In conclusion, this simulation study demonstrated the effectiveness of the detection approach based on electronic nose which in combination with an appropriate machine learning strategy could become an effective tool for monitoring meatspoilage during aerobic storage at various temperatures. The collected “volatile” data could be considered as biochemical “signature” containing information for the discrimination of meat samples in quality classes corresponding to different spoilage levels, whereas in the same time could be used to predict satisfactorily the microbial load directly from the sample surface. The realization of this strategy has been fulfilled with the development of a MIMO fuzzy-wavelet network which incorporates a clustering pre- processing stage. Classification performance was very satisfactory, while overall prediction for TVCs has been considered as very promising, although lower performance was observed especially for samples stored under MAP conditions. Prediction performances of MLP and PLS schemes revealed
First of all, I would like to thank Allah the Almighty, with His bless; I manage to complete my final year project 1 entitled “Portabale Artificial Nose to Detect Chicken MeatSpoilage Using Ammonia Sensor”. I would like to thanks all the people involved in my project especially to my supervisor, Mdm Siti Nur Suhaila Binti Mirin, with her complete guidance and share her times to discuss the project.
Nychas and Tassou (1995) have investigated the profile of water-soluble proteins as detected by HPLC during storage of chicken fillets in air, VP and MAP. Skandamis and Nychas (2001, 2002) have studied the HPLC profile of organic acids of minced beef stored in air and MAP treated or not with oregano essential oil. Tsigarida and Nychas (2001) have investigated in parallel the HPLC profile of organic acids and the profile of the volatile compounds as attributed from GC analysis of sterile beef fillets inoculated with meatspoilage bacteria and stored in air and in MAP. The volatile compounds using GC or GC/MS analysis have also been studied for inoculated and/ or naturally contaminated beef stored in air (Dainty et al., 1985a; Dainty et al., 1989b) and in MAP and/ or VP (Stutz et al., 1991; Jackson et al., 1992; Insauti et al., 2002), for chicken stored in MAP (Eilamo et al., 1998) and for cooked ham stored in MAP (Leroy et al., 2009). Finally, the GC-GC/MS volatiles profile of different types of fresh or processed meat (chicken, beef or pork) has been studied, to assess their quality characteristics, without subsequent storage of the samples (Wettasinghe et al., 2001; Marco et al., 2004; Xie et al., 2008; Rivas-Cañedo et al., 2009). However, knowledge gaps related to HPLC or GC, GC/MS analysis need to be addressed since the above studies do not include combinations regarding numerous compounds, several storage conditions (i.e. temperature and packaging), microbiological and sensory evaluation.
The antimicrobial activity may be determined by three main methods, disk diffusion, agar dilution and broth macrodilution or microdilution, according to antibiotic susceptibly tests as standardized by international committees to assay antibiotic microbial susceptibility.
Besides the applied methods, the results of the antimicrobial activity tests can be affected by many other factors, such as the microorganisms tested and the degree of solubility of each test plant-derived extract. Furthermore, the main limitations of the use of antimicrobials to activate films for meat packaging include the inactivation of compounds in contact with the meat surface and their dispersion from the surface into the meat mass. The broth meat model system has previously been developed as a methodology to test the antibacterial efficacy of plant-derived extracts directly on meat, as well as their potential as preservatives for antimicrobial packaging of fresh retail meat.
