Nondestructive Measurement in Food and Agro-products

415 Pages·2015·16.34 MB·English

by  Xiaobo Zou

Nondestructive Measurement in Food
and Agro-products
Xiaobo Zou • Jiewen Zhao
Nondestructive Measurement
in Food and Agro-products
1 3
Xiaobo Zou Jiewen Zhao
Food and Biological Engineering School of Food and Biological Engineering
Jiangsu University Jiangsu University
Zhenjiang Zhenjiang
China China

ISBN 978-94-017-9675-0 ISBN 978-94-017-9676-7 (eBook)
DOI 10.1007/978-94-017-9676-7
Springer Dordrecht Heidelberg New York London
Jointly published with Science Press, Beijing
ISBN: 978-7-03-043259-9 Science Press, Beijing
Library of Congress Control Number: 2014960208
© Science Press, Beijing and Springer Science+Business Media Dordrecht 2015
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Preface
The quality and safety of food and agro-products is a growing concern in global
trade. In recent years, nondestructive measurement (NDM) methods of quality and
safety evaluation have gained momentum and considerable attempts have been
made to develop them for objective, consistent, and accurate testing. Although well
developed in developed countries, this technology has only recently begun to gener-
ate interest in China and is developing slowly. Much research is now being directed
toward the development of nondestructive measurement devices that are versatile,
economical, and simple to use. Emphasis has been placed on the new and emerging
methods and applications.
Nondestructive measurement in food and agro-products is organized by the au-
thors’ recent topic areas. This book is divided into nine chapters; except for Chap. 1,
each chapter focuses on a major nondestructive technique, including optical, acous-
tic, and biological methods. Compared to other edited books on this topic, we con-
clude with what, in our opinion and works, is the highlight of this book.
Before introducing the technologies, a short introduction to NDM is given in
Chap. 1. Machine vision is a powerful technique to extract and quantify features
for food and agro-products assessment and control. Chapter 2 highlights the con-
struction and imaging processing of online detection by machine vision. Near in-
frared (NIR) spectroscopy has increasingly been adopted as an analytical tool in
various fields, such as the petrochemical, pharmaceutical, environmental, clinical,
agricultural, food, and biomedical sectors. Chapter 3 focuses on the variable selec-
tion methods and model simplified methods in NIR spectroscopy measurement.
Hyperspectral imaging is a powerful technique for providing high-quality spectral
and spatial information on samples. One of the main features of measurements with
any hyperspectral imaging methodology is the great amount of information col-
lected for one sample. Chapter 4 introduces principal component analysis (PCA)
and independent component analysis (ICA), which are powerful tools for data com-
pression and information extraction, along with how to use them to obtain useful
features from hyperspectral images. Electronic nose instruments are designed to
mimic the human olfactory system; they consist of an array of sensors and artificial
intelligence that achieve a faster and more objective system for evaluating aromas.
Chapter 5 introduces the most popular sensors used to develop electronic noses
v
vi Preface
for use within the food industry, and the most pattern recognition methods in elec-
tronic noses. Chapter 6 introduces the colorimetric sensors technique which can
detect a wide range of odorants using a family of metalloporphyrins immobilized on
reverse-phase silica and on hydrophobic membranes. In Chap. 7, acoustic measure-
ment is introduced, and applications of acoustic properties to monitor food quality
are described. The nondestructive evaluation of food produce requires various sen-
sors, which are more than a simple accumulation of independent sensors. Sensor
fusion provides a collaborative approach with those sensors, in order to improve
the quality assessment of the product and assure the consumer high-quality pro-
duce. In Chap. 8, a methodology of sensor fusion proposed by Steinmetz has been
introduced. An example of the three-sensor combination system for apple quality
measurement is also discussed. Chapter 9 mainly reviews the most recent develop-
ment in nondestructive techniques for food and agro-product quality analysis, such
as X-ray, Raman, magnetic resonance imaging (MRI), and Terahertz imaging.
We have written this book for graduate students, senior undergraduate students,
and researchers in academia and industry. The book should be particularly interest-
ing for researchers in the fields of food, agricultural engineering, biotechnology,
and applied mathematics. It can also serve as a handy reference for people directly
involved in the design and manufacture of nondestructive devices in food and agri-
cultural products. We hope that this book will foster better interactions, facilitate a
better appreciation of all perspectives, and help in advancing nondestructive mea-
surement in food and agricultural products.
The book is also intended to serve as a general reference for both researchers
and scientists within the food and agricultural science field as well as postgraduate
students. Each chapter includes references to the corresponding literature to serve
as valuable entry points to anyone wanting to move forward in the field, either as a
practitioner or for acquiring state-of-the-art knowledge.
