Accurate fish-freshness prediction label based on red cabbage anthocyanins

Alginate-based active and intelligent packaging: Preparation, properties, and applications

Alginate-based packaging materials have emerged as promising alternatives to conventional petroleum-based plastics due to their biodegradability, renewability, and versatile functionalities. This review provides a comprehensive analysis of the recent advances in the development and application of alginate-based films and coatings for food packaging. The composition and fabrication methods of alginate-based packaging materials are discussed, highlighting the incorporation of various functional compounds to enhance their physicochemical properties. The mechanisms of action and the factors influencing the release and migration of active compounds from the alginate matrix are explored. The application of alginate-based packaging materials for the preservation of various food products, including meat, fish, dairy, fruits, and vegetables, is reviewed, demonstrating their effectiveness in extending shelf-life and maintaining quality. The development of alginate-based pH-sensitive indicators for intelligent food packaging is also discussed, focusing on the colorimetric response of natural pigments to spoilage-related pH changes. Furthermore, the review highlights the challenges and future perspectives of alginate-based packaging materials, emphasizing the need for novel strategies to improve their performance, sustainability, and industrial adoption.

Characterization and performance evaluation of colorimetric pH-sensitive indicator based on Ҡ-carrageenan/quince seed mucilage hydrogel as freshness/spoilage monitoring of rainbow trout fillet

The aim of research was to fabricate a novel indicator by using κ-carrageenan and quince seed mucilage (QSM) hydrogels and red cabbage anthocyanin. The porosity of the hydrogel was controlled using different ratios of κ-carrageenan(C):QSM(Q) (C90:Q10, C70:Q30, and C50:Q50). The hardness of hydrogels decreased from 28.6 ± 0.3 N for C90Q10 to 11.0 ± 1.0 N for C50Q50 sample. However, according to field emission scanning electron microscopy (FE-SEM) analysis, the C50R50 sample had the best morphology with smooth surface and uniform interconnected porous network. Hydrogen bonding interactions among anthocyanins, QSM, and κ-carrageenan were confirmed by Fourier transforms infrared (FT-IR) spectroscopy. The indicator showed a color variation from red to yellow over the pH range of 2-12. Also, the indicator exhibited high sensitivity to ammonia vapors (SRGB = 115%) and good color stability. The C50QRA indicator was used for monitoring rainbow trout fillet spoilage and revealed a visually-detectable color change from red to green upon detecting total volatile basic nitrogen (TVB-N) content produced throughout storage at 4 °C. Generally, the halochromic hydrogel developed in this research can be suggested as a more sensitive and accurate freshness indicator than conventional indicator solid supports.

Prediction of the postharvest quality of Boletus wild mushrooms stored with mesoporous silica nanoparticles antibacterial film using Long Short-Term Memory model combined with the Northern Goshawk Optimization (NGO-LSTM)

This study aimed to address the challenge of extending the shelf life of Boletus wild mushrooms, which are prone to environmental and microbial contamination. An antibacterial film composed of polylactic acid (PLA) and mesoporous silica nanoparticles loaded with citral (CMP film) was developed for this purpose. Fifteen quality indices were assessed, and the data were integrated using AHP and TOPSIS to evaluate the film's efficacy. The CMP film effectively maintained the quality of mushroom over time. Additionally, a Nonlinear Global Optimization-Long Short-Term Memory (NGO-LSTM) model was employed to predict storage quality, using seven highly correlated quality indicators. The model achieved a high predictive accuracy, with the R2 exceeding 0.999. This study presents a novel packaging solution and a predictive model that together enhance the storage and quality control of Boletus wild mushrooms.

Sodium alginate/ager colourimetric film on porous substrate layer: Potential in intelligent food packaging

Colourimetric indicators have potential applications in monitoring food freshness and offer a simple, rapid, effective, and economical approach. Blending sodium alginate (SA) with agar (AG), an ideal choice for solid substrates in colourimetric indicators, can modify mechanical compliance and optical properties. However, the limitations in the water-sustaining capacity and dye migration of hydrogel substrates significantly impede the scalability and commercial application of these indicators. In this study, we designed and prepared a bilayer-structured indicator featuring an SA/AG colourimetric film on a porous Polypropylene fluoride (PVDF)/SiO2 encapsulation film. This design aims to enhance the water-sustaining capacity and reduce dye migration from the SA/AG colourimetric film. The PVDF/SiO2 composite film was prepared using a peeling-assisted phase-conversion process, which enabled the indicator to selectively allow gas, but not water, to pass through its porous substrate. Furthermore, we tested the layered indicator film by monitoring changes in shrimp freshness. The results revealed significant and distinguishable colour changes in the indicators corresponding to the freshness and spoilage of the shrimp.

