A smartphone-adaptable fluorescent probe for visual monitoring of fish freshness and its application in fluorescent dyes.

Due to increasing food safety issues, developing intelligent, on-site, and visual methods for detecting fish freshness has attracted significant attention. Here, we have prepared a benzo[h]chromene derivative BCN that can visually detect 12 biogenic amines (BAs) with high sensitivity. The mechanism for recognizing cadaverine (Cad) is that the probe reacts with Cad to produce a Schiff base derivative, which alters the charge distribution within the molecule, resulting in significant colorimetric and fluorescence changes. The sensing label BCN/FPS was prepared by loading the probe BCN on filter paper, and a visual detection platform was constructed by combining it with a smartphone. By monitoring the correspondence between label color and TVB-N content, a working curve of (R + B)/(R + B + G) with TVB-N content was obtained, enabling visual evaluation of salmon freshness using only a mobile phone. In addition, based on the good solubility and processability of BCN, its application in fluorescent dyes including impregnating dyes, printing inks, coatings, and flexible films has been explored, which opens up new directions for the application of BCN. Therefore, BCN has the potential for real-time monitoring of meat freshness and preparation of fluorescent materials.

Seabream quality monitoring throughout the supply chain using a portable multispectral imaging device.

Monitoring food quality throughout the supply chain in a rapid and cost-effective way allows on-time decision making, reducing food waste and increasing sustainability. In that framework, a portable multispectral imaging sensor was used, while the acquired data in combination with neural networks were evaluated for the prediction of fish fillets quality. Images of fish fillets were acquired using samples from both aquaculture and retail stores of different packaging and fish parts. The obtained products (air or vacuum packaged) were further stored at different temperature conditions. In parallel to image acquisition, microbial quality was estimated as well. The data were used for the training of predictive neural models that aimed to estimate total aerobic counts (TAC). The models were developed and validated using data from aquaculture and were externally validated with samples purchased from the retail stores. The set up allowed the evaluation of models for the different parts of the fish and conditions. The performance for the validation set was similar for flesh (RMSE: 0.402-0.547) and skin side (RMSE: 0.500-0.533) of the fish fillets. The performance for the different packaging conditions was also similar, however, in the external validation, the vacuum-packaged samples showed better performance in terms of RMSE compared to the air-packaged ones. Models irrespective of packaging condition are very important for cases where the products' history is unknown although the prediction capability was not as high as in the models per packaging condition individually. The models tested with unknown samples (i.e., from retail stores) showed poorer performance (RMSE: 1.061-1.414) compared to the models validated with data partitioning (RMSE: 0.402-0.547). Multispectral imaging sensor appeared to be efficient for the rapid assessment of the microbiological quality of fish fillets for all the different cases evaluated.

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.

Aerogel colorimetric label sensors based on carboxymethyl cellulose/sodium alginate with black goji anthocyanin for monitoring fish freshness.

A novel colorimetric aerogel was developed by the complexation of carboxymethyl cellulose (CMC), sodium alginate (SA), and black goji anthocyanin (BGA) followed by freeze-drying for monitoring fish (Coho salmon) freshness during storage at 4 °C and 25 °C. The various aerogels (C/S/B3:1, C/S/B2:1, C/S/B1:1, C/S/B1:2, and C/S/B1:3) externally and internally were characterized using SEM, FTIR, XRD, DSC, and TGA. Among them, the aerogel composite C/S/B1:2 exhibited the most uniform pore size, largest specific surface area, rapid color changes in various alkaline vapors (5 µM and 50 µM), and better mechanical strength. Furthermore, the colorimetric aerogel became dark blue from light purple during fish storage at temperatures of 4 °C and 25 °C when it reached pH 7.49 and 7.33, TVC 8.9 × 107 CFU/g and 8.5 × 107 CFU/g, and TVB-N 33.8 mg/100 g and 26.12 mg/100 g, respectively, indicating fish completely deteriorated. Taken together, the colorimetric aerogel composite C/S/B1:2 was promising for determining fish freshness, which could be utilized as a non-destructive and useful intelligent sensor in monitoring various fish and meat freshness and/or quality.

