Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques

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

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

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

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

Identifying freshness of various chilled pork cuts using rapid imaging analysis

BACKGROUND

Determining the freshness of chilled pork is of paramount importance to consumers worldwide. Established freshness indicators such as total viable count, total volatile basic nitrogen and pH are destructive and time-consuming. Color change in chilled pork is also associated with freshness. However, traditional detection methods using handheld colorimeters are expensive, inconvenient and prone to limitations in accuracy. Substantial progress has been made in methods for pork preservation and freshness evaluation. However, traditional methods often necessitate expensive equipment or specialized expertise, restricting their accessibility to general consumers and small-scale traders. Therefore, developing a user-friendly, rapid and economical method is of particular importance.

RESULTS

This study conducted image analysis of photographs captured by smartphone cameras of chilled pork stored at 4 °C for 7 days. The analysis tracked color changes, which were then used to develop predictive models for freshness indicators. Compared to handheld colorimeters, smartphone image analysis demonstrated superior stability and accuracy in color data acquisition. Machine learning regression models, particularly the random forest and decision tree models, achieved prediction accuracies of more than 80% and 90%, respectively.

CONCLUSION

Our study provides a feasible and practical non-destructive approach to determining the freshness of chilled pork. © 2024 Society of Chemical Industry.

Novel hollow-electrode glow discharge mass spectrometry for the quantitative analysis of protein content in food

Protein content in food is an important indicator of nutritional value and food safety. Therefore, it is of great significance to accurately detect protein content in food. In this work, a combustion furnace and novel hollow-electrode glow discharge ion source-quadrupole mass spectrometry (HGD-MS) were designed, which were used to construct a "combustion furnace + mass spectrometry" experimental platform to detect the protein content in food. Five food standard samples were selected for the analysis. The food samples were combusted in the combustion furnace at a high temperature (1300 °C) in an oxygen-rich environment. The gas products were passed into the novel hollow electrode glow discharge ion source-quadrupole mass spectrometer. A standard curve of y = 635.06x + 11 082, R2 = 0.9994 was plotted by detecting the NO+ ion intensity at a relative standard deviation (RSD) of 1.8% to 5.7%. Using the same method, food samples no. 6 and 7 were combusted and NO+ ion intensity was measured to verify the accuracy of the quantitation curve. Subsequently, the protein content was determined using a nitrogen-to-protein conversion factor of 6.25. This method provides a rapid, accurate, and environmentally friendly approach for determining protein content in food.

Discrimination among Fresh, Frozen-Stored and Frozen-Thawed Beef Cuts by Hyperspectral Imaging

The detection of the storage state of frozen meat, especially meat frozen-thawed several times, has always been important for food safety inspections. Hyperspectral imaging (HSI) is widely applied to detect the freshness and quality of meat or meat products. This study investigated the feasibility of the low-cost HSI system, combined with the chemometrics method, to classify beef cuts among fresh (F), frozen-stored (F-S), frozen-thawed three times (F-T-3) and frozen-thawed five times (F-T-5). A compact, low-cost HSI system was designed and calibrated for beef sample measurement. The classification model was developed for meat analysis with a method to distinguish fat and muscle, a CARS algorithm to extract the optimal wavelength subset and three classifiers to identify each beef cut among different freezing processes. The results demonstrated that classification models based on feature variables extracted from differentiated tissue spectra achieved better performances, with ACCs of 92.75% for PLS-DA, 97.83% for SVM and 95.03% for BP-ANN. A visualization map was proposed to provide detailed information about the changes in freshness of beef cuts after freeze-thawing. Furthermore, this study demonstrated the potential of implementing a reasonably priced HSI system in the food industry.

Application of Data Fusion in Traditional Chinese Medicine: A Review

Traditional Chinese medicine is characterized by numerous chemical constituents, complex components, and unpredictable interactions among constituents. Therefore, a single analytical technique is usually unable to obtain comprehensive chemical information. Data fusion is an information processing technology that can improve the accuracy of test results by fusing data from multiple devices, which has a broad application prospect by utilizing chemometrics methods, adopting low-level, mid-level, and high-level data fusion techniques, and establishing final classification or prediction models. This paper summarizes the current status of the application of data fusion strategies based on spectroscopy, mass spectrometry, chromatography, and sensor technologies in traditional Chinese medicine (TCM) in light of the latest research progress of data fusion technology at home and abroad. It also gives an outlook on the development of data fusion technology in TCM analysis to provide references for the research and development of TCM.

