Machine learning-enabled non-destructive paper chromogenic array detection of multiplexed viable pathogens on food

Artificial intelligence-driven quantification of antibiotic-resistant Bacteria in food by color-encoded multiplex hydrogel digital LAMP

Antibiotic-resistant bacteria pose considerable risks to global health, particularly through transmission in the food chain. Herein, we developed the artificial intelligence-driven quantification of antibiotic-resistant bacteria in food using a color-encoded multiplex hydrogel digital loop-mediated isothermal amplification (LAMP) system. The quenching of unincorporated amplification signal reporters (QUASR) was first introduced in multiplex digital LAMP. During amplification, primers labeled with different fluorophores were integrated into amplicons, generating color-specific fluorescent spots. While excess primers were quenched by complementary quenching probes. After amplification, fluorescent spots in red, green, and blue emerged in hydrogels, which were automatically identified and quantified using a deep learning model. Methicillin-resistant Staphylococcus aureus and carbapenem-resistant Escherichia coli in real fruit and vegetable samples were also successfully detected. This artificial intelligence-driven color-encoded multiplex hydrogel LAMP offers promising potential for the digital quantification of antibiotic-resistant bacteria in the food industry.

AI-based processing of future prepared foods: Progress and prospects

The prepared foods sector has grown rapidly in recent years, driven by the fast pace of modern living and increasing consumer demand for convenience. Prepared foods are taking an increasingly important role in the modern catering industry due to their ease of storage, transportation, and operation. However, their processing faces several challenges, including labor shortages, inefficient sorting, inadequate cleaning, unsafe cutting processes, and a lack of industry standards. The development of artificial intelligence (AI) will change the processing of prepared foods. This review summarizes the progress and prospects of AI applications in the sorting/classification, cleaning, cutting, preprocessing, and freezing of prepared foods, encompassing techniques such as mathematical modeling, chemometrics, machine learning, fuzzy logic, and adaptive neuro fuzzy inference system. For example, AI-powered sorting systems using computer vision have improved accuracy in ingredient classification, while deep learning models in cleaning processes have enhanced microbial contamination detection with high spectral imaging techniques. Despite challenges like managing large-scale data and complex models, AI has shown significant potential to inspire both industry practice and research. AI applications can enhance the efficiency, accuracy, and consistency of prepared foods processing, while also reducing labor costs, improving hygiene monitoring, minimizing resource waste, and decreasing environmental impact. Furthermore, AI-driven resource optimization has demonstrated its potential in reducing energy consumption and promoting sustainable food production practices. In the future, AI technology is expected to further improve model generalization and operation precision, driving the food processing industry toward smarter, more sustainable development. This study provides valuable insights to encourage further innovation in AI applications within food processing and technological advancement in the food industry.

Sensitivity-enhanced hydrogel digital RT-LAMP with in situ enrichment and interfacial reaction for norovirus quantification in food and water

Low levels of human norovirus (HuNoV) in food and environment present challenges for nucleic acid detection. This study reported an evaporation-enhanced hydrogel digital reverse transcription loop-mediated isothermal amplification (HD RT-LAMP) with interfacial enzymatic reaction for sensitive HuNoV quantification in food and water. By drying samples on a chamber array chip, HuNoV particles were enriched in situ. The interfacial amplification of nucleic acid at the hydrogel-chip interface was triggered after coating HD RT-LAMP system. Nanoconfined spaces in hydrogels provided a simple and rapid "digital format" to quantify single virus within 15 min. Through in situ evaporation for enrichment, the sensitivity level was increased by 20 times. The universality of the sensitivity-enhanced assay was also verified using other bacteria and virus. Furthermore, a deep learning model and smartphone app were developed for automatic amplicon analysis. Multiple actual samples, including 3 lake waters, strawberry, tap water and drinking water, were in situ enriched and detected for norovirus quantification using the chamber arrays. Therefore, the sensitivity-enhanced HD RT-LAMP is an efficient assay for testing biological hazards in food safety monitoring and environmental surveillance.

A xylenol orange-based pH-sensitive sensor array for identification of bacteria and differentiation of probiotic drinks

The development of straightforward and cost-efficient methods for bacterial identification is very important. In this study, we utilized xylenol orange, metal ions, and diverse bacterial carbon sources to construct a sensor array, achieving precise bacterial identification. Initially, we examined the absorbance variations of xylenol orange with five metal ions (Mn2+, Co2+, Ni2+, Cu2+, and Zn2+) at pH levels ranging from 4 to 7, observing significant changes at 570 nm or 580 nm. Given that bacteria can generate varying amounts of acid in different carbon sources, we employed a blend of xylenol orange and the five metal ions as pH-sensitive probes to characterize bacterial metabolism in three carbon sources, resulting in the development of a five-sensor array that effectively differentiated seven bacteria. Additionally, by utilizing xylenol orange with Co2+, we successfully identified six different bacterial mixtures and five types of probiotic drink products. These findings highlight the potential of our methods for broader practical application in bacterial identification in practical use.

An albumin fluorescent sensor array discriminates ochratoxins

A sensor array that can distinguish ochratoxins based on the fluorescence of the albumin-ochratoxin complex has been developed. This sensor array enabled the identification of ochratoxins and their mixtures in real food samples.

Unsupervised Learning-Assisted Acoustic-Driven Nano-Lens Holography for the Ultrasensitive and Amplification-Free Detection of Viable Bacteria

Bacterial infection is a crucial factor resulting in public health issues worldwide, often triggering epidemics and even fatalities. The accurate, rapid, and convenient detection of viable bacteria is an effective method for reducing infections and illness outbreaks. Here, an unsupervised learning-assisted and surface acoustic wave-interdigital transducer-driven nano-lens holography biosensing platform is developed for the ultrasensitive and amplification-free detection of viable bacteria. The monitoring device integrated with the nano-lens effect can achieve the holographic imaging of polystyrene microsphere probes in an ultra-wide field of view (∽28.28 mm2), with a sensitivity limit of as low as 99 nm. A lightweight unsupervised learning hologram processing algorithm considerably reduces training time and computing hardware requirements, without requiring datasets with manual labels. By combining phage-mediated viable bacterial DNA extraction and enhanced CRISPR-Cas12a systems, this strategy successfully achieves the ultrasensitive detection of viable Salmonella in various real samples, demonstrating enhanced accuracy validated with the qPCR benchmark method. This approach has a low cost (∽$500) and is rapid (∽1 h) and highly sensitive (∽38 CFU mL-1), allowing for the amplification-free detection of viable bacteria and emerging as a powerful tool for food safety inspection and clinical diagnosis.

