Meat Quality Monitoring Research Articles

Monitoring of meat quality and change-point detection by a sensor array and profiling of bacterial communities

BackgroundReal-time monitoring of food consumer quality remains challenging due to diverse bio-chemical processes taking place in the food matrices, and hence it requires accurate analytical methods. Thresholds to determine spoiled food are often difficult to set. The existing analytical methods are too complicated for rapid in situ screening of foodstuff. ResultsWe have studied the dynamics of meat spoilage by electronic nose (e-nose) for digitizing the smell associated with volatile spoilage markers of meat, comparing the results with changes in the microbiome composition of the spoiling meat samples. We apply the time series analysis to follow dynamic changes in the gas profile extracted from the e-nose responses and to identify the change-point window of the meat state. The obtained e-nose features correlate with changes in the microbiome composition such as increase in the proportion of Brochothrix and Pseudomonas spp. and disappearance of Mycoplasma spp., and with representative gas sensors towards hydrogen, ammonia, and alcohol vapors with R2 values of 0.98, 0.93, and 0.91, respectively. Integration of e-nose and computer vision into a single analytical panel improved the meat state identification accuracy up to 0.85, allowing for more reliable meat state assessment. SignificanceAccurate identification of the change-point in the meat state achieved by digitalizing volatile spoilage markers from the e-nose unit holds promises for application of smart miniaturized devices in food industry.

Deep Learning-Based Automated Cell Detection-Facilitated Meat Quality Evaluation.

Meat consumption is increasing globally. The safety and quality of meat are considered important issues for human health. During evaluations of meat quality and freshness, microbiological parameters are often analyzed. Counts of indicator cells can provide important references for meat quality. In order to eliminate the error of manual operation and improve detection efficiency, this paper proposed a Convolutional Neural Network (CNN) with a backbone called Detect-Cells-Rapidly-Net (DCRNet), which can identify and count stained cells automatically. The DCRNet replaces the single channel of residual blocks with the aggregated residual blocks to learn more features with fewer parameters. The DCRNet combines the deformable convolution network to fit flexible shapes of stained animal cells. The proposed CNN with DCRNet is self-adaptive to different resolutions of images. The experimental results indicate that the proposed CNN with DCRNet achieves an Average Precision of 81.2% and is better than traditional neural networks for this task. The difference between the results of the proposed method and manual counting is less than 0.5% of the total number of cells. The results indicate that DCRNet is a promising solution for cell detection and can be equipped in future meat quality monitoring systems.

Electrical impedance tomography for meat marbling prediction

Electrical Impedance Tomography (EIT) is a promising noninvasive technology for predicting meat quality traits, such as marbling in beef. Marbling, characterized by intramuscular fat, significantly influences beef quality by enhancing flavor, tenderness, and juiciness. This review evaluated the application of EIT in meat quality control, highlighting its ability to measure changes in conductivity to assess marbling and other quality attributes. Integrating EIT with machine-learning techniques further refines its predictive capabilities. Although EIT provides real-time, noninvasive assessments with lower costs, challenges like lower signal-to-noise ratios and complex algorithm requirements persist. Future directions include the integration of EIT with modern packaging systems and automation tools to enhance meat quality monitoring and industrial efficiency.

Intelligent Films Based on Lobeira Fruit Starch for Fresh Chicken Meat Quality Monitoring

Intelligent Films Based on Lobeira Fruit Starch for Fresh Chicken Meat Quality Monitoring

Recent Progress in Intelligent Packaging for Seafood and Meat Quality Monitoring

AbstractFood waste plays a crucial role in environmental and human health issues. To address these issues, intelligent packaging systems are proposed for the accurate assessment of the quality of food products. Intelligent packaging is a technology that integrates sensing systems with conventional packaging to provide smart sensing communication functions for real‐time food quality monitoring. The most important parts of this technology are generally classified as quantitative sensors (sensors) and qualitative sensors (indicators). Since seafood and meat products can spoil easily if not stored properly, applying intelligent packaging for real‐time monitoring of the safety of these products is beneficial. In this review, the spoiling process and essential performance characteristics are indicators of the quality of these food products are first discussed. Then, The characteristics and importance of various sensors and indicators that can be used for seafood and meat quality monitoring are presented. While discussing these topics, an updated review of recent scientific studies, preconditions, materials, advantages, and limitations are provided. Furthermore, the future need for improvements in intelligent packaging systems for real‐time quality and safety monitoring of food products is discussed. Finally, several important research examples of the challenges and perspectives of intelligent packaging applications for meat and seafood quality monitoring are presented.

