Next Article in Journal
Probiotic Potential and Cholesterol-Lowering Capabilities of Bacterial Strains Isolated from Pericarpium Citri Reticulatae ‘Chachiensis’
Previous Article in Journal
Salivary Microbiome Diversity in Kuwaiti Adolescents with Varied Body Mass Index—A Pilot Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 9
Issue 6
10.3390/microorganisms9061223
microorganisms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Microbiota of Modified-Atmosphere-Packaged Cooked Charcuterie Products throughout Their Shelf-Life Period, as Revealed by a Complementary Combination of Culture-Dependent and Culture-Independent Analysis

by
Evelyne Duthoo
1,
Geertrui Rasschaert
1,
Frédéric Leroy
2,
Stefan Weckx
2,
Marc Heyndrickx
1,3 and
Koen De Reu
1,*
1
Fisheries and Food (ILVO)—Technology and Food Science Unit, Flanders Research Institute for Agriculture, 9090 Melle, Belgium
2
Research Group of Industrial Microbiology and Food Biotechnology (IMDO), Faculty of Sciences and Bioengineering Sciences, Vrije Universiteit Brussel, 1050 Brussels, Belgium
3
Department of Pathology, Bacteriology and Avian Diseases, Ghent University, 9820 Merelbeke, Belgium
*
Author to whom correspondence should be addressed.
Microorganisms 2021, 9(6), 1223; https://doi.org/10.3390/microorganisms9061223
Submission received: 14 May 2021 / Revised: 27 May 2021 / Accepted: 28 May 2021 / Published: 4 June 2021
(This article belongs to the Section Food Microbiology)
Browse Figures
Versions Notes

Abstract

:
Although refrigeration and modified-atmosphere packaging (MAP) allow for an extended shelf life of cooked charcuterie products, they are still susceptible to bacterial spoilage. To obtain better insights into factors that govern product deterioration, ample information is needed on the associated microbiota. In this study, sliced MAP cooked ham and cooked chicken samples were subjected to culture-dependent and culture-independent microbial analysis. In total, 683 bacterial isolates were obtained and identified from 60 samples collected throughout the storage period. For both charcuterie types, lactic acid bacteria (LAB) constituted the most abundant microbial group. In cooked ham, Brochothrix thermosphacta was highly abundant at the beginning of the shelf-life period, but was later overtaken by Leuconostoc carnosum and Lactococcus piscium. For cooked chicken products, Latilactobacillus sakei was most abundant throughout the entire period. Additionally, 13 cooked ham and 16 cooked chicken samples were analyzed using metabarcoding. Findings obtained with this method were generally in accordance with the results from the culture-dependent approach, yet they additionally demonstrated the presence of Photobacterium at the beginning of the shelf-life period in both product types. The results indicated that combining culture-dependent methods with metabarcoding can give complementary insights into the evolution of microorganisms in perishable foods.

1. Introduction

According to estimates by the United Nations Environment programme, approximately 931 tons of food waste were generated in 2019, which suggests that 17% of global food production may be wasted [1]. For a large part, this is due to food spoilage [2]. Meat accounts for approximately 4% of total food waste [3], with meat losses being severest at the end of the food chain in industrialized regions. Here, almost half of meat waste occurs at the consumption level [2].
Spoilage of meat is usually of microbial origin, which is also the case for the specific category of cooked meat products. The latter are rich in proteins, fats and some (added) carbohydrates which can be broken down and metabolized by specific spoilage organisms (SSOs), resulting in off-flavors, off-odors, discoloration, gas formation, changes in texture and slime-formation, making the product unsuitable for consumption [4,5,6,7]. Although cooking eliminates the majority of the SSOs, products are recontaminated during handling operations after the cooking process, for instance during slicing and/or packaging. Microbial surface contamination of the cooked meat products thus affects the potential shelf-life stability [8,9,10], creating important food preservation challenges [11].
Refrigeration and modified-atmosphere packaging (MAP) allows for an extension of the shelf-life period of cooked meat products with minimal changes in the physical and chemical properties of the product [12,13]. Growth of psychrophilic and psychrotolerant lactic acid bacteria (LAB) and Brochothrix thermosphacta is generally favored under anaerobic MAP conditions with N2 and CO2. Due to competitive effects, the growth of acid-sensitive Gram-negative bacteria is largely inhibited [14,15,16,17]. Though LAB are themselves responsible for sensory changes, these changes are only presented once the stationary growth rate is approached, resulting in a product that is consumable for a period of approximately three weeks [8].
The end of the shelf-life period is defined by a maximum acceptable level of either SSOs or sensory alterations in the product [6]. Therefore, it is impossible to predict the shelf life without sufficient information on the composition of the microbiota present. Merely characterizing LAB as a group is insufficient, however, as more fine-grained information on their diversity is needed; specific species and even strains have different abilities to spoil meat [6,8,9].
In the present study, the microbiota of two types of MAP sliced cooked charcuterie (i.e., cooked ham and cooked chicken), produced at different production sites but sliced in the same facility, were investigated during storage using conventional culture-dependent microbiological analysis in combination with 16S rRNA gene metabarcoding during the shelf-life period. The metabarcoding approach was included to generate a culture-independent and cost-effective approach to obtain a complementary overview of the microbial composition of the samples on different taxonomic levels, although underrepresented communities may be overlooked [18,19,20]. The study aimed to yield additional insights into the diversity and evolution of the most abundant microorganisms that are present in these products during their shelf-life period. More specifically, the purpose was to unravel the differences and similarities between the microbial consortia that are present on two different charcuterie products undergoing similar processing steps of cooking, packaging, and slicing, but derived from different meats (in casu, pork and poultry).

2. Materials and Methods

2.1. Sample Collection and Storage

Two types of food products were sampled in this study: (1) cooked ham and (2) cooked chicken products. Both products were pressed into meat logs and underwent a cooking process wherein cooked ham reached a core temperature of approximately 69 °C, whereas cooked chicken reached at least 72 °C. The samples were collected within the premises of the food business operator (FBO), at the moment of slicing and packaging. Samples were taken aseptically of the log of each product type, right before slicing occurred, as well as of the sliced products derived thereof. The logs had a shelf-life period of three weeks each, and were sliced by the FBO between one and three weeks of storage at −1.5 °C, depending on the market demand. To reflect this variability, each batch of each product type was sampled twice. This was done when the log was sliced one week after its production and when another log of the same batch was sliced two weeks later, at the end of the log’s shelf-life period. A schematic overview of the sample collection procedure is presented in Figure 1.
All sliced products derived from the various logs were packaged under MAP conditions of 70% N2 and 30% CO2. Each sliced MAP product was analyzed on the day of slicing (D0) as well as after 28 d of storage (D28), corresponding with the end of the shelf-life period of the sliced product. The 28-d storage period consisted of 19 d at 7 °C and an additional 9 d at 8 °C, per usual practice of storage studies by the FBO.
Five batches were analyzed per product type, resulting in 30 analyzed samples per product type or 60 analyzed samples in total. Samples were stored and transported under cooled conditions to the laboratory for further analysis (with a transport duration of maximum 30 min).
An additional environmental sampling was conducted eight months after the original samplings. Environment samples were taken of food contact surfaces from two slicers during production; one slicer was used to slice cooked ham and another one to slice cooked chicken. Surface sampling (approximately 625 cm2 each; A4 format) was performed using sponge sticks (3M™, St. Paul, MN, USA), moistened with 10 mL of maximum recovery diluent (MRD, Oxoid, Basingstoke, Hampshire, UK).

