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
Prepacked, sliced ham samples were purchased at a local grocery store and selected from products on the retail shelf. Three different brands of smoked ham were evaluated (A, B, C) originating from 3 different establishments. All 3 products were labeled as “Ham, Water Added” and were presliced in case-ready packages. In the interest of maintaining brand anonymity, Table 1 contains select functional ingredients of each individual brand that may affect microbial growth and/or community composition. Three separate replications were purchased for each brand, with one package removed at each sampling time for analysis. A replication consisted of products of the same brand, establishment number, and sell-by date (to have been produced on the same day in the same plant). Furthermore, each of the 3 replications, respective to each brand, were from the same establishment number to ensure replications were from the same processing plant within brand, but each replicate was a unique sell-by date. Products were stored in the original packaging at the Loeffel Meat Laboratory in a covered plastic lug at approximately 1°C (± 3°C) until their respective sampling time. Samples were evaluated according the sell-by date of each replication at the following intervals: 4 wk prior to sell-by (−4), 2 wk prior to sell-by (−2), sell-by date (0), 2 wk after sell-by (+ 2), and 4 wk after sell-by (+ 4).
|Ingredients||Dextrose, modified corn starch, salt, sodium phosphate, sodium propionate, sodium erythorbate, sodium nitrite, sugar, water||Dextrose, potassium lactate, salt, sodium diacetate, sodium erythorbate, sodium nitrite, sodium phosphates, water||Dextrose, salt, sodium erythorbate, sodium nitrite, sodium phosphates, water|
Initial physicochemical analyses
Water activity, salt concentration, and proximate composition were evaluated at the initial (−4 wk) sampling time only. Samples used for water activity and salt concentration were homogenized using a food processor (Black + Decker Handy Chopper, Black + Decker, Baltimore, MD). Water activity was measured using an Aqualab water activity meter (Decagon Devices, Pullman, WA). Salt concentration was measured as described by Sebranek et al. (2001) using QuanTab high range chloride titration strips (Hach Company, Loveland, CO). Moisture, fat, protein, and ash was determined on pulverized samples. Samples were manually diced, submerged in liquid nitrogen until completely frozen, and pulverized using a Hobart commercial blender (Model 51BL32, Waring Commercial, Torrington, CT). In duplicate, 2 g of pulverized tissue was used to quantify moisture and ash content using a LECO thermogravimetric analyzer (Model TGA701, LECO Corporation, St. Joseph, MI). Using triplicate 2 g samples in a filter paper thimble, total fat was determined as outlined by AOAC (1990) using the Soxhlet extraction procedure. In duplicate, protein content was measured using a LECO nitrogen/protein analyzer (Model FP-528, LECO Corporation).
Longitudinal physicochemical analyses
Objective color and pH were evaluated at each sampling time. In duplicate, pH was measured using an Orion 410A+ pH meter (Thermo Scientific, Waltham, MA) on a slurry of 10 g of ham sample in 90 ml of double distilled water. Objective color (L*, a*, b*) was measured using a colorimeter (Chroma Meter CR-400, Konica Minolta Sensing Americas, Ramsey, NJ) using a 2° standard observer with an 8 mm aperture and a D65 illuminant, calibrated with a white tile (Y:93.15, x:0.3165, y:0.3330). A total of 6 readings were taken from 2 slices from each sample and averaged for color values.
For each respective sampling, one package was removed from storage and processed for analyses. Approximately 30 to 40 g of each sample was aseptically transferred from the retail package into a Whirl-Pak bag (Nasco, Fort Atkinson, WI), weight recorded, combined with 50 ml of sterile BBL peptone water (Becton, Dickinson and Company, Franklin Lakes, NJ), and homogenized using a bag blender (bioMérieux, Durham, NC) for 3 min. Duplicate 2 ml samples of homogenate were collected for microbial community analysis and was stored at −20°C until used for DNA extraction. Aerobic plate counts (APC) and anaerobic plate counts (AnPC) were performed using the homogenized samples. Ten-fold serial dilutions were conducted on homogenate as counts increased over shelf life to ensure accurate enumeration and then plated using the 50 μl E-Mode of an Eddy Jet spiral plater (IUL, Barcelona, Spain) on brain heart infusion agar (Thermo Fisher Scientific, Waltham, MA). Plates were then incubated at 37°C for 48 h for APC and in an anaerobic chamber containing BD GasPak EZ sachets for AnPC (BD, Franklin Lakes, NJ).