Contents
1 Introduction ................................................................................................ 1
1.1 Food Quality and Safety ...................................................................... 2
1.2 Method for Food Quality and Safety Assessment ............................... 3
1.3 Nondestructive Measurement Technology
in Food Science and Technology ........................................................ 4
Summary ...................................................................................................... 7
References and Further Reading .................................................................. 8
2 M achine Vision Online Measurements .................................................... 11
2.1 Introduction ......................................................................................... 12
2.2 Images Acquisition System ................................................................. 13
2.2.1 Lighting System ...................................................................... 13
2.2.2 Camera .................................................................................... 14
2.2.3 Lens ......................................................................................... 16
2.3 Image Processing ................................................................................ 19
2.3.1 Image Segmentation ................................................................ 20
2.3.2 Image Interpretation and Classification .................................. 21
2.4 A pplications of Machine Vision in Food
and Agricultural Products .................................................................... 22
2.4.1 Applications............................................................................. 22
2.4.2 Online Machine Vision Applications ...................................... 22
2.5 Machine Vision for Apples Grading.................................................... 26
2.5.1 Machine Vision System for Apple Shape
and Color Grading ................................................................... 26
2.5.2 Apples Defects Detection by Three-Color-Camera System .... 32
2.6 Machine Vision Online Sorting Maturity of Cherry Tomato .............. 42
2.6.1 Hardware of the Detection System ......................................... 42
2.6.2 Image Analysis ........................................................................ 42
2.6.3 Sorting Results ........................................................................ 44
2.7 Machine Vision Online Detection Quality of Soft Capsules............... 45
2.7.1 T he Hardware of Soft Capsule Online Grading System ......... 46
2.7.2 Image Process.......................................................................... 47
vii
viii Contents
2.7.3 Sorting Results ...................................................................... 48
Summary .................................................................................................... 48
References .................................................................................................. 50
3 N IR Spectroscopy Detection ................................................................... 57
3.1 Introduction ....................................................................................... 59
3.2 A Brief Review of Regression Methods in NIR ................................ 61
3.2.1 Calibration and Validation ..................................................... 61
3.2.2 Multiple linear Regression, Principal Component
Regression, and Partial Least-Squares Regression ............... 63
3.3 V ariable Selection Methods ............................................................... 66
3.3.1 Manual Approaches: Knowledge-Based Selection ............... 68
3.3.2 V ariable Selection by Single-Term Linear Regression
and Multiterm Regression ..................................................... 69
3.3.3 Successive Projections Algorithm and
Uninformative Variable Elimination ..................................... 71
3.3.4 Simulated Annealing, Artificial Neural Networks,
and Genetic Algorithm ACO ................................................. 75
3.3.5 Interval Selection Method ..................................................... 86
3.3.6 Other Wavelength Selection Methods and Software
of Wavelength Selection Methods ......................................... 94
3.4 Apple Soluble Solid Content Determination by NIR
by Different iPLS Model ................................................................... 94
3.4.1 A pple NIR Spectroscopy Acquisition and Preprocessing ..... 96
3.4.2 Determination of Apple SSC by Different PLS Models ....... 102
3.4.3 Determination of Apple SSC by
the most Predictive Models ................................................... 106
3.5 Near-Infrared Quantitative Analysis of Pigment
in Cucumber Leaves .......................................................................... 109
3.5.1 Plant Material and NIR Acquisition ...................................... 109
3.5.2 Quantitative Predication of Pigment in Cucumber Leaves ... 111
3.5.3 Results Summary and Conclusion ........................................ 117
Summary .................................................................................................... 118
References .................................................................................................. 119
4 Hyperspectral Imaging Detection ........................................................... 127
4.1 Introduction ....................................................................................... 129
4.1.1 Spectral Band Usage and Chemical Imaging ........................ 129
4.1.2 Hyperspectral Imaging .......................................................... 132
4.2 Hyperspectral Images Acquisition and Investigation ........................ 133
4.2.1 Hyperspectral Image Acquisition .......................................... 133
4.2.2 Hyperspectral Image Preprocess ........................................... 142
4.3 PCA and ICA Analysis in Hyperspectral .......................................... 143
4.3.1 Principal Component Analysis .............................................. 145
4.3.2 Independent Component Analysis......................................... 147
4.3.3 PCA and ICA in Spatial Way ................................................ 148
Contents ix
4.3.4 PCA and ICA in Spectral Way .............................................. 149
4.4 A pplications for Food Quality and Safety Analysis .......................... 150
4.5 Hyperspectral Imaging for Quantitative Analysis
of Pigments in Leaves ....................................................................... 157
4.5.1 Quantitative Analysis of Pigments in Leaves........................ 157
