Ultrasonic characterization of aqueous solutions with varying sugar and ethanol content using multivariate regression methods

Abstract

a This paper presents a multivariate regression method for simultaneous detection of sugar (sucrose as a sugar equivalent) and ethanol concentrations in aqueous solutions via temperature-dependent ultrasonic velocity. Thus, several samples of different combined concentration values were exposed to a temperature spectrum ranging from 2 to 30-C to investigate the temperature dependence of ultrasonic velocity. Model calibration was performed in order to predict the concentrations of interest. With results of proceeded experiments, the equations for calculation of unknown concentrations were carried out using polynomial regression revealing two equations with functional dependence of concentrations on each other. Further, side effects or systematic errors are still included in this model. To avoid such problems as well as to increase the accuracy with respect to the absolute errors in determining unknown probes, multivariate regression methods such as partial least squares (PLS) were tested and compared to the results obtained by polynomial regression. The accuracy achieved with chemometric models on average was three times higher. In direct comparison, the values of the error for the prediction of sucrose concentration were on average around 0.4 g/100 g in the regression model with polynomial background (RMPA) and around 0.12 g/100 g in the PLS model, and for ethanol concentration 0.13 and 0.04 g/100 g, respectively. Furthermore, calculations of the concentrations are possible without knowing the concentrations of the other solute.

NIR and PLS discriminant analysis for predicting the processability of malt during lautering

“…Description of NIPALS algorithm can be found in various literatures (e.g. Kessler [20] or Krause et al [24]). In addition, it was shown earlier that calculating principal components for PLS regression, using the kernel algorithm, requires less computational effort than NIPALS algorithm.…”

Finite element simulation for detecting the foreign body based on ultrasonic sensor

“…The application of low power ultrasound covers the assessment of the composition and physiochemical properties of components. Examples includes food characterization (Benedito, Carcel, Rossello, & Mulet, ; Cho & Irudayaraj, ; Krause, Schöck, Hussein, & Becker, ; Simal, Benedito, Clemente, Femenia, & Rosselló, ), safety (Haeggstrom & Luukkala, ; Ho, Billson, & Hutchins, ; Zhao, Basir, & Mittal, ) and quality control (Aboonajmi et al, ; Kim, Lee, Kim, & Cho, ; Schöck & Becker, ). On the other hand, high power (low frequency) ultrasound is useful for changing the physical and chemical of food properties such as crystallization (Chow, Blindt, Chivers, & Povey, ; Martini, Suzuki, & Hartel, ; Nalajala & Moholkar, ), emulsification (Leong, Wooster, Kentish, & Ashokkumar, ; Mongenot, Charrier, & Chalier, ; Pongsawatmanit, Harnsilawat, & Mcclements, ) and drying (Gallego‐Juárez et al, ; Fernandes, Linhares, & Rodrigues, ).…”

Inline Measurement of Particle Concentrations in Multicomponent Suspensions using Ultrasonic Sensor and Least Squares Support Vector Machines

“…Inline measurement of the component concentrations is an important approach to supervise industrial processes. Based on the concentration information, the process controls can be implemented and the stable product quality, the processing efficiency and the energy saving can be ensured [ 1 ]. Nevertheless, the measurement of component concentrations in multicomponent mixtures cannot be easily achieved through a low-cost and inline technique.…”