1 VISUAL QUANTIFICATION OF NON-HOMOGENEOUS COLORS IN FOODS By JOSE ALEJANDRO APARICIO A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2007
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VISUAL QUANTIFICATION OF NON-HOMOGENEOUS COLORS IN FOODS
By
JOSE ALEJANDRO APARICIO
A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE
2 LITERATURE REVIEW .......................................................................................................17
Color of Foods and Agricultural Materials.............................................................................17 Instrumental Color Measurement in Agricultural Food Products ..........................................17 Computer Vision or Machine Vision System.........................................................................18 Bakery Products......................................................................................................................20 Red Meat and Seafood............................................................................................................20 Vegetables...............................................................................................................................23 Fruits .......................................................................................................................................23 Prepared Consumer Foods......................................................................................................24 Food Container Inspection......................................................................................................24 Grains......................................................................................................................................25 Other Applications..................................................................................................................25 Visual Texture Analysis .........................................................................................................26 Visual Texture Applications in Agriculture ...........................................................................27 Correlation between Image and Visual Color Analysis .........................................................27 Preliminary Experiments ........................................................................................................28 Objectives of the Study...........................................................................................................30
3 MATERIALS AND METHODS ...........................................................................................31
Mangos and Nectarines...........................................................................................................31 Image Acquisition...................................................................................................................32 Image Analysis .......................................................................................................................32 Experimental Design ..............................................................................................................33 Method of Selection of the Reference Color Bars..................................................................34 Sensory Evaluations................................................................................................................35 Determination of Color Uniformity of Fruit...........................................................................36
Average Color: ................................................................................................................36 Color Blocks....................................................................................................................37 Color Primitives...............................................................................................................37
Calculation of Best Possible ΔE .............................................................................................39
MV Color Results of Fruits ....................................................................................................41 Non-Uniformity Analysis of Fruits ........................................................................................42 Best Possible ΔE.....................................................................................................................44 Sensory Panel Results.............................................................................................................48 Statistical Analysis..................................................................................................................50
4-1 MV color analysis for mangos...........................................................................................41
4-2 MV color analysis for nectarines .......................................................................................41
4-3 Best possible selections and minimum ΔE value possible for 8 references and 2 selections for mangos.........................................................................................................45
4-4 Best possible selections and minimum ΔE value possible for 8 references and 2 selections for nectarines.....................................................................................................45
4-5 Best possible selections and minimum ΔE value possible for 12 references and 2 selections for mangos.........................................................................................................45
4-6 Best possible selections and minimum ΔE value possible for 12 references and 2 selections for nectarines.....................................................................................................46
4-7 Best possible selections and minimum ΔE value possible for 16 references and 2 selections for mangos.........................................................................................................46
4-8 Best possible selections and minimum ΔE value possible for 16 references and 2 selections for nectarines.....................................................................................................46
4-9 Best possible selections and minimum ΔE value possible for 8 references and 4 selections for mangos.........................................................................................................47
4-10 Best possible selections and minimum ΔE value possible for 8 references and 4 selections for nectarines.....................................................................................................47
4-11 Best possible selections and minimum ΔE value possible for 12 references and 4 selections for mangos.........................................................................................................47
4-12 Best possible selections and minimum ΔE value possible for 12 references and 4 selections for nectarines.....................................................................................................48
4-13 Summary performance for panelists evaluating mangos for booth 1 ................................49
4-14 Summary performance for panelists evaluating nectarines for booth 1 ............................50
4-15 ANOVA summary absolute ΔE for mangos......................................................................51
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4-16 ANOVA summary difference ΔE for mangos ...................................................................51
4-17 ANOVA summary absolute ΔE for nectarines ..................................................................56
4-18 ANOVA summary difference in ΔE for nectarines ...........................................................57
A-1 L*a*b values for reference color bar with 8 color.............................................................70
A-2 L*a*b values for reference color bar with 12 colors .........................................................71
A-3 L*a*b values for reference color bar with 16 colors .........................................................71
A-4 Mango color primitives......................................................................................................73
A-5 Nectarine color primitives..................................................................................................74
B-1 Summary performance for panelists evaluating both fruits for booth 1 ............................75
B-2 Summary performance for panelists evaluating both fruits for booth 2 ............................76
B-3 Summary performance for panelists evaluating both fruits for booth 3 ............................76
B-4 Summary performance for panelists evaluating both fruits for booth 4 ............................76
B-5 Summary performance for panelists evaluating both fruits for booth 5 ............................77
B-6 Summary performance for panelists evaluating both fruits for booth 6 ............................77
B-7 Summary performance for panelists evaluating both fruits for booth 7 ............................77
B-8 Summary performance for panelists evaluating both fruits for booth 8 ............................78
B-9 Summary performance for panelists evaluating both fruits for booth 9 ............................78
B-10 Summary performance for panelists evaluating both fruits for booth 10 ..........................78
E-1 Mixed mode summary absolute ΔE for mangos ................................................................91
E-2 Mixed mode summary difference ΔE for mangos .............................................................92
E-3 Mixed Mode summary absolute ΔE for nectarines............................................................92
E-4 Mixed Mode summary difference in ΔE for nectarines.....................................................92
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LIST OF FIGURES
Figure page 3-1 Example of mango and nectarine on aluminum tray .........................................................31
3-2 Example of reference color bar with 8 colors added to fruit images presented to the panelists..............................................................................................................................35
4-1 Correlation between number of primitives and color change index (CCI)........................43
4-2 Correlation between number of neighbors and color change index (CCI) ........................43
4-3 Correlation between number of neighbors and number of primitives ...............................44
4-4 Comparison of ΔE values for 8, 12, and 16 reference colors, 2 selections........................49
4-4 Absolute ΔE means difference of selections of colors using mangos ...............................52
4-5 Difference in ΔE means difference of selections of colors using mangos.........................52
4-6 Absolute ΔE means for reference colors for mangos.........................................................53
4-7 Difference in ΔE means for reference colors for mangos..................................................53
4-8 Absolute ΔE means for interaction between the number of reference colors and the number of selections ..........................................................................................................54
4-9 Difference in ΔE means for interaction between the number of reference colors and the number of selections ....................................................................................................55
4-10 Absolute ΔE means for presentation for mangos...............................................................55
4-11 Difference ΔE means for presentation for mangos ............................................................56
4-12 Absolute ΔE means for reference colors for nectarines.....................................................57
4-13 Difference ΔE means for reference colors for nectarines ..................................................58
4-14 Absolute ΔE means for selection of colors for nectarine...................................................58
4-15 Difference ΔE means for selections of colors for nectarines.............................................59
4-16 Difference ΔE means for selections of colors for nectarines.............................................59
4-17 Difference ΔE means for selections of colors for nectarines.............................................60
4-18 Absolute ΔE means for presentation for nectarines...........................................................60
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4-19 Difference ΔE means for presentation for nectarines ........................................................61
A-1 Fruit tray booth 1 for image acquisition and sensory panel...............................................64
A-2 Fruit tray booth 2 for image acquisition and sensory panel...............................................64
A-3 Fruit tray booth 3 for image acquisition and sensory panel...............................................65
A-4 Fruit tray booth 4 for image acquisition and sensory panel...............................................65
A-5 Fruit tray booth for image acquisition and sensory panel.................................................66
A-6 Fruit tray booth 6 for image acquisition and sensory panel...............................................66
A-7 Fruit tray booth 7 for image acquisition and sensory panel...............................................67
A-8 Fruit tray booth 8 for image acquisition and sensory panel...............................................67
A-9 Fruit tray booth 9 for image acquisition and sensory panel...............................................68
A-10 Fruit tray booth 10 for image acquisition and sensory panel.............................................68
A-13 Reference scales presented to panelists. ............................................................................70
A-14 Example ballot for screen image evaluation......................................................................72
A-15 Example ballot for fruit evaluation. ...................................................................................73
A-16 Representation of color primitives and equivalent circles for mangos (left) and nectarines (right) with a MV system..................................................................................74
C-1 Absolute Δ E for nectarine for screen image and 8 references ..........................................79
C-2 Absolute Δ E for nectarine for screen image and 12 references ........................................79
C-3 Absolute Δ E for nectarine for screen image and 16 references ........................................80
C-4 Absolute Δ E for nectarine for tray and 8 references.........................................................80
C-5 Absolute Δ E for nectarine for tray and 12 references.......................................................81
C-6 Absolute Δ E for nectarine for tray and 16 references.......................................................81
C-7 Absolute Δ E for mango for screen image and 8 references..............................................82
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C-8 Absolute Δ E for mango for screen image and 12 references............................................82
C-9 Absolute Δ E for mango for screen image and 16 references............................................83
C-10 Absolute Δ E for mango for tray 8 references ...................................................................83
C-11 Absolute Δ E for mango for tray and 12 references...........................................................84
C-12 Absolute Δ E for mango for tray and 16 references...........................................................84
D-1 Absolute Δ E for nectarine for screen image and 8 references ..........................................85
D-2 Absolute Δ E for nectarine for screen image and 12 references ........................................85
D-3 Absolute Δ E for nectarine for screen image and 16 references ........................................86
D-4 Absolute Δ E for nectarine for tray and 8 references.........................................................86
D-5 Absolute Δ E for nectarine for tray and 12 references.......................................................87
D-6 Absolute Δ E for nectarine for tray and 16 references.......................................................87
D-7 Absolute Δ E for mango for screen image and 8 references..............................................88
D-8 Absolute Δ E for mango for screen image and 12 references............................................88
D-9 Absolute Δ E for mango for screen image and 16 references............................................89
D-10 Absolute Δ E for mango for tray 8 references ...................................................................89
D-11 Absolute Δ E for mango for tray and 12 references...........................................................90
D-12 Absolute Δ E for mango for tray and 16 references...........................................................90
E-1 Absolute ΔE means for selection of color for mangos ......................................................93
E-2 Difference ΔE Means for selection of color for mangos ...................................................93
E-3 Absolute ΔE means for reference colors for mangos.........................................................94
E-4 Difference ΔE means for reference colors for mangos......................................................94
E-5 Absolute ΔE means for presentation for mangos...............................................................94
E-6 Difference ΔE means for presentation for mangos ............................................................95
E-7 Absolute ΔE means for selections of colors for nectarines................................................95
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E-8 Difference ΔE means for selections of colors for nectarines.............................................96
E-9 Absolute ΔE means for reference colors for nectarines.....................................................96
E-11 Absolute ΔE means for presentation for nectarines...........................................................97
E-12 Difference ΔE means for presentation for nectarines ........................................................97
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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science
VISUAL QUANTIFICATION OF NON-HOMOGENEOUS COLORS IN FOODS
By
Jose Alejandro Aparicio
December 2007
Chair: Murat Balaban Major: Food Science and Human Nutrition
Color is an important quality attribute for nearly every agricultural product. Consumers
may perceive color as an indicator of freshness and wholesomeness, and color may affect their
final decision to accept/reject food. A better understanding of human perception of colors in
food would be beneficial to increase the consistency and quality of food products. The
quantification of color is becoming more important due to an emphasis on international trade and
implementation of Hazard Analysis Critical Control Points (HACCP) requiring record keeping.
