I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
15
,
No
.
4
,
A
u
g
u
s
t
20
25
,
p
p
.
4
1
2
0
~
4
1
3
2
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
15
i
4
.
pp
4
1
2
0
-
4
1
3
2
4120
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
Nea
r
-
inf
ra
red spe
ctrosco
py
and ma
chine learni
ng
t
o
detec
t
o
liv
e
o
il t
y
pe:
a
sy
stem
a
tic
revi
ew
L
eo
na
rdo
L
edesm
a
O
rt
ec
ho
,
E
nriq
ue
Ro
m
er
o
J
o
s
é,
Chri
s
t
ia
n O
v
a
lle,
H
eli
Alej
a
nd
ro
Co
rdo
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a
B
er
o
na
D
e
p
a
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t
me
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o
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g
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n
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T
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c
n
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o
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c
a
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P
e
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Li
m
a
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P
e
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ú
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
l 2
4
,
2
0
2
4
R
ev
is
ed
Ma
r
2
0
,
2
0
2
5
Acc
ep
ted
Ma
y
2
3
,
2
0
2
5
Th
e
p
re
se
n
t
st
u
d
y
e
v
a
lu
a
tes
t
h
e
e
ffe
c
ti
v
e
n
e
ss
o
f
v
isi
b
le/n
e
a
r
-
in
fra
re
d
sp
e
c
tro
sc
o
p
y
(VIS/
NIR)
c
o
m
b
in
e
d
with
m
a
c
h
i
n
e
lea
rn
in
g
i
n
o
li
v
e
o
il
t
y
p
e
d
e
tec
ti
o
n
.
A
se
a
rc
h
stra
teg
y
b
a
se
d
o
n
t
h
e
p
o
p
u
lati
o
n
,
i
n
t
e
rv
e
n
ti
o
n
,
c
o
m
p
a
riso
n
,
a
n
d
o
u
tco
m
e
(
P
IC
O
)
fra
m
e
wo
rk
wa
s
e
m
p
lo
y
e
d
t
o
fo
rm
u
late
sp
e
c
ifi
c
e
q
u
a
ti
o
n
s
u
se
d
in
S
c
o
p
u
s,
S
c
ien
c
e
Dire
c
t,
a
n
d
P
u
b
M
e
d
d
a
tab
a
se
s.
Afte
r
a
p
p
l
y
in
g
e
x
c
l
u
sio
n
c
rit
e
ria
,
5
3
st
u
d
ies
we
re
i
n
c
lu
d
e
d
i
n
t
h
e
re
v
iew
fo
ll
o
wi
n
g
p
re
fe
rre
d
re
p
o
rt
in
g
i
tem
s
fo
r
sy
ste
m
a
ti
c
re
v
iew
s
a
n
d
m
e
ta
-
a
n
a
ly
se
s
(P
RIS
M
A)
gu
i
d
e
li
n
e
s.
Th
e
re
v
iew
e
d
st
u
d
ies
d
e
m
o
n
s
trate
th
a
t
VIS/
NIR
sp
e
c
tro
sc
o
p
y
c
o
u
p
led
with
m
a
c
h
in
e
lea
rn
in
g
a
ll
o
ws
r
a
p
id
a
n
d
a
c
c
u
ra
te
id
e
n
ti
fica
ti
o
n
o
f
d
iffer
e
n
t
ty
p
e
s
o
f
o
li
v
e
o
il
,
h
ig
h
li
g
h
ti
n
g
t
h
e
d
e
tec
ti
o
n
o
f
fa
tt
y
a
c
id
s,
p
o
ly
p
h
e
n
o
ls,
a
n
d
o
th
e
r
v
it
a
l
c
o
m
p
o
u
n
d
s.
Ho
we
v
e
r,
v
a
riab
il
it
y
in
sa
m
p
les
a
n
d
p
ro
c
e
ss
in
g
c
o
n
d
it
i
o
n
s
p
re
se
n
t
sig
n
ifi
c
a
n
t
c
h
a
ll
e
n
g
e
s.
Alth
o
u
g
h
th
e
re
su
lt
s
a
re
p
ro
m
isin
g
,
fu
rt
h
e
r
re
se
a
rc
h
is
re
q
u
ire
d
to
f
u
ll
y
v
a
li
d
a
te
t
h
e
e
ffica
c
y
a
n
d
fe
a
sib
il
it
y
o
f
t
h
is
tec
h
n
o
l
o
g
y
i
n
in
d
u
strial
se
tt
in
g
s.
T
h
is
re
v
iew
p
ro
v
i
d
e
s
a
c
o
m
p
re
h
e
n
siv
e
o
v
e
rv
iew
o
f
t
h
e
a
d
v
a
n
c
e
s,
c
h
a
ll
e
n
g
e
s,
a
n
d
o
p
p
o
rtu
n
it
ies
in
t
h
is
fiel
d
,
h
i
g
h
l
ig
h
ti
n
g
th
e
n
e
e
d
to
o
p
ti
m
ize
m
a
c
h
in
e
lea
rn
in
g
m
o
d
e
ls
a
n
d
sta
n
d
a
rd
ize
a
n
a
ly
sis
p
ro
c
e
d
u
re
s
fo
r
p
ra
c
ti
c
a
l
a
p
p
li
c
a
ti
o
n
i
n
t
h
e
fo
o
d
i
n
d
u
stry
.
K
ey
w
o
r
d
s
:
Oliv
e
o
il
T
y
p
e
o
f
o
liv
e
o
il
Vis
ib
le/n
ea
r
-
in
f
r
ar
ed
s
p
ec
tr
o
s
co
p
y
Ma
ch
in
e
lear
n
in
g
Sy
s
tem
atic
r
ev
iew
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
C
h
r
is
tian
Ov
alle
E
n
g
in
ee
r
in
g
Dep
ar
tm
e
n
t
Dep
a
r
tm
en
t o
f
E
n
g
in
ee
r
in
g
,
U
n
iv
er
s
id
ad
T
ec
n
o
lo
g
ica
d
el
Per
ú
L
im
a,
Per
ú
E
-
m
ail: d
o
v
alle@
u
tp
.
ed
u
.
p
e
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
an
aly
s
is
o
f
o
liv
e
o
il
is
c
r
u
cial
d
u
e
t
o
its
im
p
o
r
tan
ce
i
n
th
e
f
o
o
d
in
d
u
s
tr
y
an
d
its
im
p
ac
t o
n
p
u
b
lic
h
ea
lth
.
T
h
u
s
,
v
is
ib
le/n
ea
r
-
in
f
r
ar
ed
s
p
ec
tr
o
s
co
p
y
(
VI
S/NIR)
an
d
m
ac
h
i
n
e
lear
n
in
g
h
a
v
e
e
m
er
g
ed
as
p
r
o
m
is
in
g
to
o
ls
f
o
r
d
etec
tin
g
th
e
ty
p
e
o
f
o
liv
e
o
il.
Pr
ev
io
u
s
r
esear
ch
h
as
ex
ten
s
iv
ely
ex
p
lo
r
ed
th
is
a
r
ea
,
d
em
o
n
s
tr
atin
g
th
at
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
c
o
m
b
in
ed
with
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
ca
n
o
f
f
er
a
r
ap
id
,
ef
f
icien
t,
a
n
d
non
-
d
estru
ctiv
e
m
eth
o
d
to
an
aly
ze
o
liv
e
o
il'
s
ch
em
ical
an
d
p
h
y
s
ical
ch
ar
ac
ter
is
tics
[
1
]
–
[
4
]
.
Ho
wev
er
,
it
is
cr
u
cial
to
r
ec
o
g
n
ize
t
h
at
th
es
e
p
r
o
m
is
es
ar
e
s
till
in
th
e
r
esear
ch
a
n
d
d
ev
elo
p
m
en
t
p
h
ase
.
Alth
o
u
g
h
s
tu
d
ies
ha
v
e
s
h
o
wn
en
co
u
r
a
g
in
g
r
esu
lts
s
o
f
ar
,
th
er
e
is
s
till
wo
r
k
to
b
e
d
o
n
e
to
f
u
lly
v
alid
ate
th
e
ef
f
ec
tiv
en
ess
an
d
f
ea
s
ib
ilit
y
o
f
th
is
tech
n
o
lo
g
y
i
n
th
e
r
ea
l w
o
r
ld
.
Stu
d
ies
h
av
e
ad
d
r
ess
ed
th
e
id
en
tific
atio
n
o
f
f
atty
ac
id
s
,
p
o
l
y
p
h
en
o
ls
,
an
d
o
th
er
c
o
m
p
o
u
n
d
s
p
r
esen
t
in
o
liv
e
o
il
u
s
in
g
a
d
v
an
ce
d
s
p
ec
tr
o
s
co
p
ic
tec
h
n
iq
u
es
[
5
]
–
[
8
]
.
T
h
ese
tech
n
iq
u
es
h
av
e
th
e
p
o
ten
tial
to
o
f
f
e
r
a
d
ee
p
er
u
n
d
er
s
tan
d
in
g
o
f
th
e
c
h
em
ical
co
m
p
o
s
itio
n
o
f
o
liv
e
o
il,
wh
ich
co
u
ld
lead
to
s
ig
n
if
ican
t
im
p
r
o
v
em
en
ts
in
its
q
u
ality
,
au
th
en
ticity
,
a
n
d
n
u
t
r
itio
n
al
v
alu
e.
T
h
is
wo
u
ld
allo
w
th
e
d
if
f
e
r
e
n
t
ty
p
e
s
o
f
o
liv
e
o
il
to
b
e
id
en
tifie
d
.
Ho
wev
er
,
d
esp
ite
th
e
p
o
s
s
ib
ilit
ies
o
f
th
ese
tech
n
iq
u
es,
th
e
y
ar
e
s
till
b
ein
g
r
esear
ch
e
d
an
d
d
ev
elo
p
e
d
.
Mo
r
e
s
tu
d
ies ar
e
n
ee
d
ed
to
v
alid
ate
its
ef
f
ec
tiv
en
ess
in
v
ar
io
u
s
o
liv
e
o
il sam
p
le
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
N
ea
r
-
in
fr
a
r
ed
s
p
ec
tr
o
s
co
p
y
a
n
d
ma
ch
in
e
lea
r
n
in
g
t
o
d
etec
t
o
live
o
il
…
(
Leo
n
a
r
d
o
Led
esma
Ort
ec
h
o
)
4121
Fu
r
th
er
m
o
r
e
,
it
is
cr
u
cial
to
ad
d
r
ess
tech
n
ical
an
d
m
eth
o
d
o
l
o
g
ical
ch
allen
g
es
,
s
u
ch
as
s
tan
d
ar
d
izatio
n
o
f
a
n
aly
s
is
p
r
o
ce
d
u
r
es
an
d
ac
cu
r
ate
in
ter
p
r
etatio
n
o
f
s
p
ec
tr
o
s
co
p
ic
d
ata.
T
h
e
in
teg
r
atio
n
o
f
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
with
m
ac
h
in
e
lear
n
in
g
tec
h
n
iq
u
es
r
ai
s
es
f
u
n
d
am
en
tal
q
u
esti
o
n
s
,
s
u
c
h
as
its
im
p
ac
t
o
n
th
e
ac
cu
r
ac
y
o
f
o
liv
e
o
il
ty
p
e
d
etec
tio
n
an
d
its
f
ea
s
ib
ilit
y
in
in
d
u
s
tr
ial
en
v
ir
o
n
m
en
ts
[
9
]
–
[
1
2
]
.
