Quantifying the effect of solution formulation on the removal of soft solid food deposits from stainless steel substrates

Improving Cleaning Efficiency through the Measurement of Food Fouling Adhesive Strength

This study aims to investigate the impacts of factors, including textural properties, surface roughness, and contact angle, on the cleaning performance of food soils and develop a preliminary mathematical model to predict the cleaning score, depending on the soil-surface properties. The force required to remove soil from the surface was determined by a texture analyzer equipped with a newly designed probe. Potato puree and egg yolk soils showed high adhesive forces compared to other deposits. Margarine required the lowest force to detach from the surfaces. A soil-surface characteristic number (SSCN) was constructed from the results of contact angle, roughness, and textural analysis to predict the cleaning score depending on the soil-surface properties. The experimental work presented indicates that a higher SSCN was associated with lower cleaning scores for soil-surface combinations. Furthermore, a predictive model was developed to define the relationship between cleaning scores and SSCN. The applicability of the model was validated by measuring the cleaning performance of caramel and pudding soils on glass, porcelain, and stainless-steel household surfaces by using an automatic method. Therefore, it can be concluded that the SSCN approach can be improved in further studies to predict cleaning scores of soil-surface combinations in the experimental rig or automatic dishwasher.

Simulated aging of draught beer line tubing increases biofilm contamination

Craft brewing is continually gaining popularity in the United States. Craft brewers are committed to producing a wide variety of products and have a vested interest in product quality. Therefore, these brewers have the expectation that the beer poured at the tap will match the quality product that left the brewery. The presence of biofilm in draught lines is hypothesized as a contributing factor when this expectation is not achieved. Clean in place strategies based on the Sinner's Circle of Cleaning are used to remediate organic and inorganic accumulation in beer draught lines, including controlling biofilm accumulation. A study was conducted to determine if repeated exposure to chemical cleaning of vinyl beer tubing impacted biofilm growth, kill/removal, and subsequent regrowth of a mixed species biofilm. The tubing was conditioned to simulate one, two, and five years of use. The data collected demonstrates a clear trend between simulated age of the tubing and biofilm accumulation on the surface. Bacterial log densities ranged from 5.6 Log10(CFU/cm2) for the new tubing to 6.6 Log10(CFU/cm2) for tubing aged to simulate five years of use. The counts for the yeast were similar. Caustic cleaning of the tubing, regardless of starting biofilm coverage, left less than 2.75 Log10(CFU/cm2) viable bacteria and yeast cells remaining on the tubing surface. This demonstrated the effectiveness of the caustic at controlling biofilm accumulation in the simulated beer draught line. The biofilm that accumulated in the five-year aged tubing was able to recover more quickly, reaching 3.6 Log10(CFU/cm2) within 24 h indicating the treatment did not fully eradicate the biofilm, suggesting that the strong chemistry used in this study would cease to be as effective over time.

Analysis of glycans in a Burnt-on/Baked-on (BoBo) model food soil using Microarray Polymer Profiling (MAPP) and immunofluorescence microscopy

Burning of food materials during cooking can increase the difficulty in removal from solid surfaces, forming residual food soils. Using molecular probe-based technologies, the aim of this work was to elucidate the composition and relative abundance of glycans within a Burnt-On/Baked-On (BoBo) model food soil and investigate enzyme systems that may facilitate soil breakdown. Microarray Polymer Profiling identified xylan, arabinoxylan, mixed-linkage glucan and mannan as target substrates for the enzymatic cleaning of BoBo residues from surfaces. Indirect immunofluorescence microscopy revealed that burning resulted in extensive structural modifications and degradation of the three-dimensional architecture of constituent polysaccharide matrices. Results from high-throughput enzyme screening indicate that inclusion of xylan depolymerising enzymes in automatic dishwashing detergents may improve cleaning of recalcitrant, plant glycan-rich BoBo soils. Collectively, this study provides new insight into the composition and removal chemistry of complex, multi-component food soils.

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression

Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes.