Meat Spoilage Detection Research Articles

Machine learning enabled microneedle-based colorimetric pH sensing patch for wound health monitoring and meat spoilage detection

Since pH can alter the biological functions, level of nutrients, wound healing process, and the behavior of chemicals, various healthcare and food industries are showing increased interest in manufacturing low-cost optical pH sensors for meat spoilage detection and wound health monitoring. To meet this demand, we have developed a simple and low-cost machine learning-enabled microneedle-based colorimetric pH sensing patch that can be used for food quality and wound health monitoring applications. The 3D–printed ultrasharp open side channel microneedle array facilitated the autonomous fluid extraction and transportation via surface tension for colorimetric pH sensing. Further, to predict the exact pH value against the obtained color on the pH-test strip, a machine learning model was prepared using experimentally collected different color images obtained from a known pH solution. Furthermore, to make the device user-friendly for older individuals and color-blind individuals, a simple and smartphone-enabled web application was prepared using the developed machine learning model. The proof-of-concept study of the developed patch was demonstrated by determining the pH of real meat samples before and after spoilage and detecting pH in two different skin-mimicking in vitro models (phantom gel and parafilm tape) using a smartphone. The analytical results demonstrated that the developed machine learning-enabled microneedle-based colorimetric pH sensing patch has excellent potential for wound health and food safety applications.

Ppb-level H2S gas sensor based on CuNi-MOFs derivatives for meat freshness detection at low temperature environment

H2S is usually used as one of the landmark gases for meat freshness detection under low temperature storage conditions. In this paper, the NiO-doped CuO composite derived from MOFs was synthesized by a low temperature (−25 ℃) precursor hydrothermal and sintering procedure for H2S detection. The influence of precursor solution temperature and sintering temperature on the structure of MOFs derivatives were discussed by SEM, TEM, and XRD characterization. The hydrothermal method of low-temperature precursor has guiding significance in improving the gas response performance of MOFs/MOFs derivatives. The sensor based on NiO-doped CuO composites derived from MOFs exhibited a ppb-level response to H2S gas. The sensor response is 106 % to 50 ppb H2S. Meanwhile, the sensor has good selectivity, and the response and recovery time are 115 s and 251 s, respectively. The low temperature precursor hydrothermal method directly affected the morphology and structure of MOFs derivatives by regulating the kinetics for MOFs nucleation and crystal growth, obtains more active adsorption sites. In addition, the composite has good sensing performance for H2S at the ambient temperature of 4 ℃ and −18 ℃, which verified the effectiveness for the detection of meat spoilage under a low temperature storage. Furthermore, the sensor based on NiO-doped CuO composites is a good candidate for H2S gas detection in meat freshness evaluation. Finally, an integrated circuit detection system was designed to detect H2S gas at different ambient temperatures. This work provides a new idea for meat freshness detection in a cold storage environment.

Pd-decorated ZnO nanoflowers as a promising gas sensor for the detection of meat spoilage

ZnO and Pd-decorated ZnO nanoflowers are synthesized by a simple wet chemical method directly on interdigitated electrodes as promising amine sensors, which can potentially indicate meat spoilage. XRD and XPS characterization show the existence of crystalline, stoichiometric ZnO along with metallic Pd in the Pd-decorated ZnO materials. SEM and TEM studies show a nanoflower morphology, with well dispersed Pd nanoparticles on the surface of the ZnO nano- “petals”. The chemiresistive gas sensing response is studied at different concentrations of methylamine (in the range of 25–400 ppm in air) and at different temperatures (25–250 °C), showing a superior response of 99.5% for Pd-decorated ZnO electrodes at 250 °C towards 400 ppm methylamine. The device also shows a promising response of 45% at the low temperature of 25 °C, making it a candidate sensing material for early detection of spoilage in meat-based products.

