Post-graduation Program In Industrial Systems and ... and Simulation I... · Direct determinations...

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


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)

3

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


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


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


Objective

Develop an alternative methodology for determinationof tannins and moisture directly in the bark of Acaciamearnsii using near infrared spectroscopy andmultivariate calibration methods.

7


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

8


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


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



PLS Partial Least SquaresIs a full spectrum method

11




iPLS Interval Partial Least Squares

The spectra are divided in intervals,which are modeled in separate toselect the best region, associated tolowest RMSCV.

12







The spectra are divided in intervals,which are modeled in separate toselect the best region, associated tolowest RMSCV.

13





siPLS Synergy interval Partial Least Square



The spectra are divided in intervals,which are modeled in the combinationform to select the best regions (two ormore), associated to lowest RMSCV.

14


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


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+

16


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)

17


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


The bark was extracted in the DBH (diameter at breast height) ≅ 1.40 m


1.40 m



3years old

4 5 6 7 8 9

Samples were collected from 7 treeamong 3 and 9 years old, in onlysampling campaign.


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


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


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


Methodology Third step – Tannin and moisture determination

BARK SAMPLE

Grinding

Gravimetric quantification2 h

for e

ach

dete

rmin

atio

n

NBR 14929

24


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



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



MODEL INTERVALSiPLS 2iPLS 3iPLS 4iPLS 8iPLS 16iPLS 32siPLS 8

siPLS16 16siPLS32 32



siPLS Synergy interval Partial Least Square

27


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%

28


Results

The profile of NIR spectra of all samples.

O-H stretchMolecular vibrations

C – H stretchAromatic

O-H stretchMolecular vibrations

29


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


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)


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)



Results

33

TANNINTannin results obtained by reference methodology and iPLSand siPLS models.No significant differences (P>0.05) among the results wereobserved.


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


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


(6333-5167)


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



Results

37

MOISTUREMoisture results obtained by reference methodology and iPLSand siPLS models.No significant differences (P>0.05) among the results wereobserved.


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


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


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.


• 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.


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


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

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