Data Processing

Vespucci is capable of several of the most common data pre-processing techniques in chemometrics, as described below.

Smoothing. Vespucci supports a number of smoothing methods including moving average filters, median filters, Savitzky-Golay smoothing and Whittaker smoothing.

Data selection. Individual spectra are easily viewed and manipulated. Spectra may be removed by threshold to reject clipped or poorly-focused spectra. Spectral data beyond a certain spatial range in a spectroscopic image may also be removed.

Normalisation. Vespucci supports min/max normalisation (subtraction of the minimum of each spectra followed by division by the maximum), unit area normalisation (dividing each spectra by its sum), standard normal variate normalisation, Z-score normalisation, normalization by the maximum intensity at a particular spectral abscissa range, scaling by a particular number, vector normalisation of each spectrum vector, and mean-centring.

Analysis and Imaging

Vespucci supports a variety of powerful and commonly-used methods for spectral data analysis, as illustrated below.

Univariate. Vespucci is capable of univariate analysis and imaging by peak intensity, peak area, and peak width (estimated full-width-at-half maximum), area ratio between two peaks, and intensity ratio between two peaks.

Multivariate. Vespucci is capable of classical principal component analysis (PCA), a widely-used method for dimension reduction, both using the singular value decomposition, and partial-least-squares (PLS) regression (Vespucci, like MATLAB, uses the SIMPLS algorithm for PLS regression [4]). Vespucci is capable of Vertex Component Analysis (VCA), an algorithm for dimension reduction and endmember extraction [8]. This algorithm finds the spectra in the dataset that are responsible for the most variance. Images can be created from the component scores generated by both methods.

Peak detection. Vespucci uses a peak-finding method based on convolution with a Mexican hat kernel, mathematically identical to the continuous wavelet transform (CWT). This method was first developed by Du for analysing proteomic mass spectrometry data [5], and was then applied to Raman spectroscopy by Zhang [12]. It uses a signal smoothed by Mexican hat kernels of varying width to determine the peak centres of the unsmoothed data. This facilitates the determination of the local extrema with smooth signals. A “chemical barcode” can then be constructed from these results, allowing the researcher to identify spectral signatures within the data and to determine which spectra contain which peaks of interest.

Imaging. Vespucci utilizes several colour scales for different purposes. The traditional, “rainbow” colour scale is implemented, along with a rainbow colour scale (“VespucciSpectral”) that runs from black to white. Vespucci implements modified versions of the ColorBrewer [2] colour scales, which are designed to increase linearly in perceived luminosity and overcome problems identified with the rainbow colour scales [1]. Scale bars of arbitrary size can be added, and images can be saved in both vector (PDF, SVG) and raster (TIFF, BMP, PNG, GIF, JPEG) formats.

External Code Interface

Code written in R, Octave, and most code written in MATLAB© can be executed from Vespucci using the external code interface. This allows researchers to use existing codebases with the software. Vespucci depends on an installation of R to execute code on objects stored in shared memory. Vespucci is distributed with a copy of the Octave interpreter as a DLL file. All data objects associated with each dataset can be exposed to the interface at the discretion of the user.

Surface-Enhanced Raman Spectroscopy (SERS)

The utility of Vespucci for several SERS studies has been shown in unpublished works and several replications of previous works. SERS is a Raman spectroscopic technique that utilises plasmonic nanomaterials, such as silver and gold nanoparticles, to enhance the intensity of Raman signals for the qualitative and quantitative determination of analytes at trace concentrations [7].

Probing nanoparticle-virion interactions. Spectroscopic data analysis is primarily concerned with the extraction of chemical information based on the presence and profile of “peaks” in spectra. When a dataset is very large, manual peak finding and determination become time consuming and are prone to human error. Automatic evaluation of peaks in vibrational spectroscopy, with nonlinear baselines and substantially broadened peaks, can be difficult. A rigorous, baseline-independent method to determine peaks of varying widths is needed in these cases. The “CWT” method fits these criteria [12], but was previously only available as a command-line R package. Vespucci provides the first implementation of this algorithm with a graphical user interface.

