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Nondestructive Measurement in Food and Agro-products 2015 ed. [Hardback]

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  • Formāts: Hardback, 407 pages, height x width: 235x155 mm, weight: 842 g, 58 Tables, black and white; 90 Illustrations, color; 55 Illustrations, black and white; XII, 407 p. 145 illus., 90 illus. in color., 1 Hardback
  • Izdošanas datums: 30-Mar-2015
  • Izdevniecība: Springer
  • ISBN-10: 9401796750
  • ISBN-13: 9789401796750
Citas grāmatas par šo tēmu:
Nondestructive Measurement in Food and Agro-products 2015 ed.Palielināt
  • Formāts: Hardback, 407 pages, height x width: 235x155 mm, weight: 842 g, 58 Tables, black and white; 90 Illustrations, color; 55 Illustrations, black and white; XII, 407 p. 145 illus., 90 illus. in color., 1 Hardback
  • Izdošanas datums: 30-Mar-2015
  • Izdevniecība: Springer
  • ISBN-10: 9401796750
  • ISBN-13: 9789401796750
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9789401796750.html
Keywords:
This book is composed of 8 chapters, each focusing on a major non-destructive technique, including optical, acoustic and biological methods. The content of each chapter is based on the author’s studies and current research developments. The book is aimed at graduate students, senior undergraduate students, and researchers in academia and industry. It will be particularly interesting for researchers in the fields of food, agricultural engineering, biotechnology and applied mathematics. It will also serve as a useful reference source for people working in the design and manufacture of non-destructive devices for food and agricultural products.
1 Introduction 1(10)
1.1 Food Quality and Safety
2(1)
1.2 Method for Food Quality and Safety Assessment
3(1)
1.3 Nondestructive Measurement Technology in Food Science and Technology
4(3)
Summary
7(1)
References and Further Reading
8(3)
2 Machine Vision Online Measurements 11(46)
2.1 Introduction
12(1)
2.2 Images Acquisition System
13(6)
2.2.1 Lighting System
13(1)
2.2.2 Camera
14(2)
2.2.3 Lens
16(3)
2.3 Image Processing
19(3)
2.3.1 Image Segmentation
20(1)
2.3.2 Image Interpretation and Classification
21(1)
2.4 Applications of Machine Vision in Food and Agricultural Products
22(4)
2.4.1 Applications
22(1)
2.4.2 Online Machine Vision Applications
22(4)
2.5 Machine Vision for Apples Grading
26(16)
2.5.1 Machine Vision System for Apple Shape and Color Grading
26(6)
2.5.2 Apples Defects Detection by Three-Color-Camera System
32(10)
2.6 Machine Vision Online Sorting Maturity of Cherry Tomato
42(3)
2.6.1 Hardware of the Detection System
42(1)
2.6.2 Image Analysis
42(2)
2.6.3 Sorting Results
44(1)
2.7 Machine Vision Online Detection Quality of Soft Capsules
45(3)
2.7.1 The Hardware of Soft Capsule Online Grading System
46(1)
2.7.2 Image Process
47(1)
2.7.3 Sorting Results
48(1)
Summary
48(2)
References
50(7)
3 NIR Spectroscopy Detection 57(70)
3.1 Introduction
59(2)
3.2 A Brief Review of Regression Methods in NIR
61(5)
3.2.1 Calibration and Validation
61(2)
3.2.2 Multiple linear Regression, Principal Component Regression, and Partial Least-Squares Regression
63(3)
3.3 Variable Selection Methods
66(28)
3.3.1 Manual Approaches: Knowledge-Based Selection
68(1)
3.3.2 Variable Selection by Single-Term Linear Regression and Multiterm Regression
69(2)
3.3.3 Successive Projections Algorithm and Uninformative Variable Elimination
71(4)
3.3.4 Simulated Annealing, Artificial Neural Networks, and Genetic Algorithm ACO
75(11)
3.3.5 Interval Selection Method
86(8)
3.3.6 Other Wavelength Selection Methods and Software of Wavelength Selection Methods
94(1)
3.4 Apple Soluble Solid Content Determination by NIR by Different iPLS Model
94(15)
3.4.1 Apple NIR Spectroscopy Acquisition and Preprocessing
96(6)
3.4.2 Determination of Apple SSC by Different PLS Models
102(4)
3.4.3 Determination of Apple SSC by the most Predictive Models
106(3)