Food Quality Monitoring Based on Hydrolysis‐Induced Au‐Catalyzed Heck Cross‐Coupling by Ag Metallization

Visual detection of meat spoilage was performed based on hydrolysis‐induced silver metallization on gold nanoparticles (Au NPs). The hydrolysis of 4‐I‐benzene‐bounded Wang resin was induced by the release of a biogenic amine followed by Au‐catalyzed Heck cross‐coupling reaction that made silver‐coated gold core‐shell NPs (Au@Ag) in the presence of Ag ions (Ag metallization). A portable sensory cap was designed by this hypothesis and the successful results were obtained for histamine, trimethylamine, and a spoilage sheep meat. With this protocol, the localized surface plasmon resonance (LSPR) is tuned for absorption of Au NPs and leads to LSPR peak blue shift of gold nanoparticles due to the Ag metallization and the preparation of Au@Ag core‐shell NPs. Au NPs and the resulting Au@Ag NPs were characterized by transmission electron microscopy (TEM), BET, ultraviolet‐visible (UV‐Vis), X‐ray diffraction (XRD), energy dispersive X‐ray (EDX), and dynamic light scattering (DLS) analyses. Also, various control experiments were set up to credit the portable sensory tube.

ForceSpun polydiacetylene nanofibers as colorimetric sensor for food spoilage detection

Due to health and economic concerns, increasing attention has been attracted to the safety and quality control of food, particularly meat and seafood. Ammonia and biogenic amines (BAs), such as trimethylamine, putrescine and cadaverine, are released during the decomposition of meat protein. Therefore, the sensitive detection of the released BAs is crucial to the assessment of the safety and quality of meat products during storage, transportation and consumption. In this study, we have developed silica-reinforced polydiacetylene (PDA) nanofiber mats by a novel Forcespinning® technique. These PDA nanofibers demonstrate optical sensing of BAs owing to their strong electrostatic interaction with carboxylate ions of the PDA and amine ions in the BA molecules. Specifically, the PDA nanofibers produce a color transition from blue-to-red plus an enhancement of its fluorescence. PDA nanofibers were prepared from two different monomers and their amine sensing properties are evaluated and compared. The PDA nanofibers made from 5,7-eicosadienoic acid (ECDA) demonstrate higher performance and can determine amine vapor concentration with a detection limit of ˜7 ppm in a few minutes. An effective colorimetric response was also obtained when evaluating the sensing ability of the nanofibers to BA vapors from actual spoiling meat products including chicken, beef, fish and pork. Overall, our forcespun PDA nanofibers demonstrated promising colorimetric detection of meat spoilage based on the released amine vapors during the food degradation. These nanofibers are suitable for real-time and in situ monitoring and assessment of meat deterioration and freshness.

Modern Trends in Detection of Microbial Spoilage of Muscle Foods - A Review

Meat spoilage and detection is very important for meat technologists, quality control agencies, and the meat industry. Thus the microbial spoilage of meat and detection methods is widely studied. For decades microbial metabolites have been used as indicators of organoleptic spoilage of meat. Detection of spoilage by screening for microbial metabolites without identifying specific bacteria is the common approach. The ability to correlate biochemical change with microbial biomass is a complex problem. Current methods for the detection of spoilage in meats are inadequate, time consuming and labour intensive. ATP bioluminescence and fluorescent methods are already in use and are popular due to quick results. Molecular methods like multiplex PCR to detect a group of spoilage bacteria have already been tried. The concept of electronic nose using the odour sensors, conducting organic polymers and metal oxide conductors are promising method. The modified spectroscopy widely studied for detection of meat spoilage. Fourier transform infrared spectroscopy, electrical impedance spectroscopy, near infrared spectroscopy is some of those methods. The use of laser especially the laser speckle imaging is already put into use in this field. Latest technology introduced in the field of meat spoilage detection is the use of smart phone with different attachments. This paper briefly reviews the microbial spoilage of meat and microbial metabolites, then discusses about the currents methods for detection of spoilage and the modern upcoming methods with their potential.