So
,
m
o
r
e
r
esear
ch
is
n
e
ed
ed
t
o
an
s
wer
t
h
e
f
o
llo
win
g
q
u
esti
o
n
:
Ho
w
d
o
es
v
is
ib
le/n
ea
r
-
in
f
r
ar
e
d
(
VI
S/
NI
R
)
s
p
ec
tr
o
s
co
p
y
co
m
b
in
ed
with
m
ac
h
in
e
lear
n
in
g
h
elp
in
th
e
ac
cu
r
ac
y
o
f
o
liv
e
o
il
ty
p
e
d
etec
tio
n
?
Fo
r
ex
am
p
le,
alth
o
u
g
h
cu
r
r
en
t
s
tu
d
ies
s
u
g
g
est
th
at
t
h
ese
h
y
b
r
id
m
eth
o
d
s
ca
n
ac
h
ie
v
e
h
ig
h
ac
c
u
r
ac
y
r
ates
in
i
d
e
n
tify
in
g
v
ar
io
u
s
o
il
ty
p
es,
in
clu
d
in
g
d
is
tin
g
u
is
h
in
g
b
etwe
en
ex
tr
a
v
ir
g
i
n
,
v
ir
g
in
,
an
d
r
e
f
in
ed
o
ils
,
f
u
r
th
e
r
r
ese
ar
ch
is
s
till
n
ee
d
ed
to
u
n
d
er
s
tan
d
th
is
tech
n
o
lo
g
y
'
s
l
im
itatio
n
s
an
d
r
estrictio
n
s
f
u
lly
.
Fu
r
th
er
m
o
r
e,
th
e
a
p
p
licati
o
n
o
f
th
ese
s
y
s
tem
s
p
r
esen
ts
ad
d
itio
n
al
ch
allen
g
es.
Op
tim
izin
g
m
a
ch
in
e
lear
n
in
g
m
o
d
els,
r
ed
u
cin
g
s
p
ec
tr
o
s
co
p
y
eq
u
ip
m
en
t
c
o
s
ts
,
an
d
ad
ap
tin
g
s
y
s
tem
s
to
v
ar
iab
le
p
r
o
ce
s
s
in
g
co
n
d
itio
n
s
ar
e
c
r
itical
asp
ec
ts
th
at
m
u
s
t
b
e
ad
d
r
ess
ed
.
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
co
m
b
in
ed
with
m
ac
h
in
e
lear
n
in
g
h
as
p
r
o
v
en
to
b
e
a
p
o
wer
f
u
l
to
o
l
in
id
en
tify
in
g
an
d
ch
ar
ac
ter
izin
g
d
if
f
er
en
t
ty
p
es
o
f
o
liv
e
o
il.
R
ec
en
t
s
tu
d
ies
h
av
e
s
h
o
wn
its
ef
f
ec
tiv
en
ess
in
id
en
tify
in
g
f
atty
ac
id
s
,
p
o
ly
p
h
en
o
ls
,
an
d
o
th
er
k
ey
co
m
p
o
u
n
d
s
th
at
d
eter
m
in
e
th
e
q
u
ality
an
d
au
th
en
ticity
o
f
o
liv
e
o
il
[
5
]
,
[
6
]
.
H
o
wev
er
,
s
a
m
p
le
v
ar
iab
ilit
y
a
n
d
p
r
o
ce
s
s
in
g
co
n
d
itio
n
s
r
ep
r
esen
t
s
ig
n
if
ic
an
t
ch
allen
g
es
th
at
r
em
ain
to
b
e
ad
d
r
ess
ed
.
T
h
i
s
s
y
s
tem
atic
r
ev
iew
will
u
s
e
A
s
ea
r
ch
s
tr
ate
g
y
b
ased
o
n
th
e
p
o
p
u
latio
n
,
in
ter
v
en
tio
n
,
c
o
m
p
ar
is
o
n
,
an
d
o
u
tco
m
e
(
PICO
)
q
u
esti
o
n
.
A
co
m
p
r
eh
en
s
iv
e
s
ea
r
ch
o
f
r
elev
an
t
s
cien
tific
d
atab
ases
will
b
e
co
n
d
u
cted
to
id
en
tify
s
tu
d
ies
ev
al
u
atin
g
th
e
ef
f
ec
tiv
en
ess
o
f
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
an
d
m
ac
h
in
e
lear
n
in
g
in
o
liv
e
o
i
l
ty
p
e
d
etec
tio
n
.
T
h
e
s
elec
ted
s
tu
d
ies
will
b
e
cr
itica
lly
e
v
alu
ate
d
to
ex
tr
ac
t
r
elev
an
t
d
ata
an
d
s
y
n
th
esize
th
e
f
in
d
in
g
s
.
T
h
e
r
esear
ch
aim
s
to
v
alid
ate
th
e
ef
f
ec
tiv
en
ess
o
f
co
m
b
in
ed
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
an
d
m
ac
h
in
e
lear
n
in
g
tec
h
n
iq
u
es
in
r
ea
l
f
o
o
d
in
d
u
s
tr
y
en
v
ir
o
n
m
e
n
ts
.
I
n
ad
d
itio
n
,
t
h
e
aim
is
to
d
ev
elo
p
a
n
d
o
p
tim
ize
m
ac
h
in
e
lear
n
i
n
g
m
o
d
els th
a
t c
an
b
e
im
p
lem
e
n
ted
ef
f
icien
t
ly
an
d
p
r
o
f
itab
ly
in
th
e
d
etec
tio
n
an
d
class
if
icatio
n
o
f
o
liv
e
o
ils
.
T
o
d
ate,
s
ev
er
al
s
tu
d
ies
h
av
e
b
ee
n
id
en
tifie
d
th
at
d
e
m
o
n
s
tr
ate
th
e
p
r
o
m
is
e
o
f
th
ese
tech
n
o
lo
g
ies
in
ac
cu
r
ately
id
e
n
tify
in
g
d
if
f
e
r
e
n
t
ty
p
es
o
f
o
liv
e
o
il.
E
a
r
ly
r
esu
lts
in
d
icate
th
at
th
e
co
m
b
i
n
atio
n
o
f
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
an
d
m
ac
h
in
e
le
ar
n
in
g
ca
n
s
ig
n
if
ican
tly
im
p
r
o
v
e
th
e
ac
cu
r
ac
y
an
d
s
p
ee
d
o
f
o
liv
e
o
il
an
aly
s
is
.
Ho
wev
er
,
m
o
r
e
r
esear
ch
is
n
ee
d
ed
to
o
v
er
c
o
m
e
cu
r
r
en
t
lim
itatio
n
s
an
d
v
alid
ate
th
ese
m
eth
o
d
s
u
n
d
er
a
b
r
o
ad
e
r
r
an
g
e
o
f
co
n
d
itio
n
s
.
o
p
er
atio
n
al.
T
h
er
ef
o
r
e,
a
co
m
p
r
eh
en
s
iv
e
s
y
s
tem
atic
r
ev
iew
m
u
s
t
ad
d
r
ess
th
ese
is
s
u
es a
n
d
ex
p
lo
r
e
th
e
m
o
s
t r
e
ce
n
t stu
d
ies in
th
e
f
ield
.
T
h
is
r
ev
iew
will
co
n
tr
ib
u
te
to
th
e
ad
v
an
ce
m
en
t
o
f
s
cien
tific
k
n
o
wl
ed
g
e
in
o
liv
e
o
il
ty
p
e
d
etec
tio
n
an
d
will
p
r
o
v
i
d
e
in
v
alu
a
b
le
g
u
id
an
ce
to
t
h
e
f
o
o
d
in
d
u
s
tr
y
an
d
p
u
b
lic
h
ea
lth
p
r
o
f
ess
io
n
als
[
1
3
]
–
[
1
6
]
.
T
h
e
p
r
o
m
is
e
o
f
th
is
r
ev
iew
lies
in
its
ab
ilit
y
to
co
n
d
en
s
e
th
e
latest
f
in
d
in
g
s
,
id
en
tify
e
m
er
g
in
g
tr
en
d
s
an
d
p
atter
n
s
,
an
d
h
ig
h
lig
h
t
ar
ea
s
f
o
r
f
u
tu
r
e
r
esear
ch
.
T
h
is
wo
u
ld
allo
w
th
e
d
ev
elo
p
m
e
n
t
o
f
m
o
r
e
s
o
lid
an
d
ef
f
ec
tiv
e
s
o
lu
tio
n
s
in
th
e
d
etec
tio
n
an
d
class
if
icatio
n
o
f
o
liv
e
o
ils
,
th
u
s
im
p
r
o
v
in
g
t
h
e
au
th
e
n
ticity
o
f
th
e
p
r
o
d
u
cts.
Fu
r
th
er
m
o
r
e
,
th
e
co
n
clu
s
io
n
s
o
f
th
is
r
ev
iew
c
o
u
ld
b
e
v
er
y
v
alu
ab
le
f
o
r
t
h
e
f
o
o
d
in
d
u
s
tr
y
,
as
th
ey
c
o
u
ld
h
elp
im
p
lem
en
t
m
o
r
e
e
f
f
ec
tiv
e
a
n
d
r
eliab
le
s
y
s
tem
s
f
o
r
class
if
y
in
g
o
liv
e
o
ils
.
T
h
is
,
in
t
u
r
n
,
wo
u
ld
h
elp
t
o
s
tr
en
g
th
en
co
n
s
u
m
er
c
o
n
f
id
e
n
ce
an
d
en
s
u
r
e
th
e
in
teg
r
ity
o
f
p
r
o
d
u
cts in
th
e
m
ar
k
et.
Fu
r
th
er
m
o
r
e
,
th
is
r
ev
iew
is
e
x
p
ec
ted
to
ad
d
r
ess
th
e
p
r
o
f
es
s
io
n
al
co
m
p
eten
ce
o
f
s
y
s
tem
s
an
aly
s
is
,
cr
itically
ev
alu
atin
g
ex
is
tin
g
o
liv
e
o
il
d
etec
tio
n
s
y
s
tem
s
an
d
p
r
o
p
o
s
in
g
p
r
ac
tical
im
p
r
o
v
e
m
en
ts
b
ased
o
n
th
e
im
p
lem
en
tatio
n
o
f
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
an
d
m
ac
h
i
n
e
lear
n
in
g
[
1
7
]
–
[
2
0
]
.
T
h
ese
im
p
r
o
v
em
en
ts
m
ay
in
v
o
lv
e
s
p
ec
if
ic
s
u
g
g
esti
o
n
s
f
o
r
eq
u
ip
m
en
t
s
elec
tio
n
,
alg
o
r
ith
m
o
p
tim
izatio
n
,
p
r
o
ce
s
s
s
tan
d
ar
d
izat
io
n
,
an
d
in
te
g
r
atio
n
o
f
em
e
r
g
in
g
tech
n
o
lo
g
ies.
T
h
ese
im
p
r
o
v
em
en
ts
co
u
ld
r
ev
o
lu
tio
n
ize
h
o
w
o
liv
e
o
il
ty
p
e
s
ar
e
d
etec
ted
an
d
class
if
ied
,
th
u
s
in
cr
ea
s
in
g
th
e
ef
f
ec
tiv
en
ess
an
d
ef
f
icien
c
y
o
f
d
etec
tio
n
s
y
s
tem
s
.
