A non-destructive method (solid state 13CNMR) for determining organic chemical components of decomposing litter

soil &I/. &whem. Vol. 2, No. 2. pp. :71-17x 1990 0038-0717 90 53.00 + 0.00 Printed tn Great Bntarn. All rights reserved Copyright T 1990 Pergamon Press plc

A NON-DESTRUCTIVE METHOD (SOLID STATE 13CNMR) FOR DETERMINING ORGANIC CHEMICAL COMPONENTS OF DECOMPOSING LITTER

SMR Research Group, Department of Organic Chemistry. University of UmeQ. 901 87 Urn&. Sweden

BJ~RN BERG

Department of Forest Site Research, Swedish University of Agricultural Sciences, WI R3 Umed. Sweden

(Accepfed 5 Atc~u.rr 1989)

Summnry--“C CP1MA.S NMR spectra were measured in order to identify and quantify various forms of carbon in decomposing fitter, The Kfason-lignin content w;1s also determined by conventional techniques. Quantitatively. the NMR results showed a decrease in carbohydrates as litter decomposition proceeded. An initial increase in polymethylene resonances ievelled off in highly decomposed samples. Further. it is shown that by using a multivariate data analysis method, in this case partial least squares, NMR data can bc used to drtermine sample-spccitic properties, such as lignin content.

Littct and humus contain organic materials capable of binding nutrients: conscqucntfy, the rate of nutri- cnt rclcasc is partially dctcrmincd by the rutcs of fitter and humic decomposition. Although a few studies on the major chcmicnt components ol’ plant fitter have been carried out, comparatively little is known about the chemical changes taking place during fitter dccomporition in nature. This lack of knowledge is somewhat surprising since the main chemical components of litlcr, namely ceffufoscs and lignin. arc the most abundant organic compounds on earth.

Tcnney and Waksman (1929) measured the decomposition mtcs of sofubfc material, hcmiccffufoses, ccffufosc and lignin in a scqucnce of litter samptes rcprtscntin8 ditTcrcnt stngcs of decomposition. Under ticfd conditions decomposition of Scats pine needle fitter has been studied in detail (Berg er ul., 1982). Chcmicaf changes have also been followed closely in decomposing chaparraf shrub foliage (Schfcsingcr and Hasey. 1981).

In gcnsral, the methods currently availabfc for chcmicaf analysis of soil organic constituents are of low specificity and require large amounts of material. Further. they are of limited vafuc in analysts of the major solid components of litter, viz. hofoccffufose and fignin. The fact that fignin is found in close association with pofysaccharidcs makes its isolation difficult. and no method cxsists that can be used to quantitatively isolate fignin in the form in which it actually occurs in plants. Those analytical methods avaifablc arc unspecific; consequently. they cannot distinguish bctwecn lignin and other related compounds. Traditionally. major litter components. such as ccfluIoses and fignin. are separated and quantified using a series of extractions followed by hydrolysis. The polymer carbohydrates are sofubifized in

hydrolytic stops whifc lignin remains a non- hydrolysabfc rcsiduc. Berg let uf. (1982) used gas chromatography to identify and quantify scvcral solid carbohydrates from one hydrolysatc. In a study on decomposing fitter Johansson et ul. (1986) was able to corrcfatc the changes in concentrations of Klason-lignin to those of lignin as dctcrmincd by the phioro~fucinof method, the dioxanc-water-HCI- method, and the afkalin~~Cu~-oxidation method.

In the study reported hcrc WC employed high resolution “C nuclear magnetic resonance (NMR) using cross-polarization and magic angle spinning (CP/MAS) in the solid state, a non-destructive method for quantitation of carbohydrates, as wcfl as aliphatic and aromatic compounds. The method was introduced in soil science by Barron and Wilson (1981) for the study of various carbon compounds in whole soil. Since then, “CCP/MAS NMR has been used to characterize a variety of compounds and complex materials, including plant components and humic materials (Himmclsbach er ul.. 1983; Macicf ef al., 1985). We want to extend our earlier studies on changes in the organic composition of needle litter during decomposition (Berg er uf., 1982) to include “C NMR in the solid state. This methodology should be useful for characterizing residual plant matter as well as decaying matter in litter. The methods study presented here includes an investigation of both the initial material and fitter during various stages of decay. Hcmpfling et uf. (1987); K&cl et al. (1987); Zech et 01. (1985) and Wilson el al. (1983). among others. have recently conducted related work.

To USC the information obtained from the NMR work to quantify the lignin we used the partial feast squares (PLS) method (Ccraldi and Kowafski 1986; Wold YI 01.. 1984) to relate our estimate of fignin content to the estimate of Kfason-fignin content obtained by the conventional method.

