Optical Methods for Nondestructive Quality Evaluation of ... · magnetic spectrum and establishing...

J. agric. Engng Res. (1985) 32,209-241

REVIEW PAPER

Optical Methods for Nondestructive Quality Evaluation of Agricultural and Biological Materials

S. GUNASEKARAN*; M. R. PAULSEN*; G. C. SHOVE*

Literature which reports on optical methods of nondestructive quality evaluation of agricultural and biological materials is reviewed. The basic laws and interaction of light with biological materials are presented with particular regard to their applicability in quality evaluation. A wide range of agricultural and biological materials such as fruits, vegetables, seeds, cereal grains, eggs and meat products are considered. Optical evaluation of maturity, viability, external damage, internal defects and other quality attributes are dealt with. The suitability of each method to successfully indicating defective samples is examined.

The phenomenon of delayed light emission (DLE) by green plant materials and the factors affecting it are presented. The potential of DLE in nondestructively evaluating the quality of fruits and vegetables is discussed and its advantages over conventional spectrophotometric methods are considered.

From the information cited in this review, data on optical properties of each product serving as an index of quality are put together in a single table to serve as a ready reference. Possible future approaches of further research in optical methods of quality evaluation are also discussed.

1. Introduction

The judgement of colour and its faithful reproduction are some of the most important aspects in the commercial and industrial world. The quality of many products may be judged by the colours they display or fail to display. It is particularly critical in cases of agricultural products such as fruits and vegetables, cereal grains and processed foods. Human evaluation has been the primary method of quality assessment for operations such as grading and sorting of food products but such an evaluation can hardly provide a general standard on a large scale and wide range of operations. Factors such as eye fatigue, lack of colour memory, variations in colour dis- crimination between individuals, personal bias and variations in lighting conditions greatly influence the decision of an individual in determining the quality of a certain product. Moreover, the human eye is greatly limited by its perceptions in a very narrow band, the visible region (400 nm to 700 nm), of the vast electromagnetic spectrum. Some quality attributes and external and internal defects are more readily detectable in the region outside the visible range. This has led to considerable research in developing instruments sensitive to a broad band of the electromagnetic spectrum and establishing indices of quality for various agricultural and biological materials. The nondestructive nature of optical methods has made them particularly attractive in on-line quality evaluation involving large number of samples for processing operations.

An attempt is made here to focus on research work where optical properties are known to be of interest. The emphasis in the review was placed on those investigations which have yielded practical results rather than those which have merely employed the optical properties of materials for hypothetical purposes. A large body of information has been examined on different agricultural products to gain insight into the methods and techniques that have already been investigated and

*Department of Agncultural Engmeering. University of Illinois. Urbana, llbnois b1801. U.S.A.

Recctved 5 Oaober 1983: accepted in rewed form 9 May 1984

209 0021~8634/85/070209+ 33 $03.00/O 0 1985 The Bntish Society for Research m Agnculturai Engmeerrng

210 OPTICAL PROPERTIES AGRICULTURAL AND BIOLOGICAL MATERIALS

NOTATION

A absorbance R reflectance c chlorophyll content, mg/lOO gfw s sorting index CIE International Commission for s sugar content, Brix

Illumination T transmittance DLE Delayed Light Emission Y detector response to DLE, V f fruit firmness, dynes difference F fluorescence ; angle, ’ aft grade of peel colour &ii gram fresh weight, g Subscripts Z incidence i incident k absorption coeficient, cm- 1 n normal I thickness of absorber, cm t transmitted LF logarithmic fluorescence parallel LR logarithmic reflectance

g0 perpendicular

n index of refraction 620:

wavelength, nm OD optical density PQI pork quality index 1120 etc. r amplitude coefficient of reflected ray 0 angle, ’

IO* IO=

IO' ommunication IO*

loss I@ Fig. 1. The electromagnetic spectrum (Shortley and Williams’)

SGUNASEKARAN ETAL. 211

the possible future options that exist. Optical methods utilizing the ultra-violet, visible, near infrared and infrared radiation are considered separately as this part of the electromagnetic spectrum is of greater consequence to human activities than any other part. Research on using delayed light emission is also discussed. Investigations employing the X-ray and gamma ray part of the electromagnetic spectrum have not been included as they are considered to be a separate area of research.

2. Electromagnetic spectrum

A brief discussion of the electromagnetic spectrum is presented as background information to give a basic understanding of optical methods employing radiation from different parts of this spectrum. Radiation is one of the basic physical processes by which energy is transferred from one place to another. The propagation of radiation through free space is the same for the entire electromagnetic spectrum. That is, radiation of all wavelengths-from the shortest gamma rays to the longest radiowaves-travels with the same speed in a vacuum.’ The observed radiations in

300 400 500 600 700

Wavelength, nm

Fig. 2. Relative sensitivity of’ the human eye to light of various wavelengths IShortley and Williams’ !

Fig. 3. The radiation incident (I) on the surface of a nonabsorbing material is shown here as either rejected (RI or transmitted (T)


the electromagnetic spectrum are given in Fig. 1. Visible light with a spectral range from 400 to 700 nm (violet to red) constitutes a very narrow band of wavelengths in the total electromagnetic spectrum. The sensitivity of the eye varies within this narrow visible range and under conditions of moderate or strong illumination the eye is most sensitive to yellow-green light of 555 nm. The spectral sensitivity curve Fig. 2 indicates this maximum and the reduced sensitivities at longer and shorter wavelengths.

3. Light: basic laws and interaction with matter

When light is incident on any material three things generally occur: some of the light will be reflected, some may be transmitted through the material and some will be absorbed within the material (Fig. 3). The degree to which these phenomena take place depends on the nature of the material and on the particular wavelength of the electromagnetic spectrum used. Commonly, the optical properties of a material are defined by the percentage of incident light reflected, transmitted and absorbed at each wavelength. Conservation of energy requires that the sum of the reflected (R), transmitted (2’) and absorbed (A) radiation should equal the total incident radiation (I), so that

Z=R+T+A

Reflection from a smooth, polished surface is called specular reflection or regular reflection (Fig. 3). The basic “Law of Specular Reflection” states that the angle at which a ray is reflected from a surface must equal the angle at which it is incident to the surface. “Fresnel Equations” define the phenomenon of regular reflection. The amplitude coefficients of a reflected ray as given by these equations are,*

r =(Wl) ’ cos Bi - [(n2/nI)’ -sin28i]1/2

’ (n2/n1)2 cos oi+ [(n2/n1)2 - sin2Bi]“2

cos Bi- [(n2/n1)2 - sin2 6i]1/2

“=Cos 0i+[(n2/nI)2 -sin2 Bi]112

The regular reflectance R= r2 = (rZI+ r2,,). For normal incidence the distinction between the parallel and perpendicular components vanishes (i.e. r2 = r21 = r2,,) leading to

The direction of the transmitted ray is given by “Snell’s Law”,

n2 sin ot=nI sin Bi

and the “Beer-Lambert Law” defines the attenuation of the transmittance of the ray in a homogeneous, nondiffusing, absorbing medium as

If the surface of incidence consists of miniature bumps (rough surface) the incident light will be scattered in all directions. The “Lambert Cosine Law”,

S. GUNASEKARAN ET AL. 213

Fig. 4. Regular refecrionfrom a rough surface (Birth’)

Fig. 5. D$?use regular rejecfion and diffuse body reflection (Birth=)

defines the distribution of this diffuse reflectance flux. Since some of the incident rays strike the rough surface more than once before being reflected (Fig. 4) it would be expected to have a lower total reflectance than a smooth surface.3

It was commonly believed that the colours of natural objects are seen by means of light reflected from the surface. Birth4 recognized that the light must be transmitted through the pigment within the cells in order to produce a coloured appearance. The light so transmitted through the object encounters randomly oriented internal interfaces and is reflected back. A fraction of this reflected radiation is transmitted back through the initial interface (Fig. 5) and is named body reflectance.’ The body reflectance is nearly always diffuse and is the most significant form of reflectance for paints, paper and foods. 5 This phenomenon of random reflection and refraction at the numerous internal interfaces is called “light scattering”.

