Spoilage Of Beef Research Articles

Assessment of a Biopolymer-Based Film Indicator for Visually Monitoring Beef and Fish Spoilage

Food safety is a worldwide public concern that has recently gained increased attention due to the emerging outbreaks of foodborne diseases. Therefore, there is a pressing need to make simple, cost-effective, and environmentally friendly methods for detecting food spoilage and to mitigate the impact of possible outbreaks. The aim of this study is to develop a biodegradable indicator film which was made by combining cellulose-nanofiber/chitosan dyed with methyl red synthesis and followed by a single layer coating of PLA on its surface, named PLA/CCM film. The microstructural properties of PLA/CCM films were studied using scanning electron microscopy (SEM) and results showed that PLA effectively adhered to the surface of CCM film. The microbial response to PLA/CCM film was determined via a bacterial plating method. It was found that the contact of film with the microbes caused a substantial color change in the film. The coating of PLA upon the CCM film decreased the percentage of the water vapor transmission rate (WVTR). The color of the film vividly altered from slightly red to yellow in response to a pH change in the range of 4–8. The PLA/CCM film was applied to monitor the spoilage of beef and fish at an ambient condition (23 °C) and revealed the onset of food spoilage by displaying the color change from red to yellow after 1 day.

Development of a polylactic acid-coated nanocellulose/chitosan-based film indicator for real-time monitoring of beef spoilage.

Food safety is one of the biggest challenges in global markets. There is a critical need to develop a simple, affordable, and environmentally friendly color indicator that can quickly and conveniently monitor and indicate the quality of packaged food products in the home, supermarkets, shops, etc. This study aimed to develop a nanocellulose/chitosan-based film coated with polylactic acid (PLA) to monitor beef spoilage in real-time. This film named PLA/NCM was fabricated by casting a suspension of a nanocellulose/chitosan mixture doped with methyl red, followed by a coating of PLA on the film surface. The film displayed a visible color change in response to different pH buffer solutions (2-10). The PLA/NCM film was applied to monitor the spoilage of beef under a refrigeration condition of 4 °C and showed an apparent color change after 5 days as a threshold for beef spoilage. The color modulation of the PLA/NCM films was processed each time via a colorimetric device and revealed substantial color difference values (ΔE) after 5 days of beef spoilage. The total viable microbial counts (TVC) and pH of the beef sample were determined, and the findings showed that the TVC and pH increased simultaneously during the beef spoilage. Although further research is necessary, the PLA/NCM film has the potential to be a color indicator for application in both smart food packaging and real-time monitoring of spoilage of beef and other meat products.

Determination of Colour and Total Viable Counts in Fresh and Charcoal Flue Gases Treated Beef Using Digital Colour Meter

Flesh from animals is one of the nutritious foods rich in vitamin B complex, iron, zinc, selenium, phosphorous, fats and magnesium. Due to this high food value, it attracts bacteria, yeasts, molds and other lower life forms that infest on it and hence undergoes natural decay process fast. Poor handling practices by small scale enterprise in the beef industry contribute to fifty percent loss, excluding post slaughter losses by livestock farmers. Therefore, there is need to develop accessible preservation methods that are supported by available infrastructure in order to curb beef losses. This study used charcoal flue gases sourced from combustion of charcoal briquettes in prolonging beef shelf life. Fresh beef samples were purchased from local abattoirs and exposed to flue gases. The optimum treatment time was between 10 to 15 hours depending on beef load mass. The reaction of the carbon monoxide and carbon dioxide with beef functional groups was studied using color determination. The colour lightness (L*) and redness (a*) values were L* 44 and a* 9 values for fresh beef, after treatment L* 30 and a* 4.5 indicating a strong bond of carboxy-myoglobin and on day 35 the L* 23 and a* 1.5 indicated onset of beef spoilage. The mean total aerobic counts of fresh beef, after treatment and onset spoilage were significantly different (p<0.05) 4.16±4.20, 2.16±2.19 and 5.16±5.20 log CFU/cm2 respectively. The results from this study, was not more than 6.00 log CFU/cm2 approved safe microbial standards of beef for consumption by FAO.

