Nondestructive detection of total volatile basic nitrogen (TVB-N) content in pork meat by integrating hyperspectral imaging and colorimetric sensor combined with a nonlinear data fusion

Combination model for freshness prediction of pork using VIS/NIR hyperspectral imaging with chemometrics

OBJECTIVE

This study aimed to develop an enhanced model for predicting pork freshness by integrating hyperspectral imaging (HSI) and chemometric analysis.

METHODS

A total of 30 Longissimus thoracis samples from three sows were stored under vacuum conditions at 4°C±2°C for 27 days to acquire data. The freshness prediction model for pork loin employed partial least squares regression (PLSR) with Monte Carlo data augmentation. Total bacterial count (TBC) and volatile basic nitrogen (VBN), which exhibited increases correlating with metabolite changes during storage, were designated as freshness indicators. Metabolic contents of the sample were quantified using nuclear magnetic resonance.

RESULTS

A total of 64 metabolites were identified, with 34 and 35 showing high correlations with TBC and VBN, respectively. Lysine and malate for TBC (R2 = 0.886) and methionine and niacinamide for VBN (R2 = 0.909) were identified as the main metabolites in each indicator by Model 1. Model 2 predicted main metabolites using HSI spectral data. Model 3, which predicted freshness indicators with HSI spectral data, demonstrated high prediction coefficients; TBC R2p = 0.7220 and VBN R2p = 0.8392. Furthermore, the combination model (Model 4), utilizing HSI spectral data and predicted metabolites from Model 2 to predict freshness indicators, improved the prediction coefficients compared to Model 3; TBC R2p = 0.7583 and VBN R2p = 0.8441.

CONCLUSION

Combining HSI spectral data with metabolites correlated to the meat freshness may elucidate why certain HSI spectra indicate meat freshness and prove to be more effective in predicting the freshness state of pork loin compared to using only HSI spectral data.

Rapid and simultaneous determination of mixed pesticide residues in apple using SERS coupled with multivariate analysis

This study aims to apply multivariate analysis algorithms for modeling the same spectra, for simultaneous determination of pymetrozine and carbendazim residues in apple. To mitigate the impact of competitive adsorption, SERS spectra are obtained from mixed solutions of pymetrozine and carbendazim at varying concentration ratios, which are then utilized for modeling. Results suggest that the PLSR model based on full-band SNV processed spectra shows the best performance for predicting pymetrozine and carbendazim contents, with R2 p of 0.9751 and 0.9779, RMSEP of 0.0492 and 0.5531 mg/L, RPD of 6.4297 and 6.8246, respectively. This model yielded R2 p of 0.9644 and 0.9671, RMSEP of 0.0747 and 0.8247 mg/L, RPD of 5.3857 and 5.6066 for pymetrozine and carbendazim in apple, respectively. The findings suggest that the proposed approach is suitable for simultaneous detection of pymetrozine and carbendazim in apples, offering a novel avenue for monitoring food safety.

An effective deep learning fusion method for predicting the TVB-N and TVC contents of chicken breasts using dual hyperspectral imaging systems

Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are important freshness indicators of meat. Hyperspectral imaging combined with chemometrics has been proven to be effective in meat detection. However, a challenge with chemometrics is the lack of a universally applicable processing combination, requiring trial-and-error experiments with different datasets. This study proposes an end-to-end deep learning model, pyramid attention features fusion model (PAFFM), integrating CNN, attention mechanism and pyramid structure. PAFFM fuses the raw visible and near-infrared range (VNIR) and shortwave near-infrared range (SWIR) spectral data for predicting TVB-N and TVC in chicken breasts. Compared with the CNN and chemometric models, PAFFM obtains excellent results without a complicated processing combinatorial optimization process. Important wavelengths that contributed significantly to PAFFM performance are visualized and interpreted. This study offers valuable references and technical support for the market application of spectral detection, benefiting related research and practical fields.

Active and smart biomass film with curcumin Pickering emulsion stabilized by chitosan-adsorbed laurate esterified starch for meat freshness monitoring

The multifunctional active smart biomass film was prepared by incorporating chitosan-adsorbed laurate esterified starch curcumin Pickering emulsion into the starch film matrix, with nano-cellulose serving as reinforcing agents. The mechanical and functional properties of the film were studied, and the film was used to monitor the freshness of pork. The results demonstrated a relatively uniform distribution of curcumin and Pickering emulsion droplets within the film matrix. Furthermore, the thermal stability was minimally impacted by the introduction of curcumin Pickering emulsion, while the tensile strength and tensile strain of the film were increased, and both its hydrophobicity and antioxidant properties were improved. The free radical scavenging rate reached 56.01 %, with sustained high antioxidant capacity even after 8 days. Additionally, the presence of curcumin provided the film with pH indicating ability and delayed pork spoilage. Therefore, this work provides an attractive strategy for constructing green, active, and smart biomass packaging films for meat packaging applications.

Characterization and interactions of spoilage of Pseudomonas fragi C6 and Brochothrix thermosphacta S5 in chilled pork based on LC-MS/MS and screening of potential spoilage biomarkers

Pseudomonas and Brochothrix are the main spoilage organisms in pork, and each of these plays an essential role in the spoilage process. However, the effect of co-contamination of these two organisms in pork has not been elucidated. The changing bacterial communities during spontaneous spoilage of pork at 4 °C were evaluated using high-throughput sequencing. The dominant spoilage bacteria were isolated and these were identified as Pseudomonas fragi C6 and Brochothrix thermosphacta S5. Chilled pork was then experimentally contaminated with these strains, individually and in combination, and the progression of spoilage was assessed by analyzing various physicochemical indicators. These included total viable counts (TVC), pH, color, total volatile basic nitrogen (TVB-N), and detection of microbial metabolites. After 7 days of chilled storage, co-contaminated pork produced higher TVC and TVB-N values than mono-contaminated samples. Metabolomic analysis identified a total of 8,084 metabolites in all three groups combined. Differential metabolites were identified, which were involved in 38 metabolic pathways. Among these pathways, the biosynthesis of alkaloids derived from purine and histidine was identified as an important pathway related to spoilage. Specifically, histidine, histamine, AMP, IMP, GMP, succinic acid, and oxoglutaric acid were identified as potential spoilage biomarkers. The study showed that the combined presence of P. fragi C6 and B. thermosphacta S5 bacteria makes chilled pork more prone to spoilage, compared to their individual presence. This study provides insights that can assist in applying appropriate techniques to maintain quality and safety changes in meat during storage and further the assessment of freshness.

