Midinfrared Spectroscopy and Partial Least-Squares Model as an

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Mid-infrared (MIR) spectroscopy and partial least squares (PLS) model as an analytical method for biodiesel and glycerol monitoring Maiquel Bonato, Alexsandra Valerio, José Vladimir Oliveira, Débora de Oliveira, and Ariovaldo Bolzan Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.7b04074 • Publication Date (Web): 22 Dec 2017 Downloaded from http://pubs.acs.org on December 28, 2017

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Industrial & Engineering Chemistry Research

Mid-infrared (MIR) spectroscopy and partial least squares (PLS) model as an analytical method for biodiesel and glycerol monitoring

Maiquel Bonato†,‡, Alexsandra Valério†, J. Vladimir Oliveira†, Débora de Oliveira†*, Ariovaldo Bolzan†

Federal University of Santa Catarina – USFC - Department of Chemical and Food

†

Engineering, Florianópolis/SC, Zip code: 88040-900, Brazil Olfar Food and Energy, S.A, Erechim/RS, Zip code: 99700-000, Brazil

‡

*Corresponding author: Débora de Oliveira ([email protected]) Department of Chemical and Food Engineering Federal University of Santa Catarina – UFSC Florianópolis, SC- Brazil - Zip code: 88040-900 Phone: +55 (48) 3721-2505

1

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ABSTRACT This work reports the application of mid-infrared (MIR) spectroscopy and partial least squares (PLS) model as an analytical method for the quality control of biodiesel production and glycerine treatment. The analytical techniques employed to control biodiesel production are expensive and laborious. On the other hand, infrared spectroscopy technique has low cost when compared with conventional techniques, simple operation, low waste generation, which makes it attractive when coupled with multivariate calibration models, such as PLS, as it constitutes a non-destructive analytical method for in-line monitoring biodiesel production reaction. In this work, spectral data were generated by MIR spectroscopy with attenuated total reflectance (ATR) accessory, which means a direct analysis. Therefore, for quantification of biodiesel samples, PLS was employed to model MIR spectroscopy data. The evaluated parameters were expressed in terms of acidity, phosphorus, monoacylglycerol (MAG), diacylglycerol (DAG), triacylglycerol (TAG), water, glycerol, and ash contents.

Keywords: Biodiesel; glycerin; MIR spectroscopy; PLS

2


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1. Introduction Biodiesel and glycerine quality are usually determined by traditional analytical techniques, such as gas chromatography and high-performance liquid chromatography, that are slow, expensive, require a long time of analysis, toxic solvents, and are not convenient for in-line monitoring of biodiesel production reaction2,3. On the other hand, infrared spectroscopy provides the opportunity to obtain quantitative and qualitative information for biodiesel due to the larger bands of spectral incidence in this region, as well as the higher intensity and specificity of the signal. Thus, Fourier Transformed Infrared spectroscopy (FTIR) has become one of the main analytical techniques used because of its screening quality, speed, and low cost4-6. ASTM D 6751 and EN 14214 standards describe the quality standards established for biodiesel, that include monoacylglycerol (MAG), diacylglycerol (DAG), triacylglycerol (TAG) content and residual products from the transesterification step directly related to the quality and concentration of raw material2,8. Thus, control of this production step is essential for rapid problems identification, avoiding extra costs with out-of-standard products, justifying the interest in a fast and efficient methodology with the possibility of development an online monitoring production system9. However, quantitative analysis by infrared spectroscopy is only possible with the use of chemometric methods, such as multiple linear regression (MLR), principal components regression (PCR) and partial least squares (PLS). The PLS model relates two data matrices, X and Y, through a multivariate linear model, which is a model capable of analyzing collinear data with multiple noises10,11. Nowadays the alkaline homogeneous catalysis process is used in most biodiesel industrial production plants. The content of free fatty acids of the oil used should be low to prevent reaction with the catalyst, leading to soap formation. The phosphorus content should be controlled, due to the difficulty in the separation of soaps, reduction of reaction yields and the 3



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quality of the final product, increasing the process cost. Thus, in this work, it was investigated the acidity in the soybean oil refining process used in the transesterification process, MAG, DAG, and TAG contents in the reaction step of fatty acid methyl esters (FAME) production, and also ash, water, and glycerol contents. These parameters are considered critical due to the direct interference in the quality and production cost7. For achieving the objectives, FTIR-MIR spectroscopy and PLS model were employed as an analytical method to monitoring biodiesel quality, which present attractive potentialities such as an analytical method simple to implement, quick, with less generated waste, without the use of toxic solvents, and able to the inline evaluation of biodiesel quality during production and also glycerin treatment.