In biological materials the body reflectance is of far more significance than the regular reflectance. Normally equipment for quality evaluation of biological materials measures total reflectance, which is the sum of the regular reflectance and body reflectance. Hence to measure the body reflectance as an indication it is necessary to ensure that the regular reflectance does not predominate.


4. Measurement systems

4.1. Measurement of ligh t Measurement of reflected and transmitted light is not difficult, however, the absorbed energy is

not available for measurement. An indication of absorbed energy can be obtained by measuring all the nonabsorbed energy and by applying the principle of energy conservation. A common expression for absorption is absorbancy, A, defined as

A=log,, I/T

Optical density, a far more commonly used expression, has the same definition but is used for applications where the transmitted ray is attenuated by both geometrical means (scatter) and absorption. Transmittance, T, is defined as

T= I,/I

Measurement of transmitted or absorbed energy is more important than reflectance measurements in certain quality evaluation problems (internal colour, defect detection, etc.). Kramer and Smith6 were the first to apply transmittance spectrophotometry to practical problems in measuring the stage of fruit maturity by making measurements on ethyl ether extracts of macerated fruit. Norris’ was the first to apply the light transmittance technique to whole intact fruit to indicate changes in internal colour and to follow maturation of fruits and vegetables. Later this technique became widely popular for internal quality evaluation.8- ’ 2 Sarkar and O’Brien’ 3 explained three approaches to analyse the optical transmittance through a fruit (tomato). The first one was based on the classical electromagnetic wave theory. Fruits contain several absorbing materials and application of this theory was more difficult as the number of material boundaries increased. Moreover, measurement of the material parameters for each composition of the fruit cannot be very reliable so an accurate prediction of the transmission phenomenon was not possible. The second approach was based on Kubelka-Munk theory14 which takes the scattering factor into account. Because of the highly complex nature of the fruit the experimental results did not agree very well with the equations derived from this theory. However, they observed that a third approach, called the systems approach, was helpful in analysing the transmitted light by considering an overall model of the fruit as a complex system.

In general the reflectance data are expressed as a linear scale, i.e. percent reflectance, while diffuse transmittance is frequently expressed as optical density.3 The advantages of using an optical density scale are:

(1) the analysis is simpler, i.e. an optical density difference is equivalent to a transmittance ratio. Differences are easier to compute;

(2) the logarithmic plot permits a wider range of intensities, i.e. a transmittance scale of 1 to 100 is two orders of magnitude, while an optical density scale may cover five orders of magnitude;

(3) there is a linear relationship between optical density and the concentration of an absorbing substance.

4.2. Establishing quality indices The success of establishing a valid index of quality depends largely on the appropriateness of

the optical property measured. Powers et al.16 stated the following basic requirements in selecting the optical property for best results with particular reference to maturity evaluation:

(1) the magnitude of relative measurement should vary as greatly as possible over the full range of maturation;

(2) the change in the measurement between consecutive stages of maturity should be great enough to permit precise colour differentiation.

Apart from the above requirements, the nature of the measurement criterion should also be


formulated judiciously so as to permit measurements insensitive to variations in the object size, shape, glossiness, the light source intensity, sensor sensitivity and amplifier gain. The measurement criterion has taken various forms and can be grouped into the following four classes.

(1) Single wavelength measurement-in which the optical property of the object at a particular wavelength is considered as an index.

(2) Difference measurement-in which the change in the optical property of the object at two different wavelengths is measured. This is an indication of the average slope of the curves in the region between the two wavelengths. The region should be chosen so as to obtain maximum change in slope of the curves representing the measurement. The effects of the object and apparatus variations are largely attenuated in this kind of measurement.‘6

(3) Ratio measurement-in which the ratio of the optical properties at two different wavelengths is the chosen criterion. This is, in general, independent of the sensitivity of the measuring instrument.

(4) Combination measurement-in which any combination of the above categories is used. For example, Birth et cd.* suggested a criterion of the form (T,,,- T520)/(T620 + T,,,) to predict the colour of tomatoes.

The following is a review of these procedures in relation to their appropriateness to agricultural and biological materials. A comprehensive list of the most significant investigations which have employed optical methods is presented in Table 1.

5. Fruits

In general, fruit quality is associated with the development of characteristic varietal flesh and skin colour. Since these colours change visually during the maturation of fruits, it should be possible to establish maturity standards on the basis of colours.”

Various fruits exhibit different physiological and chemical changes as they mature. Most common to all and perhaps the most obvious and significant is the loss of green colour. This involves the degradation of chlorophyll, resulting in the exposure of the carotenoids giving a characteristic maturity symptom of yellowing. ‘* Red colours are often observed due to forma- tion of anthocyanins, as in certain varieties of apples, or to the presence of lycopene, as in tomato. Table 2 lists the 10 different ripening stages of tomatoes and the corresponding changes in the various pigments.ls

According to Willstatter and Stoll, 2o in a colloidal solution of chlorophyll the light absorption bands are displaced toward the red end of the spectrum having its median at 678 nm. Conse- quently, due to loss of chlorophyll as the fruits mature they exhibit reduced absorbance (increased reflectance) in the 670 nm region. This has been the single most important criterion in optically judging the fruits’ quality. *,’ 1’15,2’,22 In cases where skin colour is not a true represen- tation of fruit quality or would not sufficiently indicate the stage of maturation, internal flesh colour served as an index of quality. Such measurements were also obtained nondestructively through optical methods.8,23

5.1. Apples In apples major changes in spectral characteristics were found to occur in the visible region. As

the fruits mature the percentage of reflectance increases at about 676 nm and greatly decreases between 400 and 600 nm.“~‘* The reflectance values are low in the ultra-violet region and are likely to be in error because of the influence of fluorescence, particularly from chlorophyll. In the near infrared range the reflectance would not change as the water content in the skin is not likely to change. For maturity indication a ratio measurement, R580/R620 is the best for red varieties of apples22 and an optical density difference, AOD (740-695 mm) for Golden Delicious, Stayman, Red Rome and Winesap varieties.24

Mat

eria

l an

d de

scri

ptio

n

Frui

ts

App

les D

elic

ious

Gol

den

Del

icio

us

Stay

man

R

ed

Rom

e W

ines

al

Jona

than

M

cInt

osh

Red

D

elic

ious

Gol

den

Del

icio

us

McI

ntos

h Jo

nath

an

Gol

den

Del

icio

us

McI

ntos

h R

ed D

elic

ious

Y

ello

w

Del

icio

us

Ban

ana Cav

endi

sh

Blu

eber

ries

Gra

pefr

uit

Mar

sh

Thom

pson

Le

mon

Bea

rss

Nec

tarin

es

Ora

nges

H

amlin

Pine

appl

e

TA

BLE

1

Opt

ical

ind

ices

for

var

ious

qua

lity

eval

uatio

n pr

oble

ms

in a

gric

ultu

ral

and

biol

ogic

al m

ater

ials

*

Qua

lity

eval

uati

on

prob

lem

Wat

er

core

Mat

urity

Mat

urity

Mat

urity

Bru

ise

Bru

ise

Stem

an

d ca

lyx

Mat

urity

, fir

mne

ss

and

suga

r co

nten

t M

atur

ity

Frui

t re

lativ

e co

lour

Su

rface

de

fect

s M

atur

ity

Surfa

ce

defe

cts

Mat

urity

Frui

t re

lativ

e co

lour

Su

rface

de

fect

s

Frui

t re

lativ

e co

lour

Su

rface

de

fect

s

Opt

ical

ind

ex

esta

blis

hed-

f

OD

760

4.8

OD

810

AO

D

(740

-695

)

R,s,

IR,,,

R,,,

-.520

R,,,

-8,”

DLE

AO

D

(74&

800)