2.2. Microbiological Analysis

Ten grams of each food product sample were subsampled and analyzed. After preparing appropriate dilutions in MRD, the following microbiological enumerations were performed: total psychrotolerant and psychrophilic aerobic (TAC) and anaerobic microbial counts (TANC), psychrotolerant and psychrophilic lactic acid bacteria (LAB), Enterococcus spp., Enterobacterales, sulfite-reducing Clostridia spp., Bacillus cereus, Brochothrix thermosphacta, and yeast and moulds. Plate count agar (PCA; Oxoid) was used for the enumeration of TAC, applying incubation at 21 °C during 5 d. Reinforced clostridial agar (RCA; Oxoid) was incubated at 21 °C during 5 d for the enumeration of TANC. For the enumeration of presumptive LAB, de Man, Rogosa and Sharpe agar (MRS; Oxoid) and M17 agar (Oxoid) were both incubated at 21 °C during 5 d. This was done to obtain an enhanced overview of the LAB present, as MRS medium contains acetate, whereas M17 does not. Acetate inhibits growth of Carnobacterium spp., which is known to occur in meat products [14]. For the specific enumeration of enterococci within the LAB group, Slanetz and Bartley agar (Oxoid) were incubated at 37 °C for 48 h. The confirmation of enterococci was performed by testing for growth at 44 °C, testing for growth in 40% bile and performing a catalase test. Violet-red-bile-glucose agar (VRBG; Bio Rad, Marnes-la-Coquette, France) was incubated at 37 °C for 24 h for the enumeration of presumptive Enterobacterales. Tryptose sulphite cycloserine agar (TSC; Oxoid) was incubated at 37 °C for 24 h for the enumeration of sulfite-reducing clostridia. Mannitol egg yolk polymyxin agar (MYP; Oxoid) was incubated at 30 °C for 48 h for the enumeration of presumptive B. cereus, of which the presence was further confirmed using blood agar base (Oxoid) with addition of 7% of defibrinated sheep blood (E&O Labs, Bonnybridge, UK). Blood plates were incubated at 30 °C during 24 h. Streptomycin thallous acetate actidione agar (STAA; Oxoid) was incubated at 21 °C for 48 h for the enumeration of B. thermosphacta. The confirmation of B. thermosphacta was performed by both a catalase and oxidase test (Microbact Oxidase Strips; Oxoid). Oxytetracycline-glucose-yeast extract agar (OGYE; Oxoid) was incubated at 25 °C for 5 days for the enumeration of yeast and moulds. For PCA, OGYE, STAA, Slanetz and Bartley, MRS, M17, and VRBG agar, dilutions were used to prepare pour plates, which were incubated aerobically. For MRS, M17 and VRBG, an overlay of 10 mL was added. For RCA and TSC, dilutions were used to prepare pour plates, which were incubated anaerobically using AnaeroGen 3.5 L (Oxoid) in an airtight jar. For MYP agar, the spread-plating technique was used followed by aerobic incubation. The lower limit for enumeration for all media was 1 log colony forming unit (CFU)/g.
After making appropriate dilutions in MRD, the surface swab samples were plated on PCA for the enumeration of TAC, RCA for the enumeration of TANC and MRS agar for the enumeration of presumptive LAB, following the same incubation period and temperature as mentioned above.
Water activity (aw) was measured for each food sample using the AQUALAB 4TE water activity meter (Metergroup, München, Germany). The pH was measured using a pHenomal pH 2100 L in combination with a SF113 electrode (VWR, Leuven, Belgium).

2.3. Isolate Collection

For the collection of isolates, inoculated and incubated agar media used for the enumeration of TAC (PCA), total anaerobic count (RCA) and LAB (MRS agar) were used. On the agar media representing the highest decimal dilution showing bacterial growth, five to ten colonies were randomly picked from each of the three types of media, conforming to a median level of 31% of colonies available. Pure cultures were inoculated in brain heart infusion (BHI; Oxoid) for cultures originating from PCA and RCA or in MRS broth (Oxoid) for cultures originating from MRS agar, with 15% glycerol (Thermo Fisher Scientific, Geel, Belgium), incubated for 2 d at 21 °C and kept at −80 °C. From PCA, RCA and MRS agar, a total of 259, 227 and 197 isolates were collected, respectively.

2.4. Identification of Isolates

From each isolate, except for those that could not be cultivated after storage at −80 °C (128 out of 811 isolates), DNA was collected and stored at −20 °C. DNA was extracted according to Strandén et al. [21]. On these DNA extracts (GTG)5, PCR fingerprinting was carried out [22]. The obtained fingerprints were clustered using BioNumerics version 7.6 (Applied Maths, Sint-Martens-Latem, Belgium) based on their similarity using UPGMA (unweighted pair group method with arithmetic averages algorithm) with 1% curve smoothing. Out of 259, 227 and 197 isolates (originating from PCA, RCA and MRS agar, respectively), which were included in the (GTG)5 fingerprint clusters, respective totals of 147, 144 and 104 isolates were selected for identification. They were selected as representative isolates of visually defined clusters grouping isolates with the same band patterns. For clusters containing up to three isolates, one representative isolate was selected. A minimum of two isolates was selected for identification of clusters containing four or more isolates. A 1127 bp-region of the 16S rRNA gene was amplified for identification of the selected isolates, as previously described [21]. PCR products were Sanger-sequenced by a commercial service provider (Genewiz, Takeley, UK). The sequence reads were compared to the EZBioCloud database. To identify the isolates to their presumed species level, the species in the database were used with the highest identity (of at least 98.5%) and coverage. Biodiversity indexes were calculated by using Gleason–Margalef’s index [23].
From the slicers, 148 isolates were identified, including 58 isolates from PCA plates, 53 from RCA plates and 37 from MRS agar.

2.5. Metabarcoding

Ten grams of each sample were suspended into a 1:1 dilution using MRD. This suspension was gently kneaded to allow bacterial cells to detach from the matrix’s surface. Then, the suspension was centrifuged at 200× g for 3 min to remove potentially inhibiting food matrix material. The resulting supernatant was again centrifuged at 10,000 RCF for 10 min to pellet the bacterial cells. The resulting pellet was frozen using liquid N2 and stored at −80 °C prior to DNA extraction. DNA extraction was performed using the DNeasy mericon Food Kit (Qiagen, Antwerp, Belgium) following the manufacturer’s instructions. The DNA concentration was measured using the Quantus Fluorometer (Promega Benelux, Leiden, The Netherlands) in combination with the NanoPhotometer N60 (Implen, München, Germany). Amplicon sequencing of the V3–V4 region of the 16S rRNA gene was performed as described by the Illumina protocol and with primers of Klindworth et al. [24] on an Illumina MiSeq sequencer with 2 × 300 bp reads (Admera Health, South Plainfield, NJ, USA) The sequencing data have been deposited to the BioProject accession number PRJNA734172 in the NCBI SRA database.
The amplicon sequencing dataset was demultiplexed by the sequence provider, and barcodes were clipped off. The processing pipeline was entirely performed in RStudio 1.3.1093. Reads were imported into R and the ShortRead package was used to remove primer sequences [25]. Then, quality filtering and trimming of the reads were done using the “filterAndTrim” function from the DADA2 package [26]. Forward and reverse reads were trimmed to 280 bp and 210 bp, respectively, and quality filtering was performed with a maximum expected error of 2 for the forward and 4 for the reverse reads, after which the reads were merged and count tables from the amplicon sequence variants (ASVs) were calculated. Taxonomy was assigned using the RDP naive Bayesian classifier [27] with SILVA v138 as reference database. Rarefaction curves were made using the “rarecurve” function of the Vegan package. Shannon–Wiener diversity and Chao1 richness indices were calculated using the Phyloseq package. Seventeen cooked ham samples and 14 cooked chicken samples were not used in the downstream analyses because no plateau phase was reached in the rarefaction curves. The bacterial diversity was compared between conditions by calculating the Bray–Curtis dissimilarities between all samples and constructing non-metric multidimensional scaling plots (NMDS ordination plots) using phyloseq [28].

2.6. Statistical Analysis

Analysis of variance (ANOVA, confidence interval 95%) and Tukey’s test were performed using Version 1.3.1093 (R Core Team, R Foundation for Statistical Computing, Vienna, Austria) to verify whether enumerations were significantly different (p-value < 0.05).