Rapid evaluation of fresh chicken meat quality by electronic nose

A prototype of electronic nose (e-nose) with the gas sensor system for evaluation of fresh chicken meat freshness was developed. In this paper a rapid, simple and not expensive system for fresh chicken meat spoilage detection was investigated that provides objective and reliable results. Quality changes in fresh chicken meat during storage were monitored by the metal oxide sensor (MOS) system and compared with the results of traditional chemical measurements. Gas sensor selection was tested for evaluation of volatile fatty acids (VFA) mainly representing meat spoilage.The study demonstrated that a correlation coefficient (R<sup>2</sup> = 0.89) between e-nose signals and traditional chemical method was high. These results prove that the developed e-nose prototype has a potential for assessing fresh chicken meat freshness and allows discriminating meat into fresh, unsafe and spoiled.

Application of Laser Spectrochemical Analytical Techniques to Follow Up Spoilage of White Meat in Chicken

The overall objective of this paper is to evaluate the potential of laser spectrochemical analytical techniques as rapid, cost-effective, and accurate techniques to detect the onset of spoilage in fresh chicken breast fillets in three consecutive days directly following slaughter day. Samples were periodically examined via laser-induced breakdown spectroscopy (LIBS) and laser-induced fluorescence (LIF). In the case of LIBS, the cyanide (CN) and carbon (C2) molecular spectral emission bands in the LIBS spectra of meat have been taken as indicators of protein content in the chicken breast samples. The ratio of ionic to atomic spectral lines of both magnesium and iron is found to be proportional to the chicken meat tenderness which decreases with storage time. LIF has been also exploited as a simple and fast technique for white meat spoilage detection. There was a clear inverse proportionality between the intensity of the samples’ fluorescence band and the storage period. The obtained spectrochemical results have been validated by measuring the total proteins in the investigated samples using a conventional meat analyzer. This work demonstrates the feasibility of adopting LIBS and LIF techniques in characterization of both fresh and spoiled chicken meat samples.

Application of an Electronic Nose Coupled with Fuzzy-Wavelet Network for the Detection of Meat Spoilage

Food product safety is one of the most promising areas for the application of electronic noses. During the last twenty years, these sensor-based systems have made odour analyses possible. Their application into the area of food is mainly focused on quality control, freshness evaluation, shelf-life analysis and authenticity assessment. In this paper, the performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillets stored either aerobically or under modified atmosphere packaging, at different storage temperatures. A novel multi-output fuzzy wavelet neural network model has been developed, which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the relevant quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population. Comparison results against advanced machine learning schemes indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology.

Fourier Transform Infrared and Raman Spectroscopy Studies of the Time-Dependent Changes in Chicken Meat as a Tool for Recording Spoilage Processes

Time-dependent changes of chicken meat were studied using Fourier transform IR and Raman techniques. Small pieces of intact chicken breast muscle (pectoralis major) were used in the investigations. They were stored in air at 22 °C up to 10 days and their IR and Raman spectra were measured successfully. Analysis of the obtained spectra was performed using a deconvolution of the experimental bands into Lorentz components. All integral intensities of the observed bands were standardized using the statistical R2 coefficient of determination. The R2 values were automatically created as the output of the Origin software. The time-dependent changes of the spectra were used for meat spoilage detection. The analytical relationships between the integral intensities of selected bands have been derived indicating an increase of free amino acids content as the main effect of the chicken breast muscle spoilage.

Low-Cost, High-Accuracy Method and Apparatus for Detecting Meat Spoilage

This paper introduces a novel technique for the detection of meat spoilage. While highly accurate and reliable, the technique is a low-cost technique, and is, therefore, easily within the reach of consumers and retailers. The technique is based on the principle of detecting volatile organic compounds (VOCs) that are released during the meat decomposition by means of certain chemically sensitive dyes. A group of chemically sensitive dyes are printed on paper in the form of a bar code. The bar code is then inserted inside the meat packaging. When exposed to VOCs, the dyes change their colors. A cheap handheld instrument is subsequently used to scan the bar code and instantly apply pattern recognition techniques to detect variations in the colors of the dyes. The instrument reports the condition of spoilage of the product within a fraction of a second.