Thus, it is important to provide the agricultural industry with methods to quantify and correlate
sensory and instrumental evaluations of foods.
Machine vision imitates human visual perception by using a camera and a computer with
software capable to generate precise, consistent, and cost-effective color measurement. The
comparison and correlation of instrumental and visual color analysis has been performed in
many uniformly colored agricultural products such as meat, bakery and seafood. Generally,
there is a close relationship between sensory and instrumental color analysis of homogenous
foods. However, comparison and correlation of non-homogeneous color measurements in foods
is more challenging and has not been thoroughly studied.
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Machine vision was used to quantify the degree of color uniformity of mangos and
nectarines using the number of color blocks and color primitives. The use of color primitives
provided a more accurate method to measure color uniformity of mangos and nectarines. Three
reference color bars (8, 12 and 16 colors) were created from color analysis of the fruits. A
sensory panel (n=80) visually evaluated mangos and nectarines in two presentations: screen
images captured by machine vision and fruits placed in trays. Panelists attempted to quantify
color by selecting (2, 4 or 6 colors) from the reference color bars and compare the colors in the
reference bars with those of the fruit surfaces. There were a total of 9 sessions at different days
using different panelists.
Sensory and machine vision evaluations were compared using the absolute ΔE value. ΔE
measures total color change by accounting for combined changes in L*a*b values. The concept
of the best possible ΔE or best performance under given circumstances was also evaluated. It
was apparent that the number of reference colors and color selections had an impact on the error
made by panelists. More color selections reduced the ΔE values of the visual evaluations.
Statistical analysis described significant differences between the number of reference colors, the
number of selections, presentation, and the interaction between the reference colors and the
selections. The 8 and 16 reference colors bar provided less error compared to the 12 reference
colors bar, quantified by both ΔE for both mangos and nectarines. The 12 reference colors bar
gave the most error. Two color selections provided the highest mean values. The screen images
in general had lower mean values than the fruit trays.
This study provided a better understanding of the way panelists perceive non-uniform
colors. It also suggested a new formulation of consumer panel studies involving non-uniform
visual attributes of foods.
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CHAPTER 1 INTRODUCTION
Today’s consumers have increased expectations for the quality of food they purchase. In
this competitive market there is no second chance to make a first impression. An important first
impression is the color and appearance of food. How do consumers perceive color? Humans
have difficulty in quantifying color, but are good at comparing it with a reference color.
Therefore, reference colors are used in many instances, e.g. color of a potato chip, salmon color,
egg yolk color, etc. In all these examples, the color of the food is relatively uniform. There are a
limited number of studies that correlate the uniform color of foods measured by instruments, and
by sensory panels. However, many foods have non-uniform colors, e.g. mangos, nectarines, etc.
How can we accurately measure the color in this case? Many instruments measure the average
color, but this causes loss of color information in the case of non-uniform foods. Machine vision
technology eliminates this problem by measuring all the colors at the surface of a non-uniform
food. Another difficulty is how to measure the non-uniformity of color. In this study, methods
were developed and used to quantify the non-uniformity of color with the use of machine vision
technology.
Once the non-uniformity of color is determined, how will this affect how consumers
perceive the color of non-uniform foods? Intuitively, we expect that the more non-uniform the
color, the more difficult it will be for consumers to describe or quantify it. In a preliminary
study, we found that for rabbit meat, the more non-uniform the color, the more error consumers
made in correctly quantifying it (Balaban and others, 2007).
In this study, we asked the following questions:
• Can the image of a food material, taken with a good digital camera, and under controlled conditions, be substituted for the real food, for the purposes of evaluating visual and color attributes? If this is possible, then geographical and temporal restrictions in evaluating visual attributes will be eliminated. The image of a food can then be sent anywhere in the
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world to be evaluated. Food images from different times can be compared without concern for decay. Also, the image of the food, as an accurate representation of it, can be used for record keeping.
• If reference colors are to be used in evaluating the non-uniform color of foods, how many reference colors should be presented to the sensory panelists? How will the number of reference colors affect the error that the consumer makes in quantifying the color? The answer to this question would allow optimization of the number of reference colors to use.
• From a number of reference colors, how many colors should a panelist select? Too few color choices may not allow a good representation of the actual color. On the other hand, too many colors may confuse the panelist, and may allow large errors in the quantification of real colors. The answer to this question will allow the fine-tuning of the way panelists are asked to evaluate non-uniform colors. The quantification of color is becoming increasingly important due to an emphasis on
international trade, and implementation of Hazard Analysis Critical Control Points (HACCP)
requiring record keeping. Thus, it is important to provide the agricultural industry with methods
to quantify and correlate sensory and instrumental evaluations of foods.
The overall impact of this study will be a better understanding of the way panelists
perceive non-uniform colors. This will result in a better formulation of consumer panel studies
involving visual attributes of foods.
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CHAPTER 2 LITERATURE REVIEW
Color of Foods and Agricultural Materials
Color is an important quality attribute for almost every agricultural product (Delwiche,
1987). Consumers may perceive color as an indicator of freshness and wholesomeness, and
color may affect their final decision to accept/reject food. For the meat industry, muscle color is
the primary characteristic consumers consider when evaluating the quality and acceptability of
meats (Cornforth, 1994). The discoloration of retail beef accounts for $1 billion in price
discounts annually (Mancini and Hunt, 2005). Color determines the degree of ripeness of many
vegetables and fruits (Polder and others, 2000). Different grains and their varieties are
commonly characterized according to kernel color and quality defects such as grass-green, bin-
burnt, and fungal-damaged (Lou and others, 1999).
Color measurement of food and agricultural materials can be performed subjectively by
sensory panels (Chizzolini and others, 1993). Color can also be measured by instrumental
methods (Balaban and Odabasi, 2006). The quantification of color is becoming increasingly
important due to an emphasis on international trade, and implementation of Hazard Analysis
Critical Control Points (HACCP) requiring record keeping.
Instrumental Color Measurement in Agricultural Food Products
The agricultural industry uses mostly high cost, labor intensive methods to assure control
of color quality parameters. One possibility to reduce cost is to use instrumental methods to
measure color to emulate human visual perception (Zhu and Brewer, 1999). Instruments are
cost-effective, repeatable and objective in measuring color. Instruments such as colorimeters are
commonly used to measure color in the agricultural industry. Colorimeters provide users with
fast and simple “averaged” color measurements.
