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Direct determinations of the tannin and moisture in Acacia mearnsii bark, a fast quantitative
methodology for the extractive vegetable industry
Caren Machado MenezesAdilson Ben da Costa
Post-graduation Program In Industrial Systems and Processes - Masters
Santa Cruz do Sul University – UNISC
Adilson Ben da Costa, [email protected]
Summary• Introduction• Objective• Theoretical Basis• Methodology• Results• Conclusion• References
2
Adilson Ben da Costa, [email protected]
Introduction
The extractive vegetable industry is an important industrialsegment, which supplies raw materials to other industrialsectors.
O
OH
OH
RHO
OH
O
OH
OH
R
OH
HO
OHO
R
HO
HO
OH
OH
OH
O
OH
HO
OH
OH
OH
3
4
8
4
6
a)
A
B
Tannins are polyphenoliccomplex of vegetal origin,widely used in leather tanningindustry, adhesive, oil, rubber,and pharmaceutical products.(JORGE et al., 2001; PAIVA et al., 2002;AZEREDO, 2011)
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Adilson Ben da Costa, [email protected] 4
In Brazil, Acacia mearnsii bark is most used in theproduction of tannins, and approximately 158,000 tons ofbark were extracted in the south region in 2008.It is important consider that the concentration of tannins inthe bark of Acacia mearnsii can reach up to 28% (dry weightbasis) (Santos et al., 2001).
Introduction
Adilson Ben da Costa, [email protected] 5
However, the tannins productivity depends on severalfactors, such as: genetic; soil quality; climate; cultivation techniques and management.
The interaction of these factors may lead to significantvariation in the tannin concentration in the bark purchasedby industry (RAWCHAL et al., 2001; CUNHA et al., 2006; MARTINEZ, 2006).
Introduction
Adilson Ben da Costa, [email protected] 6
Nevertheless, the market of Acaciamearnsii bark is based only on the massof the material, rather than itsconcentration of tannins, or moisture.
Introduction
Due to the official tannin methods fordetermination (NBR 11131, BRASIL,2008), needs up to 20 hours perdetermination, difficults its applicationfor the quality control in the purchasingprocess of raw materials and alongproduction process.
Extraction
Filtraction
Adilson Ben da Costa, [email protected]
Objective
Develop an alternative methodology for determinationof tannins and moisture directly in the bark of Acaciamearnsii using near infrared spectroscopy andmultivariate calibration methods.
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Adilson Ben da Costa, [email protected]
Theoretical BasisInfrared spectroscopyIR spectroscopy is one of the most common spectroscopictechniques used by organic and inorganic chemists.
X-RAY0,1 – 100 A
ULTRA-VIOLET10 - 180 nm
VISIBLE400-780 nm
INFRAREDMICROVAWE3 mm-20 cm
RADIO10 m-30 Km780 nm–0.30 mm
NEAR MID FAR
ʎ, cm-1 (wavenumber) 12820 to 4000 4000 to 400 400 to 33
ʎ, nm 780 to 2500 2500 to 25000 0,0025 to 0,03 cm
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Adilson Ben da Costa, [email protected]
Theoretical BasisInfrared spectroscopyIR spectroscopy is one of the most common spectroscopictechniques used by organic and inorganic chemists.
0,00
0,25
0,50
0,75
1,00
40005000600070008000900010000
Abso
rban
ce
Wavenumber, cm-1
NIR (near infrared) spectrum of dry tannin powder
9
Adilson Ben da Costa, [email protected]
Theoretical BasisMultivariate calibractionPartial Least Square (PLS) is the most widely usedmultivariate calibration method
PLS uses the technique of PrincipalComponent Analysis to reduce the size ofthe data set before to correlate the spectra(matrix X) and the properties of interest(matrix Y).
𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹 =∑𝒊𝒊=𝟏𝟏𝒏𝒏 (𝒚𝒚𝒊𝒊−ŷ𝒊𝒊)𝟐𝟐
𝒏𝒏Root Mean Square Error of Cross-Validation
X = T.P + E Y = T.q + f
Matrix X Matrix Y
Adilson Ben da Costa, [email protected]
Theoretical BasisMultivariate calibractionPartial Least Square (PLS) is the most widely usedmultivariate calibration method
PLS Partial Least SquaresIs a full spectrum method
11
Adilson Ben da Costa, [email protected]
Theoretical BasisMultivariate calibractionPartial Least Square (PLS) is the most widely usedmultivariate calibration method
PLS Partial Least SquaresIs a full spectrum method
iPLS Interval Partial Least Squares
The spectra are divided in intervals,which are modeled in separate toselect the best region, associated tolowest RMSCV.
12
Adilson Ben da Costa, [email protected]
Theoretical BasisMultivariate calibractionPartial Least Square (PLS) is the most widely usedmultivariate calibration method
PLS Partial Least SquaresIs a full spectrum method
iPLS Interval Partial Least Squares
𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹 =∑𝒊𝒊=𝟏𝟏𝒏𝒏 (𝒚𝒚𝒊𝒊−ŷ𝒊𝒊)𝟐𝟐
𝒏𝒏Root Mean Square Error of Cross-Validation
The spectra are divided in intervals,which are modeled in separate toselect the best region, associated tolowest RMSCV.
13
Adilson Ben da Costa, [email protected]
Theoretical BasisMultivariate calibractionPartial Least Square (PLS) is the most widely usedmultivariate calibration method
PLS Partial Least SquaresIs a full spectrum method
iPLS Interval Partial Least Squares
siPLS Synergy interval Partial Least Square
𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹 =∑𝒊𝒊=𝟏𝟏𝒏𝒏 (𝒚𝒚𝒊𝒊−ŷ𝒊𝒊)𝟐𝟐
𝒏𝒏Root Mean Square Error of Cross-Validation
The spectra are divided in intervals,which are modeled in the combinationform to select the best regions (two ormore), associated to lowest RMSCV.
14
Adilson Ben da Costa, [email protected]
Theoretical BasisMultivariate calibraction
Infrared spectroscopy+The combination of these tools resulting in analyticalmethodologies that are characterized for being:
non-destructive;
fast;
lower reagent consumption;
lower waste generation.
15
Adilson Ben da Costa, [email protected]
Theoretical Basis
Presenting itself as an efficient alternative for routineanalysis in quality control laboratory, such as: Petroleum and oil products PASQUINI and BUENO, 2007;
BORIN and POPPI, 2005; BALABIN et al., 2011
Vegetable oils and biofuels SINELLI et al., 2010; KILLNER et al., 2011; BALABIN and SAFIEVA, 2011;FERRÃO et al., 2011
Coffee powder FERRÃO et al., 2003
Wood and cellulose MAGALHÃES et al., 2005;PASQUINI et al., 2007; VENÁS and RINNAN, 2008; HEIN et al., 2010; DERKYI et al., 2011a, DERKYI et al., 2011b.
Multivariate calibractionInfrared spectroscopy+
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Adilson Ben da Costa, [email protected]
Methodology
IR Spectrum Acquisition
Tannin(NBR 11131)
Matrix Y
Matrix X
Preprocessing
Exploratory analysisPCA
PLS, iPLS e siPLS
Sample preparation
Comparison of results
Sampling
Moisture(NBR 14929)
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Adilson Ben da Costa, [email protected]
Methodology First step – Sampling and preparation
Samples werecollected in areforestation areafrom state of RioGrande do Sul,Brazil.Brazil
Samples of Acacia mearnsiibark were collected in a
reforestation area from state of Rio Grande do Sul, Brazil
Adilson Ben da Costa, [email protected]
The bark was extracted in the DBH (diameter at breast height) ≅ 1.40 m
Methodology First step – Sampling and preparation
1.40 m
Adilson Ben da Costa, [email protected]
Methodology First step – Sampling and preparation
3years old
4 5 6 7 8 9
Samples were collected from 7 treeamong 3 and 9 years old, in onlysampling campaign.
