A New Mathematical Model for Food Thermal Process Prediction
A mathematical model was developed to correlate the four heat penetration parameters of 57 Stumbo’s tables (18,513 datasets) in canned food: g (the difference between the retort and the coldest point temperatures in the canned food at the end of the heating process), fh/U (the ratio of the heating r...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
|
| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2013/569473 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832551533603979264 |
|---|---|
| author | Dario Friso |
| author_facet | Dario Friso |
| author_sort | Dario Friso |
| collection | DOAJ |
| description | A mathematical model was developed to correlate the four heat penetration parameters of 57 Stumbo’s tables (18,513 datasets) in canned food: g (the difference between the retort and the coldest point temperatures in the canned food at the end of the heating process), fh/U (the ratio of the heating rate index to the sterilizing value), z (the temperature change required for the thermal destruction curve to traverse one log cycle), and Jcc, (the cooling lag factor). The quantities g, z, and Jcc, are input variables for predicting fh/U, while z, Jcc and fh/U are input variables for predicting the value of g, which is necessary to calculate the heating process time B, at constant retort temperature, using Ball’s formula. The process time calculated using the g value obtained from the mathematical model closely followed the time calculated from the tabulated g values (root mean square of absolute errors RMS = 0.567 min, average absolute error = 0.421 min with a standard deviation SD = 0.380 min). Because the mathematical model can be used to predict the intermediate values of any combination of inputs, avoiding the storage requirements and the interpolation of 57 Stumbo’s tables, it allows a quick and easy automation of thermal process calculations and to perform these calculations using a spreadsheet. |
| format | Article |
| id | doaj-art-5af743c4855d46dfa7433e5eae39d298 |
| institution | Kabale University |
| issn | 1687-5591 1687-5605 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-5af743c4855d46dfa7433e5eae39d2982025-02-03T06:01:14ZengWileyModelling and Simulation in Engineering1687-55911687-56052013-01-01201310.1155/2013/569473569473A New Mathematical Model for Food Thermal Process PredictionDario Friso0Department of Land, Environment, Agriculture and Forestry-TESAF, Research Unit: Food Engineering, University of Padova, Viale dell’Università 16, Legnaro 35020, Padova, ItalyA mathematical model was developed to correlate the four heat penetration parameters of 57 Stumbo’s tables (18,513 datasets) in canned food: g (the difference between the retort and the coldest point temperatures in the canned food at the end of the heating process), fh/U (the ratio of the heating rate index to the sterilizing value), z (the temperature change required for the thermal destruction curve to traverse one log cycle), and Jcc, (the cooling lag factor). The quantities g, z, and Jcc, are input variables for predicting fh/U, while z, Jcc and fh/U are input variables for predicting the value of g, which is necessary to calculate the heating process time B, at constant retort temperature, using Ball’s formula. The process time calculated using the g value obtained from the mathematical model closely followed the time calculated from the tabulated g values (root mean square of absolute errors RMS = 0.567 min, average absolute error = 0.421 min with a standard deviation SD = 0.380 min). Because the mathematical model can be used to predict the intermediate values of any combination of inputs, avoiding the storage requirements and the interpolation of 57 Stumbo’s tables, it allows a quick and easy automation of thermal process calculations and to perform these calculations using a spreadsheet.http://dx.doi.org/10.1155/2013/569473 |
| spellingShingle | Dario Friso A New Mathematical Model for Food Thermal Process Prediction Modelling and Simulation in Engineering |
| title | A New Mathematical Model for Food Thermal Process Prediction |
| title_full | A New Mathematical Model for Food Thermal Process Prediction |
| title_fullStr | A New Mathematical Model for Food Thermal Process Prediction |
| title_full_unstemmed | A New Mathematical Model for Food Thermal Process Prediction |
| title_short | A New Mathematical Model for Food Thermal Process Prediction |
| title_sort | new mathematical model for food thermal process prediction |
| url | http://dx.doi.org/10.1155/2013/569473 |
| work_keys_str_mv | AT dariofriso anewmathematicalmodelforfoodthermalprocessprediction AT dariofriso newmathematicalmodelforfoodthermalprocessprediction |