Food Frontiers, 2020, 1, 134-151
https://doi.org/10.1002/fft2.30

Food Frontiers, 2020, 1, 134-151
https://doi.org/10.1002/fft2.30

Antibiotics 2020, 9(7), 383
DOI: 10.3390/antibiotics9070383
The main objective of this study was to develop a simple and efficient spectrophotometric technique combined with chemometrics for the simultaneous determination of sulfamethoxazole (SMX) and trimethoprim (TMP) in drug formulations. Specifically, we sought: (i) to evaluate the potential use of rank annihilation factor analysis (RAFA) to pH gradual change spectrophotometric data in order to provide sufficient accuracy and model robustness; and (ii) to determine SMX and TMP concentration in drug formulations without tedious pre-treatments such as derivatization or extraction techniques which are time-consuming and require hazardous solvents. In the proposed method, the spectra of the sample solutions at different pH values were recorded and the pH-spectra bilinear data matrix was generated. On these data, RAFA was then applied to estimate the concentrations of SMX and TMP in synthetic and real samples. Applying RAFA showed that the two drugs could be determined simultaneously with concentration ratios of SMX to TMP varying from 1:30 to 30:1 in the mixed samples (concentration range is 1–30 µg mL−1 for both components). The limits of detection were 0.25 and 0.38 µg mL−1 for SMX and TMP, respectively. The proposed method was successfully applied to the simultaneous determination of SMX and TMP in some synthetic, pharmaceutical formulation and biological fluid samples. In addition, the means of the estimated RSD (%) were 1.71 and 2.18 for SMX and TMP, respectively, in synthetic mixtures. The accuracy of the proposed method was confirmed by spiked recovery test on biological samples with satisfactory results (90.50–109.80%).
Food Chemistry, 2020, 33, 127460
DOI:10.1016/j.foodchem.2020.127460

Journal of Pharmaceutical and Biomedical Analysis, 2020, 190, 113428
DOI:10.1016/j.jpba.2020.113428

Food Chemistry, 2020, 330, 127197
https://doi.org/10.1016/j.foodchem.2020.127197

Food Microbiology, 2020, 92, 103554

Food Research International, 2020, 137, 109353
DOI: 10.1016/j.foodres.2020.109353

Trends in Food Science & Technology, 2020, 101, 150-164
DOI: 10.1016/j.tifs.2020.05.017

Critical Reviews in Food Science and Nutrition
Agriculture, Ecosystems & Environment, 2020, 297, 106969
DOI:10.1016/j.agee.2020.106969

Critical Reviews in Food Science and Nutrition

Different separated protein fractions by the electrophoretic method in polyacrylamide gel were used to classify two different types of honeys, Galician honeys and commercial honeys produced and packaged outside of Galicia. Random forest, artificial neural network, and support vector machine models were tested to differentiate Galician honeys and other commercial honeys produced and packaged outside of Galicia. The results obtained for the best random forest model allowed us to determine the origin of honeys with an accuracy of 95.2%. The random forest model, and the other developed models, could be improved with the inclusion of new data from different commercial honeys.