Adilson Ben da Costa, [email protected]
Methodology
Each sample was divided into 2parts, from which were collectedinfrared spectra at 3 differentpositions on the inner surface,obtained 42 infrared spectra from 7bark samples.
Acacia mearnsii bark Preparation Subsample
Perkin Elmer, model Spectrum 400
Second step – Acquisition of near infrared spectra
21
Adilson Ben da Costa, [email protected]
Methodology
Acacia mearnsii bark Preparation Subsample
Perkin Elmer, model Spectrum 400
Perkin Elmer, model Spectrum 400range from 7,500 to 4,000 cm-1,resolution of 16 cm-1 and 32 scans.
Second step – Acquisition of near infrared spectra
22
Adilson Ben da Costa, [email protected]
Methodology Third step – Tannin and moisture determination
BARK SAMPLE
Grinding
Extraction of tanninand no-tannin
Filtration
Gravimetric quantification20 h
for e
ach
dete
rmin
atio
n
NBR 11131
23
Adilson Ben da Costa, [email protected]
Methodology Third step – Tannin and moisture determination
BARK SAMPLE
Grinding
Gravimetric quantification2 h
for e
ach
dete
rmin
atio
n
NBR 14929
24
Adilson Ben da Costa, [email protected]
Methodology Fourth step – Data modeling
Software Solo 6.5.3 (Eigenvector Research, Inc.)
• Partial least squares (PLS)• Interval Partial least squares (iPLS)• Synergy Interval Partial least squares (siPLS)
http://www.eigenvector.com/software/solo.htm
25
Adilson Ben da Costa, [email protected]
Methodology Fourth step – Data modeling
To eliminate unnecessary information ofthe spectra and make the best matrixconditioning, different pretreatmenttechniques were used and evaluatedaccording to the results of RMSECV.
MATRIX X MATRIX YNone NoneNone, AutoNone, MCNormal, AutoNormal, Multiplicative Signal Correction (MSC), AutoNormal, Standard Normal Variate (SNV), AutoNormal, MSC, Auto, AutoNormal, MSC, MC, AutoNormal, MSC, 1D, MC, AutoNormal, MSC, 2D, MC, Auto
26
Adilson Ben da Costa, [email protected]
Methodology Fourth step – Data modeling
MODEL INTERVALSiPLS 2iPLS 3iPLS 4iPLS 8iPLS 16iPLS 32siPLS 8
siPLS16 16siPLS32 32
PLS Partial Least SquaresIs a full spectrum method
iPLS Interval Partial Least Squares
siPLS Synergy interval Partial Least Square
27
Adilson Ben da Costa, [email protected]
Results
Results obtained in analysis of the Acacia mearnsii bark usingthe reference methods ranged between...
NBR 11131Tannin: 7.9±0.2% - 20.9±0,4%
NBR 14929Moisture: 55.3% - 62.8%
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Adilson Ben da Costa, [email protected]
Results
The profile of NIR spectra of all samples.
O-H stretchMolecular vibrations
C – H stretchAromatic
O-H stretchMolecular vibrations
29
Adilson Ben da Costa, [email protected]
Results
Model Preprocessing LV RMSECV r (CV)PLS None (x, y) 5 2.23 0.829PLS None (x), Auto (y) 10 1.70 0.906PLS None (x), MC (y) 10 1.70 0.906PLS Normal (x), Auto (y) 12 1.35 0.942PLS Normal, MSC (x), Auto (y) 7 1.78 0.895PLS Normal, SNV (x), Auto (y) 7 1.78 0.896PLS Normal, MSC, Auto (x), Auto (y) 10 0.97 0.971PLS Normal, MSC, MC (x), Auto (y) 6 1.78 0.895PLS Normal, MSC, 1D, MC (x), Auto (y) 4 2.78 0.724PLS Normal, MSC, 2D, MC (x), Auto (y) 4 4.02 0.249
Results of calibration models for TANNIN by PLS usingdifferent data preprocessing.
