Descriptive statistical analysis (i.e., mean, variance, standard deviation), principal component analysis (PCA) and clustering were used to explore spectral data with the software R [65 ] and Unscrambler X V.10.5 (Camo Software). PCA was performed using 100 iterations of the NIPALS algorithm, with 6 or 7 principal components depending on convergence of data, 20 random cross validation segments, and mean-centered data. Hierarchical clustering by complete linkage using Spearman’s rank correlation distance was performed for clustering of ions in spectra and Ward’s method using Euclidean distance for clustering of biomass samples. K-means clustering of ions and samples (ramets) was performed in R using the packages cluster and factoextra.
Unscrambler x v 10
Manufactured by CAMO Software
Sourced in Norway
Unscrambler X V.10.5 is a software package designed for multivariate data analysis. It provides a comprehensive set of tools for processing, visualizing, and analyzing complex data sets.
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Lab products found in correlation
8 protocols using unscrambler x v 10
1
Multivariate Analysis of Spectral Data
2
Multivariate Analysis of NIRS Spectra
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The NIRS spectra data were analyzed by multivariate statistical analysis using the Unscrambler® X, v10.5 (CAMO Software AS, Oslo, Norway). Prior to creating and verifying the chemometrics model, data pretreatment was performed on the obtained NIRS spectrum. Standard normal variate (SNV) is a method that corrects the scattering and dispersion of light in the spectrum and stabilizes the baseline of the spectrum [27 (link)]. De-trending (DT) adjusts the change in curvature of the baseline of the SNV that corrects the data by moving the data along the y-axis [28 (link)]. Using the Savitzky and Golay smoothing filter, the derivation and smoothing points were adjusted to increase the signal-to-noise ratio, reducing the interference between the medium that disperses light and other materials that absorb light [29 (link)].
3
Multivariate Analysis of Spectral Data
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Descriptive statistical analysis (i.e., mean, variance, standard deviation), principal component analysis (PCA) and clustering were used to explore spectral data with the software R [66] and Unscrambler X V.10.5 (Camo Software). PCA was performed using 100 iterations of the NIPALS algorithm, with 6 or 7 principal components depending on convergence of data, 20 random cross validation segments, and mean-centered data. Hierarchical clustering by complete linkage using Spearman's rank correlation distance was performed for clustering of ions in spectra and Ward's method using Euclidean distance for clustering of biomass samples. K-means clustering of ions and samples (ramets) was performed in R using the packages cluster and factoextra.
4
Multivariate Analysis of Formulation Impacts
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Methods of principal component analysis (PCA) were used for descriptive evaluation of the experimental data. Prior to modeling, the variables were adjusted by autoscaling, that is, mean centering and scaling by standard deviation. The influence of formulation variables (Table 1 ) on the parameters of mechanical resistance, in vitro residence time, and surface pH was subsequently evaluated using multiple linear regression (MLR) with use of analysis of variance (ANOVA). MLR models was assessed on the basis of characteristics such as R-square regression (which describes each model's explained variability), R-square of prediction (which expresses the model's predictive ability), and coefficient of variation (CV%; the average modeling error expressed as a percentage of the mean). Statistical evaluations were conducted using the program Unscrambler X (v. 10.3, Camo Software, NOR).
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Methods of principal component analysis (PCA) were used for descriptive evaluation of the experimental data. Prior to modeling, the variables were adjusted by autoscaling, that is, mean centering and scaling by standard deviation. The influence of formulation variables (
5
Chemometrics Analysis of Spectral Data
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Chemometrics analysis was performed using Unscrambler X V.10.3 (CAMO/Software, Oslo, Norway). For SIMPLISMA analysis, chemometrics pre-treatments (baseline correction) were applied using Unscrambler®X (version 10.4, CAMO Software). The pre-treated matrices were then imported to Matlab® (version 7.8 R2009a, MathWorks, Natick, MA, USA) for data analysis.