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The accuracy of the instrument is assured by calibrating with standard color tiles before
measurement. The color reading is obtained by providing a controlled illuminant or standard
light source. Common standard light sources are: A=tungsten lamp, B= near sunlight, C= near
daylight, D= daylight. Colorimeters have illuminants C or D65 with color temperatures of 6774°
K or 6504° K (Oliveira and Balaban, 2006). However, it is known that other methods provide
more precise color measurements (Coles and others, 1993). Colorimeters may not measure the
observed color if the product has non-uniform colors, because all colors in their view area are
averaged. If the agricultural product is too small, or too big, or has non-uniform surfaces, then
sampling location for color measurement becomes critical. Also, careful consideration is
necessary if data are compared between industrial plants, since variations between instruments
may occur (Brewer and others, 2001).
Spectrophotometers are also used in agriculture to measure color. The working method of
these instruments is based on the generation of a spectral curve representing the transmittance or
reflectance of light from the surface of the product. This is immediately compared with the
reflectance of a reference standard. The values may be converted to different color space values.
The agricultural industry requires a better method of color measurement. In the 1960s the
use of a camera with a computer and software capable of image processing became an option for
color measurement (Brosnan and Sun 2004). The system was called computer vision or machine
vision. The capabilities of this instrument were precise, accurate and fast color measurement of
agricultural products.
Computer Vision or Machine Vision System
The computer vision or machine vision (MV) systems started in the early 1960s. Since
then, the use of machine vision in the agricultural industry has grown. Machine vision is used
for its generation of precise data, consistency, and cost effective color measurement.
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This instrument aims to emulate human visual perception by using a camera and a
computer with software capable of performing predefined visual tasks (Brosnan and Sun 2004).
Images are captured in digital form by a charge coupled device (CCD) camera. CCD cameras
can convert light into electrical charges and create high-quality, low-noise images with pixels.
They have excellent light sensitivity; they are free of geometric distortion and highly linear in
their response to light (Du and Sun 2004). The computer software then performs image
processing, which is the study of representation and manipulation of pictorial information
(Martin and Tosunoglu 2000). The pictorial information is converted to three-dimensional color
space of red (R) green (G) and blue (B) values. Further analysis provides color results.
Search for cost-reduction and increased efficiency in quality inspection has made the
agricultural industry look for techniques and instruments that provide more complex and
accurate as well as fast and objective determination of quality parameters in online inspection.
Machine vision has shown to be a useful method in this area (Blasco and others, 2003; Lee and
others 2004).
Machine vision has several other advantages over other color measurement instruments:
• Images are composed of the entire view area making the analysis more representative
• The data provided from images can be converted to different color measurement systems (O’Sullivan and others 2003) and processed beyond the capabilities of colorimeters
• Non-uniform surfaces and colors can be handled easily
The agricultural industry uses image processing and MV to classify, sort and grade
agricultural produce in diverse areas such as bakery, meats and fish, vegetables, fruits, grains,
prepared consumer foods and even food container inspection. The food industry ranks among
the top ten industries to use image processing techniques (Gunasekaran, 1996).
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Bakery Products
Bakery products are influenced by their external as well as internal appearance.
Consumer’s judgment on their appearance dictates purchasing decision and marketability, and it
is essential to meet and exceed their expectations of quality of bakery products. At the same
time it is essential to reduce cost. A MV system was used to classify defective bread loaves by
height and top slope (Scott, 1994). Cookies were studied to estimate the fraction of top surface
area covered with chocolate chip, and other physical features such as size, shape and color of
baked dough (Davidson and others, 2001). MV was capable of providing automated inspection
and could separate light from dark muffin samples (Abdullah and others, 2000).
Red Meat and Seafood
In 2006, the retail value of U.S. beef industry was $71 billion (USDA, 2006 a). More than
12 billion kilograms of beef were consumed in the U.S. in the same year, and, the beef industry
represented 4.4% of U.S. total production exports. In the U.S. nearly 15% of retail beef is
discounted due to surface discoloration, which corresponds to annual revenue losses of $1 billion
(Mancini and Hunt, 2005). The USDA beef carcass grading system consists of two parts: quality
grade and yield grade. Quality grade is evaluated by trained individuals. MV has been
recognized as an objective alternative to assessment of meat quality from fresh-meat
characteristics (Tan, 2004). Recent studies indicate MV has great capability for classification
and grading of beef muscle type, breed, age and tenderness (Basset and others, 2000; Hatem and
others, 2003; Li and others, 1997).
The purpose of grading meats is to standardize the characteristics valuable to the consumer and
those that facilitate marketing and merchandising (Hatem and others, 2003). Beef rib eye steaks
were effectively graded for quality attributes such as color and marbling scores determined by
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USDA using image processing (Gerrard and others, 1996). The results reported that MV
predicted color with an accuracy level of R²=0.86 and marbling with R²=0.84.
The pork industry has also applied MV to its processes. Pork loins were graded according
to color. Researchers used image processing with statistical and neural network models to
predict color scores of 44 pork loins (Lu and others, 2000). The scores were then compared with
trained sensory panel scores. The scores were based on visual perception ranging from 1 to 5.
Prediction error was the difference between instrumental and sensory scores. An error of 0.6 or
lower was considered not significant. Image processing and neural network models were able to
predict 93.2% of the samples with error lower than 0.6. Statistical regressions were able to
predict 84.1% of the samples with error lower than 0.6. Another study reported 90% agreement
between a MV color score and a sensory panel using 200 pork loin chops (Tan and others, 2000).
Tedious human inspection and costs are part of the grading practices in the poultry
industry. MV was used to separate defective (tumors, bruises, and torn skin and torn meat)
poultry carcasses from normal carcasses (Park and others, 1996).
In 2006, freshwater and marine fishing produced 60 million tons for human consumption
(FAO 2006). Americans consumed an average of 2.2 billion kg of seafood in 2006 (NOAA
2007). Fish represent one of the main sources of protein used in developing countries (Louka
and others, 2004).
Seafood inspection involves costly human involvement. MV was used to capture, identify
and differentiate images of three different varieties of fish: carp (Cyprinus carpio), St. Peter’s
fish (Oreochromis sp.) and grey mullet (Mugil cephalus) (Zion and others, 1999). This study
also concluded that fish mass, an important quality parameter in marketing, could be predicted
from image area with the use of image processing. Other parameters important to market these
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three types of fish were acquired. Fish species have also been sorted according to shape, length
and orientation in a processing line (Strachan, 1993). Image analysis was used to differentiate
between stocks of Haddock (Melanogrammus aeglefinus) (Strachan and Kell, 1995). Dimension
reduction derived from principal component analysis and canonical correlations was used. The
reports showed 71.7% correct sorting accuracy for shape and 90.9% and 95.6% for both stocks in
color differences. Flesh quality is important for successful development of fish farming and fish
processing (Marty-Mahe and others, 2004). Objective criteria to predict flesh redness from the
spawning coloration of fall chum salmon has been performed with image processing (Hatano and
others, 1989). Skin color development is an important quality parameter for live goldfish
(Carassius auratus), an ornamental fish of high commercial value (Chapman and others, 1997).
Objective measurement and quantification of the color of live goldfish (Carassius auratus)
raised in well water was acquired by a machine vision system (Wallat and others, 2002). The
color of dried cod fillets may go from yellow to orange, depending on the drying method used.
Image processing has been used to compare drying methods in cod fillets (Louka and others,
2004). The fillets were subject to three drying methods: hot air drying, vacuum drying, and
freeze drying. Image processing compared the three techniques to controlled instantaneous
discharge (DIC) and dehydration by successive discharge (DDS), two new techniques of drying
cod fillets. The highest whiteness value found was quantified in freeze-drying and the lowest in
air drying. Analysis of variance was used to find differences between procedures. Vacuum
drying and DIC did not have significant differences.
Catfish ranks as the fourth most popular seafood consumed in the U.S. Fresh farm-raised
catfish (Ictalurus punctatus) quality relies primarily on human inspection. MV was used to
evaluate color changes over storage time for fresh farm-raised catfish (Korel and others, 2001).
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Vegetables
Vegetables are greatly affected by quality factors such as size, shape, color, blemishes, and
diseases. Image processing resulted in more precise color measurement for potato crisp color
(Coles and others, 1993). The potato industry used a MV system and online inspection to grade
potatoes by shape (Tao and others, 1995). This study reported 89% agreement between the
instrument and human perception. The accuracy in grading potatoes was 90% by using hue,
saturation and intensity color system.
Discoloration of the mushroom cap reduced product quality, with less market value
(Brosnan and Sun, 2004). In order to maximize quality parameters, a MV system was used to
inspect and grade mushrooms based on color, stem cut, shape and cap veil opening (Hienemann
and others, 1994). MV resulted in a 20% classification error compared to two human inspectors.
The surface color of tomatoes was analyzed using a MV system classifying differences in
ripeness stages (Polder and others, 2000). Image processing from MV was used to recognize and
estimate cabbage size for a selective harvester (Hayashi and others, 1998). Surface defects,
curvature and brakes of carrots are quality parameters that influence the product’s value. MV
was used to classify standard and defective carrots (Howarth and others, 1992).