30
Adilson Ben da Costa, [email protected]
Results
31
(6333-5167)
TANNINiPLS Model3 intervals
RMSECV = 1.56R = 0.921
6 8 10 12 14 16 18 20 226
8
10
12
14
16
18
20
22
Y Measured 1 Tannin, %
Y C
V P
redi
cted
1 T
anni
n, %
Am3_A3
Am3_B1
Am3_B2 Am3_B3
Am4_A2 Am4_A3
Am5_A2 Am5_B3
Am6_B3
Am7_A1
Am7_A3
Am7_B3
Am8_A3
Am8_B3
Am9_B1 Am9_B3
Preprocessing: Normal, MSC, Auto (x), Auto (y)
Adilson Ben da Costa, [email protected]
Results
32
TANNINsiPLS Model8 intervals
RMSECV = 0.69R = 0.987 4000 4500 5000 5500 6000 6500 7000 7500
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 (10 LVs)
(# LVs)
Wavenumber, cm-¹R
MS
EC
V
1010101010101010
6 8 10 12 14 16 18 20 226
8
10
12
14
16
18
20
22
Y Measured 1 Tannin, %
Y C
V P
redi
cted
1 T
anni
n, %
Am3_B1
Am3_B3
Am4_A3
Am5_B3
Am7_A3 Am7_B3
Am8_A3 Am8_B3
Am9_B3
(6626-4442)
Preprocessing: Normal, MSC, Auto (x), Auto (y)
Adilson Ben da Costa, [email protected]
Results
33
TANNINTannin results obtained by reference methodology and iPLSand siPLS models.No significant differences (P>0.05) among the results wereobserved.
Adilson Ben da Costa, [email protected]
Results
Model Preprocessing LV RMSECV r (CV)PLS None (x, y) 7 3.14 0.502PLS None (x), Auto (y) 4 1.60 0.820PLS None (x), MC (y) 4 1.60 0.820PLS Normal (x), Auto (y) 4 1.60 0.836PLS Normal, MSC (x), Auto (y) 6 1.37 0.868PLS Normal, SNV (x), Auto (y) 6 1.37 0.868PLS Normal, MSC, Auto (x), Auto (y) 7 1.32 0.878PLS Normal, MSC, MC (x), Auto (y) 5 1.37 0.868PLS Normal, MSC, 1D, MC (x), Auto (y) 4 1.98 0.694PLS Normal, MSC, 2D, MC (x), Auto (y) 2 2.40 0.520
Results of calibration models for MOISTURE by PLS usingdifferent data preprocessing.
