Blog
Rapid Detection and Quantification of Falsified Viagra
Peer-reviewed study: portable NIR and machine learning told authentic Viagra from falsified tablets, even through the blister, with no false positives.
How NIR Spectroscopy and Machine Learning are Transforming Material Analysis Across Industries
In today’s fast-paced industrial landscape, the demand for efficient and accurate material analysis is greater than ever.
Use of Portable NIR Technology for Drug analysis in Australia
The challenge of efficiently and accurately analyzing illicit drugs remains significant in Australia due to the high volume of drugs trafficked within the country.
Current Drug Situation in Europe 2024: An Insightful Analysis
The drug landscape in Europe is continually evolving, driven by the emergence of new substances, shifting usage patterns, and changing policies.
Revolutionizing Forensic Analysis with Ultra-Portable NIR Technology
In the fast-paced world of forensic science, the quest for rapid and reliable drug testing solutions has led to significant innovations.
How Portable NIR Technology is Revolutionizing Forensic Cannabis Analysis
In the swiftly evolving field of forensic science, the demand for portable and efficient solutions has grown exponentially, particularly in the realm of drug analysis.
The Impact of Portable NIR Devices in Forensic Drug Analysis
Facing the problem of backlogs in forensic laboratories, the field of illicit drugs analyses has recently seen the development of different types of portable devices.
Textile Quality Assurance: Practical Guide for NIR
The integration of NIRLAB’s portable NIR spectrometer heralds a significant leap in textile manufacturing.
Analyzing the NIR Spectroscopy Data for Substance Analysis
Step by step: what happens between the scan of a heroin sample and the result on screen, from absorption peaks to machine learning and purity estimate.
Sugar Cane Quality Control: A Comprehensive Guide
Sugar cane is a cornerstone of the global agricultural economy, thriving amid challenges such as environmental changes and market volatility.