Indi

cati

ve s

ubst

ance

m

easu

red

Phys

ical

ch

ange

s in

app

le

tissu

e

Chl

orop

hyll

Chl

orop

hyll

and

caro

teno

ids

Chl

orop

hyll

and

caro

teno

ids

Phys

ical

ch

ange

s in

app

le

tissu

e

Phys

ical

ch

ange

s on

app

le

surfa

ce _

Ant

hocy

anin

Dis

colo

ratio

ns

caus

ed

by d

efec

t C

hlor

ophy

ll an

d ot

her

pigm

ents

Dis

colo

ratio

ns

caus

ed

by d

efec

ts

Dis

colo

ratio

ns

caus

ed

by d

efec

ts

Dis

colo

ratio

ns

caus

ed

by d

efec

ts

Corr

elat

ion

of

mea

sure

men

t

Vis

ual

ratin

g of

cut

ap

ple

Chl

orop

hyll

CIE

ch

rom

atic

ity

valu

es

CIE

ch

rom

atic

ity

valu

es

Vis

ual

ratin

g

Vis

ual

ratin

g V

isua

l ra

ting

Han

d so

rting

Vis

ual

ratin

g

Solu

ble

solid

s an

d pa

lata

bilit

y

Vis

ual

ratin

g V

isua

l ra

ting

Vis

ual

ratin

g V

isua

l ra

ting

Deg

ree

of

succ

ess f

0.90

0.96

0.99

lefe

renc

e

Val

enci

a

Satsu

ma

Mat

urity

DL

E Pa

paya

M

atur

ity

OD55

0-67

5

Peac

hes Elbe

rta

LeG

rand

s J.

H.

Hal

es

Elbe

rta

Rio

O

so

Gem

M

.A.

Bla

ke

Red

Sk

in

Mat

urity

R

io

Oso

G

em

Mat

urity

R

ange

r M

atur

ity

Pear

s Mic

higa

n 43

7 M

atur

ity

Plum

s Prun

e ty

pe

Prun

es Drie

d,

Sant

a C

lara

Firm

ness

Def

ects

: sc

ab,

skin

da

mag

e,

end

crac

k,

side

cra

ck,

expo

sed

pit

deca

y M

atur

ity

Stra

wbe

rrie

s

Tom

atoe

s R

utge

rs

Cal

M

art

Cal

A

ce

Cas

tlem

art

Roy

al

flush

H

omes

tead

Frui

t re

lativ

e co

lour

R 66

" Su

rface

de

fect

s R5

70-6

00

Mat

urity

A

OD

(7

0&74

0)

Chl

orop

hyll

Mat

urity

T7

30/T

sso

Mat

urity

~m

J4a,

Inte

rnal

co

lour

Mat

urity

M

atur

ity

Mat

urity

Mat

urity

M

atur

ity

Gre

en

to r

ipen

ing

Bre

aker

to

red

rip

e

‘%,o

I’L,

OD61

5

OD67

0

Col

our

of

extra

cted

ju

ice

Stat

istic

ally

pr

edic

ted

red/

gree

n ra

tio

(75%

w

ithin

fir

st

IO d

ays)

OD

730

R

ed-/g

reen

ra

tio

0.87

12

AOD(

51G6

00)

Chl

orop

hyll

AO

D

(600

69

0)

Lyco

pene

Dis

colo

ratio

ns

caus

ed

by d

efec

ts

Cha

nge

in i

nter

nal

flesh

col

or

Red

an

d ye

llow

pi

gmen

ts

Ave

rage

sk

in

colo

ur

Ave

rage

sk

in

colo

ur

Frui

t fir

mne

ss

Chl

orop

hyll

Phys

ical

ch

ange

s in

pru

ne

surfa

ce

Perc

enta

ge

of

surfa

ce

that

is

red

Lyco

pene

an

d ch

loro

phyl

l

Red

/gre

en

ratio

R

ed/g

reen

ra

tio

Red

/gre

en

ratio

Vis

ual

ratin

g V

isua

l ra

ting

Chl

orop

hyll

cdro

teno

ids

and

solu

ble

solid

s

Log

chlo

roph

yll

cont

ent

Solu

ble

solid

s an

d pa

lata

bilit

y

CIE

chro

mat

icity

va

lues

Pane

1 ra

ting

Pane

l ra

ting

Mag

ness

-Tay

lor

punc

h te

st

CIE

chro

mat

icity

va

lues

Han

d so

rting

0.

83

41

Ber

ries

mor

e th

an

80%

re

d

0.96

0.92

0.84

0.

76

0.72

0.80

0.95

0.98

0.

80

0.84

8

72

TAB

LE 1

Cont

inue

d

Mat

eria

l an

d de

scri

ptio

n

Vege

tabl

es

Pota

toes

Ir

ish

Cob

bler

K

atah

din

Rec

ord

Dun

bar

Rad

ish R

ed

Prin

ce

Cere

algr

ains

B

arle

y C

orn

Yel

low

Qua

lity

eval

uati

on

prob

lem

Inte

rnal

di

scol

orat

ion:

ho

llow

he

art,

blac

kspo

t, gr

eeni

ng

Diff

eren

tiate

fro

m

soil

clod

s

Sorti

ng

from

st

ones

an

d so

il cl

ods

Dis

ease

s an

d de

fect

s:

com

mon

sc

ab,

gang

rene

, bl

ight

, sk

insp

ot,

dry

rot,

gree

ning

, B

acte

rial

soft

rot

Dis

ease

s:

gang

rene

, dr

y ro

t

Def

ects

: do

wny

m

ildew

of

f co

lour

s of

whi

te,

pink

an

d lig

ht

purp

le

Bla

cksp

ot,

soft

rot

dull

whi

te,

gree

n le

af s

plit

Gra

in

adm

ixtu

re

Ker

nel

dam

age

Opt

ical

ind

ex

esta

blis

hedt

AO

D

(7 1

O-8

00)

(&,,a

-,,a/

R

,,,+w

,,)

(R

- 60

0 13

00 I

R 1

500-

2400

1

R%

X-8

90

Rw

- 75

0

Indi

cati

ve s

ubst

ance

m

easu

red

Bro

wn

subs

tanc

es

in t

he v

icin

ity

of

void

s

Phys

ical

or

ch

emic

al

chan

ges

brou

ght

abou

t by

ea

ch

defe

ct

Surfa

ce

chan

ges

and

wat

er

loss

Surfa

ce

off

colo

urs

Surfa

ce

off

colo

urs

Inte

rnal

w

hite

fle

sh

Corr

elat

ion

of

mea

sure

men

t

Han

d so

rting

Vis

ual

ratin

g

Vis

ual

ratin

g

Off

icia

l m

etho

d of

dam

age

dete

rmin

atio

n

Deg

ree

of

succ

ess$

1.0

0.8

0.9

0.90

-0

a Se

fere

nce

2 n -z

Yel

low

Y

ello

w

oats

R

ice

We

Soyb

eans

W

heat

Whe

at Fl

our,

Bra

n D

urum

H

ard

Red

Sp

ring

Har

d R

ed

Win

ter

Soft

Red

W

inte

r W

hite

Egg

and

mea

l pr

oduc

ts

Bee

f G

roun

d G

roun

d

Egg

Pork

Long

issim

us

mus

cle

Mis

crila

neou

s pr

oduc

ts

Firs

eeds

W

hite

Gra

ss M

elle

M

eado

w

Fesc

ue

Rye

gra

ss

clov

er

Sabr

ina

Gai

n ad

mix

ture

M

ould

co

ntam

inat

ion

Gra

in

adm

ixtu

re

Deg

ree

of m

illin

g D

egre

e of

mill

ing

Deg

ree

of m

illin

g

Gra

in

adm

ixtu

re

Gra

in

adm

ixtu

re

Smut

co

nten

t Sm

ut

cont

ent

Moi

stur

e co

nten

t G

rain

ad

mix

ture

G

rain

ad

mix

ture

G

rain

ad

mix

ture

G

rain

ad

mix

ture

G

rain

ad

mix

ture

Fat

cont

ent

Fat

cont

ent

Blo

od

spot

s B

lood

sp

ots

Ferti

lity

Mus

cle

qual

ity

Mus

cle

qual

ity

Via

bilit

y

Moi

stur

e co

nten

t

R,,O

-700

AL

F(44

2-60

7)

AO

D

(194

&20

80)

RS1

0-70

0 J&

0-69

, B

,,,

- ,o

o R

,,,-

700

Ru,

- 57

0

R 93

7 -

Rw,

R,,,

+ R

,,,

1565

-585

A

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65

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67

68

70

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86

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Con

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d

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n

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rt

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uatio

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TABLE 2

Chlorophyll and carotenoid compositions of “Walter” tomato fruit categorized by absorbance (A) readings* (Watada et al.19) - _

1 ?