Drop-on-demand printing of amine-responsive fluorescence-ratiometric sensor array for online monitoring meat freshness

Aiming to enable online freshness-monitoring of meat within modified-atmosphere package, we developed a ratiometric array that was fluorescently responsive to volatile organic compounds-ammonia (NH3) released by protein decaying. The array was consisted of two 3 mm × 6 mm rectangles precisely and uniformly printed with fluorescein isothiocyanate (FITC) as indicator and rhodamine B (RhB) as internal reference on the filter-paper, respectively. The fluorescence intensity of the array area was calibrated according to Green/Red ratio of the digitalized pixels extracted from images facilitated by a smartphone. The fluorescence-ratiometric sensor array displayed remarkable detection performances, including high sensitivity (LOD = 1.1 ppm), stability (91 % responding attenuation over 10 d of storage) and reproducibility (RSD < 10 %), which was further validated with real pork and shrimp samples. Subsequently, the fluorescent signals of the dual-rectangle array showed high correlation to the total volatile base nitrogen value that was officially used for indexing the meat freshness status.

Utilization of AI - reshaping the future of food safety, agriculture and food security - a critical review

Artificial intelligence is an emerging technology which harbors a suite of mechanisms that have the potential to be leveraged for reaping value across multiple domains. Lately, there is an increased interest in embracing applications associated with Artificial Intelligence to positively contribute to food safety. These applications such as machine learning, computer vision, predictive analytics algorithms, sensor networks, robotic inspection systems, and supply chain optimization tools have been established to contribute to several domains of food safety such as early warning of outbreaks, risk prediction, detection and identification of food associated pathogens. Simultaneously, the ambition toward establishing a sustainable food system has motivated the adoption of cutting-edge technologies such as Artificial Intelligence to strengthen food security. Given the myriad challenges confronting stakeholders in their endeavors to safeguard food security, Artificial Intelligence emerges as a promising tool capable of crafting holistic management strategies for food security. This entails maximizing crop yields, mitigating losses, and trimming operational expenses. AI models present notable benefits in efficiency, precision, uniformity, automation, pattern identification, accessibility, and scalability for food security endeavors. The escalation in the global trend for adopting alternative protein sources such as edible insects and microalgae as a sustainable food source reflects a growing recognition of the need for sustainable and resilient food systems to address the challenges of population growth, environmental degradation, and food insecurity. Artificial Intelligence offers a range of capabilities to enhance food safety in the production and consumption of alternative proteins like microalgae and edible insects, contributing to a sustainable and secure food system.

Polyacrylic Acid-Reinforced gelatin hydrogels with enhanced mechanical properties, temperature-responsiveness and antimicrobial activity for smart encryption and salmon freshness monitoring

Hydrogels hold great potential for use in intelligent packaging, yet they often suffer from limited functionality and inadequate mechanical strength when applied to anticounterfeiting and freshness monitoring. In this study, we present a straightforward method to create a multifunctional hydrogel by in-situ polymerizing acrylic acid (PAA) within a gelatin-Al3+ system. The resulting hydrogels exhibited an elongation at break of over 1200 %, a tensile stress of 1.20 MPa, and impressive toughness reaching 5.15 MJ/m3, significantly outperforming traditional gelatin-based hydrogels that typically achieve less than 800 % strain and below 1 MPa stress. These hydrogels also showed exceptional antifatigue and tear resistance, with a tearing energy of 5200 J/m2, greatly exceeding the 1000 J/m2 standard of typical double network hydrogels, and were capable of supporting weights 1560 times their own mass. The strong hydrogen bonding between the -COOH groups of PAA and the -NH2 groups of gelatins contributed to an upper critical solution temperature above 40°C, with adaptable PAA content allowing for anticounterfeiting applications. The hydrogel could encode information such as self-erasing numbers, QR codes, and ASCII binary codes, changing its encoded data with temperature shifts and erasing at room temperature to enhance data security. Additionally, it exhibited potent antibacterial properties against S. aureus and E. coli, immobilized anthocyanin as an ammonia-responsive indicator, and accurately tracked salmon spoilage by correlating color changes with total volatile basic nitrogen content. These characteristics make the hydrogel highly suitable for smart packaging applications within the food industry.

Optical detection and enumeration of Escherichia coli and Salmonella enterica using a low-magnification light microscope

A rapid and cost-effective method for detecting bacterial cells from surfaces is critical to food safety, clinical hygiene, and pharmacy quality. Herein, we established an optical detection method based on a gold chip coating with 3-mercaptophenylboronic acid (3-MPBA) to capture bacterial cells, which allows for the detection and quantification of bacterial cells with a standard light microscope under low-magnification (10×) objective lens. Then, integrate the developed optical detection method with swab sampling to detect bacterial cells loading on stainless-steel surfaces. Using Salmonella enterica (SE1045) and Escherichia coli (E. coli OP50) as model bacterial cells, we achieved a capture efficiency of up to 76.0 ± 2.0 % for SE1045 cells and 81.1 ± 3.3 % for E. coli OP50 cells at 103 CFU/mL upon the optimized conditions, which slightly decreased with the increasing bacterial concentrations. Our assay showed good linear relationships between the concentrations of bacterial cells with the cell counting in images in the range of 103 -107 CFU/mL for SE1045, and 103 -108 CFU/mL for E. coli OP50 cells. The limit of detection (LOD) was 103 CFU/mL for both SE1045 and E. coli OP50 cells. A further increase in sensitivity in detecting E. coli OP50 cells was achieved through a heat treatment, enabling the LOD to be reduced as low as 102 CFU/mL. Furthermore, a preliminary application succeeded in assessing bacterial contamination on stainless-steel surfaces following integration with the approximately 40 % recovery rate, suggesting prospects for evaluating the bacteria from surfaces. The entire process was completed within around 2 h, costing merely a few dollars per sample. Considering the low cost of standard light microscopes, our method holds significant potential for practical industrial applications in bacterial contamination control on surfaces, especially in low-resource settings.

