Robin F. B. Turner

Investigation of Selected Baseline Removal Techniques as Candidates for Automated Implementation

Observed spectra normally contain spurious features along with those of interest and it is common practice to employ one of several available algorithms to remove the unwanted components. Low frequency spurious components are often referred to as ‘baseline’, ‘background’, and/or ‘background noise’. Here we examine a cross-section of non-instrumental methods designed to remove background features from spectra; the particular methods considered here represent approaches with different theoretical underpinnings. We compare and evaluate their relative performance based on synthetic data sets designed to exemplify vibrational spectroscopic signals in realistic contexts and thereby assess their suitability for computer automation. Each method is presented in a modular format with a concise review of the underlying theory, along with a comparison and discussion of their strengths, weaknesses, and amenability to automation, in order to facilitate the selection of methods best suited to particular applications.

A CMOS potentiostat for amperometric chemical sensors

A simple CMOS integrated potentiostatic control circuit is described. The circuit maintains a constant bias potential between the reference and working electrodes. Chemical concentration signals are converted amperometrically to an output voltage with a slope of approximately 60 mV//spl mu/A. Redox currents from 0.1 to 3.5 /spl mu/A can be measured with a maximum nonlinearity of /spl plusmn/2% over this range. This design also provides the capability of performing differential measurements in order to null the effect of spurious background current. The total power consumption is less than 2 mW. Experimental results are reported showing the performance of the circuit as a chemical sensor control system.

Assessing differentiation status of human embryonic stem cells noninvasively using Raman microspectroscopy.

Raman microspectroscopy is an attractive approach for chemical imaging of biological specimens, including live cells, without the need for chemi-selective stains. Using a microspectrometer, near-infrared Raman spectra throughout the range 663 cm(-1) to 1220 cm(-1) were obtained from colonies of CA1 human embryonic stem cells (hESCs) and CA1 cells that had been stimulated to differentiate for 3 weeks by 10% fetal bovine serum on gelatin. Distributions and intensities of spectral bands attributed to proteins varied significantly between undifferentiated and differentiated cells. Importantly, compared to proteins and lipids, the band intensities of nucleic acids were dominant in undifferentiated cells with a dominance-reversal in differentiated cells. Thus, we could identify intensity ratios of particular protein-related bands (e.g., 757 cm(-1) tryptophan) to nucleic acid bands (784 cm(-1) DNA/RNA composite) that were effective in discriminating between spectra of undifferentiated and differentiated cells. We observed no discernible negative effects due to the laser exposure in terms of morphology, proliferation, or pluripotency of the stem cells. We conclude that Raman microscopy and complementary data processing procedures provide a rapid, noninvasive approach that can distinguish hESCs from differentiated cells. This is the first report to identify specific Raman markers for the differentiation status of hESCs.

On-line monitoring and control of methanol concentration in shake-flask cultures of Pichia pastoris.

The methylotrophic yeast Pichia pastoris can be used to express recombinant genes at high levels under the control of the methanol-inducible alcohol oxidase 1 (AOX1) promoter. Accurate regulation of the methanol concentration in P. pastoris cultures is necessary to maintain induction, while preventing accumulation of methanol to cytotoxic levels. We developed an inexpensive methanol sensor that uses a gas-permeable silicone rubber tube immersed in the culture medium and an organic solvent vapor detector. The sensor was used to monitor methanol concentration continuously throughout a fed-batch shake-flask culture of a P. pastoris clone producing the N-lobe of human transferrin. The sensor calibration was stable for the duration of the culture and the output signal accurately reflected the methanol concentration determined off-line by HPLC. A closed-loop control system utilizing this sensor was developed and used to maintain a 0.3% (v/v) methanol concentration in the culture. Use of this system resulted in a fivefold increase in volumetric protein productivity over levels obtained using the conventional fed-batch protocol.

Hollow-core photonic crystal fiber-optic probes for Raman spectroscopy

We have implemented a new Raman fiber-optic probe design based on a hollow-core photonic-crystal excitation fiber surrounded by silica-core collection fibers. The photonic-crystal fiber offers low attenuation at the pump radiation wavelength, mechanical flexibility, high radiation stability, and low background noise. Because the excitation beam is transmitted through air inside the hollow-core fiber, silica Raman scattering is much reduced, improving the quality of the spectra obtained using probes of this design. Preliminary results show that the new probe design decreases the Raman background from the silica by approximately an order of magnitude compared to solid-core silica Raman probes.