5 4 5 6 7 8 9

IO

l- Total

chlorophyll pglgfbtl

13.4 1 I.2 8.6 5.9 4.2 1.0 0.4 0.3 0.3 0.3

Lycopene, flccgldKJ

0.2 0.6 2.5

10.1 16.7 22.8 34.8 46.7

p-carotene, fig/@

y-carofene, Pgg,‘&

e-carotene, Pcglgf”’

b-zeacarotene, figI&

0.7 0.03 0.04 0.05 0.02 0.7 0.03 0.04 0.05 0.03 1.0 0.04 0.05 0.03 0.03 1.3 0.12 0.09 0.04 0.05 1.8 0.26 0.07 0.05 0.04 3.1 0.54 0.15 0.08 0.06 3.3 0.87 0.12 0.17 0.12 3.7 0.90 0.12 0.21 0.18 3.1 0.97 0.08 0.42 0.21 2.9 l-13 0.11 0.89 0.29

Pigment contenl

*Awage of three samples. each containing live tomatoes

Some of the external and internal apple defects were optically detected. Bruising on apples is a quality defect and is often a major problem in grading operations.25,26 In an earlier investigation, Rehkuglerz5 made use of the surface discontinuities in apples due to bruising as a means of detecting the presence of bruises. Light reflectance measurements indicated that bruised apple skin exhibited less average reflectance than an unbruised skin at all wavelengths between 700 and 2200 nm.26 It was thought that reflectance for a bruised surface is less than that for an unbruised surface due to a combination of cell destruction (fewer rigid cell walls to scatter light) in the bruise, an altered water-air relationship in the tissue, and a gradual chemical change in the cell materia1.26,27 A difference in reflectance for wavelengths at 800, 1200 and 1700 nm or ratio of reflectance (unbruised/bruised) at some wavelength between 1400 and 2000 nm may be useful in optical bruise detection.

Reid28 studied the typical reflectance properties of apple skin ripe, unripe, bruised, peeled, calyx and stem (Figs 6 and 7) and concluded that a detector sensitive in the wavelength range 4OCF450 nm could be used for bruise detection and one sensitive in the 725-800 nm range for stem and calyx detection.

Water core in apples is a heat initiated disorder. In the affected tissue water replaces air in intercellular spaces giving a translucent or water soaked appearance. Birth and 01sen16 developed a rapid nondestructive technique of detecting such defects in Delicious apples. Since the air spaces are eliminated in the water-cored tissue it scatters less light but transmits much more than the normal tissue (Fig. 8). At about 800 nm there are relatively few substances in normal apples that absorb energy so it was considered to be the best wavelength region to identify water-cored apples. An optical density difference, AOD (760-810 nm) gave the best results. This measurement has to be changed if apples are subjected to varymg temperature conditions to allow for the temperature changes in the optical density measurement at 810nm. Table 3 summarizes five different methods for detecting water-cored apples.

5.2. Peaches One of the earliest works on peaches was by Rood.2s He recognized that there was too much

variation in skin colour to permit a single standard for all varieties and investigated the physical,


90 t 1

Ll 600 750 700 650 600 550 500 450 400 350

Wavelength, nm

Fig. 6. ReJecrance of Red Delicious apple surface parameters. A.B, Ripe skin; C,D, bruised cheek; E,F, undamagedJIe& (Reid-)

-i 60

s 50 t ; 40

L 30

20

IO

0 600 750 700 650 600 550 500 450 400 3 io

Wavelength, nm

Fig. 7. Reflectance of Red Delicious apple surface parameters. A.B. Ripe skin; C,D. stem; E, calyx; F, polished, dewaxed. wetted; G. half ripe skin (Reid-)

chemical and physiological characteristics of peaches. The following is the list of maturity indices of peaches in order of their importance: (1) pressure test readings, (2) ground colour of skin, (3) flesh colour, (4) chlorophyll content of the flesh, (5) titrable acidity of the juice, and (6) percentage of soluble solids in the juice. Besides an increase in reflectance in a fairly narrow wavelength band at 675 nm, Sidwell et a1.2’ also reported a transmittance peak at 545 nm for fresh Elberta peaches. But colour differences among varieties of peaches are likely to cause differences in the wavelength of peak transmittance, which might not specifically be related to the stage of maturity. Fig. 9 represents the optical density values from Elberta peaches at four different stages of maturation. In general, measurement in the 700-850 nm range has been proposed as that band is fairly stable, insensitive to chlorophyll and correlates well with maturity.21*30 The maturity index AOD (70&740 nm) yielded very good correlation with eating quality of fruits2’

The value of the transmittance peak ratio T730/T850, equivalent to 0.8 and 0.9 was found to separate respectively the JH Hales and Le Grands variety of peaches into predominantly mature


4.6 - I

~<“/q, U ,/

8 \/ /

z 3.6 - 0

3.4 -

3.0 I I I 650 700 750 800 E 0

Wavelength, nm

Fig. 8. Optical density curves of a water-cored apple (A) and a sound apple (B) (Birth and 0lser1’~)

TABLE 3

Potential methods of detecting water core in apples (Birth and 01senq6)

1

2

3

4

Method Measurement

Water-core index with K=O,8 OD 760-0.8 (OD 810 nm)

Water-core index with K= 1 ,O AOD(760-810nm)

Optical density

Maturity index

OD 760+690 nm ?

Specific gravity

i AOD (74&690 nm)

Weight

Weight + buoyant force

600 700 600 9

Wavelength, nm

Fig. 9. Optical density curves offour intact Elberta peaches at progressive stages of maturation. 1, Typicalfruit harvested 3 August; 2,17 August; 3.21 August; 4,26 August, tree ripe stage (Sidwell et aL2’ j


and immature groups30 Other reported criteria for judging peach maturity based on skin colour include: transmittance ratio of 700 and 740 nm,31 reflectance ratio using one wavelength in the 550 to 590 nm region and another in the 600 to 640 nm region,22 R,,,IR,,, for Red Skin, Rio Oso Gem varietieP2 and R,,,/R,,, for Ranger variety.33

5.3. Tomatoes It can be judged from the previous pages that skin colour has predominantly served as the

index of maturity of various fruits. However, for fruits like tomatoes to be processed into puree, juice or canned the internal colour may be far more important than the skin colour. For such cases optical techniques have been developed to nondestructively determine the internal flesh colour of tomatoes as a measure of quality.8

100

0.01

Wavelength, nm

Fig. 10. Spectral transmittance curvesfor tomatoes. I, Green; 2, red-green; 3, red [Birth et al.‘)

The light absorption in tomatoes is considered to be due to chlorophyll in green and lycopene in red fruits.34 Green tomatoes were reported to have minimum transmittance at 670 nm and this was increased by more than 100 000 times as they matured to the red stage, corresponding to loss of chlorophyll (Fig. IO). On the other hand, an equivalent relative decrease in transmittance at 550 nm was observed corresponding to increase in lycopene, the red pigment. Watada et all9 estimated chlorophyll and carotenoid and other pigment contents for whole Walter tomatoes and classified them into 10 different categories. As observed in Table 2 the average chlorophyll content decreased from 13.4 ug/gfw to 0.3 ug/gfw as the fruit matured from stage 1 to stage 10. The lycopenes show an increase from O-2 pg/gfw to 46.7 ug/gfw from stage 3 to stage 10. The other carotenoids show a less pronounced increase. Linear correlations were given for absorbance readings and pigment content:

AA (710-780 nm) =0.0178+0.0380 chlorophyll

AA (570-780 nm) = 0.604 + 0.0431 lycopene

The measurement Tsz0/T67,, was found to change in the order of 30 : 1 as the tomato ripened from yellow to full red colour.* The wavelengths 670 and 620 nm were chosen because at the for- mer the spectral transmittance change is the greatest as the fruit develops more red colour and at

S. GUNASEKARAN ET-AL. 225

the latter the transmittance value is high enough so that an extremely sensitive instrument is not necessary. Likewise, the ratio T52,,/T545 was chosen for distinguishing externally green tomatoes with internal amber colour from tomatoes that are green throughout. Combining the above two criteria a new ratio (Te7,,- T520)/(T620+ T& was proposed to indicate internal colour of any tomato. This new index gave a very high correlation (O-95) with the colour of the extracted juice.