Effect of Freeze-Thaw Cycles on the Freshness of Prepackaged Penaeus vannamei

The effect of temperature fluctuations on the freshness of shrimp in simulated trays was investigated by setting a freeze-thaw (F-T) cycle of 12 h after freezing at -20 °C and thawing at 1 °C under refrigeration. The results showed that the shrimp's physicochemical properties deteriorated to different extents with the increase in F-T cycles. The total colony count of shrimp was 6.07 lg CFU/g after 21 cycles, and the volatile saline nitrogen content reached 30.36 mg/100 g, which exceeded the edible standard. In addition, the sensory quality and textural properties (hardness, elasticity, chewiness, and adhesion) declined to different degrees with increased F-T cycles. LF-NMR and protein property measurements showed that F-T cycles resulted in reduced water holding capacity and protein denaturation, which were the main factors leading to the deterioration of shrimp quality. Furthermore, flavor changes were analyzed using an electronic nose sensor to establish a freshness model. The W1W, W1S, W2S, and W5S sensors were correlated with the quality changes in shrimp and used as the main sensors for detecting the freshness of Penaeus vannamei. As a result, to better maintain the overall freshness, temperature fluctuations should be minimized in sales and storage, and fewer than 8 F-T cycles should be performed.

Non-Destructive Detection of the Freshness of Air-Modified Mutton Based on Near-Infrared Spectroscopy

Monitoring and identifying the freshness levels of meat holds significant importance in the field of food safety as it directly relates to human dietary safety. Traditional packaging methods for lamb meat quality assessment present issues such as cumbersome operations and irreversible damage. This research proposes a quality assessment method for modified atmosphere packaging lamb meat using near-infrared spectroscopy and multi-parameter fusion. Fresh lamb meat quality is taken as the research subject, comparing various physicochemical indicators and near-infrared spectroscopic information under different temperatures (4 °C and 10 °C) and different modified atmosphere packaging combinations. Through precision parameter comparison, rebound and TVB-N values are selected as the modeling parameters. Six spectral preprocessing methods (multi-scatter calibration, MSC; standard normal variate transformation, SNV; normalization; Savitzky-Golay smoothing, SG; Savitzky-Golay 1 derivative, SG-1st; and Savitzky-Golay 2 derivative, SG-2nd), and three feature wavelength selection methods (competitive adaptive reweighted sampling, CARS; successive projections algorithm, SPA; and uninformative variable elimination, UVE) are compared. Partial least squares (PLS) and support vector machine (SVM) are used to construct prediction models for chilled fresh lamb meat quality. The results show that when rebound is used as a parameter, the SG-2nd-SPA-PLSR model has the highest accuracy, with a determination coefficient R²p of 0.94 for the prediction set. When TVB-N is used as a parameter, the MSC-UVE-SVM model has the highest accuracy, with an R²p of 0.95 for the prediction set. In conclusion, the use of near-infrared spectroscopic analysis enables rapid and non-destructive prediction and evaluation of lamb meat freshness, including its textural characteristics and TVB-N content under different modified atmosphere packaging. This study provides a theoretical basis and technical support for further encapsulating the models into portable devices and developing portable near-infrared spectrometers to rapidly determine lamb meat freshness.

Enhancing the shelf life of chevon Seekh Kabab using chitosan edible film and Cinnamomum zeylanicum essential oil

Chevon Seekh Kabab is a popular meat product of India. However, due to high protein and moisture content it undergoes quick microbial spoilage and oxidative reactions leading to lower shelf life. The combination of chitosan edible film and cinnamon essential oil (CEO) was chosen to remediate this problem because of its antimicrobial and antioxidative effect. Control and chitosan edible film with CEO coated chevon Seekh Kabab samples were stored at 4 °C. The physicochemical (pH, TBARS, TVBN, moisture, colour), microbiological (APC, psychrophilic, coliform and Staphylococcal count) and sensory attributes were evaluated over a 30 days period. The maximum shelf life of 27 days was observed when 2% chitosan edible film with 0.3% CEO was coated over samples. A reduction in moisture, L* value, a* value and sensory scores along with an increase in pH, TVBN, TBARS, b* value and microbiological parameters were observed during the storage period. Reaction kinetics for the physicochemical and microbiological parameters was also established. The physicochemical, microbiological and sensory parameters were within prescribed limits till spoilage in the treated sample. This investigation may aid researchers working on scaling up of processing and preservation of Seekh Kabab.

Non-destructive and in-situ detection of shrimp freshness using mid-infrared fiber-optic evanescent wave spectroscopy

There is strong interest in non-destructive and rapid determination of food freshness in food research. In this study, mid-infrared (MIR) fiber-optic evanescent wave (FOEW) spectroscopy was applied to monitor shrimp freshness through the evaluation of protein, chitin, and calcite contents in conjunction with a Partial Least Squares Discriminant Analysis (PLS-DA) model. Shrimp shells were wiped with a micro fiber-optic probe to obtain a FOEW spectrum which quickly and nondestructively allowed evaluation of the shrimp freshness. Peaks for proteins, chitin, and calcite, which are closely related to shrimp freshness, were detected and quantified. Compared with the standard indicator for evaluating shrimp freshness (total volatile basic nitrogen), the PLS-DA model gave recognition rates for shrimp freshness using calibration and validation sets of the FOEW data of 87.27%, 90.28%, respectively. Our results show that FOEW spectroscopy is a feasible method for non-destructive and in-site detection of shrimp freshness.

