Visible and NIR Spectroscopy to biodiesel quality- Determination of alcohol and glycerol traces

Visible and NIR Spectroscopy to assess biodiesel quality:Determination of alcohol and glycerol traces

M.Pilar Dorado a ,?,Sara Pinzi a ,Antonio de Haro b ,Rafael Font b ,Juan Garcia-Olmo c

a

Department of Physical Chemistry and Applied Thermodinamics,Ed Leonardo da Vinci,Campus de Rabanales,14071Cordoba,Spain b

Department of Agronomy and Plant Breeding,IAS-CSIC,Alameda del Obispo s/n,14080Cordoba,Spain c

NIR/MIR Spectroscopy Unit,Central Service for Research Support (SCAI),University of Cordoba,Campus de Rabanales,14071Cordoba,Spain

a r t i c l e i n f o Article history:

Received 11December 2010

Received in revised form 8February 2011Accepted 15February 2011

Available online 22March 2011Keywords:NIRS

Visible region

Glycerol detection Methanol detection Biodiesel standard

a b s t r a c t

Biodiesel quality control is of relevant importance as biodiesel properties in?uence diesel engine perfor-mance.In the present work,the bene?ts of the use of visible and near-infrared Spectroscopy (NIRS)as a technique for screening undesirable contaminants,i.e.methanol and glycerol content in biodiesel are presented.Excess of methanol decreases heating value and ?ash point and increases carbon deposits,while the presence of glycerol may cause injector tip coking and deposits in the combustion chamber.Biodiesel samples contaminated with different amounts of methanol and glycerol were scanned by NIRS.Their NIR spectra were acquired at 2-nm intervals over a wavelength range from 400to 2500nm (visible plus near-infrared regions).First derivative of the spectra were calculated and correlated to the raw opti-cal data by means of modi?ed partial least-squares (MPLS)regression.First derivative equation of the optical data,pretreated by standard normal variate (SNV)and De-trending (DT)transformations,showed a coef?cient of determination r 2in the cross-validation step of 0.99and 0.81,for the samples contami-nated with methanol and glycerol,respectively.Also,the standard deviation to standard error of cross-validation ratio (RPD)was 10.0and 2.5,respectively.These statistics are indicative of the high capacity of prediction of the equations for methanol content and acceptable for glycerol content.Visible spectra also showed differences related to the samples,thus indicating it could serve to determine the presence of these contaminants.The use of NIRS technology provides a trustworthy and low-cost method to deter-mine the presence of undesirable amounts of methanol and glycerol.It also offers an important saving of time (each analysis requires less than two minutes).

ó2011Elsevier Ltd.All rights reserved.

1.Introduction

Increasing environmental concerns are leading energy policies to promote research in the ?eld of new alternative fuels,among them is biodiesel.Biodiesel is a fuel produced via transesteri?ca-tion of fatty acids from vegetable oils or animal fats,moreover con-sidered renewable energetic sources [1].Thus,the use of biodiesel in diesel engines reduces exhaust emissions,while engine perfor-mance remains constant or slightly decreases compared to the use of diesel fuel [2,3].The most popular raw materials used to produce biodiesel are rapeseed oil (center of Europe),sun?ower oil (south of Europe),soybean oil (US)and used frying oils from dif-ferent origins [4].

Presence of pollutants in biodiesel (residual alcohol,unreacted glycerides,free fatty acids,glycerol,moisture)can produce severe engine problems.In fact,excess of alcohol facilitates the formation of large carbon deposits in the engine,as well as lacquer deposits on the injector tips [5].Moreover,increasing the presence of alco-hol decreases biodiesel heating value,while increases fuel con-sumption,pressure and delays ignition,thus decreasing engine power and energetic content [6].Furthermore,residual alcohols in biodiesel decrease initial temperature of the distillation curve as well as ?ash point,due to its volatility,causing problems during start-up in cold weather [7].On the other hand,excess of alcohol in the presence of excess of catalyst inhibits complete separation of glycerol from biodiesel [1].Furthermore,excess of alcohol in-creases biodiesel production cost,thus the use of the exact amount of alcohol to reach the maximum ester yields is recommended.Glycerol residues must also to be taken into consideration.Excess of glycerol can cause problems during storage and inside engine fueling system.Deposits caused by contamination by glycerine will cause injector tip coking and deposits in the combustion chamber.This can increase aldehydes emissions and produce injection sys-tem corrosion [8].For these reasons,quality control tests are needed.In fact,biodiesel European standard EN 14214limits the presence of some pollutants,among them are alcohol and free glycerol.

0016-2361/$-see front matter ó2011Elsevier Ltd.All rights reserved.doi:10.1016/j.fuel.2011.02.015

Corresponding author.Tel.:+34957218332;fax:+34957218417.