T
h
is
s
y
s
t
em
atic
r
ev
iew
aim
s
to
g
iv
e
a
co
m
p
r
e
h
en
s
iv
e
v
iew
o
f
th
e
ad
v
an
ce
s
,
ch
allen
g
es,
an
d
o
p
p
o
r
tu
n
ities
f
o
r
d
etec
tin
g
o
liv
e
o
il ty
p
e
u
s
in
g
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
an
d
m
ac
h
in
e
lear
n
in
g
to
d
r
i
v
e
f
u
tu
r
e
r
esear
ch
an
d
p
r
ac
tical
ap
p
licatio
n
s
[
2
1
]
–
[
2
5
]
.
T
h
is
g
lo
b
al
p
e
r
s
p
ec
tiv
e
will
en
ab
le
an
u
p
-
to
-
d
ate
u
n
d
er
s
ta
n
d
in
g
o
f
th
ese
tech
n
o
lo
g
ies
i
n
th
e
f
o
o
d
i
n
d
u
s
tr
y
an
d
en
c
o
u
r
ag
e
f
u
tu
r
e
r
esear
ch
an
d
p
r
ac
tical
ap
p
licatio
n
s
th
at
b
en
ef
it
in
d
u
s
tr
y
an
d
co
n
s
u
m
e
r
s
.
2.
M
E
T
H
O
D
A
clea
r
s
ea
r
ch
s
tr
ateg
y
b
ased
o
n
th
e
PICO
q
u
esti
o
n
was
u
s
ed
to
c
o
n
d
u
ct
th
is
s
y
s
tem
ati
c
r
ev
iew,
f
r
o
m
w
h
ich
f
o
u
r
r
esear
ch
q
u
e
s
tio
n
s
an
d
a
PICO
tab
le
wer
e
g
en
er
ated
.
T
h
e
PICO
ch
a
r
t
is
a
s
tr
u
ctu
r
e
u
s
ed
to
f
o
r
m
u
late
r
esear
ch
q
u
esti
o
n
s
,
wh
ich
in
clu
d
es
Po
p
u
latio
n
(
P)
,
I
n
ter
v
en
tio
n
(
I
)
,
C
o
m
p
a
r
is
o
n
(
C
)
,
an
d
Ou
tco
m
e
(
O)
[
2
6
]
.
Sp
ec
if
ic
k
ey
wo
r
d
s
wer
e
d
ef
in
ed
,
s
u
ch
as
“
o
liv
e
o
il,
”
“
v
is
ib
le/n
ea
r
-
in
f
r
ar
ed
s
p
ec
tr
o
s
co
p
y
,
”
“
m
ac
h
in
e
lear
n
in
g
,
”
“
au
th
en
ticatio
n
,
”
a
n
d
“
ad
u
lter
atio
n
,
”
f
r
o
m
wh
ic
h
two
s
ea
r
ch
eq
u
atio
n
s
wer
e
o
b
tain
ed
f
o
r
th
r
ee
d
atab
ases
: Sco
p
u
s
,
Scien
ce
Dir
ec
t,
an
d
Pu
b
Me
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
25
:
4
1
2
0
-
4132
4122
2
.
1
.
Resea
rc
h
qu
estio
ns
T
h
e
co
m
b
i
n
atio
n
o
f
v
is
ib
le/n
e
ar
-
in
f
r
ar
e
d
(
Vis
/NI
R
)
s
p
ec
tr
o
s
co
p
y
with
m
ac
h
in
e
lear
n
in
g
h
as
ar
o
u
s
ed
n
o
tab
le
in
ter
est
in
th
e
s
cien
tific
co
m
m
u
n
ity
.
T
h
e
liter
atu
r
e
i
n
clu
d
es
a
wid
e
r
a
n
g
e
o
f
s
tu
d
i
es
th
at
ad
d
r
ess
th
e
f
ir
s
t
ap
p
licatio
n
s
o
f
t
h
ese
tech
n
o
lo
g
ies
an
d
th
e
m
o
s
t
r
ec
e
n
t
d
ev
elo
p
m
en
ts
[
2
]
,
[
2
7
]
.
R
ec
en
t
r
esear
ch
h
as
h
ig
h
lig
h
ted
h
o
w
tec
h
n
o
lo
g
ica
l
ad
v
an
ce
s
h
a
v
e
im
p
r
o
v
ed
th
e
ac
cu
r
ac
y
o
f
th
ese
m
et
h
o
d
s
,
u
n
d
er
s
co
r
in
g
t
h
e
co
n
n
ec
tio
n
b
etwe
en
tech
n
o
l
o
g
ica
l
p
r
o
g
r
ess
an
d
d
etec
tio
n
ca
p
ac
ity
.
T
h
er
ef
o
r
e,
o
r
g
an
izi
n
g
an
d
s
y
n
t
h
esizin
g
th
e
in
f
o
r
m
atio
n
co
llected
b
y
c
r
ea
tin
g
tab
les
in
a
s
y
s
tem
atic
r
ev
iew
is
cr
u
cial.
T
h
is
p
r
o
ce
d
u
r
e
p
r
o
v
i
d
es
a
clea
r
o
v
er
v
iew
o
f
th
e
ex
is
tin
g
liter
atu
r
e
an
d
f
ac
ilit
ates
th
e
co
m
p
ar
is
o
n
o
f
r
esu
lts
b
etwe
en
d
if
f
er
en
t
s
tu
d
ies
[
2
8
]
.
Fu
r
th
er
m
o
r
e
,
th
e
clea
r
an
d
c
o
n
cise
p
r
esen
tatio
n
o
f
d
ata
in
tab
les
allo
ws
r
ea
d
er
s
to
q
u
ic
k
ly
u
n
d
er
s
tan
d
t
h
e
b
r
ea
d
th
an
d
v
ar
iab
ilit
y
o
f
th
e
in
clu
d
ed
s
tu
d
ies.
T
ab
le
1
p
r
esen
ts
th
e
PICO
q
u
esti
o
n
an
d
its
co
m
p
o
n
en
ts
,
an
d
T
ab
le
2
d
etails th
e
k
e
y
wo
r
d
s
u
s
ed
in
th
e
PICO to
o
l a
n
d
its
el
em
en
ts
.
T
ab
le
1
.
PICO
q
u
esti
o
n
an
d
it
s
co
m
p
o
n
e
n
ts
P
I
C
O
Q
U
EST
I
O
N
Qu
e
st
i
o
n
:
H
o
w
d
o
e
s
v
i
si
b
l
e
/
n
e
a
r
i
n
f
r
a
r
e
d
s
p
e
c
t
r
o
sc
o
p
y
(
V
I
S
/
N
I
R
)
c
o
mb
i
n
e
d
w
i
t
h
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
h
e
l
p
i
n
t
h
e
a
c
c
u
r
a
c
y
o
f
o
l
i
v
e
o
i
l
t
y
p
e
d
e
t
e
c
t
i
o
n
?
C
O
M
P
O
N
EN
TS
R
Q1
:
H
o
w
h
a
s t
h
e
a
c
c
u
r
a
c
y
i
n
o
l
i
v
e
o
i
l
t
y
p
e
d
e
t
e
c
t
i
o
n
b
e
e
n
d
e
f
i
n
e
d
a
n
d
m
e
a
s
u
r
e
d
u
s
i
n
g
V
I
S
/
N
I
R
sp
e
c
t
r
o
sc
o
p
y
a
n
d
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
?
R
Q2
:
W
h
a
t
sp
e
c
i
f
i
c
V
I
S
/
N
I
R
sp
e
c
t
r
o
sco
p
y
t
e
c
h
n
i
q
u
e
s a
n
d
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
ms
h
a
v
e
b
e
e
n
u
se
d
i
n
t
h
e
st
u
d
i
e
s?
R
Q3
:
W
h
a
t
l
e
v
e
l
s
o
f
p
r
e
c
i
s
i
o
n
a
n
d
e
f
f
e
c
t
i
v
e
n
e
ss
h
a
v
e
b
e
e
n
o
b
t
a
i
n
e
d
b
y
t
h
e
c
o
m
b
i
n
e
d
V
I
S
/
N
I
R
a
n
d
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
me
t
h
o
d
s,
a
n
d
w
h
a
t
a
r
e
t
h
e
r
e
p
o
r
t
e
d
l
i
m
i
t
a
t
i
o
n
s?
R
Q4
:
H
o
w
d
o
t
h
e
r
e
s
u
l
t
s
o
b
t
a
i
n
w
i
t
h
t
h
e
c
o
m
b
i
n
a
t
i
o
n
o
f
V
I
S
/
N
I
R
a
n
d
mac
h
i
n
e
l
e
a
r
n
i
n
g
c
o
mp
a
r
e
d
t
o
o
t
h
e
r
o
l
i
v
e
o
i
l
t
y
p
e
d
e
t
e
c
t
i
o
n
met
h
o
d
s?
?
T
ab
le
2
.
PICO
tab
le
S
t
r
a
t
e
g
y
D
e
f
i
n
i
t
i
o
n
I
t
e
ms
K
e
y
w
o
r
d
s
S
e
a
r
c
h
E
q
u
a
t
i
o
n
P
P
r
o
b
l
e
m
o
r
P
o
p
u
l
a
t
i
o
n
D
e
f
i
c
i
e
n
c
i
a
d
e
l
a
n
á
l
i
s
i
s
d
e
l
t
i
p
o
d
e
a
c
e
i
t
e
d
e
o
l
i
v
a
“
O
l
i
v
e
O
i
l
”
,
“
T
y
p
e
o
f
O
l
i
v
e
O
i
l
”
(
“
O
l
i
v
e
O
i
l
”
)
A
N
D
(
“
M
a
c
h
i
n
e
Le
a
r
n
i
n
g
”
O
R
“
D
e
e
p
Le
a
r
n
i
n
g
”
O
R
“
V
I
S
/
N
I
R
”
)
I
I
n
t
e
r
v
e
n
t
i
o
n
U
so
d
e
V
I
S
/
N
I
R
y
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
“
V
I
S
/
N
I
R
”
,
“
M
a
c
h
i
n
e
Le
a
r
n
i
n
g
”
,
“
D
e
e
p
Le
a
r
n
i
n
g
”
C
C
o
m
p
a
r
i
so
n
M
é
t
o
d
o
s
d
e
e
v
a
l
u
a
c
i
ó
n
d
e
l
t
i
p
o
d
e
a
c
e
i
t
e
d
e
o
l
i
v
a
t
r
a
d
i
c
i
o
n
a
l
e
s
s
i
n
e
l
u
so
d
e
V
I
S
/
N
I
R
o
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
“
D
e
t
e
c
t
i
o
n
o
f
t
h
e
Ty
p
e
o
f
O
l
i
v
e
O
i
l
”
O
R
e
s
u
l
t
s
Ef
i
c
i
e
n
c
i
a
y
p
r
e
c
i
si
ó
n
e
n
l
a
d
e
t
e
c
c
i
ó
n
d
e
l
t
i
p
o
d
e
a
c
e
i
t
e
d
e
o
l
i
v
a
“
O
l
i
v
e
O
i
l
T
y
p
e
D
e
t
e
c
t
i
o
n
”
,
“
Ef
f
i
c
i
e
n
c
y
”
,
“
P
r
e
c
i
s
i
o
n
”
2
.
