Beef Freshness Research Articles

Early detection of beef-quality indicators using hyperspectral imaging combined with pixel-based segmentation method corresponding to fat and protein region

This study investigated the utilization of hyperspectral imaging (HSI) in conjunction with pixel-based segmentation to predict the thiobarbituric acid-reactive substances (TBARS) and volatile basic nitrogen (VBN) content in beef. Hyperspectral images were acquired in the visible near-infrared (VIS-NIR) and shortwave infrared (SWIR) ranges to examine temporal alterations in the fat and protein regions. A partial least squares discriminant analysis (PLS-DA) model was employed to segment fat and protein pixels, followed by a partial least squares regression (PLSR) model to predict the TBARS and VBN content from the segmented spectra. The SWIR range yielded the most accurate predictions, with an Rp2 of 0.899 for the early freshness indicators. Utilizing hyperspectral information from individual fat and protein pixels, rather than modeling the entire beef image, resulted in enhanced prediction accuracy for Rp2 of TBARS (0.814–0.899) and VBN (0.394–0.532) in the early stages of storage. These findings elucidate the potential of HSI with pixel-based segmentation as a nondestructive and real-time methodology for precise monitoring of beef freshness.

A novel carboxymethyl cellulose/gum xanthan and citric acid-based film that enhances the precision of blackcurrant anthocyanin-induced color detection for beef spoilage tracking

Leveraging blackcurrant anthocyanin (BC) as an indicator and carboxymethyl cellulose (CMC), gum xanthan (GX), and citric acid (CA) as film fabricating materials, an innovative amine-responsive beef freshness intelligent film, known as CGC-BC, was successfully created. It was found that the physical characteristics, sensitivity to the biogenic amine reaction, and original color of the film were all highly influenced by the pH of the film-forming solutions. The film's freshness monitoring ability was assessed at 4, 25, and 35 °C, and various color changes were employed to monitor beef deterioration. ΔE values and the visual color difference of the low-concentration (SCG-BC-0.08 and SCG-BC-0.16) ammonia-sensitive indicator films demonstrated significant color changes than the high-concentration (SCG-BC-0.24 and SCG-BC-0.32) films. The films biodegradation (37.16 to 51.49%) ability was enhanced with increase in the proportions of BC. As the TVB-N and pH values of beef increased with the different temperatures and time and different color changes were observed from red to pink, black to brown, and yellow.

Indicator-enhanced starch-based intelligent film for nondestructive monitoring of beef freshness: Different structural phenolic acids copigment anthocyanin

Intelligent, non-destructive visual monitoring of food freshness is an emerging trend in intelligent food packaging in which highly sensitive, intelligent films are required to achieve monitoring accuracy. Hence, a highly sensitive, pH-responsive, intelligent indicator film for the real-time monitoring of beef freshness was prepared using phenolic acid-copigmented blueberry anthocyanin. UV-scanning spectroscopy confirmed that the phenolic acids exhibited colour-enhancing effects. The specific effect was different due to the different structures. The highest M value (97.98) demonstrated that ferulic acid with methoxylated has the strongest copigmentation effect. Degradation kinetics for the copigmentation of phenolic acids with anthocyanins proved that FA had the slowest degradation rate and the longest half-life (2.89 d/21.13 d) under both light and heating conditions. This proved that FA also had the best stabilizing effect on anthocyanin. FTIR demonstrated that the two bind by hydrogen bonding and π–π stacking interactions. The thermodynamic parameters revealed that different binding energies might be responsible for the differences in colour enhancement. The highest K (5.3672) and negative ΔG (−4.1631 kJ mol−1) provide theoretical support for the optimal copigmentation that can be produced by FA. The results of pH acid-base reversible responsiveness and stability tests show that FA enhanced the colour stability and indication response sensitivity of the intelligent film through copigmentation, and improved the visual identification. The film copigmented by FA was applied to beef monitoring results, which accurately identified sub-fresh portions, indistinguishable to the naked eye, where the beef was at a threshold of spoilage. Intelligent program application further standardises individual visual behaviours and improves film accuracy. This proved that ferulic acid improved the practical application value of the intelligent indicator film loaded with anthocyanin through copigmentation.

