SmartSpectrometer—Embedded Optical Spectroscopy for
Multivariate data analysis is needed to integrate optical spectrometers as sensors. Therefore, a spectrometer with integrated artificial intelligence (AI) called SmartSpectrometer and its
AI-Powered Spectroscopy with Compact Spectrometers
AI-powered spectroscopy using compact spectrometers is the subject of this blog article. Compact spectrometers have already made spectroscopy portable and affordable; layering on
Smart Spectroscopy Systems Expand Across Industries
“The modern spectroscopy workstation is no longer just a collection of instruments — it is a connected, intelligent system designed to deliver faster, more accurate
Recent Research in Chemometrics and AI for Spectroscopy, Part II
AI and chemometrics are transforming spectroscopy into an intelligent analytical system, enhancing accuracy and interpretability across diverse applications. Innovations in explainable AI,
Artificial Intelligence in Spectroscopy: Advancing Chemistry from
Modern spectroscopic techniques (MS, NMR, IR, Raman, UV-Vis) generate an ever-growing volume of high-dimensional data, creating a pressing need for automated and intelligent
AI-powered spectrometer chip shrinks lab technology to the size of a
By combining smart silicon sensors with machine learning, it achieves lab-style spectral analysis without the bulky equipment.
Explainable artificial intelligence for spectroscopy data: a review
AI models have been developed in medicine to assist with clinical decision-making, disease diagnosis, and patient data management. One modality often used in the medical domain is
SmartSpectrometer
The modular and intelligent spectrometer system "SmartSpectrometer" developed by Fraunhofer IOSB enables the monitoring of quality-relevant material parameters in real time. Individual solutions for
Checking the quality of materials just got easier with a
SpectroGen, a new AI tool, serves as a virtual spectrometer for assessing a new material''s quality, offering a faster and cheaper option for
AI-driven ultrafast spectrometer-on-a-chip advances real-time sensing
For decades, the ability to visualize the chemical composition of materials, whether for diagnosing a disease, assessing food quality, or analyzing pollution, depended on large, expensive...
AI-driven ultrafast spectrometer-on-a-chip advances real-time sensing
While standard, bulky spectrometers lose sensitivity in the near infrared (top), this new miniature chip uses specialized surface textures and AI to detect signals that are normally invisible
SmartSpectrometer
Cost-effective modular quality and process control: The intelligent spectrometer system "SmartSpectrometer" enables the monitoring of quality-relevant material parameters in real time.
Miniature computational spectrometer with a plasmonic nanoparticles
Conventional spectrometers can be bulky and efforts are being made to develop miniaturised versions. Here, the authors present a miniature computational spectrometer based on
Real-time machine learning–enhanced hyperspectro
A metasurface makes regular cameras “super,” capturing hyperspectro-polarimetric data in real time via a neural network.
Integrating artificial intelligence with miniature mass spectrometry
In this review, we first introduce how AI methods can achieve performance enhancement of miniature mass spectrometers, and then describe the realization of intelligent analysis of miniature
A plasmonic "rainbow" chip for intelligent spectrometer
Here we report a plasmonic "rainbow" chip for spectroscopic sensing. By analyzing a single image with deep neural network, this image-based system can precisely determine the
Imaging-based intelligent spectrometer on a plasmonic rainbow chip
Here we report an intelligent on-chip spectrometer by integrating an on-chip rainbow trapping phenomenon with a compact optical imaging system.
A platform for integrated spectrometers based on solution
Performance of the integrated spectrometers Given the performance of the ultra-narrowband photodiodes, integrated spectrometers based on the photodiode arrays are demonstrated.
This reference is intended for preliminary fiber and communications infrastructure research. Optical interface, fiber type, port or core capacity, electrical input and output, enclosure protection, cooling, installation limits and applicable standards must be verified for the specific project.