Applied Energy 88 (2011) 2064–2070

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Applied Energy

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A facile and feasible method to evaluate and control the quality of Jatropha curcusL. seed oil for biodiesel feedstock: Gas chromatographic fingerprint

Rui Wang a,b, Baoan Song a,⇑, Wanwei Zhou a, Yuping Zhang a, Deyu Hu a, Pinaki S. Bhadury a, Song Yang a,⇑a State-Local Joint Laboratory for Comprehensive Utilization of Biomass, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering,Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, Chinab Department of Biology and Environment Engineering, Guiyang College, Guiyang 550005, China

a r t i c l e i n f o a b s t r a c t

Article history:Received 10 August 2010Received in revised form 28 December 2010Accepted 28 December 2010Available online 28 January 2011

Keywords:Jatropha curcus L. seed oilBiodieselGas chromatographic fingerprint technologyQuality

0306-2619/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.apenergy.2010.12.078

Abbreviations: JC, Jatropha curcas L.; FAC, fatty anumber; DS, degree of saturation; DU, degree ofpolyunsaturation; KV, kinematic viscosity; CFPP, cosimilarity analysis; HCA, hierarchical cluster analysianalysis; GC, gas chromatographic; LOD, limits ofquantifications; CC, correlation coefficient; CVVA, coRRTCP, relative retention time of common peak; Rcommon peak.⇑ Corresponding authors. Tel.: +86 851 362 0521; f

E-mail address: jhzx.msm@gmail.com (S. Yang).

To establish a facile and feasible method to evaluate and control the quality of Jatropha curcus L. seed oilfor biodiesel feedstock, Gas chromatographic (GC) fingerprint technology was introduced and employed.Initially, the chromatograms of the 13 oil samples from various plantation zones in Guizhou, China wereobtained under optimized GC conditions. Ten common peaks were selected as the characteristic peaks forchemometrics, seven of which were identified and quantified by comparing with the standards. Themean chromatogram of S7 (n = 3) was selected as the reference spectrum for similarity analysis basedon the influence of the fatty acid composition of the raw material on the fuel properties of resulting bio-diesel. Furthermore, the result of SA was confirmed by hierarchical clustering analysis and principal com-ponent analysis. By this method, all samples can be classified into three groups. The similarity value ofsamples approaching 1.000 compared with sample 7 was indicative of the desired fuel properties of bio-diesel, indicating the potential practical applications in the quality evaluation and control of biodieselfeedstock.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Biodiesel is currently regarded as a promising alternative fuelfor dwindling fossil diesel [1,2]. Unfortunately, the biodiesel pro-duced in USA, Europe and South East Asia mostly relies on edibleresources from soybean, rapeseed and palm seeds, which makesit economically non-viable and more expensive compared withfossil fuel. About 70–85% of the overall biodiesel production costarises from the utilization of these raw materials, so the productionhas not yet reached the global commercialization stage [3,4]. Giventhis, the use of sustainable non-edible oil with low cost for biodie-sel feedstock can serve as an effective alternative to reduce the costof raw materials. Besides, some emergent issues can be resolved,which related to energy crisis, food security, rural development,and poverty alleviation [1,5,6].

ll rights reserved.

cid composition; CN, cetaneunsaturation; DP, degree ofld filter plugging point; SA,s; PCA, principal component

detections; LOQ, limits ofsine value of vectorial angle;PACP, relative peak area of

ax: +86 851 362 2211.

Jatropha curcas L. (JC), a multipurpose perennial plant fromEuphorbiaceae family, has been gaining wide importance in biodie-sel production [1,2,6]. JC trees can ideally reach a height of 3–5 m,whereas under favorable conditions they can grow as tall as8–10 m. The hardy JC is resistant to drought and pests and canproduce seeds containing 27–40% oil with an annual yield of 0.1–15 tones/ha/yr depending on the cultivation methods, provenanceand climatic conditions [6–9]. There are several advantages in theproduction of biodiesel from JC. These are the lesser competitionfrom land occupation, absence of the requirement to fulfill the de-mand of food supplies due to its non-edible nature, rapid growth offruits with high oil content in 2–3 years, yearly production of seedsfor 40 years, and capability of producing in drought-prone barrenareas. More importantly, superior fuel properties with higher ce-tane number (CN) and oxidation stability (OS) are provided com-pared with other vegetable oils [7,9–11]. Therefore, JC has beenrecognized as one of the potential candidates for future biodieselproduction.