3. Results

3.1. Microbiological Analysis

The results for the microbiological enumerations (TAC, TANC and LAB) for cooked ham and cooked chicken were obtained (Figure 2 and Figure 3, respectively) and divided between samples taken of the 1-week old log, the 3-weeks old log and when these respective products were sliced and stored (see also Supplementary Table S1). Tukey’s test showed that, for each product type, there was no significant difference in bacterial counts between both storage times of the unsliced log and of the sliced products of logs sampled at day 0 and day 28, respectively. Additionally, for both product types, the counts for TAC, TANC and LAB (on both media; MRS and M17) were not significantly different at each analyzed production stage or sampling moment.
For cooked ham, the averages for TAC, TANC and LAB counted on MRS and M17 agar media varied between 7.1 and 8.6 log CFU/g at the end of the shelf-life period (Supplementary Table S1). Yeast and fungi were not often encountered; counts were below the enumeration limit in 70, 60 and 40% of the unsliced log, D0, and D28 samples, respectively. For cooked chicken samples, observations for TAC, TANC and LAB were largely similar, though average counts were slightly lower at the end of the shelf-life period than those of cooked ham, varying between 5.7 and 8.5 log CFU/g (Supplementary Table S1). Yeast and fungi were less often encountered in cooked chicken than in cooked ham samples. Despite the high levels of TAC, TANC and LAB at the end of the shelf-life period, only one sample (of cooked chicken) exhibited sensory changes at day 28, with visible slime formation and a sour smell. This was also the only sample of which counts for TAC, TANC and LAB on M17 agar exceeded 9 log CFU/g. Sulphite-reducing clostridia, Enterobacterales, Bacillus cereus, Brochothrix thermosphacta and Enterococcus spp. were rarely above the lower limit of enumeration (Table 1), but they occurred more often in the sliced cooked ham samples than in sliced cooked chicken.
Generally, slicing of the product had no immediate effect on pH and aw (Table 1). In cooked ham, a reduction in pH at day 28 was found, dropping from approximately 6.2 to 5.7. Changes in pH at the end of the shelf-life period were less pronounced or absent in cooked chicken, and mainly occurred in the logs sliced at the end of their shelf-life period.

3.2. Identification

Isolates from cooked ham and cooked chicken samples were identified at species level and the results are shown in Table 2 and Table 3, respectively, with confidence intervals for the top-three (if three were present) most abundant identifications determined per type of sample.
For the cooked ham samples, a total of 425 colonies was identified. In the unsliced log samples, B. thermosphacta was the most abundant species representing 48, 68 and 50% of the isolates obtained from PCA, RCA and MRS agar, respectively. After slicing, there was more diversity noticeable, but B. thermosphacta still showed the highest abundance, especially among RCA isolates (17, 50 and 21% of the isolates from PCA, RCA and MRS agar, respectively). Leuconostoc carnosum, although not found in the unsliced log samples, was also relatively well-represented after slicing (7, 13 and 58% of the PCA, RCA and MRS agar isolates, respectively). At the end of the shelf-life period, species diversity was lower, and L. carnosum was clearly highly abundant (76, 71 and 93% of the PCA, RCA and MRS agar isolates, respectively). Also always present at the end of the shelf-life period, albeit to a lesser extent, was Lactococcus piscium. The latter species represented 12, 7 and 4% of the PCA, RCA and MRS agar isolates, respectively. In contrast, B. thermosphacta could no longer be recovered.
For the cooked chicken samples, a total of 258 isolates was identified at species level. Latilactobacillus sakei was highly abundant throughout the shelf-life period, although this was less clearly demonstrated on PCA (representing 13, 37 and 38% of the isolates obtained from the initial log and at day 0 and 28, respectively) than on RCA (100, 75 and 39%, respectively) and MRS agar (78, 78 and 48%, respectively). Carnobacteria also showed a clear presence throughout the shelf-life period, although a successive pattern of different species was found. Carnobacterium inhibens was only found on the unsliced logs (31 and 22% of the isolates from PCA and MRS isolates, respectively) and at day 0 (26 and 11% of the PCA and MRS isolates, respectively). Carnobacterium maltaromaticum was first identified at day 0, representing 25% of the RCA isolates, and was still present at the end of the shelf-life period (6, 15 and 5% of PCA, RCA and MRS isolates, respectively). Carnobacterium divergens, on the other hand, was mostly dominant at the end of the shelf-life period, representing 23, 12 and 24% of the PCA, RCA and MRS isolates, respectively. Prior to that, it was only found at day 0 on MRS agar (11% of the isolates).
The changes in biodiversity indexes between isolates obtained from the unsliced log as well as the samples at day 0 and 28 are summarized in Figure 4. For cooked ham, a biodiversity increase was seen when comparing the microbiota on the unsliced logs to the ones found on the samples at day 0. At the end of the shelf-life period, the acquired species diversity narrowed down, with certain species clearly dominating the microbiota (i.e., L. carnosum in cooked ham samples and Lb. sakei in cooked chicken samples).
Gram-negative bacteria were rarely present at the end of the shelf-life period. None of the isolates from cooked ham obtained for the samples at day 28 showed any presence of Gram-negative bacteria. For cooked chicken, Serratia proteamaculans was identified but represented only 3% of the isolates obtained from RCA.
Identities of surface sample isolates taken from the cooked ham slicer and the cooked chicken slicer were also determined (Supplemental Figures S1 and S2, respectively). As such, 74% of the identified isolates from the cooked ham slicer corresponded with species that were also found in the cooked ham product. They showed a predominant presence of Enterococcus gilvus and Streptococcus parauberis. Coincidentally, E. gilvus was largely found in the cooked ham log before slicing, while merely two colonies were identified as S. parauberis in the cooked ham samples, both right after slicing. Brochothrix thermosphacta was only identified once in the environmental samples, while this organism was highly present in the product log and right after slicing. Additionally, surface samplings indicated the presence of C. divergens, C. maltaromaticum, L. carnosum, Psychrobacter maritimus, Psychrobacter faecalis and Vagococcus spp., all of which were not or barely found in the unsliced log samples but could be identified during the later stage of the cooked ham storage.
For the cooked chicken slicer, 72% of the identified isolates were identified as species that were also found in the cooked chicken product. The surface samples indicated the presence of, primarily, B. thermosphacta and Staphylococcus equorum. In cooked chicken samples, B. thermosphacta only occurred at the end of the shelf-life period while S. equorum was found just once after slicing. Carnobacterium divergens and C. maltaromaticum were also found in both the environmental samples and the chicken samples. Pseudomonas spp., Psychrobacter spp. and Enterococcus spp., in contrast, were only identified in the environment and not in the cooked chicken samples.

3.3. Metabarcoding

The metabarcoding analysis was performed on all samples of each product type (30 per type). Based on rarefaction curves, 13 cooked ham samples (3 of the unsliced log, 2 at day 0 and 8 at day 28) and 15 cooked chicken samples (3 of the unsliced log, 6 at day 0 and 6 at day 28) were retained for further analysis. The number of reads obtained per sample varied from 9051 to 341,236 reads, with an average of 81,489 reads per sample. Shannon–Wiener diversity and Chao1 richness indices are represented in Supplemental Figure S3. Figure 5 and Figure 6 show the relative population abundance at family and genus level, respectively. Early in the shelf-life period of both products, the genus Photobacterium was often present. In the cooked ham logs, its relative abundance varied between 43–49%; in cooked chicken logs, the relative abundance varied between 1–96%. The relative abundance of Photobacterium in samples from day 0 varied between 8–36% in the cooked ham samples and between 0–95% in the cooked chicken samples. At the end of the shelf-life period, the occurrence of Photobacterium was more rare, with a relative abundance below 5%, except in one of the eight analyzed cooked ham samples (57% relative abundance) and two of the six analyzed cooked chicken samples (22% and 83%).
In cooked ham, one genus other than Photobacterium was consistently found throughout the shelf-life period at a relative abundance of >5%, namely Pseudomonas. While the recovered taxa from the unsliced log samples showed clear similarities, the recovered taxa at day 0 of the two samples seemed more sample-specific with Aeribacillus, Enterococcus and Thermicanus being the most abundant taxa in one of the two analyzed samples and Acinetobacter, Pseudomonas, Photobacterium and Vibrio in the other. At the end of the shelf-life period, the genera Leuconostoc, Serratia and Lactococcus were most often recovered (i.e., in 7, 5 and 4 of the 8 analyzed samples and varying between 6–75%, 8–37% and 14–46%, respectively). Other more often recovered genera at D28 were Carnobacterium and Vibrio, each found in two samples.
The genera, besides Photobacterium, that were consistently recovered from the cooked chicken samples throughout the shelf-life period were reported as Lactobacillus (mainly found at day 28), and, similar to the cooked ham samples, Pseudomonas (mainly found at day 0). The taxa recovered and their abundance seemed sample-specific in cooked chicken, although similarities within batches could be found. Samples obtained from the unsliced logs in batch 3 and day-0 samples from batch 1 and 3 showed very similar recovered taxa and relative abundances within batches, while the other samples displayed different profiles. At the end of the shelf-life period, Lactobacillus was most often reported (in three of the six analyzed samples, at an abundance varying between 46–98%), followed by Carnobacterium (which was recovered in two samples at an abundance varying between 10–52%). Brochothrix was found in only one sample, but at the very high abundance of 96%.
Contrary to culture-dependent results, Gram-negative bacteria were more often recovered through metabarcoding analysis at the end of the shelf-life period. Acinetobacter, Photobacterium, Pseudomonas, Serratia and Vibrio were recovered in cooked ham samples at day 28, while cooked chicken samples from the same time point were characterized by the presence of Acinetobacter, Photobacterium, Pseudomonas and Psychrobacter.