Simultaneous quantification of total flavonoids and phenolic content in raw peanut seeds via NIR spectroscopy coupled with integrated algorithms

Peanuts are nutritionally valuable for both humans and animals due to their high content of flavonoids and phenolic compounds. Herein, we explored the potential of near-infrared (NIR) spectroscopy coupled with efficient variable selection algorithms for quantitative prediction of total flavonoids (TFC) and total phenolics content (TPC) in raw peanut seeds. Spectrophotometrically, the reference results of the extracts for TFC and TPC were analysed and recorded. The integrated application of the synergy interval coupled competitive adaptive reweighted sampling-partial least squares (Si-CARS-PLS) were used for prediction. The model performance appraisal was based on the correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The Si-CARS-PLS performed optimally for TFC (Rp = 0.9137, RPD = 2.49) and TPC (Rp = 0.9042, RPD = 2.31), respectively. Moreover, the model (Si-CARS-PLS) was found to have an acceptable fit for the analytes under study since it achieved 0.88 for TFC and 0.86 for TPC based on the external validation. Therefore, these results showed that NIR coupled with Si-CARS-PLS could be used for the quantitative prediction of flavonoids and phenolic contents in raw peanut seeds.

A comprehensive review of the principles, key factors, application, and assessment of thawing technologies for muscle foods

For years, various thawing technologies based on pressure, ultrasound, electromagnetic energy, and electric field energy have been actively investigated to minimize the amount of drip and reduce the quality deterioration of muscle foods during thawing. However, existing thawing technologies have limitations in practical applications due to their high costs and technical defects. Therefore, key factors of thawing technologies must be comprehensively analyzed, and their effects must be systematically evaluated by the quality indexes of muscle foods. In this review, the principles and key factors of thawing techniques are discussed, with an emphasis on combinations of thawing technologies. Furthermore, the application effects of thawing technologies in muscle foods are systematically evaluated from the viewpoints of eating quality and microbial and chemical stability. Finally, the disadvantages of the existing thawing technologies and the development prospects of tempering technologies are highlighted. This review can be highly instrumental in achieving more ideal thawing goals.

Application of intelligent packaging for meat products: A systematic review

BACKGROUND

Today, in response to consumer demand and market trends, the development of new packaging with better performance such as intelligent packaging has become more important. This packaging system is able to perform intelligent functions to increase shelf life, increase safety and improve product quality.

OBJECTIVES

Recently, various types of packaging systems are available for meat products, especially cooked, fresh and processed meats. But because meat products are very perishable, monitoring their quality and safety in the supply chain is very important. This systematic article briefly reviews some of the recent data about the application of intelligent packaging in meat products.

METHODS

The search was conducted in Google Scholar, Science Direct, Elsevier, Springer, Scopus, and PubMed, from April 1996 to April 2021 using a different combination of the following keyword: intelligent packaging, and meat.

RESULTS

The results showed that the intelligent packaging presents several benefits compared to traditional packaging (e.g., antimicrobial, antioxidant, and shelf life extension) at the industrial processing level. Thus, these systems have been applied to improve the shelf life and textural properties of meat and meat products.

CONCLUSIONS

It is necessary to control the number of intelligent compounds that are included in the packaging as they clearly influence the quality and nutritional properties as well as the final cost of the food products.

Field Application of a Vis/NIR Hyperspectral Imaging System for Nondestructive Evaluation of Physicochemical Properties in ‘Madoka’ Peaches

Extensive research has been performed on the in-field nondestructive evaluation (NDE) of the physicochemical properties of ‘Madoka’ peaches, such as chromaticity (a*), soluble solids content (SSC), firmness, and titratable acidity (TA) content. To accomplish this, a snapshot-based hyperspectral imaging (HSI) approach for filed application was conducted in the visible and near-infrared (Vis/NIR) region. The hyperspectral images of ‘Madoka’ samples were captured and combined with commercial HSI analysis software, and then the physicochemical properties of the ‘Madoka’ samples were predicted. To verify the performance of the field-based HSI application, a lab-based HSI application was also conducted, and their coefficient of determination values (R²) were compared. Finally, pixel-based chemical images were produced to interpret the dynamic changes of the physicochemical properties in ‘Madoka’ peach. Consequently, the a* values and SSC content shows statistically significant R² values (0.84). On the other hand, the firmness and TA content shows relatively lower accuracy (R² = 0.6 to 0.7). Then, the resultant chemical images of the a* values and SSC content were created and could represent their different levels using grey scale gradation. This indicates that the HSI system with integrated HSI software used in this work has promising potential as an in-field NDE for analyzing the physicochemical properties in ‘Madoka’ peaches.

Intelligent evaluation of free amino acid and crude protein content in raw peanut seed kernels using NIR spectroscopy paired with multivariable calibration

Given the nutritional importance of peanuts, this study examined the free amino acid (FAA) and crude protein (CP) content in raw peanut seeds. Near-infrared spectroscopy (NIRS) was employed in combination with variable selection algorithms after successful reference data analysis using colorimetric and Kjeldahl methods. Ensuing the application of partial least squares (PLS) as a full spectral model, the genetic algorithm (GA), bootstrapping soft shrinkage (BOSS), uninformative variable elimination (UVE), and random frog (RF) models were tested and assessed. A comparison of correlation coefficients of prediction (R_p), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) was performed to appraise the performance of the built models. Using RF-PLS, an unsurpassed outcome was achieved for FAA (R_p = 0.937, RPD = 3.38) and CP (R_p = 0.9261, RPD = 3.66). These findings demonstrated that NIR in combination with RF-PLS could be utilized for quantitative, rapid, and nondestructive prediction of FAA and CP in raw peanut seed samples.

Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry

Meat 4.0 refers to the application the fourth industrial revolution (Industry 4.0) technologies in the meat sector. Industry 4.0 components, such as robotics, Internet of Things, Big Data, augmented reality, cybersecurity, and blockchain, have recently transformed many industrial and manufacturing sectors, including agri-food sectors, such as the meat industry. The need for digitalised and automated solutions throughout the whole food supply chain has increased remarkably during the COVID-19 pandemic. This review will introduce the concept of Meat 4.0, highlight its main enablers, and provide an updated overview of recent developments and applications of Industry 4.0 innovations and advanced techniques in digital transformation and process automation of the meat industry. A particular focus will be put on the role of Meat 4.0 enablers in meat processing, preservation and analyses of quality, safety and authenticity. Our literature review shows that Industry 4.0 has significant potential to improve the way meat is processed, preserved, and analysed, reduce food waste and loss, develop safe meat products of high quality, and prevent meat fraud. Despite the current challenges, growing literature shows that the meat sector can be highly automated using smart technologies, such as robots and smart sensors based on spectroscopy and imaging technology.

Critical review and recent advances of 2D materials-Based gas sensors for food spoilage detection

Many people around the world are concerned about meat safety and quality, which has resulted in the ongoing advancement of packaged food technology. Since the emergence of graphene in 2004, the number of studies on layered two-dimensional materials (2DMs) for applications ranging from food packaging to meat quality monitoring has been expanding quickly. Recently, scientists have been working hard to develop a novel class of 2DMs that keep the good things about graphene but don't have zero bandgaps at room temperature. Much work has been done on layered transition metal dichalcogenides (TMDCs) like different metal sulfides and selenides for meat spoilage gas sensors. This review looks at (i) the main indicators of meat spoilage and (ii) the detection methods that can be used to find out if meat has been spoiled, such as chemiresistive, electrochemical, and optical methods. (iii) the role of 2DMs in meat spoilage detection and (iv) the emergence of advanced methods for selective classification of target analytes in meat/food spoilage detection in recent years. Thus, this review demonstrates the potential scope of 2DMs for developing intelligent sensor systems for food and meat spoilage detection with high viability, simplicity, cost-effectiveness, and other multipurpose tools.

Hyperspectral imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends

Meat products are particularly plagued by safety problems because of their complicated structure, various production processes and complex supply chains. Rapid and non-invasive analytical methods to evaluate meat quality have become a priority for the industry over the conventional chemical methods. To achieve rapid analysis of safety and quality parameters of meat products, hyperspectral imaging (HSI) is now widely applied in research studies for detecting the various components of different meat products, but its application in meat production and supply chain integrity as a quality control (QC) solution is still ambiguous. This review presents the fresh look at the current states of HSI research as both the scope and the applicability of the HSI in the meat quality evaluation expanded. The future application scenarios of HSI in the supply chain and the future development of HSI hardware and software are also discussed, by which HSI technology has the potential to enable large scale meat product testing. With a fully adapted for factory setting HSI, the inspection coverage can reliably identify the chemical properties of meat products. With the introduction of Food Industry 4.0, HSI advances can change the meat industry to become from reactive to predictive when facing meat safety issues.

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HSI has shown promising early signs in the non-destructive analysis of meat quality and safety.

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Hyperspectral imaging (HSI) is now widely applied in research studies for different meat products with the help of machine learning methods.

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With a fully adapted factory setting and robust machine learning of HSI, the inspection coverage can reach 100% of the target meat.

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HSI can change the meat industry to become from reactive to predictive when facing issues, this will be translated into fewer recalls, less meat fraud, and less waste.

Rapid Detection of Carbendazim Residue in Apple Using Surface-Enhanced Raman Scattering and Coupled Chemometric Algorithm

In order to achieve rapid and precise quantification detection of carbendazim residues, surface-enhanced Raman spectroscopy (SERS) combined with variable selected regression methods were developed. A higher sensitivity and greater density of "hot spots" in three-dimensional (3D) SERS substrates based on silver nanoparticles compound polyacrylonitrile (Ag-NPs @PAN) nanohump arrays were fabricated to capture and amplify the SERS signal of carbendazim. Four Raman spectral variable selection regression models were established and comparatively assessed. The results showed that the bootstrapping soft shrinkage-partial least squares (BOSS-PLS) method achieved the best predictive capacity after variable selection, and the final BOSS-PLS model has the correlation coefficient (R_P) of 0.992. Then, this method used to detect the carbendazim residue in apple samples; the recoveries were 86~116%, and relative standard deviation (RSD) is less than 10%. The 3D SERS substrates combined with the BOSS-PLS algorithm can deliver a simple and accurate method for trace detection of carbendazim residues in apples.

Identification of characteristic volatiles and metabolomic pathway during pork storage using HS-SPME-GC/MS coupled with multivariate analysis

Previous researches have been conducted evaluating the volatile compounds of pork. However, data regarding the changes in volatiles and metabolic pathways during pork storage were inadequately investigated. Herein, a headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC/MS) coupled multivariate analysis was proposed for characterizing the profiles of volatile compounds and metabolic pathways during pork storage. A total of 37 metabolites, including aldehydes, ketones, alcohols etc. were successfully identified. Multivariate statistical analysis revealed a substantial variation in metabolite phenotype among samples over the pork storage period, with 12 characteristic metabolites and 5 potential characteristic metabolites screened as biomarkers. Moreover, three metabolomic pathways analysis and transformation between each other (thermal reactions, lipid metabolism and amino acid metabolism) reveals the underlying mechanisms of metabolites change of pork. Therefore, the present study may provide insight into future understanding of the variation in the pork metabolite profiles.

