Hamilton, New Zealand, December 14, 2008 --(PR.com
)-- Khipu Systems, developer of the iCalibra software platform for NIR, today announced that it has developed a breakthrough in the area of NIR instrument standardization.
The iCalibra Standardization module allows single calibration models which provide accurate results from dispersive and FT instrument technologies simultaneously. Significantly, the standardization processes are integrated within the predictive models, so the models and standardization layers are capable of automated re-learning and enhancement.
This means that NIR instrument selection can become truly independent of existing calibration data sets.
“Users will be free to select the best instrument for their needs, without being influenced by the ‘legacy burden’ of years of collected spectra that locks them into their existing instrument brand. This is a development that will be welcomed by end users and instrument manufacturers alike” says Mark Stuart, Chairman, Khipu Systems.
iCalibra is a next generation software platform that revolutionizes what is possible with NIR. The iCalibra Standardization module is just one of a suite of modules derived from the iCalibra software platform, with other modules providing features such as; sample specific confidence scores, automated model building, model proving ground, automated data acquisition and exchange, auto archiving and retrieval of spectral and reference data, automated pre-treatment of calibration set data, data fusion, quality assurance modelling, classification and instrument diagnostics.
“Early deployments of iCalibra have resulted in extraordinary return on investment for users, leading to significant interest in the technology from all over the world,” says Mr Stuart.
Khipu intends to make the entire suite of modules available to customers worldwide early next year.
About Khipu Systems
Khipu Systems is the developer of iCalibra – the next generation software platform that revolutionizes what is possible with NIR. iCalibra is based on patented machine learning technology. For more information about iCalibra, please visit www.icalibra.com