4. Discussion

The microbiota of the two types of MAP cooked charcuterie products that were investigated in the present study were both dominated by LAB, whereas the counts of Gram-negative bacteria were relatively low. This is a common pattern for such products, when oxygen availability is low due to packaging [4,16,17,29]. In addition to this fairly trivial observation, more in-depth insights were obtained using a culture-dependent analysis, which were indicative of microbial interdependencies and related to the specificities of the products under study. Latilactobacillus sakei, for instance, is known to efficiently reduce the development of Enterobacterales in cooked ham [30], being a species that is cold-acclimatized and highly adapted to meat matrices [31,32]. In cooked chicken products, Lb. sakei was indeed typically dominant throughout the shelf-life period, in agreement with previous findings for MAP cooked poultry products [5,14], while Enterobacterales were not encountered. In contrast, Lb. sakei was only minimally present in cooked ham, whereas Enterobacterales were detectable in more than half of the investigated samples at the average level of 4 log CFU/g at the end of the shelf-life period. On the other hand, cooked ham samples were typified by the development of high levels of L. carnosum populations towards the end of the shelf-life period, in line with the fact that this is a commonly encountered species in such products [33,34]. Here, Leuconostoc spp. can be the cause of buttery aroma’s, slime formation, blowing of package and green discoloration [32,35]. Beyond their general acidifying effects, certain L. carnosum strains are known to display bioprotective potential, suppressing the growth of the background microbiota and limiting metabolite activity of B. thermosphacta in cooked ham environments [36]. Additionally, Lc. piscium was present, which has been shown to effectively reduce B. thermosphacta in cooked shrimp stored at 8 °C [37]. Be that as it may, presumable B. thermosphacta counts at the end of the shelf-life period of cooked ham were lower than for cooked chicken, where the presence of L. carnosum was minimal and Lc. piscium was not found.
Carnobacterium spp. were present in both products, usually throughout the shelf-life period. However, they were never able to manifest themselves to the same degree as was the case for Lb. sakei in cooked chicken or L. carnosum in cooked ham. Although carnobacteria, specifically C. divergens and C. maltaromaticum, are often described as meat spoilers when low-oxygen packaging is applied [32,38], they are less cold-adapted than Leuconostoc spp. and Lactobacillus spp. (or genera derived from Lactobacillus such as Latilactobacillus) and, therefore, less likely to prevail at temperatures below 12 °C [34]. An idiosyncratic finding for poultry was related to the high frequency in which Carnobacterium inhibens was identified in the unsliced log samples. This species is mostly recognized in relation to Atlantic salmon intestines, while carnobacteria in meat products usually belong to C. divergens and C. maltaromaticum [12,39,40]. The latter two species were nonetheless present at day 28.
Enterococci were only found in the samples derived from cooked ham, specifically at the end of the shelf-life period and in some product log samples. Enterococci can pose spoilage problems in cooked meats due to their ability to survive heating at 60 °C for 30 min [41,42]. Persistent presence of enterococci in cooked ham until the end of the shelf-life period has previously been reported, but only at elevated storage temperatures [43].
While the metabarcoding results were generally confirmative of the above-mentioned data obtained by the culture-dependent methods, for instance by identifying Leuconostoc and Lactococcus as the most abundant genera at the end of the shelf-life period, they also revealed some differences and added additional information. For instance, it was shown that most of the genera present, other than Leuconostoc, were more sample dependent than product dependent. In addition, the metabarcoding results only detected Enterococcus once with a significant relative abundance, while they yielded a high abundance of Vibrio, mainly found in the logs and at day 0, but also in some of the cooked ham samples from day 28. Raimondi et al. [13] also recovered Vibrio, which they further identified as V. rumoiensis, as a major component in cooked hams, mainly at the start of the shelf-life period. Vibrio spp. are normally considered marine bacteria, adapted to salty environments and commonly present on seafood products originating from warm water climates [44]. However, certain species still may show significant growth at temperatures below 7 °C [45].
The most revealing finding from the metabarcoding analysis, however, was that Photobacterium was often present in the unsliced logs and at day 0 for both the cooked ham and cooked chicken samples. Photobacteria have been found in meat products before when using culture-independent approaches [46,47,48,49,50], but usually they are more often associated with fish spoilage [51]. Numbers can be underestimated when general enumeration methods are used as a sufficiently high salt concentration in the growth medium is needed, and the bacteria can suffer heat injury when the pour plate technique is used [52]. Therefore, little is known at present about the spoilage-inducing abilities of photobacteria in meat products, although certain species are able to grow under anaerobic conditions at 0–20 °C, matching chilled MAP storage conditions [53]. Höll et al. [54] have predicted metabolic pathways in Photobacterium that could be relevant for spoilage and have found overlap with the metabolisms of the common meat spoilers Brochothrix and Carnobacterium. Even though Photobacterium was often recovered at the beginning of the shelf-life period, it nonetheless appeared to be outcompeted towards the end.
Both the culture-dependent and culture-independent techniques used in this study obviously come with their own set of advantages and disadvantages. One limit of the use of the former methodology is that certain viable organisms will not be cultivable under the conditions used, so that microorganisms such as Photobacterium risk being overlooked. Such microorganisms that are hard or impossible to cultivate with standard procedures can thus be recovered using the metabarcoding approach, resulting in a more complete overview of the product’s microbiota involved. A disadvantage, however, is that a reliable identification can only go down to genus level, and no distinction can be made between viable and non-viable bacteria. Another limit in this study, which affects both used methods, was the low abundance of microbiota present on the logs right before slicing. As enumerations in these types of samples are generally low, less information on the identity of the microbiota that are present can be amassed and certain bacteria can be overlooked when using the culture-dependent method. This is also an issue with the metabarcoding approach, as a high yield of pure DNA is desired after extraction to obtain reliable results, resulting in a large number of samples to be discarded after rarefaction curve analysis.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/microorganisms9061223/s1, Figure S1: Genus and species identity of isolates from the cooked ham slicer, Figure S2: Genus and species identity of isolates from the cooked chicken slicer, Figure S3: Chao1 and Shannon-Wiener diversity richness indices, Table S1: TAC, TANC and LAB counts at different production and sampling stages.

Author Contributions

Conceptualization, E.D., G.R., F.L., S.W., M.H., and K.D.R.; methodology, E.D., G.R., and K.D.R.; software, G.R.; formal analysis, E.D., G.R.; investigation, E.D.; resources, E.D.; data curation, E.D.; writing—original draft preparation, E.D.; writing—review and editing, G.R., F.L., S.W., M.H., and K.D.R.; visualization, E.D.; supervision, G.R., F.L., M.H., and K.D.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Raw 16S sequence reads are available at the Sequence Read Archive (SRA: https://www.ncbi.nlm.nih.gov/sra (accessed on 14 May 2021)) under the BioProject PRJNA734172.

