Ask the Expert: What is the meaning of Delta and Std.dev for calculating the power of a design?

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Ask the Expert: What is the meaning of Delta and Std.dev for calculating the power of a design?

Question: What is the meaning of Delta and Std.dev for calculating the power of a design?

Answer: You can regard Delta as the smallest change in response that you want to be able to detect in the experiment, and Std.dev. is the expected noise level. Together these define the signal-to-noise ratio, which is used in the calculation of power. If the signal-to-noise is low, you will need many measurements to achieve an acceptable power (or probability of detecting small, but real effects).

Ask the Expert: Can the Inlier limit and Hotelling’s T2 limit be interpreted as confidence intervals?

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Ask the Expert: Can the Inlier limit and Hotelling’s T2 limit be interpreted as confidence intervals?

Question: Can the Inlier limit and Hotelling’s T2 limit be interpreted as confidence intervals?

Answer: For the Hotelling’s limit your interpretation is correct, and you can set the desired significance level. The inlier limit is not associated with any probability limit. Rather, the pairwise Mahalanobis distance is calculated for all points in the calibration set. The minimum distance to a neighbouring point then is found for all observations. The maximum of these ‘shortest distances’ gives the inlier limit. If a new observation exceeds the inlier limit, this means that this point is farther away from any calibration point than the shortest distance between calibration points.

Seminar: Turn business and operational challenges into competitive advantages

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Seminar: Turn business and operational challenges into competitive advantages

Despite offering the potential for major business improvements, most organizations are not exploiting the value in their oceans of data across production, quality and R&D operations. We will be holding a free 2 hour seminar in Houston, Texas, showcasing how world-leading organizations have turned business and operational challenges into competitive advantages.

Free seminar: From pain to profit: Find out how world-leading organizations have turned major business and operational challenges into competitive advantages.

Time and date:  9-11am, Monday, August 27, 2012
Venue: The Royal Norwegian Consulate General, Houston, Texas

You can read more about the event and register here

up to data and CAMO deliver industry-first solution for QbD initiatives in Life Sciences

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up to data and CAMO deliver industry-first solution for QbD initiatives in Life Sciences

We have partnered with up to data professional services GmbH to deliver an industry-first solution for Quality by Design initiatives in the life science industry.

The Unscrambler® X multivariate data analysis software will be seamlessly integrated with up to data’s iStudyReporter solution for regulatory compliant submission documents, creating the groundbreaking product iStudyReporter QbD. This enables advanced analysis of process parameters and quality attributes based on real-time data from a wide range of disparate systems.

Designed specifically for life science organizations, iStudyReporter enables simultaneous real-time access to any LIMS, CDS, ERP and MES system. Combined with the powerful analytics of The Unscrambler® X, the analysis of data from R&D, clinical and enterprise manufacturing systems allows organizations to more easily implement and realize the value of Quality by Design.

You can read the full press release here

Ask the Expert: Preventing a matrix from being classified as spectra

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Ask the Expert: Preventing a matrix from being classified as spectra

Do you have a question related to multivariate data analysis? Each month our expert panel will select a handful of the most popular or unique questions to answer so you can get expert advice on choosing the right tools and scientific methods for your data analysis needs…all for free!

Question: What prevents a matrix from being classified as spectra? When I right click on a data matrix which I imported from excel or imported from excel followed by transposing, the spectra option is grayed out and cannot be selected. I am importing spectral data so it would be really helpful to set it as such, but I can’t figure out what the problem is.

Answer: To use the option to designate data as spectral, a column set needs to be defined (even if all the columns in the data are spectra. Once this is done, highlight the column set in the project navigator, right click, and toggle on “Spectra”. With this setting, the loadings plots in the PCA overview will by default be shown as a line plot, and for PLS regression, the regression coefficients will be displayed in the PLS overview as a line plot.

Have a question on using The Unscrambler® X or a general question about analyzing your data with multivariate statistics? Ask our experts here

CAMO partners with Optimal Industrial Automation for P.A.T.

