Soil Science and Plant Nutrition (2008) 54, 188–196 doi: 10.1111/j.1747-0765.2007.00239.x

© 2008 Japanese Society of Soil Science and Plant Nutrition

Blackwell Publishing LtdORIGINAL ARTICLENIR determination of lignin-derivatives in litters ORIGINAL ARTICLE

Near-infrared reflectance spectroscopy for the determination of lignin-derived compounds in the decomposed and humified litters of coniferous and deciduous temperate forests in Northern Kanto District, Central Japan

Kenji ONO1, Kouji MIKI2, Masahiro AMARI3 and Keizo HIRAI1

1Tohoku Research Center, Forestry and Forest Products Research Institute, Iwate 020-0123, 2Nihon-buchi, Tokyo 110-0008, and 3National Institute of Livestock and Grassland Science, Ibaraki 305-0901, Japan

Abstract

Methods to determine the contents of lignin-derived compounds in decomposed or humified forest litteroften involve time-consuming physical and chemical analyses. In this study, a rapid and accurate methodwith minimal pre-treatments was developed to determine the contents of recalcitrant lignin-derivedcompounds in decomposed or humified litter on a forest floor using near-infrared reflectance spectroscopy(NIR) and partial least square (PLS) regression analysis. We prepared and quantitatively and spectrophoto-metrically analyzed 92 litter samples obtained in a litter decomposition experiment at three sites, theTsukuba Experimental Site, the Tengakura Experimental Site and the Ogawa Forest Reserve, all of whichare located in the northern Kanto District of central Japan. The NIR equations to determine the respectivelignin-derived compounds at the three sites were developed by PLS regression using quantitativeand spectrophotometrical data. The NIR models developed to determine the compositional properties ofdecomposed and humified litter were extremely accurate compared with the results of previous publishedstudies using stepwise multiple linear regressions. We believe that the NIR equation developed in thepresent study has the potential to be applicable to qualifying the determination of soil organic matter(SOM) in the SOM-rich surface soil of the forests around the northern Kanto District because lignin-derived compounds can be determined by NIR in samples with a large diversity of compositional rangesand litter decomposition stages.

Key words: decomposed and humified litter, lignin-derived compounds, near-infrared reflectance spectroscopy,partial least square regression, recalcitrant compounds.

INTRODUCTION

The chemical quality of litter has a decisive influence oncarbon and nitrogen dynamics, such as the litter decom-position on a forest floor and the humification oforganic materials in forest soil (e.g. Zech and Kögel-Knabner 1994). Successful results using near-infraredreflectance spectroscopy (NIR) for the analysis of thecomposition and nutritive values of decomposed litter

and organic soils have recently been reported (Matsunaga1994; Matsunaga and Uwasawa 1992; Ono et al.2003). Ono et al. (2003) reported that NIR could beapplied to the quantification of lignin and polysaccharideon the forest floor (O horizon) as well as in fresh andsenescing leaves (needles) in the forests of Japan. Inaddition, the chemical and physical characteristics ofsoil (e.g. moisture, bulk density, carbon and nitrogenconcentrations and cation exchange capacity) showed ahigh correlation between the values obtained usingchemical analysis and the calibration values determinedby NIR (Matsunaga 1994; Ozaki and Kawata 1995).

The NIR results for decomposed and humified materialswith high ash contents, such as soil organic matter, havenot been as precise, however. Ono et al. (2003) indicatedthat lignin content is liable to be misestimated by

Correspondence: K. ONO, Tohoku Research Center, Forestryand Forest Products Research Institute, 92-25, Nabeyashiki,Shimo-Kuriyagawa, Morioka, Iwate 020-0123, Japan.Email: don@ffpri.affrc.go.jpReceived 23 July 2007.Accepted for publication 10 November 2007.

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mineral soil particle contamination when a sample’s ashcontent is more than 40%. Paul (1988) also reportedthat NIR determination of a sample’s nitrogen contentis seriously affected by excessive soil particle contami-nation. This is considered to result from the followingtwo factors. One is that all the absorption wavelengthsrepresenting each organic constituent, especially thoseof more humified materials, might not be fully adoptedin the NIR determination using stepwise multiple linearregression (SMLR). The other is that the sample popu-lation size intended for NIR determination might not beadequate. The sample population used for the predic-tion might be too large to extrapolate the compositionof more decomposed and more humified materials. Tosolve the former problem, we previously studied thespectral characteristics of decomposed beech (Faguscrenata) and Japanese red pine (Pinus densiflora)litters and clarified the specific absorption bands oflignin (Ono et al. 2007). In addition, using partial leastsquares (PLS) regression instead of the SMLR may helpto solve the first factor because in this method all thespectral data are included in the analysis of the principlecomponents (Harmon and Lajtha 1999).

