On-site colorimetric food spoilage monitoring with smartphone embedded machine learning

Integrated deep eutectic system enrichment and AI-assisted high-throughput visual detection for Hg2+ in environmental samples

INTRODUCTION

Mercury ion (Hg2+), a prevalent heavy metal, is commonly found in environmental soils and waters. Its interaction with sulfhydryl groups in proteins and lipids can cause oxidative stress and disruption of calcium homeostasis. These lead to severe health issues, including digestive, nervous, and immune system damage. Conventional Hg2+ detection methods, such as ICP-MS and AAS, require complex procedures and bulky instruments, limiting their applicability for real-time, on-site analysis. Recently, AI-assisted detection methods have emerged as promising solutions, offering portability and rapid detection capabilities. Deep eutectic solvents (DESs), and in particularly hydrophobic DESs (HDESs), provide an environmentally friendly alternative for the enrichment and detection metal ions.

OBJECTIVES

This study aims to develop a portable, cost-effective, and environmentally friendly colorimetric sensing platform based on a silver nanoparticles hydrophobic deep eutectic system (AgNPs-HDES) for Hg2+ enrichment and detection.

METHODS

AgNPs-HDES was synthesized using silver nanoparticle-containing ethylene glycol (AgNPs-EG) as the hydrogen bond donor. Electrostatic potential maps (ESP) and density functional theory (DFT) were employed to elucidate its synthesis and enrichment mechanisms. Smartphone-based image acquisition combined with YOLOv8-based AI software enabled high-throughput colorimetric analysis for Hg2+ detection.

RESULTS

A progressive color change from brownish-yellow to colorless was observed with increasing Hg2+ concentration, thereby eliminating hydrophilic interference and improving sensitivity. The AgNPs-HDES platform demonstrated a linear detection range of 1-40 μmol·L-1 (R2 = 0.9889) and a detection limit of 0.23 μmol·L-1. Recovery rates in real samples, including lake water, soil, seawater and industrial sewage, ranged from 90.3% to 123%.

CONCLUSION

The established platform enables portable, rapid, and highly accurate Hg2+ detection across multiple environmental samples simultaneously. This AI-assisted, high-throughput detection system presents a valuable tool for environmental monitoring and pollutant tracking.

Smartphone-based detection and discrimination of amine vapors by a single dye-adsorbed material

Smartphone-assisted analysis has become widely utilized for detecting various species in recent years. In such studies, multiple dyes should be employed to ensure selectivity and analyte discrimination. In our research, we have demonstrated the capability of a specially synthesized dye to selectively detect and discriminate liquid amine vapors. The developed material employs meso-toluene-α,β,α',β'-tetrabromoBODIPY immobilized on a thin-layer chromatography plate, exhibiting structure-specific color changes in response to amine vapors. The hue values of these colors, observed under both ambient and UV light, enable discrimination even among closely related amine structures. A mobile application has also been developed for the rapid interpretation of test results.

Smart Gas Sensors: Recent Developments and Future Prospective

Gas sensor is an indispensable part of modern society with wide applications in environmental monitoring, healthcare, food industry, public safety, etc. With the development of sensor technology, wireless communication, smart monitoring terminal, cloud storage/computing technology, and artificial intelligence, smart gas sensors represent the future of gas sensing due to their merits of real-time multifunctional monitoring, early warning function, and intelligent and automated feature. Various electronic and optoelectronic gas sensors have been developed for high-performance smart gas analysis. With the development of smart terminals and the maturity of integrated technology, flexible and wearable gas sensors play an increasing role in gas analysis. This review highlights recent advances of smart gas sensors in diverse applications. The structural components and fundamental principles of electronic and optoelectronic gas sensors are described, and flexible and wearable gas sensor devices are highlighted. Moreover, sensor array with artificial intelligence algorithms and smart gas sensors in "Internet of Things" paradigm are introduced. Finally, the challenges and perspectives of smart gas sensors are discussed regarding the future need of gas sensors for smart city and healthy living.