A novel fluorescence probe for ultrafast detection of SO2 derivatives/biogenic amines and its multi-application: Detecting food and fish freshness, fluorescent dye and bioimaging.

In this study, we have effectively prepared a novel fluorescent probe named HDXM based on benzopyran derivatives for the ultrafast detection (within 3 s) of SO2 derivatives or biogenic amines. HDXM showed a noticeable color change after the addition of SO2 derivatives (from purple to colorless) or biogenic amines (from purple to blue), indicating that HDXM can identify two analytes with the naked eye. It is worth noting that HDXM can be used to detect SO2 derivatives in actual sugar samples, and to image HSO3-/SO32- in living cells. More importantly, sensing labels (HDXM-loaded filter paper or agarose hydrogel) enable real-time visual monitoring of salmon freshness through colorimetric and fluorescence dual channels. Compared with the Chinese national standard method, the sensing label is an effective tool for evaluating the freshness of fish. Benefiting from its excellent solubility and fluorescence performance, HDXM can be used as a versatile fluorescent material in various applications, including flexible films, glass coatings, impregnating dyes, printing, and fingerprint ink. HDXM is expected to be a promising and valuable multifunctional tool for food safety and fluorescent materials.

Developing a Portable Fluorescence Imaging Device for Fish Freshness Detection.

Rapid detection of fish freshness is of vital importance to ensuring the safety of aquatic product consumption. Currently, the widely used optical detecting methods of fish freshness are faced with multiple challenges, including low detecting efficiency, high cost, large size and low integration of detecting equipment. This research aims to address these issues by developing a low-cost portable fluorescence imaging device for rapid fish freshness detection. The developed device employs ultraviolet-light-emitting diode (UV-LED) lamp beads (365 nm, 10 W) as excitation light sources, and a low-cost field programmable gate array (FPGA) board (model: ZYNQ XC7Z020) as the master control unit. The fluorescence images captured by a complementary metal oxide semiconductor (CMOS) camera are processed by the YOLOv4-Tiny model embedded in FPGA to obtain the ultimate results of fish freshness. The circuit for the YOLOv4-Tiny model is optimized to make full use of FPGA resources and to increase computing efficiency. The performance of the device is evaluated by using grass carp fillets as the research object. The average accuracy of freshness detection reaches up to 97.10%. Moreover, the detection time of below 1 s per sample and the overall power consumption of 47.1 W (including 42.4 W light source power consumption) indicate that the device has good real-time performance and low power consumption. The research provides a potential tool for fish freshness evaluation in a low-cost and rapid manner.

Amine vapor-responsive ratiometric sensing tag based on HPTS/TPB-PVA fluorescent film for visual determination of fish freshness.

In this study, amine vapor-sensitive films with ratiometric fluorescence attributes were developed. The pH-sensitive fluorescein 8-hydroxypyrene-1,3,6-trisulfonic acid trisodium salt (HPTS) and its tetraphenylethylene derivative (TPB) were selected as ratiometric indicators and incorporated into a polyvinyl alcohol (PVA) matrix to produce HPTS/TPB-PVA films. The films responded well to amine vapors, and the interference of aromatic vapors did not substantially affect the fluorescence signals of the films. Under UV light at a wavelength of 365 nm, the fluorescence of the films changed from dark pink to light pink and finally to yellow when the freshness of the fish was visually checked during storage. In addition, the color difference values of the films showed a positive correlation with the total volatile basic nitrogen (TVB-N), ranging from 12.7 to 24.8 mg/100 g at 25 °C and 8.4 to 25.6 mg/100 g at 4 °C, respectively. This indicates that fluorescent films have good potential for quantifying fish freshness in the near future when connected to an automatic data processing system based on color differences.

