Anal. Chem. 1993, 65, 204-208
204
Thermal Analysis of Peat Kurt Bergnerl and Christer Albanot Swedish University of Agricultural Sciences, Box 4097, S-90403 Umed, Sweden
Thermal analysls has been performed on samples of plants, peat, chemical fractlons of peat, and coal. Slmultaneow thermogravlmetry (TO) and dltferentlal scanning calorlmetry (DSC) technique has proved to be weful In classltylng and separatlngthe samples. Dueto probableredundantlnfonnatlon In the TO and DSC signals the sampling frequency has been Investlgated. Ouantltatlve predlctlons of 15 chemical and physlcalconstltuents In peat are performeduslng partlal least squares regresslon (PLSR). Predlctlon propertles are comparedwlth near Infraredreflectancespectroscopy (NI R) whlch shows that TO/DSC and N I R are comparable In predlctablllty of lnvestlgatedconstltuents. The use of simultaneousTO and DSC signals In predlctlons, compared using TO or DSC separately, shows that the comblnatlon leads to Increases In the predlctablllty, as shown by the use of standard error of predlctlon (SEP) values.
INTRODUCTION Peat is an inhomogeneous complex material with a natural variation in botanical origin and chemical and physical composition. This makes it difficult to perform consistent investigations of peat without having a good characterization of the samples. A thorough investigation of peat samples is time-consuming and expensive. By using analytical methods giving broad information in combination with a well-characterized calibration data set, multivariate statistical methods can be used to extract many properties from one experiment. This reduces time and costs of the characterization of peat. With thermal analysis, such as simultaneous thermogravimetry (TG) and differential scanning calorimetry (DSC), it is possible to get quantitative and qualitative multivariate data. DSC and differential thermal analysis (DTA) curves of peata, determined in atmospheres containing oxygen, are composed of marked exothermic effects, usually found as two peaks, in the 300-450 "C region. Stewart et al.' concluded that the degree of humification was related to the intensity of the first exothermic DTA peak. This relationship was confirmed in an investigation using DTA complemented by DTG from four Spanish peat profiles.2 In the same investigation it was found that the fiist exothermic peak was related to the carbohydrate content of the sample. Ranta3found by DTG a good correlation between the first exothermic peak and the degree of humification. He also found the first peak to represent the oxidation of sugars and cellulosic material. Further work4 on peat and peat components has indicated the dependence of the second exothermic peak on acid and cellulose contents. showed that In an earlier investigation, Persson et thermogravimetry is useful for quantitative determinations Current address: Umetri AB, Box 1454, S-90124 Umei, Sweden. (1) Stewart, J. M.; Birnie, A. C.; Mitchell, B. D. Agrochimica 1966,11, 92. (2)Almendros, G.; Polo, A,; Vizcayno, C. J. Thermal Anal. 1982,24, 175. (3)Ranta, J., SUO 1979, 30 (2),43-46. (4)Rustschey, D.;Atanasov, 0. J. Thermal Anal. 1983,27, 439. (5) Persson, J.-A.; Johansson, E.; Albano, C. Anal. Chem. 1986, 58, 1173-1178. +
0003-2700/93/0365-0204$04.00/0
of chemical and physical properties in complex samples, e.g. peat. The samples were heated in an atmosphere of nitrogen, and the properties determined were calorific value, carbon content, hydrolyzablesubstance (Rvalue), and volume weight. The calibration model was constructed from the thermogravimetric curves using the method of partial least square regression (PLSR). The model was validated, and predicted values were in good accordance with reference data. In this investigation 15chemical and physical constituents of peat have been predicted from simultaneous TG and DSC analysis. The predictability of these constituents is also compared with the near infrared reflectance spectrometry (NIR). In the present time, NIR is a commonly wed technique in the determination of many food and feed constituents as well as in peat a n a l y s i ~ . ~ ? ~ In simultaneous TG and DSC analysis of plants, peat, and coal, the signal patterns are rather similar in a visual inspection. It is therefore difficult to distinguish visually between one sample in one group from another sample in another group. Principal component analysis (PCA) is here used to reveal that information so that thermoanalytical data can be used to classify and separate the peat samples. The sampling frequency during the thermal gradient influences the resolution in the obtained data. The higher the frequency, the more redundant is the information in data points close to each other. This redundant data adds unnecessarilyto subsequent storage and calculation burdens. A comparison has been made between reduced and nonreduced data to see how much reduction can be done without losing significant information.
