Investigating and understanding fouling in a planar setup using ultrasonic methods

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.

Erratum: Acoustic Sensing and Signal Processing Techniques for Monitoring Milk Fouling Cleaning Operations

[This corrects the article DOI: 10.1002/elsc.201400235.].

Tinnitus functional index: validation of the German version for Switzerland

Background

Different standardized questionnaires are used to assess tinnitus severity, making comparisons across studies difficult. These questionnaires are also used to measure treatment-related changes in tinnitus although they were not designed for this purpose. To solve these problems, a new questionnaire - the Tinnitus Functional Index (TFI) - has been established. The TFI is highly responsive to treatment-related change and promises to be the new gold standard in tinnitus evaluation. The aim of the current study was to validate a German version of the TFI for a German-speaking population in Switzerland.

Methods

At the ENT department of the University Hospital Zurich, 264 subjects completed an online survey including the German version for Switzerland of TFI, Tinnitus Handicap Inventory (THI), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI) and sociodemographic variables. Internal consistency of the TFI was calculated with Cronbach’s alpha coefficient. Pearson correlation coefficients were used for the test-retest reliability of the TFI and to investigate convergent and discriminant validity between the THI and the BDI and BAI, respectively. Factor analysis was assessed using a principal component analysis with oblique rotation. The different factors extracted were then compared with the original questionnaire.

Results

The German version of the TFI for Switzerland showed an excellent internal consistency (Cronbach’s alpha of 0.97) and an excellent test-retest reliability of 0.91. The convergent validity with THI was high (r = 0.86). The discriminant validity with BAI and BDI showed moderate results (BAI: r = 0.60 and BDI: r = 0.65). In the factor analysis only five factors with one main factor could be extracted instead of eight factors as described in the original version. Nevertheless, relations to the original eight subscales could be demonstrated.

Conclusion

The German version of the TFI for Switzerland is a suitable instrument for measuring the impact of tinnitus. The reliability and validity of this version are comparable with the original version of the TFI. Although this study showed only five factors in the factor analysis, relations to the original eight subscales were identified. Therefore, the German version of the TFI for Switzerland can deliver relevant information regarding the different tinnitus domains.

Trial registration

Clinical trial registration number on clinicaltrial.gov: NCT01837368.