Accuracy and Precision of Manual Baseline Determination

Vibrational spectra often require baseline removal before further data analysis can be performed. Manual (i.e., user) baseline determination and removal is a common technique used to perform this operation. Currently, little data exists that details the accuracy and precision that can be expected with manual baseline removal techniques. This study addresses this current lack of data. One hundred spectra of varying signal-to-noise ratio (SNR), signal-to-baseline ratio (SBR), baseline slope, and spectral congestion were constructed and baselines were subtracted by 16 volunteers who were categorized as being either experienced or inexperienced in baseline determination. In total, 285 baseline determinations were performed. The general level of accuracy and precision that can be expected for manually determined baselines from spectra of varying SNR, SBR, baseline slope, and spectral congestion is established. Furthermore, the effects of user experience on the accuracy and precision of baseline determination is estimated. The interactions between the above factors in affecting the accuracy and precision of baseline determination is highlighted. Where possible, the functional relationships between accuracy, precision, and the given spectral characteristic are detailed. The results provide users of manual baseline determination useful guidelines in establishing limits of accuracy and precision when performing manual baseline determination, as well as highlighting conditions that confound the accuracy and precision of manual baseline determination.

Fully Automated High-Performance Signal-to-Noise Ratio Enhancement Based on an Iterative Three-Point Zero-Order Savitzky—Golay Filter

The automated processing of data from high-throughput and real-time collection procedures is becoming a pressing problem. Currently the focus is shifting to automated smoothing techniques where, unlike background subtraction techniques, very few methods exist. We have developed a filter based on the widely used and conceptually simple moving average method or zero-order Savitzky–Golay filter and its iterative relative, the Kolmogorov–Zurbenko filter. A crucial difference, however, between these filters and our implementation is that our fully automated smoothing filter requires no parameter specification or parameter optimization. Results are comparable to, or better than, Savitzky–Golay filters with optimized parameters and superior to the automated iterative median filter. Our approach, because it is based on the highly familiar moving average concept, is intuitive, fast, and straightforward to implement and should therefore be of immediate and considerable practical use in a wide variety of spectroscopy applications.

A Small-Window Moving Average-Based Fully Automated Baseline Estimation Method for Raman Spectra

A fully automated and model-free baseline-correction method for vibrational spectra is presented. It iteratively applies a small, but increasing, moving average window in conjunction with peak stripping to estimate spectral baselines. Peak stripping causes the area stripped from the spectrum to initially increase and then diminish as peak stripping proceeds to completion; a subsequent increase is generally indicative of the commencement of baseline stripping. Consequently, this local minimum is used as a stopping criterion. A backup is provided by a second stopping criterion based on the area under a third-order polynomial fitted to the first derivative of the current estimate of the baseline-free spectrum and also indicates whether baseline is being stripped. When the second stopping criterion is triggered instead of the first one, a proportionally scaled simulated Gaussian baseline is added to the current estimate of the baseline-free spectrum to act as an internal standard to facilitate subsequent processing and termination via the first stopping criterion. The method is conceptually simple, easy to implement, and fully automated. Good and consistent results were obtained on simulated and real Raman spectra, making it suitable for the fully automated baseline correction of large numbers of spectra.

Fiber-optic probes with improved excitation and collection efficiency for deep-UV Raman and resonance Raman spectroscopy.

The ability of ultraviolet resonance Raman spectroscopy (UVRRS) to determine structural, environmental, and analytical information concerning low-concentration aqueous biomolecules makes it a powerful bioanalytical and biophysical technique. Unfortunately, its utility has been limited by experimental requirements that preclude in situ or in vivo studies in most cases. We have developed the first high-performance fiber-optic probes suitable for long-term use in pulsed UVRRS applications in the deep- UV (DUV, 205-250 nm). The probes incorporate recently developed improved ultraviolet (IUV) fibers that do not exhibit the rapid solarization and throughput decay that previously hampered the use of optical fibers for delivering pulsed, DUV light. A novel 90 degrees mirrored collection geometry is used to overcome the inner-filtering effects that plague flush-probe geometries. The IUV fibers are characterized with respect to their efficacy at transmitting pulsed, DUV laser light, and prototype probes are used to obtain pulsed UVRRS data of aromatic amino acids, proteins, and hormones at low concentrations with 205-240-nm pulsed excitation. Efficient probe geometries and fabrication methods are presented. The performance of the probes in examining resonance-enhanced Raman signals from absorbing chromophores is investigated, and the optimal excitation wavelength is shown to be significantly red-shifted from the maximum of the resonance Raman enhancement profile. Generally applicable procedures for determining optimal experimental conditions are also introduced.

Absolute Quantification of Intracellular Glycogen Content in Human Embryonic Stem Cells with Raman Microspectroscopy

We present a method to perform absolute quantification of glycogen in human embryonic stem cells (hESCs) in situ based on the use of Raman microspectroscopy. The proposed quantification method was validated by comparison to a commonly used commercial glycogen assay kit. With Raman microspectroscopy, we could obtain the glycogen content of hESCs faster and apparently more accurately than with the kit. In addition, glycogen distributions across a colony could be obtained. Raman spectroscopy can provide reliable estimates of the in situ glycogen content in hESCs, and this approach should also be extensible to their other biochemical constituents as well as to other cell types.