2. Materials and Methods 2.1. Materials The reagents isopropyl alcohol, toluene, phenolphthalein, ethyl alcohol, potassium hydroxide, potassium biphthalate, xylene, n-heptane, bromine cresol blue, ethylene, sulfuric acid, hydrochloric acid, potassium iodide, sodium thiosulfate, buffers pH 4 and 7 and sodium periodate were purchased from Sigma-Aldrich. Standard organometallic 500 mg kg-1 (SpexCertiPrep), trycaprin chromatography standards, monoacylglycerol, diacylglycerol, and triacylglycerol chromatography standards, n-trimethylsilyl-n-methyl trifluoroacetamide (MSTFA, 98%), cationic and anionic Karl Fischer reagents for colorimetric were also purchased from Sigma-Aldrich. All reactants were used as received without further purification.

4


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2.2. Biodiesel synthesis Figure 1 shows the flow chart of the industrial biodiesel process studied in this research work. Due to the non-disclosure agreement and employment bond of one of the authors, details concerning biodiesel production process and operating plant conditions will not be revealed in this research work.

2.3. Determination of acidity and phosphorus contents The determination of oil acidity was carried out following the AOCS Cd 3d-63 methodology and results were expressed as wt% of oleic acid12. The determination of the phosphorus content in the oil was performed according to EN 14107 methodology and results were expressed in terms of mg kg-1 13. The results obtained in this step were used as reference to in the multivariate calibration model.

2.4. Biodiesel and glycerin characterization The quantification of MAG, DAG, and TAG content in the biodiesel samples, used as reference results, was carried out following ASTM D 6584 methodology and the results were expressed as wt%2. Determination of glycerol and ash content in the glycerin was performed according to the American pharmacopoeia methodology (version 37), and results were expressed as wt%14. Water content in the glycerin was determined following EN ISO 12937 methodology and results were expressed as wt%15. Results obtained in this step were used as reference values for the multivariate calibration model.

2.5. Mid-infrared spectroscopy with Fourier Transform (FTIR-MIR) The infrared spectrum was obtained by direct sample reading without dilution at 20 ± 1 °C (Bruker Tensor 27 model - OPUS software version 7.2.139.1294) in ATR mode (attenuated 5



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total reflection, Smith DURASAMPL IR II) with ZnSe crystal. Biodiesel samples used for the quantification of MAG, DAG and TAG content were previously centrifuged to remove glycerol excess. The FTIR spectra was analyzed in the range from 4000 to 650 cm-1, with resolution of 2 cm-1 and 20 scans.

2.6. Multivariate calibration model and validation For the multivariate calibration model, results obtained from the reference samples were correlated by OPUS software using QUANT2 tool. The regions with highest spectral differentiation were used for multivariate calibration model construction based on partial least squares regression (PLS). For the data treatment, cross-validation was used to obtain the results for mean square error of cross-validation (RMSECV, Eq. 1) and the correlation coefficient (R²). For the relative error calculation (RE, Eq. 2) and relative standard deviation (RSD, Eq. 3), 10 experiments, with samples not included in the calibration model, were used, enabling the identification of the model behavior in the work routine.

− ² = ∑

(1)

where M denotes the number of samples, Yimeas is the analysis results performed by the conventional method and Yipred represents the predicted results obtained by infrared analysis method,

% =

!" "

.100

(2)

6


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where Xlab denotes the results obtained by infrared analysis method, Xv are the samples values obtained by standard methods analysis,

'

& % = ( . 100

(3)

where )( is the mean,

=

∑ !( ²

(4)

*!

where n is the number of analysis and X is the value obtained from the FTIR-MIR analysis.

Due to the variable composition of the oil sample, in this work, the acidity and phosphate content, was determined from each process step (oil as feedstock, neutralization step, and neutralized oil), and three different multivariate calibration models were developed.