Space-Confined Amplification for In Situ Imaging of Single Nucleic Acid and Single Pathogen on Biological Samples

Direct in situ imaging of nucleic acids on biological samples is advantageous for rapid analysis without DNA extraction. However, traditional nucleic acid amplification in aqueous solutions tends to lose spatial information because of the high mobility of molecules. Similar to a cellular matrix, hydrogels with biomimetic 3D nanoconfined spaces can limit the free diffusion of nucleic acids, thereby allowing for ultrafast in situ enzymatic reactions. In this study, hydrogel-based in situ space-confined interfacial amplification (iSCIA) is developed for direct imaging of single nucleic acid and single pathogen on biological samples without formaldehyde fixation. With a polyethylene glycol hydrogel coating, nucleic acids on the sample are nanoconfined with restricted movement, while in situ amplification can be successfully performed. As a result, the nucleic acids are lighted-up on the large-scale surface in 20 min, with a detection limit as low as 1 copy/10 cm2. Multiplex imaging with a deep learning model is also established to automatically analyze multiple targets. Furthermore, the iSCIA imaging of pathogens on plant leaves and food is successfully used to monitor plant health and food safety. The proposed technique, a rapid and flexible system for in situ imaging, has great potential for food, environmental, and clinical applications.

Array-optimized artificial olfactory sensor enabling cost-effective and non-destructive detection of mycotoxin-contaminated maize

The MobileNetV3-based improved sine-cosine algorithm (ISCA-MobileNetV3) was combined with an artificial olfactory sensor (AOS) to address the redundancy in olfactory arrays, thereby achieving low-cost and high-precision detection of mycotoxin-contaminated maize. Specifically, volatile organic compounds of maize interacted with unoptimized AOS containing eight porphyrins and eight dye-attached nanocomposites to obtain the scent fingerprints for constructing the initial data set. The optimal decision model was MobileNetV3, with more than 98.5% classification accuracy, and its output training loss would be input into the optimizer ISCA. Remarkably, the number of olfactory arrays was reduced from 16 to 6 by ISCA-MobileNetV3 with about a 1% decrease in classification accuracy. Additionally, the developed system showed that each online evaluation was less than one second on average, demonstrating outstanding real-time performance for ensuring food safety. Therefore, AOS combined with ISCA-MobileNetV3 will encourage the development of an affordable and on-site platform for maize quality detection.

Microgreens Production: Exploiting Environmental and Cultural Factors for Enhanced Agronomical Benefits

An exponential growth in global population is expected to reach nine billion by 2050, demanding a 70% increase in agriculture productivity, thus illustrating the impact of global crop production on the environment and the importance of achieving greater agricultural yields. Globally, the variety of high-quality microgreens is increasing through indoor farming at both small and large scales. The major concept of Controlled Environment Agriculture (CEA) seeks to provide an alternative to traditional agricultural cultivation. Microgreens have become popular in the twenty-first century as a food in the salad category that can fulfil some nutrient requirements. Microgreens are young seedlings that offer a wide spectrum of colours, flavours, and textures, and are characterised as a "functional food" due to their nutraceutical properties. Extensive research has shown that the nutrient profile of microgreens can be desirably tailored by preharvest cultivation and postharvest practices. This study provides new insight into two major categories, (i) environmental and (ii) cultural, responsible for microgreens' growth and aims to explore the various agronomical factors involved in microgreens production. In addition, the review summarises recent studies that show these factors have a significant influence on microgreens development and nutritional composition.

Smart windows based on ultraviolet-B persistent luminescence phosphors for bacterial inhibition and food preservation

Herein, ultraviolet B (UVB) persistent luminescence phosphors containing SrAl12O19: Ce3+, Sc3+ nanoparticles were reported. Thermoluminescence (TL) spectrum analysis reveals that the shallow trap induced by Sc3+ co-doping plays an important role in photoluminescence persistent luminescence (PersL) development, while the deep trap dominates the generation of optical stimulated luminescence (OSL). Owing the appearance of deep trap, the OSL is observed under light (700 nm - 900 nm) excitation. UVB luminescence exerts good bactericidal effects on pathogenic bacteria involved in the process of food spoilage. Thus, the smart window with SrAl12O19: Ce3+, Sc3+/PDMS produces UVB PersL to efficiently inactivate Escherichia coli and Staphylococcus aureus. In addition, the presence of the smart window delays the critical point of pork decay, and greatly reduces the time of pork spoilage. It maximizes the convenience of eradicating bacteria and preserving food, thus offering a fresh perspective on the use of UV light for food sterilization and preservation.

Deep Learning Used with a Colorimetric Sensor Array to Detect Indole for Nondestructive Monitoring of Shrimp Freshness

Intelligent colorimetric freshness indicator is a low-cost way to intuitively monitor the freshness of fresh food. A colorimetric strip sensor array was prepared by p-dimethylaminocinnamaldehyde (PDL)-doped poly(vinyl alcohol) (PVA) and chitosan (Chit) for the quantitative analysis of indole, which is an indicator of shrimp freshness. As a result of indole simulation, the array strip turned from faint yellow to pink or mulberry color with the increasing indole concentration, like a progress bar. The indicator film exhibited excellent permeability, mechanical and thermal stability, and color responsiveness to indole, which was attributed to the interactions between PDL and Chit/PVA. Furthermore, the colorimetric strip sensor array provided a good relationship between the indole concentration and the color intensity within a range of 50-350 ppb. The pathogens and spoilage bacteria of shrimp possessed the ability to produce indole, which caused the color changes of the strip sensor array. In the shrimp freshness monitoring experiment, the color-changing progress of the strip sensor array was in agreement with the simulation and could distinguish the shrimp freshness levels. The image classification system based on deep learning were developed, the accuracies of four DCNN algorithms are above 90%, with VGG16 achieving the highest accuracy at 97.89%. Consequently, a "progress bar" strip sensor array has the potential to realize nondestructive, more precise, and commercially available food freshness monitoring using simple visual inspection and intelligent equipment identification.