A Novel Indicator Based on Polyacrylamide Hydrogel and Bromocresol Green for Monitoring the Total Volatile Basic Nitrogen of Fish

An intelligent indicator was developed by immobilizing bromocresol green (BCG) within the polyacrylamide (PAAm) hydrogel matrix to monitor the total volatile basic nitrogen (TVB-N) content of fish. The FTIR analysis indicated that BCG was effectively incorporated into the PAAm through the formation of intermolecular hydrogen bonds. A thermogravimetric analysis (TGA) showed that the PAAm/BCG indicator had a mere 0.0074% acrylamide monomer residue, meanwhile, the addition of BCG improved the thermal stability of the indicator. In vapor tests with various concentrations of trimethylamine, the indicator performed similarly at both 4 °C and 25 °C. The total color difference values (ΔE) exhibited a significant linear response to TVB-N levels ranging from 4.29 to 30.80 mg/100 g at 4 °C (R2 = 0.98). Therefore, the PAAm/BCG indicator demonstrated stable and sensitive color changes based on pH variations and could be employed in smart packaging for real-time assessment of fish freshness.

A colorimetric and fluorescent sensor for non-destructive screening of the freshness of shrimp and fish

The freshness of fish and shrimp is closely associated with food safety, hence it is a wide concern to develop a facile and effective method for fast, non-destructive and visual screening the freshness of fish and shrimp. Herein, we developed a chromogenic and fluorogenic sensor (RFCC) based on resorufin for sensing of biogenic amines including cadaverine and putrescine. RFCC underwent aminolysis with cadaverine or putrescine, displaying a remarkable fluorescence turn on response at 593 nm along with obvious color change from colorless to pink. RFCC was fabricated into test strips to sense cadaverine vapor, and the RGB value of test strips showed a good linear relationship with the concentration of cadaverine (0.5 - 8.2 × 10³ ppm). The RFCC tag was used to in situ screen the freshness of fish and shrimp according to obvious fluorescence change, and satisfactory results were achieved. Furthermore, this test strip was validated by total volatile base nitrogen (TVBN), providing a simple, low cost and portable tool to screen the freshness of fish and shrimp for consumers and suppliers without expensive instrumentation.

Development of a pH-sensing indicator for shrimp freshness monitoring: Curcumin and anthocyanin-loaded gelatin films

An intelligent pH-sensing indicator containing Roselle (Hibiseus sabdariffa L.) (RS) anthocyanin and curcumin (CR) was developed and characterized as on-package indicator tags to check the freshness of shrimp during the storage at 4°C. FE-SEM and FT-IR analysis showed that RS and CR were successfully immobilized in the gelatin-glycerol film-forming substrate. The addition of various natural dyes increased the thickness and antioxidant action of the colorimetric film. To assess the response to changes in the pH, the colorimetric film was immersed in different buffers. Based on volatile amines secreted by shrimp, a test application of a colorimetric film containing natural dyes at a ratio of CR:RS = 1:4 (v/v) was conducted in shrimp at 4°C. The total volatile basic nitrogen (TVB-N) and the pH of shrimp were monitored during refrigerated storage for 10 days, and the color changes of the indicator were recorded simultaneously. The results indicated that the designed colorimetric film could produce various colors, which are thought to be indicative of the freshness and spoilage of packaged shrimp. Therefore, the target film can be utilized as a promising smart packaging material for monitoring the freshness of shrimp and aquatic products in real time.

Determination of Freshness of Mackerel (Scomber japonicus) Using Shortwave Infrared Hyperspectral Imaging

Shortwave infrared (SWIR) hyperspectral imaging was applied to classify the freshness of mackerels. Total volatile basic nitrogen (TVB-N) and acid values, as chemical compounds related to the freshness of mackerels, were also analyzed to develop a prediction model of freshness by combining them with hyperspectral data. Fresh mackerels were divided into three groups according to storage periods (0, 24, and 48 h), and hyperspectral data were collected from the eyes and whole body, separately. The optimized classification accuracies were 81.68% using raw data from eyes and 90.14% using body data by multiple scatter correction (MSC) pretreatment. The prediction accuracy of TVB-N was 90.76%, and the acid value was 83.76%. These results indicate that hyperspectral imaging, as a nondestructive method, can be used to verify the freshness of mackerels and predict the chemical compounds related to the freshness.

