Technical Article
Utiliza ao de
cavacos
para
analises por
Chip
utilization
Autores
for
near
infrared NIR
Ricardo Balleirini dos Santos
Leonardo
spectroscopy
analyses
I
de Sousa I
Chagas
Jose Livio Gomidel
Palavras chave
NIR
predigao
Abstract
eavaeos
Com
0
desenvolvimento das tee
nieas de analise via espeetroseopia
infravermelho
proximo
e
a
qliente eeonomia de tempo
no
eonse
essas
que
proporeionam urn fator que pas sou a
ser de grande importaneia nesses es
tudos e
a
forma
comoas
amostras sac
proeessadas
Para
amostras
granulometria inferior
longos pe
a
com
obtengao de
a
0 05mm sac neeessarios
riodos de moagem
e
classifieagao
laboratorio Esse estudo teve
jetivo
analisar
a
no
diretamente
infravermelho
nos
eavaeos
0
utilizado foi NlRSystems
5000 da FOSS
possui
em
ob
viabilidade da obten
gao dos espeetros
proximo
equipamento
como
Esse
leitor de fibra
neeessario seeeionar
Development of near infrared spectroscopy technique NIRS
for wood
analysis resulted in substantial savings in time To take advantage of this
savings it is very important to have a compatible wood sample preparati
on
To obtain wood particle sizes smaller than 0 05mm usually required
for NIR analysis it is necessary to spend long processing time for produc
tion and classification of wood meal The objective of this study was to
analyze the viability to obtain near infrared spectrum directly from wood
chips The equipment used was a FOSS NIRSystem 5000 To be able to
obtain the NIR spectrum it was necessary to cut the wood chips to smaller
dimensions compatible with the equipment spinning Thirty different Eu
calyptus clones were usedfor this study andfor each clone 30 wood chips
were
The
analyzed The moL ture content of wood chips was close to 35
statistical correlation between conventional laboratory basic density de
termination and NIR determination was 97
with an average prediction
error
7
7
For
extractives
content the correlation was 84
m3
wood
of
Kg
with an average prediction error of 0
29 The Klason lignin content pre
a
93
sented
correlation of
and an average prediction error of 0 55
These results demonstrate the viability to use this equipment to make NIR
measurements directly on wood chips
equipamento nao
optiea tendo sido
os
eavaeos
Keyword
NIR
prediction
wood chips
para
obtengao de dimensoes eompativeis
com 0
lizados
spinning de
eavaeos
leitura Foram uti
de 30 clones
sendo
eada amostra eonstituida por 30
eos com
teor de umidade de
damente 35
l
a
Para
a
eava
aproxima
densidade basiea
eorrelagao obtida foi de 97
com erro
medio de
previsao de
teor de extrativos
foi de 84
sac de 0 29
Klason
a
osa
Vi
osa MG
Vi
e
Brasil
7 7 kglm3
Para
0
eorrelagao obtida
com erro
para
eorrelagao
o
Referencias dos autores
1
Universidade Federal de
a
medio de
previ
teor de
lignina
0
obtida foi de 93
com erro
medio de
previsao
de 0 55
Esses resultados demonstram
lidade da utilizagao desse
to para
tamente
a
a
viabi
equipamen
realizagao de leituras dire
nos
eavaeos
o
espeetro
A teeniea de
espeetroseopia
in
no
proximo tern sido bastan
empregada no Brasil com 0 obj etivo
ganhou ampla aeeitagao
que
dos trabalhos inieiados
depois
em
1900
fravermelho
por Coblents
te
tros de absorbaneia de substaneia puras
de earaeterizar
vista
madeira do ponto de
a
quimieo
teenologieo
e
raeterizagao e utilizada tanto
xiliar
Essa
ea
para
au
programas de melhoramento
os
florestal desenvolvidos
do setor de eelulose
bem para preyer
e
pelas
empresas
como tam
papel
eomportamento das
0
madeiras no proeesso de
polpagao
Segundo PASQUINI
NIR e
2003
a es
de espee
peetroseopia
tipo
troseopia de vibragao que emprega a
energia do foton numa regiao de 2 65
x 1019 a 7 96 x 1020 a qual eorrespon
de
aos
urn
eomprimentos
