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.

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.