Preliminary investigation of the use of Raman spectroscopy to predict beef spoilage in different types of packaging

In this study, pH, meat color analysis, microbial counts and Raman spectroscopic data were obtained from beef steaks stored at 4 °C for up to 21 days using two different packaging methods: vacuum (VP) and modified atmosphere packaging (MAP). Models using partial least square regression (PLSR), indicated that Raman spectroscopy was able to predict total viable counts (TVC) and lactic acid bacteria (LAB) measured at 21d post mortem (TVC in VP: R2cv = 0.99, RMSEP = 0.61; TVC in MAP: R2cv = 0.90, RMSEP = 0.38; LAB in VP: R2cv = 0.99, RMSEP = 0.54; LAB in MAP: R2cv = 0.75, RMSEP = 0.60). The results of this study demonstrate that Raman spectroscopy may have potential for the rapid determination of meat spoilage.

Fluctuations in the Microbial Community and the Volatile Organic Acids Created During Aerobic Storage of Ground Beef

ObjectivesDegradation due to microbial and chemical mechanisms occurs throughout the storage life of ground beef. These pathways are intertwined and the microbial community and the volatile organic acids (VOCs) that evolve in ground beef are dynamic. Evaluation of microbial growth using traditional culture-dependent techniques can be misleading due to the presence of unculturable organisms. Therefore, utilizing culture-independent techniques allows for a more thorough understanding of the microbial community within a meat matrix during storage life. The objective of this study was to employ 16s rRNA amplicon sequencing and VOC identification using GC–MS to explore diversity and changes of the microbial community and VOC production during shelf-life of ground beef.Materials and MethodsFinely ground beef (80/20) was procured from beef processing facilities in the West (one lot) and Midwest (two lots). The lots were separated into three physically separate replicates. Ground beef lots were transported in chub packaging to Colorado State University (Fort Collins, CO), and the chubs were stored in the dark at 2°C for either 16/17 d or 23/24 d. After dark storage, chubs were reground, and 454 g fluff-packs were placed on polystyrene trays before overwrapping with polyvinyl chloride film. The trays were placed in retail display cases maintained at 2–4°C for 5 d. Samples were collected every day of retail display for evaluation of the microbial community and VOC development. Following standardized extraction, 16S rRNA amplicon sequencing was used to explore microbial communities. Sequencing data were analyzed using the programs in the QIIME2 (version 2018.4) pipeline. Similarly, volatile organic compounds were extracted prior to analysis of targeted VOCs using a GC–MS. The project was designed as a split-plot design and was analyzed using R packages (version 3.4.3), lme4, lmerTest, and emmeans. Least squares means were separated using an α of 0.05.ResultsThe top orders of bacteria found in the meat samples were from Enterobacteriales, Lactobacillales, and Pseudomonadales. No differences (P ≥ 0.05) in Faith’s Phylogenetic Diversity Index, or a measure of diversity of the bacterial species within a sample, were observed between Days 0, 2, and 4 of retail case display. A targeted analysis identified eighteen VOCs associated with ground beef spoilage. In previous studies, the presence of hexanal, acetoin and acetic acid are identified as spoilage indicators. Hexanal, Acetoin and acetic acid increased (P ≤ 0.05) over the 5 d of retail display.ConclusionThe use of 16s rRNA amplicon sequencing technology is a relatively recent tool that has rapidly advanced the study of microbial deterioration during beef storage and shelf-life. Moreover, the combination of 16s rRNA amplicon sequencing and identification of VOCs in this study, afforded an exploration of the relationship between chemical and biological changes which occur during ground beef storage. These analytical technologies, when used in unison, can highlight the dynamic relationships and evolution of chemical and biological constituents in ground beef. Further research in ground beef shelf-life should incorporate such measures.