Biodiesel is generally produced by the transesterification of veg-etable oil or animal fat with methanol. Demirbas [12] and Ramoset al. [13] tested and compared the fuel properties of 32 biodieselsamples from different raw materials, and some direct correlationsbetween the fatty acid composition (FAC) of the raw material andthe fuel properties of the resulting biodiesel were revealed, suchas cetane number, oxidation stability, and cold-flow properties.

R. Wang et al. / Applied Energy 88 (2011) 2064–2070 2065

Knothe [14] studied the process further and generalized the rela-tionship based on the fuel properties of single fatty acid methyl es-ter. Certain interesting regulations pertaining to critical fuelparameters were summarized as follows. First, with increasingchain length, decreasing branching and unsaturation of fatty acids,CN is increased [12,13]; second, a poor OS is attributed to the pres-ence of more unsaturated components, which is sharply loweredwith the increase in the content of polyunsaturation [13,14]; third,a longer carbon chain length and increasing saturation caused thedeterioration of cold-flow properties [13,14]; fourth, kinematic vis-cosity (KV) and density increase with a longer chain length andlower unsaturation [12,14]. Furthermore, the ideal biodiesel com-position has been recommended, which consists of the high pres-ence of monounsaturated fatty acids (as C16:1 and C18:1),reduced presence of polyunsaturated acids and controlled satu-rated acid content [11,14]. Moreover, to establish the exact rela-tionship between FAC and fuel properties, some fitting equationscorrelating critical fuel properties with the FAC of raw oils were de-duced such as CN, KV and cold filter plugging point (CFPP) [13–15].

Pereyra-Irujo et al. [16] showed that resulting biodiesel samplesderived from the sunflower oils of different growing areas did nothave one fixed quality because of the difference in FAC, which wasattributed to the climate conditions, provenance, and agriculturalpractices. The FACs of JC oil samples from various sources have beenreported. The main differences were found in the contents ofpalmitic acid (C16:0, 10.05–18.97%), palmitoleic acid (C16:1,0.32–1.18%), stearic acid (C18:0, 1.30–17.00%), oleic acid (C18:1,12.80–48.80%), linoleic acid (C18:2, 10.60–47.30%), linolenic acid(C18:3, 0.00–0.21%) and eicosanoic acid (C20:1, 0.00–4.70%) respec-tively [11,17–21]. These differences were evident from the fuel prop-erties of the resulting biodiesel samples, such as the cetane number(ranging from 50 to 62) [9,11]. Clearly, even within the same species,FAC varied across many factors, such as provenance, cultivationmethods, and climatic conditions. This inevitably generated differ-ent the fuel properties of the JC biodiesel samples.

In October 2006, a JC plantation project in Guizhou Provinceencompassing a total area of 40,000 ha was approved by theNational Development and Reform Commission (NDRC) of China.An investigation on the natural source reserves of JC and its landsuitability. The Technical Regulations for Jatropha curcas Plantationin Guizhou Province were completed. To date, around 30,000 ha ofland in this province have been improved to the standard requiredfor the successful commercial production of biodiesel from JC. Inparticular, about 14,000 ha have been allocated in Zhenfeng, Luo-dian and Wangmo Counties with the appropriate geographicaland climatic conditions.

Fingerprint analysis technology for the quality evaluation andcontrol of herbal medicines has been introduced and accepted bythe World Health Organization, Food and Drug Administrationand European Medicines Agency. The technology can provide reli-able quality data with reliable quality for unknown constituentsand herbal medicine extracts from different sources by similarityanalysis (SA), hierarchical cluster analysis (HCA) and principalcomponent analysis (PCA). In addition, the technology can alsobe suitable for the quality control of wine and edible oils [22,23].Catharino et al. [24] and Eide and Zahlsen [25] developed finger-printing method based on the electrospray mass spectrometryfor biodiesel characterization, discrimination, identification, qual-ity control, and quantification from different feedstocks. However,electrospray mass spectrometry instrument is relativity expensiveand unwidespread as compared to common chromatographyinstrument.

