Evaluating the Effectiveness of Individual Cleaning Steps of a CIP Protocol in Membrane Biofilm Removal Under Dynamic Conditions

Abstract

This study evaluated the effectiveness of individual clean-in-place (CIP) steps in removing biofilms from reverse osmosis (RO) membranes under dynamic flow conditions using the Centers for Disease Control (CDC) biofilm reactor. Biofilms were developed in the laboratory under continuous flow, using mixed-species bacterial isolates obtained from 10-month-old RO membrane biofilms from a commercial facility. Individual CIP chemicals, representative of those used in commercial protocols, were tested against 24 h-old biofilms. Additionally, a complete six-step sequential CIP process was conducted under dynamic conditions, consisting of treatments with alkali, surfactant, acid, enzyme, a secondary surfactant, and sanitizer. All experiments were performed in quadruplicate, and data were subjected to statistical analysis. Among individual treatments, the acid step was the most effective, significantly outperforming the other CIP cleaning steps by reducing bacterial counts from 5.62 to 4.10 log units, a 96.98% reduction. The full six-step CIP protocol reduced counts to 2.24 log units, indicating the persistence of resistant cells. The presence of viable cells post-treatment highlights the limited efficacy of the tested CIP chemicals in fully eradicating mature biofilms. Additionally, skipping any step in the membrane cleaning can significantly compromise the efficiency and performance during production. These findings suggest that biofilms grown in vitro under dynamic conditions using the CDC reactor exhibit a more robust assessment of the CIP treatments in accomplishing the biofilm control. This study highlights the need for optimized, scientifically validated CIP protocols targeting biofilms to improve cleaning efficacy and food safety.

1. Introduction

Biofilms are defined as surface-associated, heterogeneous communities of microorganisms embedded within a self-produced extracellular polymeric matrix [1]. Biofilm formation typically starts with the reversible attachment of planktonic bacterial cells to a surface [2,3,4,5,6]. This attachment becomes irreversible as the cells begin producing extracellular polymeric substances (EPSs) [7,8,9,10,11]. The EPS matrix, primarily composed of extracellular polysaccharides and proteins, facilitates nutrient acquisition and enhances microbial resistance to environmental stressors and chemical agents [12,13,14]. This matrix also serves as a protective barrier, increasing the survivability of biofilm-embedded cells against cleaning and disinfection treatments [3,15,16,17,18]. This highlights the need for a deeper understanding of cleaning steps in disrupting biofilms, particularly by targeting the protective EPS matrix that shields embedded microorganisms.

Biofilms frequently develop on industrial surfaces such as drains, floors, conveyor belts, and food processing equipment, where they are difficult to remove [9,19]. Once matured, these biofilms can serve as a persistent source of contamination, posing a serious threat to food safety and product quality [20,21,22]. Biofilm formation is a dynamic, multistage process influenced by microbial–surface interactions and environmental conditions, including surface properties, nutrient availability, and flow dynamics [7,23]. Under continuous flow conditions, such as those simulated using the Centers for Disease Control biofilm reactor, planktonic cells are flushed out, allowing for the selective development of surface-attached biofilms [24]. Turbulent flow has been shown to support the formation of compact, cohesive biofilm structures, as opposed to loosely organized formations [3]. High-shear environments, in particular, promote stronger bacterial adhesion compared to low-shear conditions [3], while the composition of the feed and presence of contaminants significantly influence biofilm morphology and complexity [25]. Biofilms in industrial settings normally form under dynamic flow and shear conditions. Simulating these using systems, like the CDC biofilm reactor, enables more accurate evaluation of individual CIP steps, providing valuable insights into their effectiveness against mature biofilms and guiding the optimization of cleaning protocols for improved food safety and equipment hygiene.

In the dairy industry, thermophilic Bacillus species are among the most prevalent biofilm-forming microorganisms [26]. Bacillus species were detected in the evaporation stage [27], plate heat exchangers [28], preheaters [29], and throughout the powder processing plant [30]. These thermophilic bacilli typically colonize manufacturing environments operating at temperatures between 40 and 65 °C [27,28,29,30,31,32]. Bacillus species can be cultured using tryptone soy agar and include strains such as Bacillus cereus, which are known for their resistance to chemical agents, heat, and radiation [26,33]. Multispecies biofilms involving various genera such as Enterococcus, Staphylococcus, Klebsiella, Escherichia coli, Corynebacterium, Pseudomonas, Bacillus, Micrococcus, Streptococcus, and Aeromonas have been studied in different industrial and clinical settings [34,35].

Biofilm detection and characterization can be achieved through both culture-based and microscopic techniques. Scanning and transmission electron microscopy have been extensively utilized to investigate biofilm architecture and cell–surface interactions [2,36,37,38]. Culture-based methods are commonly employed for quantifying microbial populations on surfaces [39,40]. For example, Bacillus stearothermophilus biofilms have been shown to form on stainless steel surfaces after 18 h of recirculation in a laboratory reactor [41]. Additionally, a modified CDC biofilm reactor has previously been used to grow uniform Staphylococcus aureus biofilms on polyetheretherketone (PEEK) membranes [42].

