A novel fluorimetric method for glyphosate and AMPA determination with NBD-Cl and MCR-ALS.

We report the development of a new analytical method for the quantification of N-(phosphonomethyl)glycine (glyphosate) and (aminomethyl)phosphonic acid (AMPA) by combining spectrofluorimetry and multivariate calibration. In this study, fluorescence spectroscopy was used to quantify glyphosate and AMPA, which were previously derivatized with the fluorogenic reagent: 4-chloro-7-nitrobenzofurazan (NBD-Cl). Fluorescence excitation-emission matrices (EEM) were recorded by exciting between 400 and 500 nm, and measuring the emission between 500 and 610 nm. The second-order data obtained were processed using the Multivariate Curve Resolution with Alternating Least Square (MCR-ALS) methodology. The developed method was used to predict different concentrations of glyphosate and AMPA in validation samples. In addition, the presence of the herbicide was evaluated in real samples: a commercial formulation and a water sample from a cultivated area. For this purpose, the standard addition method was used to study the matrix effect in each case. The ranges of working concentrations obtained for this new method are in agreement with the amounts found in surface water samples near a direct sowing soybean growing region in Argentina.

Open-Source Assisted Laboratory Automation through Graphical User Interfaces and 3D Printers: Application to Equipment Hyphenation for Higher-Order Data Generation.

Higher-order data generation implies some automation challenges, which are mainly related to the hidden programming languages and electronic details of the equipment. When techniques and/or equipment hyphenation are the key to obtaining higher-order data, the required simultaneous control of them demands funds for new hardware, software, and licenses, in addition to very skilled operators. In this work, we present Design of Inputs-Outputs with Sikuli (DIOS), a free and open-source code program that provides a general framework for the design of automated experimental procedures without prior knowledge of programming or electronics. Basically, instruments and devices are considered as nodes in a network, and every node is associated both with physical and virtual inputs and outputs. Virtual components, such as graphical user interfaces (GUIs) of equipment, are handled by means of image recognition tools provided by Sikuli scripting language, while handling of their physical counterparts is achieved using an adapted open-source three-dimensional (3D) printer. Two previously reported experiments of our research group, related to fluorescence matrices derived from kinetics and high-performance liquid chromatography, were adapted to be carried out in a more automated fashion. Satisfactory results, in terms of analytical performance, were obtained. Similarly, advantages derived from open-source tools assistance could be appreciated, mainly in terms of lesser intervention of operators and cost savings.

Modeling four and three-way fast high-performance liquid chromatography with fluorescence detection data for quantitation of fluoroquinolones in water samples.

This paper presents a study regarding the acquisition and analytical utilization of four and three-way data, acquired by following the excitation-emission fluorescence matrices at different elution times, in a fast liquid chromatographic HPLC procedure. This kind of data were implemented for first time for quantitative purposes, and applied to the determination of two fluoroquinolones in tap water samples, as a model to show the potentiality of the proposed strategy of four-way data generation. The data were modeled with three well-known algorithms: PARAFAC, U-PLS/RTL and MCR-ALS, the latter conveniently adapted to model third-order data. The second-order advantage was exploited when analyzing samples containing uncalibrated interferences. PARAFAC and MCR-ALS were the algorithms that better exploited the second-order advantage when no peak time shifts occurred among samples. On the other hand, when the quadrilinearity was lost due to the occurrence of temporal shifts, MCR-ALS furnished the better results. Relative error of prediction (REP%) obtained were 9.9% for ofloxacin and 14.0% for ciprofloxacin. In addition, a significant enhancement in the analytical figures of merit was observed when going from second- to third-order data (reduction of ca. 70% in LODs).

Multivariate curve resolution modeling of liquid chromatography-mass spectrometry data in a comparative study of the different endogenous metabolites behavior in two tomato cultivars treated with carbofuran pesticide.

A metabonomic study based on the application of multivariate curve resolution and alternating least squares (MCR-ALS) to three-way data sets obtained by liquid chromatography coupled to mass spectrometry detection (LC-MS) was carried out for Rambo and Raf tomato cultivars treated with carbofuran pesticide. Samples were picked up during a 21 days period after treatment and analyzed by LC-MS in scan mode, along with the corresponding blank samples. Then, MCR-ALS was applied to the three-way data sets using column wise augmented matrices, and the evolutionary profiles as a function of the time after treatment were estimated for the metabolites present in both cultivars, as well as their corresponding pure spectra estimations. A comparative study using those estimations showed that some of these metabolites followed different behavior for the different cultivars after treatment. Since all treated and untreated Rambo and Raf samples were picked up according to the same sampling protocol and in a similar state of maturation, any difference in the behavior between profiles can be interpreted as an effect due to the presence of pesticide and to the kind of cultivar. Based on this hypothesis, several PLS-DA approaches were tested to check if it would be possible to classify samples by using the metabolites MCR estimations. Results showed that PLS-DA models for classification of treated or non-treated (blank) samples were the best ones obtained (98.44% of correct classifications for the validation set), which supports the stress effects related to carbofuran treatment. In addition, excellent discrimination among the four groups could be attained (89.06% of correct classifications for the validation set).

Detection of unintended stress effects based on a metabonomic study in tomato fruits after treatment with carbofuran pesticide. Capabilities of MCR-ALS applied to LC-MS three-way data arrays.

A chemometric strategy based on multivariate curve resolution and alternating least-squares (MCR-ALS) applied to LC-MS three-way data arrays has been developed to perform a metabonomic study in tomato (Lycopersicon esculentum) fruits (cultivar Rambo) following treatment with carbofuran. This methodology has proved to be adequate for the detection of unintended stress effects due to the previous treatment with this pesticide. MCR-ALS was performed on augmented matrices built with the LC-MS three-way data obtained from treated and nontreated samples through the sampling time. The strategy allowed us to obtain the concentration and spectra profiles of the main components (previously estimated with the SVD algorithm) from samples treated with pesticide as well as from blank samples, showing how they vary with time after plants treatment with the pesticide. In addition, a simple resolved mass spectrum was obtained corresponding to the peaks of a particular component in all matrices, thus avoiding ambiguity in the compound identity assignment. Different time profiles were found for some metabolites in treated and nontreated samples, which demonstrate that the presence of pesticide causes changes thorough time in the behavior of certain endogenous tomato metabolites as a result of physiological stress.

Calibration maintenance and transfer using Tikhonov regularization approaches.

Maintaining multivariate calibrations is essential and involves keeping models developed on an instrument applicable to predicting new samples over time. Sometimes a primary instrument model is needed to predict samples measured on secondary instruments. This situation is referred to as calibration transfer. This paper reports on using a Tikhonov regularization (TR) based method in both cases. A distinction of the TR design for calibration maintenance and transfer is a defined weighting scheme for a small set of new (transfer or standardization) samples augmented to the full set of calibration samples. Because straight application of basic TR theory is not always possible with calibration maintenance and transfer, this paper develops a generic solution to always enable application of TR. Harmonious (bias/variance tradeoff) and parsimonious (effective rank) considerations for TR are compared with the same TR format applied to partial least squares (PLS), showing that both approaches are viable solutions to the calibration maintenance and transfer problems.