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E-raamat: Advanced Quantitative Microbiology for Foods and Biosystems: Models for Predicting Growth and Inactivation

  • Formaat: 456 pages
  • Ilmumisaeg: 12-Apr-2006
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781420005370
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  • Formaat: 456 pages
  • Ilmumisaeg: 12-Apr-2006
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781420005370

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Presenting a novel view of the quantitative modeling of microbial growth and inactivation patterns in food, water, and biosystems, Advanced Quantitative Microbiology for Foods and Biosystems: Models for Predicting Growth and Inactivation describes new models for estimating microbial growth and survival. The author covers traditional and alternative models, thermal and non-thermal preservation, water disinfection, microbial dose response curves, interpretation of irregular count records, and how to estimate the frequencies of future outbursts. He focuses primarily on the mathematical forms of the proposed alternative models and on the rationale for their introduction as substitutes to those currently in use.

The book provides examples of how some of the methods can be implemented to follow or predict microbial growth and inactivation patterns, in real time, with free programs posted on the web, written in MS ExcelÒ, and examples of how microbial survival parameters can be derived directly from non-isothermal inactivation data and then used to predict the efficacy of other non-isothermal heat treatments. Featuring numerous illustrations, equations, tables, and figures, the book elucidates a new approach that resolves several outstanding issues in microbial modeling and eliminates inconsistencies often found in current methods.
Isothermal Microbial Heat Inactivation
1(48)
Primary Models --- the Traditional Approach
1(10)
The First-Order Kinetics and the D Value
1(3)
The ``Thermal Death Time''
4(1)
Biphasic and Multiexponential Decay Models and Their Limitations
5(4)
The Logistic Models
9(1)
Concluding Remarks to This Section
10(1)
The Survival Curve as a Cumulative Form of the Heat Distribution Resistances
11(29)
The Weibull Distribution
17(5)
Interpretation of the Concavity Direction
22(1)
The Fermi (Logistic) Distribution Function
23(4)
The Activation Shoulder
27(3)
Estimation of the Number of Recoverable Spores
30(3)
Sigmoid and Other Kinds of Semilogarithmic Survival Curves
33(1)
Sigmoid Curves
33(4)
Residual Survival (Strong ``Tailing'')
37(1)
Can an Absolute Thermal Death Time Exist?
38(2)
Secondary Models
40(9)
The ``z'' Value and the Arrhenius Equation
41(3)
The Log Logistic Model
44(2)
A Discrete b(T) vs. T
46(1)
Other Empirical Models
47(2)
Nonisothermal Heat Inactivation
49(46)
The Traditional Approach
49(4)
The F0 Value and Its Limitations
50(3)
The Proposed Alternative
53(4)
Nonisothermal Weibuillian Survival
57(11)
The Rate Model
57(2)
Heating and Cooling
59(1)
Simulation of Heating Curves by Empirical Models
59(3)
Simulated Survival Curves for Processes with Different Target Temperature and Holding Durations
62(2)
Temperate Oscillations
64(1)
Discontinuous Temperature Profiles
65(1)
The Special Case of Log Linear Isothermal Survival
66(2)
Non-Weibullian Survival Models
68(10)
Logistic (Fermian) Survival
69(1)
Extreme Tailing
70(3)
Sigmoid Survival Curves
73(2)
Isothermal Survival Model's Equation with No Analytic Inverse
75(2)
Independence of the Calculated Nonisothermal Survival Curve of the Chosen Survival Model
77(1)
Experimental Verification of the Model
78(12)
The Isothermal and Nonisothermal Inactivation Patterns of L. monocytogenes
80(1)
The Isothermal and Nonisothermal Inactivation of Salmonella
81(3)
Isothermal and Nonisothermal Survival Curves of B. sporothermodurans Spores in Soups
84(1)
The Isothermal and Nonisothermal Inactivation of E. coli
84(6)
Heat-Induced Chemical and Physical Changes
90(5)
Generating Nonisothermal Heat Inactivation Curves with Difference Equations in Real Time (Incremental Method)
95(16)
The Difference Equation of the Weibullian--Log Logistic Nonisothermal Survival Model
96(6)
Non-Weibullian Survival Curves
102(4)
Comparison between the Continuous and Incremental Models
106(5)
Estimation of Microbial Survival Parameters from Nonisothermal Inactivation Data
111(24)
The Linear Case
113(7)
Linear Survival at Constant Rate Heating
113(3)
Linear Survival at Varying Heating Rate
116(4)
The Nonlinear Case
120(10)
Weibullian--Power Law Inactivation at Arbitary Heating Rate History
120(1)
Testing the Concept with Simulated Data
120(4)
Testing the Method with Salmonella Survival Data
124(1)
Salmonella in a Growth Medium
124(5)
Salmonella in Minced Chicken Meat
