**5. Future Outlook**

In this review, we have discussed the various research approaches, nanomaterials, and methodologies in fluorescent-based detection methods used for food safety. Traditionally, fluorescence-, and image-based biosensors are used to detect contamination in food and water. Food and water are very complex matrices, which not only include several diet elements (proteins, lipids, sugars, etc.), but also consist of parts such as additives. It is important to mention that fluorphores have the challenge of the aggregation-caused quenching (ACQ) effect, which restricts their function in sensing. The development of biosensors for food safety and their in-field application deal with issues pertaining to pre-treatment of complex samples such as the development of biosensors for food safety and their in-field application deal with issues pertaining to pre-treatment of complex food sample and maintain sensitivity. Moreover, a lower concentration of bacterial contamination in food samples is also challenging for target sensitivity and detection limits. Although microarrays are effective and accurate signal-producing technology, they require technical expertise and are expensive. Therefore, microfluidics or lab-on-chip devices hold great potential due to automation, miniaturization, and portability, and their ability to produce fast signal readout. However, certain limitations due to blockage of microfluidic channels or non-specific adsorption cause problems in complex sample analysis. In this context, signal-amplification methodologies, along with deep-learning strategies, can improve foodsensing fluorescent biosensing. Regardless of the performance of fluorescence- and imagebased biosensors, they still have several challenges in real-world applications due to a high rate of false-negative or false-positive results and diet elements create autofluorescence and disrupt sensitivity and trigger false results. The nanomaterial-based fluorescent biosensors are able to address this problem. Although nano-biomaterials have benefits in operation, several parameters must be adjusted and need optimization. Extensive research, over several years, into sensing for food-safety purposes has shown that certain materials (e.g., graphene, metal nanoparticles) are usually preferred for fluorescence-sensing of food material. The advantage of using nanomaterials is the ability to achieve high signal intensity with selectivity. Nanomaterial-based biosensors have been successfully developed but suffer from constraints of stability, repeatability, and poor anti-interference ability. To overcome some major problems in fluorescence sensing, it is necessary to integrate and compare different methods to achieve optimum sensitivity. Chemometric, surfaceenhanced Raman scattering (SERS), electrochemical sensing can also be used along with fluorescence for multiplexed sensing with high sensitivity. To date, fluorescent systems are in the experimental stage and practical functions of nanomaterial-based fluorescent biosensors in food matrices continue to remain under investigation. By implementing artificial intelligence and microfluidic systems for fluorescence biosensors we may achieve the goal of developing low-cost and real-time recognition of contaminants in food matrices. Recent research has shown the possibility of achieving sensitive and precise detection of food contaminants using the smartphone by enabling artificial intelligence for signal analysis without the requirement for sophisticated equipment. This development opens the door to a stand-alone, point-of-detection device for fluorescence-based detection, showing the possibility of detection of food contaminants outside the laboratory.

**Author Contributions:** Conceptualization, K.K. and N.K.; methodology, S.K.; resources, P.G.; data curation, P.G.; writing—original draft preparation, S.K. and P.G.; writing—review and editing, K.K. and N.K.; visualization, S.K. and K.K.; supervision, K.K. and N.K.; funding acquisition, K.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** Krishna Kant acknowledges the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. (894227). Saloni Kakkar acknowledges the DST-INSPIRE fellowship for funding and CSIR-IMTECH for her carrying out PhD work. We also acknowledge University of Vigo, Spain and Graphic Era (Deemed to be University), Dehradun 248002, for providing facilities and computational resources.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We acknowledge University of Vigo, Spain, and Graphic Era (Deemed to be a University), Dehradun 248002, for providing facilities and computational resources.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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