Colorimetric Paper Sensor for Food Spoilage Based on Biogenic Amine Monitoring.

Biogenic amines (BAs), nitrogenous molecules usually present in different foods, can be considered an indicator of freshness and food quality since their amount increases during food spoilage. Their detection, possibly in real time via the use of smart packaging, is therefore of crucial importance to ensure food safety and to fulfill consumers' demand. To this end, colorimetric sensors are considered one of the most feasible solutions. Here, we report a user-friendly colorimetric sensing paper able to detect BAs via the naked eye. The sensing molecule is the aglycone genipin, a natural cross-linking agent extracted from gardenia fruit, able to bind BAs producing water-soluble blue pigments. The paper sensor was applied to chicken meat quality monitoring and a quantitative analysis was performed with image acquisition via a smartphone camera, achieving a limit of detection equivalent to 0.1 mM of putrescine. The suitability of the BA sensing paper was assessed by integrating the sensor into smart packaging and analyzing commercial chicken meat samples stored at different temperatures; the results of the sensor paralleled the "best before date" indicated on the label, confirming the potential applicability of the sensor as a smart label.

Fusion of Spectral and Textural Data of Hyperspectral Imaging for Glycine Content Prediction in Beef Using SFCN Algorithms

Glycine, the simplest free amino acid. It is one of the important factors affecting the flavor of beef. In this study, a fast and non-destructive method combining near-infrared hyperspectral (900–1700 nm) and textural data was first proposed to determine the content and distribution of glycine in beef. On the basis of spectral information pre-processing, spectral features were extracted by the interval variable iterative space shrinkage approach, competitive adaptive reweighting algorithm, and uninformative variable elimination (UVE). The glycine content prediction models were established by partial least squares regression, least squares support vector machine, and the optimized shallow full convolutional neural network (SFCN). Among them, the UVE-SFCN model was found to show better results with prediction set determination coefficient (RP2) of 0.8725. Furthermore, textural features were extracted by the gray-level co-occurrence matrix and fused with the spectral information of the best feature band to obtain an optimized UVE-FSCN-fusion model (RP2 = 0.9005, root mean square error = 0.3075, residual predictive deviation = 0.2688). Compared with the full spectrum and characteristic wavelength spectrum models, RP2 was improved by 6.41% and 3.10%. The best fusion model was visualized to represent the distribution of glycine in beef. The results showed that the prediction and visualization of glycine content in beef were feasible and effective, and provided a theoretical basis for the hyperspectral study of meat quality monitoring or the establishment of an online platform.

Development of a Portable Near-Infrared Spectroscopy Tool for Detecting Freshness of Commercial Packaged Pork.

Real-time monitoring of meat quality requires fast, accurate, low-cost, and non-destructive analytical methods that can be used throughout the entire production chain, including the packaged product. The aim of this work was to evaluate the potential of a portable near-infrared (NIR) spectroscopy tool for the on-site detection of freshness of pork loin fillets in modified atmosphere packaging (MAP) stored on display counters. Pork loin slices were sealed in MAP trays under two proportions of O2/CO2/N2: High-Ox-MAP (30/40/30) and Low-Ox-MAP (5/20/75). Changes in pH, color, thiobarbituric acid reactive substances (TBARS), Warner−Bratzler shear force (WBSF), and microbiology (total viable counts, Enteriobacteriaceae, and lactic acid bacteria) were monitored over 15 days post-mortem at 4 °C. VIS-NIR spectra were collected from pork fillets before (through the film cover) and after opening the trays (directly on the meat surface) with a portable LABSPEC 5000 NIR system in diffuse reflectance mode (350−2500 nm). Quantitative NIR models by partial least squares regression (PLSR) showed a promising prediction ability for meat color (L*, a*, C*, and h*) and microbiological variables (R2VAL > 0.72 and RPDVAL > 2). In addition, qualitative models using PLS discriminant analysis obtained good accuracy (over 90%) for classifying pork samples as fresh (acceptable for consumption) or spoiled (not acceptable) based on their microbiological counts. VIS-NIR spectroscopy allows rapid evaluation of product quality and shelf life and could be used for on-site control of pork quality.

