NBSIR 84-2829

The Role of Color in Lighting for

Meat and Poultry Inspection

U S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Center for Building Technology

Building Physics Division

Washington, DC 20234

March 1984

Sponsored by

iJJ

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34-2d29

The U.S. Department of Agriculture

Washington, DC 20250

NBSIR 84-2829

THE ROLE OF COLOR IN LIGHTING FOR

MEAT AND POULTRY INSPECTION

Belinda L. Collins

James A. Worthey

U S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Center for Building Technology

Building Physics Division

Washington, DC 20234

March 1984

Sponsored by

The U.S. Department of Agriculture

Washington, DC 20250

U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director
.

'
ABSTRACT

lie role of color in lighting for meat and poultry inspection is discussed. A

review of literature relevant to the problem of quality of illumination is presented, along with literature specific to agricultural and veterinary problems.

A psychophysical study of the accuracy of detecting and identifying selected

defects in meat and poultry was conducted under five light sources: incandescent, cool white fluorescent, cool white deluxe, high pressure sodium (HPS),

and low pressure sodium (LPS). The results indicated that more errors were made under the last two sources, and that the inspection task was rated as more difficult under these sources. In addition, spectroradiometric measurements were made of defective and adjacent "normal” tissue to document the kinds

of spectral reflectances that exist in four species: chicken, cattle, turkey,

and swine. These measurements indicated that differences in spectral reflectance

characterized much of the tissue studied. Based on these data, recommendations

are made to avoid the use of light sources with poor color rendering qualities

and low color temperatures for the inspection task.

Key words: chromaticity , color, color appearance, color rendering, energyefficient light sources, illumination, inspection, meat, poultry,

spectral reflectance.

iii
FOREWORD

This report documents the results of National Bureau of Standards (NBS)

research in support of the U.S. Department of Agriculture (USDA) , in fulfillment

of USDA/NBS Interagency Agreement No. FSIS 12-37-432, entitled "Lighting Quality

Guidelines." The report summarizes work conducted during the period July 1983

through February 1984.

We wish to acknowledge the helpful interest and guidance of the sponsor's

Project Officers, Mr. John Wu and Mr. Ralph Thompson, of the Food Safety and

Inspection Service, the willing cooperation of the USDA inspectors, and

processing facilities who participated, and the invaluable assistance of

Dr. Robert Tumquest of USDA.

DISCLAIMER

Certain commercial equipment, instruments, or materials are identified in this

report to specify the experimental procedure. Such identification does not

imply recommendation or endorsement by the National Bureau of Standards nor

does it imply that the materials or equipment identified are necessarily the

best available for the purpose.

ACKNOWLEDGMENTS

The authors wish to acknowledge the efforts of all those who participated in

this project and who provided insightful comments during the review process.

We are particularly grateful to the following people: Mrs. Mary Natrella,

Dr. Gerald Howett, Mr. Jim Heldenbrand, Dr. Arthur Rubin, Mr. Bob Booker, and

Mr. Larry Kaetzel, as well as Ms. Lisa Gray.

iv
EXECUTIVE SUMMARY

Although current U.S. Department of Agriculture (USDA) regulations for meat and

poultry inspection provide specific requirements for illumination levels (illuminance) for inspection stations, they do not provide any guidelines for the

color or quality of the illumination. As a result, a light source which does

not render color accurately can potentially be used.

Commercially available light sources vary widely in their ability to reveal

colors accurately in comparison with a reference source. Some, although energy

efficient, have poor color rendering properties.

USDA sponsored a project at the National Bureau of Standards (NBS) to explore

the role of color in the meat and poultry inspection process. Three approaches

were followed in the project: a review of published research; a psychophysical

study of the detectability and recognizability of meat and poultry defects

under light sources with different color rendering properties; and

spectroradiometric measurements of selected tissue samples in four species.

The review of the literature indicated very little research on the specific

problem of color rendering and light quality for meat and poultry inspection.

The research literature defining color and color rendering is summarized to

provide background information to those unfamiliar with these concepts. In

addition, some information is presented on the perception of colors under different light sources, as well as on some issues in lighting for veterinary and

agricultural use.

For the psychophysical experiment, meat and poultry inspectors participated in

a study designed to determine if light sources with different color rendering

properties had differing effects on the ability to detect and identify common

defects and disease. In this experiment, 18 poultry and 16 meat inspectors

observed 5 different meat or poultry tissue samples under each of 5 different

light sources. Light sources included: incandescent, cool white fluorescent,

cool white deluxe fluorescent, high pressure sodium (HPS), and low pressure

sodium (LPS).

The results indicated that more errors were made for detecting and identifying

tissue samples under HPS and LPS lamps than under the other sources. It was

also found that inspectors rated their task as more difficult under HPS and

LPS, and provided many negative comments about these two light sources. While

problems arose with selecting samples that properly represented the meat tissues

customarily seen by red meat inspectors, results for the cattle portion of the

experiment demonstrate error trends similar to the results found in the poultry

portion.

Spectroradiometric measurements were also made of the tissue samples studied in

the psychophysical experiment for chickens and cattle as well as for turkey and

swine tissue. These measurements demonstrated clear differences in lightness

and spectral reflectance between a defect and its adjacent, more normal surroundings. While further analysis is needed to assess the effects of manipulating

light source characteristics (such as spectral power distribution) on the color

v
differences believed to exist in the tissue samples, these measurements represent

one of the first field determinations of the spectral composition of meat and

poultry tissue. Such measurements are an essential first step in calculating

the effect of light source variation.

The conclusions include a recommendation that sources with color rendering

indices (CRI) (and color temperatures) equal to or less than that for HPS

should not be used for meat and poultry inspection. Sources with CRI equal to or greater than that for cool white fluorescent lights appear adequate along

with sources with color temperatures equal to or greater than that of the

incandescent source used in the psychophysical study. Further research is

needed, however, to determine if performance can be improved (or inspector

visual comfort increased) for sources with truly good rendering characteristics.

In addition, mathematical calculations are needed to predict the role of

specific light sources on the color differences associated with meat and poultry

tissue.

vi
TABLE OF CONTENTS

Page

1. INTRODUCTION 1 1.1 Background Information on the Inspection Process 1 1.2 Literature Review 2 1.2.1 Background Information on Color and Lighting 4

1.2.2 Color Rendering and Color Temperature 5 1.2.3 Visual Clarity 8

1.3 Research Relevant to Lighting for Meat/Poultry Inspection 10

2. PSYCHOPHYSICAL EXPERIMENT ON POULTRY AND RED MEAT 13

2.1 Overview 13

2.2 Apparatus 13

2.3 Participants 14

2.4 Procedure 18

2.5 Poultry Psychophysical Study 20

2.5.1 Sample Selection 20

2.5.2 Results for Pass/Reject Decisions 20

2.5.3 Identification Data 26

2.5.4 Rated Ease of Task Performance 31

2.5.5 Comments about the Light Sources 34

2.5.6 Response Time Data 34

2.6 Results from Red Meat Psychophysical Study 34

2.6.1 Sample Selection 34

2.6.2 Red Meat Pass/Reject and Identification Data 39

2.6.3 Rating, Data, Comments, and Response Time Data for Red

Meat 41

2.6.4 Discussion of Red Meat Results 44

3. SPECTRORADIOMETRIC MEASUREMENTS 50

3.1 Background Information 50

3.2 Approach 51

3.1.1 Method of Measurement 51

3.1.2 Measurement Technique 52

3.3 Spectroradiometric Measurements 53

4. CONCLUSIONS 71

5. RECOMMENDATIONS FOR FURTHER RESEARCH 72

6. REFERENCES 74

vii
LIST OF TABLES

Page

Table 1 . Light Source Data 15

Table 2. Instructions to Participant 19

Table 3. Pass/Reject Errors for All Inspectors for Poultry 23

Table 4. Comparison of Number of Errors in Pass/Reject Decisions for

All Sources for Poultry 23

Table 5. Comparison of Number of Errors in Pass/Reject Decisions for

All Samples for Poultry 24

Table 6. Decision Data for a Subset of 11 Poultry Inspectors 25

Table 7. Identification Decisions for Each Sample for Each Source for

18 Inspectors 27

Table 8. Accuracy of Identification of Samples for 18 Poultry

Inspectors 30

Table 9. Rated Ease of Doing Inspection Task for Poultry 32

Table 10. Frequency that Each Rating (1 to 5) Was Given for Each

Source for Poultry 33

Table 11. Comments About Each Light Source by Poultry Inspectors 35

Table 12. Mean Response Time for Light Sources by Poultry Inspectors ... 38

Table 13. Tabulation of Pass/Reject Data for Red Meat Inspectors 40

Table 14. Tabulation of Identification Data for Red Meat Inspectors .... 42

Table 15. Rated Ease of Doing Inspection Task for Red Meat 43

Table 16. Comments About Each Source by Red Meat Inspectors 45

Table 17. Mean Response Time Data for Red Meat Inspectors 48

viii
LIST OF FIGURES

Page

Figure 1. Chromaticity diagram 6

Figure 2. Sketch of portable light box 4

Figure 3. Relative spectral power distribution functions of three of

the light sources used in the simulated inspection experiments. These data were measured with the telescopic spectroradiometer, from the surface of the PTFE whiteness standard,

in the apparatus used for the inspection experiments 16

Figure 4. Relative spectral power distribution functions of two of the

light sources used in the simulated inspection experiments.

Measurement conditions were the same as in the previous

figures 17

Figure 5. Examples of specimens used in the pyschophysical experiment . 21

Figure 6. Spectral reflectance functions of chicken epidermis,

presented as comparisons of defective with "normal" areas ... 54

Figure 7. Spectral reflectance functions of internal organs in

chicken, presented as comparisons of diseased with "normal" areas 55

Figure 8. Spectral reflectance measurements of liver in cattle,

presented as comparisons of defective areas with "normal" areas 57

Figure 9. Spectral reflectance measurements of internal areas in

cattle, presented as comparisons of defective areas with

"normal" areas 58

Figure 10. Spectral reflectance measurements of internal areas in cow,

presented as comparisons of defective areas with "normal"

areas 59

Figure 11. Spectral reflectance measurements of internal areas in

cattle, presented as comparisons of diseased areas with

normal areas 60

Figure 12, Spectral reflectance measurements of internal organs in

cattle presented as comparisons of diseased areas with

normal areas 61

Figure 13. Spectral reflectance functions of external areas of turkey,

presented as comparisons of defective with "normal" areas ... 63

ix
LIST OF FIGURES

Page

Figure 14. Spectral reflectances of internal areas of turkey, presented

as comparisons of defective with "normal" areas 64

Figure 15. Spectral reflectance measurements of hog epidermis, presented

as comparisons of defective areas with adjacent "normal"

areas 65

Figure 16. Spectral reflectances of internal organs in hog, presented

as comparisons of diseased areas with "normal" areas 66

Figure 17. Spectral reflectance measurements of external areas of hog,

presented as comparisons of defective areas with "normal" areas 68

Figure 18. Spectral reflectance measurements of internal organs in hog,

presented as comparisons of defective areas with "normal" areas 69

Figure 19. Spectral reflectance measurements of internal areas in hog,

presented as comparisons of diseased areas with "normal" areas 70

x
INTRODUCTION

\L the present time, all meat and poultry intended for human consumption is

tully inspected by the U.S. Department of Agriculture (USDA) for disease,

contamination, and defects. The inspection is done at one or more inspection

stations staffed by USDA personnel along the normal production line. USDA

regulations for inspection specify that adequate light must be provided specifically for each inspection station. Specified illumination levels (illuminance) given in the regulations vary with the inspection station and species.

The USDA regulations currently make no specific requirement for the quality of

the illumination. The advent of high-intensity discharge (HID) light sources,

with improved energy efficiency (luminous efficacy) but often poor colorrendering properties has raised the issue of the importance of the colorrendering properties and spectral distribution characteristics of the light source used for the inspection process. At present, any light source may be

used, even one which distorts the appearance of the colors of the product.

As a result, USDA sponsored a project at the National Bureau of Standards (NBS)

in which the role of color in lighting for meat and poultry inspection was examined. In this project, several concurrent tasks were undertaken to define

the role of color in the inspection process. In the first, existing research

literature was reviewed. Second, a psychophysical experiment was conducted to

determine the accuracy of detecting defects under selected light sources for

poultry and cattle inspection. Third, the spectral reflectance of selected

samples of normal and rejected tissue from four animal and poultry species was measured spectroradiometrically

.

The present report summarizes the results of the NBS research, and provides

recommendations to USDA for minimal levels of color rendering needed for inspection stations. It also provides background information on the general role of

color in lighting as a framework for the recommendations.

1.1 BACKGROUND INFORMATION ON THE INSPECTION PROCESS

Before discussing results from the NBS research, some background information on

the meat and poultry inspection process is needed. In a typical packing house,

the animal or bird is suspended by its feet, killed, bled, and immediately processed. Following the kill, it begins its movement through the packing plant

with different processing procedures occurring along the line. It is inspected

by USDA at specified locations as it moves through the plant. Inspection

procedures for poultry and red meat differ due to size and line-speed

differences

.

There are currently two types of inspection process for young chickens

traditional and modified traditional. (USDA regulations allow either process

in a poultry plant.) For the traditional inspection process, the whole bird

including the viscera is viewed by only one inspector at a line speed which

varies according to the processing plant. Illumination levels (illuminance)

are required to be 50 footcandles (fc) at the inspection surface. For the

modified traditional inspection (MTI), the outside of the bird is first viewed

1
by one inspector who uses a mirror to see the back side. Line speed here is 70

birds per minute. Next two inspectors view the viscera, and interior cavity,

alternating birds for an effective line speed of 35 birds per minute, per

inspector. Lighting levels for MTI are required to be 150 fc. Illuminance

requirements for turkey inspection are similar to those for traditional chicken

inspection.

For red meat inspection, both swine and cattle, inspection typically occurs at

three stations along the line. At the first station, the head station, the

inspector examines the head for problems in the lymph nodes, eyes, etc. At the

second inspection station, the viscera table, the inspector examines the internal organs by sight, slicing, and touch. The third inspection occurs at a "rail" station where the carcass is examined. Although detached, the viscera

and head remain associated with the carcass until they have been checked for

signs of disease that could force condemnation of the entire carcass. Typical

speeds for red meat inspection vary widely and depend on the plant slaughter

rate. Slaughter rates vary from one per hour to 300 per hour for cattle, and

from one to 1100 per hour for swine. Illumination levels are recommended to be

50 fc. An antemortem inspection is also conducted. Lighting levels are typically lower here.

1 .2 LITERATURE REVIEW

A computerized literature search was conducted to determine if there had been

previous research on the role of lighting quality and color appearance in meat

and poultry inspection. This search included an examination of data bases for

human and veterinary medicine, agricultural inspection, color rendering and

lighting, and general engineering. Relatively little recent research on illumination engineering for meat and poultry inspection was discovered, with almost

no information on the problem of lighting quality and color. Those documents

that provide relevant information will be discussed below.

One of the few sources that directly addresses the problem of lighting for

poultry inspection was written by the Joint Committee on Farm Lighting of the

Illuminating Engineering Society and of the American Society of Agricultural

Engineers. Their report on "Lighting for the Poultry Industry" (1970) provided

guidelines for lighting in processing plants as well as poultry farms. For processing plants, it recommended a general lighting level of 70 fc (excluding

killing and unloading areas), and 100 fc for government inspection and grading

stations. It noted that factors which affect the quality of a lighting installation are: uniformity (the ratio of maximum illumination to minimum); minimization of shadow and glare (from direct beam radiation or reflection from

glossy surfaces); light source color; and environmental factors such as room surface reflectance. No mention was made of the color rendering properties of

the light source, however. The report recommended limiting use of clear mercury

and high pressure sodium (HPS) to outdoor applications, while allowing indoor

use of improved mercury and metal halide with suitably shielded luminaires. It noted, however, that all these lamps are characterized by slow restart. While

slow restart is not usually a problem for fluorescent lamps, they may not operate properly under cold conditions (requiring a special ballast and enclosure

to maintain lamp temperature). They may have problems starting under conditions

2
of high humidity, as well. In addition, a common problem in such processing

plants is that of water spray striking and breaking a hot lamp. This problem

can be minimized by careful luminaire design.

The report also presented data obtained with the Visual Task Evaluator (VTE)^

for illumination levels needed to perform different visual tasks in the poultry

inspection process. The following results were measured for poultry: to

detect white spots on liver and spleen (1.1 fc); detect bruise on carcass (2.8

fc); detect 1/2-in diameter breast blister (11.0 fc); detect synovitis (28.0

fc); detect bruise on wing joint (57.0 fc) ; and detect liver leukosis or tumor

(1000 fc). No recommendations were given for the color rendering properties of

light sources for the detection of such poultry defects.

Ries (1982) reported results from a survey of lighting for meat packing and

processing plants. He noted that use of incandescent lighting can lead to a large electric energy bill, not only for lighting but also for cooling equipment

to remove the heat from the lights. He suggested that use of incandescent lights

can account for as much as 27-44 percent of the total electric bill if the cost

of cooling is included. Ries commented that a comparison of the lumens-per-watt

output (or luminous efficacy) of seven different potential light sources for

packing plants indicated that incandescent, quartz, and mercury vapor lamps were potentially less efficient, while fluorescent, multi-vapor (metal halide)

and both high and low pressure sodium were potentially much more efficient.