An intelligent decision support system for the detection of meat spoilage using multispectral images

In food industry, quality and safety are considered important issues worldwide that are directly related to health and social progress. The use of vision technology for quality testing of food production has the obvious advantage of being able to continuously monitor a production using non-destructive methods, thus increasing the quality and minimizing cost. The performance of an intelligent decision support system has been evaluated in monitoring the spoilage of minced beef stored either aerobically or under modified atmosphere packaging, at different storage temperatures (0, 5, 10, and 15 °C) utilising multispectral imaging information. This paper utilises a neuro-fuzzy model which incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. Initially, meat samples are classified according to their storage conditions, while identification models are then utilised for the prediction of the Total Viable Counts of bacteria. The innovation of the proposed approach is further extended to the identification of the temperature used for storage, utilizing only imaging spectral information. Results indicated that spectral information in combination with the proposed modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage.

An intelligent based decision support system for the detection of meat spoilage

In food industry, safety and quality are considered important issues worldwide that are directly related to health and social progress. Meat spoilage is the result of decomposition and the formatio...

An adaptive neuro-fuzzy identification model for the detection of meat spoilage

In food industry, safety and quality are considered important issues worldwide that are directly related to health and social progress. To address the rapid and non-destructive detection of meat spoilage microorganisms during aerobic storage at chill and abuse temperatures, Fourier transform infrared (FTIR) spectroscopy with the aid of a neuro-fuzzy identification model has been considered in this research. FTIR spectra were obtained from the surface of beef samples, while microbiological analysis determined the total viable count for each sample. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from FTIR spectra. The proposed model utilises a prototype defuzzification scheme, whereas the number of input membership functions is directly associated to the number of rules, reducing thus, the curse of dimensionality problem. Results confirmed the advantage of the proposed scheme against the adaptive neuro-fuzzy inference system (ANFIS), in terms of prediction accuracy and structure simplicity. Subsequent comparison against multilayer perceptron (MLP) and partial least squares technique indicated that FTIR spectral information in combination with the proposed modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage.

An intelligent based decision support system for the detection of meat spoilage

In food industry, safety and quality are considered important issues worldwide that are directly related to health and social progress. Meat spoilage is the result of decomposition and the formation of metabolites, caused by the growth and enzymatic activity of microorganisms, and it presents not only a health hazard but an economic burden to the producer. In this research work, we explore the potential of Fourier transform infrared (FTIR) spectroscopy in combination of principal components analysis and neuro-fuzzy modelling, to determine beef spoilage microorganisms during aerobic storage at chill and abuse temperatures. FTIR spectra were obtained from the surface of beef samples, while culture microbiological analysis determined the total viable count (TVC) for each sample. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e., fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from FTIR spectra. The proposed neuro-fuzzy network model utilises a prototype defuzzification scheme, whereas the number of input membership functions is directly associated to the number of rules, reducing thus, the “curse of dimensionality” problem. Results confirmed the superiority of the adopted methodology compared to other schemes such as multilayer perceptron and the partial least squares techniques and indicated that FTIR spectral information in combination with an efficient choice of a learning-based modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage.

