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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.

During the forensic examination of textile fibers, fibers are usually mounted on glass slides for visual inspection and identification under the microscope. One method that has the capability to accurately identify single textile fibers without subsequent demounting is Raman microspectroscopy. The effect of the mountant Entellan New on the Raman spectra of fibers was investigated to determine if it is suitable for fiber analysis. Raman spectra of synthetic fibers mounted in three different ways were collected and subjected to multivariate analysis. Principal component analysis score plots revealed that while spectra from different fiber classes formed distinct groups, fibers of the same class formed a single group regardless of the mounting method. The spectra of bare fibers and those mounted in Entellan New were found to be statistically indistinguishable by analysis of variance calculations. These results demonstrate that fibers mounted in Entellan New may be identified directly by Raman microspectroscopy without further sample preparation.

The above-ground organs of plants are covered by a cuticle, an extracellular membrane performing important physiological and ecological functions, that consists of the fatty acid-derived polymer cutin and waxes. In the cuticular wax of many species, including the leaves of Prunus laurocerasus, triterpenoids are found at high concentrations. This paper investigates the potential of Raman microspectroscopy for the simultaneous detection of structurally similar triterpenoids in plant cuticles. Relative composition analysis was first performed on artificial triterpenoid mixtures consisting of α-amyrin and oleanolic acid, as well as oleanolic acid and ursolic acid, the two triterpenoids abundantly found in the cuticles of P. laurocerasus. The different triterpenoids could be distinguished in the mixture spectra and the resulting calculated triterpenoid ratios were consistent with the expected values. Qualitative analysis of the Raman spectra of P. laurocerasus cuticle demonstrated the in situ detectability of the triterpenoids using this approach. It is shown here that Raman microspectroscopy has the potential to provide useful information concerning the spatial distribution of some key chemical components of plant cuticles. This technique thus offers a valuable complement to the current standard analytical methods used for analyzing the bulk composition of plant cuticles.

Various tasks, for example, the determination of signal-to-noise ratios, require the estimation of noise levels in a spectrum. This is generally accomplished by calculating the standard deviation of manually chosen points in a region of the spectrum that has a flat baseline and is otherwise devoid of artifacts and signal peaks. However, an automated procedure has the advantage of being faster and operator-independent. In principle, automated noise estimation in a single spectrum can be carried out by taking that spectrum, shifting a copy thereof by one channel, and subtracting the shifted spectrum from the original spectrum. This leads to an addition of independent noise and a reduction of slowly varying features such as baselines and signal peaks; hence, noise can be more readily determined from the difference spectrum. We demonstrate this technique and a spike-discrimination variant on white Gaussian noise, in the presence and absence of spike noise, and show that highly accurate results can be obtained on a series of simulated Raman spectra and consistent results obtained on real-world Raman spectra. Furthermore, the method can be easily adapted to accommodate heteroscedastic noise.

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.