(Despite the high efficiency of low pressure sodium, Ries suggested that it

should be confined to outdoor use because of its monochromatic nature.)

Ries noted that the following constraints apply to lighting a processing plant:

bulb breakage and replacement (due to water hitting the bulb); fixture corrosion

due to atmospheric moisture; and cold temperatures in refrigerated rooms (where

heat from lights is a particular problem). The cost of electric lighting

includes the cost of electricity to operate the lights, replacement or relamping costs, energy to remove heat dissipated by the lamps, and initial lamp

costs

.

Ries presented calculations showing that reductions in lighting cost associated

with changing from incandescent (tungsten) lighting to HPS at one site were as great as 85 percent. At another site, a reduction of 69 percent occurred after

switching from mercury to HPS, while at a third, changing from incandescent to

fluorescent led to a 70 percent reduction in operating costs. Thus, switching

to more efficient sources has a dual potential for reducing overall electric

costs. The lamps with higher luminous efficacy require less electric power to

provide a given illuminance and consequently generate less heat.

£/ The IESNA Handbook (1982, p. 1-30) defines the VTE as a: "contrast reducing

instrument which permits obtaining a value of luminance contrast, called the

equivalent contrast C of a standard visibility reference task giving the same visibility as that of a task whose contrast has been reduced to threshold when the background luminances are the same for the task and the

reference task."

3
The Illuminating Engineering Society of North America ( IESNA) (1981) noted the

following items that must be considered in estimating lamp costs: cost to operate the lamp; lumen output; luminaire dirt depreciation; initial lamp cost;

replacement cost; and rated life. Different lamp types vary in: sensitivity

to ambient operating temperatures, warm-up and restart times, and optimal

operating position. The IESNA (1981) gives the following estimates for lamp

lif e

Incandescent —750-1000 hours

Fluorescent —10,000-20,000 hours

Metal halide—15,000-20,000 hours

Mercury—24,000+ hours

High Pressure Sodium—20,000-24,000 hours

Low Pressure Sodium—18,000 hours

Although each of these factors must be considered when selecting a light source

for meat and poultry processing, one of the most important factors has not even

been mentioned—namely, the color rendering properties of the light source. Currently available light sources vary in color rendering with the highly efficient sources often having poorer color rendering properties than less efficient

ones. The color-rendering properties of a light source determine a person's

ability to perceive colors presented under that source, and consequently play

a key role in the meat and poultry inspection process.

1.2.1 Background Information on Color and Lighting

Before discussing the color rendering properties of light sources, some background information on color is necessary. The color of an object such as a piece of meat depends on three things: the spectral power distribution of the

light source (or energy emitted by the source at each wavelength); the spectral

reflectance of the object; and the spectral sensitivity of the observer's eye.

In the present report, the observer is assumed to have normal color vision

characterized by three types of color receptors, sensitive in the blue, green,

and red regions of the visible spectrum.

One of the most common systems for specifying the color of an object or light

is the system specified by the Commission Internationale de l'Eclairage (CIE).

This system is based on the principle that three fixed colored lights (or primaries) can be mixed to match any other color. The amounts of the three primaries needed for this match have been termed the tristimulus values. The CIE

specified a Standard Observer based on the average match values given by a number of observers (Wyszecki and Stiles, 1967). The values given by the CIE

1931 Standard Observer are considered representative of the normal human eye

(for stimuli subtending visual angles between 1 and 4°).

The CIE system defines three functions of wavelength, x, y, and z, which

represent the tristumulus values of single wavelengths of the spectrum. Hurvich

(1981) points out that specifying the color of an object requires knowledge of

the reflectance spectrum of an object's surface and the spectral energy distribution of the light source illuminating the object. If these two distributions

are multiplied together, and then multiplied again by the standard observer

4
-stimulus values at each wavelength, and the resultant values added together

<r all wavelengths, one obtains a set of summed numbers termed the X, Y, and Z tristimulus values of the illuminated object. Together, these three numbers

describe the color of a light as a stimulus to color vision. For many purposes,

it is convenient to work with a different set of numbers (Y, x, y), in which Y

has the same meaning, while

x = X

X+Y+Z

and

X+Y+Z

The quantities x and y are termed "chromaticity coordinates;" chromaticity

(x,y) is the physical correlate of a light's color, independent of intensity.

The intensity information is carried by Y, and the CIE system is so contrived

that Y is equal (or proportional) to luminance as measured by ordinary light

meters.

If chromaticity (x,y) is computed for monochromatic lights throughout the

visible spectrum, and plotted as a graph with the x and y axes scaled equally,

the result is a curve called the spectrum locus; it may be labelled along its

length with wavelength in nanometers. The open end of the spectrum locus may

be closed with a straight line, called the line of purples. Any color encountered will plot on or inside this closed curve. The plot of chromaticities

containing the spectrum locus, illustrated in figure 1, is referred to as the

chromaticity diagram.

The color of an object* is dependent not only on its spectral reflectance, but

also on the spectral power distribution of the light illuminating it. The

ability of different illuminants to reveal colors is known as color rendering.

1.2.2 Color Rendering and Color Temperature

The CIE chromaticity system takes a basic scientific fact, that only three

numbers are needed to specify a light as a stimulus to color vision, and reduces

it to a practical and explicit measurement method. This is the basis of the

applied science of colorimetry, which provides a workable means of prediction

and communication for makers and buyers of dyes and pigments, color televisions,

and so forth. Colorimetry is used in illumination engineering for the specification of lamp colors. The more pressing problem in illumination, however, is one for which colorimetry does not provide a simple answer: "How will a particular light source affect the color appearance of objects?" This is known as the problem of "color rendering."

* Non self-luminous

5
VALUES OF X

Figure 1. CIE chromaticity diagram

6
v treatment of color rendering must deal with issues that are absent when one

1 1 y wishes to describe an isolated light as a stimulus to color perception:

1. The objects lighted by a source could be anything, yet one seeks to

generalize about the objects' appearance when the source is changed.

2. Whereas the CIE scheme of colorimetry is essentially a model of the initial

response (absorption) of the visual receptors, a productive discussion of

color rendering must deal with the eye's adaptation to different lighting

environments, an issue ignored in calculating chromaticity. Adaptation

data are available, of course, but models to summarize these data are a topic of current research.

3. Some standard of good color rendering is needed. It is easy to choose a standard, such as daylight or some approximation to daylight, but the

question of the reasons for this choice remains. Could another light be

better than daylight?

In subsequent sections of this paper, we try to skirt the conceptual difficulties

of the color-rendering problem by looking at specific visual tasks and specific

color contrasts.

The Color Rendering Index, like the chromaticity system, is officially defined

in a publication of the CIE (1974). It is intended to be a "measure of the

degree to which the perceived colours of objects illuminated by the source conform to those of the same objects illuminated by a reference illuminant for

specified conditions" (CIE, 1974, p. 74). A standard set of "objects" is used,

comprised of eight color chips, representing the hue circle from red through

yellow and green to blue and back to red. A family of reference illuminants is

defined, and the one to be used is that which is nearest in chromaticity to the

test source. (In large measure, this sidesteps the adaptation problem.) The

chromaticity coordinates of the eight chips in a uniform color space are com- puted for the test light, with a further small correction for adaptation if

necessary, and then the color shifts are found by which these coordinates differ

from those that the chips would have under the reference light. Finally, the

CRI is computed to be 100 minus a certain constant times the sum of the magnitudes of the color shifts. Thus a "perfect" CRI is 100, with no color shifts

at all, and any imperfect light has a CRI less than 100. Color rendering

indices of less than zero are possible. Color Rendering Index is the commercially accepted way to specify lamp spectral quality. As such, it is used in

the present report.

Another common way of specifying lamp color is known as color temperature. It

is defined by the IESNA (1981), p. 1-8, as the "absolute temperature of a blackbody radiator having a chromaticity equal to that of the light source." As the

temperature of a blackbody radiator (completely radiating source) is increased

from about 800 to 10,000 K, it shifts in color from red to yellow (3000 K)

through white (5000 K) to blue. Further, "the locus of blackbody chromaticities

on the x, y diagram is known as the Planckian locus. Any chromaticity represented by a point in this locus may be specified by color temperature"

(p. 5-13). The IESNA cautions that color temperature specifies the chromaticity

7
only of a light source and does not represent its SPD. Light sources with similar chromaticities (color temperatures) may differ widely in SPD. In addition,

the perceived color of the light source (or blackbody) depends on the observer's

adaptation state. The current CIE color rendering index compares the color

rendering of a test lamp to that of a standard lamp with the same [correlated]

color temperature. The IESNA (1981, p. 5-14, 5-15) notes that:

"When it becomes possible to compute the effects of constancy and

adaptation so that the results agree accurately with the facts of experience, it will then be possible in calculating the color rendering properties of a lamp to take correctly into consideration differences in lamp

color, as well as differences in spectral power distribution of lamps that

have the same color."

1.2.3 Visual Clarity

Aston and Bellchambers (1969, p. 259) observed that lamps of good color

rendering gave to a scene a quality which seemed to go beyond "normal"

appearance

:

For a given level of illumination (assuming this to be adequate for the

purpose) it is generally accepted that a well-designed and balanced interior colour scheme, when illuminated by fluorescent lamps giving good color

rendering, will be more attractive than the same interior illuminated by a source of poorer spectral quality. Observation has shown that the attractiveness is not due to the quality of the colour rendering of individual

hues alone , but that some additional factor, variously referred to as colour or visual 'clarity', added to the attractiveness of the interior

[Emphasis added.]

To test this idea in a systematic way, two identical colorful room-models were constructed, which could be lit by different fluorescent lamps. Observers were presented with the two models side-by-side. A dimmer was provided for one of

the lights, and observers were instructed "to adjust the level in the lefthand

cabinet ... so that the overall clarity of the scene is the same in both cabinets. By overall clarity is meant the satisfaction gained by you personally,

discounting as far as possible any obvious differences in colour and brightness"

(Aston and Bellchambers, 1969, p. 260). A lamp of good color-rendering always

appeared on the left, and it was found that observers would accept a lower

level of this light as providing equal "clarity" with a given level of lower

color-rendering light. For instance, observers considered an illuminance of

800 lux from warm-white tubes to be equivalent to about 450 lux of the high

color-rendering light.

Similar visual clarity results were found by Bellchambers and Godby (1972) and

by Thornton and Chen (1978). While it had usually been assumed that only sources with broad spectral power distributions (SPD's) could provide desirable

color rendering, Thornton and Chen's "good" lamp was a "prime color" type,

whose SPD has much of its power in narrow bands at about 435, 530, and 620 nm.

8
iton and Clien propose an explanation of visual clarity in terms of a light's

Lity to make colors more or less distinguishable. It is easy to see that

ghts do vary in this regard, because a monochromatic illuminant reduces all

objects to shades of the same color. This issue may be thought of in terms of

discrimination" or of colorfulness. Suppose that under an illuminant of good

color rendering, two object colors are just barely discriminable. If another

light has the tendency to make these colors less different, then they will not

be discriminable; they will look alike. On the other hand, a set of diverse

object colors might simply become less diverse, hence less "colorful" under the

second light.

Thornton (1972) observed that if a set of color chips, such as those used in

the definition of CRI, are lighted by a test lamp, and then their colors are plotted in a uniform color space, they define a polygon or "gamut". The area

of this gamut is a measure of how well object colors can be discriminated under

a lamp. This is more of a "performance" measure than CRI, since the standard of

a perfect SPD is not chosen in advance. Nevertheless, the rankings of illuminants by gamut area are in considerable agreement with those given by the CRI.

In other words, daylight is not just a customary standard of lighting quality,

it is actually a good performer with regard to color discrimination. A "prime

color” lamp, similar to that mentioned above, gives a gamut area slightly larger

than daylight (Thornton, 1972). With respect to such a lamp, gamut area clearly

gives a different ranking than CRI, since CRI makes daylight best by definition.

Because the gamut calculation depends on the choice of eight specific color

chips, it may appear arbitrary. Worthey (1982) found a way to describe differences among illuminants which does not depend on an arbitrary selection of

colored objects. Reasoning from a comtemporary opponent-colors model of color

vision, he derived formulas expressing a light's ability to reveal color contrasts. One computation yields a number predicting the magnitude of blue-yellow

contrasts under a light; a second computation gives a number predicting redgreen contrasts.

Calculation of these numbers for a variety of illuminants shows that while

differences in blue-yellow rendering are generally small, differences in redgreen rendering are large. Common fluorescent lamps, including cool white,

systematically reduce red-green contrasts. So do high-pressure sodium vapor

and high-pressure mercury vapor. This reduction in red-green contrasts can explain the results of the visual clarity experiments, since red-green contrasts

are known to contribute to the perceived distinctness of borders (Frome, Buck,

and Boynton, 1981). That is to say, lamps such as those which ranked poorly in

the visual clarity experiments reduce the distinctness of borders in a scene which has a variety of colored objects. A similar argument can be made that

certain lamps may reduce the perceived brightness of a scene, since color con- trasts contribute to brightness. This argument is particularly applicable to

high-pressure sodium light, since, according to Worthey 's calculations, it is

perfectly normal with respect to blue-yellow contrasts, but poor with respect

to red-green (Worthey, 1982). These conclusions regarding clarity and brightness merit further experimental work. The actual ranking of illuminants

according to red-green rendering is little different from a ranking according to

gamut area.

9
Xu (1983) also sought to quantify illuminant performance in a way that did not

involve the arbitrary selection of a set of color chips. He defined a measure

called "color-rendering capacity" which is a measure of "the maximum possible

number of different colors that can be displayed by a given illumination" (Xu,

1983, p. 1709). He observed that objects are sources of information, while

light acts as an information carrier. Thus, color-rendering capacity is a measure of a light's ability to transfer information about objects, via the

human color sense (Xu, 1983). Again, the ranking of illuminants by Xu's method

is not radically different from a ranking by other methods. Perhaps the most

important feature of Xu's work is that while Thornton (1972) and Worthey (1982) were implicitly concerned with a light's ability to reveal color information,

Xu (1983) based his calculations directly on the information-theoretical concept

of the number of different messages that a system can convey.

In summary, the visual clarity experiments, the color gamut calculations, the

opponent-color calculations, and the color-rendering capacity calculations are

all concerned with the same set of facts. That is, that color vision is a source of information about objects, and that many common light sources tend to

lose this information—to one degree or another—by making colored objects look

less different from one another than they would under normal daylight. In

these methods, daylight is not chosen arbitrarily as a standard; rather,

daylight is demonstrated to perform well in revealing color contrasts.

1.3 RESEARCH RELEVANT TO LIGHTING FOR MEAT/POULTRY INSPECTION

The lighting community has conducted extensive research on the problem of

color rendering and appearance, though not specifically for meat or poultry

inspection. Much of this research is beyond the scope of the present report,

but will be addressed briefly here to provide some insight into the effects of

varying the spectral composition of a light source on the appearance of colors

viewed under it. The illumination research has typically focused on the prediction of color appearance and subjectively rated quality for different illuminants. Researchers have often assessed the effect of varying the illuminant on

the appearance of a set of color chips. For example, Loe, Rowlands and Watson

(1982) reported the subjective assessment of light quality in an art gallery,

and on a color discrimination task involving colored chips. They varied both

the illuminant (incandescent and different fluorescent lamps were studied) and

the illuminance (either 300 or 1000 lux—30 to 100 fc).

Results from the color discrimination task indicated that increasing the

illuminance did not affect task performance, but that changing the illuminant

did. Illuminants with CRI in excess of 85 (and with a large gamut area) produced the best results. Interestingly, Loe et al. (1982) found that the tungsten light they used was not preferred for viewing paintings. They suggested

that this may have occurred because its gamut area was smaller than that of two

other fluorescent sources.

Boyce (1976) assessed performance on a color discrimination task involving

colored chips for four different fluorescent lights and two different illuminance levels. Again, he found that increasing the illuminance level from 300 to

1000 lux (30 to 100 fc) did not improve performance. Subsequently Boyce and

10
Simons (1977) found that performance did improve slightly with higher illuminance, but only for participants aged 55 and older. Boyce commented that "The

conclusion to be drawn from this is that even if colour discrimination does

improve with illuminance above, say 300 lux, the effect is likely to be small.

The implication therefore is that the use of good color rendering lamps is a considerably more relevant factor than illuminance in determining performance

at color discrimination tasks at least as long as the illuminance is above the

300 lux suggested by the IES code as the minimum illuminance for continuously

occupied interiors. It should also be noted that it will be advantageous to use good color rendering lamps at a lower illuminance rather than poor color

rendering lamps at a higher illuminance in situations where the total lighting

wattage is fixed" (Boyce, 1976, p. 11).

While Boyce's results on a color discrimination task indicated that one particular brand of fluorescent lamp (Northlight^) produced the most satisfactory results, similar research is needed for sources commercially available in

the U.S. Specific recommendations about the "best" lamp for meat and poultry

inspection can not be made on the basis of Boyce's results due to differences

in the kinds of color discriminations required, and in the spectral power distributions of commercially available sources sold in the United States and the

United Kingdom. Boyce's results do indicate that the spectral power distribution of a lamp affects the ability to discriminate colors accurately even for

different commercially available fluorescent and incandescent lamps. Boyce

commented that these results are of interest to other tasks where color discrimination is important. One such task is the "examination of foodstuffs for

freshness or the medical examination of patients for diseases where the skin

color is an important symptom. For such tasks the best light source is the one

that gives the most accurate rendering of the color relative to the source under which the internal criterion was developed" (Boyce, 1976, p. 3).