In membrane-based dairy processing systems, CIP procedures are routinely applied using various detergents and disinfectants [43,44,45,46,47,48,49,50,51]. Each step in the membrane cleaning process plays a distinct and essential role in targeting specific foulants, organic, inorganic, biological, and colloidal, that accumulate over time and hinder membrane performance. These cleaning steps are interdependent, working together to effectively restore membrane function, ensure safe and efficient operation, and minimize both the frequency of cleaning and the amount of chemicals required. This integrated approach ultimately enhances process sustainability and cost-efficiency. A standard CIP protocol for reverse osmosis membranes typically involves sequential treatments with alkaline, acidic, enzymatic, and antimicrobial agents. The selection of enzymes is guided by the composition of the soil or fouling material present on the membrane surface [52].

Cleaning efficiency is determined by several parameters, including the choice of chemical agents, mechanical action, temperature, and duration of exposure. Acidic and alkaline detergents are frequently employed in the dairy sector to target biofilms and spores [53], with combined acid–alkali treatments reported to exhibit sporicidal activity [54,55]. Enzyme-based cleaners have also demonstrated the ability to degrade the structural integrity of biofilm matrices [56,57,58,59]. Nevertheless, even after completion of a CIP cycle, visibly clean surfaces may still harbor viable microorganisms [60], emphasizing the need to understand biofilm resilience and the survival strategies of remaining microbial populations. The effectiveness of a cleaning procedure should be evaluated not only by the number of viable cells remaining after treatment but also by the presence of residual cell debris on the cleaned surface.

Persistent biofilm presence on membranes despite routine cleaning underscores the limitations of conventional CIP strategies [38]. Although continuous flow conditions can significantly influence biofilm development and structure, research in this context remains limited. The present study aims to investigate the resistance patterns of biofilms formed under dynamic conditions using the CDC biofilm reactor. Specifically, the study focuses on evaluating the efficacy of individual cleaning agents and a full six-step CIP protocol in disrupting the biofilm matrix. Breaking down CIP processes into their constituent steps and evaluating them under dynamic conditions can be an effective strategy to enhance our understanding of biofilm cleaning mechanisms. A detailed understanding of the role of each cleaning step is essential for optimizing antifouling strategies and improving biofilm control in membrane-based food processing systems.

2. Materials and Methods

2.1. Source and Propagation of Isolates

Bacterial cells embedded within biofilms from reverse osmosis whey concentration membranes collected from a commercial cheese processing facility [61] after 10 months of operation were isolated using standard microbiological procedures [62]. The identified isolates, Streptococcus sp., Staphylococcus sp., and Bacillus sp., previously reported in our earlier work [63], were preserved in cryovials at –80 °C in an ultra-low temperature freezer (Nuaire, MN). For biofilm development, the isolates were inoculated in 10 mL of Brain Heart Infusion (BHI) broth (Difco Laboratories Inc., NJ) following the protocol established in our prior study [64]. In the current investigation, mixed-species biofilms were generated using equal proportions of the three isolates. The inoculum was added to sterile whey-based medium to achieve a final concentration of approximately 7.0 log CFU mL⁻¹.

2.2. Biofilm Formation Under Dynamic Conditions Using the CDC Biofilm Reactor

Biofilms grown using the CDC biofilm reactor produced extracellular polymeric substances with clear cell–cell and cell–surface connections, offering a reliable in vitro system for studying complex biofilm behaviors [65]. To simulate biofilm development under dynamic conditions resembling industrial processes, two CDC biofilm reactors (BioSurface Technologies Corp., Bozeman, MT, USA) were employed (Figure 1). Each reactor, with a total capacity of 1 L and an effluent port positioned at approximately the 400 mL mark, operated as a continuous flow stirred-tank reactor. A baffled magnetic stir bar inside each vessel ensured uniform mixing of the reactor’s bulk fluid. Nutrients were continuously pumped into the reactor while effluent was simultaneously discharged, maintaining a steady-state environment conducive to biofilm formation.

Each reactor contained eight independent rods, mounted through a polyethylene top plate, with each rod holding a single removable coupon to support biofilm development. Spiral-wound reverse osmosis membranes were aseptically cut into 4.1 × 0.9 cm² slices to match the coupon dimensions. Membrane pieces were securely mounted on the rods using holders and nuts to prevent dislodgement during high-speed stirring. Following assembly, the entire system was sanitized with 0.5% hydrogen peroxide for 30 min. Residual disinfectant was removed by circulating sterile water through the system. The influent feed rate for each bioreactor was calibrated to 8.0 ± 0.5 mL/min using a graduated cylinder and stopwatch and adjusted using a flow regulator.

