Unscrambler

Manufactured by CAMO Software

Sourced in Norway, United States

The Unscrambler is a software application designed for the analysis and visualization of multivariate data. It provides tools for data preprocessing, exploratory analysis, and multivariate modeling.

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Lab products found in correlation

Matlab 7.11.0.564 r2010b, by MathWorks (1 mentions) Nicolet is5, by Thermo Fisher Scientific (1 mentions) Unscrambler x v 10, by CAMO Software (1 mentions) Matlab, by MathWorks (1 mentions) R statistical program, by R Project for Statistical Computing (1 mentions) Factominer, by R Project for Statistical Computing (1 mentions) Duo laser, by Bruker (1 mentions) Bravo spectrometer, by Bruker (1 mentions) Opus software, by Bruker (1 mentions) Sas version 8, by SAS Institute (1 mentions) Genstat 18th edition, by VSN International (1 mentions)

Close spelling variants of this product

Unscrambler X 10.3 (68 citations) Unscrambler X (47 citations) Unscrambler X version 10.5 (27 citations) Unscrambler X software (24 citations) Unscrambler 9.7 (20 citations) Unscrambler software (10 citations) Unscrambler 10.1 (8 citations) Unscrambler X V.10.5 (8 citations) Unscrambler software version 9.7 (3 citations) Unscrambler v9.7 (3 citations)

18 protocols using unscrambler

Proteomic Profiling of Tea Leaves

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Significance tests were conducted using Student’s t-test at p < 0.05 level. The results are represented by the mean ± standard errors (SE).
To elucidate the overall proteomic responses of tea leaves, we performed a heatmap approach by using mean centered and standard deviation scaled with The Unscrambler (Version 10.0.1, Camo Software Inc., Woodbridge, NJ, USA) for the normalization of the values to give all variables an equal chance. After normalization of all the data sets, normalized values were used in the color scales system from Microsoft Office Excel 2010 (Microsoft Corporation, Redmond, WA, USA) to generate the heatmap.
The normalized data set was used for the principal component analysis (PCA) by performing normalized volume of the differentially abundant protein spots using The Unscrambler (version 10.0.1, Camo Software Inc., Woodbridge, NJ, USA). The PCA loading plots were used to determine the separation of the differentially expressed proteins based on the presence and absence between TPL and MGL samples. The PCA loading plots were performed in triplicate (n = 3).

Zhou Q., Chen Z., Lee J., Li X, & Sun W. (2017). Proteomic analysis of tea plants (Camellia sinensis) with purple young shoots during leaf development. PLoS ONE, 12(5), e0177816.

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Multivariate Analysis of Probiotic Cheese

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The multivariate statistical analysis was performed to investigate the variability in the composition of cheese under the addition of probiotic bacteria. Principal component analysis (PCA) was applied to reduce the dimensionality of the original data and to assist the interpretation of multivariate data. The 1 H NMR spectra were used as input data for The Unscrambler (version 10.4, Camo Software, Oslo, Norway) to perform the PCA to create an overview showing trends, groupings, and outliers with a confidence level of 95%. We performed PCA using the 1 H NMR spectra of the 16 cheeses. The regions containing only noise were removed from PCA; therefore, the chemical shifts between 0.50 to 8.70 ppm were used for statistical analysis (Larsen et al., 2006) (link). In addition, the region influenced by the nondeuterated water suppression (4.75-5.00 ppm according to the saturation profile analysis) was excluded from the PCA evaluation. Before the multivariate analysis, normalize scaling parameter and baseline correction using linear fit algorithms were applied to reduce the differences in concentration and spectra noise. The data were preprocessed by mean centering because this pretreatment provided better difference between the samples (Soares et al., 2017) (link).

Balthazar C.F., Guimarães J.T., Silva R., Filho E.G.A., Brito E.S., Pimentel T.C., Rodrigues S., Esmerino E.A., Silva M.C., Raices R.S.L., Granato D., Duarte M.C.K.H., Freitas M.Q, & Cruz A.G. (2021). Effect of probiotic Minas Frescal cheese on the volatile compound and metabolic profiles assessed by nuclear magnetic resonance spectroscopy and chemometric tools. Journal of dairy science, 104(5).