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CAMO partners with Optimal Industrial Automation for P.A.T.

We are pleased to announce we have partnered with Optimal Industrial Automation Ltd, a leader in manufacturing process automation systems, to enhance Optimal’s process control software for the pharmaceutical and biotechnology sector.

The partnership enables CAMO Software’s Unscrambler® X multivariate analytical engines to be offered together with Optimal’s synTQ® Lite software, designed for PAT (Process Analytical Technology) environments in the life sciences industry.

synTQ® is designed specifically to meet the data management requirements of PAT, with the flexibility to interface with a client’s instrument or control system of choice. It can significantly speed up the development of PAT models and process understanding.

 You can read the full press release here

Process and Quality Control in food manufacturing

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Process and Quality Control in food manufacturing

We worked with a major Norwegian chocolate manufacturer to resolve a process issue causing production and quality problems

Chocolate production consists of several process steps where both raw material quality and storage conditions influence the final product quality. Although chocolate making today is based largely on science such as chemistry and food technology, the ‘human touch’ and flair based on many years of domain-specific knowledge remains an essential ingredient in the process.

Nidar, a major Norwegian chocolate producer, was experiencing a quality issue in one of their production lines, forcing them to regularly scrap batches. From a business perspective, this resulted in significant waste, downtime, energy usage and re-work costs.

When the quality issue arose, the team at Nidar realized that to fully understand the complex variables at play, multivariate process control methods and Design of Experiments (DoE) strategies were required, and consequently implemented the following 3-step process:

  • Analyzed historical data with multivariate models

A number of batches, both with and without quality problems, were selected and the process data from each was analyzed with multivariate regression methods and Principal Component Analysis, giving deeper insights into possible causes of the quality issue.

  •  Applied Experimental Design in full scale production

Secondly, the client realized that designed experiments were necessary to fully understand the issues and isolate specific problems. This began by investigating the various steps in the process with fractional factorial designs to pinpoint the important variables. This required them to stretch the process settings in different directions, occasionally allowing some batches to be scrapped in order to find the operational envelope where the process was stable even when subjected to changes. The next phase was to implement experimental plans for important variables across the entire production chain.

  •  Implemented changes in the process settings

Based on the conclusions from the above steps, the team at Nidar implemented changes in the process settings, allowing them to bring production back in line and consistently produce a high quality product which was robust towards changes in the raw material and other factors which may vary without the ability to control them.

This analysis revealed that the process of making the chocolate could not be viewed as an isolated event. For example, the speed at which the process was running was important for the production volume, thus this variable could be regarded both as a critical process variable as well as a response variable in terms of efficiency. Furthermore, the process settings when filling and cooling the product had interactions with the storage conditions such as temperature, time and humidity.

Multivariate data analysis used in combination with Design of Experiments enabled the product quality department at Nidar to get a better understanding and view of the whole process which was used to resolve a difficult quality problem.

From a business perspective, this enabled the company to save $1M per year on one production line alone and transfer the knowledge gained to other production lines.

You can download the full case study here (PDF)

 

Welcome to the DATA ANALYSIS blog by CAMO Software

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Welcome to the DATA ANALYSIS blog by CAMO Software

Hello and welcome to the data analysis blog from CAMO Software, makers of the leading MULITVARIATE DATA ANALYSIS and DESIGN OF EXPERIMENTS software, The Unscrambler® X.

On this blog we will be posting interesting science news on how multivariate data analysis is used in a wide range of fields, case studies from industry clients, product news and videos from CAMO Software, useful tips for analyzing data and much more.

Our expert panel will also answer questions about how to analyze your data using multivariate methods in The Unscrambler® X. You can post questions here

If you want a quick overview of multivariate analysis you can check out these links:

http://www.camo.com/multivariate_analysis.html

http://en.wikipedia.org/wiki/Multivariate_analysis

http://en.wikipedia.org/wiki/Multivariate_statistics

We hope you enjoy the blog, please feel free to send us feedback or suggestions for other interesting information.

 

All the best,
Paal Braathen,
CEO
CAMO Software

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