3. Results and discussion 3.1. FTIR-MIR/PLS versus conventional methodologies: oil acidity The PLS was developed through the relation between the spectrum obtained by direct samples measurement of FTIR-MIR and by conventional measurement methods. Relative standard deviation (RSD) and relative error (RE) was calculated from 10 random determinations selected in the working concentration range that had not been used for the PLS model construction. From the results, the correlation coefficient obtained in the prediction model for the acidity parameter showed an excellent linear relation. For the acidity quantification in the soybean oil refining process (Table 1), the methodology showed good accuracy even at low acid concentrations, showing the methodology efficiency. 7



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In the oil neutralization step, a high-water content was observed, as reported by Silverstein et al.16. The OH bond from water has a characteristic absorption band located at 3652, 3756 and 1596 cm-1. However, this region is also efficient for the acidity determination, justifying the analytical variation of the acidity results at this production step. As described by Hidawati et al.5, the water content present in the glycerol can be identified at 1553 cm-1. On the other hand, Pavia et al.17 describe the wavenumber at 3750-3000 cm-1 as absorption region of -OH group from water, however, this region shows interference of -OH groups from glycerin showing a no linear absorption for the water content quantification in the glycerin. Analyzing the average concentration results of acidity results at this production step, it was observed a low average value (0.08%) in relation to the difference between results obtained by FTIRMIR and conventional methodology with no problem in the quality control at this production step. Figure 2 shows the results of PLS after modeling MIR spectroscopy data for the degummed oil acidity, neutralization process step, and neutralized oil and AOCS Cd 3d-63 methodologies (titration), in the studied range of this work. From the results, it was possible to observe that the difference between standardized method (titration) and the analytical method here developed, based on MIR spectroscopy and PLS model was relatively low (0.01%). The relative standard deviation (6.34%) showed that the analytical method had some repeatability variation, however below to 10%. In general, from the results of acidity analysis by FTIR-MIR methodology using ATR, multivariate calibration, and direct quantification of the sample, it was observed a good precision and sufficient accuracy to control the oil quality to be used in the biodiesel production process. Moreover, the curves showed acceptable errors, as the methodology should be routinely verified, allowing errors identification with subsequent adjustments.

8


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3.2. FTIR-MIR/PLS versus conventional methodologies: phosphorus content Table 2 shows the results for the correlation curves between prediction model results by FTIR-MIR and EN 14107 methodology for phosphorus content in soybean oil at different processing steps. From the results obtained in this work, FTIR-MIR methodology afforded good results to quantify the phosphorus content in the feedstock with a concentration in the order of 200 mg kg-1. For both neutralization and neutralized oil steps, correlation curves were not satisfactory showing considerable error values. The phosphorus concentration observed was up to 20 mg kg-1 in the neutralization process and up to 10 mg kg-1 in the neutralized oil, which limited the methodology hence requiring more efforts towards the evolution of the prediction model. By the accuracy result, it was possible to conclude that the methodology was enough to control the production process, with relative error of 2% between FTIR-MIR and conventional method. However, the curves are not reliable enough to control the quality of the product only by infrared as demonstrated in Figure 3. Monitoring tests may be used to update the calibration curve and to improve the correlation to obtain a desired level of accuracy and precision. Nzai and Proctor18 and Xianghe et al.19 reported the quantification of phospholipids by FTIR between 1200 and 950 cm-1 related to phosphate groups (PO2; P-O-P; P-O-C + PO2; PO2) and at 1765-1720 cm-1 related to C=O. The authors reported that the region between 830-740 cm-1 can not be used due to the phosphate group orientation (PO2) and water interference, showing an unstable and low correlation coefficient, and the authors related the optimal wavelength range between 1989 - 982 cm-1. The curves depicted in Figure 3 did not show a sufficiently good correlation for quality control purposes, once the calibration curve requires a larger amount of calibration spectra. Naresh et al.20 showed that the curve should have many points as necessary to obtain a 9



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correlation and this amount should not be increased to the point of spectral noise (by the high number of calibration spectra). The precision and accuracy of the calibration curve and the number of calibration spectra to set up calibration curve must be determined by the operator experience. Thus, in this work, the correlation in the calibration curve can be improved by increasing the calibration points. From the results obtained in this work, it can be concluded that the determination of the phosphorus content in the range from 30 to 200 mg kg-1 determined by FTIR-MIR methodology using multivariate calibration model was satisfactory. On the other hand, in a range up to 30 mg kg-1, for both precision and accuracy, some effort is necessary to increase the results confidence, which consists in increasing spectra number, improve calibration curve, determination coefficient, and RMSECV.