microbiological quality as requested by EFSA’s legislation (EFSA 2005). Electronic nose and FT-IR are also convenient methods and although do provide good prediction >86% for TVC, the lack of accuracy in the case of pseudomonads in meat stored under aerobic packaging, can be considered as a drawback if these organisms is requested to be used as quality index. In general, this pipeline helps to decide which analytical technique to be used by whom (e.g. meat industry, food authority) and where (on in at line) to predict all types of bacteria of interest according to the food product being tested, and which machine learning algorithms will provide accurate predictions of their bacterial counts without having to invest in numerous and usually expensive platforms. Models developed using “MeatReg” are validated using the Monte Carlo cross validation as opposed to the more widely applied methods such as leave-one-out cross-validation (LOOCV). Though LOOCV is a very popular validation method (Argyri et al., 2010; Mohareb et al., 2016; Wang, Wang, Liu, & Liu, 2012), it has been shown that using Monte Carlo validation can avoid an unnecessary large model and decrease the risk of overfitting (Qing-Song Xu, Liang, & Du, 2004). Other common strategy is to randomly split the original dataset into training and testing datasets (Anthoula A. Argyri et al., 2013; Mataragas, Skandamis, Nychas, & Drosinos, 2007; Panagou, Mohareb, Argyri, Bessant, & Nychas, 2011; Rajamäki et al., 2006), but the calculation of the statistical parameters highly depends on which samples fell into each dataset. However, with Monte-Carlo validation the original datasets are split into training and testing datasets K number of times (as specified by the user). Training datasets are used to build the statistical model and testing datasets are used to test it. K models are built and tested and the statistical parameters for all of them are averaged. In contrast to splitting the dataset into training and testing datasets only once, this method is more reliable to assess the performance as it does not depend that much in which samples randomly fall into the training and testing subsets (Mohareb et al., 2016). Moreover, a particular effort has been placed in providing a comprehensive and easy to understand report. A heatmap that ranks all included instruments/sensors, algorithms and species counts using a red- green colour-ramp; where red represents unsuitability and green represents suitability (Figure 2). In this way, it became possible to compare and rank several possible scenarios within a single plot. Detailed information about each scenario is also provided within the generated report. For each combination of analytical platform and bacteria, all the tested machine-learning methods are ranked and statistics are provided for all of them. Performance plots are shown as well. Principal
final concentrations of 1 and 10 CFU ml -1 . The inocula were plated onto CBA and incubated anaerobically for 21 days at 10 °C to enumerate. Lamb steaks were randomly assigned to 120 barrier bags (70 μ Cryovac, New Zealand) representing five replicates for each of three inoculums, four exposure times and two temperatures. Forty packs were inoculated with 1 ml each 10 CFU ml -1 inoculum, forty with 1 ml each 1 CFU ml -1 inoculum and the remaining packs were left uninoculated (controls). The packs were vacuum-packed using a Securepak 10 Controlled Atmosphere Packaging Machine (Securefresh Pacific, Auckland, New Zealand). The packs were then subject to heat-shrinking treatment by complete immersion in 78 °C water for 2 to 3 s, to simulate best practice. ‘Best practice’ cooling was simulated by placing meat packs in boxes in a -1.5 °C chiller. After 24 h half the packs from each treatment were transferred to a 2 °C chiller. Five packs from each temperature/inoculum treatment (six treatments) were collected and opened in a 10 °C room and the steaks exposed to air for 15 min. The steaks were turned over and left for a further 15 min before being resealed in fresh barrier bags and returned to the appropriate chillers. The exposure process was repeated, with fresh packs, after 72 h and 6 days.
The main objective of this paper is to associate acquired volatile fingerprints (snapshots) of odour profile with beef spoilage during aerobic storage at various temperatures (0, 4, 8, 12, 16 and 20 °C) through the development of an advanced intelligent-based decision support system. Datasets related to enose data as well as the associated microbiological analysis (i.e. TVC) from beef fillets, were provided by the Agricultural University of Athens, Greece. The achievement of this objective, however, involves the implementation of a number of sub-tasks, related to data analysis. Due to the multi-variable nature of enose data, a dimensionality reduction algorithm was applied on the data used for training purposes. The robust PCA (RPCA) scheme has been utilized to obtain principal components that are not influenced much by outliers .
The meatspoilage bacteria: Escherichia coli, Brochothrix thermosphacta, Carnobacterium sp., Lactobacillus curvatus, Lactobacillus sakei and Leuconostoc sp. were considered in the experiment. These bacterial strains were isolated in a previous study (8) and then maintained in Microbank™ vials at -70°C. The antimicrobial activity of NCE (1mg/1ml Tween 80™) was evaluated as percent of growth reduction in appropriate liquid medium using broth macrodilution method at several NCE concentration (1:10-1:100,000).
Lactococcus lactis subsp. lactis LABW4 was able to inhibit the growth and activity of Listeria monocytogenes and was effective for long-term preservation of meat samples under refrigeration. It showed strong cidal effect and lytic mode of action on Listeria monocytogenes which cause severe meat-spoilage. Traditional methods of preservation such as refrigeration, pasteurization, and low pH are not completely effective in controlling or eliminating L. monocytogenes from food. The use of lactic acid bacteria or its metabolite(s) in combination with traditional methods of preservation could be effective in controlling L. monocytogenes to prevent spoilage of meat products. The lactic acid bacteria also have GRAS status and several probiotic activities, therefore using of such organism to preserve the food products may also confer various health benefits.
