Smart biosensing ecosystems: Integrating nanotechnology, chemometrics, and machine learning for precision analytical applications
Year: 2026
Type: article
Abstract: The growing complexity of food, environmental, and biological matrices has intensified demand for analytical technologies that are rapid, selective, and deployable outside centralized laboratories, exposing the limitations of conventional chromatographic and single-analyte biosensing approaches. This review introduces the concept of a Smart Biosensing Ecosystem, a framework in which biorecognition elements (enzymes, antibodies, nucleic acids, aptamers, whole cells), functional nanomaterials, and electrochemical or optical transducers are integrated with chemometric preprocessing, machine-learning-based interpretation, IoT connectivity, and cloud-enabled decision support into a single analytical pipeline. Within this framework, the review examines smart biosensor architecture, biorecognition mechanisms, enzyme-based catalytic sensing, electrochemical and optical transduction physics, and a comparative evaluation of graphene, MXenes, carbon nanotubes, quantum dots, and metal-based nanomaterials for signal amplification and multiplexing. It further surveys chemometric tools (PCA, PLS, LDA) and machine-learning algorithms (SVM, Random Forest, ANN, CNN, gradient boosting, ensemble learning) for model development and validation, alongside wearable, multiplexed, and hybrid electrochemical–optical platforms applied to food-quality, environmental, and biomedical analysis. Persistent challenges, including biofouling, batch-to-batch reproducibility, scalable manufacturing, regulatory translation, explainable AI, and data governance, are critically discussed as barriers limiting laboratory-to-market translation. The review concludes that continued convergence of nanotechnology, intelligent analytics, and connected infrastructure is poised to transform biosensors from isolated transduction devices into autonomous, self-calibrating, and predictive analytical ecosystems with substantial translational potential across precision food, environmental, and biomedical monitoring.
Source: Next Materials
Author: Kushagra Sharma
Institution: Jawaharlal Nehru Krishi Vishwa Vidyalaya
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,280
OpenAlex ID: W7212013524