3.5 Near-Infrared Quantitative Analysis of Pigment in Cucumber Leaves
109(9)
3.5.1 Plant Material and NIR Acquisition
109(2)
3.5.2 Quantitative Predication of Pigment in Cucumber Leaves
111(6)
3.5.3 Results Summary and Conclusion
117(1)
Summary
118(1)
References
119(8)
4 Hyperspectral Imaging Detection 127(68)
4.1 Introduction
129(4)
4.1.1 Spectral Band Usage and Chemical Imaging
129(3)
4.1.2 Hyperspectral Imaging
132(1)
4.2 Hyperspectral Images Acquisition and Investigation
133(10)
4.2.1 Hyperspectral Image Acquisition
133(9)
4.2.2 Hyperspectral Image Preprocess
142(1)
4.3 PCA and ICA Analysis in Hyperspectral
143(7)
4.3.1 Principal Component Analysis
145(2)
4.3.2 Independent Component Analysis
147(1)
4.3.3 PCA and ICA in Spatial Way
148(1)
4.3.4 PCA and ICA in Spectral Way
149(1)
4.4 Applications for Food Quality and Safety Analysis
150(7)
4.5 Hyperspectral Imaging for Quantitative Analysis of Pigments in Leaves
157(18)
4.5.1 Quantitative Analysis of Pigments in Leaves
157(2)
4.5.2 Hyperspectral Imaging Detection of Chlorophyll Distribution in Cucumber (Cucumis sativus) Leaves
159(5)
4.5.3 Chlorophyll Spectral Indices for Quantity Determination
164(6)
4.5.4 PCA and ICA in Information Extraction
170(3)
4.5.5 Estimating Chlorophyll Concentration in each Pixel of the Leaf
173(2)
4.6 Hyperspectral Imaging Detection of Total Flavonoids in Ginkgo Leaves
175(5)
4.6.1 Fresh Ginkgo Leaf Samples and Total Flavonoid Content Determination
176(2)
4.6.2 Acquisition of Hyperspectral Images and Extraction of Spectral Features
178(1)
4.6.3 MLR Calibration Model of Total Flavonoid Content
178(2)
Summary
180(2)
References
182(13)
5 Electronic Nose Measurements 195(56)
5.1 Introduction
197(5)
5.1.1 Electronic Nose Mimics Human Olfaction
197(1)
5.1.2 Structure of Electronic Nose
198(4)
5.1.3 Applications of Electronic Nose in Food Analysis
202(1)
5.2 Sensor Technologies
202(16)
5.2.1 Fiber Optic Sensors
207(2)
5.2.2 Semiconductive Gas Sensors
209(2)
5.2.3 Silicon Carbide-Based Gas Sensors
211(1)
5.2.4 Conducting Polymer-Based Sensors
212(2)
5.2.5 Mechanical Sensor
214(2)
5.2.6 Biosensor
216(2)
5.3 Electronic Nose Data Analysis
218(9)
5.3.1 Preprocessing Techniques for Gas Sensor Arrays
220(1)
5.3.2 Dimensionality Reduction
221(2)
5.3.3 Pattern Recognition
223(4)
5.4 An Example of Electronic Nose in Apple Aroma Detection
227(13)
5.4.1 Electronic Nose
227(2)
5.4.2 Apple's Aroma Determined by Electronic Nose and Gas Chromatography Combined with Mass Spectrometry
229(2)
5.4.3 Measure Results
231(9)
Summary
240(1)
References
241(10)
6 Colorimetric Sensors Measurement 251(38)
6.1 Introduction
252(3)
6.1.1 Fundamental Flaw of Normal Electronic Nose Systems
252(1)
6.1.2 Olfactory-Like Responses Converted to a Visual Output
253(1)
6.1.3 Design of a Colorimetric Sensor Array
253(2)
6.2 Porphyrins and Metalloporphyrins
255(6)
6.2.1 The Chemical Properties of Porphyrins and Metalloporphyrins
255(2)
6.2.2 Metalloporphyrins, Supporting Materials, and Corresponding Organic Compounds
257(4)
6.3 Colorimetric Sensors Measurement System
261(6)
6.3.1 Sensor Array
261(1)
6.3.2 Measurement System
262(1)
6.3.3 Sensitivity
263(1)
6.3.4 Chemometrics, Reproducibility, and Resolution
264(2)
6.3.5 Humidity Interference
266(1)
6.4 Colorimetric Sensors Measurements in the Vapor of Chemicals and Food
267(18)
6.4.1 Colorimetric Sensors Measurements in Chemicals Vapor
267(1)
6.4.2 Colorimetric Sensors Measurements in Food
268(2)
6.4.3 Traditional Vinegars Identification by Colorimetric Sensor
270(6)
6.4.4 Determination of Pork Spoilage by Colorimetric Gas Sensor Array Based on Natural Pigments
276(9)
References
285(4)
7 Acoustic Measurements 289(56)
7.1 Introduction
290(4)
7.1.1 The Perception of Sound
290(1)
7.1.2 Basic Principles of Sound for Food Analysis
291(3)
7.2 Sound Measurement Technique
294(6)
7.2.1 Microphone Measurement Technique
294(1)
7.2.2 Ultrasound Measurement Techniques
295(4)
7.2.3 Acoustic—Mechanical Methods
299(1)
