A Second-Order Fiber Optic Heavy Metal Sensor Employing Second-Order Tensorial Calibration

Trilinear chemometric analysis of two-dimensional comprehensive gas chromatography-time-of-flight mass spectrometry data

Two-dimensional comprehensive gas chromatography (GC x GC) is a powerful instrumental tool in its own right that can be used to analyze complex mixtures, generating selective data that is applicable to multivariate quantitative analysis and pattern recognition. It has been recently demonstrated that by coupling GC x GC to time-of-flight mass spectrometry (TOFMS), a highly selective technique is produced. One separation on a GC x GC/TOFMS provides retention times on two chromatographic columns and a complete mass spectrum for each component within the mixture. In this manuscript, we demonstrate how the selectivity of GC x GC/TOFMS combined with trilinear chemometric techniques such as trilinear decomposition (TLD) and parallel factor analysis (PARAFAC) results in a powerful analytical methodology. Using TLD and PARAFAC, partially resolved components in complex mixtures can be deconvoluted and identified using only one data set without requiring either signal shape assumptions or fully selective mass signals. Specifically, a region of overlapped peaks in a complex environmental sample was mathematically resolved with TLD and PARAFAC to demonstrate the utility of these techniques as applied to GC x GC/TOFMS data of a complex mixture. For this data, it was determined that PARAFAC initiated by TLD performed a better deconvolution than TLD alone. After deconvolution, mass spectral profiles were then matched to library spectra for identification. A standard addition analysis was performed on one of the deconvoluted analytes to demonstrate the utility of TLD-initiated PARAFAC for quantification without the need for accurate retention time alignment between sample and standard data sets.

New highly sensitive and selective catalytic DNA biosensors for metal ions

While remarkable progress has been made in developing sensors for metal ions such as Ca(II) and Zn(II), designing and synthesizing sensitive and selective metal ion sensors remains a significant challenge. Perhaps the biggest challenge is the design and synthesis of a sensor capable of specific and strong metal binding. Since our knowledge about the construction of metal-binding sites in general is limited, searching for sensors in a combinatorial way is of significant value. Therefore, we have been able to use a combinatorial method called in vitro selection to obtain catalytic DNA that can bind a metal ion of choice strongly and specifically. The metal ion selectivity of the catalytic DNA was further improved using a 'negative selection' strategy where catalytic DNA that are selective for competing metal ions are discarded in the in vitro selection processes. By labeling the resulting catalytic DNA with a fluorophore/quencher pair, we have made a new class of metal ion fluorescent sensors that are the first examples of catalytic DNA biosensors for metal ions. The sensors combine the high selectivity of catalytic DNA with the high sensitivity of fluorescent detection, and can be applied to the quantitative detection of metal ions over a wide concentration range and with high selectivity. The use of DNA sensors in detection and quantification of lead ions in environmental samples such as water from Lake Michigan has been demonstrated. DNA is stable, cost-effective, environmentally benign, and easily adaptable to optical fiber and microarray technology for device manufacture. Thus, the DNA sensors explained here hold great promise for on-site and real-time monitoring of metal ions in the fields of environmental monitoring, developmental biology, clinical toxicology, wastewater treatment, and industrial process monitoring.

Application of GRAM and TLD to the resolution and quantitation of real complex multicomponent mixtures by fluorescence spectroscopy

The application of the generalised rank annihilation method (GRAM) and the trilinear decomposition (TLD) method to the resolution and quantitation of fluorescence excitation-emission matrices of a ternary mixture of pesticides, carbendazim, fuberidazole, and thiabendazole, with overlapped spectra is described. The results obtained with both methods are compared and evaluated using measures of similarity (correlation coefficients) between the real and estimated spectra. Both approaches have been tested using augmented data matrices containing only two samples, but none of these methods succeeded completely in resolution of the system. When TLD was applied to augmented data matrices containing more than two samples better performance was achieved. To illustrate the application of both algorithms to real samples, they were used in the analysis of water samples containing the target pesticides. Again, TLD had an advantage over GRAM because the ability to analyse data from multiple (more than two) samples simultaneously allowed the resolution of the mixtures.