Accelerating Chromatographic Reviews with AI-Driven Automation

In regulated food and cannabis testing environments, accuracy is everything. However, ensuring precision comes at the cost of time. In this case, a lab faced a familiar bottleneck: analysts were spending close to 1.5 hours per sample reviewing chromatograms manually—a tedious process that involved identifying peaks, correcting noise, adjusting for co-elutions, and ensuring every tiny anomaly was accounted for. These reviews were then passed on to a second-level reviewer, further stretching the turnaround.

With AI stepping into the workflow, things changed dramatically. Built using historical review data, our AI model learned how analysts performed corrections and began replicating those decisions. It handled the repetitive, error-prone first-level review in minutes, not hours.

1. Heavy Manual Workload on Analysts
   Each sample required 60–90 minutes of review time. This meant a single analyst could only process about 6 samples in an 8-hour shift, significantly limiting the lab’s testing capacity. More time spent reviewing meant fewer samples tested per day.
2. Matrix Interference Issues
   Food and cannabis products often come with complex matrices (e.g., sugars, fats, flavorings) that interfere with accurate compound detection. Analysts frequently had to correct for enhancements, suppression, or co-eluted peaks manually.
3. Inconsistent Review Quality
   Different analysts applied slightly different criteria when correcting chromatograms. This led to inconsistencies in reporting and flagged several unnecessary outliers for second-level review.
4. Delayed Turnaround Times
   Clients were often waiting over 72 hours for test results due to backlog and analyst fatigue. The slow pace directly impacted client satisfaction and revenue.

Our AI solution was trained on thousands of manually reviewed chromatograms and lab SOPs to automate:

• Baseline Corrections
  Automatically smoothed noisy baselines based on learned patterns from real analyst behavior.
• Retention Time Adjustments
  Adjusted for small RT shifts due to instrument variability, ensuring peaks appeared at expected locations.
• Matrix Interference Corrections
  Recognized common suppression/enhancement patterns from sample types like gummies or beverages and auto-corrected them.
• Peak Integration and Separation
  Identified overlapping peaks and accurately split them where needed to reflect the real compound presence.

Imagine a lab technician named Maya. Before AI, Maya could review around 6 cannabis gummy samples in a full day. Each required her to manually inspect noisy chromatograms, flag overlapping peaks, and struggle with matrix interferences caused by sugar and flavoring agents. By 4 PM, she was exhausted, and samples were still waiting.

Now, with AI in the loop, Maya logs in, and the system has already pre-reviewed 12 chromatograms before lunch. She only needs to validate the AI’s decisions—a quick 5-10 minute check per sample. She spends the rest of the day either clearing more samples or assisting junior analysts.

For the lab, this means double the output without doubling the headcount. The same instruments. The same team. Just smarter workflows.

By automating the first-level chromatogram review with AI, this testing lab transformed its productivity without compromising on precision. Analysts now focus on validation rather than grunt work, and clients receive results faster than ever before. For labs looking to scale while maintaining quality, AI isn’t just a tool—it’s a game-changer.

Ready to see how your lab can double its throughput without increasing your team? 

Let’s talk.