7.3 Acoustic Signal Processing
300(4)
7.3.1 Amplitude Analysis of Sound in Food
300(1)
7.3.2 Frequency Analysis of Sounds in Food
301(1)
7.3.3 Other Analyses of Acoustic Signatures in Food
302(1)
7.3.4 Sound Analysis with Mechanical Data
302(2)
7.4 Influence Factors on Sound in Food
304(2)
7.4.1 Processing Conditions
304(1)
7.4.2 Ingredients and Hydration
305(1)
7.4.3 Other Finished Product Properties
305(1)
7.5 Acoustic Measurement in Food
306(7)
7.5.1 Acoustic Measurement Used to Characterize Crisp, Crunchy, and Crackly Food
306(2)
7.5.2 Ultrasound Measurement in Food
308(5)
7.6 Example 1: Eggshell Online Measurement by Acoustic Resonance
313(8)
7.6.1 Eggs and Acoustic Resonance Detection
314(2)
7.6.2 Results and Discussion
316(5)
7.7 Example 2: Determination of Maturity and Juiciness of Melons by Ultrasound
321(10)
7.7.1 Melons and the Tests of Elasticity, Ultrasound, Juiciness
322(5)
7.7.2 Results and Discussion
327(4)
7.8 Example 3: Measurement of Density, Ultrasonic Velocity, and Attenuation of Adulterated Skim Milk
331(7)
7.8.1 Milk and the Measurements of Particle Size, Ultrasound, Density
332(1)
7.8.2 Results
333(5)
Summary
338(1)
Reference
339(6)
8 Sensor Fusion Measurement 345(24)
8.1 Introduction to Sensor Fusion
345(4)
8.1.1 The Purpose of Sensor Management
346(1)
8.1.2 The Role of Sensor Management in Information Fusion
347(1)
8.1.3 Multisensor Management Architectures
348(1)
8.2 Sensor Fusion Method in Food and Agricultural Products
349(8)
8.2.1 Attributes Associated with Organoleptic Properties (Step 1)
351(1)
8.2.2 Reference Methods for Produce Quality Assessment (Step 2)
351(1)
8.2.3 Nondestructive Methods for Produce Quality Assessment (Step 3)
351(1)
8.2.4 Data Acquisition (Step 4)
352(1)
8.2.5 Level of Redundancy or Complementarity in the Nondestructive Sensors (Step 5)
352(1)
8.2.6 Selecting and Applying the Proper Sensor Fusion Method (Step 6)
353(3)
8.2.7 Evaluation of the Sensor Fusion System (Step 7)
356(1)
8.2.8 Acceptance, Rejection, or Improvement of the Sensor Fusion System (Step 8)
356(1)
8.3 Sensor Fusion in Food and Agricultural Products
357(2)
8.4 Quality Assessment of Apples by Fusion Machine Vision, NIR Spectrophotometer, and EN Information
359(6)
8.4.1 Three-Sensor Combination System
360(3)
8.4.2 Apple Quality Determination by Sensor Fusion Techniques
363(2)
Summary
365(1)
References
365(4)
9 Other Nondestructive Measurement Technologies 369
9.1 X-ray Measurement
370(10)
9.1.1 Transmission Imaging Measurement
371(2)
9.1.2 X-ray Computed Microtomography Measurement
373(1)
9.1.3 X-ray Fluorescent Spectroscopy Measurement
374(3)
9.1.4 Small-Angle X-ray Scattering Measurement
377(3)
9.2 Raman Spectroscopy Technique
380(10)
9.2.1 Introduction to Raman Spectroscopy in Food and Agro-products
381(1)
9.2.2 Raman Spectroscopy Equipment
382(4)
9.2.3 Raman Spectrospectry in Food and Agricultural Products
386(4)
9.3 Nuclear Magnetic Resonance
390(5)
9.3.1 Principle of NMR and MM in Food Measurement
391(1)
9.3.2 Application of NMR Spectroscopy in Food
392(1)
9.3.3 NMR Nuclear magnetic resonanceMRI Measurement in Food
393(1)
9.3.4 NMR Combined with Other Technologies
394(1)
9.4 Terahertz Spectroscopy and Imaging
395(5)
9.4.1 Terahertz Spectroscopy Systems
396(2)
9.4.2 Terahertz Measurement in Food
398(1)
9.4.3 Challenges and Limitations
399(1)
Summary
400(1)
Reference
401
Xiaobo Zou is a professor from School of Food and Biological Engineering, Jiangsu University. His research interests are in the area of quality evaluation of food and agricultural products. He has published more than 70 articles in this field, among which 40 are indexed by EI and 30 are indexed by SCI. Jiewen Zhao is a professor from School of Food and Biological Engineering, Jiangsu University. His research focuses on non-destructive measurement in agro-products and robot application in agriculture. He has published more than 140 articles, among which 70 are indexed by EI and SCI.