Draft Genome Sequence of Clostridium estertheticum subsp. laramiense DSM 14864T, Isolated from Spoiled Uncooked Beef.

Clostridium estertheticum subsp. laramiense type strain DSM 14864 (ATCC 51254) was isolated from vacuum-packaged refrigerated spoiled beef. This report describes the generation and annotation of the 5.0-Mb draft genome sequence of C. estertheticum subsp. laramiense DSM 14864T.

English

Uganda’s beef industry has been growing slowly and requires sustained monitoring of actors at post-harvest handling points in order to decrease public health risks and losses. This study documented causes of losses and estimated economic values at post-harvest handling points along the beef value chain. It was carried out at slaughter houses, transporters and butcher shops in the districts of Western, Central and Eastern Uganda. A cross sectional study was conducted among meat handlers who were interviewed to find out the losses incurred in the value chain. Microbial load from carcass swabs were collected and evaluated using standard microbiological methods to determine microbial contamination of beef. The causes of losses varied at different handling points. The actors at slaughter houses indicated the major losses were due to low beef demand (15.3%), insecurity (13.4%) and poor weight estimation methods (11.03%). Losses at the butchery included, beef waste (22.4%), drip loss (19.7%) and beef spoilage (18.4%). Microbial analysis showed the highest microbial prevalence at the butchery (70-100%) followed by slaughter (50-80%) and lastly transport (30-50%). Microbial contamination on carcass leads to spoilage and hence market loss because exportation does not admit contaminated foods. Actors reported beef waste and drip loss as the major causes of losses at the butchery. To reduce losses, public health care education for meat handlers and adherence to strict standard operating procedures (SOPs) are a key. Key words: Losses, post-harvest, beef value chain, handling points, Uganda.  

Identifying potential spoilage markers in beef stored in chilled air or vacuum packaging by HS-SPME-GC-TOF/MS coupled with multivariate analysis

Volatile organic compounds (VOCs) associated with spoilage are attracting the attention as potential markers for monitoring meat quality. The objective of the present study was to evaluate the use of spoilage-associated VOCs as marker compounds for the assessment of beef spoilage during storage at 4 °C in air and when vacuum packed. We used headspace solid-phase microextraction coupled with gas chromatography time-of-flight mass spectrometry to determine the levels of VOCs formed during storage. Correlation analysis and multivariate analyses (principal component and hierarchical clustering analysis) were used to select several VOCs that most contribute to sensory changes, and to classify beef samples based on the degree of spoilage assessed by sensory panelists, respectively. Levels of acetic acid, ethanol, 2-methylbutan-1-ol, 3-methylbutan-1-ol, 2,3-butanediol, 2-butanone, diacetyl, 2-heptanone, 3-octanone, and acetoin significantly (p < 0.05, p < 0.01) correlated with the spoilage of air-stored beef, while acetic acid, butanoic acid, pentanoic acid, ethanol, 3-methylbutan-1-ol, and 2,3-butanediol significantly (p < 0.05, p < 0.01) correlated with the spoilage of vacuum-packed beef. The multivariate-analysis results reveal that the aforementioned VOCs can be used as potential marker compounds for evaluating beef spoilage during chilled storage in air or when vacuum packed.

Application of multi-element viscoelastic models to freshness evaluation of beef based on the viscoelasticity principle.