EXPERIMENTAL SECTION Apparatus. All thermal analysis were carried out using a Stanton Redcroft STA 780 SimultaneousThermalAnalyzer. The equipmentwas used with a STA 785 DSC/TGfurnace. Collected data were transferred via diskette to a PC computer, and the SIMCA program package (with PCA and PLSR) was used for data evaluation. Samples and Pretreatment. In this investigation 116 samples were included. Of these 67 samples were of peat, representingthe main biological variationof Swediihpeat, Theae were collected from 21 Swedish mires? The samples of plants and fragments of plants (11 samples) were also collected from mires, most of them from two mires in northern Sweden. These samples were dried at 30 "C to dryness and milled to a particle size less than 1 mm before being analyzed. Some samplesof peat were chemically fractionated. Bitumen samples were prepared by extraction with chloroform/methanol (2:l). The residue was treated in sodium hydroxide (0.1 M). Humic acid was prepared by precipitating, with hydrochloric acid, dissolved humic acids in the hydroxide solution. The solid residue after the extraction steps was treated with sulfuric acid (72%)to prepare the Klason lignin. The chemical fractions of peat (31 samples of bitumen, humic acids, and Klason lignin) were analyzed without further treatment. Samples of pure glucose (2 samples) and four coal samples (ASCRM-009,AR 2772, AR 2778, and AR 2782) were also added (6) Johansson, E.; Persson, J.-A.; Albano, C. FUEL 1987,66, January.
(7)Bohlin, E.;Jonsson, C.; Linder, H. The Swedish Peat Research Foundation, Project report 32,1990. 0 1993 American Chernlcal Society
ANALYTICAL CHEMISTRY, VOL. 65, NO. 3, FEBRUARY 1, lQQ3 205
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Flgurs 1. Simultaneous TG and DSC analysis of peat in an atmosphere of air. Heating rate 5 "Clmin: (a)Carex peat, humificatlon degree 2-3 (von Post); (b) Carex peat, humification degree 7-8; (c) Sphagnum peat, humificatlondegree 3; (d) Sphagnum peat, humlficatbn degree 7-8.
to the samples and analyzed. The concentration of carbon in these coal samples covered a concentration interval ranging from 54 to 87%. Detailed information on all samples used and raw data from the measurements can be obtained from the Centre for Peat Research. Samplesof peat in small quantities are alsodistributed on request. These peat samples are well classified and have been carefully characterized.8 Measurements. Air dry samples were used in all measurements. Sample weights were kept between 5 and 10 mg. The samples were analyzed in the temperature interval between 20 and 600 "C. The heating rate was linear, 5 "C/min in an atmosphere of air (flow rate 50 mL/min). From recorded data 220 equidistant temperature intervals between 125 and 550 OC were selected fot evaluation. That means that datawere collected about every 2 "C during the analysis. Examples of simultaneous TG and DSC curves are presented in Figure 1. Analysis of Peat. The peat samples have been analyzed previously with respect to biological, physical, and chemical properties. The biological data consist of 16 variables describing the fraction of different plant fragments found in the peat. Chemicaldata represent analysisfor carbohydrates, metals,amino acids, amino sugars, Klason lignin, bitumen, and some other parameters. Available physical data are energy, particle size, ash, water content, and someother parameter~.8*~ Together there are more than 80 different variablesavailablefor the peat samples. In this investigation 15 of these variables have been used in evaluation of the simultaneous TG and DSC analysis. These variableshave been chosen because they represent the dominating constituents of peat and have only a small amount missing data for the samples analyzed. A list of the variables, concentrations, and methods can be found in Table I. Analysis of Other Samples. The samples of plants have been analyzed for only a few chemical parameters (C, H, N, and some metals), and they are therefore excluded from the mathematical modeling. No chemical analyses are available on the coal standards, the chemical fractions of peat, and the pure chemicals. This means that these materials are not available for regression modeling. (8)Bohlin, E.;Hbdiliiinen, M.; SundBn, T. Soil Sci. 1989,(4). (9)Bergner, K.;Albano, A.; Bohlin, E. 1990,STEV 216 088-1,Centre for Peat Research.