In addition to the illumination engineering reseach, recent literature on lighting for human and veterinary medicine was examined to determine if there were any research on the role of color and lighting quality in detecting disease.

Relatively little research on this subject appears to have been conducted.

One study by Charters (1966) noted that lighting for veterinary diagnosis and

treatment rooms should provide maximum assistance. "This means a high level of

illumination, freedom from unwanted shadows, and a quality of light which will

ensure the instant recognition of changes in the colour of objects seen under

it" (p. 29). He recommended use of fluorescent lighting due to its relatively

high efficiency and because it is available in a wide range of colors. One

lamp in particular was viewed as very close to "north sky daylight which is usu- ally accepted as the best light by which to make accurate colour assessments"

(p. 30).

Culver and Allard (1980) discussed the effects of lighting on the detection of

allergic reactions in the skin of veterinary patients. They noted that over- head direct lighting reduced the contrast between the light and dark areas of

the allergic reaction, whereas indirect light from the side appeared to make

the reaction easier to interpret.

11
Finney (1973) discussed various quality measurements that can be made for

agricultural products. He noted that quality characteristics include factors

such as shape, color, size, density, moisture content, firmness, tenderness, oil

content, flavor, etc. "Quality control of agricultural products, therefore, is

concerned with instruments and techniques that can be used to measure accurately

or to estimate the physical and chemical properties that are associated with the

quality of the end product" (p. 1). Finney's book deals with different nondestructive evaluation methods, such as light transmittance and reflectance techniques, sonic and ultrasonic tests, and radiographic methods; all of which are tests involving the application and subsequent measurement of energy to a product. Light transmittance and reflectance measurements have been used to measure surface appearance—an important consideration in estimating the quality of

agricultural products. This procedure is used for detecting defects as well as for sorting and analyzing different levels of quality. Most of these techniques

have been applied to fruits and vegetables rather than to meat or poultry, but

the techniques of nondestructive evaluation are equally valid for the latter.

Hall and Bobrick (1968) discussed problems related to establishing the optimal

visual conditions for a dental operating suite. They suggested an illuminance

of 2500 fc as desirable for general dental surgery. Although more recent

research might reasonably have stressed the need to maximize contrast, rather

than simply provide illuminance, recommendations for high illuminance appear

common in the medical/dental/veterinary literature. The authors noted that the

color quality of dental illumination is also extremely important due to the

need to detect disease and to match restorations and dentures to the natural

teeth for a wide variety of potential viewing conditions. In considering

possible sources with good color quality, they rejected ordinary tungsten with

a color temperature of about 3000 K because it has too much energy at the red

end of the spectrum, and not enough in the blue. They noted that "This incandescent source tends to distort the colors of tissue within the mouth and, in

particular, may mask the visual symptoms of dental disease—which emerge as a change in color with the addition of a component in the blue end of the spectrum.

Since the incandescent is lacking in blue, this clue is lost" to the dentist

(Hall and Bobrick, 1968, p. 19). The problem with fluorescent lightj^ is almost

the opposite. "Because of the strong emission lines in the blue and green of a fluorescent source, healthy gum tissue tends to appear unhealthy (instead of

vice-versa, as in the case of the incandescent source)" (Hall and Bobrick, 1968,

p. 19). As a result, Hall and Bobrick suggested the use of a 3000 K incandescent lamp filtered to appear as a source operating about 4200 K, to achieve a color rendering index of about 90. Their study is one of the few which assessed

the effect of varying light source on the color appearance of human or animal

tissue.

The study presented in the following pages is a further attempt to delimit the

role of light source in the accurate recognition of defective or diseased

animal/poultry tissue. Data from a psychophysical study of tissue recognition

under different light sources will be presented first, followed by measurements

of the spectral reflectance of different meat and poultry tissue.

1 / These authors presented spectral source data for cool white deluxe leading

one to assume that their comments apply to this source.

12
2. PSYCHOPHYSICAL EXPERIMENT ON POULTRY AND RED MEAT

2.1 OVERVIEV7

To assess the role of the spectral distribution of the light source in meat and

poultry inspection, a psychophysical experiment was conducted. This experiment

was intended to test the hypothesis that sources with poorer CRI decrease an inspector's ability to reject defective and diseased tissue. The underlying

theory is that these sources diminish the differences in color between a defectj/ and the surrounding tissue.

In the experiment, conducted at both red meat (cattle) and poultry (chicken)

packing houses, a portable light box was used to simulate the inspection station. This box allowed the presentation of different tissue specimens under

different, commonly available light sources. It provided an area through which

a tissue specimen could move in an attempt to simulate the movement on a normal

inspection line. The box was designed to control the visual environment sur- rounding a tissue specimen, so that the effect of varying the light source on

the inspection task could be studied.

For both poultry and meat, the inspector's normal task is to view each specimen

as it moves past (suspended from a chain or lying on a moving surface, depending on the inspection station), and determine if a defect or disease is present.

The inspector may condemn the specimen outright, take it off the line for veterinary inspection, or simply have a defective area "trimmed" or cut off. All

of these actions require that he/ she notice the defect or disease. For the

psychophysical experiment, the ability to perform the task was defined as accu- racy in making a pass/reject judgement and in identifying the nature of a defect. Although inspectors may also touch and slice tissue to find defects,

particularly for red meat, the present study concentrated only on visual cues.

The same experimental design was used for the psychophysical experiment on both

chickens and cattle. Five different tissue samples were selected by USDA veterinarians as representative of commonly occurring defects or diseases that

would require one of the actions noted above by the inspector. Each sample was viewed under each of five light sources by one inspector at a time. The light source was changed after an inspector had viewed the five specimens. The five sources were selected to provide a broad range of CRI, from incandescent light

to LPS. No attempt was made, however, to present the five specimens continuously,

although a normal production line is in continuous movement.

2 . 2 APPARATUS

A portable light box was constructed to accomodate a series of interchangeable

light sources (see figure 2). The box provided a viewing area, 20 in wide by

18 in deep by 14 in high, which was painted flat (medium) gray. The light

sources could be placed one at a time in an upper compartment (20 in by 18 in

1 / The term "defect" will be defined in the present report to include defect,

disease, or contamination.

13
Figure 2. Sketch of portable light box

14
by 8 in) which was painted glossy white to maximize inter-reflections. The

white upper compartment served to reduce the changes in lighting geometry

resulting from light-source substitution. The light exited through a slit at

the front of the upper compartment.

Light sources included incandescent or tungsten (TUN), fluorescent cool white

(CW), fluorescent cool white deluxe (CWX), high pressure sodium (HPS), and low

pressure sodium (LPS). These sources were chosen to provide a range of color

rendering values from very good, incandescent, to very poor, LPS. Table 1 provides a further description of the sources, along with nominal color rendering

index values from the IESNA Handbook (1981). Figures 3 and 4 present spectral

power distributions of the actual light sources used in the experiment.

Table 1 . Light Source Data

Source Composition

Measured

Color Temperature

Nominal Color

Rending Index

Incandescent TUN 2 - 150 W bulbs 2765 K 100

Fluorecent CWX 2 - 15 W bulbs 4151 K 89

Fluorescent CW 2 - 15 W bulbs 4062 K 62

High Pressure Sodium

HPS

1 - 35 W bulb 1772 K 21

Low Pressure Sodium

LPS

1 - 18 W bulb 1740 K - 44

2.3 PARTICIPANTS

All participants for both the meat and poultry psychophysical experiment were USDA inspectors who volunteered for the study. The range of inspection experience was from 6 months to at least 22 years, with most inspectors having from 6

to 10 years experience. Eighteen inspectors paticipated in the poultry study,

9 males and 9 females. Sixteen inspectors participated in the red meat study,

14 males and 2 females. Four of these participants were veterinarians serving

as the inspector-in-charge of a red meat plant.

The color vision of each inspector was screened individually by the American

Optical Pseudo-Isochromatic Plates for Testing Color Perception (1965). All

inspectors in the poultry study had normal color vision. Two of the red meat

inspectors made 6 errors on the color vision test, and one refused to take the

Source - IESNA Handbook (1981).

15
Wi

<U3o

a,

hu

o

<Ua.

QJ>

QJ 02 H m

b. Relative spectral power distribution of source #2, Incandescent

c. Relative spectral power distribution of source #3, Low-pressure Sodium. This source qualifies as monochromatic. It has the effect of eliminating color

vision, except if fluorescent materials are present

Figure 3. Relative spectral power distribution functions of three of the light sources

used in the simulated inspection experiments. These data were measured with

with the telescopic spectroradiometer , from the surface of the PTFE whiteness

standard, in the apparatus used for the inspection experiments

16
a. Relative spectral power distribution of source #4, Cool White

Deluxe Fluorescent

TJ

)

•I 0

b. Relative spectral power distribution of source #5,

High-pressure Sodium

Figure 4. Relative spectral power distribution functions of two of the light sources used in the simulated inspection experiments. Measurement conditions were the same as in the previous figure

17
test. Since American Optical considers 5 or more errors to indicate red - green deficiency, some of the red-meat data are reported with and without the

responses of the two possible color defective observers. Further testing is

required for a definitive diagnosis, however.

2.4 PROCEDURE

The following description of the experimental procedure applies to both the red

meat and poultry studies. The experiment was conducted on site at each processing plant, in a room normally used as a conference room or office. If windows

were present in the room, they were covered to eliminate supplementary light.

At the beginning of the experiment, each inspector was brought into the

experimental room, and given the Research Participant Agreement and Privacy Act

Advisory Statement. At this time, inspectors were given the opportunity to

decline to participate in the experiment. (One inspector did decline.) They

were also told that the data describing their performance would be confidential,

and that their name would not be recorded with their data, to ensure their

privacy. The experimental instructions were then read and questions answered.

Table 2 provides a copy of the instructions. The instructions asked participants to do three things after viewing each tissue sample: pass or reject it;

identify the problem if any; and rate the ease of making the decision for each

light source on a scale of 1 to 5 (where 1 meant easy and 5 meant difficult).

Time for each inspector to respond with a pass/reject judgment was also

recorded manually by an experimenter with a stopwatch. The timing for a given

trial began when the cart was started. The timing stopped when the inspector

gave his/her decision. Because the cart did not instantly enter the viewing

area, the sample was effectively obscured from view for about 2 sec. Thus, the

actual decision-making time was always about 2 sec shorter than the response

time recorded. Nevertheless, starting the stopwatch concurrently with the cart

provided a convenient and consistent starting point. Although use of a manual

timing procedure increased scatter in the data, it provided a reasonable means

of estimating the relative effects of the different sources studied.

During the experiment, tissue samples were placed in (numbered) metal pans which

could be placed on top of a motorized wooden cart (12 in by 9 in). This cart

moved from left to right through the viewing area at a speed intended to simulate normal production line speed for chickens and to limit viewing time. Flexible baffles of aluminum foil were attached to both the right and left sides of

the viewing area to obscure the approach and departure of the tissue sample and

further limit viewing time. A lace curtain was also dropped in front of the

viewing area to obscure the sample as it passed back through the box after the

inspector’s judgment. This blocked a clear view of the sample, while maintaining the experimental illumination. Room lights were turned off, but supplemental

illumination was provided through a large aperture in the top of the light box

so that source color would be the same. Horizontal illumination levels were maintained at 50 to 70 fc in the center of the viewing area, although there was variation from front to back of this area.

18
Table 2. Instructions to Participant

We are doing an experiment on lighting for meat and poultry inspection stations.

We need your help in assessing the effectiveness of different lights. This

particular study is intended to simulate your normal task on the inspection

line, but using different lights.

We will show you different samples of meat (poultry) tissue under each light.

You will see both good and bad samples. As you see each sample, we want you to

decide if you would pass or reject each sample. By "reject” we mean condemn,

hang back or trim. We would like you to make this decision as rapidly as you

normally do. Tell us either "pass” or "reject," as soon as the sample disappears

from view. We would then like you to tell us the reason for your decision.

Finally we would like you to tell us how easy your decision was for the light

source, by using a rating scale of 1 to 5 , where 1 means easy and 5 means difficult

.

To summarize, as each sample goes past you, we would like you to do three

things.

1) Decide if the tissue sample should be passed or not passed. Tell us "pass" or "reject" based on your decision. Please tell us as quickly as possible.

2) Tell us the reason for your decision.

3) Tell us how easy your decision was on a scale of 1 to 5. The number

1 means easy and 5 means difficult.

We will then go to the next meat sample. You will see 5 samples under each

light. Then we will change the lights and show you another series of samples.

A total of five different lights will be used. Each judgment should take no more than one minute, so that the whole session should be done in about 35

minutes. If you become fatigued or uncomfortable, you are free to rest or stop

the experiment.

Your data are covered by the Privacy Act and the Research Participant Agreement,

which we will ask you to initial.

Thank you very much for your participation.

Do you have any questions?

19
After an inspector had completed the color vision test, and indicated that

he/ she understood the instructions, he/she was taken out of the experimental

room. The first test light was then placed in the box. The inspector was then

brought back into the room, and seated at a comfortable viewing distance from

the light box (about 30 in). He/she was reminded to view each sample and make a pass/reject decision as rapidly as possible (to simulate the normal inspection

process), and then to identify the problem (if any) and provide a rating of the ease of their decision. Five similar types of samples were seen under each of

the five lights. Inspectors left the experimental room while each source was changed. At this time, their comments and their overall opinion of the source

for the inspection task were recorded. Because each sample was viewed under

each light source, each inspector saw a total of 25 samples. An experimental

session took about 30-40 min to complete. Order of both sample and light source presentation was randomized for each participant.

2.5 POULTRY PSYCHOPHYSICAL STUDY

2.5.1 Sample Selection

Poultry samples were selected to be representative of commonly occurring defects,

particularly those in which color might aid visual detection. Of the five

samples that were selected, four were defective, and one normal. Because a pilot study had shown that chickens dry out over time, samples were changed

after every 3-4 inspectors (or after about 2 hours). Samples were kept moist

with wet paper towels when not in the viewing area to minimize drying. Nevertheless, the samples varied throughout the experiment, with no one sample being

seen by every inspector. In an attempt to control sample uniformity, the

inspector-in-charge, a USDA veterinarian, verified that each sample selected

was a good representation of a type of defect. Figure 5 illustrates some examples of samples used in the study. A deliberate attempt was made to ensure uniformity of presentation, with all birds being about the same size, and all

shown with viscera present. (Even though not all the birds in figure 5 contain

viscera, viscera were added to them for presentation during the experiment.)

The following types of poultry samples were used: (1) normal; (2) tumor;

(3) septicemia; (4) air sacculitis; and (5) gall stain. (For the last three

inspectors, the tumor was replaced by leukosis because no chickens with tumors were available at that time.) These particular samples were chosen on the

basis of color differences, frequency of condemnation in USDA statistics, and

expected availability during the experiment.

2.5.2 Results for Pass/Reject Decisions

Data were collected and analyzed for several response categories. These

included pass/reject decisions; identification judgments; rated ease of

performing the task; response time; and comments about each source. Data

and statistical analyses will be presented for each category in turn.

20
Figure 5. Selected samples used in poultry study

21
The pass/reject judgment represents the very first decision that an inspector

makes. Inspectors were instructed that "reject" meant anything wrong with the

sample from outright condemnation to "hang back for veterinary inspection" to

"trim". "Pass" meant nothing wrong with the sample at all. A USDA veterinarian

verified that the samples represented reasonable pass/reject decisions that

would actually be made by the inspectors at each participating plant.

Table 3 presents the tabulated pass/reject errors for all samples (from 1 to 5)

for each source. Samples are identified at the top of this table, while sources

are identified along the left-hand side. The pass/reject data were tabulated

to provide frequency counts of the number of errors made for a given sample

under a given source. (This tabulation or "contingency table" results in a series of 25 entries or "cells" for the combination of five sources and samples.)

A chi square (x^) analysis of this table was performed to test whether there was an interacting effect of light source and sample type on the number of errors made; in other words, whether relatively more errors were made for some samples under some light sources, than others. This analysis was significant

(p < 0.001).

Smaller contingency tables were made to compare the number of correct and

incorrect pass/reject decisions for light sources and then for samples. Table

4 presents this comparison for light sources. A chi square analysis was used

to test whether there was a significant effect on error rate due to light source alone by looking at the total erors for all samples for each source.

This comparison was not significant. Other analyses comparing CW or TUN (they

had the same error rate) separately with CWX, HPS, and LPS, were also not

significant

.

Table 5 presents several comparisons of the errors made for the individual

samples. The first, given in 5a, compares the total errors for each sample

under all 5 light sources. The chi square analysis of this comparison was significant (p < 0.001), indicating that the number of errors in pass/reject

decisions varied depending on the sample. Examination of the table suggests

that fewer errors were made for septicemia and more for air sacculitis. Additional comparisons examined the number of errors for these samples under each

light source. Table 5b compares the number of errors and correct decisions for

the normal sample. The chi square analysis was significant (p < 0.001), indicating that more errors were made for the normal sample under LPS than under any

other light source. Table 5c presents a similar comparison for air sacculitis.

