Fabrication of a "progress bar" colorimetric strip sensor array by dye-mixing method as a potential food freshness indicator

Metal-polyphenol Multistage Competitive Coordination System for Colorimetric Monitoring Meat Freshness

A low-cost, high-precision, and secure real-time system for monitoring food freshness can significantly improve spoilage issues, yet traditional colorimetric sensor arrays often suffer from chemical dyes' high toxicity and limited color changes. Here, a metal-polyphenol network colorimetric sensor array (MPN-CSA) is built for detecting total volatile base nitrogen (TVB-N) markers of meat freshness. The multi-level competitive coordination process between the metal-polyphenol system and amine substances endows the system with color changes far beyond those of traditional dyes (reaching a detection limit of 300 ppb). By integrating convolutional neural network (CNN) technology, an online platform is developed for monitoring meat freshness, achieving an overall detection accuracy rate of 99.83%. This environmentally friendly, economically viable MPN-CSA that monitors the freshness of meat in complex storage environments can be incorporated into food packaging boxes, enabling consumers and suppliers to assess the freshness of meat in real-time, thus helping to reduce food waste and prevent foodborne illnesses.

Intelligent packaging film using watermelon peel pectin and betacyanins with UiO-66 as the gas adsorption system for monitoring the freshness of chilled pork

The demand for reliable freshness monitoring technologies is growing in the food industry to enhance food safety and reduce waste. To address this need, this study incorporated varying concentrations of UiO-66 (0.0 %, 0.5 %, 1.0 %, 1.5 %, 2.0 %) into an intelligent film composed of watermelon peel pectin and betacyanins (BCs). This design aimed to improve the sensitivity of the freshness indicator for chilled meat by leveraging the gas adsorption properties of UiO-66. This resulted in enhanced square surface roughness (Sq) with UiO-66 due to agglomeration. Low UiO-66 content (up to 1.5 %) reduced the composite film's elongation at break (EAB), water vapor permeability (WVP), oxygen permeability (OP), and light transmittance while increasing its tensile strength (TS) and thermal stability as a result of the decreased crystallinity and hydrogen bonds. The B-U-1.5 film displayed high sensitivity to pH and ammonia vapors due to heterogeneous chemical adsorption. Furthermore, the B-U-1.5 film demonstrated a distinctive color variation from red-violet to orange and then to yellow, corresponding to the freshness stages of chilled meat: fresh, sub-fresh, and spoiled. This response was more precise compared to the performance of other films. The results suggest that incorporating 1.5 % UiO-66 optimizes the film's performance for monitoring chilled meat freshness.

A Self-Adhesive Flexible and Stretchable Compliant Surface Sensor for Real-Time Monitoring of Starch-Based Food Processing

Flexible sensors have attracted great attention because of their important applications in many areas. It is important to monitor the surface of starch-based food during food processing because it can provide key information related to the appearance, texture level, and chewiness of the food. However, there is no report on real-time monitoring of the surface of steamed bread in the literature. Here, we report a self-adhesive and stretchable compliant sensor that can be mounted to the surface of starch-based food and provides real-time signals for the steaming process. The sensors consisting of biocompatible poly(3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS), poly(vinyl alcohol) (PVA), tannic acid (TA), and glycerol can be fabricated by solution processing. Because it is stretchable and self-adhesive to the dough surface, it is compliant with the expansion or contraction of the dough during food processing. Its resistance varies with the shape and volume of the dough and thus can be monitored in a real-time manner. This is the first report of a surface sensor that can monitor the steaming process of starch-based food.

An intelligent fruit freshness monitoring system using hydrophobic indicator labels based on methylcellulose, k-carrageenan, and sodium tripolyphosphate, combined with deep learning

As the demand for food quality and safety continues to rise, pH-responsive intelligent packaging technologies have found widespread application in the monitoring of food freshness. This study introduces a methylcellulose (MC)-based indicator label designed for real-time monitoring of fruit freshness. To enhance the label's structural integrity, mechanical properties, and hydrophobicity, k-carrageenan (KC) and sodium tripolyphosphate (STPP) were incorporated as crosslinkers to form a stable three-dimensional network structure. This modification led to a tensile strength of 23.33 MPa, an elongation at break of 113.4 %, and significant reductions in water content, solubility, and water vapor permeability, with the water contact angle increasing to 76.84°. To address the challenge in existing research where the ratio of mixed indicators requires time-consuming experimentation, the present study employs computer simulation techniques to model the color changes under varying chemical compositions and ratios, significantly reducing both experimental time and costs. Furthermore, an intelligent recognition method is proposed, involving the design of a label area cropping algorithm (ALC) integrated with a lightweight convolutional neural network (CNN), which effectively minimizes background interference and improves the accuracy of freshness detection. In experiments assessing the freshness of mango, kiwi, and grape, the method achieved maximum accuracies of 96.4 %, 96.3 %, and 96.3 %, respectively. Based on this approach, a mobile application has been developed for real-time monitoring of fruit freshness.

