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AI-Powered Chromatogram Analysis for Faster Laboratory Testing

How AI automated chromatogram review, reduced analysis time by 70%, and doubled laboratory sample throughput.

Client

Top US Testing Lab

Industry

Medical & Diagnostic Labs

Timeline

6 Months

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3 Min Read time

Quick Snapshot

Brief

Challenges

Solution

Impact

Meet Maya

3MIN

Read Time

Quick Snapshot

A U.S.-based testing laboratory struggled with time-consuming chromatogram analysis, manual corrections, and inconsistent first-level reviews. Technostacks developed an AI-powered chromatogram analysis solution trained on 5,000+ historical reviews, reducing review time by 70% and increasing sample throughput by 2× without additional staff.

Brief

In regulated testing environments, accuracy isn’t a luxury—it’s the baseline. 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.

Challenges

Operational Bottleneck

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. Clients were often waiting over 72 hours for test results due to backlog and analyst fatigue.

Matrix Interference Issues

Food and cannabis products often come with complex matrices (e.g., sugars, fats, flavourings) that interfere with accurate compound detection. Analysts frequently had to correct for enhancements, suppression, or co-eluted peaks manually.

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.

Solution

We trained an AI engine on 5000+ analyst-reviewed chromatograms and integrated it directly into the first-level review workflow. It resulted in :

Baseline
Corrections

Automatically smooths noisy baselines by learning real-world noise patterns.

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.

Impact

2x

Increase in Sample Throughput

Analysts who could earlier process 6–7 samples per day could now manage 11–12 with the same accuracy. The lab achieved a 2x improvement in daily output without hiring new staff.

70%

Time Savings per Sample

AI reduced the average review time from 1.5 hours to just 25 minutes, freeing up analysts for more value-added tasks.

30%

Reduction in Retesting

With consistent peak detection and reduced subjective errors, the need for retesting due to unclear results dropped significantly.

50%

Faster Turnaround, Happier Clients

The lab’s average report delivery timeline dropped from 4 days to under 2, improving client satisfaction and enabling faster go-to-market timelines for their customers.

Slash-turnaround-time-Graph

“AI model trained on 5,000+ historical reviews replicates your analysts’ judgment.”

Meet Maya

Before AI

Maya, one of the lab’s senior analysts, 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 would be exhausted, with more samples still waiting. With fatigue, her each subsequent review went slower and was more error-prone.

After AI

With our AI engine in place, Maya arrives to find 12 pre-reviewed chromatograms waiting in her queue. She only needs to validate the AI’s decisions—a quick 10 minute check per sample. She spends the rest of the day either clearing more complex 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.

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