On line kinetic analysis of permeation profiles for UV filter loaded microemulsions using an automatic system with spectroscopic detection and a chemometric approach.

The analysis of the permeation kinetics of new UV filter formulations is of great importance since the kinetic parameters are related to the effectiveness of the product over time. The dynamics of this process can be evaluated by means of the calculation of the permeation kinetic constants, which can be obtained from the respective permeation profiles. This paper is aimed at improving the analytical performance of permeation assays using an on-line automatic system with spectrometric detection avoiding the chromatographic procedure and the usually manual sampling steps required using the traditional Franz diffusion cell. Then, the kinetics of permeation of octyl p-methoxycinnamate loaded in different microemulsions through a synthetic membrane (polyamide) was analysed at real time by UV-Vis and fluorescence spectroscopies. The spectral data were obtained at regular intervals of time (5 min) during 60 min, and the concentration of the permeated UV-filter was at each time calculated using univariate linear calibration. The interference caused by the presence of basil essential oil (oily phase) in some microemulsion samples was overcome using synchronous fluorescence spectroscopy (Δλ = 60 nm) and partial least squares. In all cases, the permeation profiles were obtained (first-order kinetics) and the respective permeation kinetic constants were calculated. The validation of the proposed method was assessed by gas chromatographic-mass spectroscopy and non-significant differences for the obtained permeation kinetic constants were found between methods (p = 0.05). Additionally, a commercial sample was analysed with the proposed methods and the results were validated by high performance liquid chromatography technique.

Advances in sensing ammonia from agricultural sources.

Reducing ammonia emissions is one of the most difficult challenges for environmental regulators around the world. About 90% of ammonia in the atmosphere comes from agricultural sources, so that improving farm practices in order to reduce these emissions is a priority. Airborne ammonia is the key precursor for particulate matter (PM2.5) that impairs human health, and ammonia can contribute to excess nitrogen that causes eutrophication in water and biodiversity loss in plant ecosystems. Reductions in excess nitrogen (N) from ammonia are needed so that farms use N resources more efficiently and avoid unnecessary costs. To support the adoption of ammonia emission mitigation practices, new sensor developments are required to identify sources, individual contributions, to evaluate the effectiveness of controls, to monitor progress towards emission-reduction targets, and to develop incentives for behavioural change. There is specifically a need for sensitive, selective, robust and user-friendly sensors to monitor ammonia from livestock production and fertiliser application. Most currently-available sensors need specialists to set up, calibrate and maintain them, which creates issues with staffing and costs when monitoring large areas or when there is a need for high frequency sampling. This paper reports advances in monitoring airborne ammonia in agricultural areas. Selecting the right method of monitoring for each agricultural activity will provide critical data to identify and implement appropriate ammonia controls. Recent developments in chemo-resistive materials allow electrochemical sensing at room temperature, and new spectroscopic methods are sensitive enough to determine low concentrations in the order of parts per billion. However, these new methods still compromise selectivity and sensitivity due to the presence of ambient dust and other interferences, and are not yet suitable to be applied in agricultural monitoring. This review considers how ammonia measurements are made and applied, including the need for sensors that are suitable for routine monitoring by non-specialists. The review evaluates how monitoring information can be used for policies and regulations to mitigate ammonia emissions. The increasing concerns about ammonia emissions and the particular needs from the agriculture sector are addressed, giving an overview of the state-of-the-art and an outlook on future developments.

Fluorescence enhancement novel green analytical method for paraquat herbicide quantification based on immobilization on clay.

A new green method was developed for the quantification of paraquat (PQ) in water samples based on the fluorescence emission enhancement of the herbicide signal after adsorption on sodium montmorillonite clay (MMT). Radiant emission processes are favored by increasing the molecular rigidity of the PQ since it adopts a planar position between the nano-sheets of the MMT. The advantages of the use of this clay are nontoxic, low cost and found in abundance in natural reserves. The proposed method was successfully used in determining PQ in natural water samples with recoveries of 73% to 95%. The fluorescence emission showed a good linear relationship with PQ concentrations from 2.0 to 8.0 µmol L-1 with a detection limit of 0.37 µmol L-1. The method is simple, inexpensive and does not require the use of reagents or organic solvents; which makes it very promising to achieve the goals of green chemistry. The proposed methodology could be the beginning of the development of future green sensors.

Photolysis study of octyl p-methoxycinnamate loaded microemulsion by molecular fluorescence and chemometric approach.