Machine learning-enabled colorimetric sensors for foodborne pathogen detection

In the past decade, there have been various advancements to colorimetric sensors to improve their potential applications in food and agriculture. One application of growing interest is sensing foodborne pathogens. There are unique considerations for sensing in the food industry, including food sample destruction, specificity amidst a complex food matrix, and high sensitivity requirements. Incorporating novel technology, such as nanotechnology, microfluidics, and smartphone app development, into colorimetric sensing methodology can enhance sensor performance. Nonetheless, there remain challenges to integrating sensors with existing food safety infrastructure. Recently, increasingly advanced machine learning techniques have been employed to facilitate nondestructive, multiplex detection for feasible assimilation of sensors into the food industry. With its ability to analyze and make predictions from highly complex data, machine learning holds potential for advanced yet practical colorimetric sensing of foodborne pathogens. This article summarizes recent developments and hurdles of machine learning-enabled colorimetric foodborne pathogen sensing. These advancements underscore the potential of interdisciplinary, cutting-edge technology in providing safer and more efficient food systems.

Food-borne bacteria analysis using a diatomite bioinspired SERS platform

Rapid and sensitive detection of food-borne bacteria has remained challenging over the past few decades. We propose a surface-enhanced Raman scattering sensing strategy based on a novel bioinspired surface-enhanced Raman scattering substrate, which can directly detect dye molecular residues and food-borne pathogen microorganisms in the environment. The surface-enhanced Raman scattering platform consists of a natural diatomite microporous array decorated with a metal-phenolic network that enables the in situ reduction of gold nanoparticles. The as-prepared nanocomposites display excellent surface-enhanced Raman scattering activity with the lowest limit of detection and the maximum Raman enhancement factor of dye molecules up to 10-11 M and 1.18 × 107, respectively. For food-borne bacterial detection, a diatomite microporous array decorated with a metal polyphenol network and gold nanoparticle-based surface-enhanced Raman scattering analysis is capable of distinguishing the biochemical fingerprint information of Staphylococcus aureus and Escherichia coli, indicating the great potential for strain identification.

Ensuring food safety by artificial intelligence-enhanced nanosensor arrays

Current analytical methods utilized for food safety inspection requires improvement in terms of their cost-efficiency, speed of detection, and ease of use. Sensor array technology has emerged as a food safety assessment method that applies multiple cross-reactive sensors to identify specific targets via pattern recognition. When the sensor arrays are fabricated with nanomaterials, the binding affinity of analytes to the sensors and the response of sensor arrays can be remarkably enhanced, thereby making the detection process more rapid, sensitive, and accurate. Data analysis is vital in converting the signals from sensor arrays into meaningful information regarding the analytes. As the sensor arrays can generate complex, high-dimensional data in response to analytes, they require the use of machine learning algorithms to reduce the dimensionality of the data to gain more reliable outcomes. Moreover, the advances in handheld smart devices have made it easier to read and analyze the sensor array signals, with the advantages of convenience, portability, and efficiency. While facing some challenges, the integration of artificial intelligence with nanosensor arrays holds promise for enhancing food safety monitoring.

Microfluidic paper analytic device (μPAD) technology for food safety applications

Foodborne pathogens, food adulterants, allergens, and toxic chemicals in food can cause major health hazards to humans and animals. Stringent quality control measures at all stages of food processing are required to ensure food safety. There is, therefore, a global need for affordable, reliable, and rapid tests that can be conducted at different process steps and processing sites, spanning the range from the sourcing of food to the end-product acquired by the consumer. Current laboratory-based food quality control tests are well established, but many are not suitable for rapid on-site investigations and are costly. Microfluidic paper analytical devices (μPADs) are a fast-growing field in medical diagnostics that can fill these gaps. In this review, we describe the latest developments in the applications of microfluidic paper analytic device (μPAD) technology in the food safety sector. State-of-the-art μPAD designs and fabrication methods, microfluidic assay principles, and various types of μPAD devices with food-specific applications are discussed. We have identified the prominent research and development trends and future directions for maximizing the value of microfluidic technology in the food sector and have highlighted key areas for improvement. We conclude that the μPAD technology is promising in food safety applications by using novel materials and improved methods to enhance the sensitivity and specificity of the assays, with low cost.

Machine Learning-Assisted Portable Microplasma Optical Emission Spectrometer for Food Safety Monitoring

To meet the needs of food safety for simple, rapid, and low-cost analytical methods, a portable device based on a point discharge microplasma optical emission spectrometer (μPD-OES) was combined with machine learning to enable on-site food freshness evaluation and detection of adulteration. The device was integrated with two modular injection units (i.e., headspace solid-phase microextraction and headspace purge) for the examination of various samples. Aromas from meat and coffee were first introduced to the portable device. The aroma molecules were excited to specific atomic and molecular fragments at excited states by room temperature and atmospheric pressure microplasma due to their different atoms and molecular structures. Subsequently, different aromatic molecules obtained their own specific molecular and atomic emission spectra. With the help of machine learning, the portable device was successfully applied to the assessment of meat freshness with accuracies of 96.0, 98.7, and 94.7% for beef, pork, and chicken meat, respectively, through optical emission patterns of the aroma at different storage times. Furthermore, the developed procedures can identify beef samples containing different amounts of duck meat with an accuracy of 99.5% and classify two coffee species without errors, demonstrating the great potential of their application in the discrimination of food adulteration. The combination of machine learning and μPD-OES provides a simple, portable, and cost-effective strategy for food aroma analysis, potentially addressing field monitoring of food safety.

