Automation for food engineering : food quality quantization and process control

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.

Author Notes

Ronald E. Lacey is Associate Professor of Agricultural Engineering at Texas A&M University.

Chapter 1 Introduction	p. 1
1.1 Food quality: a primary concern of the food industry	p. 1
1.2 Automated evaluation of food quality	p. 1
1.3 Food quality quantization and process control	p. 2
1.4 Typical problems in food quality evaluation and process control	p. 7
1.4.1 Beef quality evaluation	p. 7
1.4.2 Food odor measurement	p. 8
1.4.3 Continuous snack food frying quality process control	p. 8
1.5 How to learn the technologies	p. 10
References	p. 10
Chapter 2 Data acquisition	p. 11
2.1 Sampling	p. 11
2.1.1 Example: Sampling for beef grading	p. 13
2.1.2 Example: Sampling for detection of peanut off-flavors	p. 16
2.1.3 Example: Sampling for meat quality evaluation	p. 19
2.1.4 Example: Sampling for snack food eating quality evaluation	p. 20
2.1.5 Example: Sampling for snack food frying quality process control	p. 21
2.2 Concepts and systems for data acquisition	p. 22
2.2.1 Example: Ultrasonic A-mode signal acquisition for beef grading	p. 26
2.2.2 Example: Electronic nose data acquisition for food odor measurement	p. 28
2.2.3 Example: Snack food frying data acquisition for quality process control	p. 31
2.3 Image acquisition	p. 33
2.3.1 Example: Image acquisition for snack food quality evaluation	p. 34
2.3.2 Example: Ultrasonic B-mode imaging for beef grading	p. 36
2.3.3 Example: Elastographic imaging for meat quality evaluation	p. 37
References	p. 43
Chapter 3 Data analysis	p. 49
3.1 Data preprocessing	p. 49
3.2 Data analysis	p. 54
3.2.1 Static data analysis	p. 54
3.2.1.1 Example: Ultrasonic A-mode signal analysis for beef grading	p. 56
3.2.1.2 Example: Electronic nose data analysis for detection of peanut off-flavors	p. 63
3.2.2 Dynamic data analysis	p. 66
3.2.2.1 Example: Dynamic data analysis of the snack food frying process	p. 68
3.3 Image processing	p. 71
3.3.1 Image segmentation	p. 71
3.3.1.1 Example: Segmentation of elastograms for detection of hard objects in packaged beef rations	p. 74
3.3.2 Image feature extraction	p. 74
3.3.2.1 Example: Morphological and Haralick's statistical textural feature extraction from images of snack food samples	p. 87
3.3.2.2 Example: Feature extraction from ultrasonic B-mode images for beef grading	p. 89
3.3.2.3 Example: Haralick's statistical textural feature extraction from meat elastograms	p. 90
3.3.2.4 Example: Wavelet textural feature extraction from meat elastograms	p. 90
References	p. 97
Chapter 4 Modeling	p. 99
4.1 Modeling strategy	p. 99
4.1.1 Theoretical and empirical modeling	p. 99
4.1.2 Static and dynamic modeling	p. 101
4.2 Linear statistical modeling	p. 104
4.2.1 Example: Linear statistical modeling based on ultrasonic A-mode signals for beef grading	p. 113
4.2.2 Example: Linear statistical modeling for food odor pattern recognition by an electronic nose	p. 114
4.2.3 Example: Linear statistical modeling for meat attribute prediction based on textural features extracted from ultrasonic elastograms	p. 115
4.2.4 Example: Linear statistical dynamic modeling for snack food frying process control	p. 117
4.3 ANN modeling	p. 121
4.3.1 Example: ANN modeling for beef grading	p. 130
4.3.2 Example: ANN modeling for food odor pattern recognition by an electronic nose	p. 131
4.3.3 Example: ANN modeling for snack food eating quality evaluation	p. 132
4.3.4 Example: ANN modeling for meat attribute prediction	p. 133
4.3.5 Example: ANN modeling for snack food frying process control	p. 137
References	p. 140
Chapter 5 Prediction	p. 143
5.1 Prediction and classification	p. 143
5.1.1 Example: Sample classification for beef grading based on linear statistical and ANN models	p. 144
5.1.2 Example: Electronic nose data classification for food odor pattern recognition	p. 146
5.1.3 Example: Snack food classification for eating quality evaluation based on linear statistical and ANN models	p. 148
5.1.4 Example: Meat attribute prediction based on linear statistical and ANN models	p. 149
5.2 One-step-ahead prediction	p. 150
5.2.1 Example: One-step-ahead prediction for snack food frying process control	p. 152
5.3 Multiple-step-ahead prediction	p. 154
5.3.1 Example: Multiple-step-ahead prediction for snack food frying process control	p. 162
References	p. 165
Chapter 6 Control	p. 167
6.1 Process control	p. 167
6.2 Internal model control	p. 168
6.2.1 Example: ANNIMC for the snack food frying process	p. 179
6.3 Predictive control	p. 184
6.3.1 Example: Neuro-fuzzy PDC for snack food frying process	p. 196
References	p. 200
Chapter 7 Systems integration	p. 201
7.1 Food quality quantization systems integration	p. 201
7.2 Food quality process control systems integration	p. 203
7.3 Food quality quantization and process control systems development	p. 207
7.4 Concluding remarks	p. 211
References	p. 212
Index	p. 213

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