SmartSpectrometer—Embedded Optical Spectroscopy
Multivariate data analysis is needed to integrate optical spectrometers as sensors. Therefore, a spectrometer with integrated artificial
Smart Spectroscopy Systems Expand Across Industries
A shared foundation unites these methods: Excite the sample and analyze how it interacts with light — whether through emission, scattering, or absorption —
AI-Powered Spectroscopy with Compact Spectrometers
Today''s AI-powered microspectrometers combine miniature optics, fast detector arrays, and edge compute to deliver automated preprocessing, robust
Integrating artificial intelligence with miniature mass spectrometry
In this review, we provide an overview of the recent advancements in the intelligence of miniature mass spectrometers, focusing on intelligent sample identification and AI methods that
Miniature integrated spectrometers towards high-performance and
This strategy provides guidance for the development of next-generation miniaturized integrated spectrometers. Fig. 1: Schematic of the conjugated-BIC photonic spectrometer operational...
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
Chip-scale sensor for spectroscopic metrology
Here, we present an integrated reconstructive spectrometer that enables near-infrared (NIR) spectroscopic metrology, and demonstrate a fully packaged sensor with auxiliary electronics.
An integrated single-shot spectrometer with large bandwidth-resolution
In this work, we present an integrated single-shot spectrometer relying on a silicon photonic circuit that has a footprint less than 3mm 2, but could achieve broad operation bandwidth
Using Artificial Intelligence to Drive MS Workflow Productivity
Learn how AI/automated intelligence can maximize your LC-MSn workflow performance for translational verification.
Enhanced PLS subspace-based calibration transfer method for
Comprehensive evaluation of proposed methods was conducted on the wheat dataset with 121 available standardization samples by comparing the capabilities of traditional methods
Spectroscopy-centric multimodal fusion (SCMF): Algorithmic strategies
It also provides a critical assessment of the applicability and inherent limitations of related methods, with particular emphasis on the potentially pivotal role of large language model–driven fusion between
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