Novel Interventions in Monitoring of Meat Quality and Spoilage: A Review

Novel Interventions in Monitoring of Meat Quality and Spoilage: A Review

Electrical gas sensors for meat freshness assessment and quality monitoring: A review

The simple methods for Freshness assessment have been gained attention in the last years, mainly in case of highly perishable food products as those delivered by the meat sector . Furthermore, the growing demand for fresh and safe food makes the development of reliable, low-cost and time-saving methods for freshness investigation an important need. In this context, gas sensors represent a valuable tool to food freshness and quality monitoring , avoiding the supply and consumption of rotten meat products, which may be associated with foodborne diseases. The present work gives an overview of the existing gas sensors applied to meat quality monitoring, with emphasis on a particular class of gas sensors, the electrical gas sensors . This type of sensors has been shown to have a promissory interface with wireless technologies, such as the existing NFC (near field communication) and RFID (radio frequency identification) sensor tags, which may lead to meat quality and safety assurance at consumers’ level. The presented literature studies demonstrated that the electrical properties of different materials have been exploited for the development of gas sensors. The implementation of electrical gas sensors interfaced with wireless is a promising technology in the near future because of the possibility of a rapid and accessible meat quality monitoring. • Recent gas sensors applications and prospects for meat quality are reviewed. • Gas sensors for meat monitoring were summarized based on the transduction principle. • Electrical gas sensor (EGS) versatility in meat quality and freshness assessment. • EGS combined with wireless technologies has great potential for meat monitoring.

Anthocyanin-sensitized gelatin-ZnO nanocomposite based film for meat quality assessment

Meat is considered a highly perishable food, and the interest in developing tools to monitor meat quality products has increased these years. A novel gelatin-ZnO-anthocyanin ternary nanocomposite film is proposed as a sensitive layer to meat quality monitoring in the present work. The incorporation of anthocyanin (ATH) on gelatin-ZnO (G-ZnO) film induced a sensitivity improvement of films towards ammonia vapor according to impedance measurements. G-ZnO-ATH film presented a good response (38.69 %) to the presence of ammonia vapor at 300 ppm. Also, good selectivity for ammonia was observed in the films. G-ZnO-ATH, applied to minced meat's quality monitoring at different storage conditions, showed a good performance, with a significant (p < 0.05) non-linear Spearman correlation between the response and the total volatile basic nitrogen released during meat spoilage for both storage conditions. The results suggest new perspectives in the developed film as a promising nanocomposite material for meat quality monitoring.

Evolution of raw meat polarization-based properties by means of Mueller matrix imaging.

The possibilities of using Mueller matrix (MM) imaging polarimetry to assess meat quality have not yet been sufficiently explored. In the current study, the fresh porcine muscles are imaged at room temperature with a wide-field MM imaging polarimeter over 26 hours to visualize dynamics of tissue optical properties through applying Lu-Chipman decomposition. The frequency distribution histograms (FDHs) and statistical analysis of the MM elements show prominent changes over time. The wavelength spectra of both total depolarization and scalar retardance have dips at 550 nm whereas their values continuously increase with time; the former is referred to the increase of number of scattering events and decrease of myoglobin absorption in the red part of visible spectra related to meat color and freshness, while the latter is associated with the increase in birefringence and meat tenderness. The obtained results are promising to develop a novel fast noncontact optical technique for monitoring of meat quality.

Short communication: Long term performance of near infrared spectroscopy to predict intramuscular fat content in New Zealand lamb

This study investigates the performance of a partial least squares regression model to predict intramuscular fat (IMF) in lamb M. longissimus lumborum developed using near infrared (NIR) data collected under a range of different conditions. A total of 26 independent NIR datasets were collected across 7 years, including 14 flocks, four devices and several measurement conditions. A model is developed and its performance is tested using a total of n = 3201 NIR spectra and intramuscular fat percentage measurements by wet chemistry. The model had a coefficient of determination by cross-validation of 0.52, which agrees with previous results using smaller numbers of animals. Overall the results show that near infrared models can be robust across many varying conditions. These models could potentially be implemented in an automated meat quality monitoring system.

