|POTHULA, ANAND - North Dakota State University|
|IGATHINATHANE, C - North Dakota State University|
|SHEN, JIACHENG - North Dakota State University|
|NICHOLS, K - Rodale Institute|
Submitted to: Industrial Crops and Products
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/1/2014
Publication Date: 12/13/2014
Publication URL: http://handle.nal.usda.gov/10113/60279
Citation: Pothula, A.K., Igathinathane, C., Shen, J., Nichols, K., Archer, D.W. 2015. Milled industrial beet color kinetics and total soluble solid contents by image analysis. Industrial Crops and Products. 65:159-169.
Interpretive Summary: Industrial beets could be used for biofuels and bioproducts production. However, there is a need to assess the quality of juice and pulp from the beets as they are being processed. A computer vision system using a digital camera, custom-design enclosure, and color calibration method was developed to measure color changes that happen after beets have been pulverized for juice extraction. These color changes were then used to predict industrial beet sugar content. The color measurements and mathematical model where shown to provide a good estimate of industrial beet sugar content. The computer vision system and model provides a valuable tool for processors to monitor the quality and serves an alternative method for measuring sugar content while processing beets for renewable fuels and bioproducts production.
Technical Abstract: Industrial beets are an emerging feedstock for biofuel and bioproducts industry in the US. Milling of industrial beets is the primary step in front end processing (FEP) for ethanol production. Milled beets undergo multiple pressings with water addition during raw beet juice extraction, and extracted milled beets change color during FEP. A custom designed computer vision system (CCVS) for measurement of milled beet color kinetics, consisting of a digital camera, custom-designed non-reflective enclosure, and color calibration method was developed in the present study. Beet samples of five different total soluble solids (TSS) contents were prepared by washing with cold water for color kinetics measurement. An artificial neural network (ANN) model was used for converting the RGB values of the acquired sample images to L* a* b* values. Seven color parameters (L*, a*, b*, hue, chroma, browing index (BI), and total color change (delta E)) were analyzed. Fractional conversion, Page, and user-defined polynomial models gave better fits for the experimental data with color parameters L* (R2 > 0.93), a* (R2 > 0.86), b* (R2 > 0.65), BI (R2 > 0.57) and delta E (R2 > 0.85) than the zeroth order kinetic, first order kinetic, exponential, and Peleg models. Of the color parameters studied L* (fractional R2 > 0.93, Page R2 > 0.99, user-defined polynomial R2 > 0.97 ) and delta E (fractional R2 > 0.85, Page R2 > 0.98, user-defined polynomial R2 > 0.94) gave the best description for the color change of the samples. Page model constant, kp with color parameter L* gave good prediction (R2 > 0.99) for the TSS of the samples with color change. Measurement and mathematical modeling of milled industrial beets color kinetics will serve as important quality assessment tool in processing the beets for various renewable fuel and products applications.