Recently, some new researches have been developed and ap-plied to promote JC biodiesel industry, such as gene technology[9], hand-operated decorticator [26], production optimization[27], and design and performance optimization of JC biodiesel

engine [28]. In the present study, we attempt to search for a facile,low-cost and feasible method for evaluating and controlling thequality of biodiesel feedstock, JC oil. Thus, a facile, low-cost andfeasible method, GC fingerprint technology was introduced andemployed to evaluate and control the quality of biodiesel feed-stock, JC oil. Initially, 13 representative JC seed samples were col-lected form various plantation zones in three counties inGuizhou, China. The oil content, acid value and FAC of 13 oil sam-ples were investigated. The CN, KV and CFFP of these samples werepredicted based on previous reports [13–15]. Finally, combinedwith the influence of FAC on the fuel properties of resulting biodie-sel, the GC fingerprint for the JC oil samples was established by SA,HCA and PCA. Meanwhile, the GC method was designed for thesimultaneous determination of the seven main fatty acids in JC oil.

2. Materials and methods

2.1. Materials

The details of 13 representative JC seeds from various planta-tion zones in Zhenfeng, Luodian and Wangmo in Guizhou, Chinaare summarized in Table 1. Methanol, sodium hydroxide, boron tri-fluoride and all the other regents were of analytical grade and werepurchased from Chuanhua Fine Chemical Co., Sichuan. All stan-dards: palmitic acid (C16:0), palmitoleic acid (C16:1), stearic acid(C18:0), oleic acid (C18:1), linoleic acid (C18:2), linolenic acid(C18:3) and cis-11-eicosanoic acid (C20:1) methyl ester were pur-chased from Sigma (USA).

2.2. Oil extraction, analysis, samples and standard mixture preparation

Every representative JC seed sample was oven-dried at 110 �Cfor 3 h to remove excess moisture and then the dried seed sampleswere ground into powder and passed through a 20-mesh (0.9 mm)sieve. The corresponding JC oil samples were obtained according toSarin’s method [29]. The acid value (mg KOH/g) and oil content(wt.%) of all the samples were tested by ISO 660-1996 and 659-1998 and are listed in Table 1.

All oil samples were methylated using methanolic NaOH andboron trifluoride–methanol according to ISO 5509-2001 to preparethe corresponding methyl esters. An accurately weighed sample of0.10 g was introduced into a 10 mL volumetric flask, and then di-luted with n-hexane up to the target volume. Standard mixtureswere prepared as follows: seven standards were accuratelyweighed at an appropriate amount, and dissolved in n-hexane ina 50 mL volumetric flask (C16:0, 3.71 mg/mL; C16:1, 0.24 mg/mL;C18:0, 1.55 mg/mL; C18:1, 9.47 mg/mL; C18:2, 9.31 mg/mL;C18:3, 0.13 mg/mL; and C20:1, 0.16 mg/mL) and then diluted tothe appropriate concentration ranges for the establishment of cal-ibration curves. All samples and standard solutions were stored atclose to 4 �C in the dark before use.

2.3. Apparatus and chromatographic conditions

For quantitative and fingerprint analysis, an Agilent 6890GCinstrument equipped with a flame ionization detector and auto-sample injector (Agilent 7683B) was used. HP-Innowax (30 m �0.32 mm, 0.5 lm) was used as the separation column. The chro-matographic conditions were as follows: inlet temperature: 523 K;detector temperature: 553 K; split ratio: 20:1; oven temperatureprogram: 463 K for 3 min, ramp at 15 K/min to 513 K, hold for10 min; injection volume: 1 lL; carrier gas: N2 at 1.0 mL/min; airflow: 450 mL/min; and H2 flow: 40 mL/min. The GC chromatogramsof the standards and samples were obtained under these conditionswith an injection volume of 1 lL (Figs. 1 and 2).

Table 1A summary of the tested samples.

No. Origins Harvesting time Oil content (wt.%) Acid value mg (KOH/g)

S1 Zhenfeng, Guizhou, China Aug. 2008 33.10 14.67S2 Zhenfeng, Guizhou, China Sep. 2008 35.04 6.18S3 Zhenfeng, Guizhou, China Sep. 2008 35.30 7.37S4 Zhenfeng, Guizhou, China Sep. 2008 40.12 4.41S5 Zhenfeng, Guizhou, China Sep. 2008 40.19 6.13S6 Zhenfeng, Guizhou, China Sep. 2008 38.02 7.33S7 Zhenfeng, Guizhou, China Oct. 2008 39.00 9.46S8 Zhenfeng, Guizhou, China Oct. 2008 34.16 11.90S9 Luodian, Guizhou, China Sep. 2008 35.50 8.23S10 Luodian, Guizhou, China Sep. 2008 38.09 6.02S11 Luodian, Guizhou, China Oct. 2008 35.00 7.82S12 Wangmou, Guizhou, China Sep. 2008 39.10 6.05S13 Wangmou, Guizhou, China Sep. 2008 36.10 9.40