Keywords: blown pack spoilage; C. estertheticum; antimicrobials; gelatine films; edible coatings; active food packaging
Blown pack spoilage (BPS), characterised by a putrid smell (H 2 S) with a metallic sheen on the meat with or without gas production, occurs in correctly chilled batches (0 to 2 ◦ C) after four to six weeks and is caused by psychrophilic Clostridium spp. [ 1 ]. Although Clostridium algidicarnis, Clostridium frigoris, Clostridium bowmanii, Clostridium frigidicarmis and Clostridium ruminantium have been associated with meatspoilage, they do not produce gas [ 2 – 4 ]. Blown pack spoilage is usually caused by other Clostridium spp., including C. estertheticum and C. gasigenes, which produce large volumes of gas, primarily carbon dioxide [ 3 , 5 – 7 ].
used coccidiostats, monensin and decoquinate, are effective at preventing coccidiosis, but there is limited knowledge of their comparative effect on meat goat production and goat meat quality.
The largest cost to meat production systems is feed (Qushim et al., 2016). Due to this, farmers are always looking for alternatives to feed their livestock and when to feed concentrates. One alternative with some success is sunn hemp, a summer annual legume that was once thought toxic to livestock, but has since been proven to be quite nutritious for ruminants (NRCS, 1999). Because ethnic consumers prefer goat carcasses with limited amount of fat, producers have limited the amount of time goats have been fed concentrates (Pinkerton and McMillin, 2013). This leads to the question of the optimal time to supplement grazing goats with concentrates, which may influence carcass traits and meat properties.
In Islamic tradition, meat species and safety are very important for religious and health reasons. In Islam, food containing pig meat is Haram, and horse and donkey sources is Makrooh for Muslims, and Many Muslims will not eat meat that is Haram and or Makrooh [1, 2]. In some cases, misleading labels may be harmful for individuals who have food allergies and the consumption of meat and meat products may create health concerns [3, 4]. Moreover, motivated adulteration has emerged in the whole world, and it can be led to serious threats to the health of consumers, especially for imported products. Therefore, the authenticity of meat products becomes a vital issue because the meat products are not enough for domestic consumption and this country has imported a large portion of animal products and food products from other countries. In fact, Iran imports about 100000- 150000 M. tons of meat annually majorly from Brazil and Argentine, Pakistan . According last report, 10 percent of the domestic needs are currently imported into Iran. Also, Iran is currently importing red meat from New Zealand for increasing red meat production in the country and reducing market prices . Based on this fact, the risk associated with single and multiple-choice adulterations in commercial meat products has discouraged many people from consuming meat products. Especially for cooked meat products that the adulteration rates these products are higher than raw meats.
Meat and meat products are important for nutrition and the human diet, but are also one of the major routes of human intake of contaminants. Contaminating substances may enter the food chain at many different stages. Through various constituents like fertilizer ingredients and contaminants, irrigation water, contaminants and pesticides can enter food crops through plant roots. Contaminants in forages and other feeds can be transmitted to animal products. Veterinary drugs can leave residues in animal products. Environmental chemicals such as heavy metals (e.g., lead and mercury) from many sources have sometimes been found as food contaminants. Thus range of contaminants/toxicants found in food is varied but can be broadly subdivided into:
microorganisms derived from the soil environment, irrigation water, processing facilities and handling ( Pothakos et al., 2015b ). Depending on the packaging conditions, di ﬀerent types of microbiota will prevail on fresh cut lettuce. Aerobic conditions are mainly associated with Pseudomonas spp., especially with Pseudomonas ﬂuorescens and genera of enterobacteria such as Erwinia, known for their ability to produce pectinolytic enzymes that can further disrupt the lettuce tissue to access more nutrients ( Ragaert et al., 2007 ). Moreover, Rahnella aquatilis and lactic acid bacteria (LAB) will dominate under anoxic conditions. Dif- ferent types of yeasts have been also identi ﬁed in minimally processed vegetables that could potentially induce spoilage ( Ragaert et al., 2007 ). In order to counter aerobic growth and to prevent the formation of visual defects, anoxic packaging conditions are preferred most of the time shifting the microbiota to facultative anaerobes and aerotolerant genera ( Pothakos et al., 2014b ). Leuconostoc spp. and Lactococcus spp. have been identi ﬁed as common spoilers under anaerobic conditions in fresh produce ( Jacxsens et al., 2003 ; Paillart et al., 2017 ; Pothakos et al., 2014b ; Ragaert et al., 2007 ). Additionally, psychotrophic re- presentatives of these genera have been associated with cases of spoi- lage, especially in Belgium ( Pothakos et al., 2014c ).
