A Survey of the Microbial Communities of Commercial Presliced, Packaged Deli-Style Ham Throughout Storage

Introduction

Meat spoilage is characterized as a change in the product rendering it unacceptable to the consumer, whether from chemical, biological, or physical change. Bacterial spoilage manifests itself as visible growth, textural changes, or off-odors and flavors caused by bacterial growth (Gram et al., 2002). Refrigeration and packaging type are 2 of the biggest contributors in selective growth of microorganisms (Doulgeraki et al., 2012); however, when placed under similar storage conditions, more precise factors may cause significant changes in the spoilage community, thus altering shelf life as well as the severity of spoilage defects. Lactic acid bacteria are typically identified as the primary contributors to spoilage in cooked meats stored under refrigeration and modified atmosphere packaging (Geeraerts et al., 2017, 2018); however, there may still be great variability of the bacterial community composition within lactic acid bacteria. Additionally, members of the genus Pseudomonas have been identified as specific spoilage organisms (SSOs) in vacuum-packaged products, disrupting the notion that pseudomonads are obligate aerobes (Bower et al., 2018). These findings surmount the fact that the microbial community of processed meats is more complex and adaptable than previously understood. With such mixed results regarding the dominant spoilage communities of cooked meats, steps should be taken to further characterize the predicted microbiome associated with cooked meat products under similar storage conditions.

The purpose of this study was to evaluate the spoilage microbiota of case-ready sliced and packaged ham available in the retail market. Sources of SSOs and factors modulating their growth rate are variable and contested. Miller and McMullen (2015) suggest that when comparing deli products of varying sodium concentration, the genera present on meat samples is specific to individual processing facilities. In cooked sausage, however, Hultman et al. (2015) suggest that the spoilage microbiome is more similar to that of the meat batter than of the processing environment. Tracing route of contamination and identifying processes that modulate the bacterial community of processed meats may allow for “precision shelf-life extension.” By knowing which taxa are present in specific systems, best practices can be enacted for microbial control. The aim of this study was to determine differences in the microbiota of products from the same product category (presliced ham, water-added product in gas flushed packaging) based on the variation between the postlethality processing environments of various manufacturers in the United States.

Materials and Methods

Results

Results for physiochemical metrics are displayed in Table 2, with additional storage time by brand interaction effect described in Table 3. Measures of meat pH and objective color (CIE L*, a*, b*) were recorded throughout storage time. There was a brand by storage time interaction for pH (P = 0.021), where Brand B at Week 4 had decreased pH compared with other treatments (Table 3). There were no main effects or interaction for L* or a* (P ≥ 0.244), but there was a main effect of brand for b* (P = 0.017), where Brand A displayed greater b* (yellowness) than both Brands B and C (Table 2). Salt and water activity were measured on the initial week of sampling (Week −4) only. Both salt and water activity were different between brands (P ≤ 0.007), where Brand A had a lower salt concentration than B and C and Brand B had lower water activity than A and C.

Proximate composition was measured on one sample from each brand and replication. Moisture, fat, and ash were all significantly different between brands (P < 0.012), whereas there were no differences in protein (P = 0.304). Brand A had a greater moisture and a lower fat content than Brands B and C, whereas Brand B had a greater ash percentage compared with A and C.

Means for APC and AnPC are presented in Figure 1. There was a brand by storage time interaction for AnPC (P = 0.032), but no interaction for APC (P = 0.441). For AnPC, Brand A generally increased throughout storage time, ranging from 0.89 log CFU/g at Week −4 to 5.13 log CFU/g at Week + 4. Brand B remained under 2.00 log CFU/g throughout storage time, and Brand C remained under 0.90 log CFU/g. There was a brand effect on APC (P = 0.017), where Brand A had the greatest mean APC (2.97 log CFU/g) across all sampling times, whereas Brands B and C were less with mean values of 0.40 and 0.36 log CFU/g, respectively.

Figure 1.

(a) Aerobic plate counts and (b) anaerobic plate counts of 3 brands of ham (A, B, C) throughout storage time.

In order to ensure adequate sampling depth, goods coverage was performed on the rarefied OTU table, and revealed that the depth used was able to characterize ≥ 95.8% of the total bacterial community. Diversity estimates Chao1 and observed OTUs were analyzed to determine differences in community richness or the number of different species in a sample (Figure 2). There was a brand by storage time interaction for Chao1 (P = 0.043); however, there were no significant interactions or main effects for observed OTUs (P >0.099). Using the weighted and unweighted UniFrac distance matrices, overall differences in bacterial community structure were determined. There was a main effect of brand on the weighted (P < 0.001) and unweighted (P = 0.006) UniFrac, where Brands B and C had a more similar community structure than Brand A, as shown in the principal component analysis visualization of weighted UniFrac distances in Figure 3. Brand A is separated in the top-right quadrant, whereas B and C are clustered closer on the left, illustrating that sliced hams produced in different environments can have microbiomes consisting of different community structures, but similar taxa can arise across different locations as well. To investigate which taxa are responsible for these shifts, relative abundance of taxa at the family and genus level was generated (Figure 4). Main contributors to the observed community differences are Brand A having greater proportion of Carnobacterium, Bacillus, and Prevotella than both B and C and B and C having greater proportions of Pseudomonas, Photobacterium, and Lactococcus compared with Brand A.

