An environment-friendly spot test method with digital imaging for the micro-titration of citric fruits.

A new method to determine the total titratable acidity of orange, lemon and passion fruit, based on a spot test obtained from digital images and using anthocyanins as the biodegradable indicator, is presented for the first time. The colorimetric reactions were carried out by acid-base titration on a microscale, employing anthocyanin with a microplate for spot test purposes, with detection by digital imaging. To obtain highly precise data, a chamber based on a diffuser was developed to control the illumination supplied by the light emitting diodes, and coupled to a smartphone to acquire adequate digital images. High precision was obtained with a relative standard deviation of 0.758% for n = 95. The RGB values were extracted from the digital images and used as analytical signals, the values being correlated with the micro-volume of the titrant and used to construct the titration curves and obtain the first and second derivatives, respectively. For comparative purposes, the official AOAC (Association of Official Analytical Chemists) and MAPA (Ministry of Agriculture, Livestock and Food Supply of Brazil) methods were used and the results compared by applying the paired t-test at the 95% confidence level (n = 3). No difference was found between the values and the relative errors were less than 2.8%. The micro-titrimetric method was fast, uses anthocyanins as the natural indicator, is practical, and permits a reduction of 922 times or 99.9% of the volume required in a conventional titration. It is therefore ideal for routine analyses leading to a reduction in the waste generated, according to the principles of green chemistry.

Mineral Composition of Amazonian Fruits by Flame Atomic Absorption Spectrometry Using Multivariate Analysis.

This study aimed to evaluate the mineral composition in native and non-native Amazonian fruits using flame atomic absorption spectrometry (FAAS) and multivariate methods, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA), in order to conduct a more thorough evaluation of the original data. The accuracy was checked by certified reference material analysis (Poplar leaves) and spike experiments. The results of the analysis were in agreement with the certified values, with analytical recoveries for all analytes in an acceptable range from 82 to 113%, and relative standard deviations (RSD) were in the range of 0.2-18%. Furthermore, PCA explained 68% of the total variance, while HCA confirmed the correlations found in the PCA, allowing for the evaluation of the degree of similarity between the fruits studied. These results will be used to better understand the distribution of inorganic constituents within these Amazonian fruits.

Conjunctive use of in situ gas sampling and chromatography with geospatial analysis to estimate greenhouse gas emissions of a large Amazonian hydroelectric reservoir.

Hydroelectric power reservoirs are considered potential contributors to the greenhouse effect in the atmosphere through the emittance of methane and carbon dioxide. We combined in situ sampling and gas chromatography with geostatistical and remote sensing approaches to estimate greenhouse gas (GHG) emissions of a large hydropower reservoir. We used remote sensing data to estimate the water surface and geospatial interpolation to calculate total emissions as a function of reservoir surface area. The CH4 and CO2 gas concentrations were linearly correlated to sampling time, confirming the adequacy of the in situ sampling method to measure GHG diffusive fluxes from reservoir water surfaces. The combination of high purity (99.99%) ISO-norm gas standards with a gas chromatograph, enabled us to achieve low measurement detection limits of 0.16 and 0.60 µmol mol-1, respectively, for CH4 (using a flame ionization or FID detector) and CO2 (using a thermal conductivity or TCD detector). Our results show that CO2 emissions are significantly (an order of 5.102-103) higher than those of CH4 in both the spatial and temporal domain for this reservoir. The total diffusive GHG emissions over a year (June 2011 to May 2012) of the Tucuruí hydropower reservoir being in operation, in units of tons of carbon, added up to 6.82 × 103 for CH4 and 1.19 × 106 for CO2. We show that in situ GHG sampling using small floating gas chambers and high precision gas chromatography can be combined with geospatial interpolation techniques and remote sensing data to obtain estimates of diffusive GHG emissions from large water bodies with fluctuating water surfaces such as hydropower reservoirs. We recommend that more measurements and observations on these emissions are pursued in order to support and better quantify the ongoing discussions on estimates and mitigation of GHG emissions from reservoirs in the Amazon region and elsewhere in the world.

Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration.

A method for sulfur determination in diesel fuel employing near infrared spectroscopy, variable selection and multivariate calibration is described. The performances of principal component regression (PCR) and partial least square (PLS) chemometric methods were compared with those shown by multiple linear regression (MLR), performed after variable selection based on the genetic algorithm (GA) or the successive projection algorithm (SPA). Ninety seven diesel samples were divided into three sets (41 for calibration, 30 for internal validation and 26 for external validation), each of them covering the full range of sulfur concentrations (from 0.07 to 0.33% w/w). Transflectance measurements were performed from 850 to 1800 nm. Although principal component analysis identified the presence of three groups, PLS, PCR and MLR provided models whose predicting capabilities were independent of the diesel type. Calibration with PLS and PCR employing all the 454 wavelengths provided root mean square errors of prediction (RMSEP) of 0.036% and 0.043% for the validation set, respectively. The use of GA and SPA for variable selection provided calibration models based on 19 and 9 wavelengths, with a RMSEP of 0.031% (PLS-GA), 0.022% (MLR-SPA) and 0.034% (MLR-GA). As the ASTM 4294 method allows a reproducibility of 0.05%, it can be concluded that a method based on NIR spectroscopy and multivariate calibration can be employed for the determination of sulfur in diesel fuels. Furthermore, the selection of variables can provide more robust calibration models and SPA provided more parsimonious models than GA.