Spoilage Detection of Smart Packaged Chicken Meat by ddPCR

doi:10.21203/rs.3.rs-2609429/v1

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Spoilage Detection of Smart Packaged Chicken Meat by ddPCR

https://doi.org/10.21203/rs.3.rs-2609429/v1

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Nowadays, it is important for the food industry and public health that food reaches the consumer from production to consumption without spoiling. Smart packaging technologies are one of the new technologies informing the manufacturer and customer. In order to prevent spoiled food from being consumed, it is necessary to identify the deterioration as quickly as possible. The aim of the study is to determine the applicability of smart packaging technology and usability of Digital Droplet PCR for quick and accurate spoilage detection by evaluating the quantity of genes involved in biogenic amines synthesis that occurs during spoilage. Consumption of spoiled products until products of spoilage biogenic amines, accumulated and are later on detected, may cause a threat to public health. In this study, chicken meats were analyzed on specific days in terms of microbiological, physicochemical, and molecular aspects. The 9th day was determined to be the start of the degradation when the quantity of microorganisms exceeded 10⁸ cfu/g, based on the microbiological data obtained from chicken meats. On the same day according to the ddPCR data, the gene duplication number was found to be over 50–60. Within the light of this information, the upper limit for the detection of degradation of histamine and putrescine-producing gene copy number is interpreted as 50. When the results of the microbiological analyses and ddPCR data were compared, it was shown that ddPCR method when used in combination with the smart labels, can be applicable for quick deterioration detection in smart packaging systems.

biogenic amine

ddPCR

smart packaging

spoilage

Poultry meat is a mandatory food for ideal growth and development due to its high protein, lipid, vitamin, and mineral materials (Higgs 2000). The factors such as richness of nutritious, slaughtering process, high pH value, water, redox potential, being offered to consumption with the skin as well as accumulation of microorganisms between skin folds are the cause of faster spoilage of poultry meat (Odeyemi et al. 2020; Tsafrakidou et al. 2021).

There are many substances (ammonia, hydrogen sulfide, etc.) that are released as a result of contamination and spoilage of meat with microorganisms and biogenic amines (BA) are the most crucial among those metabolites. Besides causing threat to the public health, the importance of biogenic amines has derived from their use as an indication of bacteriological quality, freshness, and food spoilage (Nychas et al. 2008; Chaidoutis et al. 2019).

The detection methods of BAs are generally divided into two categories: the detection of biogenic amines and the detection of the bacteria producing biogenic amines (Linares et al. 2011). Methods based on the direct detection of biogenic amines are especially; immunoassays, flow injection analysis (FIA), fluorometric methods, chromatographic methods (Thin-Layer Chromatography (TLC), High- Performance Liquid Chromatography (HPLC etc.)) (Önal 2007; EFSA 2011). In these methods, the measurement of BAs has traditionally been carried out in the final products and often uses lengthy and laborious analytical methods. The other method is the determination of BA-producing bacteria that has several advantages in the food industry such as providing quick results and analysis of a lot of samples in a short amount of time. In addition, through this method, it is possible to intervene by detecting the biogenic amine concentration in the products before it reaches a level that risks human health (Martínez et al. 2011).

Since biogenic amines are formed as the result of the decarboxylase enzymatic activity of amino acid with the decarboxylation of precursor amino acid, it is possible to develop molecular detection methods which are targeting these amino acids’ decarboxylation enzymes coding genes (Marcobal et al. 2006). Before the detection of biogenic amines, it is possible to determine the amount of biogenic amines involved in the last product by determining biogenic amines-producing bacteria (Linares et al. 2011). Various bacteria are able to produce biogenic amines, however, some histidine decarboxylase-positive lactic acid bacteria (LAB) are generally responsible for histamine production (Barbieri et al. 2011). As a result of meat product spoilage, Enterobacteriaceae are responsible for cadaverine, putrescine, and histamine production (Chaidoutis et al. 2019). The hdc gene catalyzed by the histidine decarboxylase (HDC) enzyme has been used to detect the amount of histamine, and the agdi gene catalyzed by the agmatine deiminase enzyme (AGDI) for the amount of putrescine (Barbieri et al. 2011; EFSA 2011).