Metabolomics and metabolite profiling

During the last 15 years, metabolomics has emerged as the latest of the so-called “omics” research fields. It deals with the analytical characterization of the metabolome, i.e. the low molecular weight metabolite complement of the biological system under investigation. A wide range of biological systems have benefited from metabolomics studies, from relatively simple microbes to plants and complex multiorganism systems such as mammals. The general goal of most metabolomics studies is to generate a snapshot of the metabolic state of a biological sample and to characterize the changes in the abundances of the measured metabolites arising from natural fluctuations or external, experimental biotic or abiotic perturbations. The topical collection in this issue of Analytical and Bioanalytical Chemistry provides a glimpse of the current activities in metabolomics and metabolite profiling. The articles presented elegantly illustrate that the general concept of metabolomics and metabolite profiling has matured significantly during the last decade and that this recent “-omics” toolbox is widely applied in many scientific disciplines, such as diagnostics of human diseases, biomarker discovery, nutrition, food safety, plant science and microbiology. Most state-of-the-art metabolomics studies use liquid chromatography (LC) or gas chromatography (GC) coupled with mass spectrometry (MS) for metabolite profiling, whereas direct infusion MS, NMR spectroscopy and IR spectroscopy are applied for metabolite fingerprinting without prior separation of the sample constituents. Other techniques such as LC with UV detection can be used to complement these techniques, as successfully illustrated by the contribution on carotenoid profiling of this issue. Moreover, the huge chemical diversity of substances contained in biological samples affords multiple analytical platforms in order to cover a wide range of metabolites. When more than one analytical method is applied, the data, which are derived from the metabolomics platforms, have to be integrated. In the present collection this topic has been addressed in the area of food safety with an untargeted GC–MS-based study using two different methods for the profiling of both volatiles and polar small metabolites for the detection of meat spoilage. Independent of the analytical technique employed, two complementary approaches are widely used in current metabolomics: targeted and untargeted analyses. In the targeted approach a set of predefined known substances is monitored, which usually allows absolute quantification and definite identification when limited to metabolites available Published in the topical collection Metabolomics and Metabolite Profiling with guest editors Rainer Schuhmacher, Rudolf Krska, Roy Goodacre, and Wolfram Weckwerth. R. Schuhmacher (*) :R. Krska Department for Agrobiotechnology (IFA-Tulln), Center for Analytical Chemistry, University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad-Lorenz-Strasse 20, 3430 Tulln, Austria e-mail: rainer.schuhmacher@boku.ac.at

Integrating multiple analytical platforms and chemometrics for comprehensive metabolic profiling: application to meat spoilage detection

Untargeted metabolic profiling has become a common approach to attempt to understand biological systems. However, due to the large chemical diversity in the metabolites it is generally necessary to employ multiple analytical platforms so as to encompass a wide range of metabolites. Thus it is beneficial to find chemometrics approaches which can effectively integrate data generated from multiple platforms and ideally combine the strength of each platform and overcome their inherent weaknesses; most pertinent is with respect to limited chemistries. We have reported a few studies using untargeted metabolic profiling techniques to monitor the natural spoilage process in pork and also to detect specific metabolites associated with contaminations with the pathogen Salmonella typhimurium. One method used was to analyse the volatile organic compounds (VoCs) generated throughout the spoilage process while the other was to analyse the soluble small molecule metabolites (SMM) extracted from the microbial community, as well as from the surface of the spoiled/contaminated meat. In this study, we exploit multi-block principal component analysis (MB-PCA) and multi-block partial least squares (MB-PLS) to combine the VoCs and SMM data together and compare the results obtained by analysing each data set individually. We show that by combining the two data sets and applying appropriate chemometrics, a model with much better prediction and importantly with improved interpretability was obtained. The MB-PCA model was able to combine the strength of both platforms together and generated a model with high consistency with the biological expectations, despite its unsupervised nature. MB-PLS models also achieved the best over-all performance in modelling the spoilage progression and discriminating the naturally spoiled samples and the pathogen contaminated samples. Correlation analysis and Bayesian network analysis were also performed to elucidate which metabolites were correlated strongly in the two data sets and such information could add additional information in understanding the meat spoilage process.