Fruits
In 2005, the U.S. fruit consumption averaged 128 kg per person (fresh-weight equivalent)
(USDA, 2006b), with bananas being the most consumed fresh fruit. Apples were the second
favorite fresh fruit. A MV system was used to evaluate the color to determine the ability of
oxalic acid to inhibit browning in banana and apple slices (Yoruk and others 2004). Golden
delicious apples were evaluated for quality parameters such as bruises, scabs, fungi or wounds
with the use of a MV system (Leemans and others 1998). The results suggested that image
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processing with different algorithms were able to detect bruises, scabs, fungi and wounds in
golden delicious apples.
In 2002, the U.S. was the world’s largest importer of mangos (Perez and Pollack, 2002).
In this same year, Mexico shipped over 90% of its exports to the U.S. The increased
consumption of mangos is related to the increased population of Latino and Asian groups.
Consumers seek mangos without external damage, with stable weight, color and consistency, at a
reasonable price (Zuñiga-Arias and Ruben 2007). However, grading mangos for export involves
hand labor and subjectivity. A MV system equipped with cameras to obtain single and multiple
view image angles was used to evaluate physical parameters like: projected area, length, width,
thickness, volume, and surface area with 96.47% accuracy (Chalidabhongse and others, 2006;
Yimyam and others, 2005).
Prepared Consumer Foods
The evaluation of cheese functional properties such as different cooking conditions, size of
samples and shred dimensions are important aspects for the marketability of pizza. Topping
types, percentage and distributions influence the appearance and the different varieties of pizza.
Pizza image acquisition is very complex due to the non-homogenous colors, shapes, overlapping,
shadows, and light reflection. Methods have been developed to quantify the color distribution
and topping exposure in pizza (Sun and Du, 2004).
Food Container Inspection
MV and image processing are used to determine shape, and check for foreign matter,
threads of bottles, sidewalls and base defects, fill levels, correct closure and label position of
food containers. MV has also been used to check for wrinkles, dents and other damages to
aluminum cans that cause leakage of contents (Seida and Frenke, 1995).
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Grains
Nigeria is the world’s leading importer of wheat (USDA 2006c). Ninety percent of the
imported wheat is supplied by the U.S. Competitive prices and product quality has lead the U.S.
increase the wheat market in Nigeria. The variety, environmental effects and class make the
classification of wheat a very complex practice even for experienced inspectors. MV systems
and image processing have been used widely in wheat (Uthu, 2000; Majumdar and Jayas, 2000;
b; Nair and others, 1997). A MV system and crush force features were used to differentiate hard
and soft wheat varieties (Zayas and others, 1996). The correct differentiation rate was 94% for
the varieties tested. Corn kernels were analyzed with MV for whiteness, mechanical and mold
damage (Liu and Paulsen, 1997). Rice has also been studied using MV and image processing.
The appearance characteristics of brown rice such as kernel shape, color, and defects were
determined using a MV system (Wan and others, 2000). An online automatic inspection system
was able to recognize cracked, chalky, broken, immature, and damaged brown rice kernels.
Other Applications
A MV system was used for online inspection of dry sugar granules and powders to
determine particle size for process control and quality improvement (Strickland, 2000). Image
processing and MV were used to detect dirt on brown eggs with stains, dark feces, white uric
acid stains, blood stains and stains caused by egg yolk (Mertens and others, 2005). The results
reported 91% overall accuracy of image processing to detect dirty eggs.
Research efforts were made to provide efficient image-based techniques to monitor
distribution and migration of fish (Nery and others, 2005). Image processing was used to
classify nine species of fish based on adipose fin, anal fin, caudal fin, head and body shape, size
and length/depth ratio of body (Lee and others, 2003). This method provided an alternative to
subjective monitoring of numbers, size and species at specific fish passages during migration.
26
Visual Texture Analysis
Visual texture is defined as how varied or patchy the color of a surface looks (Balaban,
2007). MV systems have been used in determining color, size and shape of agricultural produce.
Texture analysis with MV has great potential due to the powerful discriminating ability and
pattern recognition of this technique.
Texture information may be used to enhance the accuracy of color measurements
(Mäenpää, 2003). Texture is characterized by the relationship of the intensities of neighboring
pixels (Palm, 2004). Visual texture discriminates different patterns of images by extracting the
dependency of intensity between pixels and their neighboring pixels (Kartikeyan and Sarkar,
1991). In other words, texture is the repetition of a basic pattern. The patterns can be the result
of physical surface properties such as roughness or oriented strands, even the reflectance
differences given by a color on a surface (Tuceryan and Jain, 1998).
Visual texture analysis is divided into four main areas: statistical texture, structural texture,
model-based texture, and transform-based texture. Statistical texture describes mainly regions in
an image through high-order moments of their grayscale histograms (Bharati and others, 2004).
Structural texture is described as a composition of elements regulated by rules in images.
Model-based texture generates an empirical model of each pixel in the image based on a
weighted average of the pixel intensities in its neighborhood. Transform-order texture converts
the image into a new form using spatial frequency properties of the pixel under consideration of
its intensity variations.
Image analysis literature describes many ways to quantify texture (Bertrand and others,
1992; Mao and Jain, 1992; Reed and Du Buf, 1993; Tuceryan and Jain, 1998). A new method
used to quantify non-uniform colors is that of color primitives and color change index (Balaban,
27
2007). The methodology used to quantify color non-uniformity should be independent of
rotation, variation in size and shape (Zheng, 2006).
Visual Texture Applications in Agriculture
Image texture analysis has been used in grading and inspection for quality and safety of
agricultural products. A MV system was used with image processing and texture analysis to
quantify changes in color, shape and image texture of apple slices (Fernandez and others, 2005).
A method for texture analysis was developed to quantify non-homogeneity of color of mangos,
apples and rabbit meat using color primitives and color change index, were a color primitive was
defined as a continuous area of an image with similar light intensity (Balaban and others, 2007).
Texture analysis was used to identify the changes in textural appearance in experimental
breads caused by variations of surfactants added to flour (Bertrand and others, 1992). Iyokan
orange fruits (Miyauchi Iyokan) were used to predict sugar content of oranges (Kondo and others,
2000). Image processing and texture analysis were entered to a neural network. MV system
along with neural networks recognized relatively sweet fruit from reddish color, low height,
medium size and glossy surface. Several studies on meat tenderness characteristics
(Chandraratne and others, 2006) and classification of genotypic origins of bovine meat (Basset
and others, 2000) have been successful. Texture analysis evaluated the microstructure of food
surfaces such as potatoes, bananas, pumpkins, carrots, bread crust, potato chips and chocolates
(Quevedo and others, 2002). Texture features have demonstrated to be effective discriminating
models for classifying wholesome and unwholesome chicken carcasses (Park and others, 2002).
Correlation between Image and Visual Color Analysis
The majority of studies regarding color comparison between sensory and instrumental
measures in foods have been developed in the meat area. Research was performed to compare
28
and correlate homogeneous color measurements of pork, beef, and chicken using instrumental
and visual color analysis (Denoyelle and Berny, 1999; Lu and others, 2000; Sandusky and Heath,
1998; Zhu and Brewer, 1999). Meat and poultry were used to correlate instrumental and visual
color evaluation. A range of meat redness was studied by mixing ground poultry breast and
ground beef. High correlations between visual redness and instrumental redness were found
(Zhu and Brewer, 1999).
The comparison and correlation of instrumental and visual color analysis has also been
studied in bakery, seafood, and in medical fields. Research using cookies for color analysis
showed a strong correlation between sensory and instrumental methods (Kane and others, 2003).
The relationship between sensory and instrumental correlations using raw, baked and smoked
flesh of rainbow trout (Onchoyhychus mykiss) was studied. Close relationship between color
evaluation by sensory analysis and instrumental methods was observed (Skrede and others,
1989). A study on colorimetric assessment of small color differences on translucent dental
porcelain revealed strong correlation between instrumental and visual color analysis (Seghi and
others, 1989). However, comparison and correlation of non-homogeneous color measurements
in foods is more challenging and has not been thoroughly studied.
Preliminary Experiments
A method was developed to quantify the perception of non-homogeneous colors of foods
by sensory taste panels. The average colors of mangos, apples, and rabbit meat were measured
using MV. Differences between the average (real) colors (MV system) and those from the
sensory panel were reported as ΔE values (Balaban, 2007).
A sensory panel composed of 20 panelists performed visual evaluations of rabbit meat captured
images and 60 panelists for that of real fruit and captured sample images. The degree of non-
29
uniformity of sample colors was determined using two methods: color blocks and color
primitives.