Among molecular analysis methods, 1st generation polymerase chain reaction (PCR), gave its place to real-time PCR (qPCR) as time progressed. Droplet Digital PCR (ddPCR), which is developed thanks to the new generation technology, is the 3rd generation and promises to achieve much more accurate results by eliminating the errors that occur in qPCR. ddPCR is a new technology used to quantify the targeted nucleic acid. Dividing each sample into 20,000 droplets and analyzing each droplet separately increases the reliability of this system (Pinheiro et al. 2012; Falzone et al. 2020). Therefore, gene copy number analysis, single-cell analysis, cancer detection, gene expression analysis, genetically modified organisms (GMO) analysis, environmental analysis, pathogen, and microbiome analysis are some application fields of ddPCR. Furthermore, usage of ddPCR for detecting SARS-CoV-2, the most important disease of recent times and the causative agent of Covid-19, which has been declared a pandemic, has proven ddPCR as one of the most reliable methods (Falzone et al. 2020; Suo et al. 2020).

ddPCR has several advantages over other molecular analysis methods. ddPCR calculates thousands/millions of reactions present in water-oil droplets using flow cytometry (Morisset et al. 2013). Moreover, ddPCR is not based on an endpoint timeline, diminishing the yield of primer/prob annealing, and doesn't need standards for the qualification of the amount (Rothrock et al. 2013). Compared to qPCR, ddPCR gives trustworthy results even if there are fewer amounts of samples, has higher tolerance against inhibitors, and has more efficient GMO evaluations according to price and output (Morisset et al. 2013). In the meantime, it provides more sensitive and specific results compared with other systems based on the counting method (Rothrock et al. 2013). Other advantages include the low variation between copies and less exposure to mixed DNA in the background. However, high cost, limited output, and complex workflow are disadvantages of ddPCR (Hindson et al. 2011).

Smart (intelligent) packages can keep records of the food's history along with informing the consumer about the freshness of the food (Kalpana et al. 2019). Gas leakage (gas concentration) indicators, time-temperature indicators, and freshness indicators are among the most preferred indicator types.

The time-temperature indicator (TTI) is a physicochemical mechanism that provides information about the status and history of the temperature exposed (Müller and Schmid 2019). For refrigerated and chilled products, these labels make visible the inappropriate temperatures they are exposed to during distribution and storage (Yam et al. 2005). Freshness indicators give information directly about the quality of food and microbial spoilage (Müller and Schmid 2019). For quality control of packaged food, apart from temperature violations or leakage in the package, it should be shown whether the freshness of the product is reduced or spoilage occurred (De Kruijf et al. 2002).

In this study, ddPCR, a molecular method, was used to quantify the biogenic amine-producing genes for the rapid and reliable detection of spoilage, and these results were compared with the results of classical microbiological analysis and the employability of the method in the detection of food spoilage was examined. At the same time, the rapid detection of food spoilage and its presentation to the consumer has been investigated in the packaging of animal foods utilizing smart packaging technology, which provides direct information about the microbiological and physicochemical spoilage that may arise in foods. According to the data obtained in this study, it is aimed to contribute to public health, science, and technology by rapid detection of food spoilage.

2.1. Preparation of the Samples

Chicken meat samples were packaged by dividing them into pack containing 100 g each. The analyzes of the samples reserved for the starting day (Day 0) analysis were carried out. Istanbul University, Faculty of Veterinary Medicine, Department of Food Hygiene and Technology laboratories were used for the preparation, packaging, microbiological and physicochemical analyzes of the samples used in the study, and Iontek Inc. laboratories in Istanbul Technical University Arı Teknokent were used for ddPCR analysis.

After the samples were placed on the plates, freshness labels produced by the Teknokim put on the plates. The packages were placed into the Ponapack VTK 40SC packaging device and control groups were packaged without adding any gasses in them. For the samples belonging to the modified atmosphere packaging (MAP) 80% CO₂ – 20% N₂ gas mixture were added. Time- temperature labels, which were also produced by Teknokim, were attached to the outer surface of the films of the packages removed from the machine. The samples were stored in cold (4°C) storage until the analysis time was concluded.