The aims of this work were to develop multi-element viscoelastic models for beef and apply them to detect total volatile basic nitrogen (TVB-N) content for freshness evaluation. The deformation data were collected by a viscoelasticity detection system that employed the airflow and laser technique. Then, TVB-N contents were measured to determine the freshness of samples during storage. A universal global optimization (UGO) algorithm was applied to fit the deformation data. Various multi-element viscoelastic models including the Burgers, six-element and eight-element models were built using the obtained fitting parameters, and different viscoelastic parameters representing the degree of beef spoilage were obtained. All the viscoelastic parameters of each multi-element model and parameter combinations of the selected six-element model were employed to build mathematical models for predicting TVB-N content by support vector machine regression (SVR). In comparison, the six-element model with all the viscoelastic parameters performed the best and was determined to predict TVB-N content with correlation coefficient in the prediction set (RP ) of 0.891 and root mean squared error in the prediction set (RMSEP) of 1.467 mg/100 g. Based on the results of parameter combinations, combination (E2 , E3 , E1 , η1 , η2 ) from the six-element model performed the best, which was comparatively inferior to all the viscoelastic parameters of the six-element model. Results demonstrated that it was possible to predict TVB-N content for freshness evaluation by applying method of developing multi-element model based on the viscoelasticity with chemometrics.

The assessment of fresh and spoiled beef meat using a prototype device based on GigE Vision camera and DSP

Beef meat freshness was evaluated using artificial vision technique and pattern recognition algorithms. Color and texture features were extracted from the saturation images. The wavelet transform was used to characterize texture and a range of features was used to better characterize color. Two classes of beef meat samples were obtained from the projection of color, texture, and color associated with texture datasets using Principal Component Analysis (PCA) method. The first class corresponds to fresh beef meat samples that have undergone 6 days of cold storage and the second class presents spoiled meat. Probabilistic Neural Network (PNN) and Linear Discriminant Analysis (LDA) algorithms were used to classify and predict beef meat samples into fresh or spoiled samples. Results show that the classification and identification rates obtained by PNN are superior to LDA algorithm using the datasets of color, texture, and color associated with texture. In addition, results show that texture features associated with color features give the best classification and identification rates. An implementation of all proposed algorithms was carried out on a real time embedded system.

Recent development in electronic nose data processing for beef quality assessment

Beef is kind of perishable food that easily to decay. Hence, a rapid system for beef quality assessment is needed to guarantee the quality of beef. In the last few years, electronic nose (e-nose) is developed for beef spoilage detection. In this paper, we discuss the challenges of e-nose application to beef quality assessment, especially in e-nose data processing. We also provide a summary of our previous studies that explains several methods to deal with gas sensor noise, sensor array optimization problem, beef quality classification, and prediction of the microbial population in beef sample. This paper might be useful for researchers and practitioners to understand the challenges and methods of e-nose data processing for beef quality assessment.

Application of invasive weed optimization and least square support vector machine for prediction of beef adulteration with spoiled beef based on visible near-infrared (Vis-NIR) hyperspectral imaging

Different multivariate data analysis methods were investigated and compared to optimize rapid and non-destructive quantitative detection of beef adulteration with spoiled beef based on visible near-infrared hyperspectral imaging. Four multivariate statistical analysis methods including partial least squares regression (PLSR), support vector machine (SVM), least squares support vector machine (LS-SVM) and extreme learning machine (ELM) were carried out in developing full wavelength models. Good prediction was obtained by applying LS-SVM in the spectral range of 496–1000 nm with coefficients of determination (R2) of 0.94 and 0.94 as well as root-mean-squared errors (RMSEs) of 5.39% and 6.29% for calibration and prediction, respectively. To reduce the high dimensionality of hyperspectral data and to establish simplified models, a novel method named invasive weed optimization (IWO) was developed to select key wavelengths and it was compared with competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA). Among the four multivariate analysis models based on important wavelengths determined by IWO, the LS-SVM simplified model performed best where R2 of 0.97 and 0.95 as well as RMSEs of 4.74% and 5.67% were attained for calibration and prediction, respectively. The optimum simplified model was applied to hyperspectral images in pixel-wise to visualize the distribution of spoiled beef adulterant in fresh minced beef. The current study demonstrated that it was feasible to use Vis-NIR hyperspectral imaging to detect homologous adulterant in beef.