The chi square analysis of these data was also significant (p < 0.025). This

comparison demonstrates that fewer errors than might be expected occurred for

air sacculitis under LPS. This result may seem surprising initially until one

stops to consider that the large number of errors for the normal sample under

LPS suggests that inspectors tended to reject even good chickens under this source. Consequently, it is conceivable that the air sacculitis sample was rejected, not because of any knowledge of the sample's true condition, but

simply because it "looked bad" under LPS. The identification data (see 2.5.3) were taken to explore the possibility that the light source affected an inspector's ability to identify sample type accurately.

Inspection of the data suggested that inspector performance varied somewhat.

As a result, the performance of a subset of inspectors was analyzed. This

22
Table 3. Pass/Reject Errors for All Inspectors for Poultry

18 Inspectors

Samples

Normal Tumor Septicemia Air Sacculitis Gall Stain Total

CW 0 4 2 9 3 18

TUN 0 5 0 11 2 18

LPS 9 7 1 2 5 24

CWX 0 5 1 11 4 21

HPS 1 7C8) 1 3 9 8 28(29)

Total 10 28(29) 7 42 22 109(110)

X2 “ 42.15, df - 16, p < .001

Parentheses indicate total when leukosis is included.

All analyses done with leukosis included in the total.

Table 4. Comparison of Number of

All Sources for Poultry

Source CW

TUN

I.PS CWX

HPS

X

2 = 5.17, df

Errors in Pass/Reject Decisions for

Errors Correct

18 72

18 72

24 66

21 69

29 61

110 340

4, NS

23
Table 5. Comparison of Number of Errors in Pass/Reject Decisions

for All Samples for Poultry

a. Total Errors in Decisions for Each Sample for All Sources

Sample Normal

Tumor

Septicemia

Air Sacculitis

Gall Stain

X

2 = 49.21, df = 4, p < .001

b.

Errors Correct

10 80

29 61

7 83

42 48

22 68

Sample for Each

Errors Correct

Source CW 0 18

TUN 0 18

LPS 9 9

CWX 0 18

HPS 1 17

X

2 = 34.88, df = 4, p < .001

Distributions for Air Sacculitis for

Errors Correct

Source CW 9 9

TUN 11 7 LPS 2 16

CWX 11 7 HPS 9 9

X

2 = 12.32, df = 4, p < .025

24
Source

Table 6. Decision Data for a Subset of 11 Poultry Inspectors

a. Total Errors for All Sources and Samples - 11 Inspectors

Samples

Normal Tumor Septicemia Air Sacculitis Gall Stain Total

cw 0 0 0 2 0 2 TUN 0 2 0 5 0 7 IPS 5 3 0 1 4 13 cwx 0 3 0 4 1 8

HPS 0 4 0 4 4 12

Total 5 12 0 16 9 42

X

2 analysis inappropriate

b. Total Decisions for All Samples for Each Source

Errors Correct

Source CW 2 53

TUN 7 48

LPS 13 42

CWX 18 47

HPS 12 43

X

2 = 10.85, df - 4, P < .05

Total Decisions for Each Sample fc

Errors Correct

Sample 1 5 50

2 12 43

3 0 55

4 16 39

5 9 46

X

2 - 21.53, df - A, p < .001

25
analysis examined performance of a subset who made zero or one errors in the

pass/reject task under cool white light —the customary light for inspecting

chickens. Table 6 presents the tabulated pass/ reject data for a subset of 11

inspectors. Table 6a presents the total errors for each source and sample.

Table 6b compares errors in decisions for each light source for this subset of

inspectors. The chi square analysis was significant for this table (p < 0.05)

indicating that light source affected the error rate. Comparison of errors for

cool white with errors for LPS, HPS, and CWX were also significant (p < 0.05).

Table 6c presents the total errors for each sample. The chi square analysis

for this comparison was also significant (p < 0.001). This more detailed

analysis suggests that inspectors who made few errors under cool white light were significantly hindered in their ability to do the inspection task when the

light source was changed, particularly to HPS and LPS.

2.5.3 Identification Data

The first set of data reported (see 2.5.2) concentrated on pass/reject decisions

as the initial response. This decision, however, could allow a sample to be

"rejected" for the wrong reasons. The identification data allow a further look

at the effect of the light source on the accuracy of identifying defects with

chickens. Thus the second set of data collected was the identification of the

specific defects associated with the samples.

Table 7 tabulates the identification responses for each sample under each

source. A judgement of their correctness was made after discussion between the

researchers and the inspector-in-charge. In a number of cases, the response

given was simply "trim". This is a nominally correct action for gall stains

and single tumors, but not for air sacculitis or septicemia. Hence, the "trim"

response was counted as correct for gall stain and tumor, but incorrect for air

sacculitis and septicemia. Table 7 also presents the judgement of the correctness of the identification response. The total number of errors is greater for

table 8 than table 3, because some samples were rejected correctly (table 3),

but identified incorrectly (table 8).

Table 8a tabulates the number of errors in identifying types of samples under

the different light sources. A chi square analysis of these data was significant (p < 0.01). This result indicates that there was a significant interaction

between light source and sample on the number of errors of identifying different

samples. Smaller contingency tables were made for correct vs. incorrect identifications for each light source (table 8b) and sample (table 8c). These

analyses indicated that significant differences in the accuracy of identification occurred (p < 0.001), for both samples and sources. The greatest number

of errors was made under LPS followed by HPS. (Error rates were similar for

CWX, CW, and TUN.) In the previous section, the greatest number of errors (pass/reject) had been made for HPS, since a common response to LPS was to

"reject" samples, a spuriously correct response (see table 3). The inspectors

appeared to condemn everything under this source without knowing exactly what

the problem was, except that it "looked" bad. When asked to identify the

problem specifically, they had much more trouble, as indicated by the greater

number of identification errors for LPS. The identification data can also be

26
Table 7. Identification Decisions for Each Sample for Each Source for 18 Poultry Inspector

Source Sample

Normal Score* Tumor Score Septicemia Score AirSacculitis Score Gall Stain Score

1 1 CW Ok 1 tumor 1 sep 1 lesion on liver

1 gall 1

2 TUN Ok 1 tumor 1 sep 1 air sac 1 contam 1 3 LPS Ok 1 tumor 1 sep 1 air sac 1 Ok 0

4 CWN Ok 1 tumor 1 sep 1 poss air 1 no prob. 0

5 HPS Ck 1 tumor-*- 1 sep 1 poss sac

air sac

1 contam 1

2 1 Ok 1 tumor 1 sep 1 Ok 0 trim 1 2 Ok 1 tumor 1 sep 1 Ok 0 contam 1 3 Ok 1 tumor 1 dehydrated

contam.

1 contam 0 contam 1

4 Ok 1 tumor 1 cadaver 1 Ok 0 trim 1 5 Ok 1 tumor 1 sep 1 Ok 0 Ok 0

3 1 Ok 1 Ok 0 Ok 0 Ok 0 bile 1 2 Ok 1 Ok 0 cadaver 1 Ok 0 bile 1 3 Ok 1 Ok 0 discolored 1 grease 0 grease 0

4 Ok 1 discolored 0 cadaver 1 a lot of

red

0 bile 1

5 Ok 1 Ok 0 Ok easy 0 Ok 0 bile 1

4 1 Ok 1 tumor 1 hang back 0 air sac 1 trim 1 2 Ok 1 tumor 1 dehydrated 1 air sac 1 trim 1 3 Ok 1 tumor 1 dehydrated 1 air sac 1 Ok 0

4 Ok 1 tumor 1 dehydrated 1 air sac 1 Ok 0

5 Ok 1 tumor 1 dehydrated 1 air sac 1 Ok 0

5 1 Ok 1 tumor 1 sep 1 bad liver 1 gall 1 2 Ok 1 Ok 0 sep 1 Ok 0 gall 1 3 soot 0 tumor 1 sep 1 soot 0 diesel 0

4 Ok 1 Ok 0 sep 1 trim

viscera

1 gall 1

5 Ok 1 tumor 1 sep 1 viscera

problem

1 gall 1

6 1 Ok 1 Ok 0 trim 0 Ok 0 trim _

2 Ok 1 Ok 0 trim 0 Ok 0 gall 1

3 Ok 1 Ok 0 black 1 has spots

on it

0 grease 0

4 Ok 1 trim 1 trim 0 Ok 0 gall 1 5 Ok 1 Ok 0 Ok 0 Ok 0 Ok 0

7 1 Ok 1 tumor 1 cadaver 1 air sac 1 gall 1 2 Ok 1 tumor 1 cadaver 1 air sac 1 gall 1 3 sep 0 soot 0 sep 1 air sac 1 trim

spots

0

4 Ok 1 Ok 0 cadaver 1 air sac 1 gall 1 5 Ok 1 Ok 0 sep 1 air sac 1 Ok 0

8 1 Ok 1 tumor 1 trim back 0 trim liver 1 gall 1 2 Ok I Ok 0 trim 0 trim to get

red off

0 gall 1

] 3 Ok 1 Ok 0 trim 0 Ok 0 trim i ; 4 Ok 1 tumor 1 trim looks

red

0 Ok 0 trim i

5 Ok 1 Ok 0 trim back 0 probs. w/

liver

1 gall i

1 = correct

0 = incorrect

- = no answer

27
Table 7. Continued

Source

Sample

Normal Score Tumor Score Septicemia Score AirSacculitis Score Gall Stain Score

9 1 Ok 1 tumor 1 sep 1 Ok 0 Ok 0

2 Ok 1 tumor 1 sep 0 Ok 1 Ok 0

3 feces

swollen

liver

0 tumor 1 grease all over

0 air sac 1 grease 0

4 Ok 1 tumor 1 sep 1 Ok 0 Ok 0

5 Ok 1 tumor 1 sep 1 trim leg 0 Ok 0

10 1 Ok 1 Ok 0 sep 1 Ok 0 gall 1 2 Ok 1 tumor 1 sep 1 trim leg 0 gall 1 3 sep 0 Ok 0 sep 1 Ok 0 sep 0

4 Ok 1 Ok 0 sep 1 prob. w/

liver Ok

0 sep 0

5 Ok 1 tumor 1 sep 1 Ok 0 Ok 0

11 1 Ok 1 tumor 1 sep 1 air sac 1 gall 1 2 Ok 1 tumor 1 sep 1 air sac 1 gall 1 3 Ok 1 tumor 1 sep 1 air sac soot

1 Ok 0

4 Ok 1 tumor 1 sep 1 air sac 1 gall 1 5 Ok 1 tumor 1 sep 1 air sac 1 gall 1

12 1 Ok 1 tumor 1 sep 1 Ok 0 Ok 0

2 Ok 1 tumor 1 sep 1 Ok 0 gall 1 3 abnormal

viscera

0 trim breast 1 sep 1 prob. w/

liver

1 gall 1

4 Ok 1 trim breast 1 sep 1 Ok 0 gall 1 5 Ok 1 Ok 0 sep 1 Ok 0 gall 1

13 1 Ok 1 Ok 0 sep 1 prob. w/

liver ok

0 Ok 0

2 Ok 1 Ok 0 sep 1 Ok 0 Ok 0

3 Ok 1 unnatural

Ok

0 sep 1 sep 0 Ok 0

4 Ok 1 Ok 0 sep 1 Ok 0 Ok 0

5 leukosis 0 Ok 0 sep 1 liver ? 1 Ok 0

14 1 Ok 1 tumor 1 sep 1 air sac 1 gall 1 2 Ok 1 tumor 1 sep 1 air sac 1 gall 1 3 Ok 0 ok 0 sep 1 sep 0 greenish 0 4 Ok 1 tumor 1 sep 1 air sac 1 gall 1 5 Ok 1 tumor 1 sep 1 air sac 1 . gall 1

15 1 Ok 1 bruised

leg

0 sep 1 Ok 0 gall 1

2 Ok 1 tumor 1 sep 1 Ok 0 gall 1 3 bad

liver

0 bad skin 0 all dried

up

1 all dried

up-dark

0 grease all over

0

4 Ok 1 bruised

leg

0 sep 1 Ok 0 gall 1

5 Ok 1 Ok 0 sep 1 Ok 0 gall 1

28
Table 7, Continued

Source

Sample

Normal Score Leukosis Score Septicemia Score Air

Sacculiti8 Score Gall Stain Score

16 1 Ok 1 leukosis 1 Ok 0 Ok 0 bile 1 2 Ok 1 leukosis 1 looks dark 1 looked

hang back dry Ok 0 bile 1 3 bile 0 trim breast 0 no obvious 0 bile on 0 bile 1 outside problem wing

4 Ok 1 leukosis 1 Ok 0 Ok 0 bile 1 5 Ok 1 leukosis 1 Ok 0 Ok 0 bile 1

17 1 Ok 1 leukosis 1 sep 1 enlarged 1 gall 1 heart

2 Ok 1 leukosis 1 sep 1 Ok 0 gall 1 3 looks 0 leukosis 1 sep 1 air sac 1 gall 1 grey

4 Ok 1 leukosis 1 sep 1 Ok 0 gall 1 5 Ok 1 leukosis 1 sep i Ok 0 gall 1

18 1 Ok 1 leukosis 1 sep l air sac 1 gall 1 2 Ok 1 leukosis 1 color bad l Ok 0 gall 1 3 Ok 1 trim 1 sep l air sac 1 Ok 0

4 Ok 1 leukosis 1 sep l air sac 1 gall 1 5 Ok 1 Ok 0 sep i Ok 0 gall 1

Total

Errors of 10 32 17 52 30 Identif lcatic>n

Pass/fail 10 28 7 42 22 Errors

29
Table 8. Accuracy of Identification of Samples for 18 Poultry Inspectors

Source

a. Errors in Identification for Each Sample and Source

Samples

Normal Tumor Septicemia Air Sacculitis Gall Stain Total

cw 0 5 5 9 3 22

TUN 0 5 2 13 2 22

LPS 9 8 3 9 13 42

CWX 0 6 3 11 4 24

HPS 1 8 4 10 8 31

Total 10 32 17 52 30 141

X

2 = 32.17, df = 16, p < .01

b. Errors in Identification for Each Source for All Samples

Source

Errors Correct

CW 22 68

TUN 22 68

LPS 42 48

CWX 24 66

HPS 31 59

X

2 = 15.12, df = 4, p < .001

c. Errors in Identification for Each Sample for All Sources

Sample Normal

Tumor

Septicemia

Air Sacculitis

Gall Stain

Errors Correct

10 80

32 58

17 73

52 38

30 60

X

2 = 55.22, df = 4, p < .001

30
examined by simply tabulating the total number of errors for each source. This

tabulation (given in the TOTAL column of table 8a) indicates that the greatest

number of errors —42, or nearly half the 90 decisions —occurred for LPS. For

HPS, nearly one-third—or 31 —of the identifications were incorrect. The other

three sources showed lower frequencies of errors —about one-quarter the total

decisions. Performance was typically poor for all samples under HPS.

With respect to the samples themselves, the greatest number of identification

errors was made for air sacculitis followed by tumor and then the gall stain

(see tables 8a and 8c). It is possible that many errors occurred for air

sacculitis because this sample was located near the bottom of the viewing pan

in the viscera of the chicken, and was perhaps more difficult to see. (The

inspectors had been instructed that no defects were hidden from their direct

view, however.) Fewer errors were made for the normal chicken and the one with

septicemia. As noted before, errors of incorrectly rejecting the normal bird

occurred almost entirely under LPS.

2.5.4 Rated Ease of Task Performance

Table 9 presents mean ratings for the decision difficulty given by an inspector

for each source after viewing a sample. In this task, each inspector rated the

difficulty of making the decision^/ using a scale of 1 to 5 where 1 meant easy

and 5 meant difficult. These ratings were then summed for each source for all

samples and all inspectors and then averaged. Mean ratings for the light sources were as follows: 1.3 for TUN; 1.4 for CW; 1.6 for CWX; 2.1 for HPS;

and 3.8 for LPS, with a possible range of 1 to 5. (To meet the assumptions of

the analysis of variance with regard to normality of the distribution and homogeneity of variance, a square root transformation (Natrella, 1984) was performed

on the ratings given by each inspector.) Two separate two way analyses of

variance were then calculated for the transformed data using Minitab (1984).

The first, was a comparison of each inspector's rating for each source, to

determine if different light sources were rated differently. This analysis was significant (p < 0.001). There was also a significant tendency (p < 0.5) for

inspector ratings to vary as function of light source. The second, was a com- parison of inspectors over samples, to see if there were an effect of the

sample (in other words, were different samples rated as easier, or more difficult regardless of light source). This analysis was indicated that differences

in rating the samples were not significant. In addition, a tabulation was made

of the number of times each source was given each rating. A analysis of

this tabulation, shown in table 10, was significant (p < 0.001). This result

indicates that source 3, LPS, received ratings of 3, 4, or 5, much more frequently than CW, TUN, or CWX. Similarly, HPS tended to receive higher ratings more frequently than did the other sources. Thus, the inspectors apparently

found the task more difficult to do under both HPS and LPS.

2/ It was difficult at times to determine which decision (pass/reject or identification) was being rated, since some inspectors identified the

sample first, and then gave their rating.

