SENTERRA II

Senterra II can also be configured with an inverted microscope without compromising any functionality. The inverted Raman microscope gives open access for sample handling, which is particularly favorable in characterization of live tissue and cells.

Besides cell research, the inverted microscope also brings advantages in researches like in vitro pharmaceutical verification and in situ Raman measurement that requests better access to the microscopic stage.

New Feature: The Autonomous Composition Identifier (A.I.D.) simplifies search and identification.

The Autonomous Composition Identifier (A.I.D.) is an AI-driven software tool for identifying the chemical composition of samples based on IR or Raman spectral data. It incorporates the previous O/SEARCH features and adds a multi-step algorithm to find the best match from reference spectral databases, whether dealing with pure substances or complex mixtures. A.I.D. intelligently evaluates and recommends optimal matches with a balance of reliability and sensitivity—automatically generating diagnostics and visual reports, including residual spectra and hit quality graphs. It delivers high-performance results in seconds and supports in-depth, interactive analysis of alternative identifications.

New Feature: Co-local Measurement – Unified Analysis Across IR and Raman

The Co-local Measurement function, part of the OPUS/OBJECT package, enables seamless analysis of the same region of interest on microscopic samples using both IR and Raman microscopy. It allows the transfer of overview images and measurement areas between LUMOS II, HYPERION II, and SENTERRA II instruments. This ensures that IR and Raman data are collected from precisely the same spot, enabling complementary chemical insights. Co-local Measurement bridges the gap between two techniques, improving data correlation and boosting confidence in results for heterogeneous or complex samples.

Updated Feature: OPUS/OBJECT now combines "Find Particle" and "Cluster ID"

The Particle Identification function (part of the OPUS/OBJECT package) combines the power of “Find Particles” and “Cluster ID” into one streamlined workflow. It detects particles within IR and Raman chemical images, determines their dimensions, and identifies their chemical makeup—all in one go. This integrated approach simplifies analysis and accelerates decision-making for complex samples.

Highlight: Python Script Interface implemented into OPUS

In OPUS 8.7 SP1, we have made it easier to use customized data processing and evaluation methods with our new Python environment within OPUS. It opens a door for the users who need special algorithms for the data. The python script can be executed in OPUS right after the Raman measurement. Make the post-processing truly yours and enjoy all the power you can bring to the routine measurements with Python.

Highlight: High Performance Chemical Image Generation by New Adaptive K-means Clustering Function

We also released a new Raman data analysis function Adaptive K-means Clustering. It was created with the help of the build-in Python environment. With this new function, you can generate a chemical imaging directly after the measurement without the need of selecting specific Raman peak at all.  The system will sort the spectra into groups automatically and generates Raman images all by itself. For the applications that use a known sample, for instance a pharmaceutical tablet with several APIs, this function can provide the user Raman image immediately and save a lot of time. For the experiments of unknown samples, with the automatic Raman imaging method, users can tell at a glance if the sample is homogeneous or structured chemically.

Other new functions availa on SENTERRA II:

• “Cluster ID” Function for Identification of Classes in 3D Spectral Data
• "Find Particles" function now contains a novel particle detection method
• SENTERRA II now operates under new OVP-X routine