Peak Analysis Demo

Scientific data often requires pre-processing before meaningful parameters can be extracted. This demo showcases the Baseline Subtraction and Numerical Integration tools provided by the scichart-analysis plugin.

Interactive Example

The chart below shows a Gaussian peak on top of a linear drifting baseline (common in sensors and electrochemistry).

• Baseline Correction: Removes the linear drift.
• Integration: Calculates the area under the peak (e.g., total charge $Q$ in voltammetry).

📊 0 points 🚀 0 FPS

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Implementation Details

The analysis features are available via the PluginAnalysis API, which is auto-loaded in most scientific chart configurations.

1. Baseline Subtraction

Experimental backgrounds can be modeled as a linear trend between two points.

// Access analysis via the chart instance
const analysis = chart.getPluginAPI('scichart-analysis');

// Correct raw data using points at x=10 and x=90 as background anchors
const correctedY = analysis.subtractBaseline(rawX, rawY, 10, 90);

chart.addSeries({
  id: 'corrected-signal',
  data: { x: rawX, y: correctedY },
  style: { color: '#00f2ff' }
});

2. Peak Integration

Calculates the area under a curve within a specific range using the Trapezoidal Rule.

const analysis = chart.getPluginAPI('scichart-analysis');

// Calculate area between specific X bounds
const area = analysis.integrate(xData, yData, { xMin: 0.2, xMax: 0.8 });

console.log(`Integrated Area: ${area.toFixed(6)} units²`);

Advanced Tools

For more complex analysis, you can use the Peak Tool from the scichart-tools plugin:

// Enable interactive peak analysis tool
chart.setMode('peak');

// Listen for measurement events
chart.on('measure', (m) => {
  if (m.type === 'peak') {
    console.log('Peak Area:', m.area);
    console.log('FWHM:', m.fwhm);
  }
});

Key Features

• Interpolated Range: The integration tool automatically interpolates Y values if the specified bounds don't fall exactly on data points.
• High Performance: Optimized for large Float32Array or Float64Array buffers using hardware acceleration where possible.
• Flexible Models: Supports linear, polynomial, and moving-average based baselines.