Detection and quantification of adulterants in honey by LIBS.

The present work proposes methods for detection and quantification of honey adulterants using laser-induced breakdown spectroscopy (LIBS). The sample set consisted of 6 pure honey from different botanical sources, 2 sweetener syrups and 228 fortified samples. The spectra acquired using a spark discharge coupled to the LIBS system were used for the development of the PLS-DA (classification) and PLS (calibration) models. Several data preprocessing and variable selection methods were evaluated to obtain the best fit. The detection of adulterants was performed with 100% of accuracy. The quantification of adulterants was possible through a PLS model with the variables selected by iPLS. The PLS model was validated with external samples and presented good accuracy, selectivity, sensitivity, and linearity. The proposed methods highlighted the potential of the LIBS technique for honey authenticity certification, providing fast, simple, and clean determinations since no sample pretreatment was required.

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis.

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000-650 cm-1. The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time.

Infrared spectroscopy and multivariate methods as a tool for identification and quantification of fuels and lubricant oils in soil.

Environmental contamination caused by leakage of fuels and lubricant oils at gas stations is of great concern due to the presence of carcinogenic compounds in the composition of gasoline, diesel, and mineral lubricant oils. Chromatographic methods or non-selective infrared methods are usually used to assess soil contamination, which makes environmental monitoring costly or not appropriate. In this perspective, the present work proposes a methodology to identify the type of contaminant (gasoline, diesel, or lubricant oil) and, subsequently, to quantify the contaminant concentration using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and multivariate methods. Firstly, gasoline, diesel, and lubricating oil samples were acquired from gas stations and analyzed by gas chromatography to determine the total petroleum hydrocarbon (TPH) fractions (gasoline range organics, diesel range organics, and oil range organics). Then, solutions of these contaminants in hexane were prepared in the concentration range of about 5-10,000 mg kg-1. The infrared spectra of the solutions were obtained and used for the development of the pattern recognition model and the calibration models. The partial least square discriminant analysis (PLS-DA) model could correctly classify 100% of the samples of each type of contaminant and presented selectivity equal to 1.00, which provides a suitable method for the identification of the source of contamination. The PLS regression models were developed using multivariate filters, such as orthogonal signal correction (OSC) and general least square weighting (GLSW), and selection variable by genetic algorithm (GA). The validation of the models resulted in correlation coefficients above 0.96 and root-mean-square error of prediction values below the maximum permissible contamination limit (1000 mg kg-1). The methodology was validated through the addition of fuels and lubricating oil in soil samples and quantification of the TPH fractions through the developed models after the extraction of the analytes by the EPA 3550 method adapted by the authors. The recovery percentage of the analytes was within the acceptance limits of ASTM D7678 (70-130%), except for one sample (69% of recovery). Therefore, the methodology proposed here provides faster and less costly analyses than the chromatographic methods and it is adequate for the environmental monitoring of soil contamination by gas stations.

Potential of biohydrogen production from effluents of citrus processing industry using anaerobic bacteria from sewage sludge.

Citrus crops are among the most abundant crops in the world, which processing is mainly based on juice extraction, generating large amounts of effluents with properties that turn them into potential pollution sources if they are improperly discarded. This study evaluated the potential for bioconversion of effluents from citrus-processing industry (wastewater and vinasse) into hydrogen through the dark fermentation process, by applying anaerobic sewage sludge as inoculum. The inoculum was previously heat treated to eliminate H2-consumers microorganisms and improve its activity. Anaerobic batch reactors were operated in triplicate with increasing proportions (50, 80 and 100%) of each effluent as substrate at 37°C, pH 5.5. Citrus effluents had different effects on inoculum growth and H2 yields, demonstrated by profiles of acetic acid, butyric acid, propionic acid and ethanol, the main by-products generated. It was verified that there was an increase in the production of biogas with the additions of either wastewater (7.3, 33.4 and 85.3mmolL-1) or vinasse (8.8, 12.7 and 13.4mmolL-1) in substrate. These effluents demonstrated remarkable energetic reuse perspectives: 24.0MJm-3 and 4.0MJm-3, respectively. Besides promoting the integrated management and mitigation of anaerobic sludge and effluents from citrus industry, the biohydrogen production may be an alternative for the local energy supply, reducing the operational costs in their own facilities, while enabling a better utilization of the biological potential contained in sewage sludges.