Novel flexible sensing technology for nondestructive detection on live fish health/quality during waterless and low-temperature transportation

Fish health/quality issues are increasingly attracting attention during waterless and low-temperature transportation. Nondestructive detection has become a great need for an effective method to improve fish health/quality. Currently, emerging Internet of Things, novel flexible electronics and data fusion technology have received great interest for nondestructive detection on live fish health/quality. This paper analysized nondestructive detection mechanisms using novel flexible sensing technology to achieve high-precision sensing of key parameters, and machine learning based data fusion modeling to achieve live fish health/quality nondestructive evaluation during waterless and low-temperature transportation. Recent studies on novel flexible electrochemical and physiological biosensors development and application for solving key ambient and physiological parameter sensing were summarized. The ML based data fusion modeling framework and application for live fish health/quality nondestructive evaluation was also highlighted. The future perspective is also proposed to provide promising solutions for accurate sensing of multi-parameter and real applications of live fish health/quality nondestructive detection during waterless and low-temperature transportation.

Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion

Efficient, rapid, and non-destructive detection of pesticide residues in fruits and vegetables is essential for food safety. The visible/near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) systems were used to detect different types of pesticide residues on the surface of Hami melon. Taking four pesticides commonly used in Hami melon as the object, the effectiveness of single-band spectral range and information fusion in the classification of different pesticides was compared. The results showed that the classification effect of pesticide residues was better by using the spectral range after information fusion. Then, a custom multi-branch one-dimensional convolutional neural network (1D-CNN) model with the attention mechanism was proposed and compared with the traditional machine learning classification model K-nearest neighbor (KNN) algorithm and random forest (RF). The traditional machine learning classification model accuracy of both models was over 80.00%. However, the classification results using the proposed 1D-CNN were more satisfactory. After the full spectrum data was fused, it was input into the 1D-CNN model, and its accuracy, precision, recall, and F1-score value were 94.00%, 94.06%, 94.00%, and 0.9396, respectively. This study showed that both VNIR and SWIR hyperspectral imaging combined with a classification model could non-destructively detect different pesticide residues on the surface of Hami melon. The classification result using the SWIR spectrum was better than that using the VNIR spectrum, and the classification result using the information fusion spectrum was better than that using SWIR. This study can provide a valuable reference for the non-destructive detection of pesticide residues on the surface of other large, thick-skinned fruits.

Simultaneous quantitation of free fatty acid in rice by synergetic data fusion of colorimetric sensor arrays, NIR, and MIR spectroscopy

This study evaluated the feasibility of colorimetric sensor array (CSA), near-infrared (NIR) and mid-infrared (MIR) spectroscopy for quantitation of free fatty acids in rice using data fusion. Purposely, different data sets of low-level (CSA-NIR_LL, CSA-MIR_LL, and NIR-MIR_LL) and mid-level (CSA-NIR_ML, CSA-MIR_ML, and NIR-MIR_ML) fusion were adopted to enhance the statistical parameters. The model performance was evaluated using coefficient of determination for prediction, (R²p), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). Synergetic low-level and mid-level fusion model yielded 0.7707 ≤ R²p ≤ 0.8275, 14.4 ≤ RMSEP ≤ 16.3 and 2.19 ≤ RPD ≤ 2.48; and 0.7788 ≤ R²p ≤ 0.8571, 12.4 ≤ RMSEP ≤ 16.8 and 2.12 ≤ RPD ≤ 2.88, respectively. The CSA-NIR_ML model delivered an optimal performance for prediction of free fatty acid. The integration of CSA, NIR and MIR was feasible and could improve the prediction accuracy of free fatty acids in rice.

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

Antioxidant and ammonia-sensitive films based on starch, κ-carrageenan and Oxalis triangularis extract as visual indicator of beef meat spoilage

In this study, we first investigated the rheological property of sweet potato starch (SPS), κ-carrageenan (KC) and Oxalis triangularis extract (OTE) blends and found that the blends exhibited high apparent viscosity with an apparent shear thinning behavior. And then films based on SPS, KC and OTE were developed and their structural and functional properties were studied. The physico-chemical test results showed that OTE exhibited different colors in solutions with different pH values and the incorporation with OTE and KC could significantly increase the thickness, water vapor permeability, light barrier ability, tensile strength and elongation at break as well as the pH- and ammonia-sensitive properties of the SPS film. The structural property test results showed that some intermolecular interactions between OTE and SPS/KC occurred in SPS-KC-OTE films. Finally, the functional properties of SPS-KC-OTE films were examined and SPS-KC-OTE films showed significant DPPH radical scavenging activity as well as a visible color change in response to changes in beef meat freshness. Our results suggested that the SPS-KC-OTE films could be used as an active and intelligent food packaging material in food industry.

