Improving Sensitivity and Selectivity in Pesticide Analysis with GC–MS and GC–MS-MS
Spectroscopy E-Books
The selection of analytical methods for gas chromatography (GC)-amenable pesticides is often based on requirements for sensitivity and selectivity for regulatory needs or other monitoring requirements. Methods with both electron ionization (EI) and negative chemical ionization (NCI) are often required to cover the full range of GC–amenable pesticides at trace levels. Pesticides fragment easily in EI and CI sources such that the molecular ion is often low in abundance. NCI can provide added selectivity and sensitivity over EI methods. NCI is most commonly used in selected-ion monitoring mode. The lack of availability of parent ions for collision-induced dissociation for tandem mass spectrometry (MS) can limit the feasibility of GC–MS-MS for pesticides that significantly fragment in the ion source. Options for improving sensitivity by using of large-volume cold on column or programmable temperature vaporizer injections are presented. Read more
here
.
AI and Dual-Sensor Spectroscopy Supercharge Antibiotic Fermentation
June 30th 2025Researchers from Chinese universities have developed an AI-powered platform that combines near-infrared (NIR) and Raman spectroscopy for real-time monitoring and control of antibiotic production, boosting efficiency by over 30%.
Toward a Generalizable Model of Diffuse Reflectance in Particulate Systems
June 30th 2025This tutorial examines the modeling of diffuse reflectance (DR) in complex particulate samples, such as powders and granular solids. Traditional theoretical frameworks like empirical absorbance, Kubelka-Munk, radiative transfer theory (RTT), and the Hapke model are presented in standard and matrix notation where applicable. Their advantages and limitations are highlighted, particularly for heterogeneous particle size distributions and real-world variations in the optical properties of particulate samples. Hybrid and emerging computational strategies, including Monte Carlo methods, full-wave numerical solvers, and machine learning (ML) models, are evaluated for their potential to produce more generalizable prediction models.