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Multivariable Calibration Model Development

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Before calibration, the reflectance data was maximum normalized and then transformed to absorbance. The resulting spectra obtained with each cuvette were divided into calibration and validation sets by the Kennard-Stone method (Kennard & Stone, 1969; Li et al., 2014) using Matlab 7.11.0.564 (R2010b) software (The MathWorks, Inc., USA). 87 samples were used for multivariable calibration and the 40 left samples were used as validation set. Partial least squares (PLS) models using fullcross internal validation (FCV) were built with The Unscrambler (CAMO Software AS, Norway). The Vis/NIR region from 350 to 2200 nm was screened with a step of 50 nm for full spectrum PLS model building.

García Martín J.F. (2015). Optical path length and wavelength selection using Vis/NIR spectroscopy for olive oil's free acidity determination. International Journal of Food Science & Technology., 50(6).

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Predictive Modeling of Analytical Parameters using Vis/NIR Spectroscopy

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The predictive models for the parameters analyzed were built using 4/5 of the total corresponding sample number by Partial Least Squares method (PLS) [18] . The remaining samples (1/5) were reserved for external validation. Calibrations were obtained using PLS with The Unscrambler (CAMO Software AS, Norway) and the full spectrum was studied. The models principal components (PCs) were fixed after tests using 10 PCs initially. The procedure for spectral variable selection to be included in the models consisted of performing several consecutive cycles, eliminating variables having spectral correlation coefficients with the parameters analyzed closer to zero. Variable selection ended in the last cycle that improved the statistical model R and R CV . This selection was made on the regression coefficient graph of The Unscrumbler.which summarizes the relationship between the Vis/NIR wavelengths and the response.These cycles of elimination were conducted by means of the 'mark with rectangle' and the 'recalculate without marked' specific applications of The Unscrambler.
The reflectance data was normalized to its maximum. Standard Normal Variate Transformation (SNV) and Savitzsky-Golay second derivative, with segment width 3 and polynomial order 2 was also tested. Full-cross internal validation (FCV) was used for the models.

Guinda Á., Rada M., Benaissa M., Ourrach I, & Cayuela J.A. (2015). Controlling Argan Seed Quality by NIR. Journal of the American Oil Chemists' Society, 92(8).

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Butter Composition Analysis Protocols

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The normality of the distributions of fat contents, FFA contents, TG profiles, and FA profiles within the groups—conventional, organic, and grass butters—were checked using the Shapiro–Wilk test. Distributions appeared to be non-normal (Shapiro–Wilk: p < 0.05). Therefore, the Kruskal–Wallis test for group comparison was performed among the groups. If the Kruskal–Wallis test was significant (p < 0.05), pairwise comparisons between groups were subsequently performed by means of the Mann–Whitney U test. SPSS version 23.0.0.2 (IBM Corp., Armonk, NY, USA) was used to perform those tests. The same procedure was followed to check significant differences between samples purchased in winter and samples purchased in summer and between salted and unsalted butter samples.
Principal Component Analysis (PCA) was performed using The Unscrambler (Version X 10.3, CAMO Software, Oslo, Norway). The (I × J) data matrix consisted of I = 54 rows (butter samples) and J = 17 columns for the TGs and J = 35 columns for the FAs. Raw data were auto-scaled prior to PCA.

Pustjens A.M., Boerrigter-Eenling R., Koot A.H., Rozijn M, & van Ruth S.M. (2017). Characterization of Retail Conventional, Organic, and Grass Full-Fat Butters by Their Fat Contents, Free Fatty Acid Contents, and Triglyceride and Fatty Acid Profiling. Foods, 6(4), 26.

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PCA Analysis of Spectroscopic Data

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PCA
was performed by use
of The Unscrambler (Camo Software, Oslo, Norway) on second derivatives
in the CH stretching region (3100–2800 cm^–1), by use of the leave-out-one cross-validation approach and Nipal’s
algorithm for PCA decomposition. Six PCs were chosen for the initial
decomposition and 20 iterations were performed for each PC. Two-dimensional
(2D) (PC1 versus PC2) scores plots and the corresponding PC1 and PC2
loading plots were graphed.