Intelligent films based on dual-modified starch and microencapsulated Aronia melanocarpa anthocyanins: Functionality, stability and application

This study aims to enhance the physical properties and color stability of anthocyanin-based intelligent starch films. Three dual-modified starches, namely crosslinked-oxidized starch (COS), acetylated distarch phosphate (ADSP), and hydroxypropyl distarch phosphate (HDSP), were utilized as film matrices. Aronia melanocarpa anthocyanins were incorporated through three different pre-treatments (free, spray-drying microencapsulation, and freeze-drying microencapsulation) to assess the prepared films' functionality, stability, and applicability. The results indicate that the ADSP film exhibited an approximately two-fold increase in elongation at break (EAB) compared to native starch film. Specifically, the ADSP film's water contact angle (WCA) reached 90°, demonstrating excellent flexibility and hydrophobicity. Scanning electron microscopy (SEM) revealed stronger interactions between anthocyanins and the film matrix after microencapsulation. Furthermore, after 30 days of exposure to 37 °C heat and light radiation, the freeze-dried anthocyanin-based intelligent film (FDA film) exhibited minimal fading, displaying the highest stability among the tested films. Notably, during beef freshness monitoring, the intelligent films underwent significant color changes as the beef deteriorated. In conclusion, the developed FDA film, with its outstanding stability and responsive pH characteristics, holds immense potential as a novel packaging material for food applications.

Machine learning supported ground beef freshness monitoring based on near‐infrared and paper chromogenic array

AbstractMaintaining freshness and quality is crucial in the meat industry, as lipid oxidation can lead to undesirable odors, flavors, and potential health risks. Traditional methods for assessing meat freshness often involve time‐consuming and destructive techniques, highlighting the need for rapid, noninvasive approaches. Recent advancements in spectroscopic and chromogenic sensor array technologies have opened up new avenues for monitoring meat quality parameters, offering the potential for real‐time, accurate, and cost‐effective solutions. As thiobarbituric acid reactive substances (TBARS) value is a classic indicator of meat lipid oxidation, this study investigated the data fusion of near‐infrared spectroscopy (NIR) and paper chromogenic array (PCA) for monitoring ground beef TBARS. A standardized PCA was fabricated by photolithography with nine chemoresponsive dyes. Changes in ground beef volatile organic compounds during storage were captured in the shifts of PCA color patterns. Nippy, an open‐source Python module, was used for automated NIR spectra preprocessing. The optimal preprocessing pipeline was found by 10‐fold cross‐validation in machine learning model development. Among optimized models, partial least square regression showed the best performance in coefficient of determination (R2) of .9477, root mean squared error of prediction of 0.0545 mg malondialdehyde/kg meat, and residual prediction deviation of 4.3717. The promising result of this study indicated the potential for NIR and PCA combinations to monitor TBARS values for ground beef freshness assessment.

Development of a Sensitive Colorimetric Indicator for Detecting Beef Spoilage in Smart Packaging.

This study aimed to fabricate and characterize a novel colorimetric indicator designed to detect ammonia (NH3) and monitor meat freshness. The sensing platform was constructed using electrospun nanofibers made from polylactic acid (PLA), which were then impregnated with anthocyanins as a natural pH-sensitive dye, extracted from red cabbage. This research involved investigating the relationship between the various concentrations of anthocyanins and the colorimetric platform's efficiency when exposed to ammonia vapor. Scanning electron microscope (SEM) results were used to examine the morphology and structure of the nanofiber mats before and after the dip-coating process. The study also delved into the selectivity of the indicator when exposed to various volatile organic compounds (VOCs) and their stability under extreme humidity levels. Furthermore, the platform's sensitivity was evaluated as it encountered ammonia (NH3) in concentrations ranging from 1 to 100 ppm, with varying dye concentrations. The developed indicator demonstrated an exceptional detection limit of 1 ppm of MH3 within just 30 min, making it highly sensitive to subtle changes in gas concentration. The indicator proved effective in assessing meat freshness by detecting spoilage levels in beef over time. It reliably identified spoilage after 10 h and 7 days, corresponding to bacterial growth thresholds (107 CFU/mL), both at room temperature and in refrigerated environments, respectively. With its simple visual detection mechanism, the platform offered a straightforward and user-friendly solution for consumers and industry professionals alike to monitor packaged beef freshness, enhancing food safety and quality assurance.

