Chemometrics statistical analyses were performed using in-house MATLAB scripts and the PLS Toolbox 6.7 (Eigenvector Research, Inc., Wenatchee, WA, USA). Metabolite levels were computed from the raw (untransformed) data and expressed as mean ± SD (standard deviation). T-Student’s test was used to determine the statistical significance of differences between the means in both of the cases, and the control group and ANOVA to estimate the differences between the three categories of periodontal status. A chi- squared was used for comparative proportions. The significance level was p < 0.05. Principal component analysis (PCA) and projection to latent structures for discriminant analysis (PLS-DA) were applied to the NMR spectral datasets. Results were cross-validated using the leave-one-out to evaluate the accuracy of each classification model [22 ]; in each run one sample of the data is left out of the training and used to test the model. The whole cross validation process was run 10 times. The results of the cross validation were evaluated by the Q2 parameter. Q2 is the averaged correlation coefficient between the dependent variable and the PLS-DA predictions and provides a measure of prediction accuracy during the cross-validation process (higher values mean better prediction).
Pls toolbox 6
Manufactured by Eigenvector Research
Sourced in United States
PLS Toolbox 6.7 is a software package designed for multivariate data analysis. It provides a comprehensive suite of tools for principal component analysis (PCA), partial least squares (PLS) regression, and other related techniques. The software is suitable for a wide range of applications, including chemometrics, process monitoring, and predictive modeling.
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Lab products found in correlation
5 protocols using pls toolbox 6
1
Metabolite Profiling of Periodontal Diseases
2
Multivariate Analysis of NMR Spectra
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Chemometrics statistical analyses were performed using in-house MATLAB scripts and PLS Toolbox 6.7 (Eigenvector Research Inc., Wenatchee, WA, USA). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied to NMR spectra data matrix. PLS-DA is a classification technique that combines the properties of partial least-squares (PLS) regression with the discrimination power of discriminant analysis (DA) [20 (link)]. The main advantage of PLS-DA models is that the main sources of variability in the data are modeled by the so-called latent variables and consequently in their associated scores and loadings, allowing the visualization and understanding of different patterns and relations in the data. The PLS-DA model was tested using a leave-one-out cross-validation (CV) algorithm.
All data are expressed as mean ± standard deviation (SD). Finally, one-way analysis of variance was used for the determination of statistical significance between group means of the corresponding integrals. A difference was considered significant when P < 0.05.
All data are expressed as mean ± standard deviation (SD). Finally, one-way analysis of variance was used for the determination of statistical significance between group means of the corresponding integrals. A difference was considered significant when P < 0.05.
3
Metabolomics Analysis of Dry Eye Syndrome
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Chemometric statistical analyses were performed using inhouse MATLAB scripts and PLS Toolbox 6.7 (Eigenvector Research Inc., Wenatchee, WA, USA). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied to NMR spectra data matrix [8, 11, 32, 33, 36, 37] . Additional statistical procedures were employed. Higher values of these analyses indicated the presence of outstanding metabolites to carry out the discriminating classification. Data were processed to prepare a scatter plot combining the results from the statistics for the model variables with respect to model component scores. Data were expressed as mean ± SD. A difference was considered significant at p < 0.05. The statistical package for the social sciences (SPSS) (IBM Corp SPSS Statistics for windows version 22.0, Armonk, NY, USA) was used for statistics. The Mann-Whitney U test and the Spearman's rank correlation coefficient (rho; SR) were applied to establish the relationship between DE-MGD clinical manifestations, the morphologic/functional DE parameters, and the tear-film metabolites. Finally, p < 0.05 was considered statistically significant.
4
Tissue Classification via PLS-DA Analysis
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Partial least-squares (PLS) [19 (link)] analysis is a regression method to find a linear relationship between a response variable Y (tissue type class) and the independent variable X (spectra). PLS-DA is widely used for analysis of spectra. The method is based on finding a number of principal components that represent as much of the variance in X as is possible. PLS-DA selects the principal components which are most relevant to the response variable Y. Therefore, PLS-DA acts directly on the spectra without the need for prior feature extraction. The PLS model is generated using a training data set. A discriminant analysis (DA) method is subsequently performed to obtain thresholds for discriminating the different responses (i.e. tissue classes). Prediction of class (tissue type) on the remaining data (the validation data set) is obtained by comparing the predicted PLS scores with the DA thresholds. The measured tissue type is assigned to one of the two predefined tissue classes depending on the PLS scores. The PLS-DA algorithm scripts used PLS Toolbox 6.2 (Eigenvector Research, Inc, Wenatchee, WA).
5
PLS Modeling of Enzymatic Fermentation Analytes
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PLS regression models were built using the PLS toolbox 6.2 (Eigenvector Research, Inc., USA) using the SIMPLS algorithm [25] . Suitable spectral regions for calibration were identified from the pure PenV and POX spectra, taking into account interferences of the fermentation broth (for spectral regions used see Table 1 ). Preprocessing consisted of calculation of first derivative (Savitzky-Golay (Sav-Gol), order 2, window size 11) of spectral data and mean centering of both, spectral and concentration data. Cross-validation of PLS models was performed using random subsets (10 data splits, 3 iterations).
Model parameters and figures of merit for the POX and PenV PLS models.
| Model | Calibration range (g L−1) | Data included | Wavenumber region (cm−1) | Preprocessing steps | Latent variables | RMSECV (g L−1) | r2 CV |
|---|---|---|---|---|---|---|---|
| POX-PLS | 0–3.47 | Offline: fermentation, standards; fermentations 1–4 | 1188–1352 & 1747–1817 | Mean centering, first derivative (Sav-Gol, order 2, window 11) | 6 | 0.31 | 0.90 |
| PenV-PLS | 0–5.55 | Offline: fermentation, standards, fermentations 1 & 3–4 | 1184–1501 & 1672–1855 | Mean centering, first derivative (Sav-Gol, order 2, window 11) | 7 | 0.22 | 0.97 |
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