Optical Detection of Meat Spoilage Using Fluorescence Spectroscopy with Selective Excitation Wavelength

The native fluorescence (FL) spectra of muscle foods (meat) stored at 4 °C (refrigerated) and 25 °C (at room temperature) were measured with the selected excitation wavelength of 340 nm as a function of storage time to detect the meat spoilage status. The contributions of the principal biochemical components to the FL spectra were extracted using Multivariate Curve Resolution with Alternating Least-Squares (MCR-ALS). The change of the reduced nicotinamide adenine dinucleotide (NADH) content was found from the measured FL spectra and the MCR-ALS analysis, which reflects the microbial spoilage of muscle foods involved in the metabolic processes. This study presents the possibility that the change of relative content of NADH determined by native FL spectroscopy may be used as a "fingerprint" or criterion for monitoring the spoilage status of muscle foods.

A portable 671 nm Raman sensor system for rapid meat spoilage identification

This paper presents a portable Raman sensor system based on a miniaturized optical bench with integrated 671nm microsystem diode laser as excitation light source for the rapid in situ detection of meat spoilage. The system comprises three main components. A handheld measurement head with a dimension of 210mm×240mm×60mm containing a laser driver electronics board, the Raman optical bench, and a battery pack as power supply serves for excitation as well as collection of the Raman signals in backscattering geometry. The signal detection is realized by a custom-designed miniature spectrometer with an optical resolution of 8cm−1 and a dimension of 200mm×190mm×70mm which is fiber-optically connected to the measurement head. To control the spectrometer as well as for data storage a netbook is applied. To point out the ability of the sensor system for the rapid identification of meat spoilage porcine musculus longissimus dorsi (LD) and musculus semimembranosus (SM) were used as test samples. Stored refrigerated at 5°C the meat cuts were investigated in time-dependent measurement series up to 3 weeks after slaughter. Meat Raman spectra with an integration time of 10s can be detected with an excitation laser power of 100mW at the sample. The spectral changes of the Raman data set during storage were analyzed by principal components analysis. Specific periods of age could be discriminated in the Raman spectra which correlate very well with bacterial growth kinetics determined by microbial reference analyses. Thus, fresh meat with low bacterial load can be identified and a discrimination of spoiled samples exceeding the threshold of 106cfu/cm2 around day 7 post-mortem for both examined meat cuts was possible.

Detection of poultry spoilage markers from headspace analysis with cryoadsorption on a short alumina PLOT column

A rapid and simple diagnostic tool for the early detection of meat spoilage would be invaluable to ensure meat quality during production, distribution, and retail. Recently, we developed an improved purge and trap method for sampling low volatility, as well as volatile, compounds by applying low temperature collection on short alumina-coated porous layer open tubular (PLOT) columns. This method was applied to the analysis of both fresh and spoiled poultry to identify marker compounds that could be used as indicators for poultry spoilage. Samples of chicken breast were crimp-sealed in individual autosampler vials and maintained at 25 °C for either a day or 2 weeks. Two weeks were sufficient to ensure severe spoilage. The headspace was sampled by cryoadsorption for 10, 20, or 30 min; and the analytes were then separated, identified, and quantified with gas chromatography and mass spectrometry. Six potential markers for poultry spoilage were identified in the headspace of spoiled chicken: dimethyl disulphide; dimethyl trisulphide; phenyl sulphide; methyl thiolacetate; allyl methyl sulphide; and 2,4,6-trimethylpyridine. Additionally, isophorone was detected in the headspace of both the fresh and the spoiled chicken; its origin is unknown but is suspected to come from the packaging. The applicability of this method to detect chicken spoilage in a commercial setting was tested by sampling the air above spoiled chicken breast that was maintained in its original retail packaging, as obtained direct from a commercial vendor, for 2 weeks at 25 °C. Sampling was done via a modified, room-temperature approach with an activated PLOT column and a motorised pipette filler/dispenser. Five of the above compounds were also identified with this approach: dimethyl disulphide; dimethyl trisulphide; phenyl sulphide; 2,4,6-trimethylpyridine; and isophorone.