Sterile tryptic soy broth (TSB) served as the nutrient medium for biofilm development, which is consistent with prior studies [66]. TSB was prepared in 10 L volumes, autoclaved at 121 °C for 15 min at 15 psi, cooled to room temperature, and stored at 4 °C until use. For inoculation, 300 mL of sterile TSB was added to each reactor, and the temperature was maintained at ~37 °C using a circulating water bath (Cole-Parmer, IL, USA). A mixed-species biofilm, derived from a 10-month-old RO membrane Consortium, was initiated by inoculating the reactor with equal proportions of Streptococcus sp., Staphylococcus sp., and Bacillus sp. at a final inoculation level of 1%. The reactor stirrers were operated to induce turbulent flow, and the system was maintained in batch mode for 3 h to facilitate microbial attachment to the membrane surfaces. Subsequently, continuous feed flow (8.0 ± 0.5 mL/min) was initiated and maintained for 24 h, with temperature controlled at ~37 °C and pH maintained above 6.0 to support biofilm maturation. Feed reservoirs and waste collection tanks were replaced as necessary. Waste containers were autoclaved at 121 °C for 15 min at 15 psi to ensure inactivation of residual bacterial cells before disposal. Following autoclaving, waste was discarded, and containers were cleaned for reuse. Membrane coupons with biofilm were retrieved after 24 h of continuous flow and incubation for downstream analysis.

2.3. Scanning Electron Micrographs of Membrane Biofilms Developed Under Dynamic Conditions

Membrane coupons with biofilms were aseptically removed and gently rinsed with neutralized phosphate-buffered saline (PBS; pH 7.0), then air-dried prior to imaging by scanning electron microscopy (SEM). SEM analysis was performed at the Department of Electrical Engineering and Computer Science, South Dakota State University (SDSU). A total of four membrane samples (4.1 × 0.9 cm²), containing biofilms developed under dynamic flow conditions, were air-dried at 22 °C under laminar airflow for 24 h to minimize structural disruption of the biofilm architecture, as previously described [38]. Following dehydration, samples were sputter-coated with a 5 nm layer of gold using a CRC-150 sputtering system (Plasma Science Inc., Lorton, VA, USA). Imaging was conducted using a Hitachi S-3400N scanning electron microscope (Hitachi Science Systems Ltd., Tokyo, Japan) operated at an accelerating voltage of 5 to 10 kV.

2.4. Effectiveness of CIP Chemicals Against Biofilms Under Dynamic Conditions

The six-step CIP protocol employed in this study was based on the procedure detailed in our previous work [63]. Briefly, the sequential cleaning steps included the following: (1) alkali rinse, (2) surfactant 1, (3) acid treatment, (4) enzymatic treatment, (5) surfactant 2, and (6) sanitizer application (

Table 1. The existing CIP protocol used by a RO whey concentration plant.

Step Nos.	CIP Steps in Sequence	Temp.	Target pH Range *	Time (min)
Step 1	Alkali rinse	50 °C	11.0–11.4	12
Step 2	Surfactant 1	50 °C	11.0–11.4	30
Step 3	Acid	50 °C	1.9–2.3	30
Step 4	Enzyme	50 °C	10.5–11.0	45
Step 5	Surfactant 2	50 °C	11.0–11.4	10
Step 6	Sanitizer	21.1 °C	3.0–4.0	1

* Target pH range of CIP chemicals used by the dairy plant.

). This cleaning sequence was applied to mixed-species biofilms as described in prior methodology [64]. All cleaning agents were sourced from a commercial dairy facility and represented the same formulations used in the plant’s routine membrane cleaning operations. The standard CIP treatments were carried out at 50 °C, except for the sanitizer step, which was conducted at 21.1 °C. Concentrated stock solutions of the cleaning agents were diluted with distilled water to achieve target pH values consistent with the recommended CIP specifications (Table 1). Post-treatment, membrane surfaces were swabbed, and viable bacterial counts were determined using serial dilution and plate enumeration techniques. A sterile, neutralized phosphate buffer solution was used as the diluent for microbial recovery and enumeration.

2.4.1. Application of Individual CIP Chemicals Against Biofilms Under Dynamic Conditions

The experimental workflow diagram illustrating the use of individual CIP chemicals against mixed-species biofilms is presented in Figure 2. To assess the efficacy of individual cleaning agents, RO membrane coupons with 24 h-old mixed-species biofilms were subjected to a standardized rinsing procedure in the CDC biofilm reactor. A continuous flow of sterile water was maintained for 5 min to remove residual media and loosely attached bacterial cells. Following this, water circulation was stopped, and membrane pieces were retrieved by loosening the retaining nuts on the support rods. From each bioreactor, two of the eight membrane coupons were aseptically transferred into a sterile 100 mL beaker containing 50 mL of sterile water. After an additional rinse with sterile water, the swab technique was applied to the membrane surface, followed by serial dilution and plating on Plate Count Agar (Fisher Scientific, Pittsburgh, PA, USA). The plates were incubated at 37 °C for 24 h, and the resulting viable cell counts were recorded as pretreatment counts (Table 2). The remaining membrane coupons in the reactors were then exposed to individual cleaning agents in accordance with the standard CIP protocol described in Table 1. Each chemical was introduced into the bioreactor, and cleaning conditions were maintained as specified. Upon completion of each cleaning step, the chemical solution was drained, and the membrane surfaces were rinsed thoroughly with sterile water. Viable cell enumeration was conducted post-treatment using the swab method applied to the defined surface area of 4.1 × 0.9 cm² on the RO membranes. Swabs were vortexed in sterile phosphate-buffered saline (PBS), serially diluted, and plated to quantify the remaining viable cells. The plates were incubated at 37 °C for 24 h, and post-treatment counts were determined using the calculation method previously described [67]. These values were then compared with corresponding pretreatment counts to evaluate the reduction in biofilm viability (Table 2). The effectiveness of the individual cleaning treatment was evaluated by measuring the percentage reduction in bacterial count. It compared the number of bacteria before treatment (pretreatment count) with the number after treatment (post-treatment count). The difference between the two was divided by the original (pretreatment) count, then multiplied by 100 to express the result as a percentage. A higher percentage indicated greater bacterial reduction. The equation used for calculating the percentage reduction can be expressed as follows:

Percentage Reduction (%) = ((Pretreatment Count − Post-treatment Count)/Pretreatment Count) × 100.

2.4.2. Application of Sequential CIP Against Biofilms Under Dynamic Conditions

As previously outlined, the sequential cleaning procedure for mixed-species biofilms followed all six steps of a standard CIP cycle [64]. The experimental workflow diagram illustrating the use of sequential CIP against mixed-species biofilms is presented in Figure 3. For this experiment, two membrane coupons with biofilms were aseptically removed from each of the two CDC biofilm reactors prior to cleaning to determine pretreatment viable counts. The remaining six membrane coupons in each reactor were subjected to the full sequential CIP protocol as described in Table 1. Following each individual cleaning step, one membrane coupon was collected from each bioreactor. Viable bacterial counts were assessed separately for each sample using the swab technique on the predefined membrane area. Swabs were vortexed, serially diluted, and plated on Plate Count Agar. The plates were incubated at 37 °C for 24 h, and the resulting post-treatment counts were recorded. This process allowed for stepwise evaluation of biofilm reduction throughout the cleaning sequence. The effectiveness of each CIP step was assessed by comparing viable counts after each treatment stage with the initial pretreatment counts (Table 3), providing a quantitative measure of cleaning efficiency against mixed-species biofilms developed under dynamic flow conditions. The effectiveness of each cumulative cleaning treatment used in sequential CIP was evaluated by measuring the cumulative reduction by comparing the number of bacteria before treatment (pretreatment count) with the number after each treatment (post-treatment count). The difference between the two was divided by the original (pretreatment) count, then multiplied by 100, similar to the equation mentioned in Section 2.4.1, to express the result as a percent cumulative reduction.

2.5. Statistical Analysis

For the calculation of colony-forming units per square centimeter (CFU/cm²), the total number of colonies recovered from each membrane sample was divided by the membrane surface area (3.69 cm²). Each experiment was conducted in duplicate and repeated twice, resulting in a total of four biological replicates per treatment condition. Final microbial counts were expressed as logarithmic values (log₁₀ CFU/cm²) and reported as mean ± standard deviation. Statistical analysis was performed using analysis of variance (ANOVA) via the General Linear Models (GLMs) procedure of the SAS statistical software (version 6.12) package [67]. Mean comparisons were carried out using the Tukey post hoc test. Differences among treatments were considered statistically significant at p < 0.05.

3. Results and Discussion

3.1. Evaluation of Individual CIP Chemicals Against Mixed-Species Biofilms Under Dynamic Conditions

In this phase of the study, 24 h-old mixed-species biofilms with defined pretreatment counts were subjected to individual CIP chemical treatments to evaluate their efficacy (Table 2). The alkali treatment reduced the initial microbial load from 5.63 log₁₀ CFU/cm² to 4.61 log₁₀ CFU/cm², corresponding to a 90.45% reduction. Treatment with surfactant 1 decreased the viable count from 5.38 log₁₀ to 4.48 log₁₀, yielding an 87.41% reduction. The acid treatment was the most effective, reducing initial counts from 5.62 log₁₀ to 4.10 log₁₀ CFU/cm², a 96.98% reduction at pH 2.1. The enzymatic treatment decreased the viable count from 5.31 log₁₀ to 4.40 log₁₀ CFU/cm², representing an 87.7% reduction. Surfactant 2 reduced the initial count from 5.27 log₁₀ to 4.42 log₁₀ CFU/cm², corresponding to an 85.87% reduction. The sanitizer treatment was the least effective, reducing the viable count from 5.55 log₁₀ to 4.92 log₁₀ CFU/cm², with only a 76.56% reduction.