34
Adilson Ben da Costa, [email protected]
4000 4500 5000 5500 6000 6500 7000 75000
0.5
1
1.5
2
2.5
3
(10 LVs)
(# LVs)
Wavenumber, cm-¹R
MS
EC
V
9 1 2 410 4 2 3
Results
35
MOISTUREiPLS Model3 interval
RMSECV = 1.48R = 0.844
54 55 56 57 58 59 60 61 62 63 6454
55
56
57
58
59
60
61
62
63
64
Y Measured 1 Moisture, %
Y C
V P
redi
cted
1 M
oist
ure,
%
Am3_A1
Am3_A3
Am3_B3
Am4_A3
Am4_B2
Am4_B3
Am5_A1
Am5_A3 Am5_B2
Am5_B3
Am6_A1
Am6_B3 Am7_A2
Am7_A3
Am8_B1
Am8_B2 Am8_B3
Am9_A3
Am9_B1
Am9_B2
Am9_B3
Preprocessing: Normal, MSC, Auto (x), Auto (y)
(6333-5167)
Adilson Ben da Costa, [email protected]
4000 4500 5000 5500 6000 6500 7000 75000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
(10 LVs)
(# LVs)
Wavenumber, cm-¹R
MS
EC
V
10101010 91010101010 910 9 9101010101010101010101010 910 9101010
Results
36
MOISTUREsiPLS Model32 intervals
RMSECV = 0,71R = 0.967
(7500-7392, 7282-7065, 5647-5103,4884-4776, 4121-4013)
55 56 57 58 59 60 61 62 63 6455
56
57
58
59
60
61
62
63
64
Y Measured 1 Moisture, %
Y C
V P
redi
cted
1 M
oist
ure,
%
Am3_A2
Am3_A3
Am3_B3
Am4_A2
Am4_B2
Am4_B3
Am5_A1 Am5_A3 Am5_B3
Am7_B1
Am7_B2
Am7_B3
Am8_A1
Am8_B1 Am8_B3 Am9_A1
Am9_A3
Am9_B2 Am9_B3
Preprocessing: Normal, MSC, Auto (x), Auto (y)
Adilson Ben da Costa, [email protected]
Results
37
MOISTUREMoisture results obtained by reference methodology and iPLSand siPLS models.No significant differences (P>0.05) among the results wereobserved.
Adilson Ben da Costa, [email protected]
Conclusion
Results presented in this study show that near infraredspectroscopy (NIRS) combined with multivariatecalibration methods can be applied for the directdetermination of tannin and moisture on the Acaciamearnsii bark.
The proposed methodology has following advantagesover the reference methods:
• simple sample preparation,
• no use of reagents and no waste,
• shorter analysis time (5 minutes per sample).
38
Adilson Ben da Costa, [email protected]
Conclusion
These advantages may contribute to industry have anbetter control over the tannin manufacturing process,and purchase of raw materials, and in stock managementin industry and in forests of Acacia mearnsii.
39
Adilson Ben da Costa, [email protected]
References• DECKMANN, S. M.; POMILIO, J. A. (2010). Avaliação da Qualidade da Energia Elétrica.
Unicamp, Campinas. Accessed in December, 08, 2011. Available at:http://www.dsce.fee.unicamp.br/~antenor/pdffiles/qualidade/a1.pdf
• DUGAN et al. (2003). Electrical Power Systems Quality. 2nd ed., New York: McGraw Hill, 2003.528 p.
• DUQUE et al. (2005) Power Quality Event Detection Based on the Divide and ConquerPrinciple and Innovation Concept. IEEE Transactions on Power Delivery, v. 20, n. 4, pp. 2361 –2369.
• FERREIRA, J. C. (2009). Utilização da Transformada de Wavelet para Detectar VariaçõesAnormais de Frequência em Sistemas de Geração Distribuída. Universidade Federal deUberlândia, Uberlândia, Brazil.
• FILHO, O. D. (2003). Utilização da transformada wavelet para caracterização de distúrbios naqualidade da energia elétrica. USP, São Carlos, Brazil.
• GARCIA et al. (2009). Sistema de Consultoria de Qualidade de Energia Elétrica. In: VIIIConferência Brasileira sobre Qualidade da Energia Elétrica, 2009, Blumenau, Brazil.
• HUA et al. (2008). Recognition and Classification of Power Quality Event in Power SystemUsing Wavelet Transform. Proceedings of 27th Chinese Control Conference.
• JUNIOR, O. H. A. Desenvolvimento de uma Metodologia para Identificar e QuantificarDistúrbios da Qualidade da Energia Elétrica. (PPGEE - UFRGS), Porto Alegre, Brazil.
Adilson Ben da Costa, [email protected]
• LEBORGNE, R. (2003). Uma Contribuição à Caracterização da Sensibilidade de ProcessosIndustriais Frente a Afundamentos de Tensão. Universidade Federal de Itajubá, Itajubá, Brazil.
