Beef Freshness Research Articles

Development of a novel colorimetric sensor array based on oxidized chitin nanocrystals and deep learning for monitoring beef freshness

Food spoilage not only causes food waste, but also leads to serious foodborne illnesses. To address these concerns, a novel colorimetric sensor array (CSA) fabricated from oxidized chitin nanocrystals (O-ChNCs) combined with convolutional neural network (CNN) had been developed for real-time monitoring of beef freshness. The oxidation procedure downsized chitin to the nanoscale with enlarged surface areas and more sites that are reactive. It also converted hydroxyl groups to carboxyl groups, giving chitin nanocrystals high negative charges. These enhanced electrostatic interactions of O-ChNCs with ammonium cations, which was validated with three representative food spoilage gases (methylamine (MA), trimethylamine (TMA), and ammonia (NH3)). The detection limits reached 100, 70, and 70 ppm for MA, TMA, and NH3, respectively. All four CNN architectures succeeded with over 96 % accuracy in discriminating freshness when monitoring real beef samples stored at room temperature for 4 days in real time. The highest accuracy of 99.27 % was achieved by ResNet-50. The overall results indicated that the newly developed O-ChNCs-based CSA coupled with CNN achieved fast and reliable monitoring of beef freshness.

A Ratiometric Fluorescent Sensor Based on Silicon Quantum Dots and Silver Nanoclusters for Beef Freshness Monitoring

A ratiometric fluorescent sensor with hydrogen sulfide (H2S) and methanthiol (CH3SH) sensitivity was developed to real-time monitor beef freshness. A silicon quantum dots (SiQD) and silver nanoclusters (AgNC) complex, namely SiQD-AgNC, was used as the dual emission fluorescence materials. Due to the fluorescence resonance energy transfer (FRET) effect between SiQD and AgNC, when the fluorescence of AgNC (610 nm) was quenched by H2S or CH3SH, the fluorescence of SiQD (468 nm) recovered, resulting in an increase of the fluorescent intensity ratio (I468/I610). I468/I610 showed a linear relationship with the H2S concentration within the concentration range of 1.125-17 μM, with a limit of detection (LOD) value of 53.6 nM. Meanwhile, I468/I610 presented two linear relationships with the CH3SH concentration within the concentration range of 1.125-17 μM and 23.375-38.25 μM, respectively, with a LOD value of 56.5 nM. The SiQD-AgNC complex was coated on a polyvinylidene fluoride (PVDF) film to form a portable SiQD-AgNC/PVDF film sensor. This film showed purplish red-to-cyan color changes in response to H2S and CH3SH, with LOD values of 224 nM and 233 nM to H2S and CH3SH, respectively. When the film was used to monitor beef freshness at 4 °C, its fluorescent color gradually changed from purplish red to cyan. Hence, this study presented a new ratiometric fluorescent sensor for intelligent food packaging.

Novel “all-in-one” multifunctional gelatin-based film for beef freshness maintaining and monitoring

In this study, a novel multifunctional food packaging was developed by incorporating alizarin (AL) and oregano essential oil Pickering emulsion (OEOP) into a gelatin film matrix. The incorporation of OEOP and alizarin improved the UV–vis resistance property of the film, blocking almost all UV–vis light (decreasing 71.80% to 0.06% at 400 nm). The elongation-at-break (EBA) was 4.02 times of that of gelatin film, indicating the improved mechanical properties of the films. This film showed a significant color change from yellow to purple in the pH range of 3–11 and a considerable sensitivity to ammonia vapor within 4 min, which was attributed to the deprotonation of the alizarin molecule. The film's antioxidant and dynamic antimicrobial capacity was significantly improved owing to the sustained release effect of OEOP. Furthermore, the multifunctional film effectively slowed down the beef spoilage rate and provided real-time visual monitoring of freshness through color changes. Additionally, the color change of the beef quality was linked to the RGB values of the film through a smartphone APP. Overall, this work broadens the possibilities of applications in the food packaging industry for multifunctional food packaging film with preservation and monitoring functions.