Based on AOD (510-600 nm), Worthington” separated tomatoes into 10 different ripening classes. It was noticed that the relative value of the above measurement increased to about 1.9 as the fruits matured from green to red and then decreased. He attributed the decrease in the latter stages to the development of internal pink colour.

Light transmittance through an intact green tomato is very difficult to detect. Worthington et al.,’ ’ in their investigation found over 60% of the newly harvested tomatoes too dense to measure light transmittance. However, Nattuvetty and Chen” and Chen and Nattuvetty35 used a high density spectrophotometer with a fibre optic attachment to measure light transmittance through a small region of an intact fruit. They defined maturity as the number of days required for a fruit to attain a pre-specified surface colour when kept in a ripening room at 20°C. Optical densities at 500, 615, 670 and 730 nm were correlated with the ripening rate of tomatoes. The effects of heterogeneity of the fruits were minimized by taking the average for four observations around the fruit. A high sorting efficiency for Calmart and CalAce varieties was obtained at 615 nm, for Royal Flush at 730 nm and Castlemart at 670 nm.

5.4. Oranges and grapefruit Felsenstein and Mano? studied a few surface defects of oranges that are characterized by

change in colour. They reported that within the vicinity of 667 nm there was a difference of at least 17% in the intensity of light reflectance from the surface of a good orange and one with blemish. In a more elaborate research Gaffney3’ investigated fruit grade colour and surface defects like plug, oleocellosis, rots, moulds, windscar, scabs, thorn scratches of the Hamlin, Pine- apple and Valencia varieties of oranges and Marsh and Thompson varieties of grapefruit. The wavelength band 550 to 750 nm was sensitive to change in surface colour or nature due to various defects. A detailed listing of the wavelength bands of interest for individual defects is presented in Fig. II.

Hmnlh7 omng, Simulated plug Lota rust ma Smooth rmdscor Thorn scratch Olroc.llosis Snsn CObW Pmn*aPple orong* Simulotad plug Late rust mite Smooth rindrcar Eorty rust mite vota7c1a omng* Simutotedplup Loto rust mite Smooth rmdrear Edyrust null Thorn rcrotch outer mambrone Gwen cOlo”r Marsh. Thomrson

--__

L- __-- -_ - _--

-___ - %r ----___ ___--- -----__ ___

Wavelength, nm

Fig. II. Wavelengths suitable for detecting certain defects on Hamlin. Pineapple and Valencia oranges; Marsh and Thompson grapefruit and Bearss lemons. Solid and dashed lines indicate wavelengths for which defects reflect at least 15% less and 15% more light than normal fruit respectively. Hatched regions indicate single wavelength regions suitable .for

detecting most defects (GaffneyJ’)


5.5. Otherfruits Maturity and other quality indices for fruits such as banana,3* blueberries,3g 1emons,‘5*37

nectarines,30 papaya,23 pears,** strawberries40 were also established. In plums, Romani et ~1.~~ observed a general increase in absorption in the 500 to 600 nm region and a decrease in absorption at 680 nm with increasing maturity. Neither of these changes was found adequate to be a maturity index.

Optical measurements are also indicative of fruit firmness. For example light transmittance values at 539 to 587 nm correlated highly with loss in firmness of prune type plum9 and the light reflectance ratio R,,,/R,,, yielded a correlation of 0.72 with firmness of Ranger variety peaches.33 The damage and defects of dried prunes such as scab, sidecrack, skin damage, end crack and exposed pit were optically detected by Burkhardt and Mrozek.” They noticed that except for skin damage all other defects appeared lighter than the sound prune skin. The reflectance characteristics at 1300, 1700, and 2 100 nm are suitable for sorting dried prunes. If changes in colour of prunes upon storage is taken into consideration measurements at 1700 nm are the best index.

6. Vegetables 6.1. Potatoes

Fewer investigations to establish optical quality evaluation standards have been carried out for vegetables than for fruits. In potatoes it has been observed that the normal optical properties are modified in consistent ways in the presence of certain diseases and defects.42-44 The optical density difference at 810 and 710 nm was suggested as a nondestructive criterion to detect hollow heart in potatoes based on the brown substances in the vicinity of the void.” This measurement is also capable of indicating other discolorations of potatoes like black spot and greening. Recently, Porteous et ~1.~~ identified the diffuse reflectance at wavelengths between 590 and 890 nm and the bands near 1100 and 1400 nm as being most significant in detecting a number of diseases and defects such as: bacterial soft rot, blight, common scab, dry rot, gangrene, greening and skin spot (Fig. 22). The wavelengths suggested for detecting these defects in their order of importance are: 650, 710, 1410, 630, 750 and 830nm. Muir et al.‘” in a similar investigation

Wavelength, nm

Fig, 12. Spectral differences between healthy and severely affected areas ofpotatoes. 1, Skin spot; 2, dry rot; 3, gangrene; 4, common scab; 5, blight; 6, greening; 7, bacterial soft rot (Porteous et al:‘)

SGUNASEKARAN ETAL. 227

I I I I I I I

300 600 900 1200 1.500 1600 2100 2400 Wavelength, nm

Fig. 13. Spectral wflectunce ofpotatoes (I), stones (2) and soil clods, (3) (Story and HoNow~ay4’)

60, I

011 300 400 500 600 700 600

Wavelength,nm

Fig. 14. Comparison of light wflectance from downy mildew (I) and soft rot (2) on radish surface and that,fiom normal radish surfaces (3) ( GaffneySO)

remarked that diseased tubers indicated a progressively reduced diffuse reflectance for all cases at shorter wavelengths up to about 800 nm. At wavelengths greater than 1100 nm, where absorption by water dominates, increases in reflectance were reported due to dehydration in the affected area. With dry rot, however, simultaneous changes over the whole wavelength range were observed presumably due to the progressive loss of water as the disease advanced. The wavelength bands between 590 and 750 nm and the bands near 950, 1150, 1350, 1470 and 1850 nm were found useful in detecting various diseases studied.

The reflectance of light and infrared radiation by potatoes, stones and soil clods offers a possibility of separating them. Palmera reported that a red/blue ratio, (Rsoo to R900)/(R325 to R,,,), as a highly successful criterion for differentiating potatoes from soil clods. This ratio was found to be unaffected by size of the objects and their distance from the sensors. Story and Hollowayd6 verified this result and investigated the infrared radiation properties of potatoes, stones and soil clods. Their results (Fig. 13) indicated that potatoes in common with other plant materials show a higher reflectance over the 600 to 1300 nm region and lower reflectance outside this region. Accordingly the ratio of the reflectance in the range 600-l 300 nm to that in the range


1500-2400 nm was found to be a more distinctive indicator than the red/blue ratio used by Palmer.“5 An automatic sorting system was also developed based on this criterion.“’

6.2. Radishes Optical quality evaluation standards were established to sort Red Prince radishes based on

various defects namely, split, cut, black spot, soft rot, green stem, green leaf, downy mildew, dull white and off colour.4* The most apparent differences between the reflectance from a normal radish surface and from the defects were observed at wavelengths below 800 nm. Specifically, surface cuts, downy mildew and off colours of white pink and light purple were detected at 550 nm and the other defects, black spot, soft rot, dull white, green leaf etc. at 675 nm. Fig. 14 represents the reflectance curves for normal and downy mildew and soft rot infected radishes.