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2.4. Establishment and validation of methods for quantitative analysisand fingerprint analysis

Quantitative analysis method was performed by plotting cali-bration curves, in which every standard was tested at five differentconcentrations and every concentration. Each test was performedin triplicate. The limits of detections (LOD) and limits of quantifica-tions (LOQ) under the optimized GC conditions were determined atsignal-to-noise ratios of 3 and 10, respectively. The precision was

0 1 2 3 4 5 6 7 8 9 10

200

400

600

800

1000

1200

1400

Eicosenoic acidmethyl ester

Linolenate acid methyl ester

Linoleic acidmethyl ester

Oleic acidmethyl ester

Stearic acidmethyl ester

Palmitic acid methyl ester

Palmitoleic acid methyl ester

Retention time (min)

pA (A)

0 1 2 3 4 5 6 7 8 9 10

200

400

600

800

1000

1200

1400

10

Retention time (min)

(C)pA 8

9

6

7

543

2

1

Fig. 1. GC chromatogram for the different samples: (A) standard mixture; (B) sample frWangmo County (S12).

investigated by intraday (six times within a day) and interday var-iation with a standard mixture (three times per day, 3 consecutivedays). The reproducibility was obtained by six independently pre-pared samples (S6). The recovery test was conducted using thestandard addition method. Seven standards were added into thetransesterified samples from S6, and then processed and quantifiedin accordance with the established procedures (n = 3).

Under optimized conditions, the method for fingerprint analysiswas established and validated as follows. The precision was

0 1 2 3 4 5 6 7 8 9 10

200

400

600

800

1000

1200

1400 (B)

Retention time (min)

pA

10

8

9

6

7

543

2

1

pA

0 1 2 3 4 5 6 7 8 9 10

200

400

600

800

1000

1200

1400

Retention time (min)

(D)

10

8

9

6

7

543

2

1

om Zhenfeng County (S7); (C) sample from Luodian County (S11); (D) sample from

2 3 4 5 6 7 8 9 10

S7

S11S12

S13

S10S9

S8

S6S5

S4S3

S2

pA

Retention time (min)

S1

Fig. 2. Overlapped GC chromatogram for the 13 samples.

R. Wang et al. / Applied Energy 88 (2011) 2064–2070 2067

assessed by six repetitive injections of the same sample solution(S7), and the sample stability was investigated by analyzing thesame sample solution (S7) at 0, 4, 8, 24, 48 and 72 h (n = 5).

2.5. Analysis of data and quality evaluation

SA was performed using SPSS software (Version 13.0 forWindows, Corporation, USA). The correlation coefficient (CC) and co-sine value of vectorial angle (CVVA) of the entire chromatographicprofiles of all samples were calculated. Thirteen samples were clas-sified by HCA. To perform HCA, clusters were established by Ward’smethod as the amalgamation rule, and squared Euclidean distancewas employed as a metric in the experiment. To prove the rationalityof SA and HCA, a more visual comparison of the chromatograms wasobtained by PCA for evaluating the resemblances and differences ofthe tested samples. HCA and PCA were also performed using SPSS.

3. Results and discussion

3.1. Chromatography conditions optimization

To establish reliable and characteristic GC fingerprints of the JC oilsamples for biodiesel feedstock, various chromatographic parame-ters were selected and optimized. These included the selection ofproper separation columns, oven temperature programs, and splitratios until complete separation of the primary components andsecondary components were achieved. Among all parameters, thetype of separation column was crucial. HP-Innowax column(30 m � 0.32 mm, 0.5 lm) with high polarity showed the best selec-tivity and resolution compared with the DB-35 column(30 m � 0.32 mm, 0.5 lm) and DB-1701 column (30 m � 0.32 mm,0.5 lm) in our work. The oven rate was screened at 10 K/min,15 K/min and 20 K/min. The 15 K/min rate was chosen because itprovided complete separation and efficiency. In addition, lower splitratios resulted in the tailing of some chromatographic peaks. Conse-quently, a 20:1 of split ratio was chosen.