Potato oxidized hydroxypropyl starch/pectin-based indicator film with Clitoria ternatea anthocyanin and silver nanoparticles for monitoring chilled beef freshness

Potato oxidized hydroxypropyl starch (POHS)/pectin (P) functional and smart beef freshness indicator films were prepared using butterfly pea (Clitoria ternatea) anthocyanin (BA) and silver nanoparticles (AgNPs). BA exhibited significant pH-responsive color changes. BA and AgNPs were evenly distributed within a polymer matrix to create a compatible film with POHS/P. The films containing BA and AgNPs had good UV resistance and maintained strong mechanical strength, barrier properties, and color stability. The color of the indicator film changed from purple to green when exposed to ammonia, with the 1 % POHS/P/BA/AgNPs film showing the most sensitive response. The films also demonstrated strong antibacterial and antioxidant properties. The freshness of beef was monitored using 1 % POHS/P/BA/AgNPs films and was identified as sub-fresh and spoiled on days 4 and 7, respectively. The relationship between the color change of the indicator label and the freshness of chilled beef was established: purple for fresh meat, blue for less fresh meat, and green for spoiled meat. Thus, the new POHS/P/BA/AgNPs film can serve as a smart packaging material to indicate food freshness and extend shelf life. These results suggest that POHS/P/BA/AgNPs films have significant potential as an active and smart food packaging material.

Ammonia-Responsive Colorimetric Film of Phytochemical Formulation (Alizarin) Grafted onto ZIF-8 Carrier with Poly(vinyl alcohol) and Sodium Alginate for Beef Freshness Monitoring.

In this study, we devised a photothermally stable phytochemical dye by leveraging alizarin in conjunction with the metal-organic framework ZIF-8 (AL@ZIF-8). The approach involved grafting alizarin into the microporous structure of ZIF-8 through physical adsorption and hydrogen-bonding interactions. AL@ZIF-8 significantly enhanced the photostability and thermostability of alizarin. The nanoparticles demonstrate substantial color changes in various pH environments, showcasing their potential for meat freshness monitoring. Furthermore, we introduced an intelligent film utilizing poly(vinyl alcohol)-sodium alginate-AL@ZIF-8 (PA-SA-ZA) for detecting beef freshness. The sensor exhibited a superior water contact angle (52.34°) compared to the alizarin indicator. The color stability of the film was significantly enhanced under visible and UV light (ΔE < 5). During beef storage, the film displayed significant color fluctuations correlating with TVB-N (R2=0.9067), providing precise early warning signals for assessing beef freshness.

Smart carrageenan/carboxymethyl cellulose films combined with zein/gellan gum microcapsules encapsulated by composite anthocyanins for chilled beef freshness monitoring

The demand for smart packaging to monitor food spoilage is increasing. In the present study, composite anthocyanin microcapsules (CAM) were prepared by using zein and gellan gum as wall materials and protecting composite anthocyanin (CA). Carrageenan (Car)/sodium carboxymethylcellulose (CMC)-based indicator films (CC) containing CAM microcapsules were developed for monitoring the freshness of beef. The results demonstrated that the composite anthocyanins could be well dispersed in the CC matrix. CAM significantly reduced the effects of light and temperature on the stability of anthocyanins. CAM significantly improved the thermal stability, barrier properties and tensile strength of the films. The infrared spectra showed that the components of the indicator films were structurally similar and well-compatible. Among them, the indicator film CC/CAM-3 had the highest tensile strength (17.76 MPa), the lowest water vapor transmission rate (1.35 × 10−11 g/m·s·Pa) and a tight and neat cross-section. The color of the indicator film changed significantly in the ammonia environment: pink→blue-green→yellow. Applying different concentrations of indicator films to beef freshness monitoring, we found that beef reached sub-freshness and spoilage on days 4 and 7, respectively, and accordingly the indicator film CC/CAM-3 changed from pink to purple and green. To examine its capability as a colorimetric indicator film, we related the change in beef quality to the ΔE value of the indicator film, which proved to be the most sensitive when used to monitor the freshness grade of beef, as demonstrated by the indicator film CC/CAM-3.

A colorimetry-enhanced tri-functional film with high stability by polyphenol-anthocyanin co-pigmentation/conjugate: New prospect for active intelligent food packaging

A highly stable “tannin-anthocyanin conjugated” trifunctional active intelligent film was developed by incorporating bilberry anthocyanins (BA) as an indicator and tannin acids (TA) as a co-pigment into a sodium alginate-carrageenan polysaccharide matrix (SC-BA/TA). The doping of TA conferred outstanding antioxidant (DPPH scavenging rate > 90%) and antibacterial properties to the film, particularly effective against S. aureus. The SC-BA/TA films effectively blocked UV rays (close to 0%, effectively impeding most UVA, as well as nearly all UVC and UVB) within the range of 200–320 nm. The TA-BA co-pigment effect significantly improved the anthocyanins' storage and color stability (retention rate > 70% under UV and natural light conditions). TA forms conjugate with anthocyanins by π-π stacking and hydrogen bonding interactions with co-pigmentation rate increases of 10.5% and 11.0% for pH 2 and pH 3, respectively. The film exhibited good responsiveness to volatile amines within 4 min, and offered real-time monitoring of beef freshness, as indicated by visualizing color changes (from red to dark yellow color). Furthermore, the integration of the film's RGB value with beef quality via a smartphone App effectively reduces the variability in visual recognition among individuals. To sum up, composite films based on the “tannin-anthocyanin conjugate” approach hold great potential in the field of food freshness monitoring, opening new possibilities for the development of highly stable active smart packaging films.

