A hybrid TransCNN model integrates convolutional feature extraction with transformer-based global attention, enabling rapid and accurate detection of honey adulteration by LED-induced fluorescence.
Graphical abstract highlights the nutritional value of pulses and key adulteration types. It summarizes traditional and advanced detection methods, emphasizing computer vision and machine learning for rapid, non-destructive quality assessment.
This review summarizes ambient ionization mass spectrometry-enabled workflows for rapid, minimally prepared screening and confirmatory identification of illegal pharmaceutical adulterants in traditional Chinese medicine products.
This review discusses developments in elemental mass spectrometry, atomic absorption, emission and fluorescence, XRF and LIBS, as applied to the analysis of specimens of clinical interest, foods and beverages. Sample preparation procedures and quality assurance are also included.
Multiplexed colorimetric detection of milk.