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Summary
This summary is machine-generated.

This study introduces a fast, non-invasive ultrasonic method using partial least squares regression to predict maltose concentration in aqueous solutions, even with temperature variations. This technique is ideal for real-time monitoring in brewing fermentation processes.

Keywords:
Feature extractionMultivariate data analysisPartial least squares (PLS)Sugar concentrationUltrasound

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Area of Science:

  • Analytical Chemistry
  • Process Engineering
  • Biotechnology

Background:

  • Accurate monitoring of maltose concentration is crucial for optimizing fermentation processes, particularly in the brewing industry.
  • Traditional methods for maltose measurement can be time-consuming or invasive, limiting their applicability for real-time process control.
  • Ultrasonic signal analysis offers a potential non-invasive approach for rapid concentration determination.

Purpose of the Study:

  • To develop and validate a multivariate regression method for predicting maltose concentration in aqueous solutions using ultrasonic signals.
  • To create a unified model that accounts for temperature variations within the 10 °C to 21 °C range, relevant to brewing fermentation.
  • To assess the feasibility of online monitoring using a non-invasive ultrasonic sensor with low processing time.

Main Methods:

  • Analysis of ultrasonic signals in both time and frequency domains.
  • Development of multivariate regression models, specifically partial least squares (PLS) regression, for individual temperature points.
  • Linear approximation of model coefficients over temperature to create a unified, temperature-compensated prediction model.
  • Validation using cross-validation and extraction of signal data from buffer reflections to minimize interference.

Main Results:

  • A unified multivariate regression model was successfully developed to predict maltose concentration, effectively incorporating temperature effects.
  • The method demonstrated low processing time, suitable for online signal analysis.
  • The ultrasonic signal sections used were robust against bubble and particle interferences.
  • The minimum root mean squared error achieved was 0.64 g/100 g, indicating good prediction accuracy.

Conclusions:

  • The proposed ultrasonic multivariate regression method provides an accurate, fast, and non-invasive means for monitoring maltose concentration in aqueous solutions.
  • The temperature-compensated model enhances the applicability of ultrasonic analysis for online monitoring in industrial fermentation processes, such as brewing.
  • The use of buffer reflections for signal extraction improves the reliability of the method in complex process environments.