A color reference bar was developed for the panelists to select colors that represented those of
the samples. Panelists selected 3 colors from these reference colors, and estimated their
percentages. The “red mango” had more color blocks, and visually represented more non-
uniform colors. In the case of rabbit meat samples, there was no apparent advantage of using the
color block scheme. Clearly, a different method to quantify non-uniformity needed to be
developed for these samples. The rabbit samples had colors ranging from white to red, with
many shades in between. The lack of any other hue value may have contributed to the inability
of the color block scheme to quantify non-uniformity.
The more non-uniform samples were more difficult to evaluate, thus, the ΔE error was
higher. The non-uniformity of the samples caused more difficulty in the panelists’ matching
ability with the reference color scale, and caused higher errors.
Males (33) and females (27) were compared regarding ΔE values. The mean ΔE for males
and females was 10.58 and 10.18, respectively, with a p-value= 0.52. In this study gender did
not significantly affect ΔE. A higher number of panelists may or may not affect this outcome.
This preliminary research suggested a criteria and parameters to quantify the error panelists
made when subject to visual appraisal of non-homogenous colors in foods (Balaban, 2007;
Balaban and others, 2007). However, the number of colors that panelists selected from a
reference color bar was limited to 3 choices. More studies are needed to study the effect of the
number of colors in the reference scale, and the number of colors to choose.
The food industry could benefit from a better understanding of precise, repeatable and
accurate color measurements of foods with non-uniform surfaces and/or colors. The quantitative
30
measurement of color attributes of agricultural materials is important in quantifying quality,
maturity, defects, and various other color-dependent properties. Global market expansion and
implementations of Hazard Analysis Critical Control Points (HACCP) require record keeping.
The difference of screen captured image and real sample and its effect on human perception of
sensory evaluations has not been studied thoroughly. A properly taken image of a food sample
can be a good representation of the food itself. This may provide a usable and more flexible tool
in the analysis of visual attributes.
Objectives of the Study
The objectives of this study were:
i. To measure differences in color evaluation between sensory panel and MV system, for non-uniformly colored fruits and their images.
ii. To develop a quantitative measure of the degree of non-uniformity of color, and to
evaluate the effect of degree of non-uniformity of sample color on the difference in color evaluation.
iii. To evaluate the effect of the number of reference colors, and number of allowed color
selections on the error in color evaluation
31
CHAPTER 3 MATERIALS AND METHODS
Mangos and Nectarines
The fruits used in this study were artificial fruits to avoid color degradation due to
maturation and decay of real fruits. Mangos and nectarines generally have non-uniform colors
and surfaces. The fruits used in this study consisted of red mangos and nectarines with non-
uniform surface colors. The mangos were purchased from Amazing Produce (4470 W. Sunset
Boulevard Suite 106 Los Angeles, CA 90027) and the nectarines made of compressed polyfoam
from Zimmerman Market (254 E Main St Leola, PA 17540) (Figure 3-1). The fruits were placed
on aluminum trays. Adhesive tape was used to keep fruits from moving while images were
captured. There were a total of 10 trays with one mango and one nectarine in each. The mangos
and nectarines shown in Figures A-1 to A10 were first wrapped in grey paper (R= 128, G = 128,
B = 128) to obtain a color neutral background.
Figure 3-1. Example of mango and nectarine on aluminum tray.
32
Image Acquisition
The artificial fruits were placed inside a light box built of white acrylic sheets as shown
in Figure A-11. The light box had top and bottom lighting with 2 fluorescent lights each to
simulate illumination by noonday summer sun (D65 illumination). The door remained closed
while images were captured to assure uniformity of light inside and to minimize the effect of
outside light. Images were captured using a camera (Nikon D200 Digital Camera, Nikon Corp.,
Japan) located inside the chamber mounted to face the bottom of the light box as shown in
Figure A-11. The image acquisition set up is shown in Figure A-12. The Nikon D200 Settings
used are described in Table 3-1. After the images were captured, trays were labeled for booth
Device Nikon D200 Lens VR 18-200 mm F 3.5-5.6 G Focal length 36 mm Sensitivity ISO 100 Optimize image Custom High ISO NR Off Exposure mode Manual Metering mode Multi-pattern Shutter speed and aperture 1/3s –F/11 Exposure compensation (in camera) 0 EV Focus mode AF-S Long exposure NR Off Exposure compensation (by capture NX) 0 EV Sharpening Auto Tone compensation Auto Color mode Model Saturation Normal Hue adjustment 0 White balance Direct sunlight
Image Analysis
Each captured image included a “red” color standard with known L*, a*, and b* values
(Certified Reflectance Standard, Labsphere, ID# SCS-RD-020). Captured images of the fruits
33
were analyzed for average color, color blocks, and color-texture profiles using MV software.
The values obtained were compared with the measured L*, a*, and b* values of the red standard.
The difference of the L*, a*, and b* values was used as the correction factor for the whole
image. The images were “cleaned” using an image editing software. Each acquired pixel had
(R), (G) and (B) color intensities. The calibrated images were then used to determine the
average L*, a*, and b* values using every pixel of the fruits with Lens Eye color evaluation
software.
For color block analysis, the program read RGB values from every pixel in the captured
image, and counted that pixel a specified color block. Each pixel’s RGB values were converted
first to tristimulus values XYZ, and then to L*, a*, and b* values.
The color data generated by the software was presented in histogram form. This feature
allowed all colors present on the surface area to be seen more easily. Because all colors present
were too numerous to be considered for the color scale formation, a method was developed to
represent the most significant surface colors.
Experimental Design
For this study, a completely randomized design was used. Because the effects of two or
more factors may affect the outcome, whether or not interaction exists, a factorial experimental
design was implemented.
The independent variables considered were number of reference colors, number of colors
to choose from the reference colors, and the sensory evaluation of screen image or real fruit. The
dependent variable for this study was the ΔE values. The ΔΕ value is the color differences
between sensory and MV measured colors of each sample for each panelist. ΔE measures total
color change by accounting for combined changes in L*a*b values.
* Ref. refers to reference. DF refers to degrees of freedom. SS refers to the sum of squares.
52
It is apparent that the error means for color selections, 2 color choices had the highest mean
and was significantly different than the rest as shown in Figure 4-4 and 4-5. Panelists tend to
make more errors when selecting only 2 colors. The error decreases and panelists become more
efficient with more color choices.
The ANOVA Procedure Duncan's Multiple Range Test for ΔE Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 46.74127 Number of Means 2 3 Critical Range .8657 .9115 Means with the same letter are not significantly different. Duncan Grouping Mean N Selections A 15.2401 480 2 B 11.9144 480 4 B 11.2349 480 6
Figure 4-4. Absolute ΔE means difference of selections of colors using mangos.
The ANOVA Procedure Duncan's Multiple Range Test for DiffΔE Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 46.92962 Number of Means 2 3 Critical Range .8674 .9133 Means with the same letter are not significantly different. Duncan Grouping Mean N Selections A 13.1554 480 2 B 11.1637 480 4 B 11.0349 480 6
Figure 4-5. Difference in ΔE means difference of selections of colors using mangos.
Because of this, the rest of the color selections, 4 and 6 choices had mean values showing no
significant difference between each other for both ΔE absolute and difference in ΔE.
This same pattern was seen using the mixed mode for statistical analysis and is shown in Figures
E-1 and E-2.
53
It was also apparent that the 8 reference colors provided less error both for the ΔE absolute
and difference in ΔE as shown in Figures 4-6 and 4-7. The highest error made by panelists was
with 12 reference colors compared to 8 and 16. However, nectarines reported slightly higher
error values than mangos. This may be due to the higher non-uniformity of nectarines making the
evaluations harder for panelists. The same results were obtained using the mixed mode as shown
in Figures E-3 and E-4.
The ANOVA Procedure Duncan's Multiple Range Test for ΔE Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 46.74127 Number of Means 2 3 Critical Range .8657 .9115 Means with the same letter are not significantly different. Duncan Grouping Mean N Reference Colors A 14.3974 480 12 B 12.5116 480 16 C 11.4803 480 8
Figure 4-6. Absolute ΔE means for reference colors for mangos.
The ANOVA Procedure Duncan's Multiple Range Test for DiffΔE Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 46.92962 Number of Means 2 3 Critical Range .8674 .9133 Means with the same letter are not significantly different. Reference_ Duncan Grouping Mean N Colors A 13.4795 480 12 B 11.6834 480 16 C 10.1910 480 8
Figure 4-7. Difference in ΔE Means for reference colors for mangos.
The interaction between the number of reference colors and the number of color selections
also reported significant differences. The highest error made by panelists was when evaluating
treatment 4 or 12 reference colors and 2 color choices as shown in Figures 4-8 and 4-9 both for
absolute ΔE and difference in ΔE.