Fluctuations in the Microbial Community and the Volatile Organic Acids Created During Aerobic Storage of Ground Beef

ObjectivesDegradation due to microbial and chemical mechanisms occurs throughout the storage life of ground beef. These pathways are intertwined and the microbial community and the volatile organic acids (VOCs) that evolve in ground beef are dynamic. Evaluation of microbial growth using traditional culture-dependent techniques can be misleading due to the presence of unculturable organisms. Therefore, utilizing culture-independent techniques allows for a more thorough understanding of the microbial community within a meat matrix during storage life. The objective of this study was to employ 16s rRNA amplicon sequencing and VOC identification using GC–MS to explore diversity and changes of the microbial community and VOC production during shelf-life of ground beef.Materials and MethodsFinely ground beef (80/20) was procured from beef processing facilities in the West (one lot) and Midwest (two lots). The lots were separated into three physically separate replicates. Ground beef lots were transported in chub packaging to Colorado State University (Fort Collins, CO), and the chubs were stored in the dark at 2°C for either 16/17 d or 23/24 d. After dark storage, chubs were reground, and 454 g fluff-packs were placed on polystyrene trays before overwrapping with polyvinyl chloride film. The trays were placed in retail display cases maintained at 2–4°C for 5 d. Samples were collected every day of retail display for evaluation of the microbial community and VOC development. Following standardized extraction, 16S rRNA amplicon sequencing was used to explore microbial communities. Sequencing data were analyzed using the programs in the QIIME2 (version 2018.4) pipeline. Similarly, volatile organic compounds were extracted prior to analysis of targeted VOCs using a GC–MS. The project was designed as a split-plot design and was analyzed using R packages (version 3.4.3), lme4, lmerTest, and emmeans. Least squares means were separated using an α of 0.05.ResultsThe top orders of bacteria found in the meat samples were from Enterobacteriales, Lactobacillales, and Pseudomonadales. No differences (P ≥ 0.05) in Faith’s Phylogenetic Diversity Index, or a measure of diversity of the bacterial species within a sample, were observed between Days 0, 2, and 4 of retail case display. A targeted analysis identified eighteen VOCs associated with ground beef spoilage. In previous studies, the presence of hexanal, acetoin and acetic acid are identified as spoilage indicators. Hexanal, Acetoin and acetic acid increased (P ≤ 0.05) over the 5 d of retail display.ConclusionThe use of 16s rRNA amplicon sequencing technology is a relatively recent tool that has rapidly advanced the study of microbial deterioration during beef storage and shelf-life. Moreover, the combination of 16s rRNA amplicon sequencing and identification of VOCs in this study, afforded an exploration of the relationship between chemical and biological changes which occur during ground beef storage. These analytical technologies, when used in unison, can highlight the dynamic relationships and evolution of chemical and biological constituents in ground beef. Further research in ground beef shelf-life should incorporate such measures.

Strain-Level Diversity Analysis of Pseudomonas fragi after In Situ Pangenome Reconstruction Shows Distinctive Spoilage-Associated Metabolic Traits Clearly Selected by Different Storage Conditions.

Microbial spoilage of raw meat causes huge economic losses every year. An understanding of the microbial ecology associated with the spoilage and its dynamics during the refrigerated storage of meat can help in preventing and delaying the spoilage-related activities. The raw meat microbiota is usually complex, but only a few members will develop during storage and cause spoilage upon the pressure from several external factors, such as temperature and oxygen availability. We characterized the metagenome of beef packed aerobically or under vacuum during refrigerated storage to explore how different packaging conditions may influence the microbial composition and potential spoilage-associated activities. Different population dynamics and spoilage-associated genomic repertoires occurred in beef stored aerobically or in vacuum packaging. Moreover, the pangenomes of Pseudomonas fragi strains were extracted from metagenomes. We demonstrated the presence of specific, storage-driven strain-level profiles of Pseudomonas fragi, characterized by different gene repertoires and thus potentially able to act differently during meat spoilage. The results provide new knowledge on strain-level microbial ecology associated with meat spoilage and may be of value for future strategies of spoilage prevention and food waste reduction.IMPORTANCE This work provides insights on the mechanisms involved in raw beef spoilage during refrigerated storage and on the selective pressure exerted by the packaging conditions. We highlighted the presence of different microbial metagenomes during the spoilage of beef packaged aerobically or under vacuum. The packaging condition was able to select specific Pseudomonas fragi strains with distinctive genomic repertoires. This study may help in deciphering the behavior of different biomes directly in situ in food and in understanding the specific contribution of different strains to food spoilage.

