Homogeneity and stability assessment of a candidate to pumpkin seed flour reference material by means of computer vision based chemometrics assisted approach

This study proposes a preliminary assessment of the homogeneity and stability through digital image acquisition of a candidate for mechanically processed pumpkin seed meal reference material, exploring the concepts of homogeneity curve and the analysis of texture characteristics by Continuous-Level Moving Block through Robust Principal Component Analysis. This innovative methodology allowed us to examine the percentage of homogeneity in a set of samples, revealing an average of 41% with only one outlier in relation to the entire sample, indicating low homogeneity. In the stability study carried out after storing samples for 12 months at different temperatures, 83% of the samples were considered regular and 17% were outlier, which means that most of them were considered stable. Therefore, this methodology is useful for screening samples for homogeneity, by textural analysis, and detected non-homogeneity can be corrected in advance for quantification by standard protocols.

Presence of antibiotic resistance genes and its association with antibiotic occurrence in Dilúvio River in southern Brazil

It is known that antibiotics are widely used in human and veterinary medicine. In some countries the use is controlled, however few restrictions to their use are enforced in many countries. Antibiotics and their metabolites can reach the water bodies through sewage systems, especially in those countries with partial or absent wastewater treatment systems. The overuse and misuse of antibiotics has been linked with the increase of antibiotic resistant bacteria. The relation between the occurrence of antibiotics and resistance genes in surface waters has been widely studied worldwide evincing the great importance of this subject. In this work, a methodology for quantification of 40 antibiotics of 5 different classes, in river water, by SPE-LC-MS/MS was validated. Samples were taken during a two-year period from Dilúvio River, a stream that crosses the city of Porto Alegre (RS - Brazil) and receives in nature domestic effluent. The methodology met the requirements of validation, with Limit of Quantification varying from 20 ng L^-1 to 100 ng L^-1. A total of 48 samples was analyzed for the presence of antibiotics for two years. From the 40 antibiotics analyzed, 8 of them (Azithromycin, Cephalexin, ciprofloxacin, clindamycin, norfloxacin, sulfadiazine, sulfamethoxazole and trimethoprim) were present in all sampling points in the range of <LOQ to 344 ng L^-1. The occurrence of antibiotics resistance genes in the sampling points was determined using qPCR. Specific primers were utilized to detected resistance genes to the β-lactam cephalexin (bla_TEM gene), to the quilonones ciprofloxacin and norfloxacin (qnrS gene), to the macrolides azithromycin and clindamycin (ermB gene), and to the sulfonamides sulfadiazine and sulfamethoxazole (sulI gene), which were quantified the selected water samples. Quantitative PCR analysis revealed the presence of ARGs in all samples, with the highest concentration found for bla_TEM.

Classification of biodiesel using NIR spectrometry and multivariate techniques

This article describes the classification of biodiesel samples using NIR spectroscopy and chemometric techniques. A total of 108 spectra of biodiesel samples were taken (being three samples each of four types of oil, cottonseed, sunflower, soybean and canola), from nine manufacturers. The measurements for each of the three samples were in the spectral region between 12,500 and 4000 cm(-1). The data were preprocessed by selecting a spectral range of 5000-4500 cm(-1), and then a Savitzky-Golay second-order polynomial was used with 21 data points to obtain second derivative spectra. Characterization of the biodiesel was done using chemometric models based on hierarchical cluster analysis (HCA), principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) elaborated for each group of biodiesel samples (cotton, sunflower, soybean and canola). For the HCA and PCA, the formation of clusters for each group of biodiesel was observed, and SIMCA models were built using 18 spectral measurements for each type of biodiesel (training set), and nine spectral measurements to construct a classification set (except for the canola oil which used eight spectra). The SIMCA classifications obtained 100% accurate identifications. Using this strategy, it was feasible to classify biodiesel quickly and nondestructively without the need for various analytical determinations.