54
The ANOVA Procedure Duncan's Multiple Range Test for ΔE Alpha 0.05 Error Degrees of Freedom 1431 Error Mean Square 47.45812 Number of Means 2 3 4 5 6 7 8 9 Critical Range 1.511 1.591 1.644 1.684 1.715 1.740 1.761 1.779 Means with the same letter are not significantly different. Duncan Grouping Mean N TRT A 19.1266 160 4 B 13.9341 160 7 C B 12.6597 160 1 C B 12.4915 160 5 C D 11.8113 160 9 C D 11.7896 160 8 C D 11.5741 160 6 C D 11.4620 160 2 D 10.3192 160 3
Figure 4-8. Absolute ΔE means for interaction between the number of reference colors and the number of selections.
The lowest error made by panelists was with treatment 3 or 8 reference colors and 6
selections both for absolute ΔE and difference in ΔE. The rest of the treatments were slightly
different however, providing significant differences. It is clear that the more color selections, the
less error made by the panelists. It is possible that up to certain level of reference colors panelists
would perform more efficiently, and above that level it would too complicated for the panelists
to refer to color selections and reference colors. There may be an optimum number of reference
colors.
55
The ANOVA Procedure Duncan's Multiple Range Test for DiffΔE Alpha 0.05 Error Degrees of Freedom 1431 Error Mean Square 47.64525 Number of Means 2 3 4 5 6 7 8 9 Critical Range 1.514 1.594 1.647 1.687 1.718 1.743 1.765 1.783 Means with the same letter are not significantly different. Duncan Grouping Mean N TRT A 17.0419 160 4 B 12.0225 160 5 B 11.8493 160 7 C B 11.6113 160 9 C B 11.5896 160 8 C B D 11.3741 160 6 C B D 10.5750 160 1 C D 10.1192 160 3 D 9.8789 160 2
Figure 4-9. Difference in ΔE means for interaction between the number of reference colors and the number of selections.
The presentation (screen image vs. fruit tray) was also significantly different (p-
value=0.0001). The fruit tray had mean values higher than the screen image both for absolute
ΔE and difference in ΔE as shown in Figures 4-10 and 4-11. These same results were obtained
using the mixed mode as shown in Figures E-5 and E-6.
The ANOVA Procedure Duncan's Multiple Range Test for ΔE Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 46.74127 Number of Means 2 Critical Range .7068 Means with the same letter are not significantly different. Duncan Grouping Mean N Source A 13.6525 720 F B 11.9404 720 S
Figure 4-10. Absolute ΔE means for presentation for mangos.
56
The ANOVA Procedure Duncan's Multiple Range Test for DiffΔE Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 46.92962 Number of Means 2 Critical Range .7083 Means with the same letter are not significantly different. Duncan Grouping Mean N Source A 12.6407 720 F B 10.9286 720 S
Figure 4-11. Difference ΔE means for presentation for mangos.
Nectarines
The analysis of variance resulted in significant differences between reference colors, the
number of selections, presentation and the interaction between the reference colors and the
selection of colors for both the ΔE absolute and the difference in ΔE (p-value = 0.0001) as shown
in Tables 4-17 and 4-18.
Table 4-17. ANOVA summary absolute ΔE for nectarines. Source DF ANOVA SS MEAN Square F Value Pr >F
* Ref. refers to reference. DF refers to degrees of freedom. SS refers to the sum of squares.
The same pattern seen previously with mangos were 8 reference colors reported the lowest
value and 12 reference colors the highest value as shown in Figures 4-12 and 4-13 both for the
absolute ΔE and the difference in ΔE. Similar results were obtained using the mixed mode as
shown in Figures E-9 and E-10.
The ANOVA Procedure Duncan's Multiple Range Test for ΔE Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 47.12485 Number of Means 2 3 Critical Range .8692 .9152 Means with the same letter are not significantly different. Reference_ Duncan Grouping Mean N Colors A 14.9077 480 12 B 13.0530 480 16 C 11.4792 480 8
Figure 4-12. Absolute ΔE means for reference colors for nectarines.
58
The ANOVA Procedure Duncan's Multiple Range Test for DiffΔE Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 45.99134 Number of Means 2 3 Critical Range .8587 .9041 Means with the same letter are not significantly different. Reference_ Duncan Grouping Mean N Colors A 13.6839 480 12 B 11.7746 480 16
C 8.3653 480 8
Figure 4-13. Difference ΔE means for reference colors for nectarines.
The number of selections was also significantly different with (p-value= 0.0001). However,
when looking at the means for color selections, 2 color choices had the highest mean of the rest
of the color selections, 4 and 6 as shown in Figure 4-14 and there were no significant difference
between 4 and 6 color selections of Difference in ΔE as shown in Figure 4-15, the same case as
the mangos. Similar results were obtained with the mixed mode procedures as seen in Figures
E-7 and E-8.
The ANOVA Procedure Duncan's Multiple Range Test for ΔE Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 47.12485 Number of Means 2 3 Critical Range .8692 .9152 Means with the same letter are not significantly different. Duncan Grouping Mean N Selections A 16.3154 480 2 B 12.2299 480 4 C 10.8946 480 6
Figure 4-14. Absolute ΔE means for selection of colors for nectarine.
59
The ANOVA Procedure Duncan's Multiple Range Test for DiffΔE Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 45.99134 Number of Means 2 3 Critical Range .8587 .9041 Means with the same letter are not significantly different. Duncan Grouping Mean N Selections A 12.8803 480 2 B 10.6946 480 6 B 10.2490 480 4
Figure 4-15. Difference ΔE means for selections of colors for nectarines.
The interaction between reference colors and number of selection of colors also resulted in
significant differences with (p-value = 0.0001).
The ANOVA Procedure Duncan's Multiple Range Test for ΔE Alpha 0.05 Error Degrees of Freedom 1431 Error Mean Square 47.72086 Number of Means 2 3 4 5 6 7 8 9 Critical Range 1.515 1.595 1.649 1.688 1.719 1.745 1.766 1.784 Means with the same letter are not significantly different. Duncan Grouping Mean N TRT A 21.2744 160 4 B 14.9965 160 7 C 12.8476 160 5 C 12.6753 160 1 C 12.4578 160 8 D C 11.7046 160 9 D C 11.3842 160 2 D 10.6010 160 6 D 10.3782 160 3
Figure 4-16. Difference ΔE means for selections of colors for nectarines.
For both the absolute ΔE and difference in ΔE treatment 4 or 12 reference colors and 2 color
choices had the highest value as shown in Figures 4-16 and 4-17. Similar to mangos, panelists
had the most difficulty in matching 12 reference colors and 2 color selections. Panelist also had
difficulty in evaluating treatment 7 or 16 reference colors and 2 color selections reported as the
second highest mean error for absolute ΔE. Panelists performed best and reported the lowest
error value for difference in ΔE with treatment 2 or 8 reference colors and 4 color selections as
shown in Figure 4-16.
60
The ANOVA Procedure Duncan's Multiple Range Test for DiffΔE Alpha 0.05 Error Degrees of Freedom 1431 Error Mean Square 46.59453 Number of Means 2 3 4 5 6 7 8 9 Critical Range 1.497 1.576 1.629 1.668 1.699 1.724 1.745 1.763 Means with the same letter are not significantly different. Duncan Grouping Mean N TRT A 17.8393 160 4 B 12.8114 160 5 B 12.2578 160 8 C B 11.5613 160 7 C B 11.5046 160 9 C D 10.4010 160 6 C D 10.1782 160 3 D 9.2402 160 1 E 5.6777 160 2
Figure 4-17. Difference ΔE means for selections of colors for nectarines.
The presentation (screen image vs. fruit tray) was also significantly different (p-
value=0.0001). The fruit tray had mean values higher than the screen image as shown in Figures
4-18 and 4-19 for both ΔE and Difference in ΔE. Similar results were obtained using the mixed
mode as shown in Figures E-11 and E-12.
The ANOVA Procedure Duncan's Multiple Range Test for Delta_E Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 47.12485 Number of Means 2 Critical Range .7097 Means with the same letter are not significantly different. Duncan Grouping Mean N Source A 13.8739 720 F B 12.4193 720 S
Figure 4-18. Absolute ΔE means for presentation for nectarines.
61
The ANOVA Procedure Duncan's Multiple Range Test for DiffDE Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 45.99134 Number of Means 2 Critical Range .7011 Means with the same letter are not significantly different. Duncan Grouping Mean N Source A 12.0020 720 F B 10.5473 720 S
Figure 4-19. Difference ΔE means for presentation for nectarines.
ΔE vs. CCI
The number of primitives, number of neighbors, and CCI measure the degree of non-
uniformity of color. ΔE gave the difference between MV and panelists color appraisal. The
correlation between ΔE and non-uniformity (CCI) is shown in Figures C-1 to C-12. The ΔE for
nectarines obtained from screen images and combinations of references of colors and color
selection did not correlate well with the CCI values as shown in Figures C-1 to C-3. The same
results were reported for ΔE obtained from fruit trays as shown in Figures C-4 to C-6. This was
the same pattern for mangos in Figures C-7 to C-12.