31
Table 9

Rating Data for Poultry Inspection

a) Mean Rating Data for Each Poultry Inspector for Each Source

b)

Inspector CW TUN LPS CWX HPS

1 1.0 1.2 3.6 1.0 1.4

2 1.8 1.0 3.0 2.0 1.0

3 1.0 1.0 2.0 1.2 1.0

4 1.2 1.0 4.2 1.0 1.0

5 1.0 1.0 1.8 1.0 1.0

6 1.0 1.0 1.0 1.0 1.8

7 1.2 1.6 4.4 2.4 3.6

8 2.2 1.0 5.0 1.8 1.8

9 2.0 2.0 5.0 2.0 3.0

10 1.0 1.0 5.0 1.4 1.0

11 2.0 1.0 4.4 2.0 3.0

12 1.0 1.0 5.0 1.4 2.6

13 2.6 2.6 5.0 3.6 5.0

14 1.6 1.0 3.6 1.0 3.0

15 1.0 1.2 3.0 1.0 1.8

16 1.0 1.0 5.0 2.0 2.0

17 1.8 1.8 5.0 2.2 3.2

18 1.0 1.6 2.4 1.4 1.0

Mean 1.41 1.28 3.80 1.63 2.12

Mean Rating Data for Each Sample for Each Source Summed over Inspectors

Sample

1 2 3 4 5

Source CW 1.33 1.33 1.39 1.56 1.41

TUN 1.22 1.22 1.17 1.44 1.28

LPS 3.94 4.00 3.50 3.72 3.83

CWX 1.50 1.67 1.67 1.83 1.50

HPS 2.05 1.89 2.05 2.05 2.55

Mean 2.01 2.02 1.96 2.12 2.13

32
Table 10. Frequency That Each Rating (1 to 5) was Given for Each Light

Source for Poultry

Rating

1 2 3 4 5

Source

CW 61 21 8 0 0 90

TUN 72 11 7 0 0 90

LPS 13 3 17 13 44 90

CWX 50 26 11 3 0 90

HPS 43 9 29 2 7 90

Total 239. 70 72 18 51 450

X

2 = 299.72, df = 16, P < 0.001

33
2.5.5 Comments About the Light Sources

After each inspector had seen the five samples under a given light source,

he/ she was asked for comments about the source. These comments are tabulated by source in Table 11, just as they were given by the inspectors. In an attempt to quantify these comments, an arbitrary rating scale was devised in

which two researchers independently rated each comment. In this scale, a "poor"

light source was given a -1 , a neutral source a 0, and a "good" source a +1.

Any differences between the researchers' ratings were averaged. The range of

possible scores was from -18 to +18. This scoring procedure resulted in the

following scores: incandescent, +12; cool white, +15; HPS, -6; cool white

deluxe +7; and LPS, -17.5. An overview of the comments suggests that the

inspectors were very negative about LPS, somewhat negative about HPS, positive

about incandescent and cool white deluxe, and very positive about cool white

(the source to which they were accustomed).

2.5.6 Response Time Data

The findings from the pilot study with poultry inspectors suggested that the

inspectors responded more slowly under some light sources than others. As a result, an attempt was made to determine response time as a function of light source in the main experiment. While these data are subject to experimental

error due to the reaction time of the experimenter recording them, they provide

some hint that the inspection process may go more slowly under certain (unfamiliar, perhaps) light sources. (One experimenter made all the recordings, but

in some cases a time could not be recorded due to a malfunction of the cart or some other error.) Recorded response times included approximately 2 seconds of

initial cart travel time in which the inspector could not see the sample.

Response times were summed over all samples for all inspectors for each of the

five sources. An average response time for each source was calculated along

with a one way analysis of variance, using MINITAB (1984). The average response

time was 4.4 sec for CW and TUN; 4.9 for LPS; 4.4 for CWX; and 4.5 for HPS.

These data are presented in table 12. While the analysis of variance was significant at the p < 0.10 level, not the customary 0.05 level, it does suggest

a tendency toward longer reaction time with LPS.

2.6 RESULTS FROM RED MEAT PSYCHOPHYSICAL STUDY

2.6.1 Sample Selection

The red meat psychophysical experiment was conducted at a large cattle processing

plant, located near several other plants including a cow and bull (older cattle)

plant. Samples for the experiment were drawn from all plants in the vicinity,

but even so, it proved to be impossible to get all desired pathologies for each

inspector in the experiment. Other problems arose because the sheer size of

the cattle prohibited using the entire animal. As a result, because each sample was cut to a size that would fit on the experimental cart, no inspector

viewed an entire viscera or head as is customary during the normal inspection

process. An attempt was made to use similarly sized pieces of meat during the

experiment to avoid recognition of specific samples, but comments by some inspectors made it clear that they did remember at least some samples. Because

34
Table 11. Comments About Each Light Source by Poultry Inspectors

Cool White

Ratings Comments

1 Light fine, too

0 No Comments

1 Light much better

0.5 Normal light; no problems

1 Light pretty good

1 Better light to work with

1 Like this light

0.5 Light seemed a little softer (after tungsten)

1 Light much better; clearer; more distinct; better than previous two

lights ( LPS and HPS); liked light

1 Easy to judge; no problems with light

0 Comparable to first light (CWX); very little difference; reddish;

would have to get used to

1 Lighting just fine; similar to normal inspection; might be brighter

1 Better light; more normal; not bright enought; bird in natural

coloring

1 Can see pretty good

1 Light pretty good; similar in brightness to previous one (tungsten)

1 Like the white lights; this more soft white with less potential for

glare

1 Could see pretty good

1 Light real good

Total = +15

Incandescent

1 Light fine—No problems

0 No comments

1 Best light so far (was last light used)

1 Not bad; no problems

1 Light good (presentation position only allows me to see 60-75

percent of bird)

-1 Glare-like; would not like to work under it all the time

1 Light sharp; more the same across the bird; less change from bird to

bird; previous light seemed to dry out chicken (was CWX)

1 Liked light; seemed a bit bright

-0.5 Light not clear; leaves condition of chicken dull; with shadow

1 Bright enough

1 Best light so far; clearest; no problems with lite

1 Pretty close to first light; could see just fine

1 Light not real different from others; more what I'm used to

1 No problems with light; harder to see than under first light; not

much difference other than that

1 Liked light; no problem; bright enough

1 Nice bright white light; could see glare on chicken if bird were

wetter; like lighting, though

1 Light seemed normal; good; no problems

-0.5 Awful bright; glare problem; could work under it

Total = +12

35
Table 11. (Continued)

LPS

-1 Light makes birds dark and discolored

-0.5 Dehydrated birds; lights seemed to be brighter

-1 Chicken doesn't have normal color; looks pale and dehydrated;

wouldn't want to have to inspect under this light; difficult to

make accurate decision

-1 Too dark; chicken looks dehydrated; strange

-11 Would not want to use light to inspect chickens; changes appearance

of birds —darkness-bird appears to have soot or diesel grease on it

-1 Chicken black; grease spots; wouldn't want to work under it

-1 Light makes chicken look black; did not like light; not like to

inspect under it; chicken looked drenched with soot; more difficult

than other lighting

-1 Didn't like light at all; everything looked grey and dark; hard to

pick up abnormalities

-1 Dingy light; do not like light; would not want to work under it;

makes eyes hurt; glares into face

-1 Light is kind of difficult to see with; need a brighter light to

make decisions, particularly with fast line speed

-1 Chicken looked black; light did highlight air sac on viscera;

couldn't inspect, though, under this light

-1 Definitely would not want to work under this light; too dark;

everything looks grey

-1 Miserable light; would quit if had to work under it; can't see true

color of bird; bird looks "dead" and dehydrated; not natural

lighting; changes color totally

-1 Poor light to work by; no bird looks like normal bird; whole bird

looked greenish

-1 Birds look all dried up; too dark; skin doesn't look right; don't

like this light; wouldn't want to work under it

-1 Good for halloween party; can't tell what color skin is; things on skin don't stand out; can't see shades

-1 Skin looks grey; all look like seps; light is poor; hate to have to

work under it

-1 Wouldn't want to work under light; difficult to tell bruises, sores;

difficult light

Total = -17.5

Cool White Deluxe

1 Light, fine; bright; could see bird; see better than one before

(HPS); need a bright light to see all the way into bird

1 Birds don't looks as dehydrated under this light; lighting

reasonable; good flesh color to birds

0.5 Much bright light; problem with red birds

0 No comments

-0.5 Lights make chicken look red

1 Generally easy to do

0 Position of bird difficult to get used to

1 Light didn't seem as bright; easier on eyes

0.5 About like previous light; no great differences; could have been a hair brighter

36
Table 11. (Continued)

Cool White Deluxe Continued

1 Easy to decide; light brighter; other light dull

1 Pretty good light; problem with darkened room

-1 Light seemed darker; not quite as bright; don't prefer light

-1 Don't like light as well—seemed more strain

1 Could see real good; seemed like what I'm used to

0.5 Light seemed pretty bright

-0.5 Light is not intense enough; like the color white

1 Didn't look like fluorescent light; could see pretty well

0.5 Wasn't as good as last two [CW and HPS]; not as bright; could

work under it; normal bird looked different

Total = +7

HPS

-1 Not too bright; dimmer; yellow; not as white; hard to make

decision—bird looked dimmer

1 Light is good (but lying down position causes problems —can't see whole bird)

-1 Much better than first light (LPS) still has glow that kind of shades

chicken; wouldn't want to inspect under this light; need bright,

lighter light

-1 Light seems dimmer; not as bright

0 No problem with bird under light;

-1 Light bright and glary

-1 Bright; sort of glow; glare, wouldn't want to work under it; would

have headaches before day is over; chicken looked bluish; not good

light

0.5 Light seemed bright; room dark; sort of what I'm used to

1 Light better; clearer; no glow back into face; better than some lights

0.5 Could see with light; could be brighter

0.5 Pretty good light; not as good as first [CWX]; could work under it;

take some getting used to; reddish, yellowish tint

-1 Wouldn't want to use light; more dim than other one; would rather

have first light [CW]

-1 Yellow; don't like light; strain on eyes; not certain of what I'm

seeing; terrible light; very difficult

-1 Hard to see under (not as hard as last one [LPS]; hard to see directly

what problem is

-1 Don't like light as well as others; not bright enough; made birds

look sort of yellow;

-1 Slightly orange; makes leukosis less obvious and stand out less

-0.5 Light not as bright; not as easy as first [TUN]

1 Light seemed real good; wouldn't mind working under it

-1 Hard to see under—not as hard as last one (LPS); hard to see directly

what problem is

-1 Don't like light as well as others; not bright enough; made birds

look sort of yellow;

-1 Slightly orange; makes leukosis less obvious and stand out less

-0.5 Light not as bright; not as easy as first (TUN)

1 Light seemed real good; wouldn't mind working under it.

Total = -6

37
Table 12. Mean Response Time for Light Sources by Poultry Inspectors

Source N

1 Mean Std . Dev.

CW 78 4.4 1 . 386

TUN 86 4.4 1 . 354

LPS 87 4.9 1 . 628

CWX 85 4.4 1 . 258

HPS 83 4.5 1 . 386

F = 2.20, df = 4, 414, p <

* Total observations = 90

.10

38
an entire viscera, head, or carcass could not be presented, the overall

simulation was much less accurate than for the poultry portion, where the

entire bird plus viscera could be easily used. In addition, a greater portion

of the red meat inspection process is tactile—the tissue is sliced deliberately

to get the "feel" of it, as well as to bring hidden organs (and defects) to

view. Consequently, the red meat data must be viewed with more caution than

the poultry data.

In addition, it proved impossible to obtain similar samples throughout the

entire experiment, so that substitution of dissimilar defects had to be made.

Consequently, some types of red meat samples varied for different groups of

inspectors, while other sample types remained the same. Thus, sample 1 was

always telangiectasis (telang) of the liver (a disease characterized by black

spots); sample 5 was always an abscess or lesion (acti) of a lymph node in the

head; and sample 4 was always lung tissue, with either pneumonia or congestion

(with both being presented randomly to each subject). Sample 3 varied, during

the study, being a normal liver for the first seven inspectors, and either a normal liver or a normal spleen for the final nine. Sample 2 varied the most,

being a sawdust lesion in the liver for the first four subjects; an emaciated

heart or heart with a growth for subjects 5-7; and heart muscle with EM (eosinophilic myositis—an inflammation of muscle tissue associated with parasites),

for the remaining nine inspectors. The EM sample did not become available

until the second day of the experiment, although it had been identified as a desirable tissue from the beginning. (Anecdotal reports from the inspectors

and veterinarians had indicated that EM is very difficult to detect under HPS.)

2.6.2 Red Meat Pass/Reject and Identification Data

As might be expected from the discussion of the problems in obtaining samples

discussed above, the results from the red meat psychophysical experiment were

not nearly so clear-cut as from the poultry experiment. In addition, two of

the red meat inspectors had been identified as possibly color defective by the

AO test. Consequently, data reported the tables in 2.6.2 are given for all 16

inspectors, and in parentheses for the 14 inspectors who made fewer than five errors on the test used. Further testing would be necessary to categorize the

two inspectors as definitely color deficient.

Table 13 tabulates the pass/reject data for red meat. Table 13a gives the

number of pass/reject errors for all sources and samples. A chi square

analysis was not possible for these data due to the large number of cells with

low expected frequencies. Table 13b tabulates the number of errors/correct

responses for all samples summed over all light sources. Because of the extent

to which sample types varied in the red meat experiment, no separate

analysis was performed to examine the effect of sample. Table 13b indicates,

nevertheless, that numerous errors were made for the normal sample - more than

for any other sample type. Unlike poultry, this sample was frequently incorrectly rejected under all light sources, although the most errors did occur

under HPS and LPS. Sample 3 was varied to make the experiment more difficult

and to avoid the inspector's recognizing the liver sample, but the second

sample, a spleen, proved to be very difficult to identify accurately. This

sample was rejected as defective 15 of the 20 times that it was presented,

39
Table 13. Tabulation of Pass/Reject Data for Red Meat Inspectors

a. Tabulation of Pass/Reject Errors for 16 Meat Inspectors

Samples

Light

Source

1 2 3 (Normal) 4 5 Total

cw 1 1 (0) 1 3 (2) 2 (1) 2 (2) 6 (5)

TUN 2 0 (0) 5 (5) 1 (1) 1 (1) 9 (9)

LPS 3 1 (0) 8 (6) 0 (0) 0 (0) 12 (9)

CWX 1 2 (1) 6 (6) 3 (2) 1 (1) 13 (11)

HPS 2 0 (0) 8 (7) 2 (1) 0 (0) 12 (10)

Total 9 4 (1) 30 (26) 8 (5) 4 (4) 55 (45)

X

2 not appropriate

b. Tabulation of Errors for Each Sample for All Sources

Errors Correct

Sample 1 9 (9) 71 (61)

2 4 (1) 76 (69)

3 30 (26) 50 (44)

4 8 (5) 72 (65)

5 4 (4) 76 (66)

Comparison of Errors for Each Source for

Errors Correct

Source CW 9 (5) 71 (65)

TUN 9 (9) 71 (61)

LPS 12 (9) 68 (61)

CWX 13 (11) 67 (59)

HPS 12 (10) 68 (60)

X

2 = 1.48, df = 4, NS

d. Comparison of Correct Responses/Errors for LPS and for HP5

LPS HPS

Errors Correct Errors Correct

Sample 1 3 (3) 13 (11) Sample 1 2 (2) 14 (12)

2 1 (0) 15 (14) 2 0 (0) 16 (14)

3 8 (6) 8 (8) 3 8 (7) 8 (7)

4 0 (0) 16 (14) 4 1 (1) 15 (13)

5 0 (0) 16 (14) 5 0 (0) 16 (14)

X

2 = 22.16, df = 4 , p < 0.1 X

2 = 27.43, df = 4, p < .001

1 Numbers in parentheses indicate total errors with two color defectives

excluded

.

40
under all light sources, possibly because it had a "spotty" appearance. As a result, it proved difficult to identify the effect of light source on rejection

of normal meat tissue (unlike the poultry experiment where this type of error

only occurred for LPS and HPS). A chi square analysis of the data in table 13c

for light source was not significant, indicating no real differences in the

total error rates for each source. Chi square analyses for each sample under

LPS and then under HPS shown in table 13d were significant however, with the

most errors occuring for the normal sample. No other comparisons were statistically significant. The fewest errors typically occurred for CW and TUN, as with poultry. (The two inspectors categorized as color deficient typically

made more errors under LPS, CWX, and HPS than under CW or TUN.)

Table 14 tabulates the errors for the identification data. As for poultry,

these data were categorized and scored using a USDA pathologist to verify the

accuracy of the scoring. Again, data are given with color defectives included

and excluded (parentheses). Table 14a presents the total errors of identification for each source and sample. A chi square analysis was not appropriate for

this table. The comparison of total errors for each source, given in table 14b, was significant (p < 0.001), with more errors occurring for LPS.

Table 14c compares the total number of errors for each sample. This comparison

was signficant (p < 0.001 for the chi square analysis). Here, the problem was

a greater number of errors for sample 4, where many inspectors had problems

identifying lung congestion due to blood clots correctly. Table I4d presents

data for HPS and for LPS. Significantly more errors were made only under HPS

(p < 0.05). Both samples 3 and 4 had a large number of errors under these two

light sources. Surprisingly, relatively few errors occurred for EM and the

other samples shown as number 2. This may be because EM is normally found

after slicing the tissue, and here it had already been "found". The abscess/

lesion (number 5) and the telang (number 1) also proved relatively easy to

identify accurately. Error rates were higher for all sources for the color

defectives, although the increase was most pronounced for LPS.