Table 2. Post-treatment counts (log₁₀ cfu cm⁻²) * and the percent reduction in membrane biofilm in the embedded state using representative resistant 24 h-old mixed-species (10 mo.-old Consortium) ¹ biofilm treated against CIP chemicals ² as individual steps under dynamic conditions.

CIP Steps and Chemicals	Step 1	Step 2	Step 3	Step 4	Step 5	Step 6
	Alkali	Surfactant 1	Acid	Enzyme	Surfactant 2	Sanitizer
Pretreatment Count (log10 cfu cm−2)	5.63 ± 0.17	5.38 ± 0.40	5.62 ± 0.26	5.31 ± 0.02	5.27 ± 0.21	5.55 ± 0.24
Post-treatment Count (log10 cfu cm−2)	4.61 ± 0.12 b	4.48 ± 0.28 b	4.10 ± 0.06 a	4.40 ± 0.14 b	4.42 ± 0.20 b	4.92 ± 0.18 c
Percent Reduction	90.45	87.41	96.98	87.70	85.87	76.56

* Mean and standard deviations of four replicates. ¹ Mixed Consortium biofilms were developed using isolates of Streptococcus sp., Staphylococcus sp., and Bacillus sp. ² CIP protocol was used as mentioned in Table 1. ^a–c Means within the same row that do not share common superscripts are significantly different (p < 0.05).

Table 2. Post-treatment counts (log₁₀ cfu cm⁻²) * and the percent reduction in membrane biofilm in the embedded state using representative resistant 24 h-old mixed-species (10 mo.-old Consortium) ¹ biofilm treated against CIP chemicals ² as individual steps under dynamic conditions.

CIP Steps and Chemicals	Step 1	Step 2	Step 3	Step 4	Step 5	Step 6
	Alkali	Surfactant 1	Acid	Enzyme	Surfactant 2	Sanitizer
Pretreatment Count (log10 cfu cm−2)	5.63 ± 0.17	5.38 ± 0.40	5.62 ± 0.26	5.31 ± 0.02	5.27 ± 0.21	5.55 ± 0.24
Post-treatment Count (log10 cfu cm−2)	4.61 ± 0.12 b	4.48 ± 0.28 b	4.10 ± 0.06 a	4.40 ± 0.14 b	4.42 ± 0.20 b	4.92 ± 0.18 c
Percent Reduction	90.45	87.41	96.98	87.70	85.87	76.56

* Mean and standard deviations of four replicates. ¹ Mixed Consortium biofilms were developed using isolates of Streptococcus sp., Staphylococcus sp., and Bacillus sp. ² CIP protocol was used as mentioned in Table 1. ^a–c Means within the same row that do not share common superscripts are significantly different (p < 0.05).

The persistence of viable cells following all individual treatments highlights the resistance of the constituent bacterial species within the biofilm Consortium. Among all tested individual cleaning agents, the acid treatment exhibited the highest efficacy, with maximum bacterial reduction, whereas the sanitizer was the least effective, with minimal bacterial reduction (Figure 4). This is likely due to its limited penetration through the protective biofilm matrix. This observation is consistent with findings from our previous study [63], which also reported reduced sanitizer effectiveness against embedded biofilm cells. No statistically significant differences were observed among the treatments involving the alkali, surfactant 1, enzyme, and surfactant 2 steps.

3.2. Evaluation of Sequential CIP Against Biofilms Under Dynamic Conditions

Mixed-species biofilms with an initial viable count of 5.67 log₁₀ CFU/cm² were subjected to a six-step sequential CIP treatment protocol, replicating industrial cleaning procedures. Post-treatment viable counts were recorded after each cleaning step and analyzed statistically (Table 3). Following the first CIP step (alkali treatment), the biofilm count was reduced to 4.77 log₁₀ CFU/cm², representing an 87.41% reduction. Subsequent treatment with surfactant 1 (Step 2) further decreased the count to 4.18 log₁₀ CFU/cm², with a cumulative reduction of 96.76%.

The third step, acid treatment, resulted in a post-treatment count of 3.23 log₁₀ CFU/cm², yielding a cumulative reduction of 99.64%. Enzymatic treatment (Step 4) further decreased the viable cell count to 2.53 log₁₀ CFU/cm², with a cumulative reduction of 99.93%. After treatment with surfactant 2 (Step 5), counts were reduced to 2.32 log₁₀ CFU/cm², corresponding to a 99.96% cumulative reduction. The final step, sanitizer treatment, lowered the count to 2.24 log₁₀ CFU/cm², achieving a cumulative reduction of 99.96%.