The number of reference color or the number of selections of colors did not provide any
information in regard to a correlation between ΔE and CCI. The color change index or measure
of non-uniformity did not have a relationship with the panelist’s performance and error made
when visually evaluating mangos and nectarines. It is possible that above a certain degree of
non-uniformity of colors panelists performance is reduced and becomes too complicated to refer
to color bars.
62
CHAPTER 5 CONCLUSIONS
Colors reflect important quality parameters such as maturity, defects and other color-
dependent attributes. Most agricultural materials e.g. fruits, vegetables, grains, meat, and
seafood have non-uniform shapes, surfaces and colors. It is important to quantify the color
attributes of these materials in order to measure their quality and to help with the record keeping
associated with the new globalization requirements.
The number of color blocks did not provide a clear measure of the degree of non-
uniformity in mangoes and nectarines. However, the number of color primitives associated with
color change index and number of neighbors does provide a better measure of their degree of
non-uniformity.
Quantitative color data can be correlated with human perception. The method developed
in this study can be used to quantify the perceptions of untrained panelists regarding non-
uniformly colored foods, with objective error measurements to optimize the method parameters.
It was observed that there may be an “optimum” number of reference colors for a given food. In
our study, 12 reference colors performed poorly compared to either 8 or 16 reference colors.
Since all of the 8 reference colors were present in the set of 12 reference colors, one may argue
that increasing the number from 8 to 12 “diluted” their effect. This needs to be tested in future
studies. It is more difficult to explain why 16 reference colors performed better than 12
references. Future studies may explore this dilemma.
It was clear that the more color selections, the less the error made by the panelists, given
the reference colors provided in this study. There was also statistically significant interaction
between the number of reference colors and the number of selections. It is possible that up to
certain level of reference colors panelists would perform more efficiently, and above that level it
63
would be too complicated for the panelists to refer to color selections and reference colors. This
issue needs to be elucidated in future studies.
In this study there was no correlation between the error performance of panelists and the
degree of non-uniformity provided by the number of primitives. The concept of the minimum
possible performance level was introduced, the best possible ΔE. This provided a more realistic
way to calculate the error made by sensory panels, given a number of reference colors and color
selections.
Panelists also evaluated the color of the same sample either by looking at its image, or at a
real fruit. This study found small but statistically significant differences in the error made by
panelists between these sources. It is interesting, but expected that the error made when looking
at the image was less, since the reference colors were developed from the images. Specific
studies in the future need to clarify if images can be substituted for the real food, for visual
evaluation purposes.
It is essential to keep identifying criteria to measure the visual evaluations of panelists and
their correlations with instrumental methods of color measurements, to provide a better
understanding to the human perception of non-uniform colors. The search for better methods to
quantify and correlate instrumental and human perception data in this area should continue.
64
APPENDIX A COLOR ANALYSIS FOR ALL TRAYS
Figure A-1. Fruit Tray booth 1 for image acquisition and sensory panel.
Figure A-2. Fruit Tray booth 2 for image acquisition and sensory panel.
65
Figure A-3. Fruit Tray booth 3 for image acquisition and sensory panel.
Figure A-4. Fruit Tray booth 4 for image acquisition and sensory panel.
66
Figure A-5. Fruit Tray booth for image acquisition and sensory panel.
Figure A-6. Fruit Tray booth 6 for image acquisition and sensory panel.
67
Figure A-7. Fruit Tray booth 7 for image acquisition and sensory panel.
Figure A-8. Fruit Tray booth 8 for image acquisition and sensory panel.
68
Figure A-9. Fruit Tray booth 9 for image acquisition and sensory panel.
Figure A-10. Fruit Tray booth 10 for image acquisition and sensory panel.
69
Figure A-11. Machine Vision set-up.
Figure A-12. Light box specifications.
88 cm
46 cm 51 cm
50 cm
70
08
11
01 03 04 05 06 07
12
02 09 10
0601 03 04 05 0802 07
01
11 14
02 04 05 06 07 08 09 10
15 16
03
12 13
8 References
12 References
16 References
Figure A-13. Reference scales presented to panelists.
The main reference color bar was that with 16 colors. From that color bar, color blocks
with close/similar L*a*b or RGB values were merged to reduce the number of colors to choose
and create a color bar with 12 reference colors. The same procedure was used to create the color
reference scale with 8 references colors.
Table A-1. L*a*b values for reference color bar with 8 color. Reference color L* a* b* 1 59.14 -5.61 58.53 2 32.97 56.6 43.38 3 39.89 53.77 34.18 4 52.32 21.89 47.08 5 60.95 2.83 55.43 6 41.77 62.53 46.01 7 71.07 17.04 60.64 8 75.28 7.85 64.73
Figure A-14. Example ballot for screen image evaluation. (Fruit Image Evaluation Form)
Sensory color evaluation form
Date Panelist Age: Male □ Female □ Booth number Instructions: 1. Do not re-orient the samples, or modify their wrapping. 2. Evaluate the samples using the order given here. 3. From the screen, select only 2 colors that best represent the colors of the sample. 4. Estimate the percentage of these colors for the surface of the sample shown. 5. The sum of the 2 percentages must add to 100% Sample number (541) Color number (1 to 8)
Percent of total area
Sum=100% Sample number (397) Color number (1 to 8)
Percent of total area
Sum=100%
73
Figure A-15. Example ballot for fruit evaluation.
(Fruit Tray Evaluation Form)
Sensory color evaluation form Date Panelist Age: Male □ Female □ Booth number Instructions: 1. Do not re-orient the samples, or modify their wrapping. 2. Evaluate the samples using the order given here. 3. From the screen, select only 2 colors that best represent the colors of the sample. 4. Estimate the percentage of these colors for the surface of the sample shown. 5. The sum of the 2 percentages must add to 100% Sample number (397) Color number (1 to 8)
Percent of total area
Sum=100% Sample number (541) Color number (1 to 8)
Figure C-1. Absolute Δ E for nectarine for screen image and 8 references.
11 12 13 14 15 16 17 18 19CCI
0
10
20
30
40
Del
ta E
12 ref, 2 12 ref, 4 12 ref 6
Nectarine, abs.DE, screen
Figure C-2. Absolute Δ E for nectarine for screen image and 12 references.
80
11 12 13 14 15 16 17 18 19CCI
0
10
20
30
40
Del
ta E
16 ref, 2 16 ref, 4 16 ref, 6
Nectarine, abs.DE, screen
Figure C-3. Absolute Δ E for nectarine for screen image and 16 references.
11 12 13 14 15 16 17 18 19CCI
0
10
20
30
40
Del
ta E
8ref, 2 8 ref, 4 8 ref, 6
Nectarine, abs.DE, fruit
Figure C-4. Absolute Δ E for nectarine for tray and 8 references.
81
11 12 13 14 15 16 17 18 19CCI
0
10
20
30
40
Del
ta E
12 ef, 2 12 ref, 4 12 ref, 6
Nectarine, abs.DE, fruit
Figure C-5. Absolute Δ E for nectarine for tray and 12 references.
11 12 13 14 15 16 17 18 19CCI
0
10
20
30
40
Del
ta E
16 ref, 2 16 ref, 4 16 ref, 6
Nectarine, abs.DE, fruit
Figure C-6. Absolute Δ E for nectarine for tray and 16 references.
82
3 4 5 6 7 8 9 10 11 12CCI
0
10
20
30
40D
elta
E8ref, 2 8 ref, 4 8 ref, 6
Mango, abs.DE, screen
Figure C-7. Absolute Δ E for mango for screen image and 8 references.
3 4 5 6 7 8 9 10 11 12CCI
0
10
20
30
40
Del
ta E
12 ef, 2 12 ref, 4 12 ref, 6
Mango, abs.DE, screen
Figure C-8. Absolute Δ E for mango for screen image and 12 references.
83
3 4 5 6 7 8 9 10 11 12CCI
0
10
20
30
40D
elta
E16 ref, 2 16 ref, 4 16 ref, 6
Mango, abs.DE, screen
Figure C-9. Absolute Δ E for mango for screen image and 16 references.
3 4 5 6 7 8 9 10 11 12CCI
0
10
20
30
40
Del
ta E
8ref, 2 8 ref, 4 8 ref, 6
Mango, abs.DE, fruit
Figure C-10. Absolute Δ E for mango for tray 8 references.
84
3 4 5 6 7 8 9 10 11 12CCI
0
10
20
30
40
Del
ta E
12 ef, 2 12 ref, 4 12 ref, 6
Mango, abs.DE, fruit
Figure C-11. Absolute Δ E for mango for tray and 12 references.
3 4 5 6 7 8 9 10 11 12CCI
0
10
20
30
40
Del
ta E
16 ref, 2 16 ref, 4 16 ref, 6
Mango, abs.DE, fruit
Figure C-12. Absolute Δ E for mango for tray and 16 references.