Identifikasi Kandungan Formalin dan Kesegaran Daging Sapi dengan Image Processing

The lack of public understanding in distinguishing between fresh and spoiled meat, coupled with the unrestricted use of formalin, provides opportunities for unscrupulous traders to gain greater profits. Therefore, the development of image processing for beef freshness and formalin identification is necessary. This image processing utilizes the YOLO method, while formalin identification is done using an HCHO sensor. The system was tested every 2 hours, starting from 9:00 AM, revealing that at 9:00 AM, fresh meat was identified with a 87.89% accuracy, at 11:00 AM, fresh meat was identified with a 74.43% accuracy, and at 1:00 PM, spoiled meat was identified with a 38.85% accuracy. The results indicate that the longer the meat is left untreated, the more its freshness diminishes, eventually being identified as spoiled meat. For formalin identification in beef, results from 4 samples show that the longer the meat is exposed to formalin solution, the higher the volatility of the sensor, indicating the presence of formalin in the meat.

Design of A Beef Freshness Detector based on Color and Scent with the Mamdani Fuzzy Method

Design of A Beef Freshness Detector based on Color and Scent with the Mamdani Fuzzy Method

Visible detection of chilled beef freshness using a paper-based colourimetric sensor array combining with deep learning algorithms

This study developed an innovative approach that combines a colourimetric sensor array (CSA) composed of twelve pH-response dyes with advanced algorithms, aiming to detect amine gases and assess the freshness of chilled beef. With the assistance of multivariate statistical analysis, the sensor array can effectively distinguish five amine gases and enable rapid quantification of trimethylamine vapour with a limit of detection (LOD) of 8.02 ppb and visually monitor the fresh levels of chilled beef. Moreover, the utilization of deep learning models (ResNet34, VGG16, and GoogleNet) for chilled beef freshness evaluation achieved an overall accuracy of 98.0 %. Furthermore, t-distributed stochastic neighbour embedding (t-SNE) visualized the feature extraction process and provided explanations to understand the classification process of deep learning. The results demonstrated that applying deep learning techniques in the process of pattern recognition of CSA can help in realizing the rapid, robust, and accurate assessment of chilled beef freshness.

Live-tracking of beef freshness by sub-ppb level ammonia detection using WS2/rGO nanoflakes incorporating edge site-enriched acidic sulfur

Highly accurate, easily accessible room temperature wireless gas-sensing technology can be utilized to monitor food freshness in real time to prevent food fraud and spoiled food consumption, thus safeguarding humans from diseases.

A polylactide based multifunctional hydrophobic film for tracking evaluation and maintaining beef freshness by an electrospinning technique.

A nanofiber film was prepared by a facile electrospinning technique using polylactide (PLA), butterfly pea flower extract (BPA) and cinnamaldehyde (CIN). The as-prepared film shows the prominent antioxidative, antibacterial, colorimetric and hydrophobic properties so that the beef freshness can be monitored and maintained up to 6days at 4°C simultaneously. Besides, the nanofiber structure endows the film with a fast color responsiveness under acidic-alkaline atmospheres with different concentrations. Moreover, this film exhibits higher tensile strength (9.56 Mpa) than that of the pure PLA electrospinning film (4.40 Mpa). Especially the introduction of the BPA effectively boosts the antimicrobial ability of the CIN. The freshness, sub-freshness and spoilage levels of the beef can be easily testified by observing the color difference change of the film. So the polylactide based multifunctional film as an intelligent packaging has an excellent potential for the sub-freshness detection of meat.