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.

Identification of Burkholderia gladioli pv. cocovenenans in Black Fungus and Efficient Recognition of Bongkrekic Acid and Toxoflavin Producing Phenotype by Back Propagation Neural Network

Burkholderia gladioli pv. cocovenenans is a serious safety issue in black fungus due to the deadly toxin, bongkrekic acid. This has triggered the demand for an efficient toxigenic phenotype recognition method. The objective of this study is to develop an efficient method for the recognition of toxin-producing B. gladioli strains. The potential of multilocus sequence typing and a back propagation neural network for the recognition of toxigenic B. cocovenenans was explored for the first time. The virulent strains were isolated from a black fungus cultivation environment in Qinba Mountain area, Shaanxi, China. A comprehensive evaluation of toxigenic capability of 26 isolates were conducted using Ultra Performance Liquid Chromatography for determination of bongkrekic acid and toxoflavin production in different culturing conditions and foods. The isolates produced bongkrekic acid in the range of 0.05-6.24 mg/L in black fungus and a highly toxin-producing strain generated 201.86 mg/L bongkrekic acid and 45.26 mg/L toxoflavin in co-cultivation with Rhizopus oryzae on PDA medium. Multilocus sequence typing phylogeny (MLST) analysis showed that housekeeping gene sequences have a certain relationship with a strain toxigenic phenotype. We developed a well-trained, back-propagation neutral network for prediction of toxigenic phenotype in B. gladioli based on MLST sequences with an accuracy of 100% in the training set and an accuracy of 86.7% in external test set strains. The BP neutral network offers a highly efficient approach to predict toxigenic phenotype of strains and contributes to hazard detection and safety surveillance.

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

Real-time and on-site food spoilage monitoring is still a challenging issue to prevent food poisoning. At the onset of food spoilage, microbial and enzymatic activities lead to the formation of volatile amines. Monitoring of these amines with conventional methods requires sophisticated, costly, labor-intensive, and time consuming analysis. Here, anthocyanins rich red cabbage extract (ARCE) based colorimetric sensing system was developed with the incorporation of embedded machine learning in a smartphone application for real-time food spoilage monitoring. FG-UV-CD100 films were first fabricated by crosslinking ARCE-doped fish gelatin (FG) with carbon dots (CDs) under UV light. The color change of FG-UV-CD100 films with varying ammonia vapor concentrations was captured in different light sources with smartphones of various brands, and a comprehensive dataset was created to train machine learning (ML) classifiers to be robust and adaptable to ambient conditions, resulting in 98.8% classification accuracy. Meanwhile, the ML classifier was embedded into our Android application, SmartFood++, enabling analysis in about 0.1 s without internet access, unlike its counterpart using cloud operation via internet. The proposed system was also tested on a real fish sample with 99.6% accuracy, demonstrating that it has a great advantage as a potent tool for on-site real-time monitoring of food spoilage by non-specialized personnel.

Novel pH-responsive indicator films based on bromothymol blue-anchored chitin for shrimp freshness monitoring

The cellulose nanofibers (CNFs) based pH-sensitive indicator films were developed by mixing guar gum (GG) with bromothymol blue-anchored chitin (BTB-Chitin) as an indicator to monitor shrimp freshness. The BTB-Chitin was prepared by grafting hydroxypropyltriethylamine groups (HPTA) to chitin first, then anchoring bromothymol blue (BTB) to prepare intelligent pH response BTB-Chitin. The 0.08 BTB-Chitin films had a good tensile strength of 11.76 MPa and the water contact angle values were 125°, which displayed obvious color response to pH buffers and acid base volatile gas. Besides, the homogeneous and flexible composite films showed good color stability and reversibility. The released amount of BTB was very low from the BTB-Chitin films in heptane and corn oil. The composite films had been degraded completely in 15 days in soil. The pH and volatile base nitrogen were measured to determine the degree decay of shrimp (Litopenaeus vannamei), and the prepared pH-sensitive films changed from yellow (fresh) to cyan (spoiled) with the freshness of shrimp decreased, indicating the BTB-Chitin films could detect the shrimp freshness in real-time and high visibility.

