1.35 μg mL(-1), respectively. The samples were also analyzed by an HPLC method, and the results showed agreement with the proposed technique. The new flow injection method can be immediately used for quality control purposes in the pharmaceutical industry, facilitating monitoring in real time during the production processes of tetracycline formulations for veterinary use.

Made available in DSpace on 2020-12-12T02:44:17Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-11-30 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. Institute of Basic and Applied Chemistry of the Northeast of Argentina (IQUIBA-NEA) National Scientific and Technical Research Council (CONICET) Faculty of Exact and Natural Science and Surveying National University of the Northeast – UNNE, Av. Libertad 5470 Chemistry Institute of Araraquara São Paulo State University – UNESP, R. Prof. Francisco Degni 55 Faculty of Agricultural Sciences UNNE, Sgto. Cabral, 1213 Chemistry Institute of Araraquara São Paulo State University – UNESP, R. Prof. Francisco Degni 55

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. Fil: Pérez Rodríguez, Michael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina Fil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina Fil: Pezza, Leonardo. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil Fil: Redigolo Pezza, Helena. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil

Rice is the most consumed food worldwide, therefore its designation of origin (PDO) is very useful. Laserinduced 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. Fil: Pérez Rodríguez, Michael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina Fil: Dirchwolf, Pamela Maia. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Silva, Tiago Varão. Universidade Estadual Paulista; Brasil Fil: Villafañe, Roxana Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina Fil: Neto, José Anchieta Gomes. Universidade Estadual Paulista; Brasil Fil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina Fil: Ferreira, Edilene Cristina. Universidade Estadual Paulista; Brasil