Octyl p-methoxycinnamate (OMC) is one of the most widely used sunscreen agents. However, the efficiency of OMC as UV filter over time is affected due to the formation of the cis-isomer which presents a markedly lower extinction coefficient (εcis=12,600L mol-1cm-1 at 291nm) than the original trans-isomer (εtrans=24,000L mol-1cm-1 at 310nm). In this work, a novel carrier for OMC based on an oil-in-water microemulsion is proposed in order to improve the photostability of this sunscreen. The formulation was composed of 29.2% (w/w) of a 3:1 mixture of ethanol (co-surfactant) and decaethylene glycol mono-dodecyl ether (surfactant), 1.5% (w/w) of oleic acid (oil phase) and 69.2% (w/w) of water. This microemulsion was prepared in a simple way, under moderate stirring at 25°C and using acceptable, biocompatible and accessible materials for topical use. OMC was incorporated in the vehicle at a final concentration of 5.0% (w/w), taking into account the maximum permitted levels established by international norms. Then, a photolysis study of the loaded formulation was performed using a continuous flow system. The direct photolysis was monitored over time by molecular fluorescence. The recorded spectra data between 370 y 490nm were analyzed by multivariate curve resolution-alternating least squares algorithm. The kinetic rate constants corresponding to the photolysis of the trans-OMC were calculated from the concentration profiles, resulting in 0.0049s-1 for the trans-OMC loaded microemulsion and 0.0131s-1 for the trans-OMC in aqueous media. These results demonstrate a higher photostability of the trans-OMC when loaded in the proposed vehicle with respect to the free trans-OMC in aqueous media.

Fast Determination of Biodiesel Content in Commercial Diesel/Biodiesel Blends by Using Digital Images and Multivariate Calibration.

A new simple, rapid and inexpensive analytical method was developed to determine the biodiesel percentage in biodiesel/diesel blends through simple digital images of samples obtained by scanning with a commercial scanner. Soybean biodiesel and petroleum diesel samples were acquired from refineries currently in operation. There were prepared several mixtures within the range 1.5 to 12.0% of biodiesel in diesel oil, using the same procedure as is done in industry. The analytical signals were images recorded with a scanner. This data was decomposed with different color systems: RGB, HSV, HLS, CMYK and Grayscale. Chemometrics models based on color signals obtained from different mixtures of biodiesel/diesel were built. The quantification by using partial least squares (PLS) resulted in a RMSEP value for biodiesel of 0.9% (w/w); this load approximately 10-times smaller than the corresponding calibration range, with a correlation of 0.96 between predicted and reference values.

Fluorescent fingerprints of edible oils and biodiesel by means total synchronous fluorescence and Tucker3 modeling.

The present work proposes the use of total synchronous fluorescence spectroscopy (TSFS) as a discrimination methodology for fluorescent compounds in edible oils, which are preserved after the transesterification processes in the biodiesel production. In the same way, a similar study is presented to identify fluorophores that do not change in expired vegetal oils, to associate physicochemical parameters to fluorescent measures, as contribution to a fingerprint for increasing the chemical knowledge of these products. The fluorescent fingerprints were obtained by Tucker3 decomposition of a three-way array of the total synchronous fluorescence matrices. This chemometric method presents the ability for modeling non-bilinear data, as Total Synchronous Fluorescence Spectra data, and consists in the decomposition of the three way data arrays (samples×Δλ×λ excitation), into four new data matrices: A (scores), B (profile in Δλ mode), C (profile in spectra mode) and G (relationships between A, B and C). In this study, 50 samples of oil from soybean, corn and sunflower seeds before and after its expiration time, as well as 50 biodiesel samples obtained by transesterification of the same oils were measured by TSFS. This study represents an immediate application of chemical fingerprint for the discrimination of non-expired and expired edible oils and biodiesel. This method does not require the use of reagents or laborious procedures for the chemical characterization of samples.

Two-dimensional linear discriminant analysis for classification of three-way chemical data.