de onda de 750
verifieando
eagao de
sua
a
utilidade para
grupos funeionais
a
identifi
organieos
mareou
boom dessa teeniea De 1930
o
total de
publieagoes
com NIR
de
que sac
lentas
caras e
obter espee
A deeada de oitenta
a
A eseolha das amostras de Eu
calyptus
0
1980
proeedimen
atoria
com
570
sp foi realizada de forma ale
em urn
lote eontendo madeira
valores de densidade entre 450
kg
m3
aproximadamente
reeolhidas trinta amostras eada
senvolvimento
lignina
foi de
impulsionado prinei
palmente pelas melhorias instrumen
tais espeetrofotometros assoeiadas
com aquisigao de dados espeetrais e
seus
amostra foi determinado
umidade
Na
a
e 0
eontribuigao
uma
De eada
eavaeos
teor de
0
densidade basic a
0
teor de
teor de extrativos
realizagao desse estudo foi
eessario verifiear
a
ne
area de leitura do
feixe de luz infravermelho no spinning
devido a desunifor
eelula de leitura
eomputadores
tratamentos
No Brasil a primeira
eontendo trinta
a
sendo
aproximadamente
255 enquanto que na deeada seguin
te esse numero ehegou a 1000 A es
peetroseopia NIR eneontrou rapido de
tos
a 2500nm No infravermelho proximo
midade dos
eavaeos
que nao oeupam
aplieagao analitiea da es
peetroseopia NIR pode ter aeonteeido
todo espago de leitura A verifieagao
responsaveis pela ab
sorgao nesta regiao Os eomprimentos
de onda nos quais estas vibragoes oeor
rem para urn eomposto qualquer sac
antes de 1991
reeortadas
dente dos metodos eonveneionais de
gas
fungoes de sua estrutura e eomposigao
analises
enta
Portanto
cia para eonstrugao de modelos de
o
NIR
primeiro
0
neeessidade de analises laboratoriais
vibragoes moleeulares
as
resultam
em
sac
overtones
espeetro de NIR
0
utihzado para identifiear
moleeulares
quimieas
avaliar
em
Segundo
destaear
de
urn
as
rapidez
pode
que
Alem disso
fatoria para
a
de leitura
ser
teeniea
uso
podem
menos
e a amos
mostra satis
in line tern
aplieagao
qualquer mole
quase que universal
No entanto e
teenologia
a
falha
A
fica muitas
No
leituras
no
tra
uma
moinhos
gao
minima da amostra
adequados
valor
ao
melho
os
com 0 eon
tratamentos
dos dados tern dado muito
termo
teenologia de
infraver
Apesar de
a
a
radiagao
NIR ter sido
deteetada antes da radiagao
melho medio esta foi
a
no
ultima
infraver
regiao
do
NIR
as
e
peneiras
Diante disso
como
0
onde
em
madeira
amostras sac pre
e
trabalhoso
de classifieagao
proposto estudo
principal objetivo
com a
de
no
yam
analisar
teve
a
vi
verifieou
que
a
area de leitura eorrespon
de 1 3em de diametro
eavaeos
amostrados apresenta
dois teores de umidade 33
Para realizagao das leituras
neeessario
a
mudan
se
espeetro obtido eoncluindo
a eerea
Os
papel
forma da eelula
variagao do diametro de
corte das folhas
adaptar
a
e
10
primeiro foi
forma dos
eavaeos
forma de eelula de leitura onde foram
obtidos espeetros
espeetros
Os
realizagao das
a
na mesma
dois lados dos
madeira tern que passar por
eorrespondentes
eavaeos
no
aos
total de 1800
para eada teor de umidade
espeetros foram estudados
e
tra
tados por meio de programas estatisti
eos
de ealibragao multivariada
posterior geragao
de
alguns
com
modelos
de ealibragao Esses modelos foram va
lidados
tus
com
15 amostras de
sp retiradas do
mesmo
anteriores sendo que
anahses
essas
Eucalyp
lote que
as
eontinham
quimieas
eonveneionais
e
densidade basiea
possibihtando
a
abilidade da realizagao das leituras
eomparagao dos valores de laborato
diretamente
rio com os de
paria tempo
dinamieo
proximo
estagio de ealibra
e muito demorado
e
e
amostra
paradas em forma de serragem com
uma
granulometria muito baixa sen
do que esse tipo de preparo de amos
ou
trole instrumental
com a
garantido
normalmente para
S H
A eombina
trabalho e muito
de amilises
foi feita por meio de folhas de
0 sueesso e
