Nonlinear regression technique to estimate kinetic parameters and confidence intervals in unsteady-state conduction-heated foods

Dolan, Kirk D.; Yang, Lijian; Trampel, Christopher

doi:10.1016/j.jfoodeng.2006.06.023

Cited by 49 publications

(35 citation statements)

References 14 publications

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“…OLS estimation of kinetic parameters for nonisothermal food processes using nonlinear parameters estimation also has been discussed elsewhere (Dolan, 2003;Mishra et al, 2008;Dolan et al, 2007). Sequential estimation allows updating the parameter values as new observations are added when appropriate standard statistical assumptions permitting sequential analysis are made.…”

Section: Resultsmentioning

confidence: 99%

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Effect of amylose content on estimated kinetic parameters for a starch viscosity model

Sulaiman¹,

Dolan²

2013

Journal of Food Engineering

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Amylose Amylopectin Nonlinear kinetic parameter estimation Pasting curve Mixer viscometry Labview Brookfield viscometer Nonisothermal Inverse problem Rheology Rapid visco analyzer (RVA) a b s t r a c tThe apparent viscosity profile of starches during gelatinization varies with different amylose content. This study focused on the influence of amylose content on the kinetic parameters of a starch viscosity model for corn starches. The five parameters were: gelatinization rate constant (k g ), gelatinization activation energy (E g ), relative increase in apparent viscosity during gelatinization (A a ), relative decrease in apparent viscosity during shearing (B), and viscous activation energy (E v ). The parameters were estimated at different amylose content using both ordinary least squares nonlinear regression and the sequential method. The mixer viscometry approach was used to measure apparent viscosity. The first part of this paper presents parameter estimation results for waxy corn starch. The model was validated by using the parameters to predict viscosity for the same starch in a different measuring system, i.e., the RVA. The second part of this paper presents the estimated parameters for corn starch blends at different amylose content. The following parameters were significantly affected by amylose content: k g and E g both decreased with amylose content by an power-law relationship. Activation energy of gelatinization ranged from 121 to 1169 kJ/mol. The other parameters A a , B, and E v were not significantly influenced by amylose content. In summary, the gelatinization parameters k g and E g dramatically decreased as amylose increased from 3% to 35% (waxy corn starch blends).

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Section: Resultsmentioning

confidence: 99%

“…The MATLAB command for determing the asymptotic confidence interval (ci) of the parameters is ci = nlparci(beta, resids, J) and the procedure to determine the correlation coefficent matrix of parameters is given in detail by (Mishra et al, 2008;Dolan et al, 2007).…”

Section: Ordinary Least Squares Estimation (Ols)mentioning

confidence: 99%

Effect of amylose content on estimated kinetic parameters for a starch viscosity model

Sulaiman¹,

Dolan²

2013

Journal of Food Engineering

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“…(11). The 95% asymptotic confidence intervals (ci) of the parameters and the correlation coefficient matrix of parameters were also computed in MATLAB (Dolan et al, 2007;Mishra et al, 2008). Estimated parameters are significant when the ci does not contain zero.…”

Section: Sensitivity Coefficient Plotmentioning

confidence: 99%

Inverse method to estimate anthocyanin degradation kinetic parameters in cherry pomace during non-isothermal heating

Greiby

Mishra

Dolan

et al. 2017

Journal of Food Engineering

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“…The MATLAB command to solve nlinfit is [beta, r, J] = nlinfit(X, y, @fun, beta0), where beta is the estimated parameters, r is the residuals and J is the Jacobian (Mishra et al, 2009). The MATLAB command for determining the confidence interval (ci) of the parameters is ci = nlparci(beta, resids, J) and the procedure to determine the correlation coefficient matrix of parameters is given in detail in (Dolan et al, 2007;Mishra et al, 2008). Estimated parameters are significant when the ci does not contain zero.…”

Section: Ordinary Least Squares Proceduresmentioning

confidence: 99%

Simultaneous and sequential estimation of kinetic parameters in a starch viscosity model

Sulaiman

Dolan

Mishra

2013

Journal of Food Engineering

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Inverse problem Rapid visco analyzer (RVA) Brookfield viscometer a b s t r a c tA modified Brookfield viscometer equipped with a data acquisition system was used to study gelatinizing behavior of native corn starch. Data for the dependent variable (continuous torque), and independent variables were collected. The independent variables were time-temperature history, shear history, and temperature, corresponding to the three main regions in a pasting curve. Parameters were estimated simultaneously using both ordinary least squares, and the sequential method. The model fit well as shown by RMSE of approximately 2% of full scale, and relative standard error of all parameters estimated less than 11%. Scaled sensitivity coefficients showed that the most important parameters were time-temperature history, followed by shear history, and the temperature parameter was the least important. This work is the first to show that starch-pasting curve parameters can be estimated simultaneously and sequentially, and can be used to predict pasting curves in other systems (RVA) well.

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Nonlinear regression technique to estimate kinetic parameters and confidence intervals in unsteady-state conduction-heated foods

Cited by 49 publications

References 14 publications

Effect of amylose content on estimated kinetic parameters for a starch viscosity model

Effect of amylose content on estimated kinetic parameters for a starch viscosity model

Inverse method to estimate anthocyanin degradation kinetic parameters in cherry pomace during non-isothermal heating

Simultaneous and sequential estimation of kinetic parameters in a starch viscosity model

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