Active colorimetric bilayer polycaprolactone-eucalyptus oil@silk fibroin-bayberry anthocyanins (PCL-EO@SF-BAs) membrane with directional water transport (DWT) for food packaging

Currently, designing smart membranes with multifunctional effectiveness is crucial to food freshness monitoring and retention. Herein, an active colorimetric Janus bilayer membrane with directional water transport (DWT) performance is constructed by electrospinning, which comprises a hydrophilic layer of silk fibroin-bayberry anthocyanins (SF-BAs) and a hydrophobic layer of polycaprolactone-eucalyptus oil (PCL-EO). The entities of BAs and EO are well dispersed in the fiber matrix by hydrogen bonds and physical interactions, respectively. BAs endow the membrane colorimetric response and antioxidant activity, and EO contributes to the antibacterial activity while DWT performance is generated from the asymmetric wettability of the two layers. The bilayer membrane has an accumulative one-way transport index of 1077%, an overall moisture management capacity of 0.76 and a water evaporation rate of 0.48 g h-1. Moreover, the release of BAs and EO was predominantly controlled by Fickian diffusion. As a pH-sensing indicator, PCL-EO@SF-BAs is highly sensitive to external pH stimuli and the response is reversible. In addition to freshness monitoring, PCL-EO@SF-BAs can extend the shelf-life of pork beyond 100% at 4 °C. Also, it can extend the shelf life of shrimp by approximately 70% at 25 °C with the synergistic effect of antibacterial activity and the DWT performance.

Conformal Temperature/Impedance Sensing Patch Based on Graphene Materials for Nondestructive Detection of Fish Freshness

Rapid nondestructive detection of fish freshness is essential to ensure food safety and nutrition. In this study, we demonstrate a conformal temperature/impedance sensing patch for temperature monitoring, as well as freshness classification during fish storage. The optimization of the flexible laser-induced graphene electrodes is studied based on both simulation and experimental validation, and dimensional accuracy of 5‰ and high impedance reproducibility are obtained. A laser-assisted thermal reduction technology is innovatively introduced to directly form a reduced graphene oxide-based temperature-sensitive layer on the surface of a flexible substrate. The comprehensive performance is superior to that of most reported temperature-sensitive devices based on graphene materials. As an application demonstration, the fabricated flexible dual-parameter sensing patch is conformed to the surface of a refrigerated fish. The patch demonstrates the ability to accurately sense low temperatures in a continuous 120 min monitoring, accompanied by no interference from high humidity. Meanwhile, the collected impedance data are imported into the support vector machine model to obtain a freshness classification accuracy of 93.07%. The conformal patch integrated with crosstalk-free dual functions costs less than $1 and supports free customization, providing a feasible methodology for rapid nondestructive detection or monitoring of food quality.

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.

pH-sensitive smart indicators based on cellulose and different natural pigments for tracing kimchi ripening stages

Five natural pigments including water-soluble [butterfly pea (BP), red cabbage (RC), and aronia (AR)] and alcohol-soluble [shikonin (SK) and alizarin (ALZ)] were extracted, characterized, and loaded onto cellulose for preparing pH-sensitive indicators. The indicators were tested their color response efficiency, gas sensitivity, response to lactic acid, color release, and antioxidant activity. Cellulose-water soluble indicators showed more obvious color changes than alcohol-soluble indicators in lactic acid solution and pH solutions (1-13). All cellulose-pigment indicators exhibited prominent sensitivity to ammonia compared to acidic vapor. Antioxidant activity and release behavior of the indicators were influenced by pigment type and simulants. Kimchi packaging test was carried out using original and alkalized indicators. The alkalized indicators were more effective in showing visible color changes during kimchi storage than the original indicators, and cellulose-ALZ displayed the most distinct color change from violet (fresh kimchi, pH 5.6, acidity 0.45 %) to gray (optimum fermented kimchi, pH 4.7, acidity 0.72 %), and to yellow (over fermented kimchi, pH 3.8, acidity 1.38 %) which followed by BP, AR, RC, SK respectively. The findings of the study suggest that the alkalization method could be used to show noticeable color changes in a narrow pH range for application with acidic foods.

The production of pH indicator Ca and Cu alginate ((1,4)- β -d-mannuronic acid and α -l-guluronic acid) cryogels containing anthocyanin obtained via red cabbage extraction for monitoring chicken fillet freshness