3.3. FTIR-MIR/PLS versus conventional methodologies: MAG, DAG and TAG contents As for the acidity and phosphorous content, analysed by direct quantification using FTIRMIR methodology, the calibration curves were used to quantify MAG, DAG, TAG, and total glycerol content in the biodiesel production. These analytical parameters have extreme importance in the catalyst dosage adjustment, in order to reduce the cost of the biodiesel production and improve the final product specification. Tables 3 and 4 shown the correlation curves between the results obtained for the prediction model by FTIR and ASTM D6584 methodology for the quantification of MAG, DAG, TAG contents in the soybean oil refining process, and results for multivariate calibration model for MAG, DAG, TAG contents from transesterification process of soybean oil. The prediction model for DAG content showed good correlation with the results. On the other hand, for both MAG and TAG contents the correlation curves were not satisfactory, but of course it may be improved increasing the number of calibration experimental points within 10


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the range investigated. The obtained results for R², RSD and RE showed that the methodology used was not correlated with precision and accuracy required to follow the transesterification process under the conditions studied in this research work, but it is evident that the methodology can be improved to obtain the required efficiency. From the results of DAG content (Table 4), in particular, the determination coefficient (90 (97)) and RMSECV (average error 0.082% (w/w)), indicate that calibration curve showed a good correlation. Figure 4 illustrates the calibration curve dispersion where it can be observed that the average precision and accuracy errors are in an acceptable tolerance range. Under current conditions, the methodology proved to be useful for reaction monitoring. The methodology has potential to control the analyzed variable but requires constant maintenance to improve the precision and accuracy and ensure the confidence to allow catalyst dosage setting. In addition, by evaluating the calibration curve (Figure 4b) it can be concluded that it is possible to use FTIR-MIR methodology to control catalyst dosage in the biodiesel production. Likewise, in Table 4 for TAG results, a calibration curve with low correlation with the determination coefficient of 90 (86) and RMSECV with the average error of 0.241% (w/w) was obtained. Figure 4c shows the dispersion curve points on the calibration curve. For this work, the optimized spectral range for TAG content used to set the calibration curve in the operational system was identified in a range from 1320 to 984 cm-1, related to the characteristic absorption region of OH, CH, CH2, and CH3 groups, in accordance with results reported by Andreas

21

and Silverstein et al.16. As described for MAG prediction curve by FTIR-MIR

methodology prediction method, DAG and TAG calibration curves must be improved to better control the catalyst content added during the biodiesel process.

11



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3.4 FTIR-MIR/PLS versus conventional methodologies: glycerol, water and ash contents During the biodiesel production, glycerin is also produced in the reaction, which must be purified to achieve the required quality for the marketing. As reported by Kongjao et al.22 some of the main parameters to measure crude glycerin quality are pH, glycerol content, water content, ash content, organic material, density, viscosity and color. In order to increase the analytical efficiency, reduce costs and improve the product quality by quick corrective actions in the biodiesel production process, calibration curves to determine ash, water content and glycerol by infrared methodology were developed. Table 5 shows the results of the prediction models for glycerol, water and ash content for the treated glycerin. Figure 5a shows the dispersion points in the calibration curve and from the results, it can be concluded that FTIR-MIR method provided an acceptable error (less than 5%) and can be used to determine the water content in glycerin. The same behavior was observed for the calibration curves to determine glycerol and ash content in crude glycerin as seen in Figures 5b and 5c. The correlation coefficients obtained for glycerol parameters prediction models, water and ash content in the studied process showed an excellent linear correlation, being effective to replace the standardized method in the conditions range evaluated in this work.