272 Bo Nomizr and BJ~RV BERG

.MATERIALS AND METHODS Solid-state NY R

Sire des~riptioR~

The site used for litter sampling and incubation was located in a Scats pine (Pinus sihestris L.) forest in central Sweden. The locality (Kappsjon) was de- scribed by Berg and Lundmark (1987).

Needle collection. storage and sample preparation

Needle litter was collected at the site during the leaf abscission period in September 1978. The litter was taken from branches of ca 20-yr-old Scats pines. It was then air dried and stored at -20X prior to sample preparation. The trees were all located within an area of <50 x 50 m2.

Before weighing the needles were dried at room temperature until they reached an even moisture content (ca 68%). The greatest difference in moisture level between any one sample and the mean did not exceed fOS% U as determined on 20 samples.

The samples used for the traditional analysis were also used for obtaining 13C CP,‘MAS NMR spectra. The runs were made on a Bruker MSL-100 instrument operating at 25. I78 MHz using the broad- banded double air-bearing probe with 7.5 mm rotors made of A&O, and equipped with Kel-F caps. Exper- imental parameters were similar to those used in earlier investigations on peat (Norden et al., 1986): I ms contact time. 2.5 s repetition rate, 3700-20,000 scans of 700 data points zero-filled to 2 K and > 3.5 KHz spin velocity. The chemical shift scale was determined with reference to external adamantane whose methylene resonance was set at 38.3 parts/106. No exponential line broadening or resolution en- hancement was used for the presented spectra.

The litter bags, measuring 8 x 8 cm2, were con- structed of terylcne net with a mesh size of I mm. Each litter bag contained ca I.5 g of needle litter and a plastic tag giving the initial needle mass.

The litter bags wcrc placad on the surface of the litter htycr (A,,,) in each of 25 mcasurcment plots. Within each plot the bags were placed out in subplots (I x 1 ~1:) according to a random&d block design. The hags wcrc fastened to the ground with IO-15 cm long mctol pins. Samplings were subscqucntly made 3 times annually. On each sampling occasion one bag from each of the 25 plots was collected. The Iittcr bags wcrc transported directly to the laboratory whsrc moss. lichen and dwarf shrub remnants were rcmovcd. After drying at 82 ‘C the httcr samples were weighed individu~~lly and then pooled so that only one sample from each set was subjected to chemical analysis.

For the quantitative data analysis we used a gen- eral (PLS) regression technique (Wold er al.. 1982; Lindberg et al., 1983; Norden et al., 1983). which can explore possible relationships between a matrix of an independent set of variables (X) and another matrix (I’, which may also be a single vector), applying principal component-like (PC) algorithms. The significance of components in the data analyses is calculated using a cross-v~ilid~ition procedure (Wold, 1978). The X data matrix contains NMR spectra of I I litter atmplcs.

The spcctrn had earlier been digitized and wcrc rcprcscntcd by 71 evenly distributed data points. ranging from 186.7 to 7.9 parts/IO”, which wcrc used as variables. The intensity at each point was taken as the variable value (XC Nordcn and Albano. 1989. for discussion of PC analysis of NM R spectroscopic data).

The chemical analyses were of two kinds: a “traditional” sequence of analyses used for determining the content of various organic and inorganic compounds and tin analysis using solid state “C NMR.

The spectra used in the PLS analysis were pro- cessed on the NMR instrument as follows: 30 Hz line-broadening, Fourier-transformation, constant phase correction. base-flattening and power spectrum calculation, The linc-broadcning function was applied to increase the signal-to-noise ratio and to smooth the spectrum, which solves the probicm of digital shifting of a single high-resolution peak. Con- stant phase correction and base-flattening were applied to facilitate the power spectrum calculation. which should give the most objective spectrum as input in this type of quantitative analysis.

Tr~rdi~iottctl rrttu/y.sis

Samples were ground in a laboratory mill equipped with a I mm mesh screen. The amounts of water-soluble and ethanol-soluble substances were dete~ined by sonicating the milled sampies three times in a sonicator bath and weighing the samples after filtra- tion and drying. After extraction the dried residues were analyscd for sulfuric acid lignin (Klason lignin) by hydrolysis (I 2.0 bt H,SO, at room temperature for 2 h and refluxcd for 6 h after dilution to 0.358 sr; Bcthge YI 01.. 1971). The milled samples were also analyscd for total N content by a semi-micro Kjel- dahl procedure (Nihig~rd, 1972) with duplicate analyses.