Recent advancements in chitosan-based intelligent food freshness indicators: Categorization, advantages, and applications

With an increasing emphasis on food safety and public health, there is an ongoing effort to develop reliable, non-invasive methods to assess the freshness of diverse food products. Chitosan-based food freshness indicators, leveraging properties such as biocompatibility, biodegradability, non-toxicity, and high stability, offer an innovative approach for real-time monitoring of food quality during storage and transportation. This review introduces intelligent food freshness indicators, specifically those utilizing pH-sensitive dyes like anthocyanins, curcumin, alizarin, shikonin, and betacyanin. It highlights the benefits of chitosan-based intelligent food freshness indicators, emphasizing improvements in barrier and mechanical properties, antibacterial activity, and composite film solubility. The application of these indicators in the food industry is then explored, alongside a concise overview of chitosan's limitations. The paper concludes by discussing the challenges and potential areas for future research in the development of intelligent food freshness indicators using chitosan. Thus, chitosan-based smart food preservation indicators represent an innovative approach to providing real-time data for monitoring food quality, offering valuable insights to both customers and retailers, and playing a pivotal role in advancing the food industry.

Recent advances in artificial intelligence towards the sustainable future of agri-food industry

Artificial intelligence has the potential to alter the agricultural and food processing industries, with significant ramifications for sustainability and global food security. The integration of artificial intelligence in agriculture has witnessed a significant uptick in recent years. Therefore, comprehensive understanding of these techniques is needed to broaden its application in agri-food supply chain. In this review, we explored cutting-edge artificial intelligence methodologies with a focus on machine learning, neural networks, and deep learning. The application of artificial intelligence in agri-food industry and their quality assurance throughout the production process is thoroughly discussed with an emphasis on the current scientific knowledge and future perspective. Artificial intelligence has played a significant role in transforming agri-food systems by enhancing efficiency, sustainability, and productivity. Many food industries are implementing the artificial intelligence in modelling, prediction, control tool, sensory evaluation, quality control, and tackling complicated challenges in food processing. Similarly, artificial intelligence applied in agriculture to improve the entire farming process, such as crop yield optimization, use of herbicides, weeds identification, and harvesting of fruits. In summary, the integration of artificial intelligence in agri-food systems offers the potential to address key challenges in agriculture, enhance sustainability, and contribute to global food security.

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.

Integration of lanthanide MOFs/methylcellulose-based fluorescent sensor arrays and deep learning for fish freshness monitoring

Preserving fish meat poses a significant challenge due to its high protein and low fat content. This study introduces a novel approach that utilizes a common type of lanthanide metal-organic frameworks (Ln-MOFs), EuMOFs, in combination with 5-fluorescein isothiocyanate (FITC) and methylcellulose (MC) to develop fluorescent sensor arrays for real-time monitoring the freshness of fish meat. The EuMOF-FITC/MC fluorescence films were characterized with excellent fluorescence response, ideal morphology, good mechanical properties, and improved hydrophobicity. The efficacy of the fluorescence sensor array was evaluated by testing various concentrations of spoilage gases (such as ammonia, dimethylamine, and trimethylamine) within a 20-min timeframe using a smartphone-based camera obscura device. This sensor array enables the real-time monitoring of fish freshness, with the ability to preliminarily identify the freshness status of mackerel meat with the naked eye. Furthermore, the study employed four convolutional neural network (CNN) models to enhance the performance of freshness assessment, all of which achieved accuracy levels exceeding 93 %. Notably, the ResNext-101 model demonstrated a particularly high accuracy of 98.97 %. These results highlight the potential of the EuMOF-based fluorescence sensor array, in conjunction with the CNN model, as a reliable and accurate method for real-time monitoring the freshness of fish meat.