From Food Industry 4.0 to Food Industry 5.0: Identifying technological enablers and potential future applications in the food sector

Although several food-related fields have yet to fully grasp the speed and breadth of the fourth industrial revolution (also known as Industry 4.0), growing literature from other sectors shows that Industry 5.0 (referring to the fifth industrial revolution) is already underway. Food Industry 4.0 has been characterized by the fusion of physical, digital, and biological advances in food science and technology, whereas future Food Industry 5.0 could be seen as a more holistic, multidisciplinary, and multidimensional approach. This review will focus on identifying potential enabling technologies of Industry 5.0 that could be harnessed to shape the future of food in the coming years. We will review the state-of-the-art studies on the use of innovative technologies in various food and agriculture applications over the last 5 years. In addition, opportunities and challenges will be highlighted, and future directions and conclusions will be drawn. Preliminary evidence suggests that Industry 5.0 is the outcome of an evolutionary process and not of a revolution, as is often claimed. Our results show that regenerative and/or conversational artificial intelligence, the Internet of Everything, miniaturized and nanosensors, 4D printing and beyond, cobots and advanced drones, edge computing, redactable blockchain, metaverse and immersive techniques, cyber-physical systems, digital twins, and sixth-generation wireless and beyond are likely to be among the main driving technologies of Food Industry 5.0. Although the framework, vision, and value of Industry 5.0 are becoming popular research topics in various academic and industrial fields, the agri-food sector has just started to embrace some aspects and dimensions of Industry 5.0.

Recent Progress in Nanomaterial-Based Surface-Enhanced Raman Spectroscopy for Food Safety Detection

Food safety has recently become a widespread concern among consumers. Surface-enhanced Raman scattering (SERS) is a rapidly developing novel spectroscopic analysis technique with high sensitivity, an ability to provide molecular fingerprint spectra, and resistance to photobleaching, offering broad application prospects in rapid trace detection. With the interdisciplinary development of nanomaterials and biotechnology, the detection performance of SERS biosensors has improved significantly. This review describes the advantages of nanomaterial-based SERS detection technology and SERS's latest applications in the detection of biological and chemical contaminants, the identification of foodborne pathogens, the authentication and quality control of food, and the safety assessment of food packaging materials. Finally, the challenges and prospects of constructing and applying nanomaterial-based SERS sensing platforms in the field of food safety detection are discussed with the aim of early detection and ultimate control of foodborne diseases.

Digitalization of Colorimetric Sensor Technologies for Food Safety

Colorimetric sensors play a crucial role in promoting on-site testing, enabling the detection and/or quantification of various analytes based on changes in color. These sensors offer several advantages, such as simplicity, cost-effectiveness, and visual readouts, making them suitable for a wide range of applications, including food safety and monitoring. A critical component in portable colorimetric sensors involves their integration with color models for effective analysis and interpretation of output signals. The most commonly used models include CIELAB (Commission Internationale de l'Eclairage), RGB (Red, Green, Blue), and HSV (Hue, Saturation, Value). This review outlines the use of color models via digitalization in sensing applications within the food safety and monitoring field. Additionally, challenges, future directions, and considerations are discussed, highlighting a significant gap in integrating a comparative analysis toward determining the color model that results in the highest sensor performance. The aim of this review is to underline the potential of this integration in mitigating the global impact of food spoilage and contamination on health and the economy, proposing a multidisciplinary approach to harness the full capabilities of colorimetric sensors in ensuring food safety.

Machine Learning-Assisted Janus Colorimetric Face Mask for Breath Ammonia Analysis

Artificial olfactory systems have been widely used in medical fields such as in the analysis of volatile organic compounds (VOCs) in human exhaled breath. However, there is still an urgent demand for a portable, accurate breath VOC analysis system for the healthcare industry. In this work, we proposed a Janus colorimetric face mask (JCFM) for the comfortable evaluation of breath ammonia levels by combining the machine learning K-nearest neighbor (K-NN) algorithm. Such a Janus fabric is designed for the unidirectional penetration of exhaled moisture, which can reduce stickiness and ensure facial dryness and comfort. Four different pH indicators on the colorimetric array serve as recognition elements that cross-react with ammonia, capturing the optical fingerprint information on breath ammonia by mimicking the sophisticated olfactory structure of mammals. The Euclidean distance (ED) is used to quantitatively describe the ammonia concentration between 1 ppm and 10 ppm, indicating that there is a linear relationship between the ammonia concentration and the ED response (R2 = 0.988). The K-NN algorithm based on RGB response features aids in the analysis of the target ammonia level and achieves a prediction accuracy of 96%. This study integrates colorimetry, Janus design, and machine learning to present a wearable and portable sensing system for breath ammonia analysis.