2
.
Sea
rc
h
s
t
ra
t
eg
y
T
h
e
s
ea
r
ch
was
ca
r
r
ied
o
u
t
in
th
r
ee
d
ata
b
ases
:
Sco
p
u
s
,
Scie
n
ce
Dir
ec
t,
an
d
Pu
b
Me
d
,
with
th
e
s
ea
r
ch
eq
u
atio
n
(
“
o
liv
e
o
il”
)
AND
(
“m
ac
h
in
e
lear
n
in
g
”
OR
“d
ee
p
lear
n
in
g
”
OR
“
VI
S/NIR
”
)
f
o
r
th
e
So
p
u
s
an
d
Scien
ce
Dir
ec
t
d
atab
ases
;
an
d
f
o
r
Pu
b
Me
d
th
e
s
ea
r
ch
e
q
u
atio
n
was
u
s
ed
:
(
“
Oli
v
e
Oil
”
)
AND
(
“
Ma
ch
in
e
L
ea
r
n
in
g
”
OR
“d
ee
p
lear
n
in
g
”
OR
“n
eu
r
al
n
etwo
r
k
s
”
OR
“
ar
tific
ial
in
tellig
en
ce
”
)
AND
(
“
Sp
ec
tr
o
s
co
p
y
”
OR
“
VI
S/NIR
”
OR
“
Vis
ib
le
Nea
r
-
I
n
f
r
a
r
ed
Sp
ec
tr
o
s
co
p
y
”
)
,
s
ee
T
ab
le
3
.
Als
o
,
s
tu
d
ies
th
at
wi
ll
an
aly
ze
o
liv
e
o
il
with
VI
S/NIR
an
d
m
ac
h
in
e
lear
n
in
g
wer
e
i
n
clu
d
ed
[
2
9
]
–
[
3
1
]
,
an
d
r
ec
o
r
d
s
th
at
d
ea
lt
w
ith
o
th
er
o
ils
wer
e
ex
clu
d
ed
[
3
2
]
–
[
3
4
]
.
T
ab
le
3
.
Sear
ch
on
d
atab
ase
D
a
t
a
b
a
s
e
S
e
a
r
c
h
E
q
u
a
t
i
o
n
To
t
a
l
S
c
o
p
u
s
(
“
O
l
i
v
e
O
i
l
”
)
A
N
D
(
“
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
”
O
R
“
D
e
e
p
L
e
a
r
n
i
n
g
”
O
R
“
V
I
S
/
N
I
R
”
)
1
5
4
S
c
i
e
n
c
e
D
i
r
e
c
t
(
“
O
l
i
v
e
O
i
l
”
)
A
N
D
(
“
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
”
O
R
“
D
e
e
p
L
e
a
r
n
i
n
g
”
O
R
“
V
I
S
/
N
I
R
”
)
1
,
1
4
6
P
u
b
M
e
d
(
“
O
l
i
v
e
O
i
l
”
)
A
N
D
(
“
M
a
c
h
i
n
e
Le
a
r
n
i
n
g
”
O
R
“
D
e
e
p
L
e
a
r
n
i
n
g
”
O
R
“
N
e
u
r
a
l
N
e
t
w
o
r
k
s
”
O
R
“
A
r
t
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
”
)
A
N
D
(
“
S
p
e
c
t
r
o
s
c
o
p
y
”
O
R
“
V
I
S
/
N
I
R
”
O
R
“
V
i
si
b
l
e
N
e
a
r
-
I
n
f
r
a
r
e
d
S
p
e
c
t
r
o
sc
o
p
y
”
)
32
On
ce
th
e
s
ea
r
ch
was
ca
r
r
ied
o
u
t
in
th
e
th
r
ee
d
atab
ases
,
1
,
3
3
2
r
e
co
r
d
s
wer
e
id
en
tifie
d
,
an
d
th
e
in
clu
s
io
n
an
d
ex
cl
u
s
io
n
cr
iter
i
a
wer
e
d
ef
in
ed
,
w
h
er
e
ar
ticles
b
eg
an
to
b
e
ex
clu
d
e
d
f
o
r
d
u
p
licates,
n
o
t
m
ee
tin
g
th
e
r
eq
u
ir
ed
cr
iter
ia,
an
d
co
n
tain
in
g
ex
clu
s
io
n
cr
iter
ia
[
3
5
]
.
Af
ter
ex
clu
d
in
g
th
e
ar
ticle
s
,
5
3
r
ec
o
r
d
s
wer
e
in
clu
d
ed
in
th
e
r
ev
ie
w,
s
ee
T
ab
le
4
.
T
h
e
ar
ticle
s
elec
tio
n
p
r
o
ce
s
s
f
o
llo
wed
th
e
p
r
e
f
er
r
ed
r
ep
o
r
tin
g
item
s
f
o
r
s
y
s
tem
atic
r
ev
iews
an
d
m
eta
-
an
aly
s
es
(
PR
I
SMA
)
s
ch
em
e,
a
s
tan
d
ar
d
ized
m
eth
o
d
f
o
r
r
e
p
o
r
tin
g
s
y
s
tem
atic
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u
r
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T
ab
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4
.
I
n
clu
s
io
n
an
d
ex
clu
s
i
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I
n
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s
Fig
u
r
e
1
.
PR
I
SMA
f
lo
wch
ar
t
T
h
e
co
m
b
in
atio
n
o
f
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
a
n
d
m
ac
h
in
e
lea
r
n
in
g
o
f
f
er
s
a
n
ac
cu
r
ate
way
t
o
id
en
tify
d
if
f
er
en
t
ty
p
es
o
f
o
liv
e
o
il,
im
p
r
o
v
in
g
th
e
ac
cu
r
ac
y
o
f
tr
a
d
itio
n
al
m
eth
o
d
s
[
5
]
,
[
7
]
.
T
h
is
a
p
p
r
o
ac
h
ef
f
ec
tiv
ely
im
p
r
o
v
es
id
e
n
tific
atio
n
ac
c
u
r
ac
y
[
8
]
,
[
1
0
]
.
Dev
elo
p
i
n
g
p
r
ed
ictiv
e
m
ac
h
in
e
lear
n
in
g
m
o
d
els
tr
ain
ed
with
s
p
ec
tr
o
s
co
p
ic
d
ata
s
p
ec
if
ic
to
d
if
f
er
e
n
t
o
liv
e
o
ils
is
ess
en
tial
to
ac
h
ie
v
e
th
is
p
r
ec
is
io
n
.
Ad
d
itio
n
ally
,
u
s
in
g
VI
S/NIR
s
y
s
tem
s
o
n
p
r
o
d
u
c
tio
n
lin
es
f
ac
ilit
ates
co
n
tin
u
o
u
s
,
n
o
n
-
d
estru
ctiv
e
a
n
aly
s
is
,
allo
win
g
f
r
e
q
u
en
t
test
in
g
with
o
u
t
d
am
ag
in
g
s
am
p
les.
T
h
is
n
o
n
-
d
estru
ctiv
e
an
aly
s
is
m
eth
o
d
p
er
f
ec
tly
in
teg
r
ates
in
to
th
e
p
r
o
d
u
ctio
n
p
r
o
ce
s
s
,
o
f
f
er
in
g
c
o
n
s
tan
t
an
d
ef
f
icien
t
q
u
ality
c
o
n
tr
o
l.
T
h
e
a
b
ilit
y
to
d
etec
t
ad
u
lter
atio
n
s
in
o
liv
e
o
il
is
also
s
ig
n
if
ican
tly
im
p
r
o
v
ed
,
en
s
u
r
in
g
p
r
o
d
u
ct
au
th
e
n
ticity
th
r
o
u
g
h
m
ac
h
in
e
lea
r
n
in
g
alg
o
r
ith
m
s
th
at
id
en
tify
s
p
ec
if
ic
s
p
ec
tr
o
s
co
p
ic
p
atter
n
s
o
f
ad
u
lter
atio
n
s
.
T
h
e
an
aly
s
is
p
r
o
ce
s
s
is
au
t
o
m
ate
d
b
y
im
p
lem
en
tin
g
au
to
m
ated
s
y
s
tem
s
with
VI
S/NIR a
n
d
m
ac
h
in
e
lear
n
in
g
,
r
e
d
u
cin
g
h
u
m
a
n
in
ter
v
e
n
tio
n
an
d
ass
o
ciate
d
er
r
o
r
s
.
Gen
er
atin
g
a
n
d
m
ain
tain
in
g
a
ce
n
tr
alize
d
d
ata
b
ase
o
f
o
liv
e
o
il
s
p
ec
tr
a
is
ess
en
tial
to
u
n
if
y
t
h
e
r
esu
lts
an
d
g
u
ar
a
n
tee
co
n
s
is
ten
c
y
in
th
e
an
aly
s
es
[
1
1
]
,
[
1
7
]
.
Fu
r
th
er
m
o
r
e,
in
co
r
p
o
r
atin
g
VI
S/NI
R
an
aly
s
is
s
y
s
tem
s
with
r
ea
l
-
tim
e
m
ac
h
in
e
lea
r
n
in
g
f
u
n
ctio
n
alities
th
r
o
u
g
h
o
u
t
t
h
e
p
r
o
d
u
ctio
n
p
r
o
ce
s
s
en
ab
les
co
n
s
tan
t
m
o
n
ito
r
in
g
o
f
th
e
q
u
ality
o
f
th
e
o
liv
e
o
il,
allo
win
g
a
r
ap
i
d
r
esp
o
n
s
e
to
an
y
p
r
o
b
lem
d
etec
ted
.
Ho
wev
er
,
d
ev
elo
p
in
g
an
d
m
ai
n
tain
in
g
th
ese
s
y
s
tem
s
ca
n
in
v
o
lv
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h
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tatio
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T
ab
le
5
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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4132
4124
T
ab
le
5
.
