Green Analytical Chemistry Armenta, S.; Garrigues, S.; de la Guardia, M.
TrAC, Trends in analytical chemistry (Regular ed.),
06/2008, Letnik:
27, Številka:
6
Journal Article
Recenzirano
We discuss the origins and the fundamentals of Green Analytical Chemistry (GAC), based on the literature published about clean, environmentally-friendly or GAC methods. We pay special attention to ...the strategies and the tools available to make sample-pretreatment and analytical methods greener. We consider that the main principles are to replace toxic reagents, to miniaturize and to automate methods, making it possible to reduce dramatically the amounts of reagents consumed and wastes generated, so reducing or avoiding side effects of analytical methods. We also consider on-line decontamination or passivation of wastes to be of special interest in making analytical chemistry sustainable.
This study investigates the performances of four major global Leaf Area Index (LAI) products at 1/11.2° spatial sampling and a monthly time step: ECOCLIMAP climatology, GLOBCARBON (from ...SPOT/VEGETATION and ATSR/AATSR), CYCLOPES (from SPOT/VEGETATION) and MODIS Collection 4 (main algorithm, from MODIS/TERRA). These products were intercompared during the 2001–2003 period over the BELMANIP network of sites. Their uncertainty was assessed by comparison with 56 LAI reference maps derived from ground measurements. CYCLOPES and MODIS depict realistic spatial variations at continental scale, while ECOCLIMAP poorly captures surface spatial heterogeneity, and GLOBCARBON tends to display erratic variations. ECOCLIMAP and GLOBCARBON show the highest frequency of successful retrievals while MODIS and CYCLOPES retrievals are frequently missing in winter over northern latitudes and over the equatorial belt. CYCLOPES and MODIS describe consistent temporal profiles over most vegetation types, while ECOCLIMAP does not show any interannual variations, and GLOBCARBON can exhibit temporal instability during the growing season over forests. The CYCLOPES, MODIS, and GLOBCARBON LAI values agree better over croplands and grasslands than over forests, where differences in vegetation structure representation between algorithms and surface reflectance uncertainties lead to substantial discrepancies between products. CYCLOPES does not reach high enough LAI values to properly characterize forests. In contrast, the other products have sufficient dynamic range of LAI to describe the global variability of LAI. Overall, CYCLOPES is the most similar product to the LAI reference maps. However, more accurate ground measurements and better representation of the global and seasonal variability of vegetation are required to refine this result.
An infrared spectroscopy (IR) based methodology has been developed to determine γ-butyrolactone (GBL) in adulterated beverages. The proposed method permits the direct screening of GBL in beverages ...and involves a minimum sample treatment requiring less than 2 min for quantitative determination of GBL. Sensitivity of IR method was improved by using liquid-liquid extraction (LLE) providing detection limits of 0.023 mg g−1. Accuracy of the proposed methodology was evaluated through the analysis of soft beverages and alcoholic cocktails spiked with GBL at concentration levels ranging from 0.075 to 10 mg g−1 providing recovery values from 91 to 100%. GBL was determined in twelve blind-spiked beverages, including from mineral water to wine and cocktails. Results obtained were statistically comparable to those provided by a liquid chromatography (LC) reference methodology and consistent with the spiked values. Therefore, the use of LLE-FTIR allowed a simple, sensitive and quantitative determination of GBL in soft beverages and alcoholic cocktails, thus evidencing its use for sex submission intention.
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•Fast and simple determination of GBL in beverages.•Method based on one step liquid extraction and direct transmission IR measurements.•Forensic applications with conventional instrumentation.
The monitoring of earth surface dynamic processes requires global observations of the structure and the functioning of vegetation. Moderate resolution sensors (with pixel size ranging from 250 m to ...7 km) provide frequent estimates of biophysical variables to characterize vegetation such as the leaf area index (LAI). However, the computation of LAI from moderate resolution remote sensing data induces a scaling bias on the LAI estimate if the moderate resolution pixel is heterogeneous and if the transfer function that relates remote sensing data to LAI is non-linear.
This study provides a model to evaluate and correct the scaling bias. The model is built first for a univariate semi-empirical transfer function relating LAI directly to NDVI. The scaling bias is a function of (i) the degree of non-linearity of the transfer function quantified by its second derivative and (ii) the spatial heterogeneity of the moderate resolution pixel quantified by the variogram of the high spatial resolution (20 m) NDVI image. Then, the model is extended to a bivariate transfer function where LAI is related to red and near infrared reflectances. The scaling bias depends on (i) the Hessian matrix of the transfer function and (ii) the variograms and cross variogram of the red and near infrared reflectances.