MATHEMATICAL METHODS Introduction. The signal patterns from TG/DSC data are visually rather similar; therefore, multivariate methods were used to simplify the interpretation by visualizing relationships between objects and variables. Because of variations in sample weights (5-10 mg) some pretreatments of the data was necessary before attempting the multivariate evaluation. Data Pretreatment. All records of TG and DSC have been treated mathematically, in the temperature interval from 125 to 550 OC, using the formulas a and b, respectively.
TG = DSC =
signal (mg) weight at 125 "C (mg)
signal (mcal/s) weight at 125 "C (mg) signal at 125 "C (mcal/s) weight at 125 O C (mg)
(b)
PCA. Principal component analysis (PCA) is a useful and efficient tool to show variations in multivariate data.'O The major advantage is the good graphical display of the variation/ covariation pattern of the data. Another feature of PCA is that the method is not based on restricted statistical distributions. With use of some algoritms, PCA can handle situations with more variables than objects, missing data, etc. The variables are mean-centered by subtraction of themean value and then scaled to unit variance. The data are then modeled by orthogonal (noncorrelated) lines of best fit (least squares) to the objects in the space spanned by the original variables. These lines can be used to display plots showing the relationship between objects and variables. Similar objects are located close to each other in the plots. Combinations of components may in some cases give plots locating (10)Wold, S.;Esbensen, K.; Geladi, P. Chemom. Intell. Lab. Syat. 1987,2 (1-3),37-52.
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Table I. Variations and Concentrations in Samples Used in the Evaluation of the Simultaneous TG/DSC Analysis on Peat variable
mean
SD
max.
ash" carbonb hydrogenb nitrogenb SulfurC energyd rhamnosee fucosee arabinosee xylosee mannosee galactosee glucosee klason lignine bitumenf
3.95 59.8 6.14 1.91 0.32 23.1 1.06 0.23 1.34 3.17 1.75 2.44 12.9 54.2 5.62
3.28 2.96 0.30 0.94 0.31 1.42 0.64 0.16 0.84 1.02 0.63 0.75 6.81 9.26 2.08
% of dry solids % of org material 6.9 5.6 % of org material 4.1 0.6 % of org material 1.14 0.07 % of org material 25.4 19.8 MJ/kg org material 4.2 0.4 % of defatted dry peat 0.8 0 % of defatted dry peat 4.1 0.3 % of defatted dry peat 5.9 1.2 % of defatted dry peat 3.5 0.9 % of defatted dry peat 4.2 1.3 % of defatted dry peat 24.8 3.2 % of defatted dry peat 69.6 18.2 % of org material 10.3 1.4 % of org material
14.6 64.4
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Svensk Standard (SS)18 71 71. Instrument LECO CHNGOO. ASTMD 4239-83,1984. Svensk Standard (SS)18 71 72. e Residue after sulfuric acid hydrolysis. f Acetone extract.