Thus, as with poultry, error rates increased for passing/rejecting and

identifying samples under both HPS and LPS. Similarly, fewer errors occurred

under both CW and TUN. Unlike poultry, slightly more errors occurred under

CWX. While analysis of the errors for this source for both pass/reject and

identification data did not reach significance, the reasons for this increase,

if real, remain puzzling. Otherwise, the data are in accord with the poultry

data, and with expectations, indicating that the various tasks performed by the

inspectors were performed less accurately under HPS and LPS, and more accurately

under CW and TUN.

2.6.3 Rating Data, Comments, and Response Time Data for Red Meat

Table 15 presents the rating data given by all^ red meat inspectors for the

ease/difficulty of inspecting the samples under each light source. Table 15a

1 Color defective data were included in the analyses presented in 2.6.3 since

these inspectors were experienced, evidently able to compensate for their

defect, and represented the range of personnel at this facility.

41
Table 14. Tabulation of Identification Data for Red Meat Inspectors

Light

Source

a. Identification Errors for 16 Meat Inspectors

Samples

1 2 3(Normal

)

4 5 Total

cw 2 (1) 4 (3) 2 (2) 4 (3) 4 (3) 16 (12)

TUN 2 (2) 3 (2) 5 (5) 7 (6) 2 (1) 19 (14)

LPS 5 (3) 8 (7) 9 (7) 11 (10) 4 (3) 37 (30)

CWX 1 (1) 5 (4) 6 (6) 5 (4) 5 (4) 22 (19)

HPS 2 (1) 3 (3) 8 (7) 7 (6) 2 (1) 22 (18)

Total 12 (6) 23 (19) 30 (27) 34 (29) 17 (12) 116 (93)

2 inappropriate

b. Comparison of Identification Responses for All Sources

Errors Correct

Source CW 16 (12) 64 (58)

TUN 19 (14) 61 (56)

LPS 37 (30) 43 (40)

CWX 22 (19) 58 (51)

HPS 22 (18) 58 (52)

2 = 14.29, df = 4, NS, p < .001

c. Comparison of Identification Responses for All Samples

Errors Correct

Sample 1 12 (6) 68 (64)

2 23 (19) 57 (51)

3 30 (27) 50 (43)

4 34 (29) 46 (41)

5 17 (12) 63 (58)

2 = 19.84, df = 4, P < .001

d. Comparison of Identification Responses for LPS and HPS

LPS HPS

Errors Correct Errors Correct

Sample 1 5 (3) 11 (ID Sample 1 2 (1) 14 (13)

2 8 (7) 8 (7) 2 3 (3) 13 (11)

3 9 (7) 7 (7) 3 8 (7) 8 (7)

4 11 (10) 5 (4) 4 7 (6) 9 (8)

5 4 (3) 12 (11) 5 2 (1) 14 (13)

2 = 8.35, df = 4, NS, p < .10 2 = 10.41, df =4, p < .05

42
a.

b.

Table 15. Rating Data for Red Meat Inspection

Mean Rating Data for Each Red Meat Inspector for Each Source

Source

Inspector CW TUN LPS CWX HPS

1 1.0 1.8 4.4 1.8 2.8

2 1.0 1.0 5.0 1.0 1.0

3 1.8 1.0 4.6 1.8 2.6

4 1.2 1.6 3.4 1.0 2.6

5 1.0 1.0 3.4 1.0 1.4

6 1.0 1.0 4.6 1.0 2.6

7 1.8 1.8 3.8 1.0 3.0

8 1.0 1.0 5.0 2.2 3.8

9 1.8 2.0 5.0 2.0 4.0

10 2.0 1.0 5.0 1.6 3.4

11 2.4 1.6 5.0 1.0 2.2

12 1.4 1.2 3.8 1.6 2.0

13 1.0 1.0 5.0 1.6 3.2

14 1.8 2.8 4.0 2.2 2.8

15 1.8 1.6 5.0 1.4 3.4

16 2.0 1.0 4.0 1.8 2.4

Mean 1.5 1.4 4.44 1.5 2.7

Mean Rating Data for Each

Sample

1

Sample for

2

Each Source

3 4

s Summed

5

over Inspectors

Mean

Source CW 1.56 1.31 1.69 1.62 1.31 1.50

TUN 1.25 1.38 1.56 1.38 1.44 1.40

LPS 4.06 4.50 4.69 4.56 4.38 4.44

CWX 1.50 1.50 1.50 1.44 1.56 1.50

HPS 2.44 2.75 3.0 2.69 2.62 2.70

Frequency of Times that Each Rating

Rating

1

was Given

2

to Each

3

Source

4 4

Source 1-CW 49 24 5 2 0

2-TUN 54 22 2 2 0

3-LPS 2 2 10 11 55

4-CWX 46 29 4 1 0

5-HPS 9 25 31 11 4

Total 160 102 52 27 59

• 2 = 369.50, df = 16, sig < 0.001

43
provides the mean rating data for each inspector for each source averaged over

the 5 samples. Incandescent was rated as 1.4; cool white, 1.5; cool white

deluxe, 1.5; HPS, 2.7; and LPS, 4.4. These results suggest that red meat

inspectors found their task particularly difficult under HPS and LPS. These

two sources received noticeably higher ratings than in the poultry experiment

(2.7 vs. 2.1 for HPS, and 4.4 vs. 3.8 for LPS), while the other three sources received about the same rating.

The square root of each rating given by an inspector was calculated and two

two-way analyses of variance performed. This analysis showed significant

differences in the overall mean ratings for each source (p < 0.01). A similar

analysis of variance performed for the rating data for each sample was not

significant, indicating that the differences in ratings were due to the light

sources, and not to the samples.

Table 16 presents the comments made by the red meat inspectors about each

source. The arbitrary rating scale procedure used for the poultry inspector

comments (see 2.5.5) was applied to the comments by red meat inspectors. The

range of possible scores was from +16 to -16. This procedure resulted in the

following scores: Incandescent, 10.5; Cool White, 8.5; LPS, -16; Cool White

Deluxe, 8.5; and HPS, -6.5; As with poultry, inspectors were extremely negative about LPS, and moderately negative about HPS. As with the poultry inspectors, they were accustomed to inspecting under CW (some had been exposed to both

TUN and HPS, however). Their comments about cool white, cool white deluxe, and

incandescent were generally favorable. Many inspectors commented that HPS and

LPS caused definite problems with accurate color recognition of the meat samples

presented.

Finally, table 17 presents the mean response time data. Again, these data

include about 2 seconds when the sample was obscured from view. In addition,

the cart moved at a somewhat greater speed than the normal line speed for red

meat, although, of course, only a very small portion of the animal was presented.

Thus, these data reflect the experimental and not actual inspection response

times. Inferences about actual line speeds and reaction times cannot be made

from these data. The data were collected to determine if alterations in light source would affect the response time. An analysis of variance of response time

indicated that there was a significant difference in mean response time (p < 0.01), with that for LPS being the longest, and that for CW being the shortest.

Again, these data suggest that the task is more difficult —takes longer—for sources such as LPS and HPS.

2.6.4 Discussion of Red Meat Results

As noted earlier, the individual results for the red meat psychophysical

experiment are not as conclusive as those for the poultry inspection. The

failure to find many significant differences in the number of errors for the

pass/reject and identification tasks is believed to be due to the enormous differences between the sample presentation and the normal inspection task where

defects must be found in an entire head, viscera, or carcass. In addition, the

use of substitute samples (intended to decrease familarity with a particular

sample) resulted in many spurious errors, particularly for the normal and lung

44
Table 16. Comments About Each Source by Red Meat Inspectors

Rating

1

1

11

-1

1

1

1

10.5

-0.5

0.5

1

1

0.5

0.5

Total =

-0.5

1

1

-1

1

11

1

0

1

1

Cool White

Inspection pretty easy; liked color and texture; it's white & I can

see better; pathology readily identifiable

Liked this light much better [than LPS]; better, easier to see; used

to this light; different lighting causes problems

Looked kind of bluish; but looked good

About as easy [as CWX]; more relaxed light; easier for a longer

period of time

Seemed to strain eyes more [than CWX]

Very good in most instances

Light is a lot better than yellow one [HPS]; fairly normal light;

not much different from what I'm used to

Good light; no distortion; natural; toss up with [incandescent]; not

as bright; no reflections into face

Decent enough light; things sharp

Could work with light; pretty good; not great

Wasn't quite as sure as with other lights

Light brings EM up—brought out yellow texture; best light for EM;

but had different effect on lung - seemed duller; product looked

different

All the colors easy to see; all the white lights pretty close

Light not too bad; first one [CWX] better than this; but this is

whole lot better than second [LPS]

Seemed almost the same as earlier one [CWX]

Not too bad lights

10.5

Incandescent

Light seemed dimmer [than CW]; pneumonia wasn't as recognizable;

telang more noticeable - looked darkened

Fine; liked light

Good color; pretty true to life; true colors; sawdust lesion pretty

good

Bad glare from light

No strain on eyes

Best light so far

Pretty much normal light

Liked light; didn't distort anything except spleen; could do

inspection task under light

Not quite as sharp as first one [CW]; probably acceptable

Light convenient to eye; like light; better; more relaxing to eye

Seems brighter; definitely better; caused some problems in that

pathology didn't stand out; demarcations mellowed

45
Table 16 (Continued)

1

1

0

-1

1

Total 8

-1

-1

-1

-1

-1

-1

-1

-1

-1

-1

-1

-1

-1

-1

-1

-1

Total =

0.5

1 -1

1

Incandescent (cont.)

Light much better; gives different look [than HPS, LPS]; but not

like true light from natural sources

Same as last [CWX]; but seemed just a bit clearer; much better than

first; [LPS]; no trouble doing inspection

Light brings out yellowish tint in tissues; more so than one before

Can't stand yellowish light; harder to see what you're looking at

Lights good for task; pretty natural

.5

LPS

All coloration lost; would be almost impossible at viscera table;

whole carcass and viscera very difficult to inspect

Looks dull in color; would not like to do normnal inspection task

under it

Terrible light; hard to distinguish what was good from bad

Awful hard to tell - strange looking lesions; don't like light

Terrible light; wouldn't want to inspect with it; would miss too

much

Wouldn't recommend light; changes normality of colors; wouldn't want

to do inspection under yellow light

Everything is grey; thought man [operating cart] had died

Light no good; everything looked black

Nothing looks true to life; not acceptable

Gives appearance of grey to black to green; wouldn't want to do

inspection task under it

Very bad light; extremely hard to work under all day

Really hard to tell by what you see Don't put this light in packing house

Light is terrible; wouldn't want to work under light; turns

everything dark

All look dark; couldn't work under these conditions

Terrible light; even hand looked bad; bad light; don't want to do

inspection task

-16

Cool White Deluxe

Coloration of specimens not as pronounced as with [CW] but similar;

pneumonia not quite as defined; much better than [LPS]; [difficult

to do EM under HPS]

Liked light; no problems at all

Seems bluish; doesn't show true colors; not as good as incandescent;

hard to distinguish blues and reds; not as clear

A lot easier to see; tell what defect was; glare

46
Table 16. (Continued)

10

10

1 -1

1

1

1

1

10

Total =

+1

1 -1

-1

-0.5

-1

-1

-1

-1

-1

1

1 -1

0

-1

-1

Total =

Cool White Deluxe (cont.)

Light was good [sample goes by fast]

Natural light is best

Fine; fairly normal light source

Last one [CW] brighter; no change in color

Looked like second light [TUN]; acceptable

Too bright; appearance of tissue lighter than normal; seemed brighter

gives yellowish/orange color

Like light better than first [HPS]; good light

Enhances EM & brings out yellow; does something different to lung;

could do inspection task under it

A lot easier to see normal color of tissue

Very good light; brings out what’s wrong with tissue

Light seems easier than 1 st one; more like natural light

Not bad light; ok to do inspection

8.5

HPS

Like light; easy to work under; intensity could be brighter; might

be difficult to detect dark purple or light pink

Like light; no trouble at all

Sawdust and parasites not in true colors; light not as true, does

not show true color of product

More yellow; sawdust lesion hard to see; anything with yellow color

taken away from; not as good as other lights

Wasn’t the best light; not the worst

Better than yellow [of LPS]; still discoloring effect to actual

product

Didn't like at all; gives everything a yellow cast; could create

havoc with old cattle

Light not bright enough; kind of yellowish, distorted; normal light

is whitish; easy to see abscess and telang; hard to see pneumonia

Don't care for light; eyes did seem to adjust; but wouldn't want to

do inspection task; seemed darker & more yellow

Not best light for looking; did point out abscess; distorts

pneumonia; would not want to do inspection task; would get confused

Relatively easy to inspect under light

Much better than first [LPS]

There are a lot better lights; some colors stand out, others not; EM

particularly difficult; color of lynph node poor

Brings out yellowish tint in tissue

Worst light of 3 [TUN & CWX] ; hard to see; to distinguish something

Didn't think light very good; not what used to seeing

-6.5

47
Table 17. Mean Response Time Data for Red Meat Experiment

Source N

1 Mean Std. Dev.

CW 73 4.7 1.23

TUN 74 4.8 1.12

LPS 72 5.4 1.34

CWX 74 4.8 1.44

HPS 74 4.9 1.18

F = 3.81, df = 4, 362, p < .01

* Total observations = 80

48
samples. The significant results for the rating, the pass/reject, and identification tasks are in the same direction as the data from the poultry experiment.

Taken together, these data suggest strongly that both HPS and LPS increase

errors as well as the difficulty of doing the inspection task. The data also

suggest that cool white (CW) and incandescent (TUN) light appear to be adequate

sources, in terms of a lower error rate and rated ease of task performance.

Comments by the meat inspectors, some of whom had worked in a facility lighted

largely by HPS, suggested that HPS (and by inference, LPS) make the inspection

task much more difficult.

These data should not be taken to mean that cool white fluorescent is the ideal

light source or that it represents the best use of a given electric power allotment. General theoretical calculations (Worthey, 1982; Thornton, 1972) indicate

that cool white light diminishes color contrasts relative to natural daylight

of a similar color. Further work is necessary to determine the optimum source

for meat and poultry inspection in terms of color rendering, energy expenditure,

and visual comfort.
3. SPECTRORAD IOMETR IC MEASUREMENTS

3.1 BACKGROUND INFORMATION

Color vision may be defined as that ability by which the eye distinguishes

objects according to the shapes of their spectral reflectance functions. Under

this definition, which is not the only one in use, "color” does not include the

dimension of light versus dark. Useful information regarding such things as the identities of metals in a shop, or the ripeness of fruit, may be revealed

by color contrasts. Quite possibly, color plays a role in the discovery of

defects by meat and poultry inspectors. In this section, we assume nothing in

this regard, but undertake to show through physical data that color vision is

of potential importance in the discovery of many pathologies. To the extent

that color vision is important, then the color rendering of lighting for meat

and poultry inspection is important. These physical (reflectance) data will

not by themselves answer the regulatory question, "what light should be required,

but will address the underlying basic question, "why does color-rendering matter

at all?" Used as inputs to the appropriate calculations, these data could be

used to evaluate in detail the suitability of various lights for the inspection

task.

Based on the fundamentals of color science, we may state four rules for

interpreting these data:

1. If two objects have exactly the same spectral reflectance function, then

color vision is of very little use in telling the one from the other, or in

seeing the one against a background of the other. This is true, irrespective of the choice of lighting. If a particular discrimination made by an inspector involves only objects of identical spectral reflectance, then the

spectral power distribution (SPD) of the light source would be of little

consequence for this discrimination. (One is tempted to say that color

vision is of no consequence in such a case; however, color could still play

a role in shape perception because of the tendency for highlights to differ

in color from their background. Suppose, for instance, that an underlying

abscess causes a bump on the surface of a liver. The bump itself is "livercolored," but is made visible by shading and highlights. The highlights

are not liver-colored, but white.)

2. If the spectral reflectance of one object is proportional to that of another

object, then color vision may aid in telling them apart, but the color difference will be accompanied by a lightness difference. A particular pair

of proportional reflectances, for instance, might represent a dark red and

a lighter red. The lighter red would appear not only lighter, but also

redder (more saturated). The SPD of the light source would be of greater

importance than in the first case.

3. If two objects have different spectral reflectances (not equal and not

proportional) then the eye may be able to discriminate these objects on the

basis of color, depending on the SPD of the light source. Color may very

well convey information which is not conveyed through lightness differences

or any other cues. In general, the two objects will differ in color under

50
a broad range of lights, but some lights will "render" the color contrast

("color difference") between them as large, while other lights will make

the color difference small or even zero. As an extreme example, a light source that emits all its radiant energy in a very narrow wavelength band,

called a "monochromatic" light, loses all color differences and renders all

objects (save certain fluorescent ones) in shades of a single hue. To the

extent that spectral reflectance differences distinguish healthy tissue from

unhealthy or lesions from their backgrounds, color rendering is an important

concern.