Surveillance of pathogenic bacteria on a food matrix using machine-learning-enabled paper chromogenic arrays

Global food systems can benefit significantly from continuous monitoring of microbial food safety, a task for which tedious operations, destructive sampling, and the inability to monitor multiple pathogens remain challenging. This study reports significant improvements to a paper chromogenic array sensor - machine learning (PCA-ML) methodology sensing concentrations of volatile organic compounds (VOCs) emitted on a species-specific basis by pathogens by streamlining dye selection, sensor fabrication, database construction, and machine learning and validation. This approach enables noncontact, time-dependent, simultaneous monitoring of multiple pathogens (Listeria monocytogenes, Salmonella, and E. coli O157:H7) at levels as low as 1 log CFU/g with over 90% accuracy. The report provides theoretical and practical frameworks demonstrating that chromogenic response, including limits of detection, depends on time integrals of VOC concentrations. The paper also discusses the potential for implementing PCA-ML in the food supply chain for different food matrices and pathogens, with species- and strain-specific identification.

Dipicolylamine-Zn Induced Targeting and Photo-Eliminating of Pseudomonas aeruginosa and Drug-Resistance Gram-Positive Bacteria

The emergence of drug-resistant bacteria, particularly resistant strains of Gram-negative bacteria, such as Pseudomonas aeruginosa, poses a significant threat to public health. Although antibacterial photodynamic therapy (APDT) is a promising strategy for combating drug-resistant bacteria, actively targeted photosensitizers (PSs) remain unknown. In this study, a PS based on dipicolylamine (DPA), known as WZK-DPA-Zn, is designed for the selective identification of P. aeruginosa and drug-resistant Gram-positive bacteria. WZK-DPA-Zn exploits the synergistic effects of DPA-Zn2+ coordination and cellular uptake, which could effectively anchor P. aeruginosa within a brief period (10 min) without interference from other Gram-negative bacteria. Simultaneously, the cationic nature of WZK-DPA-Zn enhances its interaction with Gram-positive bacteria via electrostatic forces. Compared to traditional clinical antibiotics, WZK-DPA-Zn shows exceptional antibacterial activity without inducing drug resistance. This effectiveness is achieved using the APDT strategy when irradiated with white light or sunlight. The combination of WZK-DPA-Zn with Pluronic-based thermosensitive hydrogel dressings (WZK-DPA-Zn@Gel) effectively eliminates mixed bacterial infections and accelerates wound healing, thereby achieving a synergistic effect where "1+1>2." In summary, this study proposes a precise strategy employing DPA-Zn as the targeting moiety of a PS, facilitating the rapid elimination of P. aeruginosa and drug-resistant Gram-positive bacteria using APDT.

Progress in methods for the detection of viable Escherichia coli

Escherichia coli (E. coli) is a prevalent enteric bacterium and a necessary organism to monitor for food safety and environmental purposes. Developing efficient and specific methods is critical for detecting and monitoring viable E. coli due to its high prevalence. Conventional culture methods are often laborious and time-consuming, and they offer limited capability in detecting potentially harmful viable but non-culturable E. coli in the tested sample, which highlights the need for improved approaches. Hence, there is a growing demand for accurate and sensitive methods to determine the presence of viable E. coli. This paper scrutinizes various methods for detecting viable E. coli, including culture-based methods, molecular methods that target DNAs and RNAs, bacteriophage-based methods, biosensors, and other emerging technologies. The review serves as a guide for researchers seeking additional methodological options and aiding in the development of rapid and precise assays. Moving forward, it is anticipated that methods for detecting E. coli will become more stable and robust, ultimately contributing significantly to the improvement of food safety and public health.

Machine learning assisted biosensing technology: An emerging powerful tool for improving the intelligence of food safety detection

Recently, the application of biosensors in food safety assessment has gained considerable research attention. Nevertheless, the evaluation of biosensors' sensitivity, accuracy, and efficiency is still ongoing. The advent of machine learning has enhanced the application of biosensors in food security assessment, yielding improved results. Machine learning has been preliminarily applied in combination with different biosensors in food safety assessment, with positive results. This review offers a comprehensive summary of the diverse machine learning methods employed in biosensors for food safety. Initially, the primary machine learning methods were outlined, and the integrated application of biosensors and machine learning in food safety was thoroughly examined. Lastly, the challenges and limitations of machine learning and biosensors in the realm of food safety were underscored, and potential solutions were explored. The review's findings demonstrated that algorithms grounded in machine learning can aid in the early detection of food safety issues. Furthermore, preliminary research suggests that biosensors could be optimized through machine learning for real-time, multifaceted analyses of food safety variables and their interactions. The potential of machine learning and biosensors in real-time monitoring of food quality has been discussed.

Material Breakthroughs in Smart Food Monitoring: Intelligent Packaging and On-Site Testing Technologies for Spoilage and Contamination Detection

Despite extensive commercial and regulatory interventions, food spoilage and contamination continue to impose massive ramifications on human health and the global economy. Recognizing that such issues will be significantly eliminated by the accurate and timely monitoring of food quality markers, smart food sensors have garnered significant interest as platforms for both real-time, in-package food monitoring and on-site commercial testing. In both cases, the sensitivity, stability, and efficiency of the developed sensors are largely informed by underlying material design, driving focus toward the creation of advanced materials optimized for such applications. Herein, a comprehensive review of emerging intelligent materials and sensors developed in this space is provided, through the lens of three key food quality markers - biogenic amines, pH, and pathogenic microbes. Each sensing platform is presented with targeted consideration toward the contributions of the underlying metallic or polymeric substrate to the sensing mechanism and detection performance. Further, the real-world applicability of presented works is considered with respect to their capabilities, regulatory adherence, and commercial potential. Finally, a situational assessment of the current state of intelligent food monitoring technologies is provided, discussing material-centric strategies to address their existing limitations, regulatory concerns, and commercial considerations.