B
en
ef
its
an
d
im
p
lem
en
tatio
n
B
e
n
e
f
i
t
s
I
mp
l
e
me
n
t
a
t
i
o
n
R
e
f
e
r
e
n
c
e
I
mp
r
o
v
e
d
A
c
c
u
r
a
c
y
D
e
v
e
l
o
p
p
r
e
d
i
c
t
i
v
e
m
o
d
e
l
s wi
t
h
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
t
r
a
i
n
e
d
w
i
t
h
sp
e
c
t
r
o
sc
o
p
i
c
d
a
t
a
[
5
]
–
[
7
]
,
[
2
9
]
,
[
3
6
]
–
[
4
5
]
,
[
4
6
]
–
[
4
9
]
F
a
st
a
n
d
N
o
n
-
D
e
st
r
u
c
t
i
v
e
A
n
a
l
y
s
i
s
I
n
t
e
g
r
a
t
e
V
I
S
/
N
I
R
sy
st
e
ms
i
n
t
o
p
r
o
d
u
c
t
i
o
n
l
i
n
e
s
t
o
p
e
r
f
o
r
m
c
o
n
t
i
n
u
o
u
s
a
n
d
n
o
n
-
d
e
st
r
u
c
t
i
v
e
a
n
a
l
y
s
i
s
o
f
o
l
i
v
e
o
i
l
[
8
]
–
[
1
0
]
,
[
2
7
]
,
[
3
1
]
,
[
3
2
]
,
[
5
0
]
–
[
5
9
]
,
[
6
0
]
,
[
6
1
]
A
d
u
l
t
e
r
a
t
i
o
n
D
e
t
e
c
t
i
o
n
U
se
t
r
a
i
n
e
d
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
ms t
o
i
d
e
n
t
i
f
y
s
p
e
c
i
f
i
c
sp
e
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t
r
o
sc
o
p
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p
a
t
t
e
r
n
s
o
f
a
d
u
l
t
e
r
a
t
i
o
n
s
[
1
1
]
–
[
1
8
]
,
[
2
0
]
–
[
2
3
]
,
[
3
0
]
,
[
6
1
]
–
[
6
5
]
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
is
s
ec
tio
n
,
a
d
etailed
an
aly
s
is
o
f
p
r
ev
io
u
s
r
esear
ch
i
s
ca
r
r
ied
o
u
t
alo
n
g
with
a
b
i
b
lio
m
etr
ic
r
ev
iew,
wh
er
e
th
e
r
elatio
n
s
h
ip
s
b
etwe
en
th
e
ter
m
s
v
is
ib
le/n
e
ar
-
in
f
r
ar
e
d
s
p
ec
tr
o
s
co
p
y
(
VI
S/NIR)
an
d
m
ac
h
in
e
lear
n
in
g
a
r
e
ex
p
lo
r
ed
ab
o
u
t
t
h
e
id
en
tific
atio
n
o
f
th
e
ty
p
e
o
f
o
il.
Oliv
e
an
d
th
e
v
is
u
al
r
ep
r
esen
tatio
n
o
f
th
e
n
etwo
r
k
o
v
er
la
p
a
n
d
d
en
s
ity
.
I
n
th
e
s
ec
o
n
d
s
ec
tio
n
,
th
e
s
cien
tific
g
ap
o
f
th
e
ar
ticles
m
en
tio
n
ed
in
th
is
s
tu
d
y
is
ex
am
in
ed
to
d
ev
elo
p
an
a
r
ch
i
tectu
r
al
m
o
d
el
th
at
f
ac
ilit
ates
th
e
im
p
lem
en
tatio
n
o
f
a
n
al
g
o
r
ith
m
to
im
p
r
o
v
e
an
d
o
p
tim
ize
t
h
e
d
etec
tio
n
o
f
t
h
e
ty
p
e
o
f
o
liv
e
o
il.
3
.
1
.
B
ibl
io
m
et
ric
a
na
l
y
s
is
B
ib
lio
m
etr
ics
is
a
f
ield
th
at
u
s
es
s
tati
s
tical
an
d
m
ath
em
atica
l
m
eth
o
d
s
to
ex
am
in
e
s
cien
tific
p
r
o
d
u
ctio
n
.
I
t
also
ev
alu
ates
an
d
ex
am
in
es
th
e
im
p
ac
t
a
n
d
ev
o
lu
tio
n
o
f
r
esear
c
h
,
d
e
tects
tr
en
d
s
in
th
e
ev
o
lu
tio
n
o
f
s
cien
ce
an
d
te
ch
n
o
lo
g
y
,
an
d
m
ea
s
u
r
es
th
e
p
r
o
d
u
ctiv
ity
o
f
r
esear
ch
er
s
an
d
in
s
titu
tio
n
s
.
B
ib
lio
m
etr
ic
an
aly
s
is
i
s
a
v
a
lu
ab
le
to
o
l
n
o
t
o
n
ly
f
o
r
s
cien
tis
ts
b
u
t
also
f
o
r
s
cien
ce
m
an
ag
er
s
an
d
p
o
licy
m
ak
er
s
,
as
it
co
n
tr
ib
u
tes
to
en
r
ich
in
g
th
e
u
n
d
e
r
s
tan
d
in
g
o
f
s
cien
tific
r
esear
ch
an
d
its
im
p
a
ct
o
n
s
o
ciety
[
6
6
]
–
[
6
8
]
.
T
h
e
n
etwo
r
k
v
is
u
aliza
tio
n
in
Fig
u
r
e
2
s
h
o
ws
th
e
c
o
n
n
ec
ti
o
n
s
an
d
r
elatio
n
s
h
ip
s
b
etwe
en
d
if
f
e
r
en
t
r
esear
ch
ar
ea
s
in
u
s
in
g
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
an
d
m
ac
h
in
e
lear
n
in
g
f
o
r
o
liv
e
o
il
ty
p
e
d
e
tectio
n
.
T
h
is
f
ig
u
r
e
r
ev
ea
ls
h
o
w
th
e
co
n
ce
p
ts
ar
e
in
ter
r
elate
d
,
allo
win
g
u
s
to
id
en
tify
n
etwo
r
k
s
o
f
co
-
o
cc
u
r
r
en
ce
o
f
ter
m
s
an
d
r
esear
ch
s
tr
u
ctu
r
e
in
th
is
f
ield
.
No
d
es
r
ep
r
esen
t
s
p
ec
if
ic
r
esear
ch
to
p
ics,
wh
ile
lin
es
co
n
n
e
ct
ter
m
s
in
m
u
ltip
le
p
u
b
licatio
n
s
,
h
i
g
h
lig
h
tin
g
co
ll
ab
o
r
atio
n
s
a
n
d
lin
k
s
b
etwe
en
d
if
f
er
en
t
t
o
p
ics.
N
etwo
r
k
an
al
y
s
is
is
ess
en
tia
l
to
u
n
d
er
s
tan
d
h
o
w
r
esear
ch
is
s
tr
u
ctu
r
ed
a
n
d
w
h
ich
to
p
ics
ar
e
ce
n
tr
al
o
r
p
er
ip
h
er
al.
L
a
r
g
er
a
n
d
m
o
r
e
co
n
n
ec
ted
n
o
d
es,
s
u
ch
as
“
o
liv
e
o
il,
”
“
s
p
ec
tr
o
s
co
p
y
,
”
an
d
“
m
ac
h
i
n
e
lear
n
in
g
,
”
in
d
icate
ar
ea
s
o
f
g
r
ea
t
r
elev
an
ce
an
d
co
n
n
ec
tio
n
with
in
th
e
f
ield
,
s
u
g
g
esti
n
g
th
at
th
ese
t
o
p
ics
ar
e
cr
u
cial
f
o
r
cu
r
r
en
t
an
d
f
u
tu
r
e
r
esear
ch
.
Acc
o
r
d
i
n
g
to
th
e
f
ig
u
r
e,
th
e
n
o
d
es a
r
e
r
ep
r
esen
ted
in
4
g
r
o
u
p
s
.
Gr
o
u
p
1
(
R
ed
)
:
Fo
cu
s
ed
o
n
k
ey
co
n
ce
p
ts
lik
e
“
ex
tr
a
v
ir
g
in
o
liv
e
o
il,
”
“
d
is
cr
im
in
an
t
an
aly
s
is
,
”
an
d
“
f
o
o
d
an
aly
s
is
,
”
th
is
g
r
o
u
p
h
i
g
h
lig
h
ts
r
esear
ch
d
ed
icate
d
to
v
er
if
y
in
g
th
e
au
th
en
ticity
an
d
q
u
ality
o
f
o
liv
e
o
il.
Stu
d
ies
in
th
is
ca
teg
o
r
y
o
f
ten
ex
p
lo
r
e
m
eth
o
d
s
f
o
r
d
etec
tin
g
ad
u
lter
atio
n
,
class
if
y
in
g
d
if
f
e
r
en
t
o
il
g
r
ad
es,
an
d
en
s
u
r
in
g
co
m
p
lian
ce
with
f
o
o
d
in
d
u
s
tr
y
s
tan
d
ar
d
s
.
T
ec
h
n
iq
u
es
s
u
ch
as
ch
em
ical
p
r
o
f
ilin
g
an
d
s
en
s
o
r
y
ev
alu
atio
n
ar
e
f
r
eq
u
en
tly
u
s
ed
to
ass
ess
p
u
r
ity
an
d
n
u
tr
itio
n
a
l p
r
o
p
e
r
ties
.
Gr
o
u
p
2
(
Gr
ee
n
)
:
I
n
clu
d
es
ter
m
s
s
u
ch
as
“
m
ac
h
in
e
lear
n
i
n
g
,
”
“
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etw
o
r
k
s
,
”
an
d
“
s
p
ec
tr
u
m
a
n
aly
s
is
,
”
in
d
icatin
g
a
f
o
c
u
s
o
n
ap
p
ly
in
g
ar
tific
ia
l
in
tellig
en
ce
a
n
d
s
p
ec
tr
al
tec
h
n
iq
u
es
i
n
o
liv
e
o
il
r
esear
ch
.
T
h
is
g
r
o
u
p
ex
am
in
es
h
o
w
co
m
p
u
tatio
n
al
m
o
d
e
ls
an
aly
ze
s
p
ec
tr
al
d
ata
to
class
if
y
o
liv
e
o
ils
,
im
p
r
o
v
in
g
q
u
ality
co
n
tr
o
l
an
d
au
th
en
ticity
v
er
if
icatio
n
.
Ma
ch
in
e
lear
n
in
g
en
a
b
les
m
o
r
e
p
r
ec
is
e,
au
to
m
ated
ass
es
s
m
en
ts
th
at
en
h
an
ce
ef
f
ic
ien
cy
in
th
e
i
n
d
u
s
tr
y
.
Gr
o
u
p
3
(
Pu
r
p
le)
:
C
en
ter
ed
o
n
ter
m
s
lik
e
“
h
u
m
a
n
,
”
“
ad
u
lt,
”
an
d
“
f
em
ale,
”
th
is
g
r
o
u
p
s
u
g
g
ests
r
esear
ch
o
n
th
e
h
ea
lth
ef
f
ec
ts
o
f
o
liv
e
o
il
ac
r
o
s
s
d
if
f
er
en
t
d
e
m
o
g
r
ap
h
ics.
Stu
d
ies
m
ay
f
o
cu
s
o
n
its
b
en
ef
its
f
o
r
ca
r
d
io
v
ascu
lar
h
ea
lth
,
m
etab
o
lis
m
,
o
r
s
p
ec
if
ic
im
p
ac
ts
o
n
wo
m
en
’
s
h
ea
lth
.
T
h
e
in
clu
s
io
n
o
f
th
ese
ter
m
s
in
d
icate
s
an
in
ter
est in
d
ietar
y
p
atter
n
s
,
d
is
ea
s
e
p
r
ev
en
tio
n
,
a
n
d
th
e
r
o
le
o
f
o
liv
e
o
il in
lo
n
g
-
ter
m
well
-
b
ein
g
.
Gr
o
u
p
4
(
B
lu
e)
:
C
o
n
tain
s
k
ey
wo
r
d
s
s
u
ch
as
“
in
f
r
ar
e
d
d
ev
ices
”
an
d
“
ch
em
o
m
etr
ics,
”
r
e
f
lectin
g
an
in
ter
est
in
ad
v
an
ce
d
an
al
y
tical
tech
n
iq
u
es
f
o
r
o
liv
e
o
il
e
v
alu
atio
n
.