Table 3. Post-treatment counts (log₁₀ cfu cm⁻²) * and the percent cumulative reduction in membrane biofilm in the embedded state using representative resistant 24 h-old mixed-species (10 mo.-old Consortium) ¹ biofilm treated against the sequential application of CIP chemicals ² under dynamic conditions using the CDC biofilm reactor.

Chemicals	Mixed Biofilms (10 mo.-Old Consortium)
Pretreatment Count	5.67 ± 0.07
CIP Steps (Sequential) (1 through 6 below)	Post-treatment counts (log10 cfu cm−2) after sequential treatment steps	Cumulative percent reduction
Step 1 Alkali	4.77 ± 0.18 D	87.41
Step 2 Surfactant 1	4.18 ± 0.19 C	96.76
Step 3 Acid	3.23 ± 0.07 B	99.64
Step 4 Enzyme	2.53 ± 0.12 A	99.93
Step 5 Surfactant 2	2.32 ± 0.07 A	99.96
Step 6 Sanitizer	2.24 ± 0.08 A	99.96

* Mean and standard deviations of four replicates. ¹ The 24 h-old mixed biofilms were developed using isolates of Streptococcus sp., Staphylococcus sp., and Bacillus sp. ² The existing CIP protocol was followed as mentioned in Table 1. ^A–D Means within the same column that do not share common superscripts are significantly different (p < 0.05).

Table 3. Post-treatment counts (log₁₀ cfu cm⁻²) * and the percent cumulative reduction in membrane biofilm in the embedded state using representative resistant 24 h-old mixed-species (10 mo.-old Consortium) ¹ biofilm treated against the sequential application of CIP chemicals ² under dynamic conditions using the CDC biofilm reactor.

Chemicals	Mixed Biofilms (10 mo.-Old Consortium)
Pretreatment Count	5.67 ± 0.07
CIP Steps (Sequential) (1 through 6 below)	Post-treatment counts (log10 cfu cm−2) after sequential treatment steps	Cumulative percent reduction
Step 1 Alkali	4.77 ± 0.18 D	87.41
Step 2 Surfactant 1	4.18 ± 0.19 C	96.76
Step 3 Acid	3.23 ± 0.07 B	99.64
Step 4 Enzyme	2.53 ± 0.12 A	99.93
Step 5 Surfactant 2	2.32 ± 0.07 A	99.96
Step 6 Sanitizer	2.24 ± 0.08 A	99.96

* Mean and standard deviations of four replicates. ¹ The 24 h-old mixed biofilms were developed using isolates of Streptococcus sp., Staphylococcus sp., and Bacillus sp. ² The existing CIP protocol was followed as mentioned in Table 1. ^A–D Means within the same column that do not share common superscripts are significantly different (p < 0.05).

Despite the significant reductions (Figure 5), the persistence of survivor cells after all six steps used in sequential CIP indicates a significant level of resistance in the biofilm-embedded bacterial population. The stability of mixed-species biofilms has been previously documented [36], and our prior studies also demonstrated their enhanced resistance to CIP chemicals under static conditions [64]. Biofilms composed of multiple species exhibit greater tolerance to disinfectants due to structural complexity and physiological heterogeneity. Interspecies interactions in mixed-species biofilms not only improve adhesion but also confer collective protection against antimicrobial agents. This increased resilience is attributed to synergistic microbial interactions within the biofilm matrix [68,69]. These findings highlight the critical need to degrade the extracellular matrix to effectively release embedded cells, thereby improving the efficacy of later cleaning stages such as the sanitizer treatment. In the current CIP protocol tested, the sanitizer treatment is the final cleaning step and resulted in a relatively very low bacterial log reduction. However, this step remains crucial, as it plays an important role in inactivating any residual bacterial cells that may persist after previous cleaning steps.

The results obtained in the study on individual steps indicated that the enzymatic treatments, particularly those targeting polysaccharides and proteins in the extracellular matrix, could enhance biofilm removal when incorporated into multi-step cleaning protocols. Prior research has identified enzymatic agents as promising tools for disrupting biofilm structure and enhancing removal efficiency [70,71]. Furthermore, it has been established that biofilms formed under high-shear conditions, such as those in dynamic flow environments, exhibit more compact and cohesive biofilm structures with stronger adhesion and structural integrity, which are harder to disrupt compared to those formed under low-shear conditions [3,72]. When the same cleaning procedure is applied in an industrial membrane processing unit, bacterial counts can be even higher than those observed in laboratory trials. This discrepancy is largely attributed to design limitations of industrial bioreactors, where chemical usage is scaled based on membrane surface area, often resulting in suboptimal cleaning efficiency [69].