16S ribosomal RNA amplicon sequencing
Bacterial community analysis using high-throughput sequencing of the 16s ribosomal RNA (rRNA) gene was performed on each sample collection using the MiSeq Illumina Sequencing Platform as outlined by Kozich et al. (2013). Microbial DNA extraction from homogenized meat samples were performed using the Epicentre QuickExtract DNA extraction kit (Epicentre, Madison, WI). Sequencing library preparation follows protocols used in Bower et al. (2018). Approximately 1 to 5 ng of extracted DNA was amplified via polymerase chain reaction (PCR) with universal primers targeting the V4 region of 16S rRNA. Following amplification, PCR products were analyzed on a 1.5% agarose gel to confirm correct product size and amplification. Products were normalized using an Invitrogen SequalPrep Normalization Kit (Thermo Fisher Scientific). Barcoded PCR products were pooled and purified using the MinElute PCR Purification Kit (Qiagen, Germantown, MD) and further gel purified using the Pippin Prep system (Sage Science, Beverly, MA). Final concentration of the 16S rRNA libraries was determined using Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA), and the 16S libraries were sequenced using the Illumina MiSeq platform (Illumina, San Diego, CA) using the V2 500 cycle kit.
Processing of sequencing data was performed as described previously (Paz et al., 2016), using the bioinformatics pipeline Quantitative Insights Into Microbiological Ecology (QIIME; Caporaso et al., 2010). Sequences shorter than 245bp and longer than 275bp were removed and remaining sequences were trimmed to 251bp. Sequences were binned into operational taxonomic units (OTUs) at 97% similarity using the UPARSE pipeline (USEARCH v8.1; Edgar, 2010). Representative sequences from each OTU were assigned taxonomy using the UCLUST consensus taxonomy assigner method using Greengenes database release 119 as reference sequences (McDonald et al., 2012). The rarefaction of the OTU table was performed using QIIME (Caporaso et al., 2010) to a depth of 3,000 reads/sample. Samples under this threshold were removed from analysis. Good’s coverage test was performed to ensure adequate sampling depth was achieved. Alpha diversity matrices (Chao1 and observed OTUs) were calculated using QIIME. The difference in bacterial communities (beta diversity) among treatments was determined using the QIIME pipeline using distance matrices (weighted UniFrac, unweighted UniFrac) from the rarefied OTU table. The raw sequence data generated and analyzed during this study are available under BioProject accession number PRJNA722526 from the National Center for Biotechnology Information sequence read archive.
Physicochemical and microbial growth data were analyzed using R (R Core Team, 2017). For salt, water activity, and proximate composition (measured Day 0 only), data were analyzed using R (lm and anova functions), and means were separated using the agricolae package (De Mendiburu, 2017). For pH, color, APC, and AnPC, data were analyzed as a 3 (brand) by 5 (storage time) interaction, with storage time as a repeated measure with an independent covariance structure using the nlme package (Pinheiro et al., 2017). Means were separated using the lsmeans package in R (Lenth, 2016). Significance was determined at α = 0.05 throughout the study.
Interactions and main effects on mean alpha diversity were calculated using R (anova function) with storage time as a repeated measure (R Core Team, 2017). Pairwise comparisons on significant (P < 0.05) interactions and main effects of Chao1 and observed OTUs were performed using the lsmeans package in R (Lenth, 2016). To reduce variation between replications, the OTU table was filtered to include only OTUs present in all 3 replications. This filtered OTU table was used for subsequent analysis. Bacterial community composition differences were estimated using the weighted and unweighted UniFrac distance matrices as input for permutational multivariate analysis of variance in the vegan package in R (Oksanen et al., 2019) to analyze interactions and main effects. Significance was declared at P ≤ 0.05 throughout the study.
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.
|Measured trait||A||B||C||SEM||P value|
|Water activity||0.98b||0.96a||0.98b||0.001||< 0.001|
|Moisture %||78.18b||74.65a||74.66a||0.33||< 0.001|
|Ash %||3.2b||4.5a||3.59b||0.11||< 0.001|
Indicates a significant (P < 0.05) brand by storage time interaction and therefore main effects cannot be analyzed.
Means in the same row lacking a common superscript are significantly different (P < 0.05);
AnPC = anaerobic plate count; APC = aerobic plate count; SEM = standard error of the overall mean.
|Brand||Storage time (week)||pH||L*||a*||b*|
Means in the same row lacking a common superscript are significantly different (P < 0.05).
SEM = standard error of the overall mean.
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.
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.
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.
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.
No external funding supported this research. Samodha C. Fernando, author of this publication, has disclosed a significant financial interest in NuGUT LLC. In accordance with its Conflict of Interest policy, the University of Nebraska-Lincoln’s Conflict of Interest in Research Committee has determined that this must be disclosed. The rest of the authors have nothing to disclose.
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