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.

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.

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.

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.

Colorimetric Paper-Based Dual Indicator Label for Real-Time Monitoring of Fish Freshness.

Research background: Fish freshness and quality monitoring are of high importance for consumers, retailers and fishing industry. Therefore, developing novel approaches that are simple, fast, non-destructive and inexpensive to monitor fish freshness in real time is of great value. One alternative is using Intelligent or smart packaging to monitor the freshness or conditions of packaged fish. Experimental approach: On-package dual indicator label based on paper-based pH sensors was developed for real-time monitoring of the milkfish (Chanos chanos) freshness. The paper-based pH sensor was prepared using bromocresol purple (BCP) and bromothymol blue (BTB) that were immobilized onto a filter paper by dip coating. Herein, the fish degradation could be monitored visually by the dual indicator label, where the BCP changes from yellow to pink, then finally to purple, while the BTP changes from orange to green-yellow, and finally to green-blue to indicate fresh, medium fresh or spoiled product, respectively. Results and conclusion: The label responds to the pH change caused by the fish degradation and the colour of dual indicator changes to show the fish freshness at room temperature and chiller conditions. This pH change was followed by changes in the other parameters related to fish freshness, such as total volatile basic nitrogen (TVBN), total viable count (TVC), texture and odour. The threshold of fish spoilage at room temperature was observed at 8 h and under chiller conditions at 7 days when the deterioration time point was indicated by the colour changes. Thus, it can be concluded that the dual indicator label can be applied as a simple and low-cost on-package active label for fish freshness monitoring. Novelty and scientific contribution: Increasing consumer concerns about quality and safe food worldwide has boosted the search for a novel approach to food monitoring. In this work, a simple and practical on-package dual indicator label for real-time monitoring of fish freshness was developed. The colorimetric pH sensor was obtained simply by dip-coating of filter paper, yet it enables easy and accurate detection of fish spoilage with the naked eye. Similarly, the dual indicator label changes colour for other freshness parameters, such as TVBN, TVC, texture and odour.

Sensing Technology for Fish Freshness and Safety: A Review.

Standard analytical methods for fish freshness assessment are based on the measurement of chemical and physical attributes related to fish appearance, color, meat elasticity or texture, odor, and taste. These methods have plenty of disadvantages, such as being destructive, expensive, and time consuming. All these techniques require highly skilled operators. In the last decade, rapid advances in the development of novel techniques for evaluating food quality attributes have led to the development of non-invasive and non-destructive instrumental techniques, such as biosensors, e-sensors, and spectroscopic methods. The available scientific reports demonstrate that all these new techniques provide a great deal of information with only one test, making them suitable for on-line and/or at-line process control. Moreover, these techniques often require little or no sample preparation and allow sample destruction to be avoided.

Evaluation of fish freshness using impedance spectroscopy based on the characteristic parameter of orthogonal direction difference.

BACKGROUND: As a nondestructive testing technology, electrochemical impedance spectroscopy (EIS) has been applied to evaluate food quality because of its features of rapidity, low cost, nondestructiveness and portability. However, fish freshness evaluation based on existing EIS technology is affected by the differences of individual biological samples. In this study, the difference of electrical properties between two orthogonal directions was extracted to develop a new freshness indicator. A real part orthogonal direction difference parameter set (RODDS) was used to establish a prediction model for total volatile basic nitrogen (TVB-N). RESULTS: Compared with the traditional parameter of EIS, coefficient of determination between RODDS and TVB-N increased from 0.55 to 0.71 for the calibration group, and root mean squared error between predicted and measured values of TVB-N decreased from 5.46 to 3.81 for the test group. CONCLUSIONS: The results implied that RODDS could effectively offset individual differences in basic electrical properties and improve the TVB-N prediction accuracy in practical application scenarios with samples from multiple origins. The proposed method may provide a new idea for the development and improvement of EIS-based portable testing devices for fish and meat. © 2020 Society of Chemical Industry.

Tilapia fish microbial spoilage monitored by a single optical gas sensor.

As consumption of fish and fish-based foods increases, non-destructive monitoring of fish freshness also becomes more prominent. Fish products are very perishable and prone to microbiological growth, not always easily detected by organoleptic evaluation. The analysis of the headspace of fish specimens through gas sensing is an interesting approach to monitor fish freshness. Here we report a gas sensing method for monitoring Tilapia fish spoilage based on the application of a single gas sensitive gel material coupled to an optical electronic nose. The optical signals of the sensor and the extent of bacterial growth were followed over time, and results indicated good correlation between the two determinations, which suggests the potential application of this simple and low cost system for Tilapia fish freshness monitoring.

Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis.

A technique that combines the spatial resolution of a 3D structured-light (SL) imaging system with the spectral analysis of a hyperspectral short-wave near infrared system was developed for freshness predictions of gilthead sea bream on the first storage days (Days 0-6). This novel approach allows the hyperspectral analysis of very specific fish areas, which provides more information for freshness estimations. The SL system obtains a 3D reconstruction of fish, and an automatic method locates gilthead's pupils and irises. Once these regions are positioned, the hyperspectral camera acquires spectral information and a multivariate statistical study is done. The best region is the pupil with an R² of 0.92 and an RMSE of 0.651 for predictions. We conclude that the combination of 3D technology with the hyperspectral analysis offers plenty of potential and is a very promising technique to non destructively predict gilthead freshness.

Development of an SPME-GC-MS method for the specific quantification of dimethylamine and trimethylamine: use of a new ratio for the freshness monitoring of cod fillets.

BACKGROUND: Fish is a highly perishable food, so it is important to be able to estimate its freshness to ensure optimum quality for consumers. The present study describes the development of an SPME-GC-MS technique capable of quantifying both trimethylamine (TMA) and dimethylamine (DMA), components of what has been defined as partial volatile basic nitrogen (PVB-N). This method was used, together with other reference methods, to monitor the storage of cod fillets (Gadus morhua) conserved under melting ice. RESULTS: Careful optimisation enabled definition of the best parameters for extracting and separating targeted amines and an internal standard. The study of cod spoilage by sensory analysis and TVB-N assay led to the conclusion that the shelf-life of cod fillet was between 6 and 7 days. Throughout the study, TMA and DMA were specifically quantified by SPME-GC-MS; the first was found to be highly correlated with the values returned by steam distillation assays. Neither TMA-N nor DMA-N were able to successfully characterise the decrease in early freshness, unlike dimethylamine/trimethylamine ratio (DTR), whose evolution is closely related to the results of sensory analysis until the stage where fillets need to be rejected. CONCLUSION: DTR was proposed as a reliable indicator for the early decrease of freshness until fish rejection. © 2015 Society of Chemical Industry.

Recent advances in methods and techniques for freshness quality determination and evaluation of fish and fish fillets: a review.

The freshness quality of fish plays an important role in human health and the acceptance of consumers as well as in international fishery trade. Recently, with food safety becoming a critical issue of great concern in the world, determination and evaluation of fish freshness is much more significant in research and development. This review renovates and concentrates recent advances of evaluating methods for fish freshness as affected by preharvest and postharvest factors and highlights the determination methods for fish freshness including sensory evaluation, microbial inspection, chemical measurements of moisture content, volatile compounds, protein changes, lipid oxidation, and adenosine triphosphate (ATP) decomposition (K value), physical measurements, and foreign material contamination detection. Moreover, the advantages and disadvantages of these methods and techniques are compared and discussed and some viewpoints about the current work and future trends are also presented.

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.

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.

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.

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.

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.

Monitoring fish freshness with pH-sensitive hydrogel films containing quercetin or eucalyptol.

This research developed pH-sensitive smart films using carboxymethyl cellulose (CMC) and collagen (COL), combined with either quercetin (QCT) or eucalyptol (EUC), to prevent fish meat spoilage. COL, extracted from isinglass, was confirmed as type I through SDS-PAGE. The films were characterized using FESEM, FTIR, and TGA. The addition of QCT or EUC enhanced antioxidant levels to 60.16% and 70.83%, respectively, up from a baseline of 10.4%. It also increased tensile strength from 3.32 ± 0.22 to 11.8 ± 0.25 and 13.2 ± 0.27 MPa, and enhanced elongation at break from 5 ± 3.1% to 27.7 ± 1.1% and 30.15 ± 2.1%. Fish meat packaged with QCT showed a lower spoilage rate due to the antibacterial and antioxidant effects of EUC and QCT (TVBN = 7.37 ± 0.01), compared to CMC/COL film (TVBN = 10.11 ± 0.02) and non-packaged fish (TVBN = 11.23 ± 0.01). The films exhibit >80% transparency, highlighting their suitability for food packaging. CMC/COL/QCT is preferred for fish packaging because it offers better mechanical properties and lower TVB-N levels.