7. Cereal grains Hawk et aZ.Os reported reflectance characteristics of 12 different grains: hard red winter wheat,

hard red spring wheat, white wheat, durum wheat, white oats, barley, rye, yellow grain sorghum, yellow soybeans, yellow corn and flax. The different reflectance properties were used in evaluating grain samples for admixture grain, i.e. the grains other than the primary one. The results indicate that all the grains have a fairly constant reflectance of 5% across the entire ultra-violet

I 0 t . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . *...’

hkTh7k7 600 Wavelength, nm

Fig. IS. Spectral absorption curves of corn. I, Sound corn; 2,15% damage; .?,30% damage (JohnsorP)

$ 2.0 - ._ s z .k l-6- B & E 1*2- 0 : z 5 0.8 - 3

E

r c 0.4- 6 B -1 0 I I I I

0 400 500 600 700 Wavelength, nm

Fig. 16. Spectral$uorescence of sound (I) and mould contaminated (2) yellow corn (Birth and JohnsoP)


TABLE 4

Effectiveness of four optical techniques for separating 20 kernels of corn into two groups (Sound and mould-contaminated)* (Birth and JohnsoP)

Technique Measuremenl Effecfiveness, %I

I I Fluorescence Total fluorescence Transmittance Reflectance

ALF(442Z607 nm) 2.5 LF(visible) 15.0 AOD (775.-725 nm) 16.0 ALR (675~-750 nm) 21.0

*For defimtions of ,ymbolr m this Table see “Notation”

iI_he tiact~~~ of the contaminated kernels which are classltied as sound, using Ihe 95% hmlt of the \ound corn

region of the spectrum. The difference in reflectance between grains in the infrared region is small and the greatest differences occur between 450 and 700 nm. The wavelength bands that could theoretically be used to separate grain samples from admixture grains are given in Table 1.

7.1. Corn Johnsons used an absorbance measurement (A,,,- A,,,) in determining heat damaged,

sprouted, frosted, badly ground-damaged, badly weather damaged yellow corn kernels. The absorption spectra of yellow corn (Fig. Z.5) indicates larger differences between damage groups in the 650 to 750 nm range than in the 800 to 1000 nm range. However, the latter range was chosen for measurement to minimize the effect of natural colour differences in the kernels. Quantitative measurement of light transmission through corn endosperm was applied in separating opaque and normal kernels5’ The opaque kernels were reportedly more nutritious than the normal kernels.52,53 According to Paez et c11.,~’ the normal kernels allowed up to 0.67% light transmission (450-560 nm) against essentially no transmission in the opaque type. The kernel thickness was found to have an effect in reducing the percentage of light transmitted. There was. however, no difficulty in separating the kernels of different moisture content.

Mould contamination of yellow corn was detected by a nondestructive optical technique developed by Birth and Johnson. 54 This was based on the fact that the natural fluorescence from the corn was reduced when moulds were present (Fig. 16). They studied four optical properties, total fluorescence and the spectral characteristics of fluorescence, transmittance and reflectance. The best results were reported for a difference measurement of logarithm of fluoresced energy at 442 and 607 nm. Besides, this criterion takes advantage of the change of shape of the spectral fluorescence curve as a function of mould contamination. Table 4 gives the effectiveness of different optical techniques they investigated.

7.2. Wheat Smut content of wheat was also optically indicated by light transmission techniques.55,56

Birth55 observed that the slope of the transmittance curves between 750 and IOOOnm was in direct proportion to the amount of smut on the wheat sample. Excluding the water absorption band at 970 nm, optical density difference at any two wavelengths about 100 nm apart in the above range could be indicative of the total smut content. Johnson 56 investigated five different methods based on light transmittance, sedimentation, catalase activity, light reflectance and light absorption. The measurement of AOD @O&930 nm), equivalent to a transmittance ratio measurement (7’So0/T930) was found to be the best criterion.

Reflectance and transmittance of grain samples do not change greatly with moisture contents7 Nevertheless, the water absorption bands at 760,970, 1180, 1450 and 1940 nm were investigated


for spectrophotometric measurement of grain moisture content.58 The measurement of AUD (1940-2080 nm) closely predicted the moisture content of ground wheat samples. In general, it was observed that it should be possible to measure the moisture content of a wide range of materials using the absorption band at 1940 nm. The main requirement is a uniform thin sample of l-3 mm thick without interfering absorption bands. For moisture contents greater than 20% the absorption at 1940 mm becomes difficult to measure so the 970 nm band should give greater accuracy.58

7.3. Rice Most of the work with rice kernels reported the use of optical techniques for determining the

degree of milling.50~60 Degree of milling, a measure of the extent to which the germ and bran layers have been removed from the endosperm, is one of the principal factors in determining the grade of rice. Extensive milling is required for complete removal of the germ and bran layers which would consequently result in an increased percentage of broken kernels. Chemical methods such as that used for estimating percentage loss of phosphorouP’ and fat contenP* were used as indices for the degree of milling in rice. Stermer et al,59 reported the spectral transmission of rice with various degree of milling. Greater changes were observed at 660 nm and at 850 nm, accordingly the ratio measurement Ts50/T66,, was suggested as the criterion. In another method, the fact that the protein as well as oil is the greatest in the outer layers of rice was made use of in establishing a criterion indicative of degree of milling of rice.6o The oil absorption bands, 928, 1215 and 1725 nm were found to be highly correlated with surface and total lipids. Higher correlation with total lipids (0.85) than with the surface lipids (0.58) was observed presumably because the infrared energy penetrated the rice kernel sufficiently to be absorbed by all the lipids present in the kernel.

Normal rice kernels are vitreous and highly translucent but in abnormal kernels these properties are impaired to some degree. Beerwinkle and Stermer 63 utilized this translucence difference between normal and abnormal kernels to sort milled rice optically. With this feature included, the efficiency of a conventional rice sorter improved by 50 to 70%. StermeP developed objective measurements of the colour of milled white rice which could be used as an indicator of rice grade, the degree of parboiling and the extent of starch gelatinization in rice.

.

8. Eggs and meat products 8.1. Eggs

One of the earliest works on eggs was to detect blood spots, the most common defect in eggs. Brant et ~11.~~ identified three absorption bands of blood, at 415, 541 and 575 nm, in the visible region. Their method of detecting blood spots in white shell eggs was based on the relative transmittance measurement between 565 and 585 nm (Fig. 17). Though a success rate of 97.5% was reported the measurement was specific to the colour of the eggshell. Later, Norris and Rowan applied a similar technique to detect blood spots without regard to shell colour. Based on the relative absorbance measurements at 577 and 600 nm they could detect 70% of all eggs having blood spots from 3.2 to 6.3 nm in diameter and 100% having blood spots greater than 6.3 nm in diameter.

Light transmittance properties of eggs in the range 450 nm to 800 nm was investigated to group eggs according to their fertility. 67 Detectable differences among the eggs were reported in mean transmittances of the whole eggs yielding an 84% correct classification of fertile eggs. None the less it was concluded that there was too small a difference and too much variation among eggs to successfully separate fertile from infertile eggs.

8.2. Pork Unlike fruits and vegetables pigments are not necessarily the dominant factors influencing the

appearance of meat. Apart from pigments, other factors such as cellular structure, surface

SGUNASEKARAN ETAL 231

60 t

400 500 600 700 Wavelength, nm

Fig. 17. Spectral transmittance cf intact whiteshell eggs. I, Normal egg; 2, egg containing blood spot; 3, egg containing bloody white (Brant et aL6”)

roughness, and homogeneity can equally affect the appearance of meat sample.4,68 For example, there was reportedly no difference in pigment concentration between two different qualities (pale, soft and exudative and normal) of pork muscle which appear different.68 It was also found that the myoglobin and haemoglobin selectively absorb light while structural and myofibrillar protein absorb relatively less light but cause more scattering. This suggests that the general optical quality standards for meat products should take factors other than pigments into consideration.