In recent days, intelligent food packaging has gained attention due to consumers' needs and monitoring of the freshness of food. Biopolymers are used to produce matrix parts and dye chemicals, because of their unique properties, such as biodegradability and biocompatibility. In this study, alginate molecules and anthocyanins were used to produce to monitor chicken fillet freshness via pH response characteristics. Anthocyanins' color and UV characteristics at different pHs were investigated. The obtained anthocyanin solution showed visible color response at different pH level. In the red cabbage extract, the anthocyanin concentration was as 0.65 ± 0.03 mg/g. Alginate and extracted anthocyanins from red cabbage were mixed at the solution phase, then metal alginate hydrogels were synthesized via crosslinking Ca²⁺ and Cu²⁺ with alginate molecules. Due to the porous structure of the cryogels, hydrogels were freeze dried at -80 °C for 24 h at vacuum atmosphere. The obtained cryogel indicated significant color changes from pH 4 to pH 10, and at a basic environment, the color change was observed with the naked eye. The porosity amounts and sizes of the produced cryogels were examined, the average pore amount of cryogels was found to be 85.46 ± 4.36 %, and the average pore size 97.98 ± 26.20 μm. Furthermore, it was seen that the color change was not directly related to the porosity, but the interaction of anthocyanin and metal alginate matrix effected color changes degree of cryogels. Due to the electronegativity of Cu²⁺ ions, and the use of a low amount of anthocyanin was found to be more suitable for color change. The color was changed to blue-purple while total volatile basic nitrogen content increased to 46.67 mg/100 g from 14.00 mg/100 g. As a result, prepared cryogels should be a better candidates for use as a freshness indicator and intelligent packaging.

A Rice Hazards Risk Assessment Method for a Rice Processing Chain Based on a Multidimensional Trapezoidal Cloud Model

Rice is common in the human diet, making rice safety issues important. Moreover, rice processing safety is key for rice security, so rice processing chain risk assessment is critical. However, methods proposed to assess the rice processing chain risk have issues, such as the use of unreasonable thresholds for the rice processing chain and fixed weight. To solve these problems, we propose a risk assessment method for the rice processing chain based on a multidimensional trapezoidal cloud model. First, an evaluation model based on a multidimensional trapezoidal cloud model was established. Based on the historical evaluation results, Atanassov’s interval-valued intuition language numbers (AIVILNs) were introduced to determine the cloud model’s parameters. Second, the concept of dynamic weight was introduced to integrate the static and dynamic weights. An exponential function was used to construct dynamic weighting mechanisms, and the analytic hierarchy stage (AHP) was used to construct a static weight. The proposed method was validated by 104 sets of rice processing chain data, and the results show that the method could evaluate the risk level of the rice processing chain more accurately and reasonably than other methods, indicating that it can provide a sound decision-making basis for food safety supervision authorities.

rGO-PDMS Flexible Sensors Enabled Survival Decision System for Live Oysters

The shell-closing strength (SCS) of oysters is the main parameter for physiological activities. The aim of this study was to evaluate the applicability of SCS as an indicator of live oyster health. This study developed a flexible pressure sensor system with polydimethylsiloxane (PDMS) as the substrate and reduced graphene oxide (rGO) as the sensitive layer to monitor SCS in live oysters (rGO-PDMS). In the experiment, oysters of superior, medium and inferior grades were selected as research objects, and the change characteristics of SCS were monitored at 4 °C and 25 °C. At the same time, the time series model was used to predict the survival rate of live oyster on the basis of changes in their SCS characteristics. The survival times of superior, medium and inferior oysters at 4 °C and 25 °C were 31/25/18 days and 12/10/7 days, respectively, and the best prediction accuracies for survival rate were 89.32%/82.17%/79.19%. The results indicate that SCS is a key physiological indicator of oyster survival. The dynamic monitoring of oyster vitality by means of flexible pressure sensors is an important means of improving oyster survival rate. Superior oysters have a higher survival rate in low-temperature environments, and our method can provide effective and reliable survival prediction and management for the oyster industry.

Emerging Approach for Fish Freshness Evaluation: Principle, Application and Challenges

Affected by micro-organisms and endogenous enzymes, fish are highly perishable during storage, processing and transportation. Efficient evaluation of fish freshness to ensure consumer safety and reduce raw material losses has received an increasing amount of attention. Several of the conventional freshness assessment techniques have plenty of shortcomings, such as being destructive, time-consuming and laborious. Recently, various sensors and spectroscopic techniques have shown great potential due to rapid analysis, low sample preparation and cost-effectiveness, and some methods are especially non-destructive and suitable for online or large-scale operations. Non-destructive techniques typically respond to characteristic substances produced by fish during spoilage without destroying the sample. In this review, we summarize, in detail, the principles and applications of emerging approaches for assessing fish freshness including visual indicators derived from intelligent packaging, active sensors, nuclear magnetic resonance (NMR) and optical spectroscopic techniques. Recent developments in emerging technologies have demonstrated their advantages in detecting fish freshness, but some challenges remain in popularization, optimizing sensor selectivity and sensitivity, and the development of algorithms and chemometrics in spectroscopic techniques.