研究目的
To develop three selective NIR spectrometry prediction models to detect and determine concentrations of oatmeal, egg-yolk and butterfat in dishwashing liquor during automatic dishwashing, considering the variable state of homogenization, and to test their applicability in real dishwashing conditions.
研究成果
NIR spectrometry is a promising method for real-time detection of soil concentrations in dishwashing liquor, with high coefficients of determination (R2 > 0.92). The models successfully integrated homogenization states and were applicable to real dishwashing processes, showing potential for optimizing dishwashing by adapting to soil types. Further research is needed for online monitoring and generalizing models to other soils and conditions.
研究不足
The prediction models are specific to the three soils used (oatmeal, egg-yolk, butterfat) and cannot be transferred to other soil types. A constant detergent concentration was used; changes in detergent or composition may affect results. The models are sensitive to homogenization states, and absolute concentration prediction may not be precise, but changes can be tracked.
1:Experimental Design and Method Selection:
The study used NIR spectrometry with partial least squares regression and cross-validation to develop prediction models for soil concentrations. Samples were taken during dishwashing cycles to account for homogenization states.
2:Sample Selection and Data Sources:
Testing standards were emulsions of oatmeal, egg-yolk, butterfat, water, and detergent, with concentrations varied randomly. 76 standards with seven samples each were used, taken at specific times during the main washing process.
3:List of Experimental Equipment and Materials:
Equipment included an FT-NIR spectrometer (MPA-System Bruker Optics GmbH), laser diffractometer (HORIBA LA-950 V2), dishwasher (Siemens SN54M582EU/92 modified), Ultraturrax for homogenization, and software (OPUS spectroscopy software). Materials included soils (oatmeal from Peter K?lln GmbH & Co. KGaA, egg-yolk powder from Sanovo Eiprodukte GmbH & Co. KG, butterfat from DFF Dairy Fine Food GmbH), detergent D, and water.
4:Experimental Procedures and Operational Workflow:
For model development, emulsions were prepared and added to the dishwasher; samples were taken at 5, 10, 20, 30, 40, 50, and 60 minutes, stirred, and measured in transmittance mode. For applicability testing, soiled dishes were used, and the process was interrupted at the same times to take samples and predict concentrations.
5:Data Analysis Methods:
Spectra were pretreated (first derivative, SNV, MSC), and partial least squares regression with cross-validation was used to develop models, with gravimetry as the reference method.
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