Blog
Understanding Regression: Theory to Real-World Use
Regression models turn an NIR spectrum into a number: fat in olives, moisture in meat, API content in a tablet. PCR, PLS, SVR and neural networks explained.
Understanding Classification Problems: Theory to Real-World applications with NIRLAB
Classification models sort NIR spectra into categories: genuine or counterfeit, THC or CBD cannabis, PA6 or PA66. How the algorithms work, with real cases.
Portable vs. Benchtop NIR – Which One Will Lead the Future of Spectroscopy?
In the world of Near-Infrared spectroscopy, the debate between handheld and benchtop devices has been ongoing for years.
Sustainable Farming with NIRLAB’s Real-Time Analysis
What portable NIR sensors measure for farmers: moisture and NPK in soil and crop tissue, protein, fiber and fat in feed, with results in the field.
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.
Textile Quality Assurance: Practical Guide for NIR
The integration of NIRLAB’s portable NIR spectrometer heralds a significant leap in textile manufacturing.
Molecular Vibrations with NIR: A Detailed Exploration
Near-Infrared Spectroscopy (NIR) is a fascinating and versatile analytical technique widely used in various scientific and industrial fields.
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.
Navigating the Waters: Decoding the Intricacies of Water in NIR Spectroscopy
In today’s fast-paced world of scientific innovation, Near-Infrared (NIR) Spectroscopy has established itself as a vital tool across various sectors.