desse metodo de analise
vezes
easo
pois
gao dessas earaeteristieas
tipo de
ea
da qualidade dos valores de
0 sueesso
de C H N H
prepara
entre
refereneia assoeiados
gao
de referen
servem
diferenga
dependente
ligagoes
requer
estes
nesse
eula eontendo
O H
preeiso lembrar que a
ser
depen
NIR sempre vai
pois
libragao
se
nao destrutiva
se
campo de
no
Todavia eoncluido 0
inumeras vantagens
minuto por amostra
tragem
eons
NORRIS
e
teeniea apresenta
a
e
interagoes
suas
WILLIAMS
dentre
essa
que
misturas
madeira
proporgoes de diversos
tituintes alem das
2001
pode
espeeies
ser
eomplexas
no nosso easo a
as
que
transigoes harmonicas
com os
nos
e
eavaeos
tornaria
e menos
0
0
que pou
dispendioso
possivel predizer
NIR
as
via NIR
l
proeesso mais
modelos de ealibragao
sera
predigao
com
Assim
gerados
espeetros
earaeteristieas de madeira sem
A
figura
1
mostra
NIR obtido diretamente
Os
eavaeos
urn
espeetro
nos
eavaeos
utilizados
nesse
estudo
as
apresentavam
uma
uniforme do que
dos
definigao de uma
variagao dos valores do para
estudado A distribuigao esta
metro
apresentada
CHELL
tipo
sac seleeiona
quando
na natureza sem a
faixa de
xa
distribuigao mais
2
MI
Segundo
SCHlMLECK 1996
e
figura
na
de distribuigao
em
que toda
esse
fai
a
dos eonstituintes estudados apresenta
urn
numero de observagoes pareeido faz
com
gerado tenha uma
eapaeidade
predigao nao fi
restrito apenas a regiao central
0
que
modelo
melhor
eando
de
dos val ores do eonstituinte
o
primeiro
eomparar
modelos
os
umidos
vaeos
33
pas so
estudo foi
no
gerados
Espeetro NIR
nos eavaeos
desse estudo
teor de umidade de
de umidade de 10
os
1
com ea
8
teor
e com eavaeos seeos ao ar
todos
Figura
sendo que para
parametros estudados
7
os mo
6
delos com eavaeos umidos
se
apresen
0
ctl
taram
mais satisfatorios
Entao foram
C
modelos indi
gerados
viduais para eada eonstituinte utilizan
do
se
tOOo
para densidade basiea apre
digao gerado
sentou
gao
uma
essa
4
Z
3
2
0 96 eorrela
eorrelagao de
obtida
0
0 modelo de pre
espeetro
0
5
dados de valida
com os
o
gao cruzada
foi de 7 7
modelo de
0
medio de
erro
predigao
kglm3 Para a geragao desse
predigao foi necessaria a reti
rada de 5 amostras que claramente
eomportavam como outliers
eonsiderado muito bom
ma com 0 erro
obtido
pois
em
Esse
se
Os modelos de
Figura
2
Figura
3
Distribui
fao
kg m3
das densidades basieas dos
eavaeos
se
erro
e
aproxi
laboratorio Os
parametros estatistieos gerados para
modelo estao mostrados
Oensidade Sasica
na
figura
esse
3
predigao gerados
a
de serragem apresentaram eorre
lagoes menores para densidade basiea do
partir
que
quando sac gerados
mesmo
na
lote Isso
era
de
com eavaeos
se
esperar
maioria dos trabalhos que estao
do realizados
do NIR para
no
Brasil
predigao
com
do
pois
sen
ealibragao
de densidade ba
sica utilizando serragem
os
resultados
eneontrados nao tern sido satisfatorios
l
Outro
a ser
son
o
parametro muito importante
ealibrado e
teor de
0
pois segundo
lignina
SOUSA 2004
Kla
esse
tern influeneia direta
no eonsumo
reagentes quimieos
proeesso de pro
no
de
Modelo de
ao para densidade basiea
predic
dugao de eelulose
mento do
foi de 0 92
de 0 55
quando
de
predigao
senta
medio de
predigao
geragao desse modelo
a
foi necessaria a retirada de
outliers A
como
as
gerado
utilizada a validagao
5 amostras que claramente
tavam
rendi
eorrelagao obtida
a
e 0 erro
eruzada Para
no
No modelo
total
lignina
para
tambem
e
mesmo
se
eompor
figura
4 apre
earaeteristieas desse modelo
SCHULTZ
BURNS
e
eontraram valores de
1990
eorrelagao de
0 99 utilizando NIR para
lignina
predigao de 0
teor
valores obtidos
plamente
predigao
com urn erro
64
0
en
que mostra que
nesse
do
medio de
estudo foram
os
am
satisfatorios
o teor de extrativos em alcoolltolu
tambem e
