Classification of Bacteria Using FT-IR
Thermo Fisher Scientific
Articles in this issue
Miniature Spectrometers: Preventive Maintenance and Calibration
High Throughput Metabolic Stability Screening by MALDI Triple Quadrupole Analysis
Determination of Absolute Configuration
Meeting Quality Control Requirements in the Routine Inorganic Laboratory
The Importance of Using Correct Sampling Procedures in Obtaining Representative and Meaningful Analysis Results
Classification of Bacteria Using FT-IR
Determination of Estradiol in Plasma with Negative Chemical Ionization GC-MS/MS on TSQ Quantum GC
RoHS/WEEE Application of a Miniature X-Ray Spectrometer
Mid-Infrared Transmission and Reflection Microcpectroscopy: Analysis of a Novel Biological Imaging System: The Snake Infrared-Imaging Pit Organ
Photovoltaic Device Characterization Using the UVISEL Spectroscopic Ellipsometer
FT-IR Spectroscopy Inside a Glovebox
Raman Analysis of Single-Walled Carbon Nanotube (SWCNT) Films
Ultrafast Confocal Raman Imaging
Eagle III Micro-XRF: Elemental Imaging Analysis
Matrix Effects in ICP-AES Analysis
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Blood-Glucose Testing: AI and FT-IR Claim Improved Accuracy to 98.8%
February 3rd 2025Article
A research team is claiming significantly enhanced accuracy of non-invasive blood-glucose testing by upgrading Fourier transform infrared spectroscopy (FT-IR) with multiple-reflections, quantum cascade lasers, two-dimensional correlation spectroscopy, and machine learning. The study, published in Spectrochimica Acta Part A, reports achieving a record-breaking 98.8% accuracy, surpassing previous benchmarks for non-invasive glucose detection.