Multi-Omics Approaches for Freshness Estimation and Detection of Illicit Conservation Treatments in Sea Bass (Dicentrarchus Labrax): Data Fusion Applications.

Fish freshness consists of complex endogenous and exogenous processes; therefore, the use of a few parameters to unravel illicit practices could be insufficient. Moreover, the development of strategies for the identification of such practices based on additives known to prevent and/or delay fish spoilage is still limited. The paper deals with the identification of the effect played by a Cafodos solution on the conservation state of sea bass at both short-term (3 h) and long-term (24 h). Controls and treated samples were characterized by a multi-omic approach involving proteomics, lipidomics, metabolomics, and metagenomics. Different parts of the fish samples were studied (muscle, skin, eye, and gills) and sampled through a non-invasive procedure based on EVA strips functionalized by ionic exchange resins. Data fusion methods were then applied to build models able to discriminate between controls and treated samples and identify the possible markers of the applied treatment. The approach was effective in the identification of the effect played by Cafodos that proved to be different in the short- and long-term and complex, involving proteins, lipids, and small molecules to a different extent.

A colorimetric and NIR fluorescent probe for ultrafast detecting bisulfite and organic amines and its applications in food, imaging, and monitoring fish freshness.

Herein, for the first time, we have successfully constructed a novel near-infrared (NIR) emission fluorescent probe Dpyt for ultrafast detecting (within 5 s) bisulfate and organic amines based on a 1,2-dihydrocyclopenta[b]chromene-barbiturate conjugation system. Upon addition of bisulfate or organic amines, Dpyt displayed a distinct color change from blue to colorless or from purple to blue, respectively, suggesting that the Dpyt can be used to detect two analytes by the naked eye. Based on quantum chemistry calculations, the fluorescence quenching of Dpyt after the addition of HSO3- is caused by the photoinduced electron transfer (PET) process of the adduct Dpyt-HSO3-. The fluorescence enhancement of Dpyt caused by most organic amines is due to the enhanced intramolecular charge transfer (ICT) process of deprotonated Dpyt. Notably, Dpyt can be applied for detecting HSO3- in actual food samples such as red wine and sugar, as well as for imaging of HSO3- and representative propylamine in living cells. And more importantly, indicator labels constructed by filter paper loaded with Dpyt can visually monitor the freshness of salmon in real-time by daylight and fluorescence dual-mode. The comparison with national standard method of China manifests that indicator labels are a valid tool to assess the freshness of seafood.

Assessment of fish adulteration using SnO2 nanopetal-based gas sensor and machine learning.

The work presents the identification of fish adulteration and quality assessment by incorporating a chemiresistive gas sensor and machine learning (ML) techniques. Highly sensitive SnO2 nanopetals were synthesized chemically and integrated with interdigitated electrodes to fabricate a sensor device. The sensor was calibrated with formaldehyde (37 %) with a theoretical detection limit of 75 ppb and further utilized to detect the vapors emitted from fresh and formalin-adulterated fish. An extensive sensing investigation was conducted with freshly caught Rohu fish samples. The sensing behavior was examined for all the samples at different time intervals to estimate the spoilage level. The classification between fresh and adulterated fish samples was obtained with 100 % accuracy by employing ML tools. Moreover, the storage duration and spoilage level of fish samples were quantified using regression models. This work emphasizes the potential of nanomaterials combined with machine learning for the accurate detection of adulteration in food systems.

Current applications and future trends of artificial senses in fish freshness determination: A review.

