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Automation for Food Engineering: Food Quality Quantization and Process Control [Kietas viršelis]

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(Texas A&M University, College Station, Texas, USA), (Purdue University School of Agriculture, West Lafayette, IN,), (Texas A&M University, College Station, Texas, USA)
  • Formatas: Hardback, 240 pages, aukštis x plotis: 234x156 mm, weight: 600 g, 34 Tables, black and white; 100 Illustrations, black and white
  • Serija: Contemporary Food Science
  • Išleidimo metai: 28-Jun-2001
  • Leidėjas: CRC Press Inc
  • ISBN-10: 0849322308
  • ISBN-13: 9780849322303
Kitos knygos pagal šią temą:
Automation for Food Engineering: Food Quality Quantization and Process ControlDidesnis paveikslėlis
  • Formatas: Hardback, 240 pages, aukštis x plotis: 234x156 mm, weight: 600 g, 34 Tables, black and white; 100 Illustrations, black and white
  • Serija: Contemporary Food Science
  • Išleidimo metai: 28-Jun-2001
  • Leidėjas: CRC Press Inc
  • ISBN-10: 0849322308
  • ISBN-13: 9780849322303
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780849322303.html
Raktažodžiai:
In the past ten years electronics and computer technologies have significantly pushed forward the progress of automation in the food industry. The application of these technologies to automation for food engineering will produce more nutritious, better quality, and safer items for consumers. Automation for Food Engineering: Food Quality Quantization and Process Control explores the usage of advanced methods, such as wavelet analysis and artificial neural networks, to automated food quality evaluation and process control. It introduces novel system prototypes, such as machine vision, elastography, and the electronic nose, for food quality measurement, analysis, and prediction.

The book discusses advanced techniques, such as medical imaging, mathematical analysis, and statistical modeling, which have proven successful in food engineering. The authors use the characteristics of food processes to describe concepts, and they employ data from food engineering applications to explain the methods. To aid in the comprehension of technical information, they provide real-world examples and case studies from food engineering projects.

The material covers the frameworks, techniques, designs, algorithms, tests and implementation of data acquisition, analysis, modeling, prediction, and control in automation for food engineering. It demonstrates the techniques for automation of food engineering, and helps you in the development of techniques for your own applications. Automation for Food Engineering: Food Quality Quantization and Process Control is the first and only book that gives a systematical study and summary about concepts, principles, methods, and practices in food quality quantization and process control.
Introduction
1(10)
Food quality: a primary concern of the food industry
1(1)
Automated evaluation of food quality
1(1)
Food quality quantization and process control
2(5)
Typical problems in food quality evaluation and process control
7(3)
Beef quality evaluation
7(1)
Food odor measurement
8(1)
Continuous snack food frying quality process control
8(2)
How to learn the technologies
10(1)
References
10(1)
Data acquisition
11(38)
Sampling
11(11)
Example: Sampling for beef grading
13(3)
Example: Sampling for detection of peanut off-flavors
16(3)
Example: Sampling for meat quality evaluation
19(1)
Example: Sampling for snack food eating quality evaluation
20(1)
Example: Sampling for snack food frying quality process control
21(1)
Concepts and systems for data acquisition
22(11)
Example: Ultrasonic A-mode signal acquisition for beef grading
26(2)
Example: Electronic nose data acquisition for food odor measurement
28(3)
Example: Snack food frying data acquisition for quality process control
31(2)
Image acquisition
33(16)
Example: Image acquisition for snack food quality evaluation
34(2)
Example: Ultrasonic B-mode imaging for beef grading
36(1)
Example: Elastographic imaging for meat quality evaluation
37(6)
References
43(6)
Data analysis
49(50)
Data preprocessing
49(5)
Data analysis
54(17)
Static data analysis
54(2)
Example: Ultrasonic A-mode signal analysis for beef grading
56(7)
Example: Electronic nose data analysis for detection of peanut off-flavors
63(3)
Dynamic data analysis
66(2)
Example: Dynamic data analysis of the snack food frying process
68(3)
Image processing
71(28)
Image segmentation
71(3)
Example: Segmentation of elastograms for detection of hard objects in packaged beef rations
74(1)
Image feature extraction
74(13)
Example: Morphological and Haralick's statistical textural feature extraction from images of snack food samples
87(2)
Example: Feature extraction from ultrasonic B-mode images for beef grading
89(1)
Example: Haralick's statistical textural feature extraction from meat elastograms
90(1)
Example: Wavelet textural feature extraction from meat elastograms
90(7)
References
97(2)
Modeling
99(44)
Modeling strategy
99(5)
Theoretical and empirical modeling
99(2)
Static and dynamic modeling
101(3)
Linear statistical modeling
104(17)
Example: Linear statistical modeling based on ultrasonic A-mode signals for beef grading
113(1)
Example: Linear statistical modeling for food odor pattern recognition by an electronic nose
114(1)
Example: Linear statistical modeling for meat attribute prediction based on textural features extracted from ultrasonic elastograms
115(2)
Example: Linear statistical dynamic modeling for snack food frying process control
117(4)
ANN modeling
121(22)
Example: ANN modeling for beef grading
130(1)
Example: ANN modeling for food odor pattern recognition by an electronic nose
131(1)
Example: ANN modeling for snack food eating quality evaluation
132(1)
Example: ANN modeling for meat attribute prediction
133(4)
Example: ANN modeling for snack food frying process control
137(3)
References
140(3)
Prediction
143(24)
Prediction and classification
143(7)
Example: Sample classification for beef grading based on linear statistical and ANN models
144(2)
Example: Electronic nose data classification for food odor pattern recognition
146(2)
Example: Snack food classification for eating quality evaluation based on linear statistical and ANN models
148(1)
Example: Meat attribute prediction based on linear statistical and ANN models
149(1)
One-step-ahead prediction
150(4)
Example: One-step-ahead prediction for snack food frying process control
152(2)
Multiple-step-ahead prediction
154(13)
Example: Multiple-step-ahead prediction for snack food frying process control
162(3)
References
165(2)
Control
167(34)
Process control
167(1)
Internal model control
168(16)
Example: ANNIMC for the snack food frying process
179(5)
Predictive control
184(17)
Example: Neuro-fuzzy PDC for snack food frying process
196(4)
References
200(1)
Systems integration
201(12)
Food quality quantization systems integration
201(2)
Food quality process control systems integration
203(4)
Food quality quantization and process control systems development
207(4)
Concluding remarks
211(2)
References
212(1)
Index 213


Yanbo Huang, A. Dale Whittaker, Ronald E. Lacey