enD
urn
tro de
qualidade
deiras
com
importante parame
pois ma
a
gerado
para
eorrelagao foi de
0 89
de
predigao
esse
predi
fao
para
0
teor de
lignina
total
polpagao
parametro
e 0 erro
de 0 22
desse modelo de
ria
Modelo de
alto teor de extrativos po
dem prejudiear 0 proeesso de
No modelo
4
Figura
da madeira
Para
a
medio
geragao
foi necessa
predigao
retirada de 8 amostras que clara
a
mente
se
eomportavam como outliers
figura
A
do para
esse
5 mostra
0
modelo gera
parametro
o modelo para extrativos totais tam
bem apresentou resultados satisfatori
os na
validagao eruzada indieando tam
bem
a
possibilidade de utilizagao de
para predigao desse parametro
Como pode se observar os mode
los gerados com a utilizagao de eava
eavaeos
eos
se
mostraram satisfatorios
no
que
Figura
5
Modelo
gerado
0 mesmo
do que
para extrativos
em
aleoolltolueno
diz
respeito aos dados de validagao
eruzada porem sempre e necessaria
validagao
uma
var a
externa para eompro
efieaeia dos
1 mostra
os
externa para
mesmos
0
quadro
a
densidade basic a dos
com a
vali
externa para densidade basiea
estao muito satisfatorios
SEP para
os
pois
0
RM
valores de densidade ba
sica foi de 7 5 Kg m3 mostrando
a efieieneia
lor para
As
a
a
mesmas
validagao
quando
foi
validagao eruzada
erro
do modelo
medio de
zadas para
amostras utilizadas para
externa dos modelos para
para teor de
validagao dos modelos
lignina
Klason Os valores
de validagao externa para
nina estao deseritos
o RMSEP da
as
ja que va
predigao e pra
a
para
que
0
se
teor de
no
teor de
quadro
validagao
lignina
aproxima
0
do
a
possibilidade de utili
para predigao na
UFV Os dados de
para
0
apresentados
no
quadro
o RMSEP da
para
0
validagao
2
de laborato
3
validagao
externa
teor de extrativos foi de 0 4
xima do
0
externa
teor de extrativos totais estao
valor eonsiderando born
lig
externa
foi de 0 76
erro
rio indieando
zagao desse modelo
densidade basiea tambem foram utili
Os resultados obtidos
sim
utilizada
resultados de validagao
eavaeos
dagao
tieamente
erro
pois
se
urn
apro
de laboratorio
Por fim deve
se
destaear que
estudo foi desenvolvido apenas
intuito de eomprovar
a
esse
com 0
efieieneia da
l
as
o
Quadro 1
Valores de
valida
fao
Amostra
Laboratorio
predi
fao
1
extema do modelo para densidade basiea
536
Erro absoluto
535
metodologia de eoleta
retamente
los
1
gerados apresentam
mode
os
quanti
uma
2
522
507
15
3
504
498
6
4
540
546
6
5
539
544
5
6
537
537
0
7
487
489
2
8
536
541
5
9
524
530
6
10
501
500
1
11
585
542
43
12
561
555
6
lores de densidade basiea apresentaram
13
551
556
5
alta eorrelagao
utilizados eomereialmente
serem
A utilizagao do
viavel para
aquisigao
a
diretamente nos
e
547
544
3
510
502
8
rida
valida
fao
extema do modelo para
0
teor de
lignina
tro e
total
Amostra
Laboratorio
predi
fao
1
29 5
Erro absoluto
32 2
7
2
predigao
para
os va
baixo erro medio de pre
15
em
dos espeetros
eavaeos
Os modelos de
14
Valores de
aparelho NIRSys
perfeitamente
terns 5000 da FOSS e
digao sendo que
Quadro 2
pela experieneia adqui
outros estudos
melhores do que
esses se
apresen
quando
0
espee
adquirido
na
forma de serragem
Quando
os
eavaeos
com urn
33
foram lidos
teor de umidade mais alto
as
eorrelagoes foram melhores
Os modelos
gerados
0
teor de
em
alcool
2
30 6
30 0
0 6
3
29 9
30 2
0 3
lignina
4
31 9
32 5
0 6
tolueno tambem apresentaram altas eor
28 1
0 3
total
para
de extrativos
e
5
4
28
6
27 9
7
27 9
29 1
1 2
8
29 1
28 9
0 2
9
30 8
30 5
0 3
10
30 0
32 0
2 0
SAC ON V Hexenuronic acid Klason
11
30 1
30 0
0 1
nin and
12
29 1
29 0
0 1
Spectroscopy
13
28 3
4
28
0 1
on
14
27 9
7
29
1 8
15
27 6
27 0
0 6
0 5
4
28
relagoes e baixo erro medio de predigao
CALDEIRA
Quadro 3
em
Valores de
valida
fao
extema do modelo para
0
Amostra