85
APPENDIX D DELTA E VALUES FOR DIFFERENT CASES
0 1 2 3 4 5 6 7 8 9 10 11trays
0
10
20
30
40D
elta
E8 ref, 28 ref, 48 ref, 6
Nectrarine, abs.DE, screen
Figure D-1. Absolute Δ E for nectarine for screen image and 8 references.
0 1 2 3 4 5 6 7 8 9 10 11Trays
0
10
20
30
40
Del
ta E
12 ref, 2 12 ref, 4 12 ref 6
Nectarine, Abs.DE, screen
Figure D-2. Absolute Δ E for nectarine for screen image and 12 references.
86
0 1 2 3 4 5 6 7 8 9 10 11Trays
0
10
20
30
40
Del
ta E
16 ref, 2 16 ref, 4 16 ref, 6
Nectarine, abs.DE, screen
Figure D-3. Absolute Δ E for nectarine for screen image and 16 references.
0 1 2 3 4 5 6 7 8 9 10 11trays
0
10
20
30
40
Del
ta E
8 ref, 2 8 ref, 4 8 ref, 6
Nectarine, abs.DE, real fruit
Figure D-4. Absolute Δ E for nectarine for tray and 8 references.
87
0 1 2 3 4 5 6 7 8 9 10 11trays
0
10
20
30
40
Del
ta E
12 ref, 2 12 ref, 4 12 ref, 6
Nectarine, abs.DE, real fruit
Figure D-5. Absolute Δ E for nectarine for tray and 12 references.
0 1 2 3 4 5 6 7 8 9 10 11trays
0
10
20
30
40
Del
ta E
16 ref, 2 16 ref, 4 16 ref, 6
Nectarine, abs.DE, real fruit
Figure D-6. Absolute Δ E for nectarine for tray and 16 references.
88
0 1 2 3 4 5 6 7 8 9 10 11trays
0
10
20
30
40
Del
ta E
8 ref, 2 8 ref, 4 8 ref, 6
Mango, abs.DE, screen
Figure D-7. Absolute Δ E for mango for screen image and 8 references.
0 1 2 3 4 5 6 7 8 9 10 11trays
0
10
20
30
40
Del
ta E
12 ref, 2 12 ref, 4 12 ref, 6
Mango, abs.DE, screen
Figure D-8. Absolute Δ E for mango for screen image and 12 references.
89
0 1 2 3 4 5 6 7 8 9 10 11trays
0
10
20
30
40
Del
ta E
16 ref, 2 16 ref, 4 16 ref, 6
Mango, abs.DE, screen
Figure D-9. Absolute Δ E for mango for screen image and 16 references.
0 1 2 3 4 5 6 7 8 9 10 11trays
0
10
20
30
40
Del
ta E
8 ref, 2 8 ref, 4 8 ref, 6
Mango, abs.DE, real fruit
Figure D-10. Absolute Δ E for mango for tray 8 references.
90
0 1 2 3 4 5 6 7 8 9 10 11trays
0
10
20
30
40
Del
ta E
12 ref, 2 12 ref, 4 12 ref, 6
Mango, abs.DE, real fruit
Figure D-11. Absolute Δ E for mango for tray and 12 references.
0 1 2 3 4 5 6 7 8 9 10 11trays
0
10
20
30
40
Del
ta E
16 ref, 2 16 ref, 4 16 ref, 6
Mango, abs.DE, real fruit
Figure D-12. Absolute Δ E for mango for tray and 16 references.
91
APPENDIX E SOURCE CODES FOR SAS PROGRAMS
Method 1.
PROC PRINT DATA=FILE; RUN; proc sort data= File; by Object; proc anova data=file; by Object; class reference_colors selections source panelist booth; model Delta_E = reference_colors|selections|source; means reference_colors selections source reference_colors|selections|source/duncan; run;
Method 2.
proc sort data= File; by object; proc mixed DATA=File; by object; class reference_colors selections source booth panelist; model DiffDE = reference_colors|selections|source; random panelist booth; lsmeans selections|reference_colors|source /pdiff; run;
*** The model statement was interchangeable to Diff ΔE or ΔE to statistically analyze both dependent variables. Table E-1. Mixed mode summary absolute ΔE for mangos.
* Num DF refers to numerator degrees of freedom. Den DF refers to denominator degrees of freedom. Table E-3. Mixed Mode summary absolute ΔE for nectarines.
* Num DF refers to numerator degrees of freedom. Den DF refers to denominator degrees of freedom. Table E-4. Mixed Mode summary difference in ΔE for nectarines.
Figure E-3. Absolute ΔE means for reference colors for mangos.
The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Reference_Colors 8 10.1910 0.4968 1343 20.52 <.0001 Reference_Colors 12 13.4795 0.4968 1343 27.14 <.0001 Reference_Colors 16 11.6834 0.4968 1343 23.52 <.0001
Differences of Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Reference_Colors 8 12 ‐3.2885 0.4356 1343 ‐7.55 <.0001 Reference_Colors 8 16 ‐1.4924 0.4356 1343 ‐3.43 0.0006 Reference_Colors 12 16 1.7961 0.4356 1343 4.12 <.0001
Figure E-4. Difference ΔE means for reference colors for mangos.
The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F 13.6525 0.4752 1343 28.73 <.0001 Source S 11.9404 0.4752 1343 25.13 <.0001
Differences of Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F S 1.7122 0.3546 1343 4.83 <.0001
Figure E-5. Absolute ΔE means for presentation for mangos.
95
The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F 12.6407 0.4638 1343 27.25 <.0001 Source S 10.9286 0.4638 1343 23.56 <.0001
Differences of Least Squares Means
Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F S 1.7121 0.3557 1343 4.81 <.0001
Figure E-6. Difference ΔE means for presentation for mangos.
The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Reference Standard Effect Source Colors Selections Estimate Error DF t Value Selections 2 16.3154 0.7332 1343 22.25 Selections 4 12.2299 0.7332 1343 16.68 Selections 6 10.8946 0.7332 1343 14.86 Least Squares Means Reference Effect Source Colors Selections Pr > |t| Selections 2 <.0001 Selections 4 <.0001 Selections 6 <.0001 Differences of Least Squares Means Standard Effect Selections Selections Estimate Error DF t Value Pr > |t| Selections 2 4 4.0855 0.4235 1413 9.65 <.0001 Selections 2 6 5.4208 0.4235 1413 12.80 <.0001 Selections 4 6 1.3353 0.4235 1413 3.15 0.0016 Figure E-7. Absolute ΔE means for selections of colors for nectarines.
96
The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Selections 2 12.8803 0.4604 1413 27.97 <.0001 Selections 4 10.2490 0.4604 1413 22.26 <.0001 Selections 6 10.6946 0.4604 1413 23.23 <.0001 Differences of Least Squares Means Standard Effect Selections Selections Estimate Error DF t Value Pr > |t| Selections 2 4 2.6313 0.4326 1413 6.08 <.0001 Selections 2 6 2.1857 0.4326 1413 5.05 <.0001 Selections 4 6 ‐0.4456 0.4326 1413 ‐1.03 0.3031 Figure E-8. Difference ΔE means for selections of colors for nectarines.
The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Reference_Colors 8 11.4792 0.7332 1343 15.66 <.0001 Reference_Colors 12 14.9077 0.7332 1343 20.33 <.0001 Reference_Colors 16 13.0530 0.7332 1343 17.80 <.0001
Differences of Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Reference_Colors 8 12 ‐3.4285 0.4235 1343 ‐8.10 <.0001 Reference_Colors 8 16 ‐1.5737 0.4345 1343 ‐3.72 0.0002 Reference_Colors 12 16 1.8547 0.4345 1343 4.38 <.0001
Figure E-9. Absolute ΔE means for reference colors for nectarines.
The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Reference_Colors 8 8.3653 0.4604 1343 18.17 <.0001 Reference_Colors 12 13.6839 0.4604 1343 29.72 <.0001 Reference_Colors 16 11.7746 0.4604 1343 25.57 <.0001
Differences of Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Reference_Colors 8 12 ‐5.3186 0.4326 1343 ‐12.29 <.0001 Reference_Colors 8 16 ‐3.4092 0.4326 1343 ‐7.88 <.0001 Reference_Colors 12 16 1.9093 0.4326 1343 4.41 <.0001
Figure E-10. Difference ΔE means for reference colors for nectarines.
97
The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F 13.8739 0.7125 1343 19.47 <.0001 Source S 12.4193 0.7125 1343 17.43 <.0001
Differences of Least Squares Means
Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F S 1.4546 0.3458 1343 4.21 <.0001
Figure E-11. Absolute ΔE means for presentation for nectarines.
The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F 12.0020 0.4252 1343 28.23 <.0001 Source S 10.5473 0.4252 1343 24.81 <.0001
Differences of Least Squares Means
Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F S 1.4547 0.3532 1343 4.12 <.0001
Figure E-12. Difference ΔE means for presentation for nectarines.
98
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