Figure 2.

(a) Observed operational taxonomic units (OTUs) and (b) Chao1 estimates of community richness at various sampling depths for different ham brands (A, B, C) and weeks of sampling of sliced, prepackaged deli ham. All individual samples are displayed, colored by storage time with the marker shape representing brand. All samples were rarefied to an even depth of 3,000 reads.

Figure 3.

Principal coordinate analysis plot of all samples using a weighted UniFrac distance matrix. Small markers identify individual samples, whereas large markers indicate treatment means. Relative distance between samples indicates dissimilarity between overall bacterial community structure for different ham brands (A, B, C).

Figure 4.

Relative abundance (proportion) of (a) family and (b) genus classification of bacterial community according to brand of sliced, prepackaged deli ham. The top 24 most prevalent genus according to maximum relative abundance across all 3 treatments are represented.

Discussion

Physiochemical attributes suggest that tested formulations of the tested hams were generally of similar function and composition; however, the exact ingredients added and ingoing concentrations may have influenced microbial populations. Lower fat percentage and higher moisture percentage in Brand A is likely a reflection of the muscle composition of the ingoing meat block. Ham pH fluctuated over the course of the study in each brand, as displayed in Table 3. Increases in pH can be explained by the protein degradation and accrual of ammonia and biogenic amines during spoilage (Zhang and Guo, 2016), whereas decreases, such as those midstorage in Brand B, could be attributed to acids generated by fermentative organisms. A lack of brand or storage time effect on L* and a* suggests that ingoing nitrite concentrations were viable for cured color development and protection against cured meat fading. Increased ash content in Brand B is likely a reflection of its additional weight from sodium/potassium-containing antimicrobial agents and salt, compared with Brand A with less salt (Table 2) and Brand C with less added antimicrobials (Table 1).

Alongside this antimicrobial amount difference, lower salt concentration in Brand A is of considerable interest. Brand A had greater bacterial growth as well as a significantly different bacterial community structure compared with Brands B and C. Given that Brand A had less salt than both B and C, it is possible that the increased growth and the shift in community structure seen in Brand A are related to the difference in salt concentration. Salt is one of the main preservative ingredients added to meat products and as such typically decreases bacterial growth with increased concentrations (Borch et al., 1996; Bower et al., 2018). Furthermore, Brand A contained sodium propionate, an antimicrobial, whereas Brand B contained potassium lactate and sodium diacetate antimicrobials, and C contained no organic acid. Organic acids are one of the more commonly used antimicrobial agents to prevent the growth of Listeria monocytogenes in ready-to-eat products but also inhibit other organisms and may shift the bacterial community structure (Benson et al., 2014; Ahmed et al., 2015). Thus, differences in organic acids could in part explain some of the differences seen in the current study. The Week + 4 AnPC count increase in Brand A could be attributed to Carnobacterium spp. anaerobic lifestyle and permeance in the context of this ingredient system. It is important to note, however, that spoilage is a complex phenomenon based on consumer objections to product odor, color, and exudate, and microbial plate counts are only a proxy for these objections and may not truly describe consumer attitudes.

Regardless of product composition, hams generally showed a decrease in alpha diversity metric Chao1. Brand A did have more diverse bacterial composition compared with B and C; however, in all cases, bacterial populations concentrated into fewer taxa after “sell-by” date. It has been observed that spoilage coincides with microbial succession and loss of diversity (Johansson et al., 2020), potentially making this measure valuable in more accurate determination of “sell-by dates.”

Another explanation of the differences observed is simply the difference between the postlethality environment in which each of the brands was handled and packaged. Similar to how terroir plays a large role in the microbiome associated with wine and grape production (Canfora et al., 2018), it is likely that a processing plant environment contributes a unique initial contaminating bacterial community structure that shapes the eventual spoilage microbiome.