Advanced technologies in biodegradable packaging using intelligent sensing to fight food waste

The limitation of conventional packaging in demonstrating accurate and real-time food expiration dates leads to food waste and foodborne diseases. Real-time food quality monitoring via intelligent packaging could be an effective solution to reduce food waste and foodborne illnesses. This review focuses on recent technological advances incorporated into food packaging for monitoring food spoilage, with a major focus on paper-based sensors and their combination with smartphone. This review paper offers a comprehensive exploration of advanced macromolecular technologies in biodegradable packaging, a general overview of paper-based probes and their incorporation into food packaging coupled with intelligent sensing mechanisms for monitoring food freshness. Given the escalating global concerns surrounding food waste, our manuscript serves as a pivotal resource, consolidating current research findings and highlighting the transformative potential of these innovative packaging solutions. We also highlight the current intelligent paper-based food freshness sensors and their various advantages and limitations. Examples of implementation of paper-based sensors/probes for food storage and their accuracy are presented. Finally, we examined how intelligent packaging can be an alternative to reduce food waste. Several technologies discussed here have good potential to be used in food packaging for real-time food monitoring, especially when combined with smartphone diagnosis.

Recent advances on natural colorants-based intelligent colorimetric food freshness indicators: fabrication, multifunctional applications and optimization strategies

With the increasing concerns of food safety and public health, tremendous efforts have been concentrated on the development of effective, reliable, nondestructive methods to evaluate the freshness level of different kinds of food. Natural colorants-based intelligent colorimetric indicators which are typically constructed with natural colorants and polymer matrices has been regarded as an innovative approach to notify the customers and retailers of the food quality during the storage and transportation procedure in real-time. This review briefly elucidates the mechanism of natural colorants used for intelligent colorimetric indicators and fabrication methodologies of natural colorants-based food freshness indicators. Subsequently, their multifunctional applications in intelligent food packaging systems like antioxidant packaging, antimicrobial packaging, biodegradable packaging, UV-blocking packaging and inkless packaging are well introduced. This paper also summarizes several optimizing strategies for the practical application of this advanced technology from different perspectives. Strategies like adopting a hydrophobic matrix, constructing double-layer film and encapsulation have been developed to improve the stability of the indicators. Co-pigmentation, metal ion complexation, pigment-mixing and using substrates with high surface area are proved to be effective to enhance the sensitivity of the indicators. Approaches include multi-index evaluation, machine learning and smartphone-assisted evaluation have been proven to improve the accuracy of the intelligent food freshness indicators. Finally, future research opportunities and challenges are proposed. Based on the fundamental understanding of natural colorants-based intelligent colorimetric food freshness indicators, and the latest research and findings from literature, this review article will help to develop better, lower cost and more reliable food freshness evaluation technique for modern food industry.

Miniaturized wireless sensor enables real-time monitoring of food spoilage

Food spoilage results in food waste and food-borne diseases. Yet, standard laboratory tests to determine spoilage (mainly volatile biogenic amines) are not performed regularly by supply chain personnel or end customers. Here we developed a poly(styrene-co-maleic anhydride)-based, miniature (2 × 2 cm²) sensor for on-demand spoilage analysis via mobile phones. To demonstrate a real-life application, the wireless sensor was embedded into packaged chicken and beef; consecutive readings from meat samples using the sensor under various storage conditions enabled the monitoring of spoilage. While samples stored at room temperature showed an almost 700% change in sensor response on the third day, those stored in the freezer resulted in an insignificant change in sensor output. The proposed low-cost, miniature wireless sensor nodes can be integrated into packaged foods, helping consumers and suppliers detect spoilage of protein-rich foods on demand, and ultimately preventing food waste and food-borne diseases.

Neural network in food analytics

Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.

A nanomaterial-free and thionine labeling-based lateral flow immunoassay for rapid and visual detection of the transgenic CP4-EPSPS protein

Nanomaterial-based lateral flow immunoassays (LFIAs) have been widely used for the on-site detection of genetically modified components. However, the practical applications are often limited by the complex matrix, such as in red samples. In this study, a thionine (Thi) labeling-based LFIA was developed for the first time to detect CP4-EPSPS protein. The optimal labeling concentration of Thi was 0.5 mg/mL, and the antibody could be rapidly coupled to Thi in 10 min. The visual limit of detection (vLOD) levels for transgenic soybean, sugar beet, and cotton containing the CP4-EPSPS protein reached 0.05%, 0.1%, and 0.1%, respectively, and had no interference from other proteins. After storage at 4 °C for three months, the LFIA sensitivity remained unchanged and showed good stability. This method could be used to screen and detect a variety of transgenic crops containing the CP4-EPSPS protein, and the results were consistent with the current standard assay. This study pioneered the development of an immunochromatographic method using Thi as a marker and applied it to the detection of the CP4-EPSPS protein in herbicide-tolerant transgenic crops. This provides a new method for the rapid immunoassay of Thi as a dye and has good prospects for practical application.