R
esear
ch
in
th
is
ar
ea
ex
p
lo
r
es
th
e
u
s
e
o
f
in
f
r
ar
ed
s
p
ec
tr
o
s
co
p
y
an
d
s
tatis
tical
m
o
d
elin
g
to
ass
e
s
s
th
e
co
m
p
o
s
itio
n
an
d
q
u
ality
o
f
o
liv
e
o
il
in
a
n
o
n
-
d
estru
ctiv
e,
ef
f
icien
t
m
an
n
er
.
T
h
ese
tech
n
o
lo
g
ies
p
r
o
v
id
e
v
alu
ab
le
in
s
ig
h
ts
f
o
r
au
th
en
tic
atio
n
an
d
ch
em
ical
ch
ar
ac
ter
izatio
n
.
T
h
ese
ad
v
an
ce
d
tech
n
o
lo
g
ies
h
av
e
allo
wed
r
esear
ch
er
s
to
d
ev
elo
p
p
r
ec
is
e
an
d
n
o
n
-
in
v
asiv
e
m
eth
o
d
s
f
o
r
ev
al
u
atin
g
o
il
q
u
ality
.
Pro
v
id
in
g
a
n
alter
n
ativ
e
to
tr
ad
iti
o
n
al
ch
em
ical
a
n
aly
s
es.
T
ec
h
n
iq
u
es
lik
e
in
f
r
ar
ed
s
p
ec
tr
o
s
co
p
y
a
n
d
c
h
em
o
m
etr
i
cs
en
ab
le
f
aster
an
d
m
o
r
e
ef
f
i
cien
t
ass
ess
m
en
ts
wh
ile
m
ain
t
ain
in
g
ac
cu
r
ac
y
in
d
etec
tin
g
p
u
r
ity
an
d
au
th
e
n
ticity
[
3
1
]
,
[
3
2
]
,
[
5
0
]
.
T
h
e
co
m
b
in
atio
n
o
f
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
with
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
,
s
u
ch
as
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVM)
,
n
e
u
r
al
n
etwo
r
k
s
,
an
d
d
ec
is
io
n
tr
ee
s
,
h
as
m
ad
e
it
p
o
s
s
ib
le
to
an
aly
ze
th
e
s
p
ec
tr
al
ch
ar
ac
ter
is
tics
o
f
o
liv
e
o
il
s
am
p
les.
T
h
is
ap
p
r
o
ac
h
is
e
x
tr
em
ely
ef
f
ec
tiv
e
in
d
is
cr
im
in
a
tin
g
b
etwe
en
ex
tr
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
N
ea
r
-
in
fr
a
r
ed
s
p
ec
tr
o
s
co
p
y
a
n
d
ma
ch
in
e
lea
r
n
in
g
t
o
d
etec
t
o
live
o
il
…
(
Leo
n
a
r
d
o
Led
esma
Ort
ec
h
o
)
4125
v
ir
g
in
,
v
ir
g
i
n
,
a
n
d
r
ef
in
e
d
o
li
v
e
o
ils
,
ac
h
iev
in
g
u
p
to
9
8
%
ac
cu
r
ac
y
in
ce
r
tain
s
tu
d
ies
[
2
9
]
,
[
3
6
]
.
Ma
ch
in
e
lear
n
in
g
m
o
d
els
h
av
e
id
e
n
tifie
d
co
m
p
lex
s
p
ec
tr
al
p
atter
n
s
co
r
r
esp
o
n
d
in
g
to
v
ar
io
u
s
ch
e
m
ical
co
m
p
o
s
itio
n
s
an
d
o
il p
u
r
ity
lev
els
[
3
9
]
,
[
4
5
]
.
Ad
o
p
tin
g
t
h
ese
m
eth
o
d
s
h
as
also
s
im
p
lifie
d
th
e
ev
alu
atio
n
o
f
th
e
ch
em
ical
c
o
m
p
o
s
itio
n
o
f
o
liv
e
o
il
,
allo
win
g
th
e
id
e
n
tific
atio
n
o
f
s
p
ec
if
ic
co
m
p
o
u
n
d
s
,
s
u
ch
as
f
atty
ac
id
s
an
d
p
o
ly
p
h
en
o
ls
,
wh
ich
ar
e
k
e
y
in
d
icato
r
s
o
f
o
i
l
q
u
ality
.
T
h
is
im
p
r
o
v
es
class
if
icatio
n
ac
cu
r
a
cy
an
d
p
r
o
v
id
es
r
elev
a
n
t
in
f
o
r
m
atio
n
f
o
r
p
r
o
d
u
ct
au
th
en
ticatio
n
a
n
d
q
u
ality
co
n
tr
o
l
[
5
0
]
–
[
5
5
]
,
r
ep
r
esen
tin
g
a
s
ig
n
if
ican
t
ad
v
a
n
ce
in
f
o
o
d
q
u
ality
co
n
tr
o
l.
T
h
is
in
n
o
v
ativ
e
ap
p
r
o
ac
h
n
o
t
o
n
ly
co
n
tr
ib
u
tes
to
s
c
ien
tific
k
n
o
wled
g
e
b
u
t
p
r
o
v
id
es
p
r
ac
tical
b
en
ef
its
f
o
r
in
d
u
s
tr
y
an
d
co
n
s
u
m
er
s
.
Fig
u
r
e
2
.
Oliv
e
o
il st
u
d
y
n
etw
o
r
k
v
is
u
aliza
tio
n
(
VOSv
iewe
r
)
3
.
2
.
M
a
nu
s
cr
ipt
a
na
ly
s
is
T
h
ese
ad
v
an
ce
d
tech
n
o
lo
g
ies
h
av
e
en
ab
led
r
esear
ch
e
r
s
to
d
ev
elo
p
p
r
ec
is
e
an
d
n
o
n
-
in
v
asiv
e
tech
n
iq
u
es
to
ev
alu
ate
o
il
q
u
ality
,
o
f
f
er
in
g
a
n
alter
n
ativ
e
to
co
n
v
en
tio
n
al
ch
em
ical
m
eth
o
d
s
[
5
]
,
[
7
]
.
I
n
th
e
in
itial
s
tag
e
o
f
t
h
e
s
ea
r
ch
,
th
r
ee
s
cien
tific
d
atab
ases
wer
e
u
s
ed
:
Sco
p
u
s
,
Scien
ce
Dir
ec
t,
an
d
Pu
b
Me
d
.
T
h
is
ex
p
lo
r
atio
n
le
d
to
th
e
in
itial
id
en
tific
atio
n
o
f
1
,
3
3
2
r
ec
o
r
d
s
.
Af
ter
war
d
,
a
p
u
r
if
icatio
n
p
r
o
c
ess
wa
s
ca
r
r
ied
o
u
t
f
o
llo
win
g
th
e
PR
I
SMA
m
eth
o
d
o
lo
g
y
,
wh
ich
in
cl
u
d
ed
elim
in
atin
g
d
u
p
licates
an
d
e
x
clu
d
in
g
d
o
cu
m
en
ts
ir
r
elev
an
t
to
th
e
r
esear
ch
.
T
h
is
th
o
r
o
u
g
h
p
r
o
ce
d
u
r
e
r
esu
lted
i
n
a
f
in
al
s
elec
tio
n
o
f
5
3
r
elev
a
n
t
m
an
u
s
cr
ip
ts
f
o
r
r
ev
iew,
as Fig
u
r
e
1
o
f
th
e
ar
ticle
d
escr
ib
es.
Ph
ase
1
o
f
th
is
p
r
o
ce
s
s
was
f
o
cu
s
ed
o
n
t
h
e
in
itial
s
ea
r
ch
o
f
t
h
e
r
ec
o
r
d
s
.
Ph
ase
2
in
v
o
lv
e
d
r
em
o
v
i
n
g
d
u
p
licates,
wh
ich
r
ed
u
ce
d
th
e
n
u
m
b
er
to
1
,
3
2
9
r
ec
o
r
d
s
.
I
n
Ph
ase
3
,
th
ese
r
ec
o
r
d
s
wer
e
r
ev
iewe
d
,
an
d
th
o
s
e
th
at
d
id
n
o
t
m
ee
t
t
h
e
in
clu
s
io
n
cr
iter
ia
wer
e
e
x
clu
d
e
d
,
leav
i
n
g
1
0
1
d
o
cu
m
en
ts
f
o
r
f
u
r
th
er
ev
alu
atio
n
.
Fin
ally
,
4
8
ar
ticles
th
at
d
id
n
o
t
m
ee
t
t
h
e
r
e
q
u
ir
e
m
en
ts
,
s
u
ch
as
th
e
ap
p
r
o
p
r
iate
an
aly
s
is
o
f
th
e
o
il
t
y
p
e
o
r
th
e
s
p
ec
if
ic
ap
p
licatio
n
o
f
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
an
d
m
ac
h
i
n
e
lear
n
in
g
,
wer
e
d
is
ca
r
d
ed
,
r
esu
ltin
g
in
a
f
in
al
s
elec
tio
n
o
f
5
3
r
ec
o
r
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u
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p
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a
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atab
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s
o
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ce
with
3
4
m
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ilter
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Me
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u
r
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s
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atab
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
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I
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15
,
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4
,
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g
u
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20
25
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4
1
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4132
4126
Her
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u
r
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s
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ies
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ality
o
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s
in
g
ad
v
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d
m
eth
o
d
o
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o
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ie
s
.
T
ab
le
6
.
R
esu
lts
o
b
tain
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f
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ch
D
a
t
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n
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34
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u
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3
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Nu
m
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e
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ts
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PR
I
SMA
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eth
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Fig
u
r
e
4
.
Nu
m
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er
o
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m
e
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ts
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er
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o
f
p
u
b
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3
.
3
.
P
re
cisi
o
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in
t
he
det
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t
io
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o
f
t
he
t
y
pe
o
f
o
liv
e
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i
l
us
ing
VIS
/N
I
R
s
pect
ro
s
co
py
a
nd
ma
chine
lea
rning
T
h
e
ac
cu
r
ac
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o
f
o
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e
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e
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tific
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s
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f
r
ar
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s
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tr
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d
m
ac
h
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e
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n
in
g
h
as
b
ee
n
d
eter
m
in
ed
th
r
o
u
g
h
its
ab
ilit
y
to
class
if
y
d
if
f
er
en
t
o
il
ty
p
es
co
r
r
ec
tly
.
T
h
is
ac
cu
r
ac
y
h
as
b
ee
n
ev
alu
ate
d
u
s
in
g
s
tan
d
ar
d
m
etr
ics
s
u
ch
as
p
r
ec
is
io
n
,
s
en
s
itiv
ity
,
an
d
s
p
ec
if
icity
.
T
h
ese
m
etr
ics
allo
w
u
s
to
ev
alu
at
e
h
o
w
ef
f
ec
tiv
e
p
r
ed
ictiv
e
m
o
d
els
ar
e
in
class
if
y
in
g
o
liv
e
o
ils
,
s
u
r
p
ass
i
ng
tr
ad
itio
n
al
m
eth
o
d
s
in
ter
m
s
o
f
s
p
ee
d
an
d
ac
cu
r
ac
y
.
T
ab
le
7
p
r
esen
ts
v
ar
io
u
s
s
tu
d
ies
th
at
h
av
e
im
p
lem
en
te
d
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
tech
n
iq
u
es
an
d
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
to
d
etec
t
th
e
ty
p
e
o
f
o
liv
e
o
il.