Acknowledgments

The authors want to gratefully thank the FBO for their generous participation in this study. We would also like to thank Elly Engels, Kelly Van der Borght, Ann Van De Walle, Eline Dumoleijn, Sofie De Vlam, Annelies Haegeman, and Dries Schaumont for technical assistance.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. United Nations Environment Programme. Food Waste Index Report; United Nations Environment Programme: Nairobi, Kenya, 2021. [Google Scholar]
  2. Gustavsson, J.; Cederberg, C.; Sonesson, U.; Van Otterdijk, R.; Meybeck, A. Global Food Losses and Food Waste—Extent, Causes and Prevention; Food and Agriculture Organization of the United Nations: Rome, Italy, 2011. [Google Scholar]
  3. Food and Agriculture Organization. Food Wastage Footprint; Food and Agriculture Organization of the United Nations: Rome, Italy, 2013; ISBN 978-92-5-107752-8. [Google Scholar]
  4. Pellissery, A.J.; Vinayamohan, P.G.; Amalaradjou, M.A.R.; Venkitanarayanan, K. Spoilage Bacteria and Meat Quality; Elsevier Inc.: Amsterdam, The Netherlands, 2019; ISBN 9780128192337. [Google Scholar]
  5. Audenaert, K.; D’Haene, K.; Messens, K.; Ruyssen, T.; Vandamme, P.; Huys, G. Diversity of lactic acid bacteria from modified atmosphere packaged sliced cooked meat products at sell-by date assessed by PCR-denaturing gradient gel electrophoresis. Food Microbiol. 2010, 27, 12–18. [Google Scholar] [CrossRef]
  6. Kreyenschmidt, J.; Hübner, A.; Beierle, E.; Chonsch, L.; Scherer, A.; Petersen, B. Determination of the shelf life of sliced cooked ham based on the growth of lactic acid bacteria in different steps of the chain. J. Appl. Microbiol. 2010, 108, 510–520. [Google Scholar] [CrossRef]
  7. Gänzle, M.G. Lactic metabolism revisited: Metabolism of lactic acid bacteria in food fermentations and food spoilage. Curr. Opin. Food Sci. 2015, 2, 106–117. [Google Scholar] [CrossRef]
  8. Björkroth, K.J.; Korkeala, H.J. Use of rRNA gene restriction patterns to evaluate lactic acid bacterium contamination of vacuum-packaged sliced cooked whole-meat product in a meat processing plant. Appl. Environ. Microbiol. 1997, 63, 448–453. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Borch, E.; Kant-Muermans, M.L.; Blixt, Y. Bacterial spoilage of meat and cured meat products. Int. J. Food Microbiol. 1996, 33, 103–120. [Google Scholar] [CrossRef]
  10. Comi, G.; Iacumin, L. Identification and process origin of bacteria responsible for cavities and volatile off-flavour compounds in artisan cooked ham. Int. J. Food Sci. Technol. 2012, 47, 114–121. [Google Scholar] [CrossRef]
  11. Vasilopoulos, C.; De Vuyst, L.; Leroy, F. Shelf-life reduction as an emerging problem in cooked hams underlines the need for improved preservation strategies. Crit. Rev. Food Sci. Nutr. 2015, 55, 1425–1443. [Google Scholar] [CrossRef]
  12. Samelis, J. Managing microbial spoilage in the meat industry. In Food Spoilage Microorganisms; Woodhead Publishing Limited: Sawston, UK, 2006; pp. 213–286. ISBN 9781855739666. [Google Scholar]
  13. Raimondi, S.; Luciani, R.; Sirangelo, T.M.; Amaretti, A.; Leonardi, A.; Ulrici, A.; Foca, G.; D’Auria, G.; Moya, A.; Zuliani, V.; et al. Microbiota of sliced cooked ham packaged in modified atmosphere throughout the shelf life: Microbiota of sliced cooked ham in MAP. Int. J. Food Microbiol. 2019, 289, 200–208. [Google Scholar] [CrossRef] [PubMed]
  14. Geeraerts, W.; Pothakos, V.; De Vuyst, L.; Leroy, F. Variability within the dominant microbiota of sliced cooked poultry products at expiration date in the Belgian retail. Food Microbiol. 2018, 73, 209–215. [Google Scholar] [CrossRef]
  15. Rodríguez-Pérez, M.R.; Zurera-Cosano, G.; García-Gimeno, R.M.; Barco-Alcalá, E.; Castdllejo-Rodríguez, A.M. Sensory and microbiological quality evaluation of vacuum-packed sliced cooked chicken breast. Shelf-life estimation. J. Food Qual. 2003, 26, 105–122. [Google Scholar] [CrossRef]
  16. Opara, U.L.; Caleb, O.J.; Belay, Z.A. Modified Atmosphere Packaging for Food Preservation; Elsevier Inc.: Amsterdam, The Netherlands, 2019; ISBN 9780128171905. [Google Scholar]
  17. Hauzoukim, S.S.; Biswajit, M. Modified Atmosphere Packaging of Fish and Fish Products. J. Entomol. Zool. Stud. 2020, 8, 651–659. [Google Scholar] [CrossRef]
  18. Cocolin, L.; Mataragas, M.; Bourdichon, F.; Doulgeraki, A.; Pilet, M.F.; Jagadeesan, B.; Rantsiou, K.; Phister, T. Next generation microbiological risk assessment meta-omics: The next need for integration. Int. J. Food Microbiol. 2018, 287, 10–17. [Google Scholar] [CrossRef]
  19. Jagadeesan, B.; Gerner-Smidt, P.; Allard, M.W.; Leuillet, S.; Winkler, A.; Xiao, Y.; Chaffron, S.; Van Der Vossen, J.; Tang, S.; Katase, M.; et al. The use of next generation sequencing for improving food safety: Translation into practice. Food Microbiol. 2019, 79, 96–115. [Google Scholar] [CrossRef]
  20. Rouger, A.; Moriceau, N.; Prévost, H.; Remenant, B.; Zagorec, M. Diversity of bacterial communities in French chicken cuts stored under modified atmosphere packaging. Food Microbiol. 2018, 70, 7–16. [Google Scholar] [CrossRef] [PubMed]
  21. Strandén, A.; Frei, R.; Widmer, A.F. Molecular typing of methicillin-resistant Staphylococcus aureus: Can PCR replace pulsed-field gel electrophoresis? J. Clin. Microbiol. 2003, 41, 3181–3186. [Google Scholar] [CrossRef] [Green Version]
  22. Rademaker, J.L.W.; Louws, F.J.; Versalovic, J.; De Bruijn, F.J. Characterization of the diversity of ecological important microbes by rep-PCR genomic fingerprinting. In Molecular Microbial Ecolog Manual, 2nd ed.; Kluwer Academic Publishers: Dordrecht, The Netherlands, 2004; pp. 1–33. [Google Scholar]
  23. Bandeira, B.; Jamet, J.L.; Jamet, D.; Ginoux, J.M. Mathematical convergences of biodiversity indices. Ecol. Indic. 2013, 29, 522–528. [Google Scholar] [CrossRef]
  24. Klindworth, A.; Pruesse, E.; Schweer, T.; Peplies, J.; Quast, C.; Horn, M.; Glöckner, F.O. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013, 41, e1. [Google Scholar] [CrossRef] [PubMed]
  25. Morgan, M.; Anders, S.; Lawrence, M.; Aboyoun, P.; Pagès, H.; Gentleman, R. ShortRead: A bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 2009, 25, 2607–2608. [Google Scholar] [CrossRef] [Green Version]
  26. Callahan, B.J.; McMurdie, P.J.; Rosen, M.J.; Han, A.W.; Johnson, A.J.A.; Holmes, S.P. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 2016, 13, 581–583. [Google Scholar] [CrossRef] [Green Version]
  27. Wang, Q.; Garrity, G.M.; Tiedje, J.M.; Cole, J.R. Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy. Appl. Environ. Microbiol. 2007, 73, 5261–5267. [Google Scholar] [CrossRef] [Green Version]
  28. McMurdie, P.J.; Holmes, S. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE 2013, 8, e0061217. [Google Scholar] [CrossRef] [Green Version]
  29. Yost, C.K.; Nattress, F.M. Molecular typing techniques to characterize the development of a lactic acid bacteria community on vacuum-packaged beef. Int. J. Food Microbiol. 2002, 72, 97–105. [Google Scholar] [CrossRef]
  30. Héquet, A.; Laffitte, V.; Brocail, E.; Aucher, W.; Cenatiempo, Y.; Frère, J.; Fremaux, C.; Berjeaud, J.M. Development of a new method for the detection of lactic acid bacteria capable of protecting ham against Enterobacteriaceae. Lett. Appl. Microbiol. 2009, 48, 668–674. [Google Scholar] [CrossRef]