4. Conclusions FTIR-MIR methodology was used in this study to provide quite satisfactory results when compared to the conventional analytical techniques and hence can be used as a tool to determine the composition of the compounds involved in the liquid-liquid equilibrium systems related to soybean biodiesel production. The quantification curves of MAG, DAG, and TAG contents showed correlated results of 78, 97, and 86 with an error value of 4, 29, and 18%, and precision of 0, 2, and 24%, respectively, for MAG, DAG, and TAG contents. A 12


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large variation was observed, reducing the confidence degree in the calibration curves. The developed curves for the quantification of glycerol, water, and ash contents showed good correlation (97, 99, and 95), precision (0, 1, and 1%), and accuracy of 0, 1 and 1%, respectively, for glycerol, water, and ash contents. These results showed the excellent effectiveness of the methodology for the evaluated parameters. The developed models employed to quantify oil acidity, phosphorus, glycerol, water, and ash contents during soybean oil refining process showed a linear correlation and acceptable errors. After curve construction, DPR and ER estimation, it was observed that the developed methodology could be used to fit the data and operations conditions.

Acknowledgments The authors thank Olfar S.A - Food and Energy – Erechim/RS/Brazil.

References (1) Wei-Bo Zhang. Renewable and Sustainable Energy Reviews. 2012, 16, 6048. (2) ASTM D6751-15ce1, Standard Specification for Biodiesel Fuel Blend Stock (B100) for Middle Distillate Fuels, ASTM International, West Conshohocken, PA, 2015. (3) CSN EN 14105. Fat and oil derivatives - Fatty Acid Methyl Esters (FAME) Determination of free and total glycerol and mono-, di-, triglyceride contents. 2011. (4) Mckelvy M.L.; Britt T.R.; Davis B.L.; Gillie J.K.; Graves F.B.; Lentz L.A. Infrared spectroscopy. Anal. Chem. 1998, 70(12), 119. (5) HidawatI E. N.; Mimi S. A. M. Treatment of Glycerin Pitch from Biodiesel Production. J. Chem. Envir. Eng. 2011, 2 (5), 309. (6) Soares I. P.; Rezende T.F.; Fortes I.C.P. Study of the behavior changes in physicalchemistry properties of diesel/biodiesel (B2) mixtures with residual oil and its 13



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quantification by partial leasts quares attenuated total reflection-Fourier transformed infrared spectroscopy (PLS/ATR-FTIR). Energy Fuels. 2009, 23, 4143. (7) Van Gerpen J.H.; Shanks B.; Pruszko R. Biodiesel Production Technology. Report from Iowa State University for the National Renewable Energy Laboratory. NREL/SR-51036244, July 2004. (8) CSN EN 14214 (2013). Liquid petroleum products - Fatty acid methyl esters (FAME) for use in diesel engines and heating applications - Requirements and test methods. (9) Knothe G. Analyzing biodiesel: standards and methods. J. Amer. Oil Chem. Soc. 2006, 83(10), 823. (10) Wold S.; Sjostrom M.; Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom Intell Lab. Syst. 2001, 58, 109. (11) Guimarães E.; Mitsutake H.; Gontijo L. C.; de Santana F. B.; Santos D. Q.; Neto W.B. Infrared Spectroscopy and Multivariate Calibration for Quantification of Soybean Oil as Adulterant in Biodiesel Fuels. J Am Oil Chem. Soc. 2015, 92, 777. (12) AOCS (2009). Official methods and recommended practices of the American Oil Chemists’ Society. Method Cd 3d-63. (13) BS EN 14107:2003. Fat and oil derivatives. Fatty acid methyl esters (FAME). Determination of phosphorous content by inductively coupled plasma (ICP) emission spectrometry. (14) The United States Pharmacopeia. Pharmacopeia Forum 2014, 37, 3167. (15) ISO 12937:2000. Petroleum products - Determination of water - Coulometric Karl Fischer titration method. (16) Silverstein, R.M.; Webster, F.X.; Kiemle, D.J.; Bryce, D.L. Spectrometric Identification of Organic Compounds, ed. 8, Boston, USA, 2015.