SANTOS
S L
lig
International
Colloquium
Kraft
osa 2003
Vi
MICHELL A J
Pulp
SCHIMLECK L R
NIR Spectroscopy of woods from
globulus Appita Vol49 No
teor extrativos
aleoolltolueno
A F
viscosity of Pulp predicted by NIR
Eucalyptus
tus
0
pois
dade muito pequena de amostras para
tararn
l
dos espeetros di
nos eavaeos
PASQUINI
Eucalyp
1
1996
C Near Infrared
Spec
troscopy Fundamentals Practical Aspects
predi
fao
Laboratorio
Erro absoluto
2 88
74
0
1
2 14
2
2 54
43
2
0 11
3
2 26
3 34
1 08
4
2 66
4 80
2 14
5
76
1
1 61
0 15
6
78
1
1 95
0 17
and
Analytical Applications
Chern Soc
SHULTS
1 39
40
1
0 01
8
2 12
2 35
0 23
Braz
TP
BURNS
DA
Rapid
secondary analysis oflignocellulose
parison
of near infrared
NIRO
ed transform infrared FTIR
nal
7
J
Vol 14 No 2 198 219 2003
com
and fouri
Tappi
Jour
1990
SOUSA
madeira de
9
2 51
1 94
0 57
10
1 84
1 32
0 52
tus
UFV 2004
L c
ao
tra
ao da
Caracteriza
em arvores
ao de Mestrado
Disserta
grandis
69p
11
73
2
73
2
0 00
12
2 10
2 09
0 01
13
71
1
1 91
0 20
Infrared
14
1 87
1 83
0 04
Association of Cereal
15
1 21
1 11
0 10
WILLIAMS P
Paul
de Eucalyp
NORRIS K Near
Technology
2nd ed
American
Chemistry
MN USA 2001
Inc
St
o
f
Chip utilizatio
near
infrared
Authors
Ricardo Balleirini dos SantosI
Leonardo
Chagas
de Sousa1
Jose Livio Gonridel
Keywords
chips
NIR
prediction
wood
These results demonstrate the
of using this
directly
development of near
infrared spectroscopy NIR
analysis
and
the
techniques
consequent time
a factor that
them
saving provided by
became of great importance in these
studies is the way of processing the
samples To obtain samples with a
granulometry lower than 0 05mm long
refining and lab classification periods
are required The objective ofthis study
was to analyze the viability of obtaining
near infrared
spectra directly on the
chips The equipment used was a FOSS
NIRSystem 5000 This equipment has
no
optical fiber reader so that it was
necessary to cut the chips into sections
to obtain dimensions compatible with
the reading spinning Chips from 30
clones have been used each sample
consisting of 30 chips with moisture
on
equipment
chips
for
viability
readings
content ofabout 35
The correlation
obtained forthe basic
with
kg
an
average
density was 97
prediction error of7
m3 The correlation obtained for the
extractives content
prediction
average
was
error
84
with
of029
an
and
the correlation obtained for the Klason
lignin
content was 93
average
prediction
with
error
technique
an
of 0 55
infrared spectroscopy
has been
among the numerous
considerably
used in
which may be nondestructive may be
In addition the
highlighted
technique
Brazil in order to characterize the wood
shows itself to be satisfactory to be used
from chemical and
in line has almost universal
technological points
application
bonds of C
ofview This characterization is used both
any molecule
help the forest improvement programs
developed by the pulp and paper sector
companies and to predict the wood
behavior in the pulping process
According to PASQUINI 2003
H N H S H
NIR
importance to the term near infrared
technology
In spite ofthe factthat NIR radiation
to
spectroscopy is
a
type ofvibration
spectroscopy using the photon
a
range from 2 65 x 10
which
corresponds
to
19
energy in
to 7 96
X
1O
wavelengths
2Q
of
750 to 2500nm In the near infrared
NIR
the molecular vibrations resulting
containing
O H
and
requires
sample preparation The
minimum
or
combination ofthese characteristics with
the instrumental control and the suitable
data treatments has attached great
has been detected before the medium
infrared radiation this
spectrum region
one was
that had
an
the last
ample
overtones are
acceptance after the works started in
absorption in this