G-Quadruplex/Hemin-Mediated Polarity-Switchable and Photocurrent-Amplified System for Escherichia coli O157:H7 Detection

The contamination of food by pathogens is a serious problem in global food safety, and current methods of detection are costly, time-consuming, and cumbersome. Therefore, it is necessary to develop rapid, portable, and sensitive assays for foodborne pathogens. In addition, assays for foodborne pathogens must be resistant to interference resulting from the complex food matrix to prevent false positives and negatives. In this study, hemin and reduced graphene oxide-MoS2 sheets (GMS) were used to design a near-infrared (NIR)-responsive photoelectrochemical (PEC) aptasensor with target-induced photocurrent polarity switching based on a hairpin aptamer (Hp) with a G-quadruplex motif. A ready-to-use analytical device was developed by immobilizing GMS on the surface of a commercial screen-printed electrode, followed by the attachment of the aptamer. In the presence of Escherichia coli O157:H7, the binding sites of Hp with the G-quadruplex motif were opened and exposed to hemin, leading to the formation of a G-quadruplex/hemin DNAzyme. Crucially, after binding to hemin, the charge transfer pathway of GMS changes, resulting in a switch of the photocurrent polarity. Further, G-quadruplex/hemin DNAzyme enhanced the cathodic photocurrent, and the proposed sensor exhibited a wide linear range ((25.0-1.0) × 107 CFU/mL), a low limit of detection (2.0 CFU/mL), and good anti-interference performance. These findings expand the applications of NIR-responsive PEC materials and provide versatile PEC methods for detecting biological analytes, especially for food safety testing.

A review on machine learning-powered fluorescent and colorimetric sensor arrays for bacteria identification

Biosensors have been widely used for bacteria determination with great success. However, the "lock-and-key" methodology used by biosensors to identify bacteria has a significant limitation: it can only detect one species of bacteria. In recent years, optical (fluorescent and colorimetric) sensor arrays are gradually gaining attention from researchers as a new type of biosensor. They can acquire multiple features of a target simultaneously, form a feature pattern, and determine the bacteria species with the help of pattern recognition/machine learning algorithms. Previous reviews in this area have focused on the interaction between the sensor array and bacteria or the materials used to make the sensors. This review, on the other hand, will provide researchers with a better understanding of the field by discussing fluorescent and colorimetric sensor arrays based on the mechanism of optical signal generation. These sensor arrays will be compared based on the identified species. Finally, we will discuss the limitations of these sensor arrays and explore possible solutions.

A Novel Dual Bacteria-Imprinted Polymer Sensor for Highly Selective and Rapid Detection of Pathogenic Bacteria

The rapid, sensitive, and selective detection of pathogenic bacteria is of utmost importance in ensuring food safety and preventing the spread of infectious diseases. Here, we present a novel, reusable, and cost-effective impedimetric sensor based on a dual bacteria-imprinted polymer (DBIP) for the specific detection of Escherichia coli O157:H7 and Staphylococcus aureus. The DBIP sensor stands out with its remarkably short fabrication time of just 20 min, achieved through the efficient electro-polymerization of o-phenylenediamine monomer in the presence of dual bacterial templates, followed by in-situ template removal. The key structural feature of the DBIP sensor lies in the cavity-free imprinting sites, indicative of a thin layer of bacterial surface imprinting. This facilitates rapid rebinding of the target bacteria within a mere 15 min, while the sensing interface regenerates in just 10 min, enhancing the sensor's overall efficiency. A notable advantage of the DBIP sensor is its exceptional selectivity, capable of distinguishing the target bacteria from closely related bacterial strains, including different serotypes. Moreover, the sensor exhibits high sensitivity, showcasing a low detection limit of approximately 9 CFU mL-1. The sensor's reusability further enhances its cost-effectiveness, reducing the need for frequent sensor replacements. The practicality of the DBIP sensor was demonstrated in the analysis of real apple juice samples, yielding good recoveries. The integration of quick fabrication, high selectivity, rapid response, sensitivity, and reusability makes the DBIP sensor a promising solution for monitoring pathogenic bacteria, playing a crucial role in ensuring food safety and safeguarding public health.

Computer Vision-Based Artificial Intelligence-Mediated Encoding-Decoding for Multiplexed Microfluidic Digital Immunoassay

Digital immunoassays with multiplexed capacity, ultrahigh sensitivity, and broad affordability are urgently required in clinical diagnosis, food safety, and environmental monitoring. In this work, a multidimensional digital immunoassay has been developed through microparticle-based encoding and artificial intelligence-based decoding, enabling multiplexed detection with high sensitivity and convenient operation. The information encoded in the features of microspheres, including their size, number, and color, allows for the simultaneous identification and accurate quantification of multiple targets. Computer vision-based artificial intelligence can analyze the microscopy images for information decoding and output identification results visually. Moreover, the optical microscopy imaging can be well integrated with the microfluidic platform, allowing for encoding-decoding through the computer vision-based artificial intelligence. This microfluidic digital immunoassay can simultaneously analyze multiple inflammatory markers and antibiotics within 30 min with high sensitivity and a broad detection range from pg/mL to μg/mL, which holds great promise as an intelligent bioassay for next-generation multiplexed biosensing.

Precise in-field molecular diagnostics of crop diseases by smartphone-based mutation-resolved pathogenic RNA analysis

Molecular diagnostics for crop diseases can guide the precise application of pesticides, thereby reducing pesticide usage while improving crop yield, but tools are lacking. Here, we report an in-field molecular diagnostic tool that uses a cheap colorimetric paper and a smartphone, allowing multiplexed, low-cost, rapid detection of crop pathogens. Rapid nucleic acid amplification-free detection of pathogenic RNA is achieved by combining toehold-mediated strand displacement with a metal ion-mediated urease catalysis reaction. We demonstrate multiplexed detection of six wheat pathogenic fungi and an early detection of wheat stripe rust. When coupled with a microneedle for rapid nucleic acid extraction and a smartphone app for results analysis, the sample-to-result test can be completed in ~10 min in the field. Importantly, by detecting fungal RNA and mutations, the approach allows to distinguish viable and dead pathogens and to sensitively identify mutation-carrying fungicide-resistant isolates, providing fundamental information for precision crop disease management.

