Raman spectra were imported into GRAMS AI (Version 8.0 Thermo Electron Corp, Waltham, MA) for spectral averaging and baseline correction. Chemometric analysis was carried out with MATLAB version 7.2 (The Mathworks, Inc., Natick, MA), using PLS Toolbox version 7.0 (Eigenvector Research Inc., Wenatchee, WA). SERS spectra in the range 1650 – 700 cm−1 were used for classification. Prior to analysis, first derivatives of the SERS spectra were calculated using the Savitzky-Golay method with a 2nd order polynomial and a fifteen-point window. Each data set was then vector normalized and mean centered. Multivariate statistical analysis of the mycoplasma spectra was performed using principal components analysis (PCA), hierarchical cluster analysis (HCA), and partial least squares discriminant analysis (PLS-DA) using the PLS Toolbox software. The calculated principal components were used as inputs to the HCA algorithm, which used the Ward’s method algorithm to evaluate minimum variances between clusters.
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GRAMS/AI is a software application designed for spectroscopic data analysis and visualization. It provides a comprehensive suite of tools for processing and interpreting spectroscopic data from various analytical instruments.
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10 protocols using grams ai
1
Multivariate Analysis of Mycoplasma SERS Spectra
2
Raman and SERS Spectra Normalization
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The Raman and SERS spectra were normalized with respect to signal intensities by using the vector normalization method, i.e., the normalization of the signal to the standard deviation of points with respect to the average signal [27 (link),28 (link)]. A best fit numerical procedure based on the Levenberg–Marquardt nonlinear least-square method (software routines by GRAMS/AI, 2001, Thermo Electron) was used for deconvoluting the spectra in term of Lorentzian functions to determine the main component modes and their parameters (i.e., the centers, widths, and intensities). Statistical analysis was performed by using one-way ANOVA test.
3
Noise Reduction and Spectrum Analysis
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A numerical procedure based on “wavelet” algorithms was adopted to subtract the background signal and reduce the noise in the spectra.38 (link) The signal was decomposed in terms of wavenumber-scaled components (wavelets), and hierarchical representation of the data was obtained. This enabled us to remove signal components due to background and noncorrelated noise through an inverse process of signal reconstruction. The data treatment was implemented using software routines (wavelet toolbox) of MATLAB® (Version 7.6, MathWorks Inc., Natick, Massachusetts). In particular, “Bior6.8” biorthogonal wavelets were used. To compare the data, the spectrum intensities were scaled using the standard normal variate method, i.e., normalizing the signal to the standard deviation of intensities concerning the average signal.39 (link) Some examples of SERS raw data are shown in Fig. S1 of the Supplemental Material . A best-fit numerical procedure based on the Levenberg–Marquardt nonlinear least-square method (software routines by GRAMS/AI, 2001, Thermo Electron) was used to deconvolute the spectra in terms of Lorentzian functions to determine the component modes, using as fit parameters the centers, widths, and intensities of the Lorentzian functions. One-way analysis of variance (ANOVA) was used to discriminate relevant changes in the spectra.
4
Raman Spectral Deconvolution for Protein Analysis
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An evaluation of the main Raman modes in the measured signal could be achieved by performing a deconvolution of the spectrum in elemental components. In this study, a best fit of the data was performed by comparing the spectra with a model based on the overlap of a set of mixed Lorentzian/Gaussian peaks. We assumed as fitting parameters the function kind (Lorentzian or Gaussian) and its spectral positions, intensities and widths after minima localization of the second derivative spectrum, which corresponded to the positions of peaks within the band. In particular, second-derivative spectra were obtained with Savitsky–Golay derivative function algorithm for a seven data point window [24 (link)]. The best fit was done using a library routine of GRAMS/AI (2001, Thermo Scientific TM, Waltham, MA, USA). Fit convergence was evaluated by the parameter with the Levenberg–Marquardt nonlinear least-square method. Particular attention was devoted to the Amide I (1550–1750 cm ), Amide III (1200–1350 cm ) bands and the CH /CH modes (2800–3000 cm ), where significant variations were expected.
5
Raman Spectroscopy Calibration Protocol
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Utilizing both the Kaiser Hololab
calibration accessory and a Raman calibration standard, calibrations
of the spectrograph, laser excitation wavelength, and instrument spectral
response were performed to ensure high spectral quality. All Raman
spectral data were processed by GramsAI (Thermo, Inc., Waltham, MA)
for visual inspection. Chemometric calculations were performed by
using the multivariate curve resolution-alternating least squares
(MCR-ALS) in Matlab.
calibration accessory and a Raman calibration standard, calibrations
of the spectrograph, laser excitation wavelength, and instrument spectral
response were performed to ensure high spectral quality. All Raman
spectral data were processed by GramsAI (Thermo, Inc., Waltham, MA)
for visual inspection. Chemometric calculations were performed by
using the multivariate curve resolution-alternating least squares
(MCR-ALS) in Matlab.