14


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(17) Pavia, D. L.; Lampman, G. M.; Kriz, G. S. Introduction to Spectroscopy, ed. 3; Philadelphia, Australia:Brooks/cole, 2001. (18) Nzai J.M.; Proctor A. Determination of Phospholipids in Vegetable Oil by Fourier Transform Infrared Spectroscopy. JAOCS. 1998, 75 (10), 1281. (19) Xianghe M.; Qiuyue P.; Yang D.; Lianzhou J. Rapid determination of phospholipid content of vegetable oils by FTIR spectroscopy combined with partial least-square regression, Food Chem. 2014, 147, 272. (20 Naresh N. M.; Yusuf G. A. Fourier Transform Infrared Spectroscopy (FTIR) Method to Monitor

Soy

Biodiesel

and

Soybean

Oil

in

Transesterification

Reactions,

Petrodiesel−Biodiesel Blends, and Blend Adulteration with Soy Oil. Energy & Fuels. 2009, 23 (7), 3773. (21) Andreas L. Simultaneous Determination of Mono-, Di-, and Triglycerides in Multiphase Systems by Online Fourier Transform Infrared Spectroscopy. Analyt. Chem. 2011, 83, 9321. (22) Kongjao S.; Damronglerd S.; Hunsom M. Purification of crude glycerol derived from waste used-oil methyl ester plant. Korean J. Chem. Eng. 2010, 27(3), 944.

15



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Table 1. FTIR-MIR prediction results and by AOCS Cd 3d-63 methodology. Process step

Samples Bias

R²

RMSECV AOCSCd

FTIR

RSD

RE

(%)

3d-63(%)

(%)

(%)

(%)

Degummed oil

354

0.10

97

0.12

2.16

2.21

1

3

Neutralization

471

-0.11

99

0.06

0.08

0.09

6

4

Neutralized oil

250

0.01

99

0.03

0.36

0.36

4

2

16


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Table 2. FTIR-MIR prediction results, by EN 14107 methodology, and multivariate calibration model for phosphorus quantification in the soybean oil refining process.

Process step

Samples Bias

R²

RMSECV EN14107

FTIR

RSD

RE

(%)

mg kg-1

mg kg-1

(%)

(%)

Degummed oil

382

65.0

91

36

199

203

1

2

Neutralization

242

7.0

73

4.8

15

14

5

-3

Neutralized oil

239

-0.3

47

2.8

6

5

6

-21

17



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Table 3. FTIR prediction results and by ASTM D6584 methodology.

Process step

ASTM 6584 (%)

FTIR (%)

RSD (%)

RE (%)

Degummed oil

0.753

0.747

4

0

Neutralization

0.429

0.446

29

2

Neutralized oil

0.336

0.393

18

24

18


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Table 4. Multivariate calibration model for MAG, DAG, and TAG contents from transesterification process of soybean oil.

Samples

Bias

R²

RMSECV (%)

Monoacylglycerol (MAG)

186

0

78.6

0.074

Diacylglycerol (DAG)

180

0

97.2

0.082

Triacylglycerol (TAG)

165

0

86.6

0.241

19



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Table 5. FTIR-MIR prediction, standardized methodology, and multivariate calibration models for glycerol, water, and ash contents in the glycerin.

Standard

FTIR

RSD

RE

methodology

(%)

(%)

(%)

Samples

Bias

R²

RMSECV (%)

(%)

Glycerol

82.8

82.9

0

0

162

0

97.4

1.25

Water

13.3

13.4

1

1

166

0

99.3

0.55

Ash

4.0

4.1

1

1

161

0

95.2

0.12

20


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Figure 1. Flow chart of the biodiesel industrial production process.

21



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(a)

(b)

(c) Figure 2. Correlation between calibration curve and experimental data for the oil acidity content by FTIR-MIR and AOCS Cd 3d-63 methodology (titration) for the degummed oil (a), neutralization process step (b), and neutralized oil (c). 22


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(a)

(b)

(c) Figure 3. Correlation between calibration curve and experimental data for the phosphorus content by FTIR-MIR and EN 14107 methodology (titration) for the degummed oil (a), neutralization process step (b), and neutralized oil (c).

23



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(a)

(b)

(c) Figure 4. Correlation between calibration curve and experimental data for MAG (a), DAG (b), and TAG (c) content by FTIR-MIR and ASTM D 6584 (gas chromatography) methodology. 24


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(b)

(c)

(c) Figure 5. Correlation between calibration curve and experimental data for water content (a), glycerol content (b), and ash content(c), determinate by FTIR-MIR and standardized methodology.

25



142x83mm (96 x 96 DPI)


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Midinfrared Spectroscopy and Partial Least-Squares Model as an

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