2.2. Microbiological Analyses

Microbiological analyses of the samples were performed at the days 0, 1, 3, 5, 7, 9, 12 and 15. TEMPO device and classical culturing methods were preferred for microbiological analysis. Homogenate obtained by taking 10g from the samples and diluting with 1/9 with Maximum Recovery Diluent (MRD) then mixed with Stomached Lab-Blender (400) for a minute. Dilutions were prepared according to the dilutions to be used (Gabis et al. 1976). TEMPO device was used for analysis of total aerobic mesophilic bacteria (AC), yeast – mold (YM), Enterobacteriaceae (EB), lactic acid bacteria (LAB), total coliform (TC), Escherichia coli (E. coli) (EC). For TEMPO analyses, powdered media were activated with diluting those using different ratios of MRD depending on the microorganism variety. After the homogenate was diluted and placed, the bottles containing the medium-sample mixture were placed in the TEMPO preparation unit and automatic filling started. After inoculation, sample cartridges were incubated for analysis accordingly; 72–76 hours at 25°C for yeast and mold (parallel with the EN ISO 21527 and BAM part 18 standards), 24–48 hours at 30°C for total aerobic mesophilic bacteria (parallel with the EN ISO 4833), 35°C for Enterobacteriaceae (parallel with the ISO 21528-2), for LAB 40–48 hours at 30°C (parallel with the NF ISO 15214), for total coliform 22–27 hours as defined in FDA-BAM at 30°C, E. coli were incubated at 37°C for 24–27 hours (parallel with the EN ISO 16649-2). Cartridges whose incubation periods concluded were taken from the oven and placed in the TEMPO reading device, and the processed reading results from the computer were recorded (Biomerieux 2016).

Classical analysis methods were used for the analysis of Pseudomonas spp., Lactabacillus spp. and total psychrophilic bacteria. Pseudomonas agar to which C-F-C supplement was added was incubated for 10 days at 7°C ± 1°C after transferring the samples. Typical colonies were counted after incubation. Rogosa agar, whose pH was adjusted to 5.5, was incubated at 35°C for 48–72 hours after using spread plate technique. Results were obtained by counting typical colonies after incubation. After transferring each dilutions to Plate Count Agar (PCA), it was incubated at 7°C ± 1°C for 10 days. Petri dishes that finished the incubation period were taken and all the typical colonies that had grown were counted and results obtained in cfu/g (Gilliland et al. 1976; Sandine et al. 1976).

2.3. pH Analysis

pH measurements were made in order to determine the spoilage by psychochemical means. 10 g of the samples were taken and divided into small pieces, diluted with 90 ml of distilled water then homogenized and pH measurements were obtained using a pH meter.

2.4. Molecular Analysis

2.4.1. DNA Extraction

High Pure PCR Template Preparation Kit (Roche) was used for DNA extraction. Direct DNA extraction from the tissues performed following the manufacturer’s instructions. DNA was stored at -20°C until PCR analysis in the Eppendorf.

2.4.2. DNA Quantification

After the DNA extraction from the foods samples, 2 µl extract was taken, and DNA amounts and purity were determined by Epoch microplate spectrophotometer.

2.4.3. ddPCR

After the optimization processes were completed, the ddPCR system was used to quantify the presence of suitable genes in the samples. For this purpose, EvaGreen master mix and DNAs were mixed and loaded into the cartridge. After the oils were loaded, the cartridges were placed in the ddPCR Droplet Generator device to prepare droplets. 40 µl of droplets were transferred to the plates and placed in the PCR device after the plates were sealed. After the PCR conditions were selected, the device was started to amplify the genes. When the Thermal Cycler finished amplifying, the plate was placed in the QX200 Droplet Reader and the droplets were measured, and then the results were evaluated using the Quanto Soft program. The results were obtained by multiplying the data from the results of the study with the dilution coefficients.