• MISITI et al. (2012). Wavelet Toolbox For Use with MATLAB®. User’s Guide, Revised forVersion 4.10 (Release 2012b), The MathWorks, Inc.
• PAZOS, R. P. (2006). Transformada Wavelet Haar. (UNISC) Accessed in December, 21, 2012.Available at: http://rpanta.com/downloads/material/271006_RPP_DSP04.pdf>
• RAMOS, F. R. et al. (2002). On signal processing approach for event detection andcompression applied to power quality evaluation. In IEEE 10th International Conference onHarmonic and Quality of Power, pp. 133–138, 2002
• SANTOSO et al. (2000). Characterization of distribution power quality events with Fourier andwavelet transforms. IEEE Transactions on Power Delivery, v. 15, n. 1, pp. 247–254.
• SILVA et al (2009). Eficiência Energética na Indústria. In: VIII Conferência Brasileira sobreQualidade da Energia Elétrica, Blumenau, SC, Brazil.
• SOLA, A. V. H.; KOVALESKI, J. L. (2004). Eficiência energética nas indústrias: cenários &oportunidades. In: XXIV ENEGEP, X International Conference on Industrial Engineering andOperations. Florianópolis, Brazil.
• SOLÓRZANO, K. M. L. (2004). Uma Contribuição ao Estudo de Sobretensões em SistemasElétricos de Pequeno Porte Contendo Cargas Não - Lineares. (CPG-E), Universidade Federalde Itajubá, Itajubá, Brazil.
References
Inscrições abertas a partir de 1º de outubro
Acknowledgement
Thank you for your attention.
Inscrições abertas a partir de 1º de outubro
Acknowledgement
Thank you for your attention.
Adilson Ben da Costa, [email protected]
Results
Results of calibration models for tannin by PLS, iPLS andsiPLS in samples of Acacia mearnsii bark, using different datapreprocessing.
Model Interval NIR region, cm-1 LV RMSECV r (CV)iPLS2 1 5750-4001 7 2.18 0.839iPLS3 2 6333-5167 7 1.56 0.921iPLS4 2 5750-4876 8 1.98 0.871iPLS8 5 6189-5753 4 2.72 0.741
iPLS16 10 6192-5975 3 2.95 0.688iPLS32 11 5211-5103 2 3.72 0.475siPLS8 3,4,5 6189-4879 10 0.96 0.972siPLS8 2,3,4,5,6 6626-4442 10 0.69 0.987siPLS16 2,4,5,6,7,8,9,10,11 6410-4667, 4448-4231 10 0.65 0.988
siPLS32 3,7,11,14,15, 21, 29
7173-7065, 6301-6193,5647-5430, 5211-5103,4775-4667, 4339-4231
9 1.21 0.954
44
Adilson Ben da Costa, [email protected]
Results
Results of calibration models for moisture by PLS, iPLS andsiPLS in samples of Acacia mearnsii bark, using different datapreprocessing.
Model Interval NIR region, cm-1 LV RMSECV r (CV)iPLS2 1 5750-4001 6 1.69 0.798iPLS3 2 6333-5167 7 1.44 0.850iPLS4 1 4875-4001 8 1.48 0.845iPLS8 4 5752-5316 10 1.48 0.844
iPLS16 7 5538-5321 5 1.67 0.797iPLS32 14 5538-5430 2 1.94 0.712siPLS8 3,4,8 7500-7064, 5752-4879 7 1.00 0.932siPLS8 2,3,4,5,6,8 7500-7064, 6626-4442 10 1.13 0.911
siPLS16 6,7,9,14,15 7282-6847, 5974-5757, 5538-5103 8 0.76 0.961
siPLS321,8,11,12,13,
14,15,29,30,32
7500-7392, 7282-7065, 5647-5103,
4884-4776, 4121-401310 0.71 0.967
45