6
Enzymatic Activity Determination Protocol
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Endoglucanase activity was determined, according to Nava-Cruz et al., [13] (link) by analyses of reducing sugar released during hydrolysis of 1% sodium carboxymethyl cellulose in 50 mM citrate-phosphate buffer (pH 4.8) by enzyme solution incubated at 50 • C for 30 min.
Exoglucanase activity was performed by using filter paper Whatman (1 cm × 5 cm) in sodium citrate buffer (50 mM, pH 4.8) at 50 • C for 60 min, according with Mandels et al. [14] Enzymes determinations mentioned above were stopped on a bath with ice for 5 min.
An enzyme activity was defined as the amount of enzyme that catalyzes the release of 1 μmol of glucose per minute under the assay conditions. The activity of the enzyme is expressed as units per gram dry matter (U g -1 DM). • Principal component analysis (PCA) is a tool currently used in chemometrics and were described in a previous study. [15] Software Chemometrics analysis was performed using Unscrambler X V.10.3 (CAMO/Software, Oslo, Norway).
Exoglucanase activity was performed by using filter paper Whatman (1 cm × 5 cm) in sodium citrate buffer (50 mM, pH 4.8) at 50 • C for 60 min, according with Mandels et al. [14] Enzymes determinations mentioned above were stopped on a bath with ice for 5 min.
An enzyme activity was defined as the amount of enzyme that catalyzes the release of 1 μmol of glucose per minute under the assay conditions. The activity of the enzyme is expressed as units per gram dry matter (U g -1 DM). • Principal component analysis (PCA) is a tool currently used in chemometrics and were described in a previous study. [15] Software Chemometrics analysis was performed using Unscrambler X V.10.3 (CAMO/Software, Oslo, Norway).
7
River Microbial and Chemical Distribution Analysis
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For the evaluation of microbial and chemical distribution across a river section, one-way ANOVA at the 5% significance level followed by a post hoc test (Tukey’s HSD multiple comparison) was performed for each investigated parameter by using the software SPSS Statistics 27.
Multivariate statistics was performed through a Principal Component Analysis (PCA), which allows a visual presentation of relationships between samples and variables. The advantage of a PCA is that it can reveal patterns that may not be easily discovered when using classical statistics. In a PCA, a large dataset of possibly correlated variables is transformed into a new, smaller dataset. The transformation is performed by identifying directions, called principal components (PCs), where the maximum variation in the dataset can be found. The results from the PCA are presented as scores, describing variation in samples, and loadings, describing variations in variables. The confidence region in the PCA plots was based on Hotelling’s T2 test, which is a multivariate version of Student’s t test. The confidence limit was selected to be 95%. The PCA was performed using the software The Unscrambler X v. 10.5 (CAMO Software AS, Norway).
Multivariate statistics was performed through a Principal Component Analysis (PCA), which allows a visual presentation of relationships between samples and variables. The advantage of a PCA is that it can reveal patterns that may not be easily discovered when using classical statistics. In a PCA, a large dataset of possibly correlated variables is transformed into a new, smaller dataset. The transformation is performed by identifying directions, called principal components (PCs), where the maximum variation in the dataset can be found. The results from the PCA are presented as scores, describing variation in samples, and loadings, describing variations in variables. The confidence region in the PCA plots was based on Hotelling’s T2 test, which is a multivariate version of Student’s t test. The confidence limit was selected to be 95%. The PCA was performed using the software The Unscrambler X v. 10.5 (CAMO Software AS, Norway).
8
Multivariate Analysis of Fruit Phytochemicals
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The data collected were subjected to the analysis of variance (ANOVA) using a statistical software (GenStat 17.1, VSN International, Hemel Hempstead, United Kingdom). Fischer's least significant differences were calculated and used to separate means at 5% level of significance. In an attempt to gain a better insight of the interaction between measured PGRs and RBD, the dataset of fruit from different canopy positions was also subjected to chemometric data analysis. This multivariate data analysis was based on the principal component analysis (PCA) and executed using The Unscrambler chemometric software (The Unscrambler® X v10.5, CAMO SOFTWARE AS, Oslo Science Park, Norway).
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