Vespucci’s peak detection methods have been used to determine spectral regions of interest for subsequent analysis. The goal of this work is to use the chemical information found in the peaks to determine the chemical environment inhabited by AgNPs when interacting with virions. Dengue virus samples were incubated with AgNPs then inactivated and deposited on glass slides. All Raman signals were smoothed with a median filter of window size 7 (Figure 2) and normalized to the glass signal centred near 2600 cm^–1 (Figure 3). Spectra of glass slides without sample were recorded as a control measurement. The average glass spectrum was fitted with a Voigt curve in Origin 8.0. The glass signal fit was subtracted from all samples, and those samples whose maximum signal intensity was less than one half the intensity of the glass spectrum were removed. The CWT-based peak finding method (Figure 4) was then applied to produce a “chemical barcode”, a bar graph plotting the total detected peak centres against wavenumber (Figure 5). The general peak regions corresponding to each peak were then determined by estimating the width of the peaks in the bar graph. Vespucci provides, for the first time, an automatic and reproducible system for determining potential spectral regions of interest for the interaction of AgNPs and virions. Pre-processing and subsequent analysis were completed with only a few clicks of the mouse. This approach may also be applicable for other nanobiological studies (e.g., cell-nanoparticle and biomatrix-nanoparticle interactions) when analyte concentrations are very low.

SERS substrate assessment. The univariate imaging features of Vespucci can be used to assess the SERS capabilities of silver nanorods (AgNRs). AgNRs were constructed by the vapour deposition of silver onto silicon platforms at two different temperatures (100 and 300 K). The resulting SERS substrates were then exposed to a solution of rhodamine-6G (R6G) as a test probe and imaged. In addition to determining the spatial distribution of SERS “hotspots” (areas where R6G may experience higher enhancement) on each substrate, the two samples could be easily compared by using a common colour scale between them. The data was processed with a median filter of window size 7, followed by standard normal variate normalization. Univariate images of the Riemann sum of the spectral region from 1625 to 1675 cm^–1, corresponding to a xanthine breathing marker mode (Figure 6 and 8), were compared to determine differences in overall enhancement. The values were larger for the substrates synthesized at 100 K than for the substrates synthesized at 300 K (Figure 7), because the colder temperature provided favourable kinetics to produce denser and better-aligned AgNR surfaces [10].

Raman Spectroscopy

Bone mineralisation. Bone consists of both organic (bone marrow, collagen) and inorganic (hydroxyapatite) components. While increase in inorganic components and subsequent crystallisation corresponds to bone strengthening, abnormal bone mineralisation interferes with vascularization, inhibiting growth. The intensity of spectral signatures corresponding to inorganic moieties can be used to assess the degree of mineralisation of biomaterials. A toxicological study was devised to examine the interaction between platinum group metal (PGM) salts and developing chick embryos [11]. This work utilises the data processing and univariate analysis features of Vespucci to demonstrate how PGM salts interfere with the mineralisation process.

Raman spectral scans were performed on slices of chick embryo tibiotarsi. Embryos were exposed to PGM solutions in ova on the 7^th and 11^th day of incubation in order to study potential bone structural changes due to exposure to PGMs. Crystallisation was assessed and spatially determined by observing intensity near the ν₁ band of phosphate (search range 946–976 cm^–1). The crystalline structure can be observed (Figure 9) in multiple colour scales.

Use of C++ API

The Vespucci C++ API allows for the creation of specialty programs to perform the same task on multiple datasets. Here, a simple program to pre-process and perform VCA of all datasets in a particular folder is demonstrated in the Examples folder in the source tree. The procedural maths library, combined with a few Qt classes, was used to automate the data analysis and pre-processing workflow.