Hyperspectral imaging maps chemistry across spaces to identify multiple pathogens on surfaces and in food, enabling non-invasive quality control.

Rapid bacterial detection is advancing with optical methods and AI, potentially shrinking culture timelines from weeks to minutes or hours.

Near-infrared spectroscopy offers deeper sample penetration at lower cost, but requires preprocessing and chemometric models for interpretation.

Digital inline holography and terahertz sensing are pushing toward lensless, field-deployable detection with potential single-cell resolution.

Artificial intelligence, including CNNs, transformers, YOLO, and transfer learning, is essential for interpreting high-dimensional spectral and imaging data, with cautions about data leakage and external validation.

Regulatory readiness and standardized datasets are highlighted as prerequisites for deployment, aiming for label-free, non-invasive, real-time pathogen detection to reduce contamination and guide antibiotic use.

The future is integrative: multimodal platforms that combine spectroscopy, imaging, and AI, miniaturized with LEDs and smartphone optics, validated across food safety and clinical contexts.

Fourier transform infrared spectroscopy provides chemical fingerprints to distinguish bacteria and strains, with portable instruments enabling on-site use.

Laser speckle imaging and time-lapse shadow imaging capture real-time growth dynamics and rapid colony detection without extensive sampling.

Raman spectroscopy and SERS enhance signal to identify pathogens, including single-cell classification and improved sensitivity with nanostructured substrates.