4. It is helpful to think of colors and the effect of lighting upon them, in

terms of color contrasts between particular objects, rather than of the

appearance of colors in isolation. There are two reasons for this. First,

there are cases in which color is important to vision, even though no one color is abnormal; a broken and protruding bone contrasts in color with

muscle, even if both bone and muscle are normal in color. Second, the eye

"adapts" to different lights, so that no simple physical measure has a precise connection with perceived color. By speaking in terms of specific

contrasts, we can make practical statements that do not mislead, while

minimizing any detailed discussion of the adaptation problem. (The more common approach to color rendering (IESNA, 1981) asks how much individual

surfaces are displaced in color. This is not the key practical question

however, and it makes the adaptation problem a little more obtrusive than

it needs to be. The idea of emphasizing contrast is borrowed from the

retinex theory (McCann, McKee and Taylor, 1976), which describes adaptation

quantitatively with the hypothesis that contrasts at borders govern

perception.

)

In what follows, we first describe how the spectral reflectance measurements

were taken. Then we show that many spectral reflectance differences—potential

color differences—are available to aid the meat and poultry inspection tasks.

3 . 2 APPROACH

3.2.1 Method of Measurement

Spectroradiometric measurements were taken with a Photo ResearchTM Model PR-710

Spot SpectrascanTM Spectroradiometer. Important features of this instrument

for the present work include:

1. A telescopic viewing and measuring system which permits the operator to

aim and focus on the exact surface desired, and to observe whether the

specular component of reflection is included or excluded.

2. A high-resolution, low-flare lens which can be focused from 44 mm to

infinity. The measured area is circular and subtends 0.5 degrees. At

44 mm, the measured spot is approximately 0.5 mm across, which is just

small enough to permit measuring the spectral radiance of a large leukotic

spot on a chicken liver, for instance.

51
3. The instrument can be transported for field work.

4. It incorporates a microcomputer and one floppy disk drive which:

a. permit long-term storage of spectral radiance data on the floppy disk.

b. can output numerical and graphical data to the printer-plotter.

c. can pass data to another computer system.

d. can divide a spectral radiance function by the previously stored

spectral radiance of a white standard, in order to provide spectral

reflectance.

e. provide automatic output of derived quantities such as luminance and

chromaticity.

5. It has a fixed spectral range of 390 to 730 nanometers (nm), covering the

visible spectrum, and reads out at intervals of 2 nm.

3.2.2 Measurement Technique

Reflectance of a surface is a measure of the fraction of incident light that an object reflects; a very white object has a reflectance of almost 1.0, while a very black object has a reflectance approaching 0.0. Reflectance measured as a function of wavelength is called spectral reflectance . Many surfaces are partly

mirror-like ("specular"); if they are illuminated by a single light-bulb for

instance, some of the incident light contributes to highlights, which are essentially little mirror images of the light bulb. The rest of the light is either

absorbed or reflected diffusely. In measuring reflectance, it is important to

know if one is observing the reflected light within a highlight or away from a highlight (Nicodemus, Richmond, Hsia, Ginsberg, and Limparis, 1977). This is

described as measuring reflectance with " specular included " or

" specular

excluded .

On non-metallic objects, highlights generally have the approximate color of the

incident light, while non-highlight areas have the characteristic color of the

object (Raloff, 1982). While highlights may provide the eye with important

information on object geometry, the "specular excluded" reflectance conveys the

color of the object. For this reason, we measured spectral reflectance of

healthy and diseased tissue as specular excluded, with a few exceptions.

The measurement involved these steps:

1. With a small source at a selected reference distance (usually 7 inches)

along the normal to the sample's surface, we measured the spectral radiance

of a standard white surface of packed polytetraf luoroethylene (PTFE) (Weidner

and Hsia, 1981). The small source was an incandescent bulb, whose voltage

was kept constant to within a few percent. The packed PTFE surface is such

a good approximation to a perfect white, that its spectral reflectance may

be taken as 1.0 throughout the visible spectrum.

Within the general area to be measured, such as the normal epidermis of an entire chicken, we chose a spot for the measurement. We put the small source at the reference distance along the normal to the spot. A special

apparatus was constructed to simplify proper placement of the light source.

52
3. We focused the spectroradiometer on the spot. The angle of viewing was assumed not to be critical, except that any highlight should be excluded

from the measured area. Exclusion of the highlight is done by direct

observation through the telescopic viewing system.

4. We measured the spot's spectral radiance.

5. The radiance found in step 4 was divided by that found in step 1, to get

the spectral reflectance (technically, the spectral reflectance factor).

The procedure and provision for lighting used are inferior in sophistication to

those which would be used in a physics laboratory. The net effect of this is

to put some uncertainty on the absolute magnitude—but not the shape —of the

reflectance curves obtained. However, the direct-viewing spectroradiometer

does permit good reliability in the task of aligning the intended spot and

including or excluding specular reflection. Furthermore, since this instrument

measures the entire spectrum simultaneously , the shape of the spectral radiance

functions should be unaffected by small time variations in incident light

intensity. As the eye also collects and uses data in a way that does not hinge

on precise measurement of absolute reflectance, we can say that these data have

satisfactory accuracy in the context of vision and lighting.

3.3 SPECTRORADIOMETRIC MEASUREMENTS

Spectroradiometric measurements were made of tissue samples from each of four

species. These included: chickens, turkeys, swine, and cattle. Tissue samples were selected on the basis of availability on the day measurements were made.

Consequently, not all major defects responsible for condemnation in each species

are represented. Reflectance measurements were typically made for a defect and

for nearby "normal" tissue, to indicate the extent to which differences in color occur in samples actually seen by inspectors. The surrounding tissue is "normal"

relative to the defect—its spectral reflectance may well differ from the

corresponding tissue in a truly normal sample.

The spectral radiance data for normal and defective samples for four species were transformed to spectral reflectance data relative to the white standard.

Then the data for each defect and its adjacent "normal" tissue were plotted in

a series of comparison graphs (see figure 6 for example). These graphs present

percent reflectance as a function of visible wavelength from 390 to 730 nm. Note that the range of values for percent reflectance varies for different

samples. At least two kinds of comparison may be made for each figure. The

first is that of differences in spectral characteristics, represented by changes

in the shape of the curve (and normally perceived as differences in color).

The second is that of differences in lightness, represented by changes in the

height of the curve (or percentage reflectance). Combinations of the two may

also be observed. Inspection of these graphs indicates, that for many of the

defects measured, differences in the shape of the spectral curve (or in color)

play an important role.

Figures 6 and 7 present data for chicken defects, similar to those studied in

the psychophysical portion of the experiment. Figure 6a shows different areas

53
a) Solid: normal chicken skin.

Dashed: bruised bird, yellowish area. Dotted: bruised bird, bluish area.

c) Solid: septicemic chicken, dark

area. Dashed: normal epidermis, same data as

solid curve in (a).

b) Solid: gall stain.

Dashed: more normal area, same bird.

d) Solid: squamous cell carcinoma.

Dashed: adjacent "normal" area of

epidermis.

Figure 6. Spectral reflectance functions of chicken epidermis, presented as comparisons of defective with "normal" areas.

54
a) Solid: yellowish spot in leukotic

liver.

Dashed: more normal area, same liver.

b) Solid: yellowish spot in leukotic

spleen.

Dashed: more normal area, same spleen

c) Solid: "normal" area of a liver

with air sacculitis.

Dashed: "normal" area of a leukotic

liver. This is a "control" comparison

between similar surfaces.

Figure 7. Spectral reflectance functions of internal organs in chicken,

presented as comparisons of diseased with "normal" areas.

55
of bruising on a chicken, showing both bluish (lower curve) and yellowish

areas, as well as more normal nearby tissue. Figure 6b compares a gall stain

similar to that used in the psychophysical portion of the experiment with

normal chicken epidermis. Figure 6c compares a septic (septicemic) epidermis

with normal epidermis, while figure 6d presents a comparison of a skin carcinoma

with more normal skin.

The comparisons shown in figure 7 are for internal chicken tissues, characterized

by much lower reflectances. Figure 7a presents a yellowish spot on a leukotic

liver with more normal liver tissue. Similarly, figure 7b shows a yellowish

spot of leukosis on the spleen. Figure 7c compares air sacculitis on a chicken

liver with an unaffected area.

Figures 6 and 7 are characterized by differences in spectral characteristics

and lightness between a defective and normal area. The leukotic spots and

bluish bruise show some of the greatest differences in lightness, but the graphs

generally show differences in spectral characteristics to a greater or lesser

extent.

Figures 8 to 12 present spectral reflectance data for cattle. Figure 8a

compares abscessed tissue, containing both the abscess and the immediately adjacent inflamed area, with more distant normal liver tissue. There is a marked

lightness difference for the white area along with some difference in spectral

characteristics. Figure 8b compares the white area of two different liver abscesses which differ more in lightness than in spectral characteristics. Figure

8c compares the small abscess of 8b with more normal liver tissue in the immediate surround. Figure 8d compares the "normal" tissue surrounding each abscess.

In this case, the two spectral reflectances are very similar with differences

only at very short wavelengths. Figure 9 presents data primarily for cattle

viscera (except figure 9d) . Figure 9a compares inflamed mammary tissue with a more normal area of tissue, while figure 9b compares a sawdust lesion in the

liver with more normal liver tissue. Figure 9c compares fecal material on the

spleen with an uncontaminated area of spleen. Finally figure 9d shows two different areas of bruising with non-bruised flank tissue. Again differences in

spectral characteristics emerge for the various comparisons, particularly for

9b and 9c.

Figure 10 presents additional data for cattle viscera. Figure 10a shows a pulmonary adhesion with adjacent "normal” tissue. Figure 10b compares both a white and inflamed red area of an abscess with a normal area of diaphragm.

Finally, figure 10c shows another comparison of fecal contamination with an uncontaminated area, this time for lung tissue. The figure 10 comparisons are

.a less dramatic depiction of wavelength and lightness differences (except 10c)

than some of the other spectral reflectance data for cattle.

Figures 11 and 12 present data for some of the defects studied in the cattle

psychophysical experiment. Figure 11a presents the spectral reflectances found

for EM, compared with normal heart and fat tissues. Figure lib presents liver

tissue with and without the dark spots of telang. Figure 11c shows comparisons

for a kidney with chronic nephritis (not used in the psychophysical study).

Figure lid shows data for acti (an abscess of the lymph nodes in the head).

56
a) Solid: liver, white area of abscess

in steer.

Dashed: liver, inflammation near

abscess.

Dotted: liver, adjacent "normal" area.

b) Solid: liver, white area of small

abscess.

Dashed: liver, white area of larger

abscess, same liver. Note that this

is a "control" comparison of nominally

similar areas.

c) Solid: liver, small abscess.

Same data as previous graph.

Dashed: liver, more normal area.

Hsvelength. nm

d) Solid: liver, normal area, steer.

Dashed: liver, another normal area,

different liver. This is another

"control" comparison, showing the

variability within "normal" liver.

Figure 8. Spectral reflectance measurements of liver in cattle, presented

as comparisions of defective areas with "normal" areas.

57
area. Dashed: mammary tissue, more normal

area.

h) Solid: liver, "sawdust lesion."

Dashed: liver, more normal adjacent

area.

Wavelength, ntn c) Solid: spleen, contaminated with

fecal material.

Dashed: spleen, adjacent "normal"

area.

Wavelength, noi d) Solid: flank, bruise in peripheral

tissue.

Dashed: flank, bruised, peritoneal

side.

Dotted: flank, more normal tissue.

Figure 9. Spectral reflectance measurements of internal areas in cattle presented as comparisions of defective areas with "normal" areas.

58
Navelength, nti a) Solid: lung, pulmonary adhesion.

Dashed: lung, adjacent "normal" area.

b) Solid: diaphragm with white

abscess.

Dashed: diaphragm, adjacent "normal"

area. Dotted: diaphragm, inflamed red area.

c) Solid: lung, contaminated with

fecal material.

Dashed: lung, uncontaminated area.

Figure 10. Spectral reflectance measurements of internal areas in cow,

presented as comparisions of defective areas with "normal" areas. 59
a. Solid: Eosinophilic myositis in

cattle heart, greenish (yellowish)

spot.

Dashed: Adjacent normal pink heart

muscle.

Dotted: Nearby white fat with some

blood on it normal.

chronic nephritis.

Dashed: Abnormal yellowish (whitish) area of kidney with chronic nephritis.

b. Solid: Telangiectasis in liver,

dark spot with no surrounding red area

included. (Only a large spot cam be

isolated in this way.)

Dashed: Nearby, more normal, area of

same liver.

head.

Dashed: "Normal" pinkish-gray area of

same lymph node.

Figure 11. Spectral reflectance measurements of internal areas in cattle,

presented as comparisons of diseased areas with normal areas.

60
whitish area. reddish area. Dashed: "Normal" reddish area, same Dashed: "Normal" pinkish area, same

liver. congested lung.

Wavelength. na

c. Solid: Pneumonic lung, dark area. Dashed: "Normal" pinkish area, same

pneumonic lung.

Figure 12. Spectral reflectance measurements of internal organs in cattle,

presented as comparisons of diseased areas with normal areas.

61
Both figures lib and lid demonstrate noticeable differences in spectral

characteristics and lightness.

Figure 12a presents additional data for a liver abscess, characterized by both

color and lightness differences from normal liver tissue. Figures 12b and 12c

present data for both congested and pneumonic lung tissue. Figure 12b shows

greater differences in spectral reflectance between adjacent tissue samples,

than does 12c, although some differences are apparent for the latter, as well.

Figures 13 to 1A present data for turkeys. Figure 13a shows reflectance data

for various portions of a septic turkey, compared with a normal epidermis.

Figure 13b compares two different areas of a fresh wing bruise with more normal

epidermis. Figure 13c shows an older healing bruise on a humerus. This bruise

was characterized by a distinctive greenish cast. Figure 13d also shows a healing bruise, again with a greenish cast. Figure 13 demonstrates marked differences in response to spectral composition for all comparisons of defective and

normal tissue samples. There are also differences in percent reflectance for

the various samples. Figure 1A presents data for turkey viscera and internal

areas. Except for lAc, the turkey viscera are characterized by much lower

reflectances than the epidermal samples. Figure lAa compares a whitish granuloma with a more normal area of turkey liver, while lAb shows a dark area of

liver with bile build-up along with a more normal red area of liver. Figure

lAc compares data from the gizzard of a septic turkey. Air sacculitis was also

present on the gizzard. Finally, lAd compares fecal contamination with an uncon taminated area of peri-renal fat. Again the data shown in figure 1A demonstrate

marked differences in the pattern of spectral reflectance for various portions

of defective and normal tissue.

Figures 15-19 present comparison data for swine. Figure 15 provides a series

of comparisons for swine epidermis. Thus, figures 15a and 15b compare two

different hair tufts with adjacent skin. (The normal skin in 15b is characterized by a much lower spectral reflectance than that of 15a. Note the difference in the scale of the ordinate.) Figures 15c and d compare different melanotic lesions with surrounding epidermal tissue. The melanotic lesions are characterized by very low spectral reflectance, unlike the surrounding epidermis

Since this lesion appears as a black spot on a light background, it is one for

which color vision is probably not important.

Figure 16 presents comparison data for internal swine tissue. Figure 16a

compares pulmonary edema with normal lung tissue, while 16b compares two pneumonic splotches (with almost identical spectral reflectances) with normal lung

tissue. Figure 16c presents an unusual and severe abscess. An abscess in the

lung of a pig had extended through the pleura to reach the spine. The surface

of this growth had the normal color of the pleura although the growth's shape,

of course, was not normal. Measurements were made of a highlighted area (specular) and a non-highlighted area (specular excluded) to investigate the

role of color in contributing to the perception of the shape of the abscess.

Figure 16d compares pus in the parotid gland with more normal tissue. This

last figure is characterized by only slight differences in spectral

characteristics and lightness.

62
Dashed: Septicemic leg.

Dotted: normal epidermis, wing of

another turkey.

area. Dashed: "gray" area of same

bruise green overlying red.

Dotted: pink surrounding tissue.

b) Solid: fresh bruise on wing, dark

red area. Dashed: same bruised wing, lighter ST6&*

Dotted: normal epidermis (nearly

white), same wing.

d) Solid: leg, old healing bruise.

Dashed: normal muscle, red area

without skin.

Figure 13. Spectral reflectance functions of external areas of turkey,

presented as comparisons of defective with "normal" areas.

63
a) Solid: whitish area of granuloma in

liver.

Dashed: adjacent "normal" area.

c) Solid: exudate of air sacculitis,

overlying white fibrous area of

gizzard.

Dashed: white area of same gizzard,

air sac pulled away.

Dotted: septicemia, same gizzard, dark

red area.

b) Solid: dark area of bile build-up

in liver.

Dashed: red area of same liver.

d) Solid: fecal contamination on

peri-renal fat.

Dashed: peri-renal fat, not

contaminated.

Figure 14. Spectral reflectances of internal areas of turkey, presented as comparisons of defective with "normal" areas.

64
a) Solid: hair tuft on background of

skin. The measurement spot covered

parts of several hairs and also some

skin.

Dashed: normal cream -colored skin.

c) Solid: white surround of melanotic

lesion.

Dashed: dark area of melanotic lesion.

Note the large lightness difference.

Dotted: normal cream -colored area of

skin.

b) Solid: hair tuft remeasured.

Dashed: normal skin, no hair.

d) Solid: black area of melanoma.

Dashed: normal skin near melanoma.

Figure 15. Spectral reflectance measurements of hog epidermis, presented as comparisons of defective areas with adjacent "normal" areas.