Am
o
n
g
th
e
s
p
ec
tr
o
s
co
p
y
tech
n
iq
u
es,
r
ef
lecta
n
ce
an
d
tr
an
s
m
is
s
io
n
s
p
ec
tr
o
s
co
p
y
s
tan
d
o
u
t,
as th
e
y
ar
e
u
s
ed
to
o
b
tain
d
etailed
s
p
ec
tr
al
d
ata
o
f
o
liv
e
o
il
[
1
]
–
[
8
]
.
N
u
mb
e
r
o
f
ma
n
u
scri
p
t
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
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&
C
o
m
p
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I
SS
N:
2088
-
8
7
0
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N
ea
r
-
in
fr
a
r
ed
s
p
ec
tr
o
s
co
p
y
a
n
d
ma
ch
in
e
lea
r
n
in
g
t
o
d
etec
t
o
live
o
il
…
(
Leo
n
a
r
d
o
Led
esma
Ort
ec
h
o
)
4127
R
eg
ar
d
in
g
m
ac
h
in
e
lear
n
i
n
g
a
lg
o
r
ith
m
s
,
v
ar
i
o
u
s
m
eth
o
d
s
h
a
v
e
b
ee
n
u
s
ed
,
in
clu
d
in
g
:
−
Su
p
p
o
r
t
v
ec
t
o
r
m
ac
h
in
es
(
SVM)
:
T
h
ey
ar
e
u
s
ed
f
o
r
th
eir
a
b
ilit
y
to
h
an
d
le
h
i
g
h
-
d
im
e
n
s
io
n
al
d
ata
an
d
f
in
d
th
e
h
y
p
er
p
la
n
e
th
at
b
est
s
ep
ar
ates
th
e
class
es
o
f
o
liv
e
o
il.
T
h
is
m
eth
o
d
is
p
a
r
ticu
lar
l
y
ef
f
ec
tiv
e
in
d
is
tin
g
u
is
h
in
g
s
u
b
tle
d
if
f
er
en
c
es in
s
p
ec
tr
al
d
ata,
en
s
u
r
in
g
ac
cu
r
ate
class
if
icatio
n
[
9
]
–
[
1
8
]
,
[
1
9
]
,
[
2
0
]
.
−
Ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
(
ANN)
:
T
h
ese
m
o
d
els
lev
er
a
g
e
m
u
ltip
le
lay
er
s
o
f
p
r
o
ce
s
s
in
g
to
ca
p
tu
r
e
co
m
p
lex
r
elatio
n
s
h
ip
s
b
etwe
e
n
s
p
ec
tr
al
v
ar
iab
les
an
d
o
liv
e
o
il
ty
p
es.
T
h
eir
ab
ilit
y
to
lear
n
p
atter
n
s
f
r
o
m
lar
g
e
d
atasets
m
ak
es
th
e
m
h
ig
h
ly
ef
f
ec
tiv
e
in
p
r
ed
ictin
g
o
il
class
if
icat
io
n
s
with
im
p
r
o
v
ed
p
r
ec
is
io
n
[
2
1
]
–
[
2
5
]
.
−
R
an
d
o
m
f
o
r
est:
b
y
c
o
m
b
in
in
g
m
u
ltip
le
d
ec
is
io
n
tr
ee
s
,
th
e
s
e
alg
o
r
ith
m
s
e
n
h
an
ce
ac
cu
r
a
cy
an
d
r
ed
u
c
e
o
v
er
f
itti
n
g
,
m
ak
in
g
th
em
we
ll
-
s
u
ited
f
o
r
class
if
y
in
g
s
p
ec
t
r
al
d
ata.
T
h
eir
r
o
b
u
s
tn
ess
all
o
ws
th
em
to
m
an
ag
e
v
a
r
iab
ilit
y
in
o
liv
e
o
il
s
am
p
les an
d
p
r
o
v
id
e
r
eliab
le
class
if
icatio
n
r
esu
lts
[
2
6
]
–
[
3
0
]
.
−
K
-
n
ea
r
est
n
eig
h
b
o
r
s
(K
-
NN)
:
T
h
is
p
r
o
x
im
ity
-
b
ased
class
if
icatio
n
m
eth
o
d
is
u
s
ef
u
l
f
o
r
i
d
en
tify
in
g
o
il
ty
p
es
b
ased
o
n
s
im
ilar
ities
in
VI
S/NIR
s
p
ec
tr
a.
I
ts
s
im
p
licit
y
an
d
ef
f
icien
cy
m
a
k
e
it
a
p
r
ac
tical
ch
o
ice
f
o
r
q
u
ick
an
d
ef
f
ec
tiv
e
class
if
i
ca
tio
n
task
s
[
3
1
]
–
[
3
5
]
.
−
L
in
ea
r
d
is
cr
im
in
an
t
an
aly
s
is
(
L
DA)
:
T
h
is
s
tati
s
tical
ap
p
r
o
ac
h
is
u
s
ed
to
f
in
d
th
e
lin
ea
r
c
o
m
b
in
atio
n
o
f
f
ea
tu
r
es
th
at
b
est
s
ep
ar
ates
d
if
f
er
en
t
class
es
o
f
o
liv
e
o
il
.
I
t
is
wid
el
y
a
p
p
lied
f
o
r
d
im
en
s
io
n
ality
r
ed
u
ctio
n
a
n
d
im
p
r
o
v
in
g
class
if
icatio
n
p
er
f
o
r
m
an
ce
in
s
p
ec
t
r
al
an
aly
s
is
[
3
6
]
–
[
4
0
]
.
T
h
ese
m
ac
h
in
e
lear
n
in
g
tec
h
n
iq
u
es
ar
e
co
m
p
lem
en
te
d
b
y
p
r
ep
r
o
ce
s
s
in
g
o
f
th
e
s
p
ec
tr
al
d
at
a,
s
u
ch
as
n
o
r
m
aliza
tio
n
a
n
d
b
aselin
e
co
r
r
ec
tio
n
,
im
p
r
o
v
in
g
p
r
ed
icti
v
e
m
o
d
els'
ac
cu
r
ac
y
[
2
1
]
–
[
2
5
]
.
T
h
e
ev
alu
atio
n
m
etr
ics
u
s
ed
,
s
u
ch
as
p
r
ec
is
io
n
,
s
en
s
itiv
ity
,
an
d
s
p
ec
if
icity
,
allo
w
th
e
ef
f
ec
tiv
en
ess
o
f
th
ese
m
o
d
els
to
b
e
m
ea
s
u
r
ed
[
2
7
]
–
[
3
1
]
.
T
h
an
k
s
to
th
ese
tech
n
iq
u
es,
h
ig
h
p
r
ec
is
io
n
in
o
liv
e
o
il
class
if
icatio
n
h
as
b
ee
n
ac
h
iev
e
d
[
3
2
]
–
[
4
1
]
.
T
h
ey
h
av
e
also
d
em
o
n
s
tr
ated
s
ig
n
if
ican
t
im
p
r
o
v
em
en
ts
in
s
p
ee
d
an
d
ac
c
u
r
ac
y
co
m
p
a
r
ed
to
tr
ad
itio
n
al
m
eth
o
d
s
[
4
2
]
–
[
5
0
]
.
T
ab
le
7
.
T
ec
h
n
iq
u
es o
b
tain
ed
#
Te
c
h
n
i
q
u
e
s
C
a
n
t
i
d
a
d
R
e
f
e
r
e
n
c
i
a
1
V
I
S
/
N
I
R
r
e
f
l
e
c
t
a
n
c
e
a
n
d
t
r
a
n
sm
i
ssi
o
n
sp
e
c
t
r
o
sc
o
p
y
8
[
1
]
–
[
8
]
2
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
ms
l
i
k
e
S
V
M
12
[
9
]
–
[
1
8
]
,
[
1
9
]
,
[
2
0
]
3
D
a
t
a
p
r
e
p
r
o
c
e
ss
i
n
g
:
N
o
r
mal
i
z
a
t
i
o
n
a
n
d
b
a
s
e
l
i
n
e
c
o
r
r
e
c
t
i
o
n
6
[
2
1
]
–
[
2
6
]
4
Ev
a
l
u
a
t
i
o
n
me
t
r
i
c
s
:
A
c
c
u
r
a
c
y
,
s
e
n
si
t
i
v
i
t
y
,
s
p
e
c
i
f
i
c
i
t
y
5
[
2
7
]
–
[
3
1
]
5
H
i
g
h
p
r
e
c
i
si
o
n
i
n
o
l
i
v
e
o
i
l
c
l
a
ssi
f
i
c
a
t
i
o
n
10
[
3
2
]
–
[
4
1
]
6
I
mp
r
o
v
e
m
e
n
t
s
i
n
sp
e
e
d
a
n
d
a
c
c
u
r
a
c
y
c
o
m
p
a
r
e
d
t
o
t
r
a
d
i
t
i
o
n
a
l
m
e
t
h
o
d
s
9
[
4
2
]
–
[
5
0
]
#
To
t
a
l
50
3
.
4
.
P
re
cisi
o
n lev
els a
nd
co
m
pa
riso
n o
f
m
e
t
ho
ds
in t
he
det
ec
t
io
n o
f
t
he
t
y
pe
o
f
o
liv
e
o
il
Pre
d
ictiv
e
m
o
d
els
b
ased
o
n
m
ac
h
in
e
lear
n
in
g
h
a
v
e
d
em
o
n
s
tr
ated
h
ig
h
p
r
ec
is
io
n
a
n
d
e
f
f
icien
cy
i
n
d
etec
tin
g
an
d
class
if
y
in
g
o
liv
e
o
il
ty
p
es
u
s
in
g
VI
S/NIR
tech
n
iq
u
es
[
1
0
]
–
[
1
7
]
.
T
h
ese
m
o
d
els
r
ep
licate
co
m
p
lex
an
d
ad
ap
tiv
e
p
atter
n
s
,
allo
win
g
f
o
r
ac
cu
r
ate
p
r
o
d
u
ct
class
if
icati
o
n
.
Ho
wev
er
,
ce
r
tain
lim
itatio
n
s
h
av
e
b
ee
n
r
e
p
o
r
ted
,
s
u
ch
as
s
am
p
le
v
ar
iab
ilit
y
an
d
p
r
o
ce
s
s
in
g
co
n
d
itio
n
s
,
wh
ich
m
ay
af
f
ec
t
th
e
ac
cu
r
ac
y
an
d
co
n
s
is
ten
cy
o
f
t
h
e
r
esu
lts
.
T
h
e
n
ee
d
f
o
r
co
n
s
tan
t
o
p
tim
iz
atio
n
an
d
ad
ju
s
tm
en
ts
to
th
e
m
o
d
els
is
also
an
im
p
o
r
tan
t b
a
r
r
ier
th
at
m
u
s
t b
e
o
v
er
co
m
e
to
im
p
r
o
v
e
t
h
e
im
p
l
em
en
tatio
n
o
f
th
ese
m
eth
o
d
s
.
T
h
e
co
m
b
in
atio
n
o
f
VI
S/NI
R
an
d
m
ac
h
in
e
lear
n
in
g
tec
h
n
iq
u
es
o
f
f
er
s
ad
v
a
n
tag
es
c
o
m
p
ar
ed
to
tr
ad
itio
n
al
o
liv
e
o
il
-
ty
p
e
d
etec
tio
n
m
eth
o
d
s
.