c
unsimilar objects close to each other. It is therefore important to interpret the plots carefully. Each component is composed of a linear combination of the original variables. PLSR. Partial least square regression (PLSR) can be seen as an extension to PCA by introducing a second block of variables (responses, Y-variables, dependent variables) to the multivariate analysis." PLSR is used to calculate regression models between the two blocks of data. Like PCA, PLSR is also a projection method, and the results can easily be shown with plots that help the interpretation. In this paper the PLSR has been used for modeling of simultaneous TG/DSC data and peat properties. These models have been used to estimate the repeatability of the TG/DSC analysis, to compare the predictability of TG/DSC with NIR,to study the redundancy of TG/DSC data, and to see how scaling of data influences the prediction quality of the models. Rank of the Models. To minimize prediction error, an optimum number of dimensions in the PLS model has to be used. In this paper the standard error of prediction (SEP) is used. SEP is calculated on the predicted values according to
This makes SEP a measure of how much the predictions deviate from the measured (and considered true) values. A high SEP means a bad correspondence between measured and predicted values. The number of components that gives the lowest SEP is considered the right model dimensionality. Validation. In the PLSR predictions the validation was done in the following way. One peat sample was excluded from the data matrix, and a PLSR model was calculated with the thermo analysis data in X and the selected peat constituents in Y. The y-variables for the excluded sample were predicted from the model. This procedurewas repeated for each peat sample. The number of PLSR factors giving the minimum prediction error sum of squares (PRESS) was used. Scaling. The initial variance of a variable partly determines its importance in the model. Variance scaling of data is used to give the same strength of influenceto all variables. By that the ability of each variable to influence the model is regulated.1° Included in this study is a comparison of scaling (11) Geladi, P.;Kowalski,
B. R. Anal. Chim. Acta
1986, 185, 1-17.
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methods. The comparison here was made by treating the data in four ways. These are no scaling of data, scaling of all data, scaling of only x-data, and scaling of only y-data. Classification. The plotting of data after PCA analysis sometimes allowsthe detectionof clusters of objects (samples) or clusters of variables. The classification used was an unsupervised one. All samples are classified as belonging or not to a defined class. Here the groups of data found in the PCA analysis are tested to see if the groups are separate classes or not. The classification can be done in two ways. The fist is to build a PCA model for each separate group and then fit objects to all models. An F test is then used to test which class an object belongs to. Another way is to use PLS discriminant analysis by introducingdummy variables (1and 0 ) describing the different classes. The number of dummy variables is equal to the number of classes. In this study both methods have been used.
ANALYTICAL CHEMISTRY, VOL. 65.
Table 11. Repeatability af Simultaneous T d a n d DSC Analysis on Sugars and Klason Lianin. predicted variable analyzedmean mean SD relativeSD rhamnose 0.91 1.08 0.185 11 fucose 0.10 0.15 0.015 10 arabinose 0.77 0.84 0.095 11 xylose
mannose galactose glucose Klason lignin
3.13 2.12 3.05 20.1 55.1
2.94 1.87 2.56 17.0 54.6
0.239 0.135 0.144 1.35 3.07
8.1 7.2 5.6 7.9 5.6
' h e sample analyzed is a high humified Sphagnum peat (1109). The reduced data set is used and the x- and y-variables are scaled. The concentration is given in percent of defatted dry peat.