  31. Björkroth, J.; Ridell, J.; Korkeala, H. Characterization of Lactobacillus sake strains associating with production of ropy slime by randomly amplified polymorphic DNA (RAPD) and pulsed-field gel electrophoresis (PFGE) patterns. Int. J. Food Microbiol. 1996, 31, 59–68. [Google Scholar] [CrossRef]
  32. Pothakos, V.; Devlieghere, F.; Villani, F.; Björkroth, J.; Ercolini, D. Lactic acid bacteria and their controversial role in fresh meat spoilage. Meat Sci. 2015, 109, 66–74. [Google Scholar] [CrossRef]
  33. Samelis, J.; Björkroth, J.; Kakouri, A.; Rementzis, J. Leuconostoc carnosum Associated with Spoilage of Refrigerated Whole Cooked Hams in Greece. J. Food Prot. 2016, 69, 2268–2273. [Google Scholar] [CrossRef]
  34. Dušková, M.; Kameník, J.; Lačanin, I.; Šedo, O.; Zdráhal, Z. Lactic acid bacteria in cooked hams—Sources of contamination and chances of survival in the product. Food Control 2016, 61, 1–5. [Google Scholar] [CrossRef]
  35. Andreevskaya, M.; Jääskeläinen, E.; Johansson, P.; Ylinen, A.; Paulin, L.; Björkroth, J.; Auvinen, P. Food Spoilage-Associated Leuconostoc, Lactococcus, and Lactobacillus Species Display Different Survival Strategies in Response to Competition. Appl. Environ. Microbiol. 2018, 84, 1–16. [Google Scholar] [CrossRef] [Green Version]
  36. Vasilopoulos, C.; De Mey, E.; Dewulf, L.; Paelinck, H.; De Smedt, A.; Vandendriessche, F.; De Vuyst, L.; Leroy, F. Interactions between bacterial isolates from modified-atmosphere-packaged artisan-type cooked ham in view of the development of a bioprotective culture. Food Microbiol. 2010, 27, 1086–1094. [Google Scholar] [CrossRef]
  37. Fall, P.A.; Leroi, F.; Cardinal, M.; Chevalier, F.; Pilet, M.F. Inhibition of Brochothrix thermosphacta and sensory improvement of tropical peeled cooked shrimp by Lactococcus piscium CNCM I-4031. Lett. Appl. Microbiol. 2010, 50, 357–361. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Casaburi, A.; Piombino, P.; Nychas, G.J.; Villani, F.; Ercolini, D. Bacterial populations and the volatilome associated to meat spoilage. Food Microbiol. 2015, 45, 83–102. [Google Scholar] [CrossRef] [PubMed]
  39. Laursen, B.G.; Bay, L.; Cleenwerck, I.; Vancanneyt, M.; Swings, J.; Dalgaard, P.; Leisner, J.J. Carnobacterium divergens and Carnobacterium maltaromaticum as spoilers or protective cultures in meat and seafood: Phenotypic and genotypic characterization. Syst. Appl. Microbiol. 2005, 28, 151–164. [Google Scholar] [CrossRef]
  40. Leisner, J.J.; Laursen, B.G.; Prévost, H.; Drider, D.; Dalgaard, P. Carnobacterium: Positive and negative effects in the environment and in foods. FEMS Microbiol. Rev. 2007, 31, 592–613. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Ben Braïek, O.; Smaoui, S. Enterococci: Between Emerging Pathogens and Potential Probiotics. BioMed Res. Int. 2019, 2019, 5938210. [Google Scholar] [CrossRef]
  42. Malinowska-Pańczyk, E.; Królik, K.; Skorupska, K.; Puta, M.; Martysiak-Żurowska, D.; Kiełbratowska, B. Microwave heat treatment application to pasteurization of human milk. Innov. Food Sci. Emerg. Technol. 2019, 52, 42–48. [Google Scholar] [CrossRef]
  43. Vasilopoulos, C.; Ravyts, F.; De Maere, H.; De Mey, E.; Paelinck, H.; De Vuyst, L.; Leroy, F. Evaluation of the spoilage lactic acid bacteria in modified-atmosphere- packaged artisan-type cooked ham using culture-dependent and culture-independent approaches. J. Appl. Microbiol. 2008, 104, 1341–1353. [Google Scholar] [CrossRef]
  44. Petruzzi, L.; Corbo, M.R.; Sinigaglia, M.; Bevilacqua, A. Microbial Spoilage of Foods: Fundamentals; Elsevier Ltd.: Amsterdam, The Netherlands, 2017; ISBN 9780081005033. [Google Scholar]
  45. Lages, M.A.; Balado, M.; Lemos, M.L. The expression of virulence factors in vibrio anguillarum is dually regulated by iron levels and temperature. Front. Microbiol. 2019, 10, 1–11. [Google Scholar] [CrossRef] [Green Version]
  46. Pennacchia, C.; Ercolini, D.; Villani, F. Spoilage-related microbiota associated with chilled beef stored in air or vacuum pack. Food Microbiol. 2011, 28, 84–93. [Google Scholar] [CrossRef]
  47. Delhalle, L.; Korsak, N.; Taminiau, B.; Nezer, C.; Burteau, S.; Delcenserie, V.; Poullet, J.B.; Daube, G. Exploring the Bacterial Diversity of Belgian Steak Tartare Using Metagenetics and Quantitative Real-Time PCR Analysis. J. Food Prot. 2016, 79, 220–229. [Google Scholar] [CrossRef]
  48. Stoops, J.; Ruyters, S.; Busschaert, P.; Spaepen, R.; Verreth, C.; Claes, J.; Lievens, B.; Van Campenhout, L. Bacterial community dynamics during cold storage of minced meat packaged under modified atmosphere and supplemented with different preservatives. Food Microbiol. 2015, 48, 192–199. [Google Scholar] [CrossRef]
  49. Hilgarth, M.; Fuertes, S.; Ehrmann, M.; Vogel, R.F. Photobacterium carnosum sp. nov., isolated from spoiled modified atmosphere packaged poultry meat. Syst. Appl. Microbiol. 2018, 41, 44–50. [Google Scholar] [CrossRef] [PubMed]
  50. Nieminen, T.T.; Dalgaard, P.; Björkroth, J. Volatile organic compounds and Photobacterium phosphoreum associated with spoilage of modified-atmosphere-packaged raw pork. Int. J. Food Microbiol. 2016, 218, 86–95. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. Labella, A.M.; Arahal, D.R.; Castro, D.; Lemos, M.L.; Borrego, J.J. Revisiting the genus Photobacterium: Taxonomy, ecology and pathogenesis. Int. Microbiol. 2017, 20, 1–10. [Google Scholar] [CrossRef] [PubMed]
  52. Betts, G. Other Spoilage Bacteria; Woodhead Publishing Limited: Sawston, UK, 2006; ISBN 9781855739666. [Google Scholar]
  53. Fuertes-Perez, S.; Hauschild, P.; Hilgarth, M.; Vogel, R.F. Biodiversity of Photobacterium spp. Isolated From Meats. Front. Microbiol. 2019, 10, 1–13. [Google Scholar] [CrossRef] [PubMed]
  54. Höll, L.; Hilgarth, M.; Geissler, A.J.; Behr, J.; Vogel, R.F. Prediction of in situ metabolism of photobacteria in modified atmosphere packaged poultry meat using metatranscriptomic data. Microbiol. Res. 2019, 222, 52–59. [Google Scholar] [CrossRef]
Microorganisms 09 01223 g001 550
Figure 1. Schematic overview of sample collection procedure, resulting in six samples per batch. This scheme is applicable for each of the product types (per product type, five batches were analyzed).
Figure 1. Schematic overview of sample collection procedure, resulting in six samples per batch. This scheme is applicable for each of the product types (per product type, five batches were analyzed).
Microorganisms 09 01223 g001
Microorganisms 09 01223 g002 550
Figure 2. Log(CFU/g) values for total psychrotolerant and psychrophilic aerobic (TAC), and anaerobic counts (TANC) and lactic acid bacteria counts (M17 and MRS agar) found on cooked ham samples. The boxplots represent the 1st quartile, median, 3rd quartile and, if applicable, outliers of each dataset. Each boxplot contains five data points, except for when a sample was below the enumeration limit of 1 log CFU/g. In these cases, n will deviate. Deviation in data points is visualized by pie charts: white representing the countable samples and black representing the samples below the enumeration limit.