For the detection of putresin producing genes, agdif (5'-ATGCCCGGTGAATTTGAA-3') and agdir (5'-TTGCGCTGGTTTAGCACC-3') primer pairs (GenBank: AF446085.5), for the detection of histamine producing genes hdcRTfw (5'-ACTCAATCGGTGTTTCCGGC-3') and hdcRTrv (5'-TGTGACCGTTACGTGAACCG-3') primer pairs (GenBank: KC771251) were used (Nannelli et al. 2008; Ferrario et al. 2014). The times and temperatures used as thermal protocol are as the following: an initial denaturation step 5 min at 95°C, followed by 40 Cycle, denaturation 30 sec at 95°C, binding 1 min at 60°C, a final extention step 5 min at 4°C, enzyme deactivation 5 min at 90°C.

2.5. Statistical Analysis

SPSS program (SPSS, version 20.0 for Windows, SPSS Inc.) used for evaluating statistical similarity between the MAP and control groups on the basis of each parameter (microbiological and pH analysis data) according to Friedman's method (Chantawannakul et al. 2002; Koistinen et al. 2007).

Before the samples were packaged with the MAP method, the first day analyses were made, and these groups were named as the starting day. After the samples were packaged, microbiological, physicochemical and molecular analyses were carried out, and the changes in the labels were also observed.

3.1. Microbiological Analysis Results

According to the results of microbiological analysis, there are clear distinctions depending on the different packaging methods of the chicken meats (Fig. 1 and Fig. 2). Total aerobic mesophilic bacteria counts increased from 4.77 log₁₀ cfu/g to 5.11 log₁₀ cfu/g on the 15th day in the MAP group, and to 7.79 log₁₀ cfu/g in the control group, although the rise curve was low, indicating complete spoilage of the food. The difference in total coliform counts between the groups was observed as more than 4 times. In the same way, yeast-mold and Pseudomonas spp. also showed the difference by increasing approximately twice as much. When the MAP and control groups were compared in terms of spoilage, it was evaluated that the chicken meat did not reach the values to be accepted as spoiled on the 15th day in the MAP groups, but in the control groups’ spoilage criteria was met on the 9th day. When the relationship between MAP and control group samples statistically compared with Friedman method, the difference between EB value is p < 0.05, the difference between EC value is p < 0.05, the difference between Pseudomonas spp. value is p < 0.05, the difference between total psychrophilic bacteria value is p < 0.05, difference between LAB value is p < 0.05, difference between Lactobacillus spp. value is p < 0.05, difference between TC value is p < 0.05 and difference between yeast mold values at p < 0.05 level are found.

3.2. pH Analysis Result

The pH values obtained as a result of the analysis are given in Table 1. The pH value of the products was determined on the analysis days, and as a result of these analyzes, the MAP and control group products determined to have a difference. The difference in the MAP and control groups was about 0.10, and both had an increase compared to baseline day analyses. When the relationship between pH values of MAP and control group samples was evaluated statistically by Friedman's method, the difference between pH values was found to be significant at the p < 0.05 level.

Table 1

MAP and Control Group pH Analysis Results
pH	Starting Day	1st Day	3rd Day	5th Day	7th Day	9th Day	12th Day	15th Day
MAP	6.23	6.26	6.31	6.15	6.15	6.10	6.11	6.32
Control	6.23	6.03	6.12	6.14	6.18	6.25	6.34	6.46

3.3. Molecular Analysis (ddPCR) Results

The results of the ddPCR analysis performed on the chicken meat samples are given in Table 2, Fig. 3 and Fig. 4. As can be seen in the figures created according to the values in the table, it has been observed that the number of repeats of the genes that cause histamine and putrescine production in both MAP and control chicken meats increased with the advancing storage period. In both groups, the values began to increase from the 3rd day. When we look at the microbiological analysis findings, it has been seen that the values have increased starting from the 5th day and the products have reached the spoilage limits. When these two results were evaluated together, a parallel increase was detected. When the MAP and control groups were compared among themselves, it was determined that the increases were very high for both biogenic amines. On the 9th day, the histamine value of MAP chicken meat samples was 8.50, 65.50 in the control group, and putrescine was 7.00 in the MAP group, and 55.00 in the control group.