Table 111. Rematability of Standard Methods. % of defatted dry peat variable mean SD relative SD. % rhamnose 2.08 0.544 26 0.06 0.69 2.54 2.64 2.89 19.6 42.4
fucose
arabinose xylose
mannose galadose glucose
Klason lignin
0.092 0.154 0.131 0.112 0.081 0.814 1.47
164 22 5.2 4.2 2.8 4.2 3.5
T h e sample, a low humified (H3-4)Sphagnum peat (TEo53),is analyzed 13 times for sugars and 9 times for Klason lignin during a time of about 2 months. 81
RESULTS AND DISCUSSION The simultaneous analysis of changes in energy flow and weight during the linear temperature gradient are presented as curves vs temperature (see examples in Figure 1). Due to similarities in signals it is sometimes very difficult to classify and separate samples from each other. It is also difficult to quantify concentrations of constituents due to these similarities. The multivariate methods of PCA and PLSR are used to overcome the difficulties of visual interpretation of the curves by giving an objective view over similarities, dissimilarities, and relationships. 1. PCA, Classification. All of the 115 objects and 440 variables form a matrix of 115 by 440. This matrix waa analyzed hy PCA. The results, Figure 2a-2, show that the first three components separate the data into six separate clusters. This is an unsupervised clustering. The clustering was found to match background information availeble for the samples. The classes are coal, Klason lignin, glucose, bitumen, acids, and peat + plants. The last cluster, peat + plants, is not separated completely even if all five significant components are used. The samples of peat that are not separated from the plants are of Sphagnum type and of low humification. This means that Sphagnum peat of low degree of humification and plants are similar in a thermoanalytical point of view. It also indicates that peat and plant materials should be comparable when heating in an atmosphere of air as is done in furnaces. 2. Prediction. AUsamplesofpeathave beencharacterized in earlier investigations and analyzed botanically, chemically, and physi~ally.~,~ Some of these chemical results have been used to model data from the thermoanalyzer to predict concentrations of different constituents in peat. In this investigation 15 of a total of over 80 variables have been chosen for modeling. PLSR was used for modeling, and the SEP values served for optimizing the number of components for each variable. The model includes 220 variables from variations in weight (TG) and 220 variables from variations in energy flux (DSC) collected during the thermal analysesof the samples. This makes altogether 440variahles (the x-matrix). That means that TG and DSC values have been collected every second degree in the temperature gradient. Fifteen variables from analysis with conventional methods have all together been used as y-matrix, see Table I. To study the repeatability of the TG/DSC analysis one of the sampleshas been analyzed repeatedly (seven times) during a time of more than 2 months. The sample analyzed is a high-humified Sphagnum peat. Predicted results of Klason lignin and carbohydrates are compared with analyzed results from used standard methods. Predicted values, on a significance level of 95%,do not significantly differ from analyzed values, see Table 11, except for galactose. The repeatability
NO. 3, FEBRUARY 1. lDD3 207
"
Ash
C H N S En. Rha Fuc AIB
Xyl Man Gal GI" KI. Bi.
Variable Figure 3. The predlCtion error measured as SEP. A comparison of TGlDSCwith NIRanalysesof 15 constituentslnpeat. Thecomprlson Is made on the same 41 peat samples, and all data are scaled. 51
0-
Ash C
H
N
S En. Rha FuC A m Xyl Man Gal Glu KI. Bi.
Variable Data reduction of simultaneous TO and DSC analysis of 67 peat samples. Xdata are reduced from 440 to 44 variables (every tenth variable). Both x- and ydata are scaled. The Influence of data reduction measured as SEP values ere tested on 15 peat variables. Figure 4.
5 4
ti3 In
2 1 n "
Ash
C
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N
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Variable Fbum 5. Results of scaling of data measured as SEP valws. Data are treated In four ways: no scaling of data,scaling of Y 4 t a . scallng of Xdata, and scaling 01 X- and Ydata. of predicted values, measured as relative SD, is comparable with used standard methods, see Table 111. Compared with the NIR technique the simultaneous TG/ DSC analysis is good. The SEP values are of the same order for most of the constituents. In two cases (ash and Klason lignin) the SEP value is much better for the simultaneous TG/DSC technique and in one case (glucose)it is much better for the NIR technique, see Figure 3. Thermal analysis seems as good as NIR in predicting constituents in peat. The only
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ANALYTICAL CHEMISTRY, VOL. 65, NO. 3, FEBRUARY 1, 1993
values and reduces the SEP values by up to 25% compared with the heat predictions of TG or DSC. The strongest effect is reached for the variables of energy, glucose, and Klason lignin.