Preparation of a composite-based thin film and its sensing application for detecting beef freshness

In order to improve the stability of polyphenols anthocyanidins as freshness indicator in food packaging, in this study, gellan (GL), grape-skin anthocyanidins (GSAC) and chitosan-polyvinyl alcohol (CHPA) were prepared as a composite film to protect beef and indicate the beef freshness for visual detection. The results indicated that the composite film appears good mechanical properties. The results of scanning electron microscopy showed that when the composite film were added into CHPA, the structure compatibility and stability of the film was the best. Then the complicate film was used to indicate the beef freshness and the beef was stored at 4 °C, the total volatile base nitrogen content of beef had increased continuously with the extension of storage time. After 6 days, the total volatile base nitrogen content was 35.65 mg/100 g, the thiobarbituric acid reaction product value was 1.005 mg, and pH value was 6.96. It can be seen directly with the naked eyes that the color of the film changed from dark red to mauve, and finally to blue brown, suggesting that the beef had been rotten. To sum up, the composite film made up of CHPA and gellan (GL) anthocyanin (AC) can not only reduce the oxidation of anthocyanin and improve its stability, but also be used as an indicator of meat freshness.

Nondestructive determination of common indicators of beef for freshness assessment using airflow-three dimensional (3D) machine vision technique and machine learning

Assessment of beef freshness is critical to ensure a safe and sustainable beef supply chain. This work aims to investigate the feasibility of the viscoelasticity of beef to evaluate its freshness using the airflow-3D machine vision technique and machine learning models. The 3D camera was continuously used to acquire deformation images under the action of the airflow. The obtained images were preprocessed using region of interest ( ROI ) segmentation, filtering denoising, and down-sampling. And then the depth and volume of processed images were obtained by the Oriented Bounding Box ( OBB ) algorithm and volume algorithms, separately. Two four-element viscoelastic models were established to fit depth and volume respectively for obtaining viscoelastic characteristics. Finally, regression models were built and compared using viscoelastic characteristics to determine the optimum prediction models and methods for two freshness indicators. The backpropagation neural network ( BPNN ) and SVR based on selected features were the best prediction models for pH and total volatile basic nitrogen ( TVB-N ) content evaluation in beef, and the correlation coefficients ( R c and R p ) of the calibration set and prediction set were 0.7636, 0.9036, and 0.7669, 0.8388, respectively. • A test electronic instrument based on airflow-3D machine vision was independently developed. • The depth and volume of spatial deformation were quantified by algorithms of point cloud image quantization. • Two four-element viscoelastic models of the creep recovery phase were established to extract viscoelastic characteristics. • Machine learning models for simultaneously predicting two common freshness indicators were built. • The prediction effects of different viscoelastic feature fusions and different models were compared.

Non-destructive measurements for the evaluation of beef freshness based on airflow interaction and a structured light 3D imaging technique and comparison with pH value and total volatile basic nitrogen content

Non-destructive measurements for the evaluation of beef freshness based on airflow interaction and a structured light 3D imaging technique and comparison with pH value and total volatile basic nitrogen content

Preliminary Investigation on the Relationship between Raman Spectra of Beef and Metmyoglobin and Metmyoglobin Reductase Activity.

A hand-held Raman spectroscopic device was used as a rapid nondestructive testing device to predict the metmyoglobin (MetMb) and metmyoglobin reductase activity (MRA) values on the surface layer of fresh beef. Longissimus dorsi muscles were from 10 young bulls (Holstein-Friesian) from two different cattle farms (group A = 5 and B = 5). The Raman spectra of 100 samples were correlated with the MetMb and MRA values using partial least squares regression (PLSR). Two groups could be discriminated, and the separate correlation models were better than the joint correlation model for the fresh beef. The coefficients of determination are R2 = 0.81 (group A) and R2 = 0.87 (group B) for MetMb and R2 = 0.80 (group A) and R2 = 0.85 (group B) for MRA. The results show the usefulness of Raman spectra in predicting the inner traits such as MetMb and MRA during meat storage. In conclusion, it is feasible to determine the MetMb and MRA values by Raman spectroscopy. Color is an important indicator of beef freshness and can vary depending on the age, sex, and breed of the cow. They play a very important role in human nutrition. The color of meat is an important indicator of meat freshness, and many researchers are already investigating the causes of color changes. The research was conducted in this environment.