65
a) Solid: lung with edema in pneumonic

area. Dashed: lung, adjacent normal tissue.

b) Solid: lung, red splotch in

pneumonic area. Dashed: lung, adjacent "normal"

tissue.

Dotted: lung, different red splotch in

pneumonic area.

Havelength, not

c) Solid: spine, abscess from lung,

highlight

.

Dashed: spine, same abscess, specular

excluded. This surface had the normal

color of mesentery, but these data

indicate a role for color in the

perception of shape.

gland.

Dashed: head, pus in parotid lymph

gland.

Figure 16. Spectral reflectances of internal organs in hog, presented as comparisons of diseased areas with "normal" areas.

66
Figure 17 compares different areas of pig skin. Figure 17a shows two different

areas of erythematous skin along with normal "cream-colored" skin. Figure 17b

also shows erythema due to trampling along with normal skin. Figure 17c com- pares a white and red area of exostosis, while figure 17d presents foot tissue

with and without arthritis. Figure 17 is characterized by differences in both

spectral characteristics and lightness.

Figure 18 shows data for various internal pig organs. Figure 18a compares a melanotic lesion in mammary tissue with a normal fatty surround. Figure 18b

presents a lung abscess against "normal" lung, while 14c shows splenitis and

normal spleen. Figure 18d compares a kidney cyst filled with urine against a background of kidney, with the kidney alone. Since the cyst is transparent,

its spectral reflectance depends strongly on the geometry of the lighting and

viewing conditions.

Figure 19 provides more internal organ comparisons for swine. Figure 19a

compares a spot of white TB in the mesentery surrounding the intestine with a normal lymph node. Figure 19b shows two white milk spots on the liver, as well

as normal liver. Finally, figure 19c presents an abcess with both the capsule

and pus present along with a normal jowl.

In the preceding pages, the presence of differences in both lightness and

spectral composition between defective and nearby more normal tissue has been

documented.

Additional analytical calculations are needed to predict the extent to which

changes in light source spectral distribution will affect differences in

color between defective and adjacent "normal" tissue. Small color differences,

apparent under incandescent light or even cool white fluorescent light, may

disappear under a source with poor color rendering such as HPS. In the psychophysical portion of the experiment, the samples most affected by light source manipulation were the gall stain, air sacculitis and normal tissue in chickens,

and various lung problems in cattle. Observation of these samples indicated

that they were characterized by small color differences—differences likely to

be minimized by HPS and LPS.

67
area. Dashed: skin, erythetna, dark brown

area. Dotted: skin, normal cream-colored

area.

c) Solid: exostosis, white area. Dashed: exostosis, red area.

trample mark.

Dashed: skin, adjacent "normal" area.

d) Solid: foot, dark area with

arthritis.

Dashed: foot, adjacent "normal" area.

Figure 17. Spectral reflectance measurements of external areas of hog,

presented as comparisions of defective areas with "normal" areas.

68
a) Solid: mammary tissue, melanotic

lesion.

Dashed: mammary tissue, normal fatty

surround.

Wavelength, na

c) Solid: spleen, red spots in

splenitis.

Dashed: spleen, adjacent "normal"

area.

b) Solid: lung, multiple lesion;

yellowish area of abscess.

Dashed: lung, adjacent "normal" area.

d) Solid: kidney, cyst with urine

against background of kidney. The

cyst is actually transparent, so its

color depends strongly on the geometry

of lighting and viewing.

Dashed: normal red area of same

kidney.

Figure 18. Spectral reflectance measurements of internal organs in hog,

presented as comparisons of defective areas with "normal" areas.

69
a) Solid: mesentery, white tubercular

spot in lymph node.

Dashed: mesentery, more normal area in

lymph node.

b) Solid: liver with "milk spots,"

white area. Dashed: liver with "milk spots," a second white area. Dotted: liver, adjacent "normal" area.

12C .

IOC .

I «.

I

8

* BO - §•Cj Cj 3 4C - $

2C _

Spectral Reflectances

C iiniiiilniiiiiJiliimiiJiliiiiiiiiilimiiiiil

300 400 500 BOO 700 BOO Wavelength, nn

c) Solid: jowl, abscess, pus.

Dashed: jowl, capsule area of abscess.

Dotted: jowl, normal fatty tissue.

Figure 19. Spectral reflectance measurements of internal areas in hog,

presented as comparisions of diseased areas with "normal" areas.

70
4. CONCLUSIONS

The major analyses performed in the present research project suggest two

important conclusions: 1) differences in spectral reflectance between defective and normal tissue exist; and 2) at least two light sources, HPS, and LPS,

decrease the inspector's ability to inspect meat and poultry tissue accurately.

Thus, the spectral reflectance data indicate that potential color differences

differences in the shapes of the spectral reflectance functions —do exist

between defective and adjacent normal tissue for all four species measured.

Along with the accompanying lightness differences, color differences appear to

play a major role in the successful identification of tissue for condemnation,

judging by the results of the psychophysical experiment. Those data indicate

that the two sources with the poorest color rendering (HPS and LPS) had some of

the poorest performance for the various measures studied. These included pass/

reject judgments; accuracy of identification; rated ease of task performance

under each source; overall rating of source for the inspection task; and

response time. While these results were more pronounced for the poultry study,

due perhaps to the greater veracity of the simulation, the trends for the

cattle study were in the same direction.

An alternate interpretation of the data would be that sources of higher color

temperature yield higher performance. The independent variables of color

rendering and color temperature were not studied independently in the psychophysical study. Our assumption in this report has been that color rendering is

the more important variable. The most cautious interpretation would be that

both low CRI and low color temperatures should be avoided.

In conclusion, the data obtained in the psychophysical experiment indicate that

sources with color rendering indices as low as that of HPS (21) do not allow

meat and poultry inspection to be done accurately and reliably. These sources were also characterized by low color temperatures, around 1750 K. Sources with

color rendering indices equal to or better than cool white fluorescent (62)

appear to cause no problems. (It should be noted, however, that no source,

such as improved mercury, with a CRI between 60 and 20 was tested.) The data

also suggest that performance is better for sources with higher color temperatures (above the 2800 K of the incandescent lamp used) but the effects of color

temperature were not studied independently of CRI. The CRI of cool white

fluorescent is lower than might be considered ideal (Worthey, 1982), but since

it is the source under which many of the inspectors were accustomed to working,

it is, in effect, their reference standard. They were able to perform equally

well for sources better than this, particularly incandescent lighting, but not

for ones that were markedly "worse" in terms of conventional CRI specifications.

Consequently, if recommendations for minimum CRI for USDA meat and poultry

inspection were to be made, they would be that sources with CRI below 62, and

with color temperatures below 2800 K, are not likely to be adequate.

71
5. RECOMMENDATIONS FOR FURTHER RESEARCH

A question left unanswered by the psychophysical research is the effect of

light sources with CRI better than that of cool white fluorescent, such as cool

white deluxe light. The poor performance with this source in the meat study was not well understood. Neither the inspectors' ratings nor their comments

indicated that this source caused problems. Comments by the poultry inspectors

indicated that they were unused to seeing the "reds" brought out by this source,

since the more customary cool white fluorescent tends to dull reds. They

associated the red with a chicken killed by scalding (for which the carcass is

condemned). As a result, it is conceivable, that after sufficient familiarization, inspection performance might improve if the task were performed under a source such as cool white deluxe with a CRI higher than that of cool white.

The performance of a source such as metal halide with a CRI almost equivalent

to that of cool white should also be evaluated, because of its high luminous

efficacy and relatively high color temperature.

As a result, it appears desirable to conduct a full scale field study under a series of plausible light sources. Instrumenting an actual inspection station,

and conducting the study over several days, seems reasonable. This would allow

use of actual red meat and poultry samples, thus avoiding the problems of sample

size and familiarity noted earlier, as well as providing an opportunity for

training inspectors before performance is assessed. It would also allow the

inspectors to adapt properly to the light source, and to work under it long

enough to report fatigue or strain associated with the light. In addition, it

would permit the geometry of the viewing situation, including general plant

lighting, to be more realistic.

Other needed research would build on the spectroradiometric data already

collected for the four species. These data indicate that differences in spectral composition and lightness characterize almost all sample pairs studied.

Further research is needed to determine the extent to which color differences

are affected by changes in the spectral composition of the light source. Two

researchers, Worthey (1982) and Xu (1983) recently presented some new conceptual

approaches to evaluating the color rendering properties of light sources. Both

noted that some light sources can systematically reduce object color contrast

and decrease overall visual clarity and brightness.

The goal of lighting of meat and poultry inspection should be to transfer the

most information about the tissue for the lighting dollar. While Xu (1983)

showed that certain lamps have a greater "capacity" for information transfer

than others, it is difficult to write an exact formula for "information per

chicken" in the inspection context. The discussion in section 1.2.3, comparing

the work of Thornton (1972), Worthey (1982), and Xu (1983), suggests that contrast (or differences in color and lightness) can serve as a good proxy for

information transfer. In a specific case, if a lesion does not contrast with

nearby tissue, then no information can be transferred to the eye about it. If an illuminant reduces many of the color contrasts arising in meat and poultry

inspection, relative to what would be seen in daylight, then it gives poor

information transfer.

72
The spectral reflectance data already collected for meat and poultry samples

could be used to compute the color and black-and-white contrasts which could be seen by an inspector under various light sources. For example, figure 6b,

compares the curves for normal chicken skin, and chicken skin stained with

gall. The graphs show that there are potential color and lightness differences

between these areas. Given the spectral power distribution of a light source,

one can compute the luminous reflectance and chromaticity of each area under

that light. These values can be transformed to measures that assess the lightness contrast, the chromatic contrast, and the total contrast between the two

samples, under the particular light. Through such calculations, light sources

could be compared on the basis of their ability to reveal gall stain, as well

as other defects for which the spectroradiometric data were taken. The most

appropriate color space for this calculation would be a uniform color space

that incorporates a correction for the observer's adaptation, such as CIELAB

space (CIE, 1978).

This analysis would allow one to predict the effect of a specific light source

on the size of the color differences between defective and normal tissue measured in the present study. Such an analysis can provide direction for determining optimal lighting sources which maximize information transfer while

minimizing energy costs for meat and poultry inspection.

Finally, another topic for further research, which has not been formally

examined in this report although it was hinted at in the inspectors' remarks, is

whether lamp color temperature has an important effect on the meat and poultry

inspection task. Although illuminant color has an effect on the appearance of

objects, this problem is usually kept separate from color rendering. In the

CRI calculation, for instance, an infinite family of reference illuminants is

defined, one for each color temperature. When one looks at color rendering as

a matter of performance, and not just as a matter of normal versus abnormal

appearance, however, then the effect of lamp color must be considered.

No experiment has ever been done (to our knowledge) in which an observer's

performance was measured, while color temperature and color-rendering were

separately and systematically varied. Because of the eye's known ability to

adapt to changes in illuminant color (Judd, 1940; McCann, McKee, and Taylor,

1976), it would not be expected that small changes in lamp color would drastically affect performance. However, if a light of low color temperature is used

(which emits relatively little blue light) , blue-yellow contrasts should be

harder to detect, even if luminance is maintained at a reasonably high level.

Thus as a light is made yellower—lower in color temperature—it becomes effectively a dimmer and dimmer light as far as stimulation of the blue-sensitive

receptors in the eye is concerned. The other receptors, those that respond to

red and green light, have spectral sensitivities similar to that of a light

meter, so that fixing illuminance with this meter keeps their level of stimulation approximately constant. From this clue, we may infer that lights of

very low color temperature can impair perception of blue-yellow contrasts

(Ronchi and Stefanacci, 1978). The computational project will provide information on the existence of blue-yellow contrasts in mean and poultry tissues

contrasts which might be affected by lowering color temperature.

73
Future research in this area should also include an analytical type of

experiment in which lamp color and color-rendering properties are varied

independently, and psychophysical performance is then evaluated.
6.

REFERENCES

1. American Optical Corporation, Pseudo-Isochromatic Plates for Testing

Color Perception, 1965.

2. Aston, S.M. and Bellchambers , H.E. Illumination, colour rendering and

visual clarity. Lighting Research and Technology , 1, pp. 259, 261, i969.

3. Bellchambers, H.E. and Godby, A.C. Illumination, colour rendering and

visual clarity. Lighting Research and Technology . 4, pp. 104-106, 1972.

4. Billmeyer, F.W. and Saltzman, M. Principles of color technology . 2nd

Ed., New York: John Wiley and Sons, 1981.

5. Boyce, P.R. Illuminance, lamp type and performance on a colour

discrimination task. Capenhurst, England: Electricity Research Council,

ECRC/M-943, June 1976, NTIS Accession //PB-276 753.

6. Boyce, P.R. and Simons, R.H. , Hue discrimination and light sources. Lighting Research and Technology . 9, pp. 125-140, 1977.

7. Charters, A.D. Practice improvement — V. Lighting. Journal of Small

Animal Practice . 7, pp. 929-31, 1966.

8. Commission Internationale de l’Eclairage (CIE). Method of measuring and

specifying colour rendering properties of light sources. Paris: CIE No.

312 (TC-3.2), 1974.

9. Commission Internationale de l'Eclairage (CIE). Recommendations on uniform

color spaces. Supplement No. 2 to CIE publciation No. 15 (E-l.3.1), 1971/

(TC-1.3). Paris: CIE, 1978.

10. Culver, M.J. and Allard, A.W. Intradermal testing for allergy. Veterinary

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11. Finney, E.E. Measurement techniques for quality of agricultural products . Pittsburgh, Pa: Instrument Society of America, 1973.

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separate and combined effects of color differences, luminance contrast,

and luminance level. Journal of the Optical Society of America, 71,

pp. 145-150, 1981.

13. Hall, G.L. and Bobrick, M. Improved illumination of the dental treatment

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1968.

14. Hurvich, L.M. Color Vision. Sunderland, Mass: Sinauer Assoc., 1981.

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15. Illuminating Engineering Society of North America ( IESNA) . IES Handbook,

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17. Judd, D. B. Hue saturation and lightness of surface colors with

chromatic illumination. Journal of the Optical Society of America , 30, p. 2-32, 1940.

18. Loe, D.L., Rowlands, E. , and Watson, N.F. Preferred lighting conditions

for the display of oil and watercolour paintings. Lighting Research and

Technology , 14, 1982.

19. McCann, J.J., McKee, S.P. and Taylor, T.H. Quantitative studies in retinex

theory. Vision Research , 16, pp. 445-458, 1976.

20. Minitab, (Statistical computing package) Release 82.1, Copyright Penn

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Limperis, T. Geometrical Considerations and Nomenclature for Reflectance,

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1977.

23. Raloff, J. Avoiding the "plastic look" in computer-generated color

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24. Ries, K.M. Relighting Case Studies. Paper presented at the Second Annual

Cost Control Workshop of the American Meat Institute. Chicago, October

15, 1982.

25. Ronchi, L. and Stefanacci, S. Colour discrimination in the frame of

illuminating engineering. In Guy Verriest, Ed., Color Vision Deficiencies

IV, Modern Problems in Ophthalmology , 19, p. 88-91; also, L. Ronchi,

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26. Thornton, W.A. Color-discrimination index. Journal of the Optical Society

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27. Thornton, W.A. and Chen, E. What is visual clarity? Journal of the

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29. XU, H. Color rendering capacity of illumination, Journal of the Optical

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77
N BS-1 14A irev. 2 -ec) U.S. DEPT. OF COMM.

BIBLIOGRAPHIC DATA

1. PUBLICATION OR

REPORT NO.

2. Performing Organ. Report No. 3. Publication Date

SHEET (See instructions) 84-2829 March 1984

4. TITLE AND SUBTITLE

The Role of Color in Light for Meat and Poultry Inspection

5. AUTHOR(S)

Belinda L. Collins and James A. Worthey

6. PERFORMING ORGANIZATION (If joint or other than NBS, see instructions) 7 . Contract/Grant No. national bureau of standards

DEPARTMENT OF COMMERCE 8. Type of Report 8, Period Covered

WASHINGTON, D.C. 20234

9.

SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street. City. State, ZIP

)

U.S. Department of Agriculture

14th & Independence Ave.

Washington, DC 20250

10.

SUPPLEMENTARY NOTES

J Document describes a computer program; SF-185, FIPS Software Summary, is attached.

11.

ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant

bi bl iography or literature survey, mention it here) The role of color in lighting for meat and poultry inspection is discussed. A review

of literature relevant to the problem of quality of illumination is presented, along

with literature specific to agricultural and veterinary problems. A psychophysical

study of the accuracy of detecting and identifying selected defects in meat and

poultry was conducted under five light sources: incandescent, cool white fluorescent,

cool white deluxe, high pressure sodium (HPS), and low pressure sodium (LPS). The

results indicated that more errors were made under the latter two sources, and that

the inspection task was rated as more difficult under these sources. In addition,

spectroradiometric measurements were made of defective and adjacent "normal" tissue

to document the kinds of spectral reflectance that exist in four species: chicken,

cattle, turkey, and swine. These measurements indicated that differences in spectral

reflectance characterized much of the tissue studied. Based on these data,

recommendations are made to avoid the use of light sources with poor color rendering

qualities in the inspection task.

12.

KEY WORDS (Six to twelve entries ; alphabetical order; capitalize only proper names; and separate key words by semicolon s) Chromaticity ; color; color appearance; color rendering; energy-efficient light

sources; illumination; inspection; meat poultry; spectral reflectance.

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