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Analysis Package P11:
NIR Prediction Package

We have developed unique proprietary models that allow the lignocellulosic composition of biomass samples to be predicted from their near infrared (NIR) spectra. This package provides the following advantages over the standard wet-chemical methods for analysis:

  • Rapid Analysis:

      We typically provide data within one day of receipt of the samples.

  • Lower Cost:

      The NIR method involves less laboratory work than the wet-chemical analytical methods which allows us to provide the service at a lower cost.

  • Effective Sample Screening:

      As a result of the increased speed and reduced cost of analysis the NIR method allows a far greater number of samples to be analysed than would otherwise be possible with standard methods. This means that you can screen more samples in order to find those that are most appropriate for your desired end-use.

  • Click here to read more about our NIR analysis service.

    Constituents Determined

    These constituents are predicted using near-infrared models developed at Celignis. Statistics concerning the quality of these models can be viewed for each constituent by clicking on the links above. In our analysis reports for this Package we provide the predicted value along with the estimated deviation in prediction for each analyte. Examples of the data reports generated can be viewed on the Celignis Database. Please log on to the guest account using email "" and password "celignis".

    Click here to place an order for determining NIR Prediction Package.

    Sample Weight Requirements

    Under nornal conditions there is a minimum requirement of 5g of sample for this analysis package with a recommended weight of 50+g of sample.

    However, it may be possible for us to undertake the anaysis with lower quantities than the minimum specified above. Please email us at to find out.

    Analytical Procedure for NIR Prediction Package

    Step 1: Scanning the Sample with the NIR Device
    Step 2: Application of NIR Models to the Spectrum

    Equipment Used for NIR Prediction Package