LC.Predictor 0.2.1

What is LC.Predictor?

LC.Predictor is a .NET Standard 2.0 (C#) interface used in communication between a predictor software backend and a client software frontend. The chemometrics prediction API adds predictor capabilities to the client software using LabCognition or other third party backend software offering prediction capabilities.

The SamplePredictor implementation is demonstrating how to use this API within a third party client frontend software and Labcognition software as predictor backend software.

Previous Versions

0.2.1

• add net8 target
• remove explicit 3rd-party license acknowledgements. This must be handled by extracting the references using a tool.

0.2.0

• add 3rd-party license acknowledgements are deployed with the package

0.1.0

LC.Predictor superseeds parts of LabCognition.Interface (1.2.0). Interfaces and methods used in LabCognition.Interface have been revised and renamed. Here is a matrix depicting the changes:

What is Prediction?

Prediction means the calculation of a physical property like a concentration value from spectroscopic data using a multivariate calibration model.

Input

The input of such prediction method is a spectrum in the form of x,y data and a calibration model created with LabCognition software.

Output

The output of the prediction method are one or more quantitative and/or qualitative prediction results. Each result may be a numeric or a string value generated by the calibration model algorithm when performing a prediction.

Preparing the Input Data

Create a Calibration Model

At first you must create a calibration model with LabCognition or the modelling software implementing the LC.Predictor. Once created, the calibration model is typically a single file containing the recipe to convert spectroscopic data into the desired prediction result. A detailed description on how to do this is given elsewhere.

Record Sample Spectra

Performing prediction requires suitable spectra taken from physical samples as files in scientific file format or as x,y values in memory.

Additional Calibration Metadata

In case additional metadata shall be calibrated in combination with spectral data, it must be appended to the x,y data of the spectrum in order to transport it through the interface.

Operating the LC.Predictor from the client frontend software

The calibration modelling software or the predictor software used in the backend providing the prediction capabilities must implement the Interfaces IPredictorFactory and IPredictor. This may be the LabCognition or another third party software. In the client frontend software you may implement the counterparts using these two interfaces as follows:

Creating IPredictor Instance

Create an instance of the application using one of the IPredictorFactory.ReadModelFromXXXX interface method. It will create an instance or the predictor software granting communication through IPredictor interface.

Additionally, it loads the desired calibration model into that instance:

• IPredictorFactory.ReadModelFromFile Load a calibration model from a file on hard disc
• IPredictorFactory.ReadModelFromStream Load a calibration model from a stream (which can also be a memory stream)
• IPredictorFactory.ReadModelFromMemory Load a calibration model from a blob in memory

Gathering possible Prediction Results

You might need information upfront on all possible outputs a calibration model will return when running a prediction. The GetResultPreview() method returns a list of all the results you will get when performing a prediction.

Prediction Result

Each result comes with some properties describing the result:

• Value The main output you are interested in. In principle two value types are supported:

  • numeric (double)
  • text (string)

• Unit This is an optional text describing the value's unit, if any

• Property The result type such as predicted, residual, spectral residual, Mahalanobis disctance, etc. Some results refer to a particular constituent and others refer to general spectral properties.

• Constituent The name of the predicted feature e.g. fat, protein, etc. in wheat. For spectral properties this is empty.

Since the value type is not unique for all results they are returned as IPredictionResult. Use the GetValueType() method in the IPredictionResult interface to identify the value type.

Run Prediction

Call the Predict(double[] dataX, double[] dataY) method with your spectrum's x,y data to obtain the prediction results.