Simultaneous quantification of deoxymyoglobin and oxymyoglobin in pork by Raman spectroscopy coupled with multivariate calibration

Because of the nutritional advantages and customer acceptance, it is vital to ensure pork meat quality. This study examined the quantification of myoglobin proportions (deoxymyoglobin and oxymyoglobin) by coupling Raman spectroscopy with efficient variables selection chemometrics. Prior to acquiring Raman spectroscopic data, the fractions of myoglobin were determined. Afterward, multivariate calibration methods like partial least square (PLS), competitive adaptive reweighted sampling (CARS-PLS), genetic algorithm-PLS (GA-PLS), and random frog-PLS (RF-PLS) were applied and evaluated. The models' performance was assessed using correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The RF-PLS model achieved optimal results for both deoxymyoglobin and oxymyoglobin, with Rp = 0.8936; RMSEP = 2.91 and RPD = 1.97 for the former and Rp = 0.9762; RMSEP = 1.23 and RPD = 4.47 for the latter, respectively. Therefore, this work demonstrated that Raman spectroscopy paired with RF-PLS could be employed for nondestructive, fast, and easy detection of deoxymyoglobin and oxymyoglobin.

A sensitive fluorescence-visualized sensor based on an InP/ZnS quantum dots-sodium rhodizonate system for monitoring fish freshness

A fluorescence-visualized sensor based on 3-mercaptopropionic acid (MPA)-capped indium phosphide/ zinc sulfide quantum dots (InP/ZnS QDs) and sodium rhodizonate (SR) was designed to sensitively monitor fish freshness. MPA-InP/ZnS QDs, which exhibit orange-red fluorescence, were synthesized by a solvothermal method. In the MPA-InP/ZnS QDs-SR system, the fluorescence of MPA-InP/ZnS QDs was quenched by SR due to the combined function of the inner filter effect (IFE) and static quenching effect (SQE) at pH = 3. When ammonia was added, the fluorescence was recovered, and the color changed from colorless to bright orange-red under UV light (365 nm). The sensing performance for volatile amine gas was studied, and the sensor demonstrated good linearity between the fluorescence intensity, the total volatile basic nitrogen (TVB-N) and the total color change (ΔE) of bighead carp stored at room temperature (25 °C) and refrigerated temperature (4 °C). The proposed sensor has potential applications in monitoring fish freshness.

Detection of Foreign Materials on Broiler Breast Meat Using a Fusion of Visible Near-Infrared and Short-Wave Infrared Hyperspectral Imaging

Foreign material (FM) found on a poultry product lowers the quality and safety of the product. We developed a fusion method combining two hyperspectral imaging (HSI) modalities in the visible-near infrared (VNIR) range of 400–1000 nm and the short-wave infrared (SWIR) range of 1000–2500 nm for the detection of FMs on the surface of fresh raw broiler breast fillets. Thirty different types of FMs that could be commonly found in poultry processing plants were used as samples and prepared in two different sizes (5 × 5 mm2 and 2 × 2 mm2). The accuracies of the developed Fusion model for detecting 2 × 2 mm2 pieces of polymer, wood, and metal were 95%, 95%, and 81%, respectively, while the detection accuracies of the Fusion model for detecting 5 × 5 mm2 pieces of polymer, wood, and metal were all 100%. The performance of the Fusion model was higher than the VNIR- and SWIR-based detection models by 18% and 5%, respectively, when F1 scores were compared, and by 38% and 5%, when average detection rates were compared. The study results suggested that the fusion of two HSI modalities could detect FMs more effectively than a single HSI modality.

A study on shelf life of prepackaged retail-ready Korean native black pork belly and shoulder butt slices during refrigerated display

OBJECTIVE

In most retail centers, primal pork cuts for sale are usually prepared into retail-ready slices and overwrapped with air-permeable plastic film. Also, meat of Korean native black pig (KNP) is reputed for its superior quality, however, its shelf life during retail display has not been studied. Thus, the objective of this study was to evaluate shelf life of prepackaged retail-ready KNP belly and shoulder butt slices during refrigerated display.

METHODS

Bellies and shoulder butt obtained at 24 h post-mortem from finishing KNP were used. Each belly or shoulder butt was manually cut into 1.5 cm-thick slices. The slices in each cut type were randomly taken and placed on white foam tray (2 slices/tray) overwrapped with polyvinyl chloride film. The retail-ready packages were then placed in a retail display cabinet at 4°C. Shelf life and sensory quality of the samples were evaluated on day 1, 3, 6, 9, 12, and 15 of display.

RESULTS

The shoulder butt reached the upper limit (20 mg/100 g) of volatile basic nitrogen for fresh meat after 9 days while, the belly remained within this limit throughout the display time (15 days). Both the cuts reached a thiobarbituric acid reactive substances level of above 0.5 mg malondialdehyde/kg after 9 days. The a* (redness) values remained unchanged during first 9 days in both cuts (p>0.05). After 9 days, off-flavor was not found in either cut, but higher off-flavor intensity was found in shoulder butt after 12 days. The shoulder butt was unacceptable for overall eating quality after 12 days while, belly still was acceptable after 12 days.

CONCLUSION

The belly showed a longer shelf life compared to the shoulder butt, and a shelf life of 9 and 12 days is recommended for the prepackaged retail-ready KNP shoulder butt and belly slices, respectively.