129(1)
Concluding Remarks
130(5)
Isothermal Inactivation with Stable and Dissipating Chemical Agents
135(30)
Chemical Inactivation under ``Constant'' Agent Concentration
137(2)
Microbial Inactivation with a Dissipating Chemical Agent
139(18)
Traditional Models
140(2)
Alternative General Model
142(3)
Dissipation and Inactivation
145(1)
Monotonic Agent Dissipation
145(3)
Agent Dissipation with Regular and Random Oscillations
148(6)
Agent Replenishment
154(3)
Estimation of Survival Parameters from Data Obtained during Treatments with a Dissipating Agent
157(6)
Demonstrations of the Procedure with Published Data
161(2)
Discrete Version of Survival Model
163(2)
High CO2 and Ultrahigh Hydrostatic Pressure Preservation
165(24)
Microbial Inactivation under High CO2 pressure
167(10)
Effect of Pressure Level and Treatment Duration
170(4)
Is the Pressurization Rate a Factor?
174(3)
Ultrahigh Pressure
177(9)
Ultrahigh-Pressure Treatment in a Perfectly Insulated Vessel
182(3)
Treatment in an Uninsulated Vessel
185(1)
How to Use the Model?
186(3)
Dose--Response Curves
189(16)
The Fermi (Logistic) Distribution
190(6)
The Weibull Distribution
196(4)
Mixed Populations
200(5)
Isothermal and Nonisothermal Bacterial Growth in a Closed Habitat
205(42)
The Traditional Models
205(9)
The Logistic Equation and the Logistic Function
206(5)
The Gompertz, Baranyi and Roberts, and Other Growth Models
211(2)
The Lag Time
213(1)
The Logistic--Fermi Combination Model
214(6)
Simulation of Nonisothermal Growth Pattern Using the Logistic--Fermi Model
220(11)
Monotonic Temperature Histories
226(1)
Regular and Random Temperature Oscillations
226(5)
Prediction of Nonisothermal Growth Patterns from Isothermal Growth Data
231(16)
The Growth of Pseudomonas in Refrigerated Fish
235(6)
The Growth of E. coli
241(6)
Interpretation of Fluctuating Microbial Count Records in Foods and Water
247(30)
Microbial Quality Control in a Food Plant
249(1)
The Origins and Nature of Microbial Count Fluctuations
250(1)
Asymmetry between Life and Death
251(1)
Estimating the Frequency of Future Outbursts---the Principle
252(2)
Testing the Counts' Independence
254(4)
Uneven Rounding and Record Derounding
258(3)
Choosing a Distribution Function
261(8)
Nonparametric Distributions
261(1)
Parametric Distributions
262(1)
Calculation of a Distribution's Parameters
262(3)
The Q--Q Plot
265(2)
Truncated Distributions
267(2)
Exinction and Absence
269(1)
Special Patterns
270(7)
Populations with a Detection Threshold Level
270(3)
Records of Positive/Negative Entries
273(1)
Records with a True or Suspected Trend or Periodicity
274(3)
Estimating Frequencies of Future Microbial High Counts or Outbursts in Foods and Water --- Case Studies
277(44)
Microbial Counts in a Cheese-Based Snack
278(11)
Analysis of Raw Records
278(7)
Analysis of Normalized Data
285(4)
Rating Raw Milk Sources
289(4)
Frozen Foods
293(5)
E. coli in Wash Water of a Poultry Plant
298(9)
Fecal Bacteria in Lake Kinneret
307(14)
Characterization of Count Distributions
311(1)
Nonlogarithmic Transformations of the Counts
311(1)
Finding a Truncated Distribution
312(1)
Distribution of Fecal Bacteria in the Lake's Water
312(3)
Estimating the Frequency of Future Outbursts
315(2)
Issues of Concern
317(4)
A Probabilistic Model of Historic Epidemics
321(12)
The Model
322(2)
Mortality from Smallpox and Measles in 18th Century England
324(7)
Smallpox
324(4)
Measles
328(3)
Potential Uses of the Model
331(2)
Aperiodic Microbial Outbursts with Variable Duration
333(26)
Microbial Fluctuations in a Water Reservoir
334(14)
Determination of Model Parameters
338(2)
Fluctuation Parameters of the Massachusetts Water Reservoir
340(2)
Validation of the Threshold Estimation Method
342(6)
A Model of Pathogen Outbursts in Foods
348(7)
Other Potential Applications of the Model
355(4)
Outstanding Issues and Concluding Remarks
359(20)
Inactivation Models
359(10)
Determination of Survival Parameters from Inactivation Curves Determined under Nonisothermal Conditions
359(3)
Modeling and Predicting Survival Patterns when Several Influential Factors Vary Simultaneously
362(3)
Non-Weibullian Inactivation Patterns
365(1)
Systems in which the Inoculum Size May Affect Inactivation
366(1)
Robustness and Sensitivity
367(1)
Relationship between Survival Parameters and Inactivation Mechanism
368(1)
Alternative Inactivation Technologies
368(1)
Growth Models
369(3)
Terminology
369(1)
Growth under Changing Conditions
369(1)
Growth under Arbitrary Conditions
370(1)
Simultaneous Growth and Inactivation or Inactivation and Growth
371(1)
Fluctuating Records in Water and Foods
372(2)
Censored Data
372(1)
Sampling at Different Locations
373(1)
Risk Assessment
373(1)
A Few Last Remarks
374(5)
References 379(10)
Freeware 389(2)
Index 391


Peleg, Micha