Scanning electron microscopy analysis of membrane samples supported these findings. Biofilms formed under dynamic conditions (Figure 6) displayed dense structural organization and strong surface attachment. Rod-shaped Bacillus spp. and coccoid cells, corresponding to Streptococcus and Staphylococcus spp., respectively, were visible, reflecting the composition of the mixed-species inoculum. The architecture and morphology of the biofilms were consistent with the polymeric secretions and interactions of the participating microbial species [4]. The SEM images confirmed the formation of dense, multi-layered biofilm aggregates, reinforcing the observed microbial resistance. The remaining viable cell population of 2.24 log₁₀ CFU/cm² poses a significant concern for membrane systems, where multiple membranes operate in parallel and even small populations of resilient cells may contribute to system-wide contamination and biofouling. These findings highlight the potential to optimize CIP protocols not only through overall process improvements but also by refining each individual cleaning step. Collectively, the research offers valuable insights into the shortcomings of conventional CIP methods and supports the advancement of more effective, targeted cleaning strategies.

This study corroborates earlier findings demonstrating the successful in vitro development of resistant biofilms using the CDC biofilm reactor system [66]. This reactor model has proven to be a robust platform for generating reproducible, mature biofilms under dynamic conditions, facilitating the systematic evaluation of CIP protocols. In this study, mixed-species biofilms were developed and exposed to a six-step sequential CIP treatment simulating commercial dairy processing conditions. Despite undergoing the complete cleaning cycle, viable microbial populations were still recovered from the membrane surfaces, indicating the limited efficacy of the tested CIP protocol against complex, mature biofilms. These findings are consistent with prior reports showing that residual embedded microorganisms, even in low numbers, can serve as persistent sources of contamination, potentially compromising food product safety and quality [73,74]. Among the individual CIP steps evaluated, acid treatment emerged as the most effective, a result that aligns with our previous studies and the other published literature [64]. In contrast, sanitizer treatment was consistently less effective, reinforcing earlier observations that certain cleaning agents exhibit limited penetration through the protective biofilm matrix.

These findings have several important implications, including the potential to develop more effective cleaning protocols that enhance product quality, improve plant performance, and increase economic returns. As production trends shift toward longer runs, more complex equipment, greater automation, and stricter microbiological standards, the risk of contamination from bacterial biofilms has become a growing concern for dairy manufacturers. Each cleaning step, including pre-rinse, alkaline wash, acid clean, enzymatic treatment, sanitization, and final rinse, plays a targeted role in removing specific types of foulants. Omitting even one cleaning step can lead to incomplete cleaning, accumulation of residual contaminants, accelerated membrane degradation, and hygienic integrity of membrane systems. Therefore, optimizing cleaning procedures is crucial to prevent biofilm formation and ensure the longevity and efficiency of membrane filtration systems.

The present study highlights the complexity of cleaning and sanitation in membrane systems and food processing environments, emphasizing the need for targeted, efficient, and sustainable cleaning strategies. Even significant reductions in microbial load may not suffice if residual contamination remains, as it can lead to rapid recontamination [75]. The limitations of enzymatic treatments, as observed in our study, are consistent with previous findings, which attributed reduced efficacy to poor wettability and limited action on specific biofilm components [76]. The type of microorganism forming the biofilm is equally important. Previous studies emphasized differentiating between spore-forming and non-spore-forming microbes and accounting for food residues such as proteins, minerals, and fats when designing cleaning protocols [77]. Additionally, time, temperature, and concentration must be carefully optimized. Some microorganisms are particularly resilient due to their location in areas with limited fluid flow, like gasket–metal junctions or rough surfaces [78].

Cleaning is a multifaceted challenge involving dynamic, non-linear systems and complex materials such as biofilms. This study emphasizes the need for a deeper understanding of fouling and cleaning mechanisms, as conventional manufacturer guidelines may fall short in delivering optimal outcomes. In the food and beverage industries, cleaning is especially critical due to the risk of microbiological contamination, which can compromise product quality and safety. Membrane cleaning and disinfection have long been seen as technical barriers, limiting the wider adoption of membrane technologies. Strategic selection and sequencing of cleaning agents are critical for maximizing membrane recovery while reducing chemical usage, operational costs, and environmental impact. These findings align with previous studies, which stressed the importance of refining cleaning protocols for RO membranes in advanced wastewater reclamation [79]. A nuanced understanding of fouling mechanisms, as emphasized in previous studies, supports the development of more effective cleaning procedures by enabling the appropriate selection of agents and fine-tuning of process parameters [80].

The study reviewed current evaluation criteria for membrane cleaning and introduced a lab-scale experimental model to assess the efficacy of cleaning agents on embedded microbial cells. SEM imaging confirmed biofilm formation and cohesive layers on membrane surfaces, which protect bacteria and hinder cleaning effectiveness. Biofilm resistance is further complicated by extracellular polymeric substances, which protect embedded bacteria, and by biofilm maturation stages that influence cellular resistance [81]. In food processing, Bacillus cereus spores are notably problematic due to their strong adhesion to stainless steel and potential to form biofilms, reinforcing the critical need for robust cleaning practices [82]. The diminished effectiveness of sanitizers against adhered cells, compared to suspended ones, corroborates earlier work, which found significantly reduced efficacy of common sanitizers on surface-attached microbes [83]. Effective biofilm removal requires a combined approach, physical, chemical, and enzymatic, alongside the specific strengths of selected cleaning agents.