Figure 2. Log(CFU/g) values for total psychrotolerant and psychrophilic aerobic (TAC), and anaerobic counts (TANC) and lactic acid bacteria counts (M17 and MRS agar) found on cooked ham samples. The boxplots represent the 1st quartile, median, 3rd quartile and, if applicable, outliers of each dataset. Each boxplot contains five data points, except for when a sample was below the enumeration limit of 1 log CFU/g. In these cases, n will deviate. Deviation in data points is visualized by pie charts: white representing the countable samples and black representing the samples below the enumeration limit.
Microorganisms 09 01223 g002
Microorganisms 09 01223 g003 550
Figure 3. Log(CFU/g) values for total psychrotolerant and psychrophilic aerobic (TAC) and anaerobic counts (TANC) and lactic acid bacteria counts (M17 and MRS agar) found on cooked chicken samples. The boxplots represent the 1st quartile, median, 3rd quartile and, if applicable, outliers of each dataset. Each boxplot contains five data points, except for when a sample was below the enumeration limit of 1 log CFU/g. In these cases, n will deviate. Deviation in data points is visualized by pie charts: white representing the countable samples and black representing the samples below the enumeration limit.
Figure 3. Log(CFU/g) values for total psychrotolerant and psychrophilic aerobic (TAC) and anaerobic counts (TANC) and lactic acid bacteria counts (M17 and MRS agar) found on cooked chicken samples. The boxplots represent the 1st quartile, median, 3rd quartile and, if applicable, outliers of each dataset. Each boxplot contains five data points, except for when a sample was below the enumeration limit of 1 log CFU/g. In these cases, n will deviate. Deviation in data points is visualized by pie charts: white representing the countable samples and black representing the samples below the enumeration limit.
Microorganisms 09 01223 g003
Microorganisms 09 01223 g004 550
Figure 4. Biodiversity indexes for number of identified species found based on total psychrotolerant and psychrophilic aerobic (TAC) and anaerobic counts (TANC) and lactic acid bacteria counts (MRS agar) for cooked ham and cooked chicken at the different sampling moments.
Figure 4. Biodiversity indexes for number of identified species found based on total psychrotolerant and psychrophilic aerobic (TAC) and anaerobic counts (TANC) and lactic acid bacteria counts (MRS agar) for cooked ham and cooked chicken at the different sampling moments.
Microorganisms 09 01223 g004
Microorganisms 09 01223 g005 550
Figure 5. Cumulated histograms of the relative abundance of taxa identified by metabarcoding at family levels for cooked ham and cooked chicken samples. The taxa representing <5% in relative abundance were merged in the category “Others”. Sample codes: H, cooked ham; C, cooked chicken. B, batch number; L, time of slicing at either one (L1) or three weeks after log production (L3); and D, time of analysis at either the day of slicing (D0) or after 28 days of storage (D28).
Figure 5. Cumulated histograms of the relative abundance of taxa identified by metabarcoding at family levels for cooked ham and cooked chicken samples. The taxa representing <5% in relative abundance were merged in the category “Others”. Sample codes: H, cooked ham; C, cooked chicken. B, batch number; L, time of slicing at either one (L1) or three weeks after log production (L3); and D, time of analysis at either the day of slicing (D0) or after 28 days of storage (D28).
Microorganisms 09 01223 g005
Microorganisms 09 01223 g006 550
Figure 6. Chord diagrams of the relative abundance of taxa identified by metabarcoding at genus level for cooked ham and cooked chicken samples. The taxa representing <5% in relative abundance were merged in the category “Others”. Sample codes: H, cooked ham; C, cooked chicken; B, batch number; L, time of slicing at either one (L1) or three weeks after log production (L3); and D, time of analysis at either the day of slicing (D0) or after 28 days of storage (D28).
Figure 6. Chord diagrams of the relative abundance of taxa identified by metabarcoding at genus level for cooked ham and cooked chicken samples. The taxa representing <5% in relative abundance were merged in the category “Others”. Sample codes: H, cooked ham; C, cooked chicken; B, batch number; L, time of slicing at either one (L1) or three weeks after log production (L3); and D, time of analysis at either the day of slicing (D0) or after 28 days of storage (D28).
Microorganisms 09 01223 g006
Table
Table 1. Values at three different production stages [unsliced log, after slicing (D0) and at end of the shelf-life period (D28)] for yeast and fungi (OGYE), sulphite-reducing clostridia (TSC), Enterobacterales (VRBG), presumptive Bacillus cereus (MYP), Brochothrix thermosphacta (STAA) and Enterococcus (Slanetz & Bartley), as well as the corresponding average (in bold) and standard deviation for pH and aw values. For microbial counts, mean (in bold) and standard deviation log (CFU/g) values are given when there is more than one countable sample. In other instances, the log CFU/g value is given, in case of only one sample being countable, or the value is assigned as not detectable, when no samples were countable. The percentage of countable samples for each parameter was calculated based on the total number of samples taken at this specific production stage.
Table 1. Values at three different production stages [unsliced log, after slicing (D0) and at end of the shelf-life period (D28)] for yeast and fungi (OGYE), sulphite-reducing clostridia (TSC), Enterobacterales (VRBG), presumptive Bacillus cereus (MYP), Brochothrix thermosphacta (STAA) and Enterococcus (Slanetz & Bartley), as well as the corresponding average (in bold) and standard deviation for pH and aw values. For microbial counts, mean (in bold) and standard deviation log (CFU/g) values are given when there is more than one countable sample. In other instances, the log CFU/g value is given, in case of only one sample being countable, or the value is assigned as not detectable, when no samples were countable. The percentage of countable samples for each parameter was calculated based on the total number of samples taken at this specific production stage.
OGYETSCVRBGMYPSTAASlanetz & Bartley
pHawCountable (%)Mean ± SDCountable (%)Mean ± SDCountable (%)Mean ± SDCountable (%)Mean ± SDCountable (%)Mean ± SDCountable (%)Mean ± SD
Cooked ham
1 week
Log6.18 ± 0.090.9752 ± 0.00282 (40)1.0 ± 0.00 (0)na0 (0)na0 (0)na0 (0)na0 (0)na
D06.11 ± 0.160.9759 ± 0.00372 (40)1.3 ± 0.50 (0)na0 (0)na0 (0)na1 (20)1.90 (0)na
D285.73 ± 0.180.9738 ± 0.00422 (40)2.0 ± 0.10 (0)na3 (60)4.4 ± 1.10 (0)na2 (40)4.2 ± 1.72 (40)5.5 ± 0.1
3 weeks
Log6.17 ± 0.160.9755 ± 0.00171 (20)1.01 (20)2.00 (0)na1 (20)1.00 (0)na0 (0)na
D06.23 ± 0.150.9756 ± 0.00332 (40)1.0 ± 0.00 (0)na0 (0)na0 (0)na0 (0)na1 (20)1.3
D285.74 ± 0.100.9742 ± 0.00264 (80)2.4 ± 1.10 (0)na4 (80)3.9 ± 0.70 (0)na3 (60)4.1 ± 2.13 (60)5.7 ± 0.3
Cooked chicken
1 week
Log6.04 ± 0.250.9727 ± 0.00190 (0)Na a1 (20)1.00 (0)na0 (0)na0 (0)na0 (0)na
D06.08 ± 0.080.9735 ± 0.00210 (0)na0 (0)na0 (0)na0 (0)na0 (0)na0 (0)na