Comparison of Superchilling and Supercooling on Extending the Fresh Quality of Beef Loin.

This study compared the effects of superchilling and supercooling preservations for 15 days on the freshness and quality characteristics of beef loin. Beef freshness was evaluated by total aerobic count (TAC), total volatile basic nitrogen (TVB-N), and thiobarbituric acid reactive substances (TBARS), and instrumental color, drip loss, cooking loss, and texture profile analysis (TPA) were determined as quality parameters. All assays were compared with fresh control and normal chilling conditions (4 °C). The mean preservation temperatures of superchilling and supercooling were −3.9 °C and −2.1 °C, respectively. The freshness parameters indicated that both superchilling and supercooling extended the freshness of beef loin for 15 days, while chilled beef could not maintain the standard of freshness conditions. For quality parameters, there was no difference between the control and supercooling treatments, whereas superchilling exhibited higher drip loss and toughness compared to the control (p < 0.05). Therefore, this study demonstrated that supercooling was the best preservation technique to extend the freshness and quality of beef loin, but superchilling was not suitable to guarantee the quality of beef.

Olfactory visualization sensor system based on colorimetric sensor array and chemometric methods for high precision assessing beef freshness

Beef is easily spoiled, resulting in foodborne illness and high societal costs. This study proposed a novel olfactory visualization system based on colorimetric sensor array and chemometric methods to detect beef freshness. First, twelve color-sensitive materials were immobilized on a hydrophobic platform to acquire scent information of beef samples according to solvatochromic effects. Second, machine vision algorithms were used to extract the scent fingerprints, and principal component analysis (PCA) was employed to compress the feature dimensions of the fingerprints. Finally, four qualitative models, k-nearest neighbor, extreme learning machine, support vector machine (SVM), and random forest, were constructed to evaluate the beef freshness according to the value of total volatile basic nitrogen (TVB-N) and total viable counts (TVC). Results demonstrated that SVM had a preferable prediction ability, with 95.83% and 95.00% precision in the training and prediction sets, respectively. The results revealed that the simple constructed olfactory visualization sensor system could rapidly, robustly, and accurately assess beef freshness.

Non-destructive determination of beef freshness based on colorimetric sensor array and multivariate analysis

Rapid and reliable detection of beef freshness is essential for diet safety and resource-saving. In this study, the colorimetric sensor array (CSA) integrated with whale optimization algorithm (WOA) and back-propagation neural network (BPNN) had been innovatively developed for the quantitative determination of beef freshness, regarding the total volatile basic nitrogen (TVB-N) and total viable counts (TVC) contents. Firstly, the CSA comprising twelve color-sensitive dyes was designed to acquire the scent fingerprints (RGB triplets) during beef storage. Secondly, WOA-BPNN was used to optimize the color components combination from the preprocessed CSA to acquire the dominant color components. Finally, the BPNN models were constructed based on the optimized color components, with the BPNN topology of 8–12–1 for TVB-N prediction and 6–6–1 for TVC prediction. The results revealed that the BPNN model combined with optimized color components could be utilized to quantitatively determine beef freshness. The overall results demonstrated that the CSA integrated with appropriate multivariable analysis methods could realize rapid and reliable quantitative determination of beef freshness. Furthermore, the WOA-BPNN could effectively extract the dominant color components, which was beneficial for improving detection accuracy and robustness of the BPNN, as well as time- and cost-saving.

