Unscrambler 9.8 software

Unscrambler Software. Camo Process AS, www.camo.no, Oslo, Norway, 2004. 

Other estimates of the standard error of prediction have been proposed. A fairly popular and user-friendly one, although with limited value for some data, is contained in the Unscrambler software (CAMO ASA, Oslo, Norway). 

From the total sample set (48 samples), 45 samples were used as calibration samples. The three samples excluded from the calibration set were selected on the basis of a representative variation of their active ingredient concentrations, and finally used as unknown test samples to predict the content of their active ingredients. Partial least squares (PLS) models for each active ingredient were developed with the Unscrambler Software (version 9.6 CAMO Software AS, Oslo, Norway) from the MSC-pretreated median spectra of all pixels of each of the 45 calibration sample images. Based on these calibration models, the predictions of the active ingredient content for each pixel of the imaging data of the three test samples and their evaluation as histograms, contour plots and RGB plots was performed with Matlab v. 7.0.4 software (see below). 

An outlier is a sample which looks so different from the other that either is not well described by the model or influences the model too much. In regression, there are many ways for a sample to be classified as an outlier. It may be outlying according to the X-variables only, or to the Y-variables only, or to both. It may also not be an outlier for either separate set of variables, but become an outlier when you consider the (X,Y) relationship. Outliers can be detected in the Unscrambler software using X-Y relation outliers, Y-residual vs. predicted Y, the influence plot, the score plots, Y residuals, leverages and normal probability plot. 

Fig. 5. Piincijjal component analysis scores scatter plot of the FTIR data set in the spectral window of 3000-600 cnri wavenumber (1700 data points) of propolis samples produced in the southern Brazil (Santa Catarina State). NRi, NR2 and HL refer to propolis samples originated from northern and highland regions of Santa Catarina State. The calculations were carried out using The Unscrambler software (v. 9.1, Oslo - Norway). PCI and PC2 accoimts for 88% of the variance preserved.

UNSCRAMBLER software, Camo AS, Olav Tryggvasonsgt, 24, N-7011 Trondheim, Norway. 

For this example, the commercial software products The Unscrambler (Unscrambler 2004) has been used for PLS and MobyDigs (MobyDigs 2004) for GA results have been obtained within few minutes. Some important aspects of multivariate calibration have been mentioned together with this example others have been left out, for instance, full CV is not always a good method to estimate the prediction performance. 

Unscrambler, Camo Software Inc., Woodbridge, USA, http //www.camo.com/ (Accessed on January 31, 2008). 

Many commercial statistical or chromatographic software packages also allow to set up a ruggedness test. This is for instance the case with Statgraphics Plus, Unscrambler II and DryLab for Windows . This list is far from complete. 

One of die difficulties is to decide what software to employ in order to analyse the data. This book is not restrictive and you can use any approach you like. Some readers like to program their own mediods, for example in C or Visual Basic. Others may like to use a statistical packages such as SAS or SPSS. Some groups use ready packaged chemometrics software such as Pirouette, Simca, Unscrambler and several others on the market. One problem widi using packages is that they are often very... 

Samples of each polymer were equilibrated at different relative humidities by storage over saturated salt solutions in desiccators. The equilibrated samples were then examined using FT Raman spectroscopy and differential scanning calorimetry (DSC). Gravimetry was used to assess the water vapor sorption profile. Chemometric analysis of spectroscopic data was performed using a commercial software package. Unscrambler (Camo.). 

PCA and related two-way multivariate techniques can be performed in several commercial chemometrics softwares, such as SIMCA-P/P + (Umet-rics AB, Umea, Sweden) and The Unscrambler (Camo Inc., Woodbridge, New Jersey, USA). Batch analysis can be performed in SIMCA-P+ only. 

The main purpose of multivariate methods would be information extraction. The simplest form of information extraction and data reduction is the PCA technique. The history of PCA can be traced to an article by Pearson (1901). It is a statistical method that can be performed in a wide variety of mathematical, statistical, or dedicated computer software such as Matlab (The MathWorks, Inc.), SPSS (SPSS, Inc.), or The Unscrambler (Camo, Inc.). We will here give a short nonmathematical introduction to this method, and we refer the reader to one of the many available text books on this topic for a more in-depth, formal presentation. 

The Unscrambler (version 7.08, CAMO AS, Trondheim, Norway) software program was used in the PLS calibration of the NIR profiles with the adulterant percentage as the dependent variable in limited spectral regions and different data pretreatment methods. All of these different procedures were evaluated to find the spectral region and the data pretreatment method that give the best prediction and classification. The following procedure was found to serve the purpose of quantification and classification of an adulterant in olive oil in an acceptable manner. 

Present data acquisition and/or analysis tools such as OPUS (Bruker Optics), Perkin Elmer s Spotlight software (Perkin Elmer), Resolutions Pro (Varian), Grams (Thermo Fisher Scientific), The Unscrambler (CAMO), CytoSpec (www.cytospec.com) and various Matlab (The MathWorks) toolboxes allow for the easy recording and evaluation of infrared (IR) spectra. However, care has to be taken concerning the particular choice of data acquisition parameters, pre-treatment of spectra and data analysis procedures. With the attempt to move biomedical IR spectroscopy from bench top to bedside further questions of reproducibility and standardisation arise. 

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