Different dietary sources of selenium alter meat quality, shelf life, selenium deposition, and antioxidant status in Hu lambs

Thirty-two male Hu lambs (32.31 ± 3.31 kg; 4-months-old) were randomly assigned to four treatments: (1) control (CON), (2) selenium-enriched yeast (SeY, 0.8 mg/kg), (3) selenized glucose (SeGlu, 0.8 mg/kg), and (4) sodium selenite (SS, 0.8 mg/kg) to evaluate their effects on Hu lamb production and slaughter performance, antioxidant capacity, hematological parameters, meat quality and shelf-life. The production and slaughter performances were not different (P > 0.05) among treatments. SeGlu and SeY increased (P < 0.05) the total antioxidant capacity in serum and muscle selenium content while decreasing (P < 0.05) the malondialdehyde (MDA) contents both in serum and muscle. SeGlu extended muscle shelf-life by 7.7 h compared with CON and decreased (P < 0.05) yellowness (b*) and lightness (L*) in meat stored for 24 h. In summary, the effects of SeGlu were similar to those of SeY and better than those of SS in improving serum and muscle antioxidant status, prolonging muscle shelf-life, and increasing selenium deposition in muscle.

Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review

With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend.

Assessment of the Microbial Spoilage and Quality of Marinated Chicken Souvlaki through Spectroscopic and Biomimetic Sensors and Data Fusion

Fourier-transform infrared spectroscopy (FT-IR), multispectral imaging (MSI), and an electronic nose (E-nose) were implemented individually and in combination in an attempt to investigate and, hence, identify the complexity of the phenomenon of spoilage in poultry. For this purpose, marinated chicken souvlaki samples were subjected to storage experiments (isothermal conditions: 0, 5, and 10 °C; dynamic temperature conditions: 12 h at 0 °C, 8 h at 5 °C, and 4 h at 10 °C) under aerobic conditions. At pre-determined intervals, samples were microbiologically analyzed for the enumeration of total viable counts (TVCs) and Pseudomonas spp., while, in parallel, FT-IR, MSI, and E-nose measurements were acquired. Quantitative models of partial least squares–Regression (PLS-R) and support vector machine–regression (SVM-R) (separately for each sensor and in combination) were developed and validated for the estimation of TVCs in marinated chicken souvlaki. Furthermore, classification models of linear discriminant analysis (LDA), linear support vector machine (LSVM), and cubic support vector machines (CSVM) that classified samples into two quality classes (non-spoiled or spoiled) were optimized and evaluated. The model performance was assessed with data obtained by six different analysts and three different batches of marinated souvlaki. Concerning the estimation of the TVCs via the PLS-R model, the most efficient prediction was obtained with spectral data from MSI (root mean squared error—RMSE: 0.998 log CFU/g), as well as with combined data from FT-IR/MSI (RMSE: 0.983 log CFU/g). From the developed SVM-R models, the predictions derived from MSI and FT-IR/MSI data accurately estimated the TVCs with RMSE values of 0.973 and 0.999 log CFU/g, respectively. For the two-class models, the combined data from the FT-IR/MSI instruments analyzed with the CSVM algorithm provided an overall accuracy of 87.5%, followed by the MSI spectral data analyzed with LSVM, with an overall accuracy of 80%. The abovementioned findings highlighted the efficacy of these non-invasive rapid methods when used individually and in combination for the assessment of spoilage in marinated chicken products regardless of the impact of the analyst, season, or batch.