A novel intelligent film with high stability based on chitosan/sodium alginate and coffee peel anthocyanin for monitoring minced beef freshness

SummaryThis study aimed to develop a novel pH‐sensitive intelligent film for monitoring minced beef freshness based on chitosan/sodium alginate (CS–SA) and coffee peel anthocyanin (CPA). The different CPA content effects on the internal structure, physical properties and pH sensitivity of the CS–SA films were investigated. The results showed low contents of CPA (≤2%) could be evenly distributed within the CS–SA matrix with good compatibility. The addition of CPA significantly improved the physical properties and pH sensitivity of the CS–SA films. The CS–SA film containing 2% CPA (CS–SA–CPA2) showed the best tensile strength, water vapour barrier performance and most noticeable colour change. Moreover, this film exhibited good colour stability after 120 days. The CS–SA–CPA2 film changed colour from light yellow to brown to dark brown during minced beef storage, successfully indicating freshness. Therefore, the CS–SA–CPA2 film has the potential to be a pH‐sensitive material for monitoring minced beef freshness.

Preparation of Intelligent Indicator Label and Its Application in Beef Freshness Monitoring

Preparation of Intelligent Indicator Label and Its Application in Beef Freshness Monitoring

Comprehensive Identification of Beef Freshness with Multi-index Using Visible/Near-Infrared Spectroscopy

Comprehensive Identification of Beef Freshness with Multi-index Using Visible/Near-Infrared Spectroscopy

Rapid and non-destructive spectroscopic method for classifying beef freshness using a deep spectral network fused with myoglobin information

A simple, novel, rapid, and non-destructive spectroscopic method that employs the deep spectral network for beef-freshness classification was developed. The deep-learning-based model classified beef freshness by learning myoglobin information and reflectance spectra over different freshness states. The reflectance spectra (480–920 nm) were measured from 78 beef samples for 17 days, and the datasets were sorted into three freshness classes based on their pH values. Myoglobin information showed statistically significant differences depending on the freshness; consequently, it was utilized as a crucial parameter for classification. The model exhibited improved performance when the reflectance spectra were combined with the myoglobin information. The accuracy of the proposed model improved to 91.9%, whereas that of the single-spectra model was 83.6%. Further, a high value for the area under the receiver operating characteristic curve (0.958) was recorded. This study provides a basis for future studies on the investigation of myoglobin information associated with meat freshness.

Perception of beef quality for Spanish and Brazilian consumers

Understanding the priorities of beef consumers is important for planning strategies to attract and maintain each customer. Using self-administered interviews with Spanish and Brazilian adults who regularly consume beef, we analyzed their purchasing behaviors in relation to their perceptions and priorities regarding beef quality that guide their choices. The current study identifies the influences of information (labeling and marketing) requirements, concerns about humane production systems, and the intrinsic characteristics of beef. To evaluate the data extracted from the interviews, the questions were grouped into four main factors. While extrinsic factors are the highest valued in both Spain and Brazil, those such as beef freshness and tenderness present inverse concerns between the two countries based on cultural dissimilarities. The data also reveals a clear symmetry among consumers who increasingly seek specific pre-purchase information and products with guaranteed superior quality. Despite other characteristics that differentiate developed and developing countries, the perceptions of quality among consumers in Spain and Brazil are decidedly similar.

Nitroso-hemoglobin-ginger conjugates effects on bacterial growth and color stability in a minced beef model

This study aims to enhance the color and microbiological qualities of a raw beef using natural ingredients. Nitroso-hemoglobin (NO-Hb) integrated with vitamin C (VC), calcium lactate, and ginger complexation were used as natural inhibitors against the growth of aerobic and pathogenic bacteria, namely (Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), and Salmonella. NO-Hb inhibited E. coli, S. aureus, and Salmonella, and enhanced the color stability more than nitrite in the minced beef model. After the multiexponential analysis of relaxation decays, the water component (T2b) was analyzed using the low-field NMR. The results indicated that, at the 7th d of cold-storage the third component (T2) was detected. Significant correlations were observed between T21 and T22 relaxation times and water-holding capacity in minced beef, implying that the LF-NMR measurements could be an efficient method for the determination and prediction of beef freshness. NO-Hb- ginger mixture, as a novel ingredient, could be used instead of nitrite in terms of meat safety.