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. A portable multispectral imaging sensor was used for the rapid prediction of microbiological quality of fish fillets. Seabream fillets, packaged either in aerobic or vacuum conditions, were collected from both aquaculture and retail stores, while images were also acquired both from the skin and the flesh side of the fish fillets. In parallel to image acquisition, the microbial quality was also estimated for each fish fillet. The data were used for the training of predictive artificial neural network (ANN) models for the estimation of total aerobic counts (TACs). Models were built separately for fish parts (i.e., skin, flesh) and packaging conditions and were validated using two approaches (i.e., validation with data partitioning and external validation using samples from retail stores). The performance of the ANN models for the validation set with data partitioning was similar for the data collected from the flesh (RMSE = 0.402-0.547) and the skin side (RMSE = 0.500-0.533) of the fish fillets. Similar performance also was obtained from validation of the models of the different packaging conditions (i.e., aerobic, vacuum). The prediction capability of the models combining both air and vacuum packaged samples (RMSE = 0.531) was slightly lower compared to the models trained and validated per packaging condition, individually (RMSE = 0.510, 0.516 in air and vacuum, respectively). The models tested with unknown samples (i.e., fish fillets from retail stores-external validation) 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. Hence, these outcomes could be beneficial not only for the industry and food operators but also for the authorities and consumers.

Fraud-proof methylcellulose-based fish freshness indicator: Reversibility in halochromic sensing of basic volatiles is tailored by ionic strength.

Food from animal sources (e.g., fish) represents the food group most likely to disseminate diseases to humans. To prevent food contamination and foodborne illnesses, intelligent packaging has been developed to monitor fish freshness by real-time tracking their physicochemical attributes and informing consumers about their conservation state. In this context, we investigated the influence of ionic strength (IS) provided by CaCl2 on the chromatic response of anthocyanin açai extracts incorporated into methylcellulose (MC) within hydrocolloid-based colorimetric sensors for monitoring the freshness of Lambari fish. The color sensitivity of the sensors was modulated by IS in the presence of NH3 volatile and/or TVB-N. Increasing IS led to a plasticizing effect in the MC matrix, which influenced the chromatic properties of anthocyanin in the presence of NH3 and/or TVB-N. The perception of distinct colors by untrained eyes improved from 10 min with the control sensor to 2.5 min for sensors with IS >50 mM. Adjusting the IS to 500 mM with LiCl, CaCl2, or MgCl2 resulted in gray-green, blue, or moss-green colors, respectively, diverging from the control sensor's color (pink and gray) after 10 min of ammonia exposure, confirming salt-induced copigmentation. Color irreversibility in the sensors was achieved when the IS exceeded 250 mM. Through principal component analysis, we statistically validate the efficacy of the sensor in assessing the freshness of Lambari fish. The sensor maintained its color-change capability even after 60 d of storage and was able to classify Lambari fish freshness according to Brazilian and European standards. This study elucidates the interrelation between the structures and properties of natural compounds such as MC, anthocyanin, and CaCl2, providing a method to control the chromatic properties of sensors intended to monitor food quality, safety, and shelf-life.

A pH-sensitive film based on chitosan/gelatin and anthocyanin from Zingiber striolatum Diels for monitoring fish freshness.

As a new type of packaging method, the anthocyanin-based pH-sensitive indicator film has gained much attention owing to low cost, small size, and visually informative property. In this study, an intelligent film based on chitosan/gelatin (CG) matrix with Zingiber striolatum Diels (ZSD) anthocyanin for fish freshness monitoring was developed. The film properties, including thickness, moisture content, color, mechanical properties, UV-vis light barrier property, as well as pH and ammonia sensitivity, were evaluated. The CG-ZSD films exhibited a more compact structure when compared with the CG film. The CG-ZSD20 film showed the highest elongation at break (6.33 ± 0.62%) and lowest tensile strength (20.0 ± 0.58 MPa). FTIR spectra revealed the strong hydrogen bond interactions between ZSD and polymer matrix. Film incorporated with 15% anthocyanin extract has increased melting temperature at 118.9 °C, and a lower weight loss (13.8%) at melting temperature. In pH 1-14 buffer, the color of CG-ZSD films underwent a significant change from red to yellow-green. The CG-ZSD15 film was utilized for monitoring fish freshness and showed visible color changes from deep purple to brown. The total volatile basic nitrogen content and pH value changes of fish were closely related to the visual color changes in film. This demonstrated that the film was a highly pH-sensitive film for quantifying fish freshness in real-time.