Development of a novel chromophore reaction-based fluorescent probe for biogenic amines detection

Biogenic amines (BAs) are important biomarkers to monitor meat spoilage. However, the design of efficient BA fluorescent probes with distinct colorimetric and ratiometric fluorescent dual-channels is still a critical challenge because of similar chemical properties and basicity between BAs and other amines. Herein, pyrrolopyrrole cyanine (PPCy-1) is reported to display distinctly high reactivity toward BAs through an ultrasensitive irreversible chromophore reaction for the first time. The reaction mechanism is ascribed to synergistic aza-Michael addition and B-N detachment, followed by hydrolysis to produce low-conjugated diketopyrrolopyrrole and heteroaromatic acetonitrile compounds. As a result, colorimetric and ratiometric fluorescent dual-channel (Δλ_ab = 188 nm and Δλ_em = 151 nm) signals and a limit of detection up to 62.1 nM level for BA solution are acquired. In addition, the colorimetric detection of volatile amine vapor using the PPCy-1-loaded filter paper, showing a color change from green to yellow, is feasible. A simple and cost-effective fluorescence "turn on" method using the filter paper or the CAD-40 resin loaded with PPCy-1 to detect TVB (total volatile bases) originating from shrimp spoilage is further demonstrated.

Rapid detection of chlorpyrifos residue in rice using surface-enhanced Raman scattering coupled with chemometric algorithm

Due to the continuous development and progress of society and more and more attention to the quality and safety of food, rapid testing of pesticides in food is of great significance. In this paper, surface-enhanced Raman spectroscopy (SERS) and chemometric algorithms were employed collectively to quantify chlorpyrifos (CP) residues in rice samples. The SERS spectra from different concentrations (0.01-1000 μg/mL) of CP were collected using AgNPs-deposited-ZnO nanoflower (NFs)-like nanoparticles (Ag@ZnO NFs) SERS sensor. Four quantitative chemometric models for CP were comparatively studied, and the competitive adaptive reweighted sampling-partial least squares model achieved the best prediction and practical applicability for predicting CP levels with a limit of detection of 0.01 µg/mL. The results of the student's t-test showed no significant difference between this method and high-performance liquid chromatography (HPLC), and good relative standard deviation (RSD) indicated that this method could be used for the detection of CP in rice.

Coating with chitosan containing lauric acid (C12:0) significantly extends the shelf-life of aerobically - Packaged beef steaks during refrigerated storage

The present research aimed at investigating the application potential of a newly developed chitosan/lauric acid edible coating in preservation of fresh beef under refrigerated storage and aerobic packaging conditions. The 2-cm thick steaks were coated with 2% chitosan (CHI), 1 mM lauric acid in 2% chitosan (CHI/1 mM LA) or 3 mM lauric acid in 2% chitosan (CHI/3 mM LA), and over-wrapped in permeable film. Non-coated samples were used as a control (CON). Results showed that the inhibitory effects against the spoilage bacteria growth, volatile basic nitrogen formation and lipid oxidation of the chitosan coating was increased with the incorporation of lauric acid (p˂0.05). More importantly, the incorporation of lauric acid almost completely protected the meat samples against the discoloration after 21 days of storage. The coating with chitosan or chitosan/lauric acid completely inhibited the formation of bacterial spoilage-derived volatile compounds. Overall, coating of chitosan containing 1-3 mM lauric acid could be a promising method in preservation of fresh beef to improve safety and quality under aerobic packaging condition.

Effects of Vacuum Frying on the Preparation of Ready-to-Heat Batter-Fried and Sauced Chub Mackerel (Scomber japonicus)

Chub mackerel (CM) is a commercial fish in Korea, owing to its availability and nutritional values. This study aimed to develop a ready-to-heat (RTH) Korean preparation of CM, known as Godeungo gangjeong. We utilized vacuum frying technology to fry the CM and evaluated its quality. Conventional frying with a deep fryer was performed in parallel to assess the superiority of the vacuum fryer. We optimized the frying conditions of vacuum frying (VBF) and deep frying (DBF) using response surface methodology. At optimum conditions of 95 °C for 7 min 42 s, VBF produced better sensory, chemical, and microbial properties than DBF at 190 °C for 5 min 30 s. The nutritional values, including amino acid and fatty acid contents, were investigated and found to be higher in VBF than in DBF. Sensory properties also showed better scores on VBF than DBF, especially in appearance, aroma, taste, and overall acceptability. The VBF produced lower volatile basic nitrogen (VBN), thiobarbituric acid reactive substances (TBARS), and total bacterial count (TBC) than DBF. The findings confirmed that vacuum frying is a better option to produce RTH Godeungo gangjeong, since it provides less oxidation and maintains the product quality. Using the Arrhenius approach, the product was concluded to preserve both quality and safety for 9 months of storage at −18 °C.

Effect of α-Terpineol on Chicken Meat Quality during Refrigerated Conditions

The present study was designed to evaluate the in vitro antimicrobial properties of nine bioactive compounds (BACs). Applying the disc paper and minimum inhibitory concentration (MIC) assays, we found that the BACs with the widest spectrum of in vitro antibacterial activity against the studied bacteria were carvacrol and α-terpineol (αTPN). Subsequently, αTPN was selected and applied at different concentrations into the fresh minced chicken meat. The meat was then vacuum packaged and stored for 14 days at 4 °C. Physicochemical properties, lipid oxidation (thiobarbituric acid reactive substances, TBARS), electronic-nose-based smell detection, and microbiological characteristics were monitored. At day 14, meat treated with higher concentrations of αTPN (MIC-2 and MIC-4) exhibited a significantly increased pH and lightness (L*), increased yellowness (b*), decreased redness (a*), caused a significant decrease in water holding capacity (WHC), and decreased lipid oxidation by keeping TBARS scores lower than the control. Although αTPN showed perceptibly of overlapped aroma profiles, the E-nose was able to distinguish the odor accumulation of αTPN between the different meat groups. During the 2-week storage period, αTPN, particularly MIC-4, showed 5.3 log CFU/g reduction in aerobic mesophilic counts, causing total inhibition to the Pseudomonas lundessis, Listeria monocytogenes, and Salmonella Typhimurium. These promising results highlight that αTPN is exploitable to improve the shelf life and enhance the safety of meat and meat products.