3.2. Establishment and validation of methods for quantitative andfingerprint analysis

To establish and confirm the quantitative analysis, the calibra-tion curves, correlation coefficient (r2), LOD and LOQ of seven stan-dards were obtained (Table 2). The precision, reproducibility, andrecovery test are listed in Table 3. To establish and validate thefingerprint analysis, peak 7 (oleic acid methyl ester) was selected

as the reference peak because it was a strong signal peak andwas located in the middle of the retention time (close to6.0 min). The relative peak areas of common peaks (RPACP) andthe relative retention time of common peaks (RRTCP) were de-duced as compared with this reference compound. The validationof the fingerprint analysis method was as follows: the RSD ofRPACP and RRTCP for precision and stability was lower than3.01% and 0.62% respectively (n = 6). The satisfactory chromato-graphic conditions have been established for the quantitative anal-ysis and fingerprint analysis of the JC oil samples.

3.3. Identification and quantitation of components in fingerprintchromatograms

Usually, relative retention times were utilized as the primarycriterion for the identification of peaks when all the main compo-nents can be completely separated. By comparison with the GCchromatograms of the standard mixture (Fig. 1), Peaks 2, 3, 5, 6,7, 8 and 9 were identified as C16:0, C16:1, C18:0, C18:1, C18:2,C18:3 and C20:1 respectively. Their contents were calculated bythe external standard method (Table 4). Considerable differenceswere noted in terms of FAC in the 13 samples, which inevitablyinfluenced the fuel properties of the biodiesel produced from them.

3.4. Establishment of GC fingerprint for JC oil

Therefore, 13 mean GC chromatograms were analyzed with thesame assay method and overlapped (Fig. 2). The results indicatedthat there were considerable FAC differences in the JC oil in thesame species but of different provenances and conditions in culti-vation (Table 1). Ten common peaks were selected and marked bynumbers (Figs. 1 and 2). In combination with the data shown in theTables 4 and 5, some similarities and differences are easily be ob-servable, particularly in terms of changes in C18:1 (peak 6) andC18:2 (peak 7) contents. These differences are evidently responsi-ble for the fluctuation in fuel properties.

3.5. Similarity analysis (SA)

Chromatographic fingerprint should be used to evaluate simi-larity of samples, which can be obtained from the calculation ofthe CC and CVVA from the original data. In previous reports, thesimilarity of a set of chromatographic fingerprints was calculatedbased on mean chromatogram using SPSS software [30,31]. Fortu-nately, the prediction of the ideal JC oil for the resulting biodieselwas based on its FAC, which can in turn guide us to the optimum

Table 2Calibration curve and other parameters for quantitative analysis.

Standards Calibration curve r2 Linear range (lg/mL) LOD (lg/mL) LOQ (lg/mL)

C16:0 A = 1487.1242 � C + 4.7838 0.9994 1484–3710 1.6 5.6C16:1 A = 1249.9530 � C + 0.0745 0.9996 24–240 2.0 6.6C18:0 A = 1371.8760 � C + 2.3446 0.9997 62–1550 2.5 8.5C18:1 A = 1528.9013 � C + 16.1897 0.9997 378.8–9470 2.3 7.8C18:2 A = 1370.4193 � C + 18.8098 0.9996 372.4–9310 2.8 9.4C18:3 A = 1449.9591 � C + 0.0184 0.9995 5.2–130 1.1 3.9C20:1 A = 1421.8660 � C + 3.3729 0.9998 6.4–160 1.3 4.6

Table 3Data on precision, reproducibility, and recovery test for quantitative analysis.

Standards/content (lg/mL) Precision Reproducibility (S6, n = 6) Recovery (n = 3)

Intraday (n = 6) Interday (n = 9)

MCa (lg/mL) RSD (%) MCa (lg/mL) RSD (%) MCa (mg/g) RSD (%) Spiked (mg) Recovery (%) RSD (%)

C16:0/1484 1460.32 1.87 1489.59 1.91 135.56 2.29 7.07 99.20 2.80C16:1/96 96.86 2.97 93.53 3.43 11.88 4.47 1.49 96.67 1.69C18:0/620 614.21 2.90 622.46 3.08 41.62 3.14 4.07 99.33 0.90C18:1/3788 3802.27 1.77 3791.91 2.16 287.53 3.27 25.12 99.96 3.47C18:2/3724 3712.09 2.49 3699.34 2.28 514.63 2.81 23.91 100.12 2.39C18:3/52 53.29 3.01 52.91 3.61 1.54 3.35 1.03 98.85 0.66C20:1/64 62.43 2.87 63.22 3.30 1.70 4.07 1.32 101.16 1.54

a The mean content.