Convergence of a Disease and Litigation Leading to Increased Scallop Discard Mortality and Economic Loss in the Georges Bank Fishery

AbstractRotational management in the Atlantic sea scallop Placopecten magellanicus fishery has focused on closing areas to fishing when a high proportion of small scallops is observed in a discrete area, but less attention has been paid to reopening closed areas to prevent poor meat quality. In this fishery there are several large marine protected areas, including Closed Area I of Georges Bank. Intense drop camera surveys of Closed Area I in 1999 led to incorporating a 700‐km2 area into the rotational management scheme. There was a larger abundance of scallops to the south of this area, and these were made available for harvest in 2005, but this fishery was closed in 2006 when the management plan was overturned by a federal court decision. When the area was reopened again in 2011, however, the expected harvest was not achieved. Although fishermen harvested the scallops and opened the shells to extract the meat, they found that many of the meats were gray and not of marketable quality and so they discarded them. The gray meats were linked to a highly pathogenic apicomplexan infection. There was a 52% decline in exploitable biomass between 2011 and 2013 (7,300 metric tons, representing a gross revenue of US$170 million) that could not be attributed to a reduction in landings, resulting in a failure to complete the allotted trips. The scallop rotation management plan has been extremely successful overall, with 69 of the access area trips harvesting over 80% of the allocation; however, increased monitoring of meat quality would further improve management.

Noise filtering framework for electronic nose signals: An application for beef quality monitoring

Beef is one of the most popular and widely consumed foodstuffs in the world. Nevertheless, it can easily decay if not properly treated during distribution and storage. The consumption of low quality beef causes a serious health hazard. The electronic nose (e-nose) is a rapid and low-cost instrument for beef quality classification. Hence, the development of a mobile e-nose for online meat quality monitoring is appealing. In the last few years, e-noses have been used to classify different grades of beef and to predict the number of the microbial population in beef samples. Several methods are used to deal with these classification and regression problems. Especially in multiclass beef classification and regression, signals contaminated with noise can significantly degrade the performance of the pattern recognition module. Therefore, the presence of internal and external noise in e-nose signals is a major challenge in beef quality monitoring. In this study, a noise filtering framework based on a fine-tuned discrete wavelet transform (DWT) was developed to handle noisy signals generated by an e-nose sensor array. To the best of our knowledge this is the first time the problem of e-nose signal noise in beef quality classification is tackled. The proposed framework was integrated and tested on several machine learning algorithms that were used in previous studies, i.e. k-nearest neighbor (k-NN), support vector machine (SVM), quadratic discriminant analysis (QDA), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS). Furthermore, the effect of noise filtering was investigated in the classification with two, three, and four classes of beef. The effect of noise filtering was also observed in regression tasks to predict the size of microbial population in beef samples. The experimental results showed that the proposed framework provides a significant improvement in multiclass classification and regression tasks.

An overview of smart packaging technologies for monitoring safety and quality of meat and meat products

The food packaging sector has experienced much development since its inception. In the past few decades, innovations in packaging sector have led to the development of smart packaging (SP) systems that carve a niche in a highly competitive food industry. SP systems have great potential for improving the shelf‐life, and safety of food products apart from their basic roles of protecting the products against unwanted biological, chemical, and physical damage and keeping them clean. Indicators and sensors, SP components, are used for real‐time monitoring of meat quality and subsequently inform the retailers and consumers about the freshness, microbiological, temperature, and shelf life status of the products. Barcodes and radio‐frequency identification tags are employed in meat packaging for real‐time information about the authenticity, and traceability of the products in the supply chain. Recently, innovations in SP technologies resulted in fast, sensitive, and effective detection, sensing, and record keeping of freshness, microbiological, and shelf life status of meat and meat products. The SP system shows promise for extensive utilization in the meat industry in response to the consumer appreciation for safe, and quality meat products, as well as their waste reduction notions. This paper gives an updated overview of ongoing scientific research, and recent technological advances that offer the perspectives of developing smart meat packaging systems that are capable of monitoring the physical, microbial, and chemical changes of the package contents from producer to the point of sale and even beyond, and remediating potential adverse reactions.