Comparative evaluation of spoilage-related bacterial diversity and metabolite profiles in chilled beef stored under air and vacuum packaging

Microbial spoilage is a complex event to which different bacterial populations and metabolites can contribute depending on the storage conditions. This study explored the evolution of spoilage and related volatile organic compounds (VOCs) in chilled beef under air and vacuum packaging (VP). The results suggested that different storage conditions affected changes in bacterial communities and metabolites in beef and consequently affected the odor properties of the stored beef, thereby leading to spoilage. Bacterial species belonging to Pseudomonadaceae (Pseudomonas spp.) and lactic acid bacteria (Lactobacillus sp.) dominated the bacterial communities in beef stored under air and VP, respectively, with several VOCs associated with off-odors of the stored beef and most likely produced by both bacteria. Our results suggested several microbial VOCs that could be used as potential spoilage indicators, including acetic acid, butanoic acid, and 2-butanone in VP-stored beef and 3-methylbutan-1-ol, ethyl acetate, acetoin, 2-butanone, and diacetyl in air-stored beef. These findings might provide valuable information regarding the quality monitoring of beef during storage.

Development of a multiplex qPCR for the species identification of Clostridium estertheticum, C. frigoriphilum, C. bowmanii and C. tagluense-like from blown pack spoilage (BPS) meats and from wild boars

Psychrophilic and psychrotolerant clostridia (n = 110) were isolated from vacuum-packed meat (beef and lamb), fresh venison and from skin and fecal samples of wild boars. They were identified to species level using MALDI-TOF MS, sequence and phylogeny analysis of the 16S rRNA and species specific multiplex qPCR. The results of all three methods were concordant. The majority of isolates were identified as C. tagluense-like Group I (n = 34) and Group II (n = 42). Thirty-five isolates could be identified to species level as follows: C. estertheticum (n = 15), C. frigoriphilum (n = 13), C. frigidicarnis (n = 1) and C. bowmanii (n = 5). This is the first report of detection and identification of C. frigoriphilum and C. tagluense-like Group II as causative agents of blown pack spoilage of beef. The species specific multiplex qPCR developed in this study could be applied to identify and to quantify the Clostridium species described above in suspicious meat juice samples.

Application of Artificial Fish Swarm Algorithm for Synchronous Selection of Wavelengths and Spectral Pretreatment Methods in Spectrometric Analysis of Beef Adulteration

In this paper, we proposed artificial fish swarm algorithm (AF) for synchronous selection of wavelengths and pretreatment methods during quantification of beef adulteration with spoiled beef based on the analysis of ultraviolet (UV), visible and near-infrared (Vis-NIR), and UV-Vis-NIR spectra. The best partial least squares regression model was obtained when the wavelength-then-pretreatment scheme was adopted in the Vis-NIR range where 11 wavelengths were utilized to produce an RPD value of 2.68. The coefficients of determination R2 and root mean squared errors of the model were 0.91 and 0.08 as well as 0.87 and 0.11 for calibration and prediction, respectively. It was demonstrated that AF was a useful tool for model optimization as compared with genetic algorithm. Moreover, the sequence for selecting wavelengths and spectral pretreatment methods had great influence in model performance and could be decided by try-out approach since the performance due to selection sequence was sensitive to spectral range and optimization algorithm employed. Nevertheless, wavelength-then-pretreatment scheme was preferred due to simpler model structure and reduced computation loads. It was shown that Vis-NIR spectroscopy was feasible in quantifying beef adulteration, and AF was an advance method for optimizing model performance. Moreover, AF could be expended in more studies for optimizing food quality and safety analysis.