Non-destructive determination of chemical and microbial spoilage indicators of beef for freshness evaluation using front-face synchronous fluorescence spectroscopy

We investigated the potential of front-face synchronous fluorescence spectroscopy (△λ = 75 nm) to non-destructively evaluate beef freshness and quality decline during chilled storage. The total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS) and total viable count (TVC) values were used as standard freshness indicators. The fluorescent substances, including amino acids, collagen and conjugated Schiff bases, were highly correlated with the chemical and microbial deterioration of the beef. Quantitative models for simultaneously predicting the three freshness indicators were built combined with partial least squares (PLS) algorithm and showed good reliability. For TVB-N and TBARS values, Rc2 and Rp2 were both above 0.900, and for TVC values Rc2 and Rp2 were 0.912 and 0.871, respectively. The qualitative model established by partial least squares discriminant analysis (PLS-DA) algorithm could accurately classify beef samples as fresh, acceptable or spoiled. The accuracy of the calibration and validation sets were 92.54% and 86.96%, respectively.

Polyphenols from thinned young kiwifruit as natural antioxidant: Protective effects on beef oxidation, physicochemical and sensory properties during storage

For the first time, polyphenols extracted from deserted thinned young kiwifruits were studied as natural antioxidant and assessed for their preservative effects on beef quality during 7 days of refrigerated storage. Young kiwifruit polyphenols (YKP) displayed high phenolic content (0.53 g epicatechin equivalent/g) and strong antioxidant activity (0.54 g epicatechin equivalent/g) with major constituents identified as epicatechin, quercitrin and catechin. Compared to control, epicatechin and potassium sorbate, YKP application was found to be able to inhibit TBARS and TVB-N formation, reduce fatty acid oxidation, alleviate discoloration and stabilize textural properties of beef. At day 7, YKP treated beef exhibited marked PUFA increment of 1.77-fold (0.88-fold in control) without significant alteration of sensory properties, evidenced by the overall sensorial score of 5.6 in YKP group and 5.1 in control. Our results provide theoretical support for the realistic application of YKP as natural replacer for synthetic preservatives in maintaining beef freshness.

A feasibility study of rapid nondestructive detection of total volatile basic nitrogen (TVB-N) content in beef based on airflow and laser ranging technique

To investigate the feasibility of airflow and laser ranging technique for nondestructively determination of TVB-N in beef, a viscoelasticity detection system was employed to collect deformation information of beef samples. Then 12 characteristic parameters were extracted from deformation curves. Multiple linear regression (MLR), principal component regression (PCR) and partial least squares regression (PLSR) were used to establish prediction models for TVB-N. The results showed that the PLSR model using 12 characteristic parameters performed the best with correlation coefficients in calibration and prediction sets (Rc and Rp) of 0.847 and 0.821, root mean squared errors in the calibration and prediction sets (RMSEC and RMSEP) of 1.750 and 2.560 mg/100 g. This result demonstrated that it was feasible to use airflow and laser ranging technique for rapid and nondestructive detection of beef freshness. This study made possible that the deformation characteristics of livestock and poultry meat could be expressed in a quantitative method.

Pengembangan Label Pintar untuk Indikator Kesegaran Daging Sapi pada Kemasan

&#x0D; Meat is a high nutritional food that rich of protein, fat, and sugar. Smart label has been applied as a beef freshness detector. Two types of pH indicator have been used i.e bromocresol purple (BCP) and methyl red (MR) as dual indicator freshness. The objective of this research was to determine the beef freshness using smart label at room temperature. The color change of the smart label was examined by imageJ software to determine the freshness degree using the mean RGB value. The beef was examined every 2 hours for pH and total volatile base (TVB-N) analysis during the 24 hours storage at room temperature. The result showed that color indicator will change according to the beef freshness, bromocresol purple turned from yellow to purple (mean RGB 171.465 ± 1.122) and methyl red turned from red to yellow (mean RGB 162.082 ± 1.315). The beef freshness at room temperature decreased as the pH increase from 5.61 to 6.23 along with the color change of smart label. Furthermore, the color would change when 0.022 %N of TVB-N has been reached. Therefore, the beef freshness can be determined by using smart label based on dual indicator of bromocresol purple and methyl red in room temperature. &#x0D; &#x0D; Keywords: beef freshness, smart label, pH, TVB&#x0D;