CRISPR-based biosensors for pathogenic biosafety

Pathogenic biosafety is a worldwide concern. Tools for analyzing pathogenic biosafety, that are precise, rapid and field-deployable, are highly demanded. Recently developed biotechnological tools, especially those utilizing CRISPR/Cas systems which can couple with nanotechnologies, have enormous potential to achieve point-of-care (POC) testing for pathogen infection. In this review, we first introduce the working principle of class II CRISPR/Cas system for detecting nucleic acid and non-nucleic acid biomarkers, and highlight the molecular assays that leverage CRISPR technologies for POC detection. We summarize the application of CRISPR tools in detecting pathogens, including pathogenic bacteria, viruses, fungi and parasites and their variants, and highlight the profiling of pathogens' genotypes or phenotypes, such as the viability, and drug-resistance. In addition, we discuss the challenges and opportunities of CRISPR-based biosensors in pathogenic biosafety analysis.

Design of Biodegradable, Climate-Specific Packaging Materials That Sense Food Spoilage and Extend Shelf Life

The AgriFood systems in tropical climates are under strain due to a rapid increase in human population and extreme environmental conditions that limit the efficacy of packaging technologies to extend food shelf life and guarantee food safety. To address these challenges, we rationally designed biodegradable packaging materials that sense spoilage and prevent molding. We nanofabricated the interface of 2D covalent organic frameworks (COFs) to reinforce silk fibroin (SF) and obtain biodegradable membranes with augmented mechanical properties and that displayed an immediate colorimetric response (within 1 s) to food spoilage, using packaged poultry as an example. Loading COF with antimicrobial hexanal also mitigated biotic spoilage in high-temperature and -humidity conditions, resulting in a four-order of magnitude decrease in the total amount of mold growth in soybeans packaged in silk-COF, when compared to cling film (i.e., polyethylene). Together, the integration of sensing, structural reinforcement, and antimicrobial agent delivery within a biodegradable nanocomposite framework defines climate-specific packaging materials that can decrease food waste and enhance food safety.

Rapid Identification and Monitoring of Multiple Bacterial Infections Using Printed Nanoarrays

Fast and accurate detection of microbial cells in clinical samples is highly valuable but remains a challenge. Here, a simple, culture-free diagnostic system is developed for direct detection of pathogenic bacteria in water, urine, and serum samples using an optical colorimetric biosensor. It consists of printed nanoarrays chemically conjugated with specific antibodies that exhibits distinct color changes after capturing target pathogens. By utilizing the internal capillarity inside an evaporating droplet, target preconcentration is achieved within a few minutes to enable rapid identification and more efficient detection of bacterial pathogens. More importantly, the scattering signals of bacteria are significantly amplified by the nanoarrays due to strong near-field localization, which supports a visualizable analysis of the growth, reproduction, and cell activity of bacteria at the single-cell level. Finally, in addition to high selectivity, this nanoarray-based biosensor is also capable of accurate quantification and continuous monitoring of bacterial load on food over a broad linear range, with a detection limit of 10 CFU mL^-1 . This work provides an accessible and user-friendly tool for point-of-care testing of pathogens in many clinical and environmental applications, and possibly enables a breakthrough in early prevention and treatment.

Paper-based sensors for bacteria detection

The detection of pathogenic bacteria is essential to prevent and treat infections and to provide food security. Current gold-standard detection techniques, such as culture-based assays and polymerase chain reaction, are time-consuming and require centralized laboratories. Therefore, efforts have focused on developing point-of-care devices that are fast, cheap, portable and do not require specialized training. Paper-based analytical devices meet these criteria and are particularly suitable to deployment in low-resource settings. In this Review, we highlight paper-based analytical devices with substantial point-of-care applicability for bacteria detection and discuss challenges and opportunities for future development.

Single-cell pathogen diagnostics for combating antibiotic resistance

Bacterial infections and antimicrobial resistance are a major cause for morbidity and mortality worldwide. Antimicrobial resistance often arises from antimicrobial misuse, where physicians empirically treat suspected bacterial infections with broad-spectrum antibiotics until standard culture-based diagnostic tests can be completed. There has been a tremendous effort to develop rapid diagnostics in support of the transition from empirical treatment of bacterial infections towards a more precise and personalized approach. Single-cell pathogen diagnostics hold particular promise, enabling unprecedented quantitative precision and rapid turnaround times. This Primer provides a guide for assessing, designing, implementing and applying single-cell pathogen diagnostics. First, single-cell pathogen diagnostic platforms are introduced based on three essential capabilities: cell isolation, detection assay and output measurement. Representative results, common analysis methods and key applications are highlighted, with an emphasis on initial screening of bacterial infection, bacterial species identification and antimicrobial susceptibility testing. Finally, the limitations of existing platforms are discussed, with perspectives offered and an outlook towards clinical deployment. This Primer hopes to inspire and propel new platforms that can realize the vision of precise and personalized bacterial infection treatments in the near future.

Visual-Olfactory Synergistic Perception Based on Dual-Focus Imaging and a Bionic Learning Architecture

The synergistic interaction of vision and olfaction is critical for both natural and artificial intelligence systems to recognize and adapt to complex environments. However, current bioinspired systems with visual and olfactory sensations are mostly assembled with separate and heterogeneous sensors, inevitably leading to bulky systems and incompatible datasets. Here, we demonstrate on-chip integration of visual and olfactory sensations through a dual-focus imaging approach. By combining lens-based visual imaging and lensless colorimetric imaging, a target object and its odor fingerprint can be captured with a single complementary metal-oxide-semiconductor imager, and the obtained multimodal images are analyzed with a bionic learning architecture for information fusion and perception. To demonstrate the capability of this system, we adapted it to food detection and achieved 100% accuracy in identifying meat freshness and category with a 10 s sampling time. In addition to the highly integrated sensor design, our approach exhibits superior accuracy and efficiency in object recognition, providing a promising approach for robotic sensing and perception.