Evolution of the bulk optical properties of bovine muscles during wet aging

The bulk optical properties (BOP) of two bovine muscles were studied in the 500nm to 1850nm wavelength range. Over a two-week period of wet aging, the BOP of the biceps femoris (BF) and longissimus lumborum (LL) were determined and related to moisture content, tenderness and cooking loss. The absorption by myoglobin and reduced scattering coefficient were higher in the BF compared to the LL. The scattering anisotropy factor was relatively high (>0.95 for LL), representing dominant forward scattering. Two-toning effects in the BF could be attributed to significant scattering differences, as no differences in absorption properties were observed. During wet aging, the anisotropy factor decreased, while tenderness increased. It was hypothesized that this might be related to proteolysis of cytoskeletal proteins. The results show the potential use of BOP to monitor tenderization and the cause of color differences in beef muscles. Moreover, this information could be used to develop and optimize optical sensors for non-destructive meat quality monitoring.

Development of mobile electronic nose for beef quality monitoring

Meat is one of foodstuff that widely consumed in the world. Unfortunately, the quality of meat can easily degrade if not handled properly and become the serious health hazards if consumed. Hence, the food safety system is very important to guarantee the quality of food to be consumed. In this study, we introduced the development of mobile electronic nose for beef quality detection and monitoring. This system is developed using low-cost hardware and possible to integrate with cooling box or refrigerator for real time monitoring and analysis during distribution and storage processes. K-Nearest Neighbor with signal preprocessing is used to classify two, three, and four classes of beef. The experimental results show that the system can perfectly distinguish fresh and spoiled beef. Moreover, it has promising classification accuracy for binary, three classes, and four classes classification with 93.64%, 86.00%, and 85.50%, respectively. Hence, this system has a potential solution to provide low-cost, easy to use, and real-time meat quality monitoring system.

Non-destructive mobile monitoring of microbial contaminations on meat surfaces using porphyrin fluorescence intensities

A non-destructive mobile system for meat quality monitoring was developed and investigated for the possible application along the whole production chain of fresh meat. Pork and lamb meat was stored at 5°C for up to 20days post mortem and measured with a fluorescence spectrometer. Additionally, the bacterial influence on the fluorescence signals was evaluated by different experimental procedures. Fluorescence of NADH and different porphyrins could be correlated to the growth of diverse bacteria and hence used for contamination monitoring. The increase of porphyrin fluorescence started after 9days p.m. for pork and after 2days p.m. for lamb meat. Based on the results, a mobile fluorescence system was built and compared with the laboratory system. The corrected function of the meat slices showed a root mean square error of 1156.97 r.u. and a mean absolute percentage error of 12.59%; for lamb the values were 470.81 r.u. and 15.55%, respectively. A mobile and non-invasive measurement system would improve the microbial security of fresh meat.

Development of on package indicator sensor for real-time monitoring of meat quality.

Aim:The aim was to develop an indicator sensor for real-time monitoring of meat quality and to compare the response of indicator sensor with meat quality parameters at ambient temperature.Materials and Methods:Indicator sensor was prepared using bromophenol blue (1% w/v) as indicator solution and filter paper as indicator carrier. Indicator sensor was fabricated by coating indicator solution onto carrier by centrifugation. To observe the response of indicator sensor buffalo meat was packed in polystyrene foam trays covered with PVC film and indicator sensor was attached to the inner side of packaging film. The pattern of color change in indicator sensor was monitored and compared with meat quality parameters viz. total volatile basic nitrogen, D-glucose, standard plate count and tyrosine value to correlate ability of indicator sensor for its suitability to predict the meat quality and storage life.Results:The indicator sensor changed its color from yellow to blue starting from margins during the storage period of 24 h at ambient temperature and this correlated well with changes in meat quality parameters.Conclusions:The indicator sensor can be used for real-time monitoring of meat quality as the color of indicator sensor changed from yellow to blue starting from margins when meat deteriorates with advancement of the storage period. Thus by observing the color of indicator sensor quality of meat and shelf life can be predicted.