Fish is a highly demanding food product and the determination of fish freshness is crucial as it is a fundamental factor in fish quality. Therefore, the fishery industry has been working on developing rapid fish freshness determination methods to monitor freshness levels. Artificial senses that mimic human senses are developed as convenient emerging technologies for fish freshness determination. Computer vision, electronic nose (e-nose), and electronic tongue (e-tongue) are the emerging artificial senses for fish freshness determination. This review article is uniquely worked upon to investigate the current applications of the artificial senses in fish freshness determination while describing the steps, and fundamental principles behind each artificial sense, comparing them with their advantages and limitations, and future trends related to fish freshness determination. Among the artificial senses, computer vision determines the freshness of fish in a completely nondestructive way while the e-tongue determines the freshness of fish in a completely destructive way. There are developed e-noses for fish freshness determination in both destructive and nondestructive ways. By analyzing visual cues such as color, computer vision systems can assess fish quality without the need for physical contact and it makes computer vision suitable for large-scale industrial fish quality assessing applications. Overall, this review study reveals artificial senses as a proven replacement for traditional sensory panels in determining fish freshness precisely and conveniently. As future trends, there is a demand for developing applications for consumers to determine fish freshness based on artificial senses.

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.

Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence.

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future.

Droplet-based luminescent sensor supported onto hydrophobic cellulose substrate for assessing fish freshness following smartphone readout.

In this work, two sensitive droplet-based luminescent assays with smartphone readout for the determination of trimethylamine nitrogen (TMA-N) and total volatile basic nitrogen (TVB-N) are reported. Both assays exploit the luminescence quenching of copper nanoclusters (CuNCs) produced when exposed to volatile nitrogen bases. In addition, hydrophobic-based cellulose substrates demonstrated their suitability as holders for both in-drop volatile enrichment and subsequent smartphone-based digitization of the enriched colloidal solution of CuNCs. Under optimal conditions, enrichment factors of 181 and 153 were obtained with the reported assays for TMA-N and TVB-N, respectively, leading to methodological LODs of 0.11 mg/100 g and 0.27 mg/100 g for TMA-N and TVB-N, respectively. The repeatability, expressed as RSD, was 5.2% and 5.6% for TMA-N and TVB-N, respectively (N = 8). The reported luminescent assays were successfully applied to the analysis of fish samples, showing statistically comparable results to those obtained with the reference methods of analysis.

Application of HR-NMR for the Metabolic Kinetic Assessment of Red Mullet (Mullus barbatus) and Bogue (Boops boops) Samples during Different Temperature Storage.

Fish freshness and quality can be measured through several indices that can be both chemical and physical. The storage temperature and the time that elapses following the catching of the fish are fundamental parameters that define and influence both the degree of freshness and nutritional quality. Moreover, they particularly effect the kind of fish we considered. In this research, it was observed how different temperatures of storage and shelf-life (+4 °C and 0 °C) may affect the metabolic profile of red mullet (Mullus barbatus) and bogue (Boops boops) fish samples over time, specifically observing the alteration of freshness and quality. In particular, a High-Resolution Nuclear Magnetic Resonance (HR-NMR)-based metabolomics approach was applied to study the metabolic profile changes that occur in fish spoilage. The HR-NMR spectroscopy data were useful for calculating a kinetic model that was able to predict the evolution of different compounds related to fish freshness, such as trimethylamine (TMA-N) and adenosine-5'-triphosphate (ATP) catabolites for the K-index. Furthermore, NMR in combination with chemometrics allowed us to estimate a further kinetic model able to represent the spoilage evolution by considering the entire metabolome. In this way, it was also possible to detect further biomarkers characterizing the freshness and quality status of both red mullets and bogues.

Downregulation of Peroxidase Activity of Platinum Cube Enables Minute-Time Scale Colorimetric Signaling of Hypoxanthine for Fish Freshness Monitoring.

