NXT Spectra provides an AI‑driven platform that fully automates GC‑MS data analysis, translating raw mass spectra into molecular structures without manual peak‑picking or database limits. Its hybrid algorithms and virtual spectral libraries identify unknown compounds directly from EI spectra, processing samples in seconds and delivering ready‑to‑use reports on any PC via a browser interface. The solution integrates with standard GC‑MS instruments, enabling faster, more reliable non‑target and quality‑control workflows.
Funding
Funding not disclosed
Founders
Product
Problem
GC‑MS data analysis currently requires multiple manual steps such as peak picking, background subtraction, and noise reduction, demanding expert time and limiting throughput. Existing library‑based identification also fails for unknown compounds, restricting coverage and slowing research and quality‑control workflows.
Solution
NXT Spectra provides a browser‑based platform that automates the entire GC‑MS workflow, from raw data import to structured reporting, using chemistry‑driven AI. The system reads raw files from common GC‑MS instruments, applies automated peak detection, denoising, and background subtraction, and then predicts molecular structures directly from electron‑impact spectra, even when no reference library entry exists. Results are delivered in seconds, enabling higher identification coverage—up to 200 % more than traditional methods—while maintaining scientific precision. The platform requires no installation or training, allowing analysts to start immediately on any PC.
Target Audience
Primary users are analytical chemists and scientists in fragrance, fine chemicals, pharmaceuticals, and related industries who perform GC‑MS‑based identification, as well as quality‑control labs and research teams needing rapid, high‑coverage analysis of known and unknown compounds.
Features
- Direct structure prediction from EI‑mass spectra using AI models, bypassing database limitations
- Fully automated signal processing (peak picking, denoising, background subtraction) with no manual intervention
- Compatibility with raw data formats from all major GC‑MS instruments
- Instant web‑based reporting with reproducible, AI‑validated results
- Virtual spectral library generation to support non‑target and unknown compound identification
- Scalable workflow suitable for routine analysis, reaction monitoring, quality control, and competitive intelligence