Table 4Main fatty acid composition and predicted fuel properties of the tested samples.

No. Fatty acid composition/wt.%a, n = 3 Predictive value DS [13] DU [13] DP [13]

C16:0 C16:1 C18:0 C18:1 C18:2 C18:3 C20:1Peak 2 Peak 3 Peak 6 Peak 7 Peak 8 Peak 9 Peak 10 CN [13,14] CFPP (�C) [13,14] KV [15] (40 �C, mm2/s)

S1 13.44 0.85 7.21 43.48 33.62 0.10 0.21 56.76 �0.93 4.20 20.65 132.77 33.72S2 13.01 0.84 7.14 42.28 34.90 0.12 0.21 56.13 �1.17 4.16 20.15 134.16 35.02S3 13.08 0.95 6.44 38.79 38.93 0.12 0.19 55.11 �2.25 4.11 19.52 136.84 39.05S4 13.56 1.19 4.56 28.75 51.46 0.14 0.16 52.87 �5.05 4.05 18.12 149.14 51.6S5 13.69 1.30 4.21 28.42 49.60 0.15 0.16 51.80 �5.56 3.92 17.9 145.22 49.75S6 13.56 1.19 4.16 28.75 51.46 0.15 0.17 52.47 �5.68 4.02 17.72 150.16 51.61S7 13.17 0.84 7.56 44.37 32.62 0.11 0.22 57.00 �0.46 4.21 20.73 132.67 32.73S8 13.06 1.12 4.65 29.99 50.65 0.19 0.14 52.90 �5.07 4.06 17.71 146.79 50.84S9 13.76 0.99 6.94 37.65 39.39 0.13 0.20 55.76 �1.25 4.14 20.7 137.68 39.52S10 13.06 1.04 5.40 40.94 39.07 0.20 0.26 55.38 �3.89 4.18 18.46 146.52 39.27S11 12.60 1.84 7.11 30.33 46.20 0.13 0.37 53.88 �1.35 4.00 19.71 161.83 46.33S12 13.37 0.97 7.09 41.10 34.95 0.18 0.24 55.83 �1.14 4.12 20.46 136.33 35.13S13 12.99 1.21 5.76 37.42 40.67 0.20 0.32 54.40 �3.35 4.07 18.75 152.37 40.87

a The mean value.

Table 5Mean value (n = 3) of the relative peak area of common peaks (RPACP) of the 13 JC oil samples over a reference compound (oleic acid methyl ester, peak 7).

Peak Sample

1 2 3 4 5 6 7 8 9 10 11 12 13

1 0.0012 0.0012 0.0013 0.0029 0.0028 0.0024 0.0011 0.0024 0.0015 0.0015 0.0017 0.0013 0.00212 0.3006 0.2992 0.3272 0.4580 0.4675 0.4254 0.2886 0.4287 0.3554 0.2866 0.4218 0.3165 0.33023 3.9664 3.6674 4.1928 4.6175 4.9586 4.8937 3.7821 4.3911 4.3420 2.7543 15.2251 3.5408 2.57034 0.0028 0.0029 0.0031 0.0044 0.0042 0.0040 0.0028 0.0039 0.0028 0.0046 0.0043 0.0025 0.00345 0.0010 0.0010 0.0011 0.0024 0.0024 0.0020 0.0009 0.0020 0.0012 0.0012 0.0014 0.0011 0.00176 0.1487 0.1494 0.1491 0.1301 0.1329 0.1378 0.1528 0.1411 0.1654 0.1184 0.2133 0.1679 0.13517 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.00008 0.6947 0.7414 0.9007 1.6178 1.6375 1.4745 0.6606 1.5325 0.9387 0.8565 1.3822 0.7832 0.95399 0.0040 0.0044 0.0048 0.0073 0.0075 0.0066 0.0041 0.0070 0.0050 0.0075 0.0033 0.0055 0.0075

10 0.0084 0.0077 0.0076 0.0073 0.0081 0.0075 0.0082 0.0052 0.0077 0.0098 0.0094 0.0073 0.0120