6
FTIR-based Protein Secondary Structure Analysis
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All spectra were treated with the OPUS software. The analysis of the secondary structure of the proteins was carried out by curve fitting using the GRAMS AI software from Thermo Scientific. The second derivatives were calculated from the ATR–FTIR spectra after smoothing over nine consecutive points. The absorption bands at low wavenumbers were free from features originating from water vapor, as judged from the peaks above 1750 cm−1. A straight baseline passing through the ordinate at 1700 and 1610 cm−1 was subtracted before the curve fitting. The baseline was again modified by the least-squares curve-fitting software, which allows for a horizontal baseline to be adjusted as an additional parameter to obtain the best fit. The second derivative spectrum was used to determine the initial peak positions for curve fitting, and the peaks were fitted using Gauss functions. The area under the entire band was considered as 100%, and each component after fitting was expressed as a percent fraction.
7
Raman Spectroscopy Peak Fitting Analysis
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In order to determine the basic vibrational modes that contribute to the Raman signal, the spectra were analyzed in terms of convoluted peak functions by using a best-fit peak-fitting routine of GRAMS/AI (2001, Thermo Scientific™, Waltham, MA, USA) program, which is based on the Levenberg-Marquardt nonlinear least-square method. In particular, the wavenumber band of Amide I (1550–1750 cm−1) was considered, where significant variations were expected due to protein configuration changes. A mixed Gaussian and Lorentzian peak shape was used [12 (link)]. Peaks constituting the spectrum were manually selected in order to define the starting conditions for the best-fit procedure. The best fit was then performed to determine convolution peaks with optimized intensity, position and width. Its performance was evaluated by means of the λ2 parameter.
8
Raman Spectroscopy Analysis of Glass Sponge Axial Filaments
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All the experiments were performed in triplicates and repeated at least three times (n = 3), unless otherwise stated. Statistical analyses were performed using GraphPad Prism software v. 5.01 (GraphPad Prism software Inc., La Jolla, CA, USA) using unpaired Student's t‐test for comparison of two groups. Data if necessary is presented as mean ± SD (standard deviation) (see Table S7 , Supporting Information). The differences were considered to be statistically significant if p < 0.05 for either test used. Each Raman spectrum was acquired in 80 s or 200 s, i.e., a CCD integration time of 2 s was used in both measurements and several spectra (n = 40 with RamanRxn1 and n = 100 with Apha 300S) were averaged in order to improve the signal‐to‐noise ratio. The fluorescence background was removed with a multi‐point linear baseline using the software GRAMS/AI (Thermo Fisher Scientific, USA Inc, Waltham, MA, USA). Four different positions were measured on axial filaments extracted from glass sponges, in order check for homogeneity. All spectra acquired on each sample resulted totally similar; a representative one is displayed in Figure S23 (Supporting Information).
9
XRD Analysis of Glycolated Clay Minerals
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The XRD patterns of glycolated samples were modelled using the software NEWMOD 3.2.1, which allows for simulating the distribution and crystal chemical attributes of endmembers and interstratified clay minerals (Reynolds and Reynolds, 1996) . The following parameters were simulated: the position of the 001 peaks, the octahedral Fe content (on O10[OH2] basis for 2:1 minerals and O5[OH]4 basis for 1:1 minerals), K in the illite interlayer, the average and maximum number of layers per coherent domain (Nmean and Nmax parameters), and the final proportion (wt. %) of each mineral. The experimental 060 peaks were mathematically decomposed and calculated individual components fitted into the experimental pattern using the program GRAMS/AI (Thermo Fisher Scientific TM Waltham, MA, US) in order to identify the occurrence of a di-or tri-octahedral clays. The decomposition of the experimental curves was performed by adding curves with Gaussian shape. The position of each maximum was used to identify the occupation of di-or tri-octahedral sites and each curve area was used to estimate their proportion in the samples (Deocampo et al., 2009) .
10
Cellulose Crystallinity Characterization by WAXD
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As outlined above, after each alkali treatment, the alkaline media were exchanged with DI water by repeated centrifugation and re-dispersion, until reaching neutral pH 7.0. Neutralized samples were freeze-dried ready for wide-angle X-ray diffraction (WAXD) measurements (RINT 2000 V or SmartLab, Rigaku Corp., Tokyo, Japan). The WAXD profiles were acquired using Ni-filtered CuKα radiation (λ = 0.1542 nm) at 40 kV and 20 mA in 5-40°of 2θ. The scan rate and step were 0.5-2° min -1 and 0.05°, respectively. Carboxymethylated ACC-CNFs (CM-CNFs) were also measured via WAXD under the same conditions.
Peak separation of the WAXD profiles [26] (link)[27] (link)[28] (link) was conducted based on the second derivative of the profile data using the spectral data processing software GRAMS/AI (Thermo Fischer Scientific, Inc. USA), prior to the following calculations. The crystallinity and the extent of transformation into cellulose II (Cellulose II ratio (%)) were calculated from the WAXD profiles (n = 3-5) according to previous references [27, (link)[29] (link)[30] (link)[31] (link):
Peak separation of the WAXD profiles [26] (link)[27] (link)[28] (link) was conducted based on the second derivative of the profile data using the spectral data processing software GRAMS/AI (Thermo Fischer Scientific, Inc. USA), prior to the following calculations. The crystallinity and the extent of transformation into cellulose II (Cellulose II ratio (%)) were calculated from the WAXD profiles (n = 3-5) according to previous references [27, (link)[29] (link)[30] (link)[31] (link):
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