4.5.2 Hyperspectral Imaging Detection of Chlorophyll
Distribution in Cucumber (Cucumis sativus) Leaves ........... 159
4.5.3 Chlorophyll Spectral Indices for Quantity Determination .... 164
4.5.4 PCA and ICA in Information Extraction ............................... 170
4.5.5 Estimating Chlorophyll Concentration in each Pixel
of the Leaf ............................................................................. 173
4.6 Hyperspectral Imaging Detection of Total Flavonoids
in Ginkgo Leaves .............................................................................. 175
4.6.1 Fresh Ginkgo Leaf Samples and Total Flavonoid
Content Determination .......................................................... 176
4.6.2 A cquisition of Hyperspectral Images and Extraction
of Spectral Features ............................................................... 178
4.6.3 MLR Calibration Model of Total Flavonoid Content ........... 178
Summary .................................................................................................... 180
References .................................................................................................. 182
5 Electronic Nose Measurements ............................................................... 195
5.1 Introduction ....................................................................................... 197
5.1.1 Electronic Nose Mimics Human Olfaction ........................... 197
5.1.2 Structure of Electronic Nose ................................................. 198
5.1.3 A pplications of Electronic Nose in Food Analysis ............... 202
5.2 Sensor Technologies .......................................................................... 202
5.2.1 Fiber Optic Sensors ............................................................... 207
5.2.2 Semiconductive Gas Sensors ................................................ 209
5.2.3 Silicon Carbide-Based Gas Sensors ...................................... 211
5.2.4 Conducting Polymer-Based Sensors ..................................... 212
5.2.5 Mechanical Sensor ................................................................ 214
5.2.6 Biosensor ............................................................................... 216
5.3 Electronic Nose Data Analysis .......................................................... 218
5.3.1 Preprocessing Techniques for Gas Sensor Arrays ................. 220
5.3.2 Dimensionality Reduction ..................................................... 221
5.3.3 Pattern Recognition ............................................................... 223
5.4 An Example of Electronic Nose in Apple Aroma Detection ............. 227
5.4.1 Electronic Nose ..................................................................... 227
5.4.2 A pple’s Aroma Determined by Electronic Nose
and Gas Chromatography Combined with
Mass Spectrometry ................................................................ 229
5.4.3 Measure Results .................................................................... 231
Summary .................................................................................................... 240
References .................................................................................................. 241
x Contents
6 Colorimetric Sensors Measurement ....................................................... 251
6.1 Introduction ....................................................................................... 252
6.1.1 Fundamental Flaw of Normal Electronic Nose Systems ...... 252
6.1.2 Olfactory-Like Responses Converted to a Visual Output ..... 253
6.1.3 Design of a Colorimetric Sensor Array ................................. 253
6.2 Porphyrins and Metalloporphyrins .................................................... 255
6.2.1 T he Chemical Properties of Porphyrins
and Metalloporphyrins .......................................................... 255
6.2.2 Metalloporphyrins, Supporting Materials,
and Corresponding Organic Compounds .............................. 257
6.3 Colorimetric Sensors Measurement System ..................................... 261
6.3.1 Sensor Array .......................................................................... 261
6.3.2 Measurement System ............................................................ 262
6.3.3 Sensitivity .............................................................................. 263
6.3.4 Chemometrics, Reproducibility, and Resolution .................. 264
6.3.5 Humidity Interference ........................................................... 266
6.4 Colorimetric Sensors Measurements in the Vapor
of Chemicals and Food ...................................................................... 267
6.4.1 Colorimetric Sensors Measurements
in Chemicals Vapor ............................................................... 267
6.4.2 Colorimetric Sensors Measurements in Food ....................... 268
6.4.3 T raditional Vinegars Identification
by Colorimetric Sensor ......................................................... 270
6.4.4 Determination of Pork Spoilage by Colorimetric
Gas Sensor Array Based on Natural Pigments ...................... 276
References .................................................................................................. 285
7 A coustic Measurements ........................................................................... 289
7.1 Introduction ....................................................................................... 290
7.1.1 T he Perception of Sound ....................................................... 290
7.1.2 Basic Principles of Sound for Food Analysis ........................ 291
7.2 Sound Measurement Technique ........................................................ 294
7.2.1 Microphone Measurement Technique ................................... 294
7.2.2 Ultrasound Measurement Techniques ................................... 295
7.2.3 A coustic–Mechanical Methods ............................................. 299
7.3 Acoustic Signal Processing ............................................................... 300
7.3.1 A mplitude Analysis of Sound in Food .................................. 300
7.3.2 Frequency Analysis of Sounds in Food ................................. 301
7.3.3 Other Analyses of Acoustic Signatures in Food .................... 302
7.3.4 Sound Analysis with Mechanical Data ................................. 302
7.4 Influence Factors on Sound in Food ................................................. 304
7.4.1 Processing Conditions ........................................................... 304
7.4.2 Ingredients and Hydration ..................................................... 305
7.4.3 Other Finished Product Properties ........................................ 305