2068 R. Wang et al. / Applied Energy 88 (2011) 2064–2070

fuel properties. To search for the ideal reference spectrum for SA,the predicted CN, KV, CFFP, degree of saturation (DS), degree ofunsaturation (DU) and degree of polyunsaturation (DP) werecalculated through the fitting equations and calculation method[13–15] (Table 4). Fluctuation clearly occurred in the fuel proper-

ties because of the differences in FAC, especially in CN (51.80–57.00) and CCFP (�5.68 to �0.46). With an increased DS and a de-creased DP, CN and KV increased [11,14]. The highest CN (57.00)was found in S7. It can provide a superior OS, which is attributedto the lowest DU (132.67) and DP (32.73). Although S7 showed

R. Wang et al. / Applied Energy 88 (2011) 2064–2070 2069

the worst CFPP (�0.46 �C) and KV (4.21 mm2/s) among all samples,the CFPP and KV values of S7 can be disregarded because biodieselis commonly mixed with petroleum diesel in different proportions(such as B5, B10, and B20). CFPP is regarded as a ‘‘soft’’ specifica-tion in EN 14124 and ASTM D6751 standards [14]. Also, the�0.46 �C of CCFP is acceptable in most areas in south China. There-fore, biodiesel with a higher CN, OS and appropriate cold-flowproperties is desired. As mentioned above, the ideal vegetable oilbiodiesel has a high C16:1 and C18:1 content, a low percentageof polyunsaturated acids, and limited saturated acids content[11]. Therefore, the chromatogram of sample 7 was chosen as thereference spectrum. Thus, the CC and CVVA value for every meanchromatogram were calculated (S1–S13, Table 6) by comparingthem to the mean S7 reference spectrum. Taking the data of S1,S2, and S12 into account, the similarities varied from 0.995 to1.000 compared with that of S7. The mean CC and CVVA were0.998 and 0.999, respectively. These four samples showed a similarCN (56.76, 56.03, 57.00, 55.38, respectively), CFPP (�0.93, �1.33,�0.46, �1.14 �C, respectively) and KV (4.20, 4.16, 4.21, 4.12 mm2/s, respectively). The data corresponding to S3, S9, S10 and S13range from 0.977 to 0.986, and the mean values of CC and CVVAwere 0.981 and 0.984, respectively. The similarities of S4, S5, S6,S8 and S11 ranged from 0.860 to 0.905 as compared with that ofS7, and the mean values of CC and CVVA were 0.879 and 0.923,respectively. They had an inferior CN (52.87, 51.80, 52.47, 52.90,and 53.88, respectively) and a superior CFPP (�5.05, �5.56,�5.68, �5.07, and �1.35 �C). To study further, the cluster relation-ship of samples from various plantations, HCA and PCA wereapplied.

3.6. Hierarchical clustering analysis (HCA)

To assess the quality characteristics of these samples, HCA of 13mean fingerprint chromatograms (n = 3) was done using SPSS. A13 � 9 matrix was formed by the RPACP of the 10 common constit-uents from 13 mean fingerprint chromatograms to reveal the sim-ilarities and differences among the samples. After an appropriatedistance (Level I) was chosen to differentiate the samples, all sam-ples were divided into three clusters: samples 1, 2, 7 and 12 incluster I, samples 3, 9, 10, and 13 in cluster II, and cluster III con-sisted of samples 4, 5, 6, 8, and 11 (Fig. 3). An interesting phenom-enon was observed: the distance between cluster I and cluster IIwas shorter than that between clusters I, II, and cluster III, whichcan be attributed to the differences in FAC among the samples.The resulting biodiesel in cluster I provided the highest DS, thelowest DU and DP among the three clusters (Table 4). Therefore,the samples with high CN, CFFP, KV and OS were located in clusterI. If a larger distance level (Level II) was considered, the samplescan be divided into two clusters, indicating that the fuel propertiesof the resulting biodiesel from cluster III were poorer than those ofcluster I and cluster II, as shown by the highest DP. As seen in Table4, the distribution of CN was completely localized in the corre-sponding cluster completely: cluster I (55.83–57.00), cluster II(54.40–55.76), cluster III (51.80–53.88). Further, some abnormal

Table 6Similarity comparison between the chromatographic patterns of all samples.