E-mail address:qf1dopem@uco.es (M.Pilar Dorado).

Heretofore,the assessment of biodiesel quality has been con-ducted by gas chromatography(GC)and high-performance liquid chromatography(HPLC)[9,10].A method via GC for analyzing mono-,di-,triacylglycerides,methyl esters and glycerol in one run was developed[11].Darnoko et al.[12]and Arzamendi et al.

[13]developed size exclusion chromatography(SEC)methods in order to monitor glyceride contents and FAME yield during biodie-sel production.Pinzi et al.[14]developed an ultrasound assisted automatic on-line method for the determination of bounded and free glycerol in biodiesel.

On the other hand,near infrared spectroscopic(NIRS)methods for quality control have been reported.NIRS is a technique that uses the radiation absorbed by a set of samples in a region from700to 2500nm to develop calibration curves related to sample properties. After calibration,the regression equation permits fast analysis of many other samples by prediction of data on the basis of the spec-tra.The most attractive features of NIRS analysis are measurement speed,minimal sample preparation and non-destructiveness,mak-ing it possible to conduct large numbers of analyses in a short time. NIRS has been widely used for the last decades as fast and accurate method for qualitative and quantitative analysis of biological and non-biological materials in different?elds.Petrochemical industry has used NIRS to analyze petroleum derivatives and properties, quality of lubricants and other by-products,to predict physical properties of mixtures of hydrocarbons,etc.[15–17].This method-ology could help to improve the quality control process related to both biodiesel production and testing.In fact,Knothe successfully monitorized for completion the transesteri?cation reaction,as-sessed biodiesel quality through the NIR method and determined the blend level of biodiesel in conventional diesel fuel[18,19].Fur-thermore,Felizardo et al.[20]reported the development of calibra-tion models for water and methanol in biodiesel,whereas Baptista et al.developed calibration models to predict total methyl ester and principal fatty acid contents in biodiesel[21]and other biodiesel quality parameters such as iodine value,CFPP,kinematic viscosity and density[22].However,compared to other techniques,NIRS is a relative methodology,so calibration samples must be previously analyzed using a reference method.It is not selective,so multivar-iate analysis has to be used to extract relevant information and it is necessary to obtain a large number of samples to develop robust calibration equations.

The target of this work was to assess the suitability of NIRS and visible spectra to determine methanol and glycerol traces in bio-diesel.As far as we know,only NIR wavelength range has been used to determine biodiesel quality.However,visible region could provide valuable information about the same topic,thus decreas-ing costs.

2.Materials and methods

2.1.Raw materials

Biodiesel from used oil,complying with biodiesel fuel speci?ca-tions according to European standard EN14214,was supplied by Stocks del Vallès,S.A.(Llerona,Spain).Some biodiesel properties are given in Table1.Methanol for analysis,ACS-ISO and glycerol PA-ACS-ISO were acquired from PANREAC QUíMICA SA(Barcelona, Spain).

2.2.NIRS analysis

A set of50biodiesel samples was contaminated with methanol (subset1)in a range from0.003%to0.433%m/m.A set of25bio-diesel samples was contaminated with glycerol(subset2)in a range from0.005%to0.050%m/m.Both ranges were chosen in or-der to include the maximum authorized value for methanol and glycerol residues,respectively,according to the European biodiesel standard EN14214.Each sample(10l l volume)was placed in the NIRS gold sample holder(3cm diameter).The lens holder has an anodized aluminum base made of crystal quartz.Samples were scanned in a NIR spectrometer(NIRSystems model6500, Foss-NIRSystems,Inc.,Silver Spring,MD,USA)in double transmit-tance mode,equipped with a transport module,acquiring their NIR spectra at2nm intervals over a wavelength range from400to 2500nm(visible together with NIR regions).

Using the WinISI II v.1.50program(Infrasoft International,LLC, Port Matilda,PA,USA),different calibration equations for the methanol and glycerol content were developed on both calibration sets.Each NIR spectrum was correlated to the each sample with different content of methanol or glycerol,so two matrices contain-ing the spectral values as the independent variable,and the values of methanol or glycerol as the dependent variable,were generated. Calibration equations were computed using?rst derivative of the optical data(log(1/R),where R is re?ectance),with a standard nor-mal variate(SNV)and De-trending(DT)mathematical pretreat-

Table1

Biodiesel properties.