Study on starch content detection and visualization of potato based on hyperspectral imaging

Starch is an important quality index in potato, which contributes greatly to the taste and nutritional quality of potato. At present, the determination of starch depends on chemical analysis, which is time consuming and laborious. Thus, rapid and accurate detection of the starch content of potatoes is important. This study combined hyperspectral imaging with chemometrics to predict potato starch content. Two varieties of Kexin No.1 and Holland No.15 potatoes were used as experimental samples. Hyperspectral data were collected from three sampling sites (the top, umbilicus, and middle regions). Standard normal variate (SNV) was used for spectral preprocessing, and three different methods of competitive adaptive reweighted sampling (CARS), iterative variable subset optimization (IVSO), and the variable iterative space shrinkage approach (VISSA) were used for characteristic wavelength selection. Linear partial least-squares regression (PLSR) and nonlinear support vector regression (SVR) models were then established. The results indicated that the sampling site has a considerable impact on the accuracy of the prediction model, and the umbilicus region with CARS-SVR model gave best performance with correlation coefficients in calibration (Rc) of 0.9415, in prediction (Rp) of 0.9346, root mean square errors in calibration (RMSEC) of 15.9 g/kg, in prediction (RMSEP) of 17.4 g/kg, and residual predictive deviation (RPD) of 2.69. The starch content in potatoes was visualized using the best model in combination with pseudo-color technology. Our research provides a method for the rapid and nondestructive determination of starch content in potatoes, providing a good foundation for potato quality monitoring and grading.

Effect of Quinoa (Chenopodium quinoa Willd.) Starch and Seeds on the Physicochemical and Textural and Sensory Properties of Chicken Meatballs during Frozen Storage

The effects of starch (corn and quinoa) and quinoa seeds on chicken meatballs' physicochemical, textural, and sensory properties were investigated during frozen storage. The chicken meatballs were prepared with corn starch (CS), quinoa starch (QS), quinoa seeds (Q), and combinations of corn starch and quinoa seeds (CS-Q), and quinoa starch and quinoa seeds (QS-Q), which were subjected to five freeze-thaw (F-T) cycles of temperature fluctuation conditions during frozen storage. Regardless of the type used (CS or QS), adding starch resulted in fewer cooking, drip, and reheating losses in chicken meatballs during frozen storage. The values of the hardness, gumminess, and chewiness of chicken meatballs with CS or QS were half those of chicken meatballs without starch, indicating that the addition of starch inhibited the change in the meatballs' texture. The total volatile basic nitrogen (TVB-N) and thiobarbituric acid reactive substance (TBARS) values were progressive but did not dynamically increase during five F-T cycles. Chicken meatballs containing CS-Q or QS-Q showed significantly lower TBARS values than those with CS, QS, or Q after five F-T cycles. Adding quinoa seeds significantly increased the antioxidant activity and the chewiness of meatballs (p < 0.05) compared with starch only. The addition of the combination of QS-Q to chicken meatballs increased the values of taste, texture, and overall acceptability, indicating that quinoa starch and seeds may be introduced as premium ingredients to frozen meat products.

The Effect of Multiple Freeze-Thaw Cycles on the Microstructure and Quality of Trachurus murphyi

Temperature fluctuation in frozen food storage and distribution is the perpetual and core issue faced by the frozen food industry. Ice recrystallisation induced by temperature fluctuations under cold storage causes microstructural changes in fish products and irreversible damages to cells and tissues, which lower the frozen fish quality in the food chain. This study is intended to explore how repeated freezing-thawing affected the microstructure and quality of Trachurus murphyi during its frozen storage. The results showed the consistency between the increase in ice crystal diameter, volume, and porosity in frozen fish and the increase in centrifugal loss (from 22.4% to 25.69%), cooking loss (from 22.32% to 25.19%), conductivity (from 15.28 Ms/cm to 15.70 Ms/cm), TVB-N (from 16.32 mg N/100 g to 19.94 mg N/100 g), K-value (from 3.73% to 7.07%), and amino acid composition. The muscle structure change observed by Fourier-Transform Infrared spectroscopy (FT-IR) showed that the content of α-helix reduced from 59.05% to 51.83%, while the β-sheet fraction grew from 15.44% to 17.11%, β-turns increased from 5.45% to 7.58%, and random coil from 20.06% to 23.49%. Moreover, muscular structure exhibited varying degrees of deterioration with increasing cycles of freezing and thawing as shown by scanning electron microscopy (SEM). We studied the muscular morphology, which included the measurement of porosities (%) of pore that increased (from 1.4% to 4.3%) and pore distribution, by X-ray computed tomography (uCT). The cycles of the freeze-thaw resulted in structural changes, which seemed to be closely associated with ultimate quality of frozen fish products.