Table 2

ddPCR Analysis Results of Modified Atmosphere Packaging and Control Group Chicken Meats (Gene Region Copy Numbers)
ddPCR	Starting Day	3rd Day	5th Day	9th Day
MAP- Histamine	3.50	1.60	4.00	8.50
Contol- Histamine	3.50	0.00	35.50	65.50
MAP- Putrescine	0.00	0.00	4.00	7.00
Control- Putrescine	0.00	1.50	35.00	55.00

3.4. Smart Label Implementation and Evaluation

In freshness indicators, the labels must retain their blue color in order to qualify the products as fresh, and the color must turn yellow to qualify as spoiled. On the other hand in time - temperature indicators, when the white part turns blue, it indicates that temperature breakage occurs, and the amount of color change indicates the elapsed time. In the light of this information, labels used as time-temperature indicator and freshness indicator were followed up and recorded.

As a result of our study, through our observation there were no color change in the control groups unless freshness indicators were deteriorated, in the MAP groups color change was observed even shortly after packaging. Since the freshness indicators used in our study are based on color change according to the pH changes in the packages, unexpected changes that occurred in the MAP groups show that the indicator interacted with the gases used in the MAP. Time-temperature indicators were monitored together with the temperature monitoring device located outside of the cold storage room and no color change was observed unless there was a temperature change. On the other hand, it was observed that the indicator changed color in the products whose cold chain was broken and stored at room temperature.

In addition to threatening public health, food spoilage should be identified as soon as possible, the underlying cause should be revealed, and treatment should be initiated as soon as possible due to increased hospital costs, incapacity to work, and economic problems as a result of the consumption of foods that couldn’t be detected at the time that they are spoiled (Odeyemi et al. 2020; Pellissery et al. 2020). Classical microbiological analyzes take longer to complete, and ddPCR analyzes provide both fast and quantitatively reliable results. For this reason, the usability of ddPCR in the detection of food spoilage was investigated, the amount of histamine and putrescine biogenic amine, which is a spoilage product, was determined by ddPCR, and the parallelism of the increase in this product with the spoilage was investigated in this thesis study. There are several microorganisms that produce putrescine and histamine, but it is impossible to study each one separately. In this study, the quantity of hdc and agdi genes responsible for the production of these biogenic amines was therefore determined. This number of hdc and agdi genes was compared with the results of the classic microbiological analysis, and it was investigated whether this method could be used to determine food spoilage.

Smart packaging, one of the new packaging systems, allows monitoring of the condition of the food as well as providing information about different changes during transportation and storage (Kalpana et al. 2019). Time-temperature and freshness indicators are the smart packaging systems used for this purpose (Fang et al. 2017). In this study, with the help of these indicators, it was evaluated whether two of the sub-members of the smart packaging system could be used in the detection of food spoilage. In the freshness labels, the results related to the pH change were observed in the control groups, it was determined that the color of the label changed as the pH increase occurred, and the indicators used were observed to be successful. Successful results have been obtained in many studies in which the freshness of chicken meats is monitored with pH-sensitive indicators (Riahi et al. 2019; Lee et al. 2019; Wu et al. 2021). In this study, deviations were observed in freshness indicators in MAP packaged product groups. When the studies in this area are reviewed, it has been observed that the freshness indicator, which is sensitive to other spoilage products such as sulphured compounds, is generally used in MAP products (Smolander et al. 2002; Opara et al. 2022). When the performance of the freshness indicators was evaluated, it was interpreted that the factors other than the product in the package (such as the additional gas in the package) should be further developed for the use of pH-sensitive freshness indicators in the MAP system, since the indicators show a color (pH) change that is not due to spoilage.