Biofilms, primarily formed by bacteria Bacillus cereus, resist conventional chemical cleaning due to their protective EPSs and structural complexity [84,85]. Chemical cleaning protocols effectively removed both organic and inorganic deposits and achieved the lowest microbial cell counts, even in the presence of resistant genera like Bacillus sp. [86]. Chemical cleaning, while effective, raises sustainability concerns due to harsh chemical use and, hence, requires enzyme-based cleaning. Previous findings advocate for more environmentally friendly alternatives, such as self-cleaning membranes [87]. Tailoring cleaning protocols to fouling types is essential, as no single method works universally across all biofilm stages. Previous studies recommend enzyme-based or detergent-denaturant combinations over heavy reliance on bactericides for better membrane compatibility and effectiveness [70].

Enzymatic cleaning has emerged as a promising alternative, targeting key biofilm components like proteins, polysaccharides, and DNA. Enzymes such as proteases, glycosidases, alginate lyase, and β-mannosidase have demonstrated significant biofilm reduction, especially when used in combination [88,89]. Investigations revealed that enzyme mixtures can outperform traditional cleaners, achieving up to 71% biofilm removal in some cases [84]. Additionally, enzyme-functionalized nanocarriers and mechanical-enhanced enzymatic treatments further improve efficacy [85,89]. While enzymatic methods reduce biomass and microbial load, complete eradication of spore-forming and resistant genera is still difficult [90]. Research continues to optimize enzyme formulations and explore synergistic strategies incorporating surface engineering, synthetic biology, and artificial intelligence [91]. These innovations aim to enhance membrane longevity and support sustainable biofouling control across industrial applications.

A successful CIP protocol depends on optimizing parameters such as cleaning time, temperature, flow rate, chemical concentration, and the sequence of chemical application. Evaluating cleaning performance through the enumeration of surviving viable cells helps in understanding biofilm resistance. These insights are essential for refining cleaning strategies that ensure effective biofilm removal, maintain membrane integrity, and support long-term operational efficiency. Digitizing and automating CIP processes has the potential to improve food safety, optimize resource use, and align with Industry 4.0 standards. This advanced solution offers a scalable model for smarter, more sustainable manufacturing in the food and beverage sector, enhancing hygiene, operational efficiency, and traceability throughout production [92].

4. Conclusions

The present study underscores the utility of the CDC biofilm reactor as a valuable laboratory-scale tool for biofilm research under dynamic conditions, particularly in the context of food manufacturing environments. The system enables the formation of biofilms on membrane materials under realistic flow conditions, providing an experimental framework for assessing both existing and novel CIP agents, including chemical and enzymatic treatments. These capabilities are especially relevant to the dairy industry, where biofilm-associated contamination poses significant operational and regulatory challenges. The resistance of embedded cells and the observed inefficacy of traditional sanitizers highlight the critical need to refine existing CIP protocols. The potential for surviving biofilm populations to regrow between cleaning cycles raises concerns about long-term system hygiene, product integrity, and economic sustainability. Therefore, a comprehensive understanding of each chemical’s role within the CIP sequence is essential to improve cleaning outcomes. Scanning electron microscopy further confirmed the presence of dense bacterial aggregates and extracellular polymeric substances on the membrane surface. These structural features contributed to the persistence of the biofilm despite chemical treatment, demonstrating the protective function of the EPS matrix. Effective biofilm removal depends on the interplay of mechanical forces, chemical dissolution, enzymatic degradation, and optimized operational parameters such as time, temperature, flow rate, and agent concentration.

Overall, this study emphasizes the importance of a well-structured, scientifically validated CIP protocol, incorporating appropriate sequencing of cleaning agents to target both microbial cells and the matrix components that shield them. The present study enhances our understanding of how each cleaning step interacts with biofilms developed under realistic conditions and reinforces the need for optimized cleaning protocols in the dairy industry. The results also underscore the need to tailor CIP protocols based on fouling characteristics, microbial species, and system design. Suboptimal cleaning in industrial setups further compounds the risk of system-wide biofouling. Ineffective cleaning not only risks microbial regrowth but also impacts product quality, regulatory compliance, and economic performance. Comprehensive CIP validation and proper step sequencing are crucial for minimizing biofilm-related risks in food production, making the optimization of each cleaning step essential. Ultimately, a deeper understanding of biofilm behavior and cleaning dynamics will drive the development of more effective, targeted, and sustainable cleaning protocols aligned with the evolving demands of the food and beverage industry. Future research should focus on evaluating enhanced enzyme formulations and synergistic cleaning combinations within dynamic flow systems to improve the efficacy of sanitation in dairy membrane operations.