Bibliometric Review on the Volatile Organic Compounds in Meat

Meat flavor is an important aspect of meat quality that also influences consumer demand, and is therefore very important for the meat industry. Volatile organic compounds (VOCs) contribute in large part to the flavor of meat, and while increasing numbers of articles are published on this topic, reviews of these articles are very scarce. Therefore, our aim was to perform a bibliometric analysis of the scientific publications on VOCs in meat over the period 2000-2020. We selected 611 scientific sources from the Scopus database related to VOCs in meat (seafood excluded). The bibliometric information retrieved included journals, authors, countries, institutions, keywords, and citations. From this analysis, we drew up a list of the most important journals, authors, countries, and institutions, and the trends in VOC research on meat. We conducted a social network analysis (SNA) to identify the collaborations among the many authors and countries, and a keyword analysis to generate a network map of the authors' keywords. We also determined which meat species were most frequently chosen as research subjects, traced the evolution of the various methods/instruments used, and explored the research tendencies. Finally, we point out the need for further research in defining meat quality, improving meat flavor, identifying adulterants, and certifying the authenticity of meat.

Real-Time Monitoring the Effects of Storage Conditions on Volatile Compounds and Quality Indexes of Halal-Certified Kimchi during Distribution Using Electronic Nose

The food logistics system is an essential sector for maintaining and monitoring the safety and quality of food products and becoming more crucial, especially during and after the pandemic of COVID-19. Kimchi is a popular traditional fermented food originally from Korea and easily changes because of the storage conditions. This study aims to evaluate the effects and the contributions of temperature to volatile compounds, quality indexes, and the shelf life of Halal-certified Kimchi, and to identify alcohol and find the correlation between the identified variables using an electronic nose and conventional method with the integration of multivariate analysis. Thirty-two volatile compounds (VOCs) were detected and correlated with pH, titratable acidity (TA), and lactic acid bacteria (LAB) counts during storage time. Ethanol was also found in the ripened Kimchi and possibly became the critical point of halal Kimchi products besides total acidity, pH, and LAB. Furthermore, the correlation between pH and benzaldehyde, titratable acidity and 3-methylbutanoic acid, and among lactic acid bacteria with ethanol, acetic acid, ethyl acetate, and 3-methylbutanoic acid properly can be used as a given set of variables in the prediction of food quality during storage and distribution.

UV-fluorescence imaging for real-time non-destructive monitoring of pork freshness

This study aimed to develop a cost-effective fluorescence imaging system to rapidly monitor pork freshness indicators during chilled storage. The system acquired fluorescence images of pork and the color features were extracted from these images to establish partial least squares regression (PLSR) models to predict total volatile basic nitrogen (TVB-N), total viable count (TVC), pH for pork. For TVB-N, TVC and pH values, R_p were 0.92, 0.88 and 0.74, residual predictive deviation (RPD) were 2.24, 2.03, and 1.19, respectively. For TVB-N and TVC indicators showed that the predictive ability of this model was largely comparable to that of fluorescence hyperspectral imaging. However, combining fluorescence and color imaging improved the model's predictive ability. For TVB-N, TVC and pH, R_p were 0.94, 0.93 and 0.85, RPD were 2.62, 2.59, and 1.95, respectively. Therefore, this study developed a system with great potential for detecting the value of most pork quality indicators in real-time.

Rapid and non-destructive freshness evaluation of squid by FTIR coupled with chemometric techniques

BACKGROUND

Freshness is an important quality of squid with respect to determining the market price. The methods of evaluation of freshness fail to be widely used as a result of the lack of rapidity and quantitation. In the present study, a rapid and non-destructive quantification of squid freshness by Fourier transform infrared spectroscopy (FTIR) spectra combined with chemometric techniques was performed.

RESULTS

The relatively linear content change of trimethylamine (TMA-N) and dimethylamine (DMA-N) of squid during storage at 4 °C indicated their feasibility as a freshness indicator, as also confirmed by sensory evaluation. The spectral changes were mainly caused by the degradation of proteins and the production of amines by two-dimensional infrared correlation spectroscopy, among which TMA-N, DMA-N and putrescine were the main amines. The successive projections algorithm (SPA) was employed to select the sensitive wavenumbers to freshness for modeling prediction including partial least-squares regression, support vector regression (SVR) and back-propagation artificial neural network. Generally, the SPA-SVR model of the selected characteristic wavenumber showed a higher prediction accuracy for DMA-N (R² _P  = 0.951; RMSE_P  = 0.218), whereas both SPA-SVR (R² _P  = 0.929; RMSE_P  = 2.602) and Full-SVR (R² _P  = 0.941; RMSE_P  = 2.492) models had a higher predictive ability of TMA-N.

CONCLUSION

The results of the present study demonstrate that FTIR spectroscopy coupled with multivariate calibration shows significant potential for the prediction of freshness in squid. © 2021 Society of Chemical Industry.