wavelengths in which these
vibrations occur for a given compound
1900 by Coblents the first one to obtain
in harmonic transitions
responsible
for the
range The
are
7
near
to WILLIAMS and
2001
advantages presented by this technique
the quickness of reading less
than a
minute per sample and the sampling
the
With the
The
According
NORRIS
functions of its
composition
spectrum
molecular
structure
Therefore
and
the NIR
be used to
identify
species in complex chemical
can
mixtures in the present
case
the wood
and to evaluate the proportions ofseveral
constituents
interactions
in addition to their
pure substance
absorbency spectra
discovering its usefulness to identify
organic functional groups
The
80
technique
s
marked the boom of this
From 1930 to 1980 the total
amount of publications about NIR pro
nearly 255 increasing to
1000 in the following decade The NIR
spectroscopy had a quick development
mainly stimulated by the instrumental
cedures
was
l
as
Authors references
1
Federal University
osa
ofVi
osa MG
Vi
Brazil
improvements speetrophotometers as
sociated with acquisition of spectral data
and toilsome
and their treatments computers
classifYing screens In view ofthat the
proposed study had as main purpose to
analyze the viability of performing the
readings directly on the chips which
In Brazil the first contribution in
the field ofanalytical application ofthe
NIR
spectroscopy
may have occurred
before 1991
must
would
However it should be born in mind
that the NIR technology will be
the
always
conventional
dependent
analytical methods as these ones serve
as reference for
building calibration
on
models The difference between
success
be
procedure as the wood
processed by mills and
save
time and make the process
dynamic and less expensive Thus
generated it
be possible to predict with NIR
more
of the cell where with the variation
will
of the
spectra the wood
characteristics
without
laboratory analyses which are
expensive and slow being required
reference
values associated with the
Nevertheless
after
calibration stage the
sample
concluding the
success
ofthis
method of analysis is often assured
In case of wood
analyses to carry
out the NIR readings the samples are
usually prepared in the form of sawdust
with a very low granulometry this type
of sample preparation being a very slow
carrying out this study it was
verifY the reading area of
the infrared light beam in the spinning
reading cell due to the non
uniformity of the chips that do not
occupy the whole reading space This
verification was made by means of
paper sheets cut out in the same shape
with the calibration models
and failure in this kind of work is very
dependent on the quality ofthe
When
necessary to
The
Eucalyptus
sp
samples
been selected at random
in
have
a
lot
containing wood with density values
ranging approximately from 450 to 570
kglm3 totaling thirty samples each of
them containing thirty chips Then the
moisture content the basic density the
lignin content and the extractives content
have been determined for each sample
cutting diameter of the sheets
changes were found out in the spectrum
obtained from which
it followed that
the
reading area corresponds to
approximately 1 3 em diameter
The sampled chips had two moisture
contents 33
and 10
To perform the
readings first it was necessary to adapt
the shape ofthe chips to the shape ofthe
cell
where
reading
spectra
to
both
corresponding
chip sides have
been obtained totaling 1800 spectra for
each moisture content
The spectra were studied andtreated
by
means
of statistical multivariate
ra
co m
a
imen
rar
oq
u
calibration programs
with
later
of some calibration models
generation
validated with 15
These models
were
Eucalyptus