All calculations were performed on an IBM com- patible personal computer to which NMR data were transferred from the Bruker Aspect 3000 computer after initial conversion to ASCII files. Computer programs for PC analysis and PLS regression are commercially available (SIMCA package-available from Scpanova AB, Enskedc, Sweden or from Princi- pal Data Components, Columbia, MO., U.S.A.). The lcvcl of significance of PLS components and linear correlations in this paper are based on F tests and a 95% level of confidence

For some very rcccnt PLS applications in solid state “C NMR see Nordcn et ul. (1989) and Wall- b&cks CI NI. (1989).

RFSCLTS AND KEXXSSION

The data set including litter mass loss and traditional lignin analyses was originally published by Berg and Lundmark (I 987).

lttifial ntutrrial

In the initial, undecomposed material the “C signals from the carbohydrate region were evident,

Plant litter analysis by NMR 273

200 150 100 M Iv:

Fig. I. (A) “C NMR spectra of original sample: (8) a sample after dccomposilion for 55.t days (45.976 mass loss); and (C) a sample after dccomporition for IJ?R days (73.2%

“lilSS loss).

vis. in the region ranging from 60 to 100 parts/IO” (t:ig. I). The signals at 60-65 part/IO” wcrc assigned to hydroxymcthylcnc (C-6) groups of pyrunoscs both in monomer and polymer form (see Fig. 2 for ring numbering systems of carbohydrates). Signals from carbon 5 in furanoscs should also appear hcrc. The pair of peaks at 70-75 part/IO” is attributable to the ring carbons (C-7.3.5) of pyranosc units (in polymer form) as well as furanosc ring carbons. In our cast pyranoscs were rcprcscntcd by ccllulosc. galactans and mannans, and l’uranoscs by arabinans and xylans (cf. Berg PI 01.. 1982).

Further downfield (81 parts/IO*) a broad resonance peak appeared that had several contributing carbohydrate constituents originating from carbon 4 (Fig. I). Monomer and polymer units of hcmicellulose and amorphous cellulose resonate at this magnetic field as

A

OH

Fig. 2. Ring numbering system of (A) the furanose skeleton and (B) the cellulose subunit. /J-D-glucopyranose.

does carbon 4 in starch. The peak at 87 parts/IO” is attributable to cellulose carbon (C-4). which in pure preparations has been defmcd as crystalline cellulose (Atalla and VanderHart, 1984).

The anomcric ccl1ulosc carbon (acctal carbon, C-l). which is the most dcshicldcd carbon in carbohydrates. appeared at 103 parts/IO0 (Fig. I). The broad base of the anomcric region (ccntrc cu 100 parts/IO’) could have arisen from carbon 4 in hcmicclluloscs.

The mostly dcshicldcd peak (I71 parts/IO”) was ass&cd to the carbonyl carbon of amino acids in protcin, the carbonyl carbon of acctyl groups in some hcmicclluloscs, the carbonyl in lignin aldchydc groups and the carbonyl in waxes, partly explaining the large resonance in the aliphatic rcyion.

The peaks ~40 parts/IO” in the aliphatic region of the spectra indicate that long polymethylenc chains, probably in fatty acids are responsible for the resonance at 31 parts/lob. Shorter or more branched alkyl chains from short fatty acids and lignin should increase the broadness of the aliphatic response. explaining why shorter polymcthylene chains were observed at 29 parts/IO6 and why terminal methyl

T&lc I. Mass loss and some chemical changes in decomposing Scour pine (P. sikrrrris) nccdlc litter dccomwxinn in a field experiment

Conccnlration

7x1113 0 0 - 15.9 790522 190 IS.1 0.3x 5.8 790112x 2x8 31.0 0.47 5.8 x00519 553 4s.9 0.65 8.4 8OQ902 659 57.6 I.25 9.4 801029 719 61.3 I .Ob 6.1 Xl0524 923 61.0 I .os 7.3 XIOR3I IO?? 66.6 I.19 6.6 RI 1027 I079 66. I I .07 7.3 R?OS?b I :YO 70. I I.Rl 6.7 X?IO?I I438 73.2 0 84 6.4

Dala from Berg and Lundmark (1987).

7.8 265 4.4 9.1 3311 4.7 8.3 373 3.4 8.6 401 6.7 9.2 411 9.2

10.6 435 8.7 8.3 459 8.5 8.0 469 Il.0 8.0 470 IO.1 9.0 472 Il.0

Il.0 458 I I.2

273 Bo NOR&N and BJ~RS BERG

groups were found at 25 parts/ 106. The resonance at 20 parts/IO6 was probably produced by the methyl carbon of acetyl substituents of certain hemicelluloses (Kolodziejski er al.. 1982).