A three-in-one versatile sensor for concise detecting biogenic amines and beef freshness

Biogenic amines (BAs), as important indicators for evaluating food spoilage caused by fermentation processes or microbial activities, present significant risks of food safety. Consequently, the development of a simple, sensitive, and selective detection method for amines is of great importance. In this study, we proposed a three-in-one sensor 3,6-bis(dimethylamino)-9-(ethylthio)xanthylium (PSE) for high sensitivity and selectivity detecting BAs with multimodal responses, including olfactory, colorimetric, and fluorescent signals, thus facilitating convenient real-time detection of BAs. Mechanism study indicated that the nucleophilic substitution of PSE with BAs induced such rapid multi-responses with a low detection limit (LOD = 0.03 μM). We further fabricated PSE loaded paper for portable detection of BAs vapors. And the accurate determination of BAs levels is achieved through analyzing the RGB color mode. Finally, we successfully applied these test strips for non-destructive assessing meat beef freshness with the assistance of a smartphone in on-site scenarios.

An intelligent chitosan/gelatin film via improving the anthocyanin-induced color recognition accuracy for beef sub-freshness differentiation monitoring

A novel volatile amine-responsive food freshness smart film, CG-BAE, has been developed utilizing chitosan (CS) and gelatin (GL) as film substrates and butterfly pea flower anthocyanin extract (BAE) as the indicator. The pH value of the film-forming solution was found to significantly impact the physical properties, biogenic amine response sensitivity, and initial color of the film. The hydrogen bonding interaction between anthocyanins and the film matrix was identified as the main binding force, as confirmed by FT-IR spectroscopy and molecular docking simulation analysis. Notably, CG-BAE-2.0 films have demonstrated excellent color stability, antioxidant activity, and color responsiveness. The performance of the film in monitoring beef freshness was evaluated and proved clear differentiation between four stages: freshness, sub-freshness, onset of spoilage, and complete spoilage. The color change of the film was assessed using RGB values obtained from a smartphone App, reducing the difference in color recognition by individual naked eyes. Therefore, this work addresses the limitation that BAE-based intelligent films cannot be distinguished at the starting point of meat spoilage, and presents a promising approach to sub-freshness indication, opening new possibilities for food freshness differentiation monitoring.

A pH-Responsive colourimetric sensor array based on machine learning for real-time monitoring of beef freshness

A pH-Responsive colourimetric sensor array based on machine learning for real-time monitoring of beef freshness

κ−Carrageenan−Based flexible antibacterial and pH−Sensitive hydrogels with phenanthroline covalent conjugation groups

Development of antibacterial materials are essential for many fields. In this paper, κ−carrageenan−based flexible antibacterial and pH−sensitive hydrogels with phenanthroline covalent conjugation groups were prepared. The swelling behavior demonstrated the hydrogels were with fairly good resistance to aqueous with pH values of 3–8. The bend, twist and compressing test revealed the hydrogels had fairly good flexibility and mechanical performance. The hydrogels were pH−sensitive because they can change color visible by the naked eye with the variation of pH values in the range of 3–5 and 9−13. The antibacterial performance against natural miscellaneous bacteria, S. aureus, E. coli and M. albican was investigated in detail. OD600 measurement, bacterial suspensions, antibacterial ring assay and morphology for the S. aureus, E. coli and M. albican showed that the hydrogels exhibited quite high antibacterial activity against both S. aureus and E. coli. The antibacterial rates against S. aureus and E. coli can reach 99% and 98% after 9 h incubation, respectively. The experiments for application in monitoring beef freshness demonstrated that the hydogels are potential materials for real–time monitoring the freshness of high protein food such as the beef.

Cellulose/Salep-based intelligent aerogel with red grape anthocyanins: Preparation, characterization and application in beef packaging

The purpose of this study was to prepare a porous intelligent aerogel for food packaging applications, in particular for monitoring minced beef freshness, using cellulose extracted from grape stalk, Salep as a copolymer, and red grape anthocyanins as a pH-sensitive pigment. Aerogels based on cellulose 1% (w/v) and Salep 1% (w/v) at ratios of 1:3, 3:1, and 1:1 were prepared by lyophilization. Aerogel with high porosity, low density, and higher mean pore size was chosen for preparing intelligent colorimetric aerogel (ICA) with the addition of 0.44 mg/100 mL of anthocyanins. Based on the color parameters, the stability of ICA was satisfactory when exposed to both light and dark conditions, as well as when stored at either 4 or 25 °C. Additionally, X-ray diffraction and thermogravimetric analyses indicated that an amorphous aerogel was formed, with a thermal decomposition temperature of 320 °C. The color of the ICA changed from purple on the first and 3rd days of packaging to blue-gray on the 6th day. As the spoilage process continued, the color of the indicator became dark brown. Taken together, ICA showed a quick response to minced beef spoilage with the ability to differentiate between fresh and spoiled meat during storage at 4 °C.