D286.04 ± 0.390.9729 ± 0.00422 (40)1.6 ± 0.50 (0)na0 (0)na0 (0)na2 (40)6.2 ± 1.40 (0)na
3 weeks
Log6.21 ± 0.110.9730 ± 0.00282 (40)1.5 ± 0.70 (0)na0 (0)na0 (0)na0 (0)na0 (0)na
D06.18 ± 0.160.9719 ± 0.00150 (0)na0 (0)na0 (0)na0 (0)na0 (0)na0 (0)na
D285.90 ± 0.400.9711 ± 0.00432 (40)3.3 ± 2.30 (0)na0 (0)na1 (20)1.01 (20)8.10 (0)na
a Not applicable.
Table
Table 2. Genus and presumptive species identity of isolates from PCA, RCA and MRS agar media for the cooked ham samples. Percentages of isolates were calculated on the total of identified isolates per medium. Confidence intervals for percentages of number of identified isolates are calculated for the three most abundant (if three were present) identifications per sampling moment from PCA, RCA and MRS agar media and are presented between square brackets.
Table 2. Genus and presumptive species identity of isolates from PCA, RCA and MRS agar media for the cooked ham samples. Percentages of isolates were calculated on the total of identified isolates per medium. Confidence intervals for percentages of number of identified isolates are calculated for the three most abundant (if three were present) identifications per sampling moment from PCA, RCA and MRS agar media and are presented between square brackets.
Presumptive SpeciesIsolates from Cooked Ham (n = 425)
Log (n = 64)D0 (n = 125)D28 (n = 236)
PCA (%)
n = 31		RCA (%)
n = 21		MRS (%)
n = 12		PCA (%)
n = 54		RCA (%)
n = 38		MRS (%)
n = 33		PCA (%)
n = 78		RCA (%)
n = 86		MRS (%)
n = 72
Gram positive (n = 381)
Arthrobacter glacialis0001 (2)00000
Arthrobacter psychrochitiniphilus1 (3)00000000
Brochothrix thermosphacta15 (48 [31–66])15 (68 [49–88])6 (50 [22–78])9 (17 [9–27])19 (50 [34–66])7 (21 [7–35])000
Carnobacterium divergens00002 (5)1 (3)1 (1)3 (4)0
Carnobacterium maltaromaticum1 (3)0002 (5)1 (3)8 (10 [4–17])5 (6 [1–11])0
Corynebacterium testudinoris0002 (4)00000
Enterococcus devriesei02 (11 [0–24])001 (3)0000
Enterococcus gilvus2 (7)06 (50 [22–78])00002 (2)0
Enterococcus malodoratus000001 (3)001 (1)
Latilactobacillus fuchuensis000001 (3)01 (1)0
Latilactobacillus sakei00000002 (2)0
Lactococcus piscium0002 (4)3 (8 [0–17])2 (6 [0–14])9 (12 [4–19])6 (7 [2–12])3 (4 [0–9])
Leucobacter aridicollis1 (3)00000000
Leucobacter spp.2 (6)00000000
Leucobacter luti0001 (2)00000
Leuconostoc carnosum0004 (7 [0–14])5 (13 [2–24])19 (58 [41–74])59 (76 [66–85])61 (71 [61–81])67 (93 [87–99])
Leuconostoc gelidum000001 (3)04 (5)1 (1)
Luteococcus japonicas00002 (5)0000
Microbacterium oxydans0001 (2)00000
Paeniglutamicibacter antarcticus0001 (2)00000
Staphylococcus saprophyticus0002 (4)00000
Streptococcus parauberis0001 (2)1 (3)0000
Vagococcus fluvialis0000001 (1)1 (1)0
Vagococcus salmoninarum00001 (3)0000
Vagococcus spp.00000001 (1)0
Gram negative (n = 44)
Brevundimonas intermedia0001 (2)00000
Comamonas koreensis0001 (2)00000
Flavobacterium ardleyense0003 (6)00000
Limnohabitans planktonicus0001 (2)00000
Paracoccus aminophilus0001 (2)00000
Proteus terrae0002 (4)00000
Pseudochrobactrum spp.0001 (2)00000
Pseudomonas canadensis0002 (4)00000
Pseudomonas gessardii0002 (2)00000
Pseudomonas lactis0001 (2)00000
Pseudomonas psychrophila0001 (2)00000
Pseudomonas mandelii3 (10 [0–20])00000000
Pseudomonas thivervalensis1 (3)00000000
Psychrobacter faecalis0001 (2)00000
Psychrobacter maritimus0008 (15 [5–24])00000
Psychrobacter pulmonis0001 (2)00000
Rhizobium radiobacter0001 (12)00000
Serratia myotis5 (16 [3–29])4 (21 [4–39])002 (5)0000
Shewanella putrefaciens0001 (2)00000
Sphingobacterium anhuiense0001 (2)00000
Sphingobacterium faecium0001 (2)00000
Table
Table 3. Genus and presumptive species identity of isolates from PCA, RCA and MRS agar media for the cooked chicken samples. Percentages of isolates were calculated on the total of identified isolates per medium. Confidence intervals for percentages of number of identified isolates are calculated for the three most abundant (if three were present) identifications per sampling moment from PCA, RCA and MRS agar media and are presented between square brackets.
Table 3. Genus and presumptive species identity of isolates from PCA, RCA and MRS agar media for the cooked chicken samples. Percentages of isolates were calculated on the total of identified isolates per medium. Confidence intervals for percentages of number of identified isolates are calculated for the three most abundant (if three were present) identifications per sampling moment from PCA, RCA and MRS agar media and are presented between square brackets.
Presumptive SpeciesIsolates from Cooked Chicken (n = 258)
Log (n = 30)D0 (n = 36)D28 (n = 192)
PCA (%)
n = 13		RCA (%)
n = 8		MRS (%)
n = 9		PCA (%)
n = 19		RCA (%)
n = 8		MRS (%)
n = 9		PCA (%)
n = 64		RCA (%)
n = 66		MRS (%)
n = 62
Gram positive (n = 247)
Bacillus licheniformis1 (8)00000000
Brochothrix thermosphacta00000014 (22 [12–32])16 (24 [14–35])6 (10 [2–17])
Carnobacterium divergens000001 (11)15 (23 [13–34])8 (12 [7–24])15 (24 [14–35])
Carnobacterium inhibens4 (31 [6–56])02 (22 [0–49])5 (26 [7–46])01 (11)000
Carnobacterium maltaromaticum00002 (25 [0–55])04 (6)10 (15)3 (5)
Enterococcus malodoratus000000001 (2)
Enterococcus viikkiensis0000001 (2)00
Kocuria rhizophila0001 (5)00000
Latilactobacillus sakei2 (13 [0–35])8 (100 [100])7 (78 [51–100])7 (37 [15–59])6 (75 [45–100])7 (78 [51–100])24 (38 [26–49])27 (41 [28–51])30 (48 [36–61])
Leuconostoc carnosum0000001 (2)1 (2)1 (2)
Micrococcus caseolyticus1 (8)00000000
Staphylococcus spp.0001 (5)00000
Staphylococcus sciuri0001 (5)00000
Vagococcus spp.000000001 (2)
Vagococcus silagei0000005 (8)2 (3)5 (8)
Gram negative (n = 11)
Acinetobacter albensis0002 (11 [0–24])00000
Comamonas spp.0001 (5)00000
Pseudomonas migulae1 (8)00000000
Pseudomonas weihenstephanensis1 (8)00000000
Psychrobacter maritimus0001 (5)00000
Ralstonia mannitolilytica1 (8)00000000
Serratia proteamaculans00000002 (3)0
Sphingobacterium cladoniae1 (8)00000000
Sphingobacterium pakistanense1 (8)00000000
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Duthoo, E.; Rasschaert, G.; Leroy, F.; Weckx, S.; Heyndrickx, M.; De Reu, K. The Microbiota of Modified-Atmosphere-Packaged Cooked Charcuterie Products throughout Their Shelf-Life Period, as Revealed by a Complementary Combination of Culture-Dependent and Culture-Independent Analysis. Microorganisms 2021, 9, 1223. https://doi.org/10.3390/microorganisms9061223

AMA Style

Duthoo E, Rasschaert G, Leroy F, Weckx S, Heyndrickx M, De Reu K. The Microbiota of Modified-Atmosphere-Packaged Cooked Charcuterie Products throughout Their Shelf-Life Period, as Revealed by a Complementary Combination of Culture-Dependent and Culture-Independent Analysis. Microorganisms. 2021; 9(6):1223. https://doi.org/10.3390/microorganisms9061223

Chicago/Turabian Style

Duthoo, Evelyne, Geertrui Rasschaert, Frédéric Leroy, Stefan Weckx, Marc Heyndrickx, and Koen De Reu. 2021. "The Microbiota of Modified-Atmosphere-Packaged Cooked Charcuterie Products throughout Their Shelf-Life Period, as Revealed by a Complementary Combination of Culture-Dependent and Culture-Independent Analysis" Microorganisms 9, no. 6: 1223. https://doi.org/10.3390/microorganisms9061223

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop