The Unscrambler

The Unscrambler X is a commercial software product for multivariate data analysis, used for calibration of multivariate data which is often in the application of analytical data such as near infrared spectroscopy and Raman spectroscopy, and development of predictive models for use in real-time spectroscopic analysis of materials. The software was originally developed in 1986 by Harald Martens[1] and later by CAMO Software.

The Unscrambler X
Developer(s)CAMO Software
Stable release
10.1 / January 2011
Operating systemMicrosoft Windows
TypeMultivariate statistics, Multivariate Analysis, Design of Experiments Chemometrics, Spectroscopy, Sensory analysis
LicenseProprietary
Websitewww.camo.com/unscrambler/

Functionality

The Unscrambler X was an early adaptation of the use of partial least squares (PLS).[2] Other techniques supported include principal component analysis (PCA),[3] 3-way PLS, multivariate curve resolution, design of experiments, supervised classification, unsupervised classification and cluster analysis.[4]

The software is used in spectroscopy (IR, NIR, Raman, etc.), chromatography, and process applications in research and non-destructive quality control systems in pharmaceutical manufacturing,[5][6] sensory analysis[7][8] and the chemical industry.[9][10]

gollark: But if we allowed *any* language, you'd somewhat lock people out if they did not know *one* of them.
gollark: Yes it is. If we used different languages it would probably not work very well.
gollark: What isn't needed?
gollark: It has to be something and it's a lowest common denominator.
gollark: Approximate and heavily biased summary: palaiologos suddenly demanded that all (future?) events not require specifically Python after being annoyed by HelloBoi's event somehow, and ubq shut down the code guessing event and left.

References
  1. Harald Martens, Terje Karstang, Tormod Næs (1987) Improved selectivity in spectroscopy by multivariate calibration Journal of Chemometrics 1(4):201-219 doi:10.1002/cem.1180010403
  2. Abdi, H. (2003) Partial least squares regression In Lewis-Beck, M., Bryman, A., Futing, T. (Eds.). Encyclopedia for Research Methods for the Social Sciences. Thousand Oaks, CA Sage, pp. 1–17
  3. S. De Vries, Cajo J.F. Ter Braak (1995) Prediction error in partial least squares regression: a critique on the deviation used in The Unscrambler Chemometrics and Intelligent Laboratory Systems 30:239-245 PDF
  4. Kristian Helland (1991) UNSCRAMBLER 11, version 3.10: A program for multivariate analysis with PLS and PCA/PCR Journal of Chemometrics 5(4):413-415 doi:10.1002/cem.1180050410
  5. M.R. Maleki, A.M. Mouazen, H. Ramon and J. De Baerdemaeker (2006) Multiplicative Scatter Correction during On-line Measurement with Near Infrared Spectroscopy Biosystems Engineering 96(3):427-433 doi:10.1016/j.biosystemseng.2006.11.014
  6. Tatavarti AS, Fahmy R, Wu H, Hussain AS, Marnane W, Bensley D, Hollenbeck G, Hoag SW. Assessment of NIR Spectroscopy for Nondestructive Analysis of Physical and Chemical Attributes of Sulfamethazine Bolus Dosage Forms AAPS PharmSciTech. 2005; 06(01): E91-E99. doi:10.1208/pt060115
  7. Yusop, S.M., O'Sullivan, M.G., Kerry, J.F. and Kerry, J.P. (2009), "Sensory Evaluation Of Indian-style Marinated Chicken by Malaysian and European Naive Assessors" Journal of Sensory Studies, 24: 269–289. doi: 10.1111/j.1745-459X.2009.00210.x
  8. Yusop, S.M., O'Sullivan, M.G., Kerry, J.F. and Kerry, J.P. (2009), "Sensory Evaluation Of Chinese-style Marinated Chicken by Chinese and European Naive Assessors" Journal of Sensory Studies, 24: 512-533. doi: 10.1111/j.1745-459X.2009.00224.x
  9. Agustina Guiberteau Cabanillas, Teresa Galeano Díaz, Nielene María Mora Díez, Francisco Salinas, Juan Manuel Ortiz Burguillos and Jean-Claude Viré (2000) Resolution by polarographic techniques of atrazine–simazine and terbutryn–prometryn binary mixtures by using PLS calibration and artificial neural networks Analyst 125:909-914 doi:10.1039/b000726i
  10. Emilio Marengo,Elisa Robotti,Daniela Cecconi,Mahmoud Hamdan,Aldo Scarpa & Pier Giorgio Righetti (2004) Identification of the regulatory proteins in human pancreatic cancers treated with Trichostatin A by 2D-PAGE maps and multivariate statistical analysis Analytical and Bioanalytical Chemistry 379(7):992-1003 doi:10.1007/s00216-004-2707-x
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