87
CONCLUSION
Ash C
H
N
S En RhaFuc Ara Xyl ManGal Glu KI
R8
Variable Flgure 6. Comparison of predictability of peat Constituents measured as SEP values: (a) predicted from TG data. (b) predicted from DSC data, (c) predicted from simultaneous TG and DSC data.
drawback is the need of longer analysis times. To decrease the analysis times the heating rate has been doubled,but this giveaproblems withspontaneousignitionof thesamp1e.Tests with a reduced amount of sample have also been performed, hut this gives a higher noise to signal ratio and problems to get reproducible samples. 3. Data Reduction. Thermal analyzers produce huge amounts of data. In this investigation 2 X 220 data points are collected from each sample run. This corresponds to a sampling frequency of about one every second degree. Processing data from 115 samples and 440 variables from each sample is time consuming. To reduce the computing time and to see if the information from the thermoanalyzer is redundant, the data was reduced. Every tenth variable from the original x-data was used as a new reduced x-data set. The distance hetween data points in the thermogram is now about 20 "C. To test if information is lost in x-dataafter reduction, the 67 samples of peat were tested on the 15 y-variables. No loss in information was found by this data reduction,seeFgure4, whereSEPvaluesarecompmed before and after reduction. 4. Scaling. In Figure 5 the results from the different ways of scaling are presented. The best results taken as a whole were received if all datawere scaled,hut the differences in SEP values between these procedures are small and prohahly not significant. By scaling both x- and y-variables, the optimum number of components was reduced compared with no scaling and scaling of only x- or y-variables. The conclusion must he that the effects of scaling on the SEP values for these variables are small. 5. TG, DSC, and Simultaneous TWDSC. Here the simultaneous TG and DSC signals are split into two data sets, one with only TG and the other with only DSC data. These are compared with each other and the simultaneous TG and DSC data set. The DSC signal gives better results than the TG signal, measured aa SEP values on tested variables,witbone exception. The predictionof ashcontents shows a lower SEP value using the TG signal as x-matrix, see Figure 6. Simultaneous TG and DSC gives even lower SEP
In this paper, it is shown that thermal analysis may be used to classify and separate classes of samples. The technique used for doing this is based on a PCA analysis on the thermograms. In addition, the relationship of peat to some other energy sources (coal, plant material) analyzed hy the same equipment is also established. The possibility of classification is a useful feature for handling peat samples of unknown origin and for fmdingrelationship betweensamples. Thedescrihed thermal techniqueis alsousefulin predicting constituents in peat via a multivariate regression relation. Thermal analysisgives predictions of concentrations and other properties that are in good agreement with those of another multivariate technique, NIR spectrometry. For some constituents, like for Klason lignin and ash content, thermal analysis outperformed NIR. For positioning of the sampling points during the thermal analysis, it is found that sampling intervals of less than one sampling each 20 "C are enough. For the prediction of some of the constituents, longer sampling intervals may be used without losing information in collected data, but for other constituents the predictability will be reduced. In the used thermoanalytical equipment, data is collected describing simultaneous changes in weight and energy flow during the temperature gradient. In an earlier article6 the changes in weight in an atmosphere of nitrogen were used to predict constituents in peat. In this study an atmosphere of air was used. A comparison was made between using only changes in weight or energy flow or a combination of these two. It was found that the use of simultaneousTG and DSC signals increases the predictability, measured as SEP values, by up to 25% compared with the best results of TG or DSC separately. The general conclusion is that thermal analysis methods are very useful in the study of energy-related materiala and peats in particular and that they offera flexibilityin parameter selection that allows their use in many different situations, demanding different answers from the analysis.
ACKNOWLEDGMENT Financial support from the Swedish National Board for Industrial and Technical Development, NUTEK, through grant 216 088 - 2 is gratefully acknowledged. The interest and support received from the other members of the project staff is also gratefully acknowledged.
RECEIVED for review March 30, 1992. Revised manuscript received October 13, 1992. Accepted October 21,1992.