Parameter Method Unit Result Biodiesel European standard EN14214:2008

Density at15°C ISO3675kg/m3883.9860–900

Viscosity at40°C EN ISO3104mm2/s 4.64 3.5–5.0

Flash point EN ISO22719°C176>101

CFPP EN116°Cà2<0(it depends on the climate)

Sulfur content EN ISO20846%m/m0.0015<0.01(10mg/kg)

Carbon residue(original)EN ISO10370%m/m0.02<0.3

Cetane number FIA100–67>51.0

Sulfated ash ISO3987%m/m<0.01<0.02

Water content EN ISO12937mg/kg7<500

Total contamination EN12662mg/kg1<24

Corrosivity to copper EN ISO2160Corr.degree1Class1

Acid index EN14104mg KOH/g0.147<0.5

Methanol content EN14110%m/m0.003<0.2

Free glycerol EN14105%m/m0.005<0.02

Monoglycerids EN14105%m/m0.10<0.8

Diglycerids EN14105%m/m0.04<0.2

Triglycerids EN14105%m/m<0.01<0.2

Total glycerol EN14105%m/m0.04<0.25

Iodine number EN14111g Iodine/100g100<120

Phosphorous content EN14107mg/kg<1<4

Alkali content EN14109mg/kg<1<5

2322M.Pilar Dorado et al./Fuel90(2011)2321–2325

ments to correct the Scatter effect[23].The aim of the use of deriv-ative spectra instead of raw optical data to perform calibration is to solve those problems associated with overlapping peaks and base-line correction[24].To correlate spectral information of the sam-ples,and both methanol and glycerol contents,modi?ed partial least-squares(MPLS)was used as regression method,by using wavelengths from400to2500nm every2nm.This regression method reduces the huge number of spectral data points and elim-inates the intercorrelation of the absorbance values presented by neighboring wavelengths[25].

Equations were validated by means of cross-validation.Cross-validation is based on an iterative algorithm,which selects part of the sample set population to develop the calibration equation. And then,it uses this equation to predict the content of both meth-anol and glycerol on the remaining unselected samples.This pro-cess is repeated until all samples are predicted.The standard error of cross-validation(SECV)is calculated as the square root of the mean square of the residuals,considering N-1degrees of free-dom and where the residual equals the actual value minus the pre-dicted one.

The accuracy of the developed calibration models were deter-mined on the basis of the ratio of performance to deviation (RPD),statistic calculated as the ratio of the standard deviation (SD)of the methanol and glycerol values to the standard error of prediction found in the cross-validation step(SECV)[26],as well as the coef?cient of determination in the cross-validation(r2). The RPD statistic is considered a useful indicator to evaluate the prediction capability of an equation to predict the component of interest.The higher the RPD value,the greater the probability of the model to predict accurately new samples was found.If the RPD is>3,the calibration models are considered acceptable for analytical purposes[26,27].

3.Results and discussion

3.1.Methanol contamination

Fig.1shows the average and SD spectra from biodiesel samples contaminated with methanol,obtained by trans?ectance mode on the visible and NIR regions.As can be seen,average spectra show NIR absorption bands corresponding to the–CH,–CO and–CC groups(around1720,1750,2150,2300and2360nm).In the visible region(400–780nm),the spectrum shows maximum absorption at440nm,caused by the yellowish color of the sample.

Moreover,SD spectrum indicates regions exhibiting higher differences among all biodiesel samples.The main spectral differ-ences are observed around the absorption bands at440,1720, 1750,2150,2300and2360nm,but also around590nm(visible re-gion).At this wavelength,associated to the yellow or orange color of the sample,any spectral absorption band(only a shoulder)is found.Although,different log(1/R)values are depicted,depending on the amount of methanol in the sample.This?nding indicates it may be of interest to evaluate results not only from visible together with NIR region(400–2500nm),but also from the visible region only(400–780nm),provided the easiness and cost saving of the instruments that work in this spectral range.For this purpose,cal-ibration equations were built considering methanol content data and each speci?c region,thus helping to evaluate the in?uence of the spectral region on the prediction capability.

Table2gives the calibration statistics values considering differ-ent spectral regions to predict methanol content in biodiesel sam-ples.As can be seen,the coef?cients of determination(r2)are very high and reach values close to1(0.99and0.98for equations devel-oped considering visible plus NIR regions,and only visible region, respectively).The high values of the coef?cients of determination in the cross-validation step indicate calibration models are able to explain more than98%of the variability of the reference data. According to Shenk and Westerhaus[28],r2values higher than 0.95show an excellent predictive ability.The SECV value for the equation to predict methanol content by using the visible plus NIR spectra is0.013%m/m with a RPD value of10.0.Slightly less accurate statistical results were obtained considering only the vis-ible region(0.020%m/m for SECV and6.5for RPD statistic).How-ever,in both cases statistics indicate that equations to determine methanol content in biodiesel samples exhibit high prediction abil-ity,considering different spectral range[26,27].

Fig.2presents the relationship between reference and NIR pre-dicted values obtained from the equation to predict methanol con-tent in biodiesel samples,using visible plus NIR spectral data.This ?gure con?rms the excellent correlation between experimental and predicted data provided by the NIR calibration model.