samples taken from the
previous ones which
same
lot
sp
as
the
contained conventional
chemical
and basic
analyses
density making
possible to compare the lab values with
those predicted via NIR
Figure 1 shows a NIR spectrum ob
tained directly on the chips
The chips used in this study showed
a more
uniform distribution than when
selected in the
without the
nature
Figure
1
NIR
spectrum on
the chips ofthis
study
definition of a variation range of the
values of the studied parameter The
distribution is shown in
According
to
SCHIMLECK
Figure
2
and
MICHELL
this type of
1996
distribution where the whole range of
the studied constituents shows a similar
number of observations
causes
the
generated model to have a better
prediction capacity without being
limited just to the central region ofthe
en
c
o
Q
en
0
o
a
Q
0
E
J
Z
constituent values
The fIrst step ofthe
study was to
compare the models generated with wet
chips 33 moisture content and air
dried chips 10 moisture content the
wet chip models having been found to
be more satisfactory for all studied
Basic
Figure
2
Chip
basic
density
density kg m3
distribution
parameters
Then individual models
generated
for each constituent
whole spectrum The
generated
were
for basic
using the
prediction model
density showed a
correlation of 0 96 which was obtained
with the
average
cross
validation data
prediction
error
was
7 7
The
kgm3
To generate this prediction model it was
necessaryto
remove 5
clearly behaving
as
samples
thatwere
outliers This
is considered to be very
error
good
as
approaches the laboratory obtained error
The statistical parameters generated for
this model are shown in Figure 3
The prediction models generated
from
sawdust presented
lower
correlations for basic density thanwhen
generated with chips from the same lot
l
as
o
Figure
3
Basic
density prediction
model
Table 1
This was to be
External validation values of the basic density model
Sample
Prediction
Laboratory
Absolute error
1
536
535
1
2
522
507
15
3
504
498
6
4
540
546
6
5
539
544
5
6
537
537
0
7
487
489
2
8
536
541
5
9
524
530
6
10
501
500
1
11
585
542
43
12
561
555
6
being
551
14
547
15
510
as
in most works
calibration for basic
density prediction
using sawdust the results obtained have
not been satisfactory
Another very important parameter to
be calibrated is Klason lignin content
as
according to SOUSA 2004 it has
direct influence on the consumption of
chemical reagents in the pulp production
process as well as on its yield In the
model generated for total lignin the
correlation obtained
prediction
average
13
expected
carried out in Brazil with NIR
556
5
544
3
502
8
the
cross
was
error
validation
0 92 and the
0 55
generate this
when
used
To
model it
was
was
prediction
samples thatwere
clearly behaving as outliers Figure 4
necessary to remove 5
Table 2
External validation values of the total lignin content model
presents the characteristics ofthis model
SCHULTZ and BURNS
Prediction
Laboratory
1
29 5
32 2
7
2
2
30 6
30 0
0 6
3
29 9
30 2
0 3
1990
using
NIR for lignin content prediction with
an average prediction error of 0 64
4
31 9
32 5
0 6
which shows that the values obtained in
5
4
28
28 1
0 3
this study
6
27 9
7
27 9
29 1
1 2
8
29 1
28 9
0 2
9
30 8
30 5
0 3
10
30 0
32 0
2 0
11
30 1
30 0
0 1
important wood quality
woods with high
parameter
extractives content may impair the
pulping process In the model generated
12
29 1
29 0
0 1
for this parameter the correlation
Sample
Absolute error
found correlation values of 0 99
0 5
4
28
were
widely satisfactory
The alcohol toluene extractives
content is also an
as
13
28 3
4
28
0 1
0 89 and the average
14
27 9
7
29
1 8
0 22
15
27 6
27 0
0 6
model
Table 3