Partly decomposed material

The major qualitative trend found in the spectra was the steady reduction that occurred in the relative amount of carbohydrates as decomposition of the litter proceeded (resonances between 60 and II0 parts/106). Furthermore, there was an apparent increase in the polymethylene resonances at 31 parts/106, an increase which occurred rather rapidly (i.e. at 45.9% mass loss) and was observed in the spectrum as a sharp peak. This peak may have been due to an enrichment of undegraded waxes. As shorter methylene chains were not seen it is not possible to comment on changes in lignin at a mass loss of 45.9% (553 days). However, the initial increase in polymethylene chains seemed to level off as the decomposition proceeded further. Other changes in the spectra that appeared as decomposition pro- cccdcd were small.

The mcasurcment of lignin content in hcteroge- ncous samples like litter is hampered by the broad signals that usually occur in solid state “C NMR. In studies of dclignification. attempts were made to quantify the lignin content of wood used in the pulping process by using NMR and intcnsitics of the aromatic carbon rcsonanccs (C-3 and C-4) of lignin (Huw 1st al., 1984). Howcvcr. in our cast this np- preach was not fcasiblc owing to the low resolution of our spectra. Ccncrally, in chemically complex s;lmplcs like soil, peat and litter it is rcasonnhlc to assume that this type of information can only bc considcrcd as semi-quantitative.

Using an alternative approach the sum of all aromatic carbons can be dctcrmincd by integration of the “C NMR data. Howcvcr, Zech et al. (1985) have shown that this estimate dots not reilcct any of the chemical changes occurring during litter dccomposi- tion and humification.

In the present study we used an approach where the whole spectrum was taken into account for the quantification. For this purpose we employed the PLS technique which relates latent information to indcprndont measurements or properties. In a rclatcd PLS modclling work Holmgren and Nordin (1988) searched for correlations bctwcen Fourier- transformed i.r. (FT-i.r.) values from various peat samples and calorific value. dcgrce of decomposition and the presence of certain chemical consitituents. Using PLS modclling other investiga- tors have been able to model the carbohydrate content (NordL:n et al., 1989) and to improve the accuracy in predicting lignin concentrations. espe- cially at low lignin Icvcls, (Wallbticks et al., 1989). Thcsc authors used spectral information from solid state “C NMR to determine the carbohydrate and lignin contents of birch wood samples used in the kraft pulping process.

In the present work we were able to quantify the Klason-lignin concentration in our litter samples by using the PLS approach (Fig. 3) which related Klason-lignin concentrations (obtained by traditional chemical methods) to the PLS-predicted Klason-

'"

31.0 . 290 l

27 0 y 0* .,, ., . . . . . .,, .., ,

0 290 330 370 L1.0 450 L9.0 Cont. of lignin i%)

as measured by hydrolysis

Fig. 3. Linear regression for concentration of measured Klason-lipnin vs lignin concentration predicted from

“C NMR (r2 = 0.927; P < 0.001: n = I I ).

lignin concentrations based on resonances from the whole spectral region (see Materials and Methods). The prediction was obtained by using two statistically significant PLS-components. which togcthcr ac- counted for 91% of the variance in the measured lignin data (r’ = 0.927) (Fig. 3). However, the cross- validation proccdurc (see Materials and Methods) indicated the cxistcnce of two additional PLS-compo- ncnts. and all four PLS-components togcthcr should explain 99% of the variation in Klason-lignin obtained by the chemical method. Obviously the Iattcr analysis would result in a bcttcr correlation (r’ = 0.993, P < O.OOl), but we could not justify four PLS-components with our data set which only con- tninod I I samples. Thus, in order not to ovcrparamc- tcrizc we have only prcsentcd the curve for two components.

An examination of the loadings would give us information about which resonances in the NMR spectrum were responsible for the obtained result. The total explained variance of the mcasurcd lignin data was after the first component 20% and after the second component 91% (component three: 93% and component four: 99%).

The overall lineshape is rccognizcd in loading one which more or less resembles the original material. Loading one might also reflect the different signal-to- noise ratios in the collection of spectra.

The main correlation of the lignin content was in the second component and an examination of loading two showed that the aliphatic, aromatic and carbonyl regions were positively correlated. The largest posi- tive correlation was found for the methoxyl resonance (-OCH, at 56 parts/IO”). The carbohydrate region (60-l IO parts/IO”) was negatively corrclatcd mainly through peaks at 66, 73, 88 and 106 parfs/lOO, respectively. The reason was that thcrc was a gradual decrease in concentration of carbohydrates accompa- nied by a relative increase of Klason-lignin concentration as the material decomposed.

Aclinot~led~m~enr-The authors are grateful to the Swedish National Energy Administration for financial support.

Plant litter analysis by NMR 27s

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A non-destructive method (solid state 13CNMR) for determining organic chemical components of decomposing litter