Davis et aLps investigated the reflectance spectra of longissimus muscle from pork loins of the qualities PSE (pale, soft and exudative), normal and DFD (dark, firm and dry). A pork quality index (PQI) was suggested based on light reflectance measurement at 633 and 627 nm.

PQI = - 1.67 -254 log l/R,,, +258 log l/R,,,

This index yielded a correlation of 0.8 with visual rating of quality. However, they commented that these measurements involving pigments might be affected by the chemical reactions involving porcine myoglobin and by other external factors such as bacterial growth and oxidation. The light scattering properties of muscle, independent of the above factors, was suggested as a desirable alternative for evaluating muscle quality. Following that, Birth et ~1.‘~ researched the possibility of establishing light scatter coefficients as a measure of pork muscle quality. They concluded that the relationship between the distribution of the radiation in the muscle and the muscle qualities can be used as a basis for determining the meat quality of pork loins. Scatter coefficients and absorption coefficients were computed in the 550 to 1150 nm range. High correlation was obtained between the scatter coefficient at 800 nm and the transmittance of Hart extract.”

8.3. Ground beef Norri?’ studied the light absorption characteristics of ground beef samples. Of the observed

absorption bands at 540, 575, 640 and 760 nm, he selected the one at 760 nm for estimating fat content of ground beef. The other absorption bands were rejected as they are closely related to light absorption by blood. A fat absorption band at 928 nm was reported by Massie.‘= From the spectral reflectance data (Fig. 18) he observed that the change in the slope of the curves at 940 nm


Fig. 18.

301 I I I I I I I 800 850 900 950 IO00

Wavelength, nm

Reflectance curves ofground beef containing33,3% far (1) and 183% fat (2) (Massie”)

indicated a change in fat content with a high degree of success. He proposed an empirical formula,

% fat=M+N(R,,, -R,,,)I(Rw +R,,,)

where M and N are constants.

9. Miscellaneous products

Kramer et a1.73 investigated the light absorption properties of Virginia type cured, unroasted peanut halves without skin. Optical density difference, AOD (48CL5 10 nm) was found useful as a peanut maturity indicator. Due to the absorption band at 445 and 470nm of the pigment xanthophyll in immature peanuts the above measurement values were higher for immature peanuts than for mature peanuts. They also found good correlation between AOD (48&587 nm) and the taste panel rating of off-flavour in peanuts. Beasley and Dickens“’ indicated that oil extracted from peanuts could also be used as a maturity index. They reported that oil from mature peanuts generally transmitted more light at about 425, 456 and 480 nm than oil from immature peanuts. Apart from xanthophyll, D-carotene and lutein were also suggested as influencing the spectral properties which are related to maturity. Moisture content of whole intact peanut cotyledons were also spectrophotometrically determined.s8 It was reported that AOD (970-900 nm) predicted the peanut moisture content within + 0.7%.

The light transmission and reflectance characteristics of white fir seeds were studied to obtain an index of seed viability.25 In the reflectance spectrum of 500 to 2500 nm range significant seed to seed changes were observed only around 660 nm. A sorting index (S) was assigned based on the measurements around 660 nm,

s= Rae -f&o R 660

Table 5 presents the different S values and the percentage germination in 5 weeks. These results indicate the possibility of optically detecting the viability of seeds. Though the mechanism involved in this procedure was not understood it was presumed that phytochrome reaction which is active at about 660 nm may be an influencing factor. A viability indicator of delinted cotton seeds was also developed based on an optical density difference measurement.76*77 The AOD


(720-790 nm) was reported to be useful but was found to be cultivar dependent.‘* Later it was indicated that the condition of the seed coat may have influenced the OD measurements.”

Tyson and Clarkso developed a technique to determine the allatoxin contamination of Stuart variety pecans. They used sound pecan halves soaked and incubated in chloroform aflatoxin solution. With a fluorescence ratio measurement on the bottom side of the pecan halves they detected aflatoxin contamination of at least 20 p.p.m. with a 90% confidence. The measurement criterion was R,,,/R,,, or R,,,/R,,,. Aflatoxin contamination of Iranian pistachio nuts was also optically indicated. Dickens and Weltye’ observed that nuts emitting bright greenish yellow (BGY) fluorescence under long wave ultraviolet (LWUV) light had a high probability of aflatoxin contamination. Tests by Farsaie et aL8* with 360 nm light source clearly indicated that BGY fluorescent nuts can be separated from other normal nuts. Based on the ratio of fluorescent energies at 490 and 420 nm a photometer was developed to sort the aflatoxin contaminated nuts.83

McCIure84 studied the browning reaction of tobacco. The browning in tobacco leaves is usually associated with physical damage and hence low market value. In the region of 425 to 700 nm there was an increase in absorption between 500 and 700 nm due to the increase in brown pigments in the affected portions of leaves. However, the characteristic feature of the absorption spectrum of intact leaves which relates to browning is the increase in absorption at 575 nm. Based on the assumption that the optical density was a linear function of the concentration of brown pigmentation, he found that the model describing the reaction will range from zero order to higher depending on the thermal treatment.

Norris and Harts8 determined the moisture content of lima beans with intact cotyledons by optical methods. Diffuse reflectance measurement at near infrared wavelengths were also used in estimating moisture content of four grass species. 85,86 Three reflectance ratio measurements, Rr,,,lRr~~o, Rr4JRr66a~ and R,,,,IR,,,, were found to give good correlations with the moisture content of whole grass samples. Generally, the fresher and leafier specimens gave better correlations and there was no significant difference between the spectra of different grass species. Due to the small sample size and small sample surface exposed to radiation, single measurements of reflectance at selected wavelengths estimated moisture content accurate to f22.0%. It was suggested that hundreds of independent readings be made to obtain a useful measurement.

TABLES

Five week germination results for white fir seeds (Miles and Moehle’“)

I I I Range of Percentage

sorting index. S germinated Torul

sample size

Overallprminatlonpercentage:60%

10. Delayed tight emission

Strehler and Arnold*’ discovered that green plants which have been irradiated give off light for a considerable period of time after illumination. This phenomenon is known as delayed light emission (DLE). They explained it as a reflection of certain early reactions in photosynthesis


which, by virtue of their reversibility, are capable of releasing a portion of their stored chemical energy through a chemiluminescent mechanism, including at least one enzymatic reaction.

Preliminary measurements of the colour of the emitted light indicated that it corresponded closely to the colour of the fluorescent light. However, it was identified that the luminescence is more closely related to photosynthesis than to fluorescence. 87 The similarities between the luminiscent reaction and photosynthesis include:

(1) the nature of temperature dependence; (2) the rate at which the reactions are destroyed by ultra-violet light; (3) the range of saturation intensity; (4) the compounds that inhibit the reactions. Later investigations*8**a suggested an interpretation of the physical process leading to DLE in

terms of semiconductor theory. The emitted light has been shown to be due to an electronic transition between the first excited singlet state of chlorophyll and the ground state.so Contrary to Strehler and Arnold’s interpretation that luminescence phenomena is a consequence of the reversibility of some of the early enzymatic photosynthetic reactions it was proved to be nonenzymatic.9’~92 But evidence is presented to support the contention that the later stages of emission are of an enzymatic nature.g2

10.1. Factors ufjkting DLE The emitted light is affected by temperature, excitation intensity, wavelength of excitation,

excitation area, duration of illumination, time after illumination, successive dark and light periods and amount of chlorophyll in the plant material. The temperature dependence of DLE was reported to be extremely complex.92~93 However, this is not a major problem with regard to agricultural products because of the relatively small influence on the DLE in the range of temperature encountered with them.94

Fig. 19 shows the broad activation spectrum which produces DLE from intact green peaches. This explains the strong dependence of DLE on the wavelength of excitation. The peak of DLE intensity is observed at about 680 nm. A similar response was reported for oranges.93 The intensity of DLE bears a direct relationship with the excitation intensity until saturation. Beyond the saturation level additional illumination has little effect in producing an increase in DLE intensity. 93-95 The higher the illumination intensity, the shorter the time required for saturation.94

Ltr%eTk Wavelength of excitation, nm

Fig. 19. Action spectrum of green peach measured 2 s after illumination (Jacob et aLs4/

S.GUN ASEKARAN ETAL. 235

Fig. 20. Decay curves of chforelfa luminescence at two different temperatures (Streher and AmoM’)

With bananas Chuma et aLs3 observed that maximum DLE intensity was reached for an excitation time of 2 s or less. They also found that excitation times up to 60 s degraded the DLE intensity but for times longer than 60 s the DLE intensity was constant.