The scaling bias is investigated on several distinct landscapes from the VALERI database. Adjusting for scaling bias is critical on crop sites which are the most heterogeneous sites at the landscape level. Regarding the univariate transfer function, the magnitude of the scaling bias increases rapidly with pixel size until this size is larger than the typical spatial scale of the data. For the bivariate transfer function, it results from the addition of several components that may add up or cancel each other out. It is thus more difficult to analyze.
The accuracy of the model to estimate the scaling bias is discussed. It depends mainly on the ability of the variograms and cross variogram to represent the local dispersion variances and covariance within the moderate resolution pixel. The model is generally highly accurate at 1000 m spatial resolution for the univariate transfer function and less accurate for the bivariate transfer function.
The exposure to smoking related products has been evaluated through urine illness risk marker determination through the analysis of urine samples of smokers and vapers. Biomarkers and their ...metabolites such as
N
-acetyl-
S
-(2-cyanoethyl)-L-cysteine (CEMA),
N
-acetyl-
S
-(3,4-dihydroxybutyl)-L-cysteine (DHBMA),
N
-acetyl-
S
-1-(hydroxymethyl)-2-propen-1-yl)-L-cysteine (MHBMA),
N
-acetyl-
S
-(3-hydroxypropyl)-L-cysteine (3HPMA), 2
R
-
N
-acetyl-
S
-(4-hydroxybutan-2-yl)-L-cysteine (HMPMA), and
N
-acetyl-
S
-(3-carboxy-2-propyl)-L-cysteine (CMEMA) together with nicotine and cotinine were identified and quantified by LC-HRMS and LC-MS/MS, and data found normalized to the creatinine level. One hundred two urine samples were collected from smokers, non-smokers, and vapers, spanning an age range from 16 to 79 years. Results obtained showed that CEMA was only detected in urine samples from smokers and MHBMA was in the same order of magnitude in all the urine samples analyzed. HMPMA was found in the urine of vapers at the same order of concentration as in non-smokers. 3HPMA in vapers was lower than in the urine of smokers, presenting an intermediate situation between smokers and non-smokers. On the other hand, DHBMA in vapers can reach similar values to those found for smokers, while CMEMA shows concentrations in the urine of vapers higher than in the case of non-smokers and traditional smokers, requiring new research to link this metabolite to the use of electronic cigarettes and possible alternative metabolomic routes. In general, this study seems to verify that traditional smoking practice constitutes a major source of carcinogenic chemicals compared with substitutive practices, although those practices are not free of potential harm.
Graphical abstract
The monitoring of earth surface dynamic processes at a global scale requires high temporal frequency remote sensing observations which are provided up to now by moderate spatial resolution sensors. ...However, the spatial heterogeneity within the moderate spatial resolution pixel biases non-linear estimation processes of land surface variables from remote sensing data. To limit its influence on the description of land surface processes, corrections based on the quantification of the intra-pixel heterogeneity may be applied to non-linear estimation processes. A complementary strategy is to define the proper pixel size to capture the spatial variability of the data and minimize the intra-pixel variability.
This work provides a methodology to characterize and quantify the spatial heterogeneity of landscape vegetation cover from the modeling of the variogram of high spatial resolution NDVI data. NDVI variograms for 18 landscapes extracted from the VALERI database show that the land use is the main factor of spatial variability as quantified by the variogram sill. Crop sites are more heterogeneous than natural vegetation and forest sites at the landscape level. The integral range summarizes all structural parameters of the variogram into a single characteristic area. Its square root quantifies the mean length scale (i.e. spatial scale) of the data, which varies between 216 and 1060 m over the 18 landscapes considered. The integral range is also used as a yardstick to judge if the size of an image is large enough to measure properly the length scales of the data with the variogram. We propose that it must be smaller than 5% of the image surface. The theoretical dispersion variance, computed from the variogram model, quantifies the spatial heterogeneity within a moderate resolution pixel. It increases rapidly with pixel size until this size is larger than the mean length scale of the data. Finally based on the analysis of 18 landscapes, the sufficient pixel size to capture the major part of the spatial variability of the vegetation cover at the landscape scale is estimated to be less than 100 m. Since for all the heterogeneous landscapes the loss of NDVI spatial variability was small at this spatial resolution, the bias generated by the intra-pixel spatial heterogeneity on non-linear estimation processes will be reduced.