14. Effect of Curry Leaf (Murraya koenigii) Extract to Early Spoilage of Beef

This study aimed to determine early spoilage of beef after addition of curry leaf extract. The sample used is beef Semimembranosus muscle which is divided into 42 parts each weight is 5 grams. This research used experimental method which consists of three treatments with two repetitions. P1 group is without curry leaf extract (control), group P2 with curry leaf extract 25% used and group P3 with curry leaf extract 50%. The entire sample is stored in a refrigerator at 4 ° C. For 7 days early spoilage test is conducted every day at 9am and 4pm. The result showed difference early spoilage time between each treatment. In P1 positive result of early spoilage time is on the 4th day and P2 positive result of early spoilage time is on the 5th day. While in P3 has not obtained positive result of early spoilage time at 7 days storage. Based on the results of this study we concluded that curry leaf extract can slow down the early spoilage time of beef

Potentially active spoilage bacteria community during the storage of vacuum packaged beefsteaks treated with aqueous ozone and electrolyzed water

The microbial contamination that occurs during the slaughtering process and during handling of the meat results in a shortening of the shelf-life of meat. In this study, which has had the aim of extending the shelf life of beefsteaks, pilot-scale treatments were carried out with aqueous ozone (AO) and electrolyzed water (EW) before vacuum packaging (VP). The development of the potentially active microbiota and the associated volatilome were followed over 15days of storage under refrigerated conditions (4°C), in order to define the potential long-term effects of the treatments and storage condition on microbiota.The targeted RNA-based amplicon sequencing identified Pseudomonas fragi as the most frequent species before and after the treatments with AO and EW, as well as in the untreated control. The tested treatments did not reduce the overall presence of this species, but they affected the intra-species distribution of its oligotypes, albeit slightly. With the progression of the refrigerated storage and the reduction of the oxygen availability, Lactobacillus sakei, Leuconostoc gasicomitatum and Lactococcus piscium became the dominant, potentially active, beef microbiota, as confirmed by microbiological data. When the OTU abundances and volatilome were coupled, a significant association was observed between the organic acids, esters and aldehydes and these lactic acid bacteria species.In spite of the limited effectiveness of the treatments over the short and long term, this study has provided a detailed view of beef spoilage using RNA as the sequencing target, strengthening and confirming the current knowledge based on DNA-amplicon sequencing.

Comparison of Two Volatile Sampling Techniques Based on Different Loading Factors in Determination of Volatile Organic Compounds Released from Spoiled Raw Beef

Evaluation of meat volatiles is an increasing concern due to the development studies for spoilage sensors in order to inform consumers if meat spoiled or not. Since the reliable measuring of volatiles in headspace is important for designing a sensor, preconcentration techniques for meat matrix should be investigated. This study describes the effect of three sample amounts: total volume ratio (loading factors) with 0.025, 0.05, and 0.10 kg/L applied in two preconcentration sampling techniques: solid phase microextraction (SPME) and gas flushing on Tenax with thermal desorption to explain the interactions and relationship between sample amount and extracted analytes for spoiled beef striploin. The ratio of the sample amount of beef meat in the chamber and the total volume of chamber is called as “loading factor”. The results showed that the volatile profiles did not vary significantly, but the levels were affected with the extraction technique and loading factor. The volatile organic compounds identified in the early stages of meat spoilage include alcohols, aldehydes, ketones, esters, and amine compounds. While amines were measured only with Tenax, ethyl acetate was detected only with SPME. The total peak area of volatiles with Tenax (~390 × 10−6 AU at 0.05 kg/L) were higher than SPME sampling (~290 × 10−6 AU at 0.05 kg/L) in each loading factor. Although optimum loading factor depended on the type of volatile, 0.05 kg/L loading factor resulted the most efficient extraction by the two preconcentration techniques for most of the volatiles generally.