External validation values of the alcohol toluene extractives con
tent model
was
prediction
prediction
samples that were clearly
error
To generate this
8
behaving as outliers had to be removed
Figure 5 shows the model generated
for this parameter
The model for total extractives also
Prediction
Laboratory
Absolute error
1
2 14
2 88
0 74
validation also indicating the
2
2 54
2 43
0 11
of
Sample
3
2 26
3 34
1 08
4
2 66
4 80
2 14
5
1 76
1 61
0 15
6
78
1
1 95
0 17
7
1 39
1 40
0 01
8
2 12
2 35
0 23
9
l
o
2 51
1 94
0 57
showed
satisfactory
using chips
results at the
for
eross
possibility
predicting this
parameter
As it
can
be observed the models
generated with chip utilization have been
found to be
the
satisfactory
cross validation data
validation is
with
regard
to
but an external
always required
to prove
10
1 84
1 32
0 52
their effectiveness Table 1 shows the
11
2 73
2 73
0 00
external validation results for the basic
12
2 10
2 09
0 01
density ofthe chips
13
71
1
1 91
0 20
14
1 87
1 83
0 04
15
1 21
1 11
0 10
The results obtained with the
external validation for basic
very
satisfactory
as
density are
the RMSEP for the
density values was 7 5 kgm3 thus
showing the efficiency of the model
since the value for the averageprediction
error is practically the same as when the
basic
validation
cross
The
same
was
used
samples
used for the
external validation of the basic
density
models have beenalso used for validating
the models for Klason lignin content The
external validation values for the
lignin
content are indicated in Table 2
The RMSEP ofthe externalvalidation
lignin content was 0 76 which
approaches the laboratory error
indicating the possibility of using this
prediction model at UFV The external
for the
validation data for the total extractives
content are shown in Table 3
The
RMSEP of the
external
validation for the extractives content was
4
Figure
Prediction model for the total
lignin
content
4
0
a value considered to be good as it
approaches the laboratory error
Finally it should be stressed that this
study was only developed in order to
demonstrate the efficiency of the
methodology of collecting the spectra
directly on the chips as the generated
models present too small
samples
to be
Using
amount of
an
commercially used
FOSS NIRSystems 5000
apparatus is perfectly viable for
obtaining spectra directly onthe chips
The prediction models for the
basic density values showed high
correlation and low average prediction
error and based on the experience
gained from other studies these ones
Figure
5
Model
generated
for alcohol toluene extractives
showed themselves better than when
the spectrum is obtained in the form
lignin
ofsawdust
by NIR Spectroscopy
When the
higher
correlations
were
read at
a
the
33
lloquium
yO
sa
were better
The models
lignin
chips
moisture content
generated
high
and low average prediction
viscosity
on
for the total
correlations
of
Pulp predicted
International Co
Kraft
Eucalyptus
Vi
Pulp
2003
L R NIR
A J
SCHIMLECK
Spectroscopy
of woods from
Eucalyptus globulus Appita VoL49
1
PASQUINI
Spec
Practical As
pects and Analytical Applications
SANTOS S L
SACON V Hexenuronic acid
Klason
Braz
219
Chern
2003
BURNS D A
of
secondary analysis
comparison
of
near
Soc
Vol 14
NO 2
J
198
Rapid
lignocellulose
infrared NIRO and
Tappi
Journal 1990
SOUSA
deira de
LC
trayao
Caracterizayao
em
arvores de
2004
da ma
Eucalyptus
de Mestrado UFV
grandis Dissertayao
C Near Infrared
troscopy Fundamentals
CALDEIRA A F
NO
1996
error
SHULTS T P
fouried transform infrared FTIR
MICHELL
and alcohol toluene extractives
contents also showed
and
l
69p
WILLIAMS
Infrared
P
NORRIS K Near
Technology
Association of Cereal
2nd ed
American
Chemistry
Paul MN USA 2001
Inc
St
as
o