Soils play a key role in shaping the environment and in risk assessment. We characterized the soils of bare agricultural plots using TerraSAR-X (9.5
GHz) data acquired in 2009 and 2010. We analyzed ...the behavior of the TerraSAR-X signal for two configurations, HH-25° and HH-50°, with regard to several soil conditions: moisture content, surface roughness, soil composition and soil-surface structure (slaking crust).
The TerraSAR-X signal was more sensitive to soil moisture at a low (25°) incidence angle than at a high incidence angle (50°). For high soil moisture (>
25%), the TerraSAR-X signal was more sensitive to soil roughness at a high incidence angle (50°) than at a low incidence angle (25°).
The high spatial resolution of the TerraSAR-X data (1
m) enabled the soil composition and slaking crust to be analyzed at the within-plot scale based on the radar signal. The two loamy-soil categories that composed our training plots did not differ sufficiently in their percentages of sand and clay to be discriminated by the X-band radar signal.
However, the spatial distribution of slaking crust could be detected when soil moisture variation is observed between soil crusted and soil without crust. Indeed, areas covered by slaking crust could have greater soil moisture and consequently a greater backscattering signal than soils without crust.
► Huge database for a reliable relationship between X-band signal and soil moisture. ► Effect of soil roughness on TerraSAR signal: weak and function of the soil moisture. ► The TerraSAR signal is not sensitive to slight differences in soil composition. ► Hydric state of slaking crust explain the signal variations at the within-plot scale.
Two new procedures, based on infrared spectroscopy in the near infrared (NIR) and mid infrared (MIR), have been developed for the authentication of tea tree oil (TTO) commercial samples. Infrared ...measurements were made on untreated samples by transmission NIR and attenuated total reflectance (ATR) followed by partial least square discriminant analysis (PLS-DA). These methods offer a fast and low cost alternative to enantiomeric two-dimensional gas chromatography coupled to mass detection usually employed to discriminate between authentic and non-authentic samples. In these studies, a set of 267 samples, including authentic and non-authentic labelled tea tree oil samples, were used to build the models based on the wavenumber range, data pre-processing and latent variables number selection. Infrared methods can be discriminant for authentic and non-authentic TTO samples with a 98% certainty for both ATR and NIR methodologies, employing 92 and 142 external samples respectively. Developed PLS-DA infrared based methodologies and the reference methodology have been evaluated and compared from a Green Analytical point of view.
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•Infrared spectroscopy used for direct authentication of tea thee oil samples.•Neither sample pretreatment nor analyte extraction was required.•ATR-PLS-DA and NIR-PLS-DA models show adequate predictive capability for authentication.•Fast, cheap and environmentally friendly green alternatives to enantiomeric gas chromatography based methods.
A chemometric method has been developed for the determination of acidity and peroxide index in edible oils of different types and origins by using near infrared spectroscopy (NIR) measurements. ...Different methods for selecting the calibration set, after an hierarchical cluster analysis, were applied. After discrimination of olive oils from maize, seed and sunflower, the prediction capabilities of partial least squares (PLS) multivariate calibration of NIR data were evaluated. Several preprocessing alternatives (first derivative, multiplicative scatter correction, vector normalization, constant offset elimination, mean centering and standard normal variate) were investigated by using the root mean square error of validation (RMSEV) and prediction (RMSEP), as control parameters. Under the best conditions studied, the validation set provides RMSEP values of 0.034 and 0.037% (w/w) for acidity in (I) olive oil group and (II) sunflower, seed and maize oils group. RMSEP values for peroxide in both sample groups, expressed as mequiv.
O
2
kg
−1, were, respectively 1.87 and 0.79. The limit of detection of the methodology developed was 0.03% for acidity in both groups of edible oils (I and II), and 0.9 and 0.8
mequiv.
O
2
kg
−1 for peroxide in the olive oil and other edible oils groups, respectively. In fact, the methodology developed is proposed for direct acidity quantification and for the screening of peroxide index in edible oils, requiring less than 30
s per sample without any previous treatment.
A methodology based on the ultrasound-assisted extraction with ethanol and the dry film attenuated total reflectance infrared spectroscopy (DF-ATR-FTIR) measurement of extracts has been developed for ...a fast evaluation of non-conventional ("exotic") solid-sized cocaine samples. The method provides quantitative results in less than three minutes with a limit of detection in the solid sample of 1.6 μg g
of cocaine with a variation coefficient lower than 7%. Results found for seized samples of different natures were compared with those obtained by a reference gas chromatography method and the greenness of the whole proposed procedure was evaluated and compared using the analytical eco-scale, green analytical procedure index (GAPI), and analytical greenness metric (AGREE). The green evaluation of the proposed methodology provided green scores by considering different evaluation criteria.