Due to its unique biological composition, aquatic products, especially fish, are extremely perishable compared to other muscle products. Herein, we proposed an artificial nanozyme-based colorimetric detection of hypoxanthine (Hx), the indicator of fish freshness, in a minute-time scale without the assistance of a natural enzyme (hypoxanthine oxidase). The principle is based on the interaction between Hx and polyvinylpyrrolidone-modified platinum cubic nanomaterials (PVP-PtNC), in which the catalytic active sites of PVP-PtNC's surface were blocked by Hx. This causes the downregulation of PVP-PtNC's catalytic ability and weakened its ability to catalyze the oxidization of 3,3',5,5'-Tetramethylbenzidine (TMB) by H2O2. Accordingly, the decrease in the UV-vis absorption and the weakening of the colorimetric reaction color is proportional to the Hx concentration. On this basis, a target-triggered colorimetric method for detecting Hx is developed for fish freshness monitoring with a fast detection speed, low cost, high accuracy, and simplified operation. Experiments reveal that the correlation response of Hx is from 0.5 µM to 10 mM with a limit of detection of 0.16 µM. In particular, the Hx detected from real fish indicates that the method possesses a promising potential for practical application. All of these features are expected to promote the development of online detection tools for food safety monitoring.

Metal-oxide-semiconductor resistive gas sensors for fish freshness detection.

Fish are prone to spoilage and deterioration during processing, storage, or transportation. Therefore, there is a need for rapid and efficient techniques to detect and evaluate fish freshness during different periods or conditions. Gas sensors are increasingly important in the qualitative and quantitative evaluation of high-protein foods, including fish. Among them, metal-oxide-semiconductor resistive (MOSR) sensors with advantages such as low cost, small size, easy integration, and high sensitivity have been extensively studied in the past few years, which gradually show promising practical application prospects. Herein, we take the detection, classification, and assessment of fish freshness as the actual demand, and summarize the physical and chemical changes of fish during the spoilage process, the volatile marker gases released, and their production mechanisms. Then, we introduce the advantages, performance parameters, and working principles of gas sensors, and summarize the MOSR gas sensors aimed at detecting different kinds of volatile marker gases of fish spoiling in the last 5 years. After that, this paper reviews the research and application progress of MOSR gas sensor arrays and electronic nose technology for various odor indicators and fish freshness detection. Finally, this review points out the multifaceted challenges (sampling system, sensing module, and pattern recognition technology) faced by the rapid detection technology of fish freshness based on metal oxide gas sensors, and the potential solutions and development directions are proposed from the view of multidisciplinary intersection.

Enzymatic determination of hypoxanthine in fish samples as a freshness indicator using the CUPRAC colorimetric sensor.

Fish consumption is essential for a healthy diet. However, all seafood including fish are susceptible to deterioration unless properly preserved. Controlling the freshness of fresh or packaged fish is a challenging issue for the food industry in terms of human health and shelf life determination. One of the main indicators showing the freshness of fish is undoubtedly the amount of hypoxanthine (Hx). As soon as the organism dies, Hx begins to be released with the cessation of ATP synthesis and shows a gradual increase over time. Therefore, Hx determination is an important indicator in the control of fish freshness. Based on this fact, a colorimetric method for the enzymatic determination of Hx using the CUPRAC (Cupric ion Reducing Antioxidant Capacity) sensor was developed. Uric acid (UA) and H2O2 are enzymatically produced by xanthine oxidase (XOD) from Hx, and both products respond to the CUPRAC reagent to produce the cuprous neocuproine (Cu(I)-Nc) chromophore chelate formed in situ on a Nafion anionic membrane on which the cationic Cu(II)-Nc complex was fixed. Hx was measured at different time intervals in the meat samples taken from sea bass (Dicentrarchus labrax), which was left to stand at room temperature for a time period between 0 and 24 h; the level of spoilage was determined from the coloration of the CUPRAC membrane sensor (via absorbance measurement at 450 nm). It was observed that there was a linear increase in the amount of Hx during the measurement period. The method was optimized for Hx determination, verified with interference analysis and standard additions to real samples, and validated against HPLC. The linear detection range of the developed method for Hx was 2.0-32.0 µM with an LOD of 0.79 µM, and early stages of fish degradation could be detected at several nanomoles of Hx per gram of fish meat. The proposed method was demonstrated to have distinct superiority over many recent colorimetric sensors of fish freshness in regard to its lower LOD for Hx, wider linear range, capability to cope with interferents (including biologically important antioxidants, such as cysteine, reduced glutathione, ascorbic acid, UA and α-tocopherol) and applicability to real samples.