A smartphone-adaptable fluorescent sensing tag for non-contact and visual monitoring of the freshness of fish

Fish-based food products play important roles in our daily diet. The related food safety is vitally essential for human health, thus it is very necessary to screen the freshness of fish-based foods. In this work, we presented a ratiometric fluorescent probe PTCN for the determination of cadaverine, a metabolic biomarker of the spoilage of fish. PTCN displayed a ratiometric fluorescence response towards cadaverine with good specificity, high sensitivity (LOD = 46 nM) and ultra-fast response (<15 s), and thus has been successfully utilized to determine cadaverine from the spoilage of fish. PTCN was fabricated into cheap and portable sensing tags, which can visually detect gaseous cadaverine with obvious fluorescence color transformation from red to green and a low detection limit (8.65 ppm). Moreover, the PTCN tags were used as smart fluorescent tags for non-contact and visual monitoring of cadaverine in fish. Furthermore, the ratiometric fluorescence signals were utilized to create a smartphone-adaptable digital sensing profile for indicating cadaverine in fish products.

Microbial Inactivation Property of Pulsed Corona Discharge Plasma and Its Effect on Chilled Pork Preservation

Although plasma, especially atmospheric plasma generated by corona discharge, has been proven to be effective in sterilization and food preservation, its disinfection mechanism on chilled pork is poorly understood. In this research, the bactericidal and preservation effect of corona discharge plasma (CDP) was investigated. The maximum bactericidal effect was found after 20 kV 4 kHz CDP treatment, with 2.77 log (colony-forming unit [CFU]/g), 2.41 log (CFU/g), and 1.36 log (CFU/g) reduction for Pantoea agglomerans, Serratia liquefaciens, and Kurthia zopfii, respectively, after 10 min of exposure. The efficiency of microbial inactivation was attributed to the increase of ozone, hydrogen peroxide and morphological changes. It was observed that the microbial level and total volatile binding nitrogen value of CDP-treated chilled pork samples were suppressed during storage, whereas the increase of thiobarbituric acid reactive substances value and the changes of color were still worthy of attention. The aim of this study was to explore the effect of pulsed CDP on the inactivation of spoilage microorganism inoculated on the surface of fresh pork. The prospect of this technology in meat preservation industry was also investigated.

Toward the Development of Combined Artificial Sensing Systems for Food Quality Evaluation: A Review on the Application of Data Fusion of Electronic Noses, Electronic Tongues and Electronic Eyes

Devices known as electronic noses (ENs), electronic tongues (ETs), and electronic eyes (EEs) have been developed in recent years in the in situ study of real matrices with little or no manipulation of the sample at all. The final goal could be the evaluation of overall quality parameters such as sensory features, indicated by the "smell", "taste", and "color" of the sample under investigation or in the quantitative detection of analytes. The output of these sensing systems can be analyzed using multivariate data analysis strategies to relate specific patterns in the signals with the required information. In addition, using suitable data-fusion techniques, the combination of data collected from ETs, ENs, and EEs can provide more accurate information about the sample than any of the individual sensing devices. This review's purpose is to collect recent advances in the development of combined ET, EN, and EE systems for assessing food quality, paying particular attention to the different data-fusion strategies applied.

A smartphone-adaptable fluorescent sensing tag for non-contact and visual monitoring of the freshness of fish

Fish-based food products play important roles in our daily diet. The related food safety is vitally essential for human health, thus it is very necessary to screen the freshness of fish-based foods.

A smartphone-adaptable fluorescent sensing tag for non-contact and visual monitoring of the freshness of fish

Fish-based food products play important roles in our daily diet. The related food safety is vitally essential for human health, thus it is very necessary to screen the freshness of fish-based foods.

Development, characterization and stability of a white cachama pâté-type product (Piaractus brachypomus)

The objective of the work was to formulate, characterize and evaluate the stability of a product based on white cachama (Piaractus brachypomus). From four lipid sources (pork back fat, canola oil, olive oil or sacha inchi oil), the one with the highest acceptance rate was selected based on the acceptance index and sensory characteristics. The formulation was optimized using the response surface method; 15 formulations were used in triplicate, evaluating the pH, moisture, colour and sensory acceptance values. The macronutrient composition and lipid profile of the optimal formulation was determined; its stability was evaluated under refrigeration conditions by measuring lipid and protein degradation, changes in colour, texture, changes at the microbiological and sensory levels. It was found an optimal proportion of inclusion of 50% white cachama pasta, 21% canola oil and 23% water. The stability of the final product obtained was 42 days, with 82% of acceptability index. This product could be an alternative to other pâté-type products from other species.

