All chemical reagents were of analytical grade and were used with no additional purification unless otherwise stated. Uric acid (≥99%), hydroxylamine hydrochloride (99%), and water (HPLC grade) were purchased from Sigma Aldrich Ltd. (Dorset, UK). Potassium dihydrogen phosphate, dipotassium phosphate, sodium hydroxide, silver nitrate, hydrochloric acid and nitric acid were of analytical grade and obtained from Fischer Scientific (Loughborough, UK). Clinical pre-preeclampsia urine samples were kindly donated by Professor B.
Biocatalyst discovery and directed evolution are central to many pharmaceutical research programs, yet the lack of robust high-throughput screening methods for large libraries of enzyme variants generated (typically 10(6)-10(8)) has hampered progress and slowed enzyme optimization. We have developed a label-free generally applicable approach based on Raman spectroscopy which results in significant reductions in acquisition times (>30-fold). Surface enhanced Raman scattering (SERS) is employed to monitor the enzyme-catalyzed conversion by xanthine oxidase of hypoxanthine to xanthine to uric acid. This approach measures the substrates and products directly and does not require chromogenic substrates or lengthy chromatography, was successfully benchmarked against HPLC, and shows high levels of accuracy and reproducibility. Furthermore, we demonstrate that this SERS approach has utility in monitoring enzyme inhibition illustrating additional medical significance to this high-throughput screening method.
One of the current limitations surrounding surface‐enhanced Raman scattering (SERS) is the perceived lack of reproducibility. SERS is indeed challenging, and for analyte detection, it is vital that the analyte interacts with the metal surface. However, as this is analyte dependent, there is not a single set of SERS conditions that are universal. This means that experimental optimisation for optimum SERS response is vital. Most researchers optimise one factor at a time, where a single parameter is altered first before going onto optimise the next. This is a very inefficient way of searching the experimental landscape. In this review, we explore the use of more powerful multivariate approaches to SERS experimental optimisation based on design of experiments and evolutionary computational methods. We particularly focus on colloidal‐based SERS rather than thin film preparations as a result of their popularity. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd.
This study investigates the feasibility of using surface-enhanced Raman scattering (SERS) for the quantification of absolute levels of the boar-taint compounds skatole and androstenone in porcine fat. By investigation of different types of nanoparticles, pH and aggregating agents, an optimized environment that promotes SERS of the analytes was developed and tested with different multivariate spectral pre-processing techniques, and this was combined with variable selection on a series of analytical standards. The resulting method exhibited prediction errors (root mean square error of cross validation, RMSECV) of 2.4 × 10(-6) M skatole and 1.2 × 10(-7) M androstenone, with a limit of detection corresponding to approximately 2.1 × 10(-11) M for skatole and approximately 1.8 × 10(-10) for androstenone. The method was subsequently tested on porcine fat extract, leading to prediction errors (RMSECV) of 0.17 μg/g for skatole and 1.5 μg/g for androstenone. It is clear that this optimized SERS method, when combined with multivariate analysis, shows great potential for optimization into an on-line application, which will be the first of its kind, and opens up possibilities for simultaneous detection of other meat-quality metabolites or pathogen markers. Graphical abstract Artistic rendering of a laser-illuminated gold colloid sphere with skatole and androstenone adsorbed on the surface.
For enzyme‐catalysed biotransformations, continuous in situ detection methods minimise the need for sample manipulation, ultimately leading to more accurate real‐time kinetic determinations of substrate(s) and product(s). We have established for the first time an on‐line, real‐time quantitative approach to monitor simultaneously multiple biotransformations based on UV resonance Raman (UVRR) spectroscopy. To exemplify the generality and versatility of this approach, multiple substrates and enzyme systems were used involving nitrile hydratase (NHase) and xanthine oxidase (XO), both of which are of industrial and biological significance, and incorporate multistep enzymatic conversions. Multivariate data analysis of the UVRR spectra, involving multivariate curve resolution‐alternating least squares (MCR‐ALS), was employed to effect absolute quantification of substrate(s) and product(s); repeated benchmarking of UVRR combined with MCR‐ALS by HPLC confirmed excellent reproducibility.
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