AIMC Topic: Spectroscopy, Fourier Transform Infrared
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Rapid discrimination of different primary processing Arabica coffee beans using FT-IR and machine learning.
Food research international (Ottawa, Ont.)
Feb 10, 2025
In this study, fourier transform infrared spectroscopy (FT-IR) analysis was combined with machine learning, while various analytical techniques such as colorimetry, low-field nuclear magnetic resonance spectroscopy, scanning electron microscope, two-...
Contaminant detection in flexible polypropylene packaging waste using hyperspectral imaging and machine learning.
Waste management (New York, N.Y.)
Feb 10, 2025
Flexible plastic packaging (FPP) constitutes one of the largest post-consumer plastic streams processed in recycling facilities. To address the key challenges of its sorting and quality control, this study developed and tested a classification proced...
Determination of quality differences and origin tracing of green tea from different latitudes based on TG-FTIR and machine learning.
Food research international (Ottawa, Ont.)
Jan 27, 2025
Latitude differences can significantly affect the quality of tea, while in-depth research in this field is lacking. This study investigates green teas from different latitudes in China using thermogravimetric analysis coupled with infrared spectrosco...
Machine Learning-Based Spectral Analyses for Cultivar Identification.
Molecules (Basel, Switzerland)
Jan 25, 2025
is a plant species with high cultural and biological relevance. Besides being used as an ornamental plant species, has relevant biological properties. Due to hybridization, thousands of cultivars are known, and their accurate identification is mand...
An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach.
Scientific reports
Jan 23, 2025
This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inferen...
Optimizing the production and efficacy of antimicrobial bioactive compounds from in combating multi-drug-resistant pathogens.
Frontiers in cellular and infection microbiology
Jan 6, 2025
BACKGROUND: The rise of antibiotic-resistant pathogens has intensified the search for novel antimicrobial agents. This study aimed to isolate from local soil samples and evaluate its antimicrobial properties, along with optimizing the production of ...
Bacillus subtilis
Escherichia coli
Spectroscopy, Fourier Transform Infrared
Streptomyces
Culture Media
RNA, Ribosomal, 16S
Microbial Sensitivity Tests
Anti-Bacterial Agents
Soil Microbiology
Candida albicans
Drug Resistance, Multiple, Bacterial
Anti-Infective Agents
Staphylococcus aureus
DNA, Bacterial
From Spectra to Signatures: Detecting Fentanyl in Human Nails with ATR-FTIR and Machine Learning.
Sensors (Basel, Switzerland)
Jan 3, 2025
Human nails have recently become a sample of interest for toxicological purposes. Multiple studies have proven the ability to detect various analytes within the keratin matrix of the nail. The analyte of interest in this study is fentanyl, a highly d...
Machine learning outperforms humans in microplastic characterization and reveals human labelling errors in FTIR data.
Journal of hazardous materials
Dec 31, 2024
Microplastics are ubiquitous and appear to be harmful, however, the full extent to which these inflict harm has not been fully elucidated. Analysing environmental sample data is challenging, as the complexity in real data makes both automated and man...
Differentiation of glioblastoma G4 and two types of meningiomas using FTIR spectra and machine learning.
Analytical biochemistry
Dec 27, 2024
Brain tumors are among the most dangerous, due to their location in the organ that governs all life processes. Moreover, the high differentiation of these poses a challenge in diagnostics. Therefore, this study focused on the chemical differentiation...
Photonic platform coupled with machine learning algorithms to detect pyrolysis products of crack cocaine in saliva: A proof-of-concept animal study.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Dec 18, 2024
The non-invasive detection of crack/cocaine and other bioactive compounds from its pyrolysis in saliva can provide an alternative for drug analysis in forensic toxicology. Therefore, a highly sensitive, fast, reagent-free, and sustainable approach wi...
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