After a certain duration of excitation DLE is observed over varying periods of times. At room temperature, DLE is observable from 0.01 s to several minutes. 87 However, the intensity of DLE decreases with the time after excitation, known as decay of DLE. The decay curves of chlorella luminiscence (Fig. 20) indicate that the lower the temperature, the faster is the decay. In mature oranges just the opposite trend was observed. s4 chlorophyll content.

The decay was also found to vary with s3 The effect of continuous illumination after dark periods has also been

investigated.87’93

11. Quality evaluation based on DLE

Delayed light emission is probably produced by all vegetables, fruits and plant materials under- going photosynthesis and can be related to the chlorophyll concentration or apparent greenness

1

e I 1 I I

1 20 40 60 80 100 120 Time, 5

Fig. 21. DLEfor lemons of different chlorophyll content. 1, Darkgreen; 2. light green: 3, silver; 4, yellow (Jacob et al.9’)


Colour grade

Fig. 22. Relation between colour grade and DLE intensity of bananas during ripening. (dark period= IO min; illumination intensity= 5500 lx; exciting time = 4 s; illumination area= 4.4 cm”; decay period= 0.7s) (Chuma et aLs’)

of the product. s4 The importance of chlorophyll with regard to judging the quality of fruits and vegetables has been previously discussed. Hence, there is a very definite possibility of applying DLE measurements for quality evaluation of plant materials. Jacob et aLs4 reported that DLE related to apparent chlorophyll concentration has been observed in apple, apricot, banana, bell pepper, nectarine, olive, onion, orange, peach, persimmon, plum, pomegranate and tomato.

The DLE measurements offer two advantages.over the spectrophotometric methodss4 (1) Illumination need not be spectrally well defined; this permits wide choice and simple design

of the exciting source. (2) Illumination and measurement are not simultaneous, i.e. illumination can be removed in

time and position from measurement. Another convenience is that beyond a saturation level additional illumination has little effect in producing an increase in DLE intensity; therefore, small changes in illumination will not interfere with quantitative measurement of DLE.

Jacob et ~1.~’ observed large differences among four commercial colour grades of lemon (Fig. 21). Based on a similar observation they sorted tomatoes under field conditions. Chuma and Nakajis6 observed an almost linear relationship between the chlorophyll content of tomatoes and the DLE intensity. They sorted the green tomatoes with high accuracy whereas the sorting of red stages were only partly successful. Chuma et aLs3 applied this technique in automatic selection of Satsuma oranges. They developed a relationship between the chlorophyll content (c) and the detector response to DLE (Y) as,

Sorting efficiencies of 545,90, 83.8, 85-7 and 100% were reported for five colour groups ranging from dark green to orange. The surface treatments of oranges such as brushing, brushing and waxing had negligible effects on DLE suggesting difficulty in detecting such treatments.

Measurements of DLE on Cavendish banana indicated that DLE decreased with ripening and bananas just beyond optimum ripening had only a trace of DLE." The grade of peel colour (gpc) of bananas has been related to DLE as

y= 10.9 e-o.72 9pc

Fig. 22 indicates that maturity of bananas can be determined with DLE measurement. Other quality attributes of banana such as sugar content and firmness were also related to DLE. The DLE decreases with sugar content(s) according to

S. GUNASEKARAN ET AL.

Y= -0.773 s+3.26

237

and increased with firmness (f> according to

Y=(l.40fx lo-6)-3.50.

12. Discussion and conclusions

Optical properties of agricultural and biological materials vary widely and are dependent upon many factors. Quality evaluation based on these properties requires accurate optical property values. For any particular substance under a particular set of conditions precise values can probably be obtained by careful measurements. However, good estimates can be made for many materials on the basis of the investigations already reported. These optical property estimates would permit a quick evaluation of various properties that have served as quality indices and help in the selection of those most suitable for any particular application.

In general the nondestructive quality evaluation of agricultural and biological materials focus on three major aspects:

(1) maturity and/or ripeness evaluation; (2) composition analysis; (3) intenal and external defect detection.

For maturity evaluation the interaction of various pigments and their change as the product matures is taken as the prime indicator. The importance of chlorophyll in this regard has been adequately discussed. Moisture and fat or oil content and other compositions have been analysed based on certain absorption bands in the electromagnetic spectrum. For example, water has five prominent absorption bands at 760,970, 1190, 1450 and 1940 nm and oil at 928, 12 15 and 1725 nm. Defects, both external and internal, on the other hand, have been found to affect the normal properties of interaction of light with products in consistent ways. Hence, defect detection is accomplished by comparing optical properties of a normal product with those of the defective product.

The wide variety of size and shapes of agricultural products makes most commercial instruments difficult to use for measuring optical characteristics since special geometric designs are needed. With the advent of fibre optic technology the problem has been partially overcome. There are numerous instances of fibre optics applications in measurements of biological objects. 35,54,63.83.s7 Chen and Nattuvetty35 reported the application of fibre optics as having the advantages of detecting light of extremely low intensities by providing a good light seal and eliminating the effects of variations such as fruit size and distance from the source. This has also been found to be very useful in high speed operations. In addition, new sources of light have also been explored. Low power lasers can be of great help because the laser beam is highly direc- tional, coherent, very bright and has a well defined beam diameter. Also these properties make the laser beam more adaptable to optical manipulations compared to other conventional sources. There are recent reports of laser applications in quality detection of biological products.70,s8,gg

The spectrophotometric techniques so far investigated relied heavily on empirical data and statistical analyses. For a better engineering approach the interaction of light with agricultural products should be analysed analytically. Chen and Nattuvetty 35 indicated that if a mathematical model can be developed to represent the distribution of diffused light in the fruit, the effects of various parameters can be studied more thoroughly.

Many agricultural products may be considered as turbid or translucent,loO i.e. incident light energy does not traverse the object in a rectilinear manner but is scattered from its original direction of travel. Thus not only absorption of energy within the sample, but also scatter and changes in the direction of travel of energy as it traverses the object, must be considered. Accord- ing to Birth’OO light scattering is the primary process for creating diffuse reflectance which


enables us to observe a nonmetallic object from any direction, regardless of the direction of incident light. Mathematical description of scatter, diffuse reflectance and diffuse transmittance are extremely valuable in providing a means of obtaining insights into the interaction between light and biological materials where multiple scattering predominates.5 The Kubelka-Munk theory has been widely accepted for analytical analysis of body reflectance.“’ The Twersky approach of mathematical analysis of absorption and multiple scattering of biological suspen- sions resulted in more accurate descriptions of biological materials from the visible and near infrared wavelengths.5 Sarkar and 0’Brien13 presented an analytical model describing optical transmission phenomena based on spectrophotometric measurements on tomatoes. The distribution of light transmitted through agricultural materials, the effects of distance between the light source and detector, and the size and location of the absorber have also been investigated.35’s8

Even though there have been recent advances in analytical approach and mathematical modelling of interaction of light with agricultural and biological products, further investigations are required to gain a better understanding of the optical characteristics and for facilitating more precise and accurate measurements. Hopefully an automated, computerized quality evaluation procedure can eventually be developed. In addition to further spectrophotometric studies, properties such as DLE of green plants should be more thoroughly investigated as potential indicators of quality. The advantages of DLE measurements over the spectrophotometric measurements might help in the development of simpler and less expensive instrumentation for quality evaluation of agricultural and biological materials.

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