The cooking process inactivates many microorganisms present on the raw meat; therefore, it is important to consider the introduction of spoilage organisms on products after thermal processing. In the case of the studied hams, products would be sliced and packaged prior to distribution and thus would each be handled in a different, unique postlethality processing environment. It has been shown that Pseudomonas is commonly found on the contact surfaces of meat slicers (Mertz et al., 2014), which could contribute to the predominance of the taxa in this study. Furthermore, model systems have indicated that sanitation practices on slicing lines, as well as types of products sliced on said line, can influence the microbial load and shelf life of meat products; microbes from fermented products can migrate to nonfermented goods manufactured in the same facility without thorough sanitation between batches, leading to spoilage (Holley, 1997). It is possible that the Lactobacillaceae in Brand B are indigenous contaminants from the postprocess environment that are equipped for survival in the cold-stored modified atmosphere conditions and thus flourish as SSOs, as observed in other instances (Pothakos et al., 2015).

This study also serves to disrupt conventional notions regarding the taxa of the meat microbiome. Traditional wisdom would suggest that the conditions and environment of cooked ham either in vacuum packaging or low-oxygen modified atmosphere packaging would suppress the growth of Pseudomonas because they were believed to be obligate aerobes (Walker, 1980; Sun and Holley, 2012; Rossaint et al., 2015). It has been a recent surprise that hams in this study along with other studies of vacuum-packaged meats evaluated using 16s rRNA sequencing and traditional plating methods have revealed pseudomonads as a considerable proportion of the microbial community at time of spoilage (Wang et al., 2017; Hilgarth et al., 2019). It is clear that pseudomonads are more complex and adaptable than they are given credit; generalizations of behavior at the genus level have led to a misunderstanding regarding the environments in which they can flourish. This large genus does contain obligate aerobes but also contains obligate respirators or anoxic persisters that can grow without the presence of oxygen, utilizing additional fermentative pathways (Kolbeck et al., 2021). It has even been observed that Pseudomonas fragi strains in vacuum-packaged beef utilize more protolytic behavior and less aerobic respiration behaviors than counterparts stored aerobically (De Filippis et al., 2018). Although suppression may be observed in some strains, such as those in Brand B, vacuum or MAP packaging may not be an adequate solution for all pseudomonads, like those of Brand C. Beyond the scope of storage, metabolically repressed pseudomonads in MAP systems may gain function and quickly spoil products once the package is opened and exposed to oxygen, issuing a further line of consideration for microbiome characteristics in the context of consumer usage. These findings further illustrate the importance of understanding the microbiome of specific meat products and processing environments when devising shelf-life extension strategies.

One additional taxon of note is Photobacterium. Although this genus is generally associated with fish, recent works utilizing next generation sequencing have been uncovering Photobacterium as a sizable portion of the spoilage microbiome of some meats (Fuertes-Perez et al., 2019). Photobacterium are notability difficult to culture, so these organisms were likely underrepresented in studies utilizing traditional plate media commonly used to isolate organisms from meat surfaces, as shown by Hilgarth et al. (2018). Unculturability is a widespread phenomenon, with many organisms deemed unable to readily grow in laboratory settings, leading to gaps in understanding regarding microbial diversity. The fact that these organisms were not widely recognized as a part of the meat microbiome until recently displays the capability that next generation sequencing has in finding problematic spoilage microbiota and possibly under characterization of microbial communities as a result of lack of diversity of locations sampled. Photobacterium spp. were a substantial portion of the bacterial community in Brand C, less portion in Brand B, and minimally in Brand A, as shown in Figure 4. Facility C was the closest (less than 10 miles) to a large freshwater lake and operated in a county that used said lake as its main water source, Brand B was near an estuary (less than 20 miles), and Brand A was more than 500 miles from any major water bodies. Considering the genus is generally associated with aquatic environments, it is possible these bacteria entered the food system via these nearby sources or employees that interfaced with these waters. Each processing plant contains a unique microbiome influenced by their operations and surroundings; scientists and processors should consider the utilization of precise sequencing methods to identify issues without the cumbersome preparation and incomplete view traditional methods impose.

Conclusions

The results of this study surmount the fact that bacteria of the meat spoilage microbiome are more adaptable and diverse than traditional methodologies describe. Technologies utilized to modulate bacterial communities and delay their growth must consider efficacy in the context of the system applied. In the context of the studied presliced ham, spoilage microbiomes were generally dominated by Pseudomonas, Photobacterium, Lactococcus, or Carnobacterium; however, the proportion of these taxa and other accessorial taxa varied across brands. Factors that influence microbial composition in ready-to-eat products are numerous but could include geographical location, sanitation practices, employee hygiene practices, temperature of processing and storage environments, or line speed and postlethality exposure time, among others. Although it is difficult to identify any one factor individually, the results presented indicate that one or a combination of these factors influence the microbial community to cause differences between brands. Products made with similar ingredients and through similar processes still have unique spoilage bacterial communities, which are likely impressed on them from the environment in which they were handled and packaged.