The time-temperature indicators used in this study showed that they can be used effectively in the smart packaging system by not changing color as long as there is no deviation in temperatures in both MAP and control groups. Studies in the field of TTI also support that this product can be used effectively for the monitoring of food spoilage (Mataragas et al. 2019; Vaikousi et al. 2009; Gao et al. 2020). Results will be much more effective when the time-temperature and freshness indicators are combined with other smart and active packaging systems.

It has been confirmed by previous studies that the amount of biogenic amines, which is one of the food spoilage products, increases as a result of the spoilage of chicken meats (Wojnowski et al. 2019; El-Nour et al. 2017). Rokka et al. reported that there was no significant change in the amount of putrescine below + 6.1°C, on the other hand, an increase in the amount of putrescine was reported after 7 days when it was above this temperature (Rokka et al. 2004). In another study, it was observed that the amount of putrescine and histamine in chicken meat stored at 4°C began to increase significantly on the 15th day (Silva and Glória 2002). Gallas et al. observed that there was an increase in putrescine on the 9th day, and a slight increase in histamine in their study on MAP packaged chicken meat, while Balamatsia et al. pointed out that the amount of putrescine detected by the HPLC method increased from the 5th day (Gallas et al. 2010; Balamatsia et al. 2006). In another study, it was disclosed that putrescine began to increase from the 3rd day and histamine began to increase from the 7th day in chicken meat that is in cold storage (Baston and Barna 2010). Baston et al. stated that putrescine in chicken meats in cold storage increased after the 7th day and histamine increased after the 20th day (Baston et al. 2008). In this study, we noticed that the number of bacteria producing histamine and putrescine gene started to increase on the 3rd day in both MAP and control groups. This is an indication of the onset of spoilage from the 3rd day. However, since the studies are based on the direct detection of the presence of biogenic amines, the presence of spoilage was detected a couple of days later. There is a serious risk of a public health threat since the product will not be detected to be spoiled during this period and will be consumed.

Many bacteria cause the production of histamine and putrescine, which are closely related to spoilage (Moniente et al. 2021; EFSA 2011; Chaidoutis et al. 2019). Since it is not possible to analyze all these bacteria separately, both economically and practically, the numbers of genes responsible for the production of these biogenic amines were determined and this amount was compared with the classical microbiological analysis findings and it was investigated whether or not this method could be used in food spoilage detection. Previous studies have shown that PCR or Real-time PCR methods have been generally used in the detection of these genes (Moniente et al. 2021; Nurilmala et al. 2020).

In our literature review, we could not find any study that the ddPCR method was used for the detection of spoilage, therefore, unlike other molecular methods, we aimed to use the ddPCR method in this study. Studies in the field of ddPCR have proven the reliability of this method. As a result of their study, Pinheiro et al. found ddPCR to be successful and made successful measurements even in samples with less than 5% DNA density (Pinheiro et al. 2012). Contrary to qPCR, it is not sensitive to inhibition of amplification in DNA extractions and can give accurate results even in very small sample amounts (Morisset et al. 2013). Being able to analyze even a single copy is a reason for preference, especially in areas of vital importance such as cancer studies (Galbiati et al. 2019). In the food industry, it is used in many areas such as the determination of the different meat types added for fraudulent purposes (Ren et al. 2017; Naaum et al. 2018), pathogen detection (Cremonesi et al. 2016; Pan et al. 2020) and GMO analysis (Košir et al. 2019). However, since no study was found using ddPCR to detect food spoilage, the results of this study were expected to serve as a roadmap for future research in this field.

Until 7 log₁₀ cfu/g microorganisms are detected, there may be no odor or taste change in food, or the consumer may not accept that the food is spoiled for thriftiness and consume it, which may cause food poisoning (Sperber 2009). This process is affected by many factors such as the structure of the food, its type and storage conditions. Hinton et al, in their study, observed that there was an increase in spoilage bacteria on the 7th day in chicken meat stored in cold storage, and a significant increase in the number of Pseudomonas spp. on the 7th day (Hinton et al. 2004). Also, the psychotropic nature of Pseudomonas spp. allows it to be used as a spoilage indicator in foods stored at low temperatures (Martínez et al. 2011). In chicken meats, the development of Pseudomonas spp. is rapid and these bacteria can be used as a freshness indicator (Bruckner et al. 2013). Studies show that lactic acid bacteria and Enterobacteriaceae are also the dominant flora, especially in MAP chicken meat, and cause food spoilage (Tsafrakidou et al. 2021; Lauritsen et al. 2019). Our study showed parallelism in with the high levels of biogenic amine producing genes of those bacteria in spoiled chicken meats.

Jaafresh et al, in their study based on the detection of spoilage in MAP packaged chicken meat, considered the group between 4–9 days as semi-deteriorated while considering the group after the 10th day as spoiled and determined 9 days as the storage period (Jaafreh et al. 2018). In our study, it was accepted that there was an increase, especially in the psychrophilic group from the 9th day and although the storage times of the products in MAP packaging were longer, the control group deteriorated by the 9th day.

As microbial deterioration begins, pH changes also begin and this is reflected in the data. In MAP and air-packed chicken meats, pH was measured between 5.8 and 6.4 as a result of spoilage, and no differences were observed according to gas ratios (Rossaint et al. 2015). Herbert et al. also stated that gas differences and time do not affect pH and the pH is generally between 5.7–6.2 in chicken meats (Herbert et al. 2013). Bruckner et al. found that the pH increased from 6.02 to 6.23 in chicken meats stored at 4°C (Bruckner et al. 2013). In this study, there was an increase in pH values for each group, similar to other studies. In addition, the pH changes of MAP, which has lower microbiological values, were relatively less than the control groups.

In this study, considering that genes producing biogenic amines can be found in more than one bacterium, a connection was tried to be determined between the increase in gene amount and spoilage, and according to ddPCR data, the difference was observed between chicken meat MAP and control groups in terms of both putrescine and histamine producing genes. In both values, the number increased as the day progressed, and when the MAP and controls were compared, the values were found to be higher in the control group compared to the other group. The ddPCR method was found to be reliable in terms of detecting spoilage since microbiological deterioration was not detected on the 15th day in the MAP groups and there was no significant increase in the amount of histamine and putrescine producing genes. Although control chicken meat samples reached the threshold value on the 9th day, MAP samples did not reach the microbiological threshold values even on the 15th day. In particular, it was evaluated that the values of the hdc and agdi gene regions, which were predicted to be reached on the 9th day of the control chicken meat samples and amplified by the ddPCR method, quantitatively made approximately 50–60 repetitions, could be used as the spoilage threshold. Thus, it is thought that the ddPCR method could be used for the rapid detection of spoilage in meat and meat products, and with further studies, this can be used as a preliminary detection method in cases such as inspection and food poisoning cases where the rapid decision needs to be made. However, further studies are needed in terms of testing and applicability of this method, as our study is the first study in this field and it is needed to be supported by further studies with more data.

Funding

This study was supported by the Research Fund of the İstanbul University, İstanbul, Turkey (Project No: 47011). This manuscript has been summarized from PhD dissertation of Gülay Merve BAYRAKAL.

Contribution

Gürhan Çiftçioğlu and Gülay Merve Bayrakal designed the work, prepared samples, analyzed the experimental data and discussed the research results. Gülay Merve Bayrakal performed the experiments, data analysis, literature search and wrote the final manuscript. Gürhan Çiftçioğlu supervised the work and reviewed/edited the manuscript. All the authors read and approved the final manuscript.

Conflict of Interest

Gülay Merve Bayrakal declares that she has no conflict of interest. Gürhan Çiftçioğlu declares that he has no conflict of interest.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Spoilage Detection of Smart Packaged Chicken Meat by ddPCR

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Preparation of the Samples

2.2. Microbiological Analyses

2.3. pH Analysis

2.4. Molecular Analysis

2.4.1. DNA Extraction

2.4.2. DNA Quantification

2.4.3. ddPCR

2.5. Statistical Analysis

3. Results

3.1. Microbiological Analysis Results

3.2. pH Analysis Result

3.3. Molecular Analysis (ddPCR) Results

3.4. Smart Label Implementation and Evaluation

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

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