Prediction of the freshness of horse mackerel (Trachurus japonicus) using E-nose, E-tongue, and colorimeter based on biochemical indexes analyzed during frozen storage of whole fish.

Electronic nose (E-nose), electronic tongue (E-tongue) and colorimeter combined with data fusion strategy and different machine learning algorithms (artificial neural network, ANN; extreme gradient boosting, XGBoost; random forest regression, RFR; support vector regression, SVR) were applied to quantitatively assess and predict the freshness of horse mackerel (Trachurus japonicus) during the 90-day frozen storage. The results showed that the fusion data of the E-nose, E-tongue and colorimeter could contain more information (with a total variance contribution rate of 94.734 %) than that of the independent one. ANN, RFR and XGBoost showed good performance in predicting biochemical indexes with the RP2 (the square correlation coefficient of the Test set) ≥ 0.929, 0.936, 0.888, respectively, while SVR models showed a bad performance (RP2 ≤ 0.835). In addition, among the established quantitative models, the RFR model had the best prediction effect on K value (freshness index) with Rp2 of 0.936, ANN model had the highest fitting degree in predicting carbonyl content (protein oxidation degree) with Rp2 of 0.978, XGBoost model had the best performance in predicting the TBA value (lipid oxidation degree) with Rp2 of 0.994, RFR model was the best strategy for predicting Ca2+-ATPase activity (protein denaturation degree) with Rp2 of 0.969. The results demonstrated that the freshness of frozen fish can be effectively evaluated and predicted by the combination of electronic sensor fusion signals.

Fabrication and Characterization of Intelligent Multi-Layered Biopolymer Film Incorporated with pH-Sensitive Red Cabbage Extract to Indicate Fish Freshness.

This study aimed to fabricate an intelligent monolayer and multi-layered biodegradable films incorporated with red cabbage extract (RCE) to act as a safe and reliable freshness indicator. A film-forming solution (FFS) of gelatin, carboxymethyl cellulose (CMC) and chitosan was prepared and fortified with 0.5% (w/v) of RCE for developing intelligent monolayer films. The intelligent multi-layer film was prepared via layer by layer casting of gelatin, chitosan (added with 0.5% of RCE) and CMC biopolymers. The thickness of the multi-layered film was the highest (0.123 ± 0.001 mm) compared to gelatin-, CMC- and chitosan-based monolayer films (p < 0.05). Chitosan film has the highest tensile strength (p < 0.05), followed by multi-layer, CMC and gelatin films. Elongation at break was slightly higher in CMC (35.67 ± 7.62%) compared to the multi-layer film (33.12 ± 9.88%) and gelatin film (p > 0.05). Water vapor permeability was higher in the multi-layer film (1.244 ± 0.05 × 10−5 g mm h−1cm−2 P−1) than the other monolayer films. Moisture content was highest in chitosan film followed by the multi-layered film (p < 0.05) and then the CMC and gelatin films. CMC film showed the highest solubility compared to multi-layered and chitosan film (p < 0.05). Additionally, transmittance and transparency values in the multi-layered film were the lowest compared to the chitosan-, CMC- and gelatin-based films. L* and a* values were the lowest, while b* values increased in the multi-layered film compared to the other film samples (p < 0.05). pH sensitivity and ammonia gas tests revealed similar color changes in chitosan and multi-layer films. However, FTIR spectra confirmed that dye leaching was not detected for the multi-layered film soaked in ethanol. The biodegradability test showed rapid degradation of multi-layered and chitosan films within 1 month. Based on the optimum results of the multi-layered film, it was applied to monitor the fresh quality of tilapia fish fillets at 4 °C for 10 days. The results of freshness acceptability were noted on day 6 due to the change in color of the multi-layer film with an estimated total volatile basic nitrogen content of 21.23 mg/100 g. Thus, the multi-layered film can be used as an indicator to monitor the quality of the fish freshness without leaching dye onto the food surface.