Khoshmanesh A., Dixon M.W., Kenny S., Tilley L., McNaughton D, & Wood B.R. (2014). Detection and Quantification of Early-Stage Malaria Parasites in Laboratory Infected Erythrocytes by Attenuated Total Reflectance Infrared Spectroscopy and Multivariate Analysis. Analytical Chemistry, 86(9), 4379-4386.

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Partial Least Squares Discrimination Analysis

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PLS was also
performed by use
of The Unscrambler (Camo Software, Oslo, Norway) on second derivatives
in the CH stretching region (3100–2800 cm^–1), by use of the leave-out-one prediction approach. Partial least-squares
discriminant analysis (PLS-DA) finds a linear regression model by
projecting the predicted variables and the observable variables to
a new space. A separate column was designated the category variable,
which included the percentage parasitemia in this case. The regression
plot and associated regression coefficients were plotted. The number
of factors in the analysis varied from 6 to 8 latent factors. The
number of factors was determined by the “goodness of fit”
and analysis of the regression coefficients to ensure that the coefficients
were based on real spectral bands and not spurious artifacts.

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Chemical Profile Analysis Using PCA

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Statistical analysis was carried out using The Unscrambler (Camo software). Principal component analysis was performed for comparison and for identification of outliers with distinguishable chemical profile.

Tangerina M.M., Furtado L.C., Leite V.M., Bauermeister A., Velasco-Alzate K., Jimenez P.C., Garrido L.M., Padilla G., Lopes N.P., Costa-Lotufo L.V, & Pena Ferreira M.J. (2020). Metabolomic study of marine Streptomyces sp.: Secondary metabolites and the production of potential anticancer compounds. PLoS ONE, 15(12), e0244385.

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FTIR-Based Adulteration Detection in High-Oleic Peanut Oil

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Twenty-five training sets and calibration samples of adulterated peanut samples were used for each study conducted with vegetable oil, canola oil, and almond oil adulterants. The training set and calibration samples (n = 25) of varying compositions of adulterated HRPO with either VO, CO, or AO, ranging from 1–90% (wt/wt), were prepared in sample vials. The samples were kept at room temperature for approximately 48 hours to facilitate homogenization of HRPO and the adulterant oils. The FTIR spectra of the adulterated HRPOs were measured using an ATR-FTIR spectrometer (Thermo Scientific NiCOLET iS5, Waltham, MA, USA). The FTIR spectrum of each sample was scanned 25 times with a resolution of 4 cm⁻¹ over a 600 cm⁻¹ to 4000 cm⁻¹ wavenumber range. Partial-least-regression and chemometric data analysis was performed using the software The Unscrambler (CAMO Software, 9.8, Oslo, Norway).

Smithson S.C., Fakayode B.D., Henderson S., Nguyen J, & Fakayode S.O. (2018). Detection, Purity Analysis, and Quality Assurance of Adulterated Peanut (Arachis Hypogaea) Oils. Foods, 7(8), 122.

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Multivariate Analysis of SERS Data

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Data analysis was performed using The Unscrambler (X version 10.1, CAMO Software, Oslo, Norway) and it consisted of background removal by linear baseline correction, followed by mean normalization and principal component analysis-linear discriminant analysis (PCA-LDA) using the first 15 principal components (PCs). PCA reduces the dimensionality of the data by projecting all spectra onto PCs. Linear discriminant analysis is a supervised statistical analysis method that requires prior knowledge regarding the type of sample (e.g., cancer or control) for building a mathematical model that can discriminate between the sample type. To combine spectral information with PSA levels, the PSA values were normalized by dividing them to their median value. Next, the normalized values were appended to the SERS spectra matrix and the PCA-LDA was performed on the resulting matrix of data. The D'Agostino & Pearson omnibus normality tests, unpaired t-test, Mann-Whitney nonparametric tests and receiver operating characteristic (ROC) curve analysis were performed with GraphPad Prism (Version 6.0, GraphPad Software).

Stefancu A., Moisoiu V., Couti R., Andras I., Rahota R., Crisan D., Pavel I.E., Socaciu C., Leopold N, & Crisan N. (2018). Combining SERS analysis of serum with PSA levels for improving the detection of prostate cancer. Nanomedicine (London, England), 13(19).

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