Article Type: Configuration / How-To
Audience: Solution Architects, Application Developers, Quality Engineers
Module: Fuuz Script Editor / Transform Nodes
Industry: Biopharmaceutical, Life Sciences, Cell & Gene Therapy
Estimated Time: 30-45 minutes
Last Updated: January 2026

1. Overview

This guide walks you through creating a Quality Batch Golden Record Analysis Tool in Fuuz. When complete, your tool will automatically analyze batch record data to identify the best-performing "golden" reference batch, detect out-of-spec conditions, and recommend corrective actions based on root cause analysis.

The tool you build will:

• Process batch record data from any connected data source
• Automatically identify the highest-quality "golden" reference batch
• Detect statistical deviations across all process parameters
• Map deviations to probable root causes with corrective action recommendations
• Generate structured JSON output for dashboards, reports, or downstream workflows

2. Prerequisites

Before you begin, ensure the following requirements are met:

2.1 Required Data Format

Your batch record data must include these fields:

Example record:

{
  "batch": "BRX-2024-1847",
  "ts": "2024-03-15T08:30:00Z",
  "phase": "Exponential",
  "eq": "BR-001",
  "TT-001": 37.02,
  "AT-001": 7.21,
  "AT-002": 45.3,
  "AT-010": 8.5,
  "ST-001": 150
}

3. Step-by-Step Procedure

Step 1: Create a New Flow

1. Navigate to your Fuuz application in the Designer
2. Click Application Designer in the left navigation panel
3. Click + New Data Flow and name it Quality Batch Analyzer

Step 2: Add Your Data Source

1. Drag a Source node onto the flow canvas
2. Configure the connector to retrieve your batch record data:
   • REST API: Configure endpoint URL and authentication
   • Database: Write a query to select batch records
   • MQTT: Subscribe to the batch data topic
   • File: Point to your JSON data file
3. Test the connection to verify data is retrieved successfully

Step 3: Add a Transform Node

1. Drag a Javascript Transform node onto the canvas
2. Connect the output of your Source to the Transform input
3. Double-click the Transform node to open the configuration panel
4. Click Script Editor to access the JavaScript editor

Step 4: Add the Pattern Analyzer Script

1. In the Script Editor, delete any existing code
2. Copy and paste the complete Fuuz Pattern Analyzer script (provided separately)
3. The script expects data in the $$ variable (full payload)

Script structure overview:

// The script contains these main components:

// 1. Parameter Knowledge Base - Maps tag prefixes to root causes
var parameterKnowledge = {
  "TT-": { name: "Temperature", highCauses: [...], lowCauses: [...] },
  "AT-001": { name: "pH", variabilityCauses: [...] },
  // ... additional parameters
};

// 2. Statistical Functions - Calculate mean, std dev, CV, z-scores
function calculateStats(values) { ... }

// 3. Golden Batch Identification - Scores batches by quality
function findGoldenBatch(batchStats) { ... }

// 4. Deviation Detection - Identifies out-of-spec conditions
function detectDeviations(batchData, globalStats) { ... }

// 5. Root Cause Mapping - Links deviations to probable causes
function mapRootCauses(deviation, paramInfo) { ... }

// 6. Main Analysis - Orchestrates the full analysis
return analyzeData($$);

Step 5: Customize Parameter Knowledge (Optional)

The default script includes knowledge for common bioreactor parameters. To add your own:

1. Locate the parameterKnowledge object near the top of the script
2. Add entries for your custom tag prefixes:

// Add custom parameter definitions
parameterKnowledge["WT-"] = {
  name: "Weight",
  highCauses: ["Scale drift", "Tare error", "Material buildup"],
  lowCauses: ["Incomplete fill", "Leak detected", "Calibration needed"],
  highActions: ["Verify scale calibration", "Check tare procedure"],
  lowActions: ["Inspect fill system", "Check for leaks"]
};

parameterKnowledge["LT-"] = {
  name: "Level",
  highCauses: ["Overfill condition", "Sensor fouling"],
  lowCauses: ["Low feed rate", "Drain valve issue"],
  variabilityCauses: ["Control loop oscillation", "Pump pulsation"]
};

Step 6: Configure Output Handling

1. Add an output node to handle the analysis results:
   • REST API: POST results to a quality management system
   • Database: Store results in a batch analysis table
   • Notification: Send alerts for OUT_OF_SPEC batches
   • Dashboard: Feed results to a real-time quality dashboard
2. Connect the Transform output to your chosen destination

Step 7: Test the Flow

1. Click Save to save your workflow
2. Click Button on Source to execute the workflow
3. Review the Transform output in the execution log
4. Verify the output contains:
   • summary - Record counts, batch counts, status breakdown
   • referenceBatch - The identified golden batch
   • batchRanking - All batches ranked by quality score
   • outOfSpecBatches - Detailed issues with root causes

4. Verification

Confirm your implementation is working correctly:

5. Example Output

Here is sample output from analyzing a 20-batch bioreactor dataset:

6. Troubleshooting

7. Next Steps

After implementing your Quality Batch Golden Record Analysis Tool:

• Build a dashboard to visualize batch rankings and quality trends over time
• Configure alerts to notify quality teams when OUT_OF_SPEC batches are detected
• Integrate with your QMS to automatically create deviation records
• Expand the knowledge base with process-specific root causes and corrective actions
• Schedule automated runs for continuous process verification (CPV)

8. Related Resources

• Fuuz Script Editor Reference Guide
• Transform Node Configuration
• Working with Data Connectors
• Biopharma Training Dataset Download
• Contact Support