Incorporation of Selected Strains of Pediococcus spp. on Quality Characteristics of Dry Fermented Sausage during Fermentation and Ripening

This research investigated the physio-chemical and microbial quality characteristics of dry fermented sausage from selected Pediococcus strains: P. acidiliactici (PE1) and P. pentosaceus (PE2) as compared to commercial starter culture (COS) during fermentation and ripening. Treatments showed no substantial variation (p<0.05) in water activity (a_w) values across the study period. PE1 and PE2 treatments exhibited similar (p>0.05) pH values and presented remarkable (p<0.05) lower volatile basic nitrogen (VBN) and thiobarbituric acid reactive (TBARS) content than COS treatment throughout the ripening period. However, the pH values in COS batch were considerably lower than others. PE1 samples presented a significant highest (p<0.05) counts both in lactic acid bacteria (LAB) and total plate count (TPC) than COS and PE2 treatments at 7 days fermentation, and it resulted in a similar and higher TPC count as COS after the ripening period. After the ripening process, treatments are ordered based on LAB counts as follows: COS>PE1>PE2. All batches presented similar redness and yellowness attributes since the 7 days of fermentation and in lightness across the study period. Treatments were similar (p>0.05) in springiness and chewiness traits across the study period and in hardness characteristics in the ripened products. Cohesiveness was higher in PE1 and COS batches. No variation (p>0.05) in aroma and sourness sensory attributes of treatments. The color attribute was highest (p<0.05) in PE1 and PE2 treatments and PE1 had the highest overall acceptability. The overall outstanding merit exhibited by PE1 can be utilized in the commercial production of high-quality dry fermented sausage.

Analysis of Microbial Diversity and Dynamics During Bacon Storage Inoculated With Potential Spoilage Bacteria by High-Throughput Sequencing

Staphylococcus xylosus, Leuconostoc mesenteroides, Carnobacterium maltaromaticum, Leuconostoc gelidum, and Serratia liquefaciens were investigated for their roles in in the spoilage of sterilized smoked bacon. These five strains, individually and in combination, were applied as starters on sliced bacon at 4–5 log₁₀ CFU/g using a hand-operated spraying bottle and stored for 45 days at 0–4°C. Dynamics, diversity, and succession of microbial community during storage of samples were studied by high-throughput sequencing (HTS) of the V3–V4 region of the 16S rRNA gene. A total of 367 bacterial genera belonging to 21 phyla were identified. Bacterial counts in all the inoculated specimens increased significantly within the first 15 days while the microbiota developed into more similar communities with increasing storage time. At the end of the storage time, the highest abundance of Serratia (96.46%) was found in samples inoculated with S. liquefaciens. Similarly, for samples inoculated with C. maltaromaticum and L. mesenteroides, a sharp increase in Carnobacterium and Leuconostoc abundance was observed as they reached a maximum relative abundance of 97.95 and 81.6%, respectively. Hence, these species were not only the predominant ones but could also have been the more competitive ones, potentially inhibiting the growth of other microorganisms. By analyzing the bacterial load of meat products using the SSO model, the relationships between the microbial communities involved in spoilage can be understood to assist further research.

Machine Learning-Reinforced Noninvasive Biosensors for Healthcare

The emergence and development of noninvasive biosensors largely facilitate the collection of physiological signals and the processing of health-related data. The utilization of appropriate machine learning algorithms improves the accuracy and efficiency of biosensors. Machine learning-reinforced biosensors are started to use in clinical practice, health monitoring, and food safety, bringing a digital revolution in healthcare. Herein, the recent advances in machine learning-reinforced noninvasive biosensors applied in healthcare are summarized. First, different types of noninvasive biosensors and physiological signals collected are categorized and summarized. Then machine learning algorithms adopted in subsequent data processing are introduced and their practical applications in biosensors are reviewed. Finally, the challenges faced by machine learning-reinforced biosensors are raised, including data privacy and adaptive learning capability, and their prospects in real-time monitoring, out-of-clinic diagnosis, and onsite food safety detection are proposed.