Total volatile basic nitrogen and trimethylamine in muscle foods: Potential formation pathways and effects on human health

The use of total volatile basic nitrogen (TVB-N) as a quality parameter for fish is rapidly growing to include other types of meat. Investigations of meat quality have recently focused on TVB-N as an index of freshness, but little is known on the biochemical pathways involved in its generation. Furthermore, TVB-N and methylated amines have been reported to exert deterimental health effects, but the relationship between these compounds and human health has not been critically reviewed. Here, literature on the formative pathways of TVB-N has been reviewed in depth. The association of methylated amines and human health has been critically evaluated. Interventions to mitigate the effects of TVB-N on human health are discussed. TVB-N levels in meat can be influenced by the diet of an animal, which calls for careful consideration when using TVB-N thresholds for regulatory purposes. Bacterial contamination and temperature abuse contribute to significant levels of post-mortem TVB-N increases. Therefore, controlling spoilage factors through a good level of hygiene during processing and preservation techniques may contribute to a substantial reduction of TVB-N. Trimethylamine (TMA) constitutes a significant part of TVB-N. TMA and trimethylamine oxide (TMA-N-O) have been related to the pathogenesis of noncommunicable diseases, including atherosclerosis, cancers, and diabetes. Proposed methods for mitigation of TMA and TMA-N-O accumulation are discussed, which include a reduction in their daily dietary intake, control of internal production pathways by targeting gut microbiota, and inhibition of flavin monooxygenase 3 enzymes. The levels of TMA and TMA-N-O have significant health effects, and this should, therefore, be considered when evaluating meat quality and acceptability. Agreed international values for TVB-N and TMA in meat products are required. The role of feed, gut microbiota, and translocation of methylated amines to muscles in farmed animals requires further investigation.

Fast quantification of total volatile basic nitrogen (TVB-N) content in beef and pork by near-infrared spectroscopy: Comparison of SVR and PLS model

With application of PLS regression and SVR, quantitation models of near infrared diffuse reflectance spectroscopy were established for the first time to predict the content of volatile basic nitrogen (TVB-N) content in beef and pork. Results indicated that the best PLS model based on the raw spectra showed an excellent prediction performance with a high value of correlation coefficient at 0.9366 and a low root-mean-square error of prediction value of 3.15, and none of those pretreatment methods could improve the prediction performance of the PLS model. Moreover, comparatively the model obtained by SVR showed inferior quantitative predictive ability (R = 0.8314, RMSEP = 4.61). Analysis on VIP selected wavelengths inferred amino bond containing compounds and lipid may play important roles in the development of PLS models for TVB-N. Results from this study demonstrated the potential of using NIR spectroscopy and PLS for the prediction of TVB-N in beef and pork while more efforts are required to improve the performance of SVR models.

Assessment of quality characteristics and bacterial community of modified atmosphere packaged chilled pork loins using 16S rRNA amplicon sequencing analysis

Modified atmosphere packaging (MAP) is widely applied in packaging meat and meat products. While most studies had employed culture-dependent microbiological analyses or polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE), the recent application of high-throughput sequencing (HTS) has been effective and reliable in detecting the microbial consortium associated with food spoilage. Since MAP application is limited in China, applying HTS in assessing the microbial consortium of meat and meat products in the country becomes imperative. In this study, quality indexes and bacterial enumeration often used as spoilage indicators were employed to assess MAP fresh pork under chilled (4 °C) storage for 21 d. The results indicated that 70%O₂/30%CO₂ (Group A) retained more redness (a*) content, while 70%N₂/30%CO₂ (Group B) markedly reduced spoilage indicators compared to the control group. Notably, high-throughput sequencing indicated that Group B and 20%O₂/60%N₂/20%CO₂ (Group C) inhibited the growth of abundant spoilers, Pseudomonas spp. and Brochothrix spp. Thus, MAP (Group B and C) has promising potential in inhibiting predominant meat spoilers during chilled storage. This study provides valuable information to food industries on the potential application of MAP to control meat spoilage in Chinese markets.

Determination of Fatty Acid Content of Rice during Storage Based on Feature Fusion of Olfactory Visualization Sensor Data and Near-Infrared Spectra

This study innovatively proposes a feature fusion technique to determine fatty acid content during rice storage. Firstly, a self-developed olfactory visualization sensor was used to capture the odor information of rice samples at different storage periods and a portable spectroscopy system was employed to collect the near-infrared (NIR) spectra during rice storage. Then, principal component analysis (PCA) was performed on the pre-processed olfactory visualization sensor data and the NIR spectra, and the number of the best principal components (PCs) based on the single technique model was optimized during the backpropagation neural network (BPNN) modeling. Finally, the optimal PCs were fused at the feature level, and a BPNN detection model based on the fusion feature was established to achieve rapid measurement of fatty acid content during rice storage. The experimental results showed that the best BPNN model based on the fusion feature had a good predictive performance where the correlation coefficient (R_P) was 0.9265, and the root mean square error (RMSEP) was 1.1005 mg/100 g. The overall results demonstrate that the detection accuracy and generalization performance of the feature fusion model are an improvement on the single-technique data model; and the results of this study can provide a new technical method for high-precision monitoring of grain storage quality.

Application of benchtop NIR spectroscopy coupled with multivariate analysis for rapid prediction of antioxidant properties of walnut (Juglans regia)

Benchtop near-infrared (NIR) spectroscopy coupled with multivariate analysis was used for the classification and prediction of antioxidant properties of walnut. Total phenolic content (TPC), total flavonoid content (TFC), ABTS assay and FRAP assay were performed spectrophotometrically. The synergy interval partial least square coupled competitive adaptive reweighted sampling (Si-CARS-PLS) was used for the prediction. A decent discrimination using principal component analysis (PCA) was observed by mean of spectroscopic and antioxidant properties data with total cumulative variance of 99.26% (PC1 = 95.07%, PC2 = 2.98%, PC3 = 1.21%) and 96.60% (PC1 = 64.28%, PC2 = 32.32%) respectively. The Si-CARS-PLS yielded optimal performance, R_P = 0.9616, RPD = 3.807 for TPC, R_P = 0.9657, RPD = 3.367 for TFC, R_P = 0.9683, RPD = 2.728 for ABTS assay, and R_P = 0.914, RPD = 2.669 for FRAP assay. These findings revealed that NIR integrated with Si-CARS-PLS could be used for the prediction of antioxidant properties of walnut.