The two-dimensional linear discriminant analysis (2D-LDA) algorithm was originally proposed in the context of face image processing for the extraction of features with maximal discriminant power. However, despite its promising performance in image processing tasks, the 2D-LDA algorithm has not yet been used in applications involving chemical data. The present paper bridges this gap by investigating the use of 2D-LDA in classification problems involving three-way spectral data. The investigation was concerned with simulated data, as well as real-life data sets involving the classification of dry-cured Parma ham according to ageing by surface autofluorescence spectrometry and the classification of edible vegetable oils according to feedstock using total synchronous fluorescence spectrometry. The results were compared with those obtained by using the spectral data with no feature extraction, U-PLS-DA (Partial Least Squares Discriminant Analysis applied to the unfolded data), and LDA employing TUCKER-3 or PARAFAC scores. In the simulated data set, all methods yielded a correct classification rate of 100%. However, in the Parma ham and vegetable oil data sets, better classification rates were obtained by using 2D-LDA (86% and 100%), compared with no feature extraction (76% and 77%), U-PLS-DA (81% and 92%), PARAFAC-LDA (76% and 86%) and TUCKER3-LDA (86% and 93%).

Single excitation-emission fluorescence spectrum (EEF) for determination of cetane improver in diesel fuel.

A highly sensitive spectrofluorimetric method has been developed for the determination of 2-ethylhexyl nitrate in diesel fuel. Usually, this compound is used as an additive in order to improve cetane number. The analytical method consists in building the chemometric model as a first step. Then, it is possible to quantify the analyte with only recording a single excitation-emission fluorescence spectrum (EEF), whose data are introduced in the chemometric model above mentioned. Another important characteristic of this method is that the fuel sample was used without any pre-treatment for EEF. This work provides an interest improvement to fluorescence techniques using the rapid and easily applicable EEF approach to analyze such complex matrices. Exploding EEF was the key to a successful determination, obtaining a detection limit of 0.00434% (v/v) and a limit of quantification of 0.01446% (v/v).

Screening analysis of biodiesel feedstock using UV-vis, NIR and synchronous fluorescence spectrometries and the successive projections algorithm.

This paper investigates the use of UV-vis, near infrared (NIR) and synchronous fluorescence (SF) spectrometries coupled with multivariate classification methods to discriminate biodiesel samples with respect to the base oil employed in their production. More specifically, the present work extends previous studies by investigating the discrimination of corn-based biodiesel from two other biodiesel types (sunflower and soybean). Two classification methods are compared, namely full-spectrum SIMCA (soft independent modelling of class analogies) and SPA-LDA (linear discriminant analysis with variables selected by the successive projections algorithm). Regardless of the spectrometric technique employed, full-spectrum SIMCA did not provide an appropriate discrimination of the three biodiesel types. In contrast, all samples were correctly classified on the basis of a reduced number of wavelengths selected by SPA-LDA. It can be concluded that UV-vis, NIR and SF spectrometries can be successfully employed to discriminate corn-based biodiesel from the two other biodiesel types, but wavelength selection by SPA-LDA is key to the proper separation of the classes.

Automatized flow-batch method for fluorescent determination of free glycerol in biodiesel samples using on-line extraction.

An automatic method, based on flow-batch (FB), for determining glycerol in biodiesel was developed. The FB systems draw upon the useful features of flow, batch and multi-commutation approaches. The standards and samples preparation, as well as, derivatization and analysis were fully automated. For that purpose, a homemade chamber was built. The proposed method is based on liquid-liquid extraction of glycerol and simultaneous oxidation with periodate, generating formaldehyde that reacts with acetylacetone. A fluorescent product of 3,5-diacetyl-1,4-dihydrolutidine was obtained. The fluorescence signal was recorded at λ(ex) =417 nm and λ(em) = 514 nm. A linear response was observed from 0.10 to 5.00 mg L(-1) glycerol, variation coefficient 1.5%, sampling rate 14 h(-1) and detection limit 0.036 mg L(-1) glycerol. The procedure was successfully applied to the analysis of biodiesel samples, and the results agreed with the reference method (ASTM D6584-07) at 95% confidence level.

A new automated approach to determine monosodium glutamate in dehydrated broths by using the flow-batch methodology.

The advantages of the flow-batch methodology were exploited to implement a simple system with nephelometric detection for the determination of monosodium glutamate (MSG) in food samples. The method is based on the inhibitory effect of the MSG over the crystallization of L-lysine in an isopropanol/acetone mixture. The calibration curve was prepared on-line. The method was linear over the range of 2.8 x 10(-3) to 1.1 x 10(-2)gL(-1) and a detection limit of 9.7 x 10(-5)gL(-1) was achieved. It was successfully applied to determine the MSG concentration in food samples, without a previous treatment. A recovery study was carried out on real samples and the percentages were between 98 and 106%.