Artificial Intelligence-Assisted Digital Immunoassay Based on a Programmable-Particle-Decoding Technique for Multitarget Ultrasensitive Detection

The development of a multitarget ultrasensitive immunoassay is significant to fields such as medical research, clinical diagnosis, and food safety inspection. In this study, an artificial intelligence (AI)-assisted programmable-particle-decoding technique (APT)-based digital immunoassay system was developed to perform multitarget ultrasensitive detection. Multitarget was encoded by programmable polystyrene (PS) microspheres with different characteristics (particle size and number), and subsequent visible signals were recorded under an optical microscope after the immune reaction. The resultant images were further analyzed using a customized, AI-based computer vision technique to decode the intrinsic properties of polystyrene microspheres and to reveal the types and concentrations of targets. Our strategy has successfully detected multiple inflammatory markers in clinical serum and antibiotics with a broad detection range from pg/mL to μg/mL without extra signal amplification and conversion. An AI-based digital immunoassay system exhibits great potential to be used for the next generation of multitarget detection in disease screening for candidate patients.

Ultrasensitive multichannel immunochromatographic assay for rapid detection of foodborne bacteria based on two-dimensional film-like SERS labels

Rapid and sensitive detection of multiple foodborne bacteria without DNA amplification is still challenging. Here, we proposed an immunochromatographic assay (ICA) with multiplex analysis ability and high sensitivity for direct detection of bacteria in real food samples, based on an improved surface-enhanced Raman scattering (SERS) sensing strategy. Multifunctional Au shell-coated graphene oxide nanosheets (GO@Au) were fabricated and for the first time introduced into the ICA system as a two-dimensional film-like SERS label, which possessed huge surface area, excellent stability, and superior SERS activity. Different from the conventional spherical nanotags, the antibody-conjugated GO@Au nanosheet effectively and rapidly adhered to bacterial cells, improved the dispersibility of bacteria-nanolabel complexes on the ICA strips, and provided numerous stable hotspots for SERS signal enhancement. The combination of GO@Au labels and the ICA system achieved the multiplex and ultrasensitive determination of three major foodborne pathogens, namely, Staphylococcus aureus, Escherichia coli O157:H7, and Salmonella typhimurium, in a single test, with low detection limits (8, 10, and 10 cells/mL) and short detection time (20 min). The proposed biosensor demonstrated high stability and good accuracy in various food samples and is thus a promising tool for the rapid identification of bacteria.

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.

An Integrated Food Freshness Sensor Array System Augmented by a Metal-Organic Framework Mixed-Matrix Membrane and Deep Learning

The static labels presently prevalent on the food market are confronted with challenges due to the assumption that a food product only undergoes a limited range of predefined conditions, which cause elevated safety risks or waste of perishable food products. Hence, integrated systems for measuring food freshness in real time have been developed for improving the reliability, safety, and sustainability of the food supply. However, these systems are limited by poor sensitivity and accuracy. Here, a metal-organic framework mixed-matrix membrane and deep learning technology were combined to tackle these challenges. UiO-66-OH and polyvinyl alcohol were impregnated with six chromogenic indicators to prepare sensor array composites. The sensors underwent color changes after being exposed to ammonia at different pH values. The limit of detection of 80 ppm for trimethylamine was obtained, which was practically acceptable in the food industry. Four state-of-the-art deep convolutional neural networks were applied to recognize the color change, endowing it with high-accuracy freshness estimation. The simulation test for chicken freshness estimation achieved accuracy up to 98.95% by the WISeR-50 algorithm. Moreover, 3D printing was applied to create a mold for possible scale-up production, and a portable food freshness detector platform was conceptually built. This approach has the potential to advance integrated and real-time food freshness estimation.

LISzyme Biosensors: DNAzymes Embedded in an Anti-biofouling Platform for Hands-free Real-Time Detection of Bacterial Contamination in Milk

Nonspecific binding is a significant challenge associated with biosensors in complex food textures. To overcome this, we have developed LISzymes, which are DNAzymes incorporated in lubricant-infused surfaces (LISs). Using milk as a complex background matrix, we show that LISzyme biosensors are significantly more effective in preventing nonspecific binding compared to other commonly used "blocking" methods. The use of lubricant infusion to treat sensing surfaces results in a 4-fold increase in the signal-to-noise ratio obtained with the DNAzyme with respect to untreated surfaces, when detecting the presence of specific bacteria in milk. This is a striking improvement upon previous DNAzyme sensors. We also show that the use of LISs does not affect the DNAzyme's ability to effectively and specifically detect its target─a protein specifically produced by Escherichia coli (E. coli), in a complex sample matrix such as milk. LISzymes drastically improve DNAzyme performance, resulting in target detection associated with E. coli at concentrations as low as 250 CFU/mL in milk in less than an hour, which is currently not possible using other optical platforms. LISzymes are promising tools for the real-time monitoring of food contamination and may prove valuable within many other biosensing applications.

Nondestructive multiplex detection of foodborne pathogens with background microflora and symbiosis using a paper chromogenic array and advanced neural network

We have developed an inexpensive, standardized paper chromogenic array (PCA) integrated with a machine learning approach to accurately identify single pathogens (Listeria monocytogenes, Salmonella Enteritidis, or Escherichia coli O157:H7) or multiple pathogens (either in multiple monocultures, or in a single cocktail culture), in the presence of background microflora on food. Cantaloupe, a commodity with significant volatile organic compound (VOC) emission and large diverse populations of background microflora, was used as the model food. The PCA was fabricated from a paper microarray via photolithography and paper microfluidics, into which 22 chromogenic dye spots were infused and to which three red/green/blue color-standard dots were taped. When exposed to VOCs emitted by pathogens of interest, dye spots exhibited distinguishable color changes and pattern shifts, which were automatically segmented and digitized into a ΔR/ΔG/ΔB database. We developed an advanced deep feedforward neural network with a learning rate scheduler, L₂ regularization, and shortcut connections. After training on the ΔR/ΔG/ΔB database, the network demonstrated excellent performance in identifying pathogens in single monocultures, multiple monocultures, and in cocktail culture, and in distinguishing them from the background signal on cantaloupe, providing accuracy of up to 93% and 91% under ambient and refrigerated conditions, respectively. With its combination of speed, reliability, portability, and low cost, this nondestructive approach holds great potential to significantly advance culture-free pathogen detection and identification on food, and is readily extendable to other food commodities with complex microflora.