No. Correlative coefficient Cosine value of vectorial angle

S1 0.999 0.999S2 0.998 0.999S3 0.983 0.989S4 0.864 0.912S5 0.860 0.910S6 0.888 0.929S7 1.000 1.000

predicted data existed in the three clusters (Table 4). For example,the KV (4.12 mm2/s) of S12 from cluster I was lower than those ofS9 (4.14 mm2/s) and S10 (4.18 mm2/s) in cluster II; the CFFP(�1.25 �C) of S9 in cluster III was higher than S3 (�2.25 �C), S13(�3.35 �C) and S10 (�3.89 �C). However, the CN and CFFP of S12and the CN and KV of S9 were similar to those of the other samplesin their respective clusters. Fingerprint analysis was performed andestablished based on ‘‘integrity’’ and ‘‘fuzziness’’ or ‘‘sameness’’and ‘‘difference’’ [32]. In this study, only CN, CFFP, and KV werepredicted and listed. The similarity and difference of the samplescannot be interpreted and evaluated sufficiently through thesethree fuel properties. In addition, the fitting error of the equations[13–15] should also be considered. However, the results werefound consistent by comparing HCA with SA.

3.7. Automatic classification by PCA (unsupervised classification byPCA)

In this experiment, the RPACP of 10 common peaks from 13mean chromatograms was employed as input data for PCA. Threeprincipal components accounting for 91.46% of the total variance,and were considered significant based on eigenvalues >1. Fromthe projected dots of the 13 mean chromatograms in Fig. 4, a morevisual comparison of all chromatograms was provided. The PCA interms of the three principal components showed that all samplescan be classified into three groups. Based on SA and HCA, groupIII was considerably different from groups I and II, and group IIIwas similar to group I, suggesting that the samples from groups Iand II were more suitable for biodiesel feedstock than those ofgroup III. Moreover, the results of SA, HCA, and PCA were well iden-tical with each other and provided a strong foundation for thequality evaluation and control of JC oil samples.

4. Conclusions

At present, multiple plantations of JC L. have already beenestablished to a considerable scale in Guizhou, China, encompass-ing a total area of about 30,000 ha, for the commercialization ofbiodiesel production. The chemical content and quality of the finalproduct, such as oil content, acid value, and fatty acid composition(Tables 1 and 4), are greatly dependent on provenance, climaticconditions, soil quality, and cultivation conditions, among otherfactors. Therefore, the development of an effective method forthe quality evaluation and control of JC seed oil is imperative. Inthis study, the fingerprint analysis technology derived from qualityevaluation and control of herbal medicines has been utilized toascertain and to evaluate the quality of biodiesel feedstock, JC oil.For this purpose, the chromatographic conditions were first opti-mized, and the main fatty acid composition and distribution wereanalyzed for all samples. Ten common chromatographic peakswere found, among seven of which were identified successfully(peaks 2, 3, 6–10) in the chromatograms. Due to its ideal FAC(the lowest DU and DP) and superior predicted fuel properties,the chromatogram of sample 7 was selected as the reference

No. Correlative coefficient Cosine value of vectorial angle

S8 0.878 0.922S9 0.977 0.986

S10 0.986 0.992S11 0.905 0.941S12 0.995 0.997S13 0.978 0.984

Fig. 3. Clustering dendrogram of the JC oil samples using Ward’s method andEuclidean distance.

Fig. 4. 3D-projection plots of PCA based on the three principal components of the13 samples. PC1, PC2, and PC3 are the first three principal components using RPACPas input data.

2070 R. Wang et al. / Applied Energy 88 (2011) 2064–2070

spectrum for similarity analysis based on the influence of FAC ofthe raw material on the fuel properties of the resulting biodiesel.The results showed that through chemometric techniques suchas SA, HCA, and PCA, the samples can be objectively classifiedand divided into three distinct clusters. A similarity value fromsample close to 1.000 as compared with sample 7, and was indic-ative of better fuel properties in the resulting biodiesel. In sum-mary, a facile and feasible method for the quality evaluation andcontrol of JC seed oil has been developed. Useful data for the breed-ing, planting and harvesting of Jatropha curcus L. have also beenprovided.

Acknowledgements

This work was financially supported by International Science& Technology Cooperation Program of China (2010DFB60840),Key Science and Technology Project of Guizhou Province (No.20076004), the National Key Technology R&D Program(2006BAD07A12), and Guizhou S&T Program (SZ[2009]3011).

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