An
aly
s
es
p
er
f
o
r
m
ed
u
s
in
g
VI
S/NIR
an
d
m
ac
h
in
e
lear
n
in
g
a
r
e
f
aster
an
d
n
o
n
-
d
estru
ctiv
e
,
s
i
g
n
if
ican
tly
im
p
r
o
v
in
g
th
e
ef
f
icien
cy
o
f
th
e
d
etec
tio
n
p
r
o
c
ess
.
Fu
r
th
er
m
o
r
e,
q
u
ality
co
n
tr
o
l
a
u
to
m
atio
n
r
e
d
u
ce
s
h
u
m
an
i
n
ter
v
en
tio
n
a
n
d
m
in
im
izes
er
r
o
r
s
,
allo
win
g
a
r
ap
id
r
esp
o
n
s
e
to
q
u
ality
p
r
o
b
lem
s
[
2
5
]
–
[
2
8
]
.
I
n
co
m
p
a
r
is
o
n
,
tr
ad
itio
n
al
m
et
h
o
d
s
ar
e
ty
p
ically
s
lo
wer
an
d
m
o
r
e
d
estru
ctiv
e
,
wh
ich
ca
n
lim
it
th
eir
ap
p
licab
ilit
y
in
lar
g
e
-
s
ca
le
p
r
o
d
u
ctio
n
en
v
i
r
o
n
m
en
ts
[
3
0
]
–
[
3
7
]
.
T
h
e
g
en
er
atio
n
an
d
cu
r
atio
n
o
f
ce
n
tr
alize
d
d
ata
b
ases
also
im
p
r
o
v
e
p
r
o
d
u
ct
tr
ac
ea
b
ilit
y
a
n
d
a
u
th
en
ticit
y
,
o
f
f
er
in
g
a
m
o
r
e
in
teg
r
ated
an
d
r
o
b
u
s
t a
p
p
r
o
ac
h
to
o
liv
e
o
il q
u
ality
c
o
n
tr
o
l
[
4
0
]
–
[
4
4
]
; f
o
r
m
o
r
e
d
etails,
s
ee
T
ab
le
8
.
T
ab
le
8
.
Nu
m
b
er
o
f
tech
n
o
lo
g
ical
ap
p
r
o
ac
h
es
#
Te
c
h
n
o
l
o
g
i
c
a
l
A
p
p
r
o
a
c
h
e
s
Q
u
a
n
t
i
t
y
R
e
f
e
r
e
n
c
e
s
1
P
r
e
d
i
c
t
i
v
e
m
o
d
e
l
s
w
i
t
h
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
4
[
1
0
]
–
[
1
7
]
2
A
u
t
o
ma
t
i
o
n
a
n
d
o
p
t
i
m
i
z
a
t
i
o
n
o
f
q
u
a
l
i
t
y
c
o
n
t
r
o
l
4
[
2
5
]
–
[
2
8
]
3
G
e
n
e
r
a
t
i
o
n
a
n
d
c
o
n
ser
v
a
t
i
o
n
o
f
c
e
n
t
r
a
l
i
z
e
d
d
a
t
a
b
a
ses
o
f
o
l
i
v
e
o
i
l
s
p
e
c
t
r
a
7
[
3
0
]
–
[
3
7
]
4
I
mp
l
e
me
n
t
a
t
i
o
n
o
f
r
e
a
l
-
t
i
me
a
n
a
l
y
s
i
s
sy
st
e
ms f
o
r
r
a
p
i
d
r
e
s
p
o
n
s
e
t
o
q
u
a
l
i
t
y
p
r
o
b
l
e
ms
7
[
4
0
]
–
[
4
4
]
#
To
t
a
l
22
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
25
:
4
1
2
0
-
4132
4128
4.
CO
NCLU
SI
O
N
T
h
e
co
m
b
i
n
atio
n
o
f
v
is
ib
le/n
e
ar
-
in
f
r
ar
e
d
s
p
ec
tr
o
s
co
p
y
(
VI
S
/NI
R
)
an
d
m
ac
h
in
e
lear
n
in
g
t
ec
h
n
iq
u
es
h
as
b
ee
n
h
ig
h
lig
h
ted
as
an
ef
f
ec
tiv
e
an
d
ef
f
icie
n
t
to
o
l
f
o
r
d
etec
tin
g
an
d
class
if
y
in
g
o
liv
e
o
il.
T
h
e
s
y
s
tem
atic
r
ev
iew
p
r
o
ce
s
s
ad
d
r
ess
ed
th
r
ee
m
ain
p
h
ases
:
in
itiall
y
,
a
c
o
m
p
r
eh
e
n
s
iv
e
s
ea
r
ch
o
f
r
ec
o
r
d
s
was
p
er
f
o
r
m
ed
,
r
esu
ltin
g
in
1
3
2
9
d
o
cu
m
e
n
ts
.
Su
b
s
eq
u
en
tly
,
a
f
ter
th
e
eli
m
in
atio
n
o
f
d
u
p
licates
an
d
r
ev
iew
ac
co
r
d
in
g
to
in
clu
s
io
n
cr
iter
ia,
1
0
1
ar
ticles
wer
e
s
elec
ted
f
o
r
f
u
r
th
er
ev
a
lu
atio
n
.
Fin
ally
,
af
ter
ap
p
ly
i
n
g
s
tr
ict
cr
iter
ia,
5
3
r
ec
o
r
d
s
wer
e
in
cl
u
d
ed
th
at
m
et
th
e
s
p
ec
if
ic
r
eq
u
ir
em
en
ts
o
f
o
il
ty
p
e
an
aly
s
is
an
d
a
p
p
li
ca
tio
n
o
f
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
to
g
eth
er
with
m
a
ch
in
e
lear
n
in
g
.
Acc
o
r
d
in
g
to
th
e
r
esu
lts
,
th
e
d
is
tr
ib
u
tio
n
o
f
th
ese
ar
ticles,
Sco
p
u
s
s
to
o
d
o
u
t
as
th
e
p
r
im
ar
y
s
o
u
r
ce
with
3
4
ar
ticles,
m
ain
tain
in
g
th
e
h
ig
h
est
n
u
m
b
er
ev
e
n
af
ter
ap
p
ly
in
g
th
e
r
elev
a
n
t
f
ilter
s
.
Scien
ce
Dir
ec
t
co
n
tr
ib
u
ted
1
5
r
elev
a
n
t
ar
ticl
es,
wh
ile
Pu
b
Me
d
co
n
tr
ib
u
te
d
f
o
u
r
m
o
r
e.
T
h
ese
r
esu
lts
ev
id
en
ce
a
g
r
o
win
g
in
ter
est
an
d
d
ev
elo
p
m
e
n
t
in
ad
v
an
ce
d
an
al
y
tical
m
eth
o
d
s
to
im
p
r
o
v
e
th
e
au
th
e
n
ticity
an
d
tr
ac
ea
b
ilit
y
o
f
o
liv
e
o
il,
wh
ich
ar
e
c
r
u
cial
asp
ec
ts
o
f
q
u
ality
ass
u
r
a
n
ce
in
th
e
p
r
o
d
u
ctio
n
c
h
ain
.
T
h
e
p
o
ten
tial
o
f
r
a
p
id
,
n
o
n
-
d
estru
ctiv
e
an
aly
s
is
u
s
in
g
VI
S/NIR
s
p
ec
tr
o
s
co
p
y
,
co
u
p
led
with
th
e
p
r
ed
ictiv
e
p
o
w
er
o
f
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es,
is
tr
u
ly
tr
an
s
f
o
r
m
ativ
e.
T
h
is
p
r
o
m
is
in
g
ap
p
r
o
ac
h
is
s
et
to
r
ev
o
lu
tio
n
ize
q
u
ality
an
d
s
af
ety
s
tan
d
ar
d
s
in
th
e
o
liv
e
o
il
in
d
u
s
tr
y
.
I
t
o
p
tim
izes
q
u
ality
co
n
tr
o
l
p
r
o
ce
s
s
es
an
d
eq
u
ip
s
u
s
with
p
r
ac
tical
to
o
ls
to
ad
d
r
ess
em
er
g
in
g
c
h
allen
g
es
in
th
e
au
t
h
en
ticity
an
d
tr
ac
ea
b
ilit
y
o
f
h
i
g
h
-
d
em
an
d
f
o
o
d
p
r
o
d
u
cts
s
u
ch
as
o
liv
e
o
il.
T
h
is
in
f
o
r
m
atio
n
s
h
o
u
ld
in
s
p
ir
e
u
s
to
s
tu
d
y
th
e
f
u
tu
r
e
o
f
o
liv
e
o
il
an
aly
s
is
.
ACK
NO
WL
E
DG
E
M
E
NT
S
W
e
wo
u
ld
lik
e
to
ex
p
r
ess
o
u
r
g
r
atitu
d
e
t
o
th
e
Un
iv
e
r
s
id
ad
T
e
cn
o
l
ó
g
ica
d
el
Per
ú
f
o
r
p
r
o
v
id
in
g
u
s
with
s
u
p
p
o
r
t
an
d
ass
is
tan
ce
,
wh
ich
m
ad
e
it
p
o
s
s
ib
le
to
ca
r
r
y
o
u
t
th
is
s
y
s
tem
atic
r
ev
ie
w.
Ad
d
itio
n
ally
,
we
ex
ten
d
o
u
r
s
in
ce
r
e
a
p
p
r
ec
iati
o
n
to
o
u
r
p
r
o
f
ess
o
r
s
f
o
r
th
ei
r
g
u
i
d
an
ce
an
d
v
alu
a
b
le
in
s
i
g
h
ts
th
r
o
u
g
h
o
u
t
t
h
e
r
esear
ch
p
r
o
ce
s
s
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
is
r
esear
ch
was
co
n
d
u
cted
with
o
u
t
an
y
in
ter
n
al
o
r
e
x
te
r
n
al
f
in
an
cial
s
u
p
p
o
r
t.
T
h
e
a
u
th
o
r
s
co
n
f
ir
m
t
h
at
n
o
f
u
n
d
in
g
s
o
u
r
c
es in
f
lu
en
ce
d
th
e
d
ev
elo
p
m
en
t
o
r
o
u
tco
m
es o
f
t
h
is
s
tu
d
y
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
L
eo
n
ar
d
o
L
ed
esm
a
Or
tech
o
✓
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RE
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NC
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S
[
1
]
E.
B
o
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r
à
s
a
n
d
o
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s
,
“
P
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[
2
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N
.
A
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.
[
3
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X
.
M
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[
4
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L.
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.
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[
5
]
G
.
S
.
F
o
l
l
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d
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s,
“
F
o
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A
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4
.
[
6
]
D
.
Za
p
p
i
,
C
.
S
a
d
u
n
,
L
.
G
o
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,
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.
L.
A
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,
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A
n
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m
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c
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se
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r
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.
[
7
]
A
.
T
a
y
,
R
.
K
.
S
i
n
g
h
,
S
.
S
.
K
r
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s
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n
,
a
n
d
J.
P
.
G
o
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e
,
“
A
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.
[
8
]
T.
B
h
o
w
mi
k
,
A
.
C
h
o
w
d
h
u
r
y
,
a
n
d
S
.
K
.
D
a
s
M
a
n
d
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l
,
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D
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l
N
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w
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Pr
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[
9
]
J.
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Evaluation Warning : The document was created with Spire.PDF for Python.