When the genome organizations of 30 native isolates belonging to a wine spoilage yeast, Dekkera (Brettano- myces) bruxellensis, a distant relative of Saccharomyces cerevisiae, were examined, the numbers of chromosomes varied drastically, from 4 to at least 9. When single gene probes were used in Southern analysis, the corre- sponding genes usually mapped to at least two chromosomal bands, excluding a simple haploid organization of the genome. When different loci were sequenced, in most cases, several different haplotypes were obtained for each single isolate, and they belonged to two subtypes. Phylogenetic reconstruction using haplotypes revealed that the sequences from different isolates belonging to one subtype were more similar to each other than to the sequences belonging to the other subtype within the isolate. Reanalysis of the genome sequence also confirmed that partially sequenced strain Y879 is not a simple haploid and that its genome contains approx- imately 1% polymorphic sites. The present situation could be explained by (i) a hybridization event where two similar but different genomes have recently fused together or (ii) an event where the diploid progenitor of all analyzed strains lost a regular sexual cycle, and the genome started to accumulate mutations.
From this present study, I concluded that the spoiled vegetables collected from market harbor the pathogenic bacteria (Pseudomonas aeruginosa, Escherichia coli, Vibrio cholerae and Salmonella typhi) and fungi (Aspergillus niger, Penicillium chrysogenum and Mucor sp.) which cause harmful diseases to human beings. It is must to dispose the spoiled vegetables from the market to prevent the spread of disease causing microorganisms and spoilage of fresh vegetables.
To prevent untrustworthiness of the exposed desired norm and consequently make it conceivable, it will be stated in general (“... that’s why people eat less meat”), since stating an amount of meat per week might be unrealistic and therefore in risk of not being believable. With this, when for example stating that it is “the norm” to eat meat 4 days a week, people who eat less meat on a weekly basis, might consider increasing their meat intake because of the “boomerang effect”. Furthermore, it might not motivate individuals that already eat meat 4 days a week, to lower their meat consumption. The “boomerang effect” will be prevented, since there will be no statement of an average meat intake of which individuals can compare their intake to. Also, regarding the boomerang effect, there will be no attention drawn to the negative behavior (most people eat meat), but on the contrary, the desired behavior will be stated (most people do not eat meat), which again, should prevent the “boomerang effect” from occurring. The content of the messages (besides the norms), will contain information about the effects of meat consumption on the environment and animal welfare, to emphasize the importance of the desired (normative) behavior.
Singh et al., (2002) observed that the product packed under vacuum or nitrogen gas remain organoleptically acceptable for 10 and 15 days under refrigerated and frozen storage as against 8 and 10 days long shelf life of aerobically packed samples under same storage conditions. Bhoyar et al., 1998) reported that sensory evaluation of both aerobically packaged and vacuum packaged restructured chicken steak sample showed that the product from both packaging group were quit acceptable at the end of day 60 during frozen storage. However, vacuum packaged product wre rated higher in colour, flavor, juiciness, texture and overall acceptability, whereas, finding of Rajkumar et al.,(2004) proposed that vacuum packaging has definite advantages in preserving the sensory quality of patties but not enable extension of shelf life beyond 15 days. Sahoo et al., (1998) described the sensory quality of frozen ground buffalo meat by preblending with natural antioxidant and vacuum packaging and they observed that, vacuum packaging samples had lower amount of salt extractable proteins and colour and odour score as compared to aerobically packed samples. Zhao et al., (1996) studied the physical chemical and sensory characteristics of irradiated pork loin cut and reported that improvement of surface colour and odour in irradiated pork can be achieved by suitable packaging environment i.e. vacuum and CO 2 atmospheres. Debashis Bhattacharyya, et al., (2013) found that all the sensory parameters of
A total of 2140 (71.3%) positive fungi isolates are recorded in Table 1 as follows: L. esculentus 410 (19.2%), E. guineensis 415 (19.4%), I. batatas 200 (9.3%), S. tuberosum, 380 (17.8%), M. sapientum 120 (5.6%), D. ca- rota 110 (5.1%), M. paradisiaca 115 (5.4%), C. papaya 78 (3.6%), P. americana 67 (3.1%), C. lanatus 110 (5.1%) and C. chinense 135 (6.3%). The rates of isolation of the 6 different genera from the various samples are displayed in Tables 2-4. Figure 1 shows the distribution of the fungi species responsible for the spoilage/soft rots as follows: A. niger (20.42%), F. accuminatum (12.57), F. oxysporum (11.59%), R. nigrican (9.77%), F. moniliforme (6.92%), M. indicus (6.40%), R. stolonifer (6.03%), A. flavus( 4.95%), A. fumigatus (4.11%), M. racemosus (3.46%), M. hiemalis (3.08%), R. nigra (1.78%), M. amphibiorum (1.68%), F. eqiuseti (1.68%), F. solani (1.31%), P. oxalicum (1.26%), F. dimerum (0.97%), C. albicans (0.93%), R. oligosporus (0.84%), P. ex- pansum (0.51%), R. oryzae (0.28%) and P. digitatum (0.19%).