3.2.Glycerol contamination

Fig.4shows the average and SD spectra obtained from biodiesel samples contaminated with glycerol,considering visible and NIR

Table2

Equations to predict methanol and glycerol presence in biodiesel samples according

to different spectral ranges(Standard deviation,SD;standard error of prediction in

cross-validation,SECV;ratio of performance to deviation,RPD).

Spectral range a Mean SD SECV r b RPD

Methanol(%)Visible+NIR0.2220.1300.0130.9910.0

Visible0.2220.1300.0200.98 6.5

Glycerol(%)Visible+NIR0.0300.0150.0070.75 2.1

Visible0.0300.0150.0060.81 2.5

a Visible+NIR:400–2500nm.

b Visible:400–780

nm.

M.Pilar Dorado et al./Fuel90(2011)2321–23252323

regions.The average spectrum is similar to the one produced when biodiesel was contaminated with methanol(Fig.1),comprising NIR absorption bands at around1720,1750,2150,2300,2360nm (which correspond to–CH,–CO and–CC groups)and440nm(re-lated to the color of the samples).The absence of visually percep-tible differences in absorption bands may be attributed to the low concentration of contaminants in biodiesel samples.

Fig.3depicts SD spectrum including the spectral regions show-ing higher differences among biodiesel samples adulterated with glycerol.Main spectral differences may be found in the region of 1720,1750,2150,2300and2360nm due to different content of –CH,–CO and–CC groups in each contaminated sample.In the vis-ible region of the spectrum,the evidence of differences according to the presence of methanol are displayed at450nm(this wave-length is associated to the blue color).

Table2provides calibration statistics values to predict glycerol content on biodiesel samples,considering different spectral re-gions.These equations show r2values of0.75and0.81,considering visible plus NIR and only visible regions,respectively.It means that above75%of the optical data variability may be explained by these calibration models.In fact,according to Shenk and Westerhaus [28],r2values higher than0.70show a good predictive ability. The SECV value for the equation used to predict glycerol content by using the visible plus NIR spectra is0.007%m/m,with a RPD va-lue of2.1.Statistical results slightly improve when only the visible region is taken into consideration(0.006%m/m for SECV and2.5for RPD statistic).In this sense,according to Williams and Sobering [26],calibration models with a RPD value between2and3indicate approximate quantitative predictions.As may be seen from statis-tics included in Table2,calibration models developed on the basis of visible and NIR regions to determine glycerol content in biodie-sel samples present acceptable prediction ability.

Fig.4presents the correlation between reference and NIR predicted values obtained from the equation developed to predict glycerol content in biodiesel samples,using visible spectral data. As may be seen,predicted data?t experimental ones with a corre-lation of almost1:1.However,it exhibits a wider data dispersion, and thus a lower prediction capability,compared to the model developed for methanol traces prediction(Fig.2).

According to our results it is not possible to evaluate the repro-ducibility.However,the best statistic to estimate the repeatability of the NIRS calibration equations is SECV.SECV value is calculated as the standard error of the difference between NIRS predicted and reference values during the cross-validation step.Thus,it can be used as a measure of the standard deviation of the repeatability for the NIRS analytical method.According to the ISO5725-2stan-dard(Accuracy-trueness and precision-of measurement methods and results)and the values included in Table2,the root standard deviation of residual,RSD r(%),for the NIRS prediction of methanol and glycerol traces could be around6%and20%respectively.

4.Conclusions

As a result of the calibration process,the equation with the high-est prediction ability is able to determine methanol and glycerol traces in biodiesel samples.The validation set must be provided with suf?cient accuracy to identify those samples.Visible region analysis also shows signi?cant differences due to the presence of different contaminants and concentrations.The interesting correla-tion between color and presence of some contaminants in visible region may be used as an economical method to assess biodiesel quality.Moreover,visible and NIRS regions offer an important sav-ing of time(2min/analysis)and cost of analysis compared with other standard reference analysis.From the NIRS calibration and validation results shown in this work,it can be concluded that NIR and visible Spectroscopy are able to predict methanol and glyc-erol traces in biodiesel samples.However,further work is still needed to improve the calibration statistics for the estimation of glycerol content on biodiesel samples.

Acknowledgments

The authors thank to Gloria Fernandez Marin(IAS-CSIC)for her support in the lab and to Miquel Vila(Stocks del Vallès,S.A.‘‘BDP–BioDiesel Peninsular’’)for providing us with biodiesel sam-ples and analytical analysis to determine biofuel speci?cations. This work was funded by Junta de Andalucia,Spain(TEP4994) and Ministry of Science and Education,Spain(ENE2007-65490/ ALT).

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