The main objective of the present study was todevelop a highly accurate NIR method for quantifyingrecalcitrant lignin-derived compounds in samples thatinclude decomposed and humified materials on forestfloors. We prepared litter samples of various species atvarious stages of decomposition and humification at thefollowing three forest stands: the Tsukuba Experimen-tal Site (TKB), the Tengakura Experimental Site (TGR)and the Ogawa Forest Reserve (OFR). We analyzeddecomposed and humified litter samples quantitativelyand spectrophotometrically, compared their chemicalanalytical data and near-infrared spectral data, anddeveloped a method of NIR determination using PLSregression. In this study, we narrowed the analyticaltarget to lignin-derived compounds whose specificabsorption was clarified by Ono et al. (2007).

MATERIALS AND METHODS

Experimental sitesGeneral information regarding the experimental sitesis shown in Table 1. These sites included the followingthree forest stands: a 99-year-old Japanese cedar(Cryptomeria japonica) plantation with Japanese laulel(Aucuba japonica) as an understory at TKB, a 61-year-old Japanese cypress (Chamaecyparis obtusa) plantationwith no understory species at TGR, and a mixed deciduoussecondary forest at OFR dominated by beech (Faguscrenata) and oak (Quercus crispula) with bamboo grass(Sasa nipponica). The TKB and TGR sites are located inthe Tsukuba Mountains massif and OFR is located in thesouthern part of the Abukuma Mountains. The soil typeis Dystric Cambisols at TKB and OFR and Humic Andosolsat TGR according to the World Reference Base for SoilResources 2006 (The International Union of Soil Sciences,International Soil Reference and Information Centre, Foodand Agriculture Organization 2006). The parent materialis crystalline schist and late Quaternary volcanic ash at TKB,granite and late Quaternary volcanic ash at TGR (EconomicPlanning Agency 1973), and metamorphic rock and lateQuaternary volcanic ash at OFR (Yoshinaga et al. 2002).

Sampling and preparation of the decomposed materialsTo obtain decomposed and humified litter samples atthe respective sites, we experimented regarding litterdecomposition using the litterbag method (Crossley andHoglund 1962) (mesh bag size: 1-mm mesh polyethylenebags [150 mm × 200 mm]) for 3 years. The litterbag methodis one of the most commonly used field techniques.This method offers the advantage of understanding thedynamics of organic compounds during litter decompositionalong a time series. The samples used in the decompositionexperiment were litterfalls and fresh needles or leavesof the Japanese cedar (Cryptomeria japonica), Japanesecypress (Chamaecyparis obtusa), beech (Fagus crenata)

Table 1 Dominant species, mean annual temperature, annual precipitation, slope, soil type and parent material at the sites

Study sites Survey dateDominant

speciesUnderstory

speciesMAT (ºC)

AP (mm)

Slope

Soil type† BedrockPosition Grad. Direct.

Tsukuba Experimental Site (TKB)

2003.10.10 Japanese cedar

Japanese laulel

13.2‡ 1360§ Upper 25º N60ºE Dystric Cambisols

Crystalline schist late Quaternary volcanic ash¶

Tengakura Experimental Site (TGR)

2003.11.7 Japanese cypress

No vegetation

13.2‡ 1360§ Middle 10º N40°E Humic Andosols

Granite, late Quaternary volcanic ash¶

Qgawa Forest Reserve (CFR)

2003.10.23 Beech oak Bamboo grass

10.7†† 1910†† Upper 22º N5°W Dystric Cambisols

Metamorphic rock, late Quaternary volcanic ash‡‡

†Soil types were from IUSS, ISRIC and FAO (2006). ‡Mean annual temperatures (MAT) were obtained at Kasama meteorological station by AMeDAS (automated meteorological data acquisition system) from 1978 to 2006. §Annual precipitation (AP) was obtained at Kakioka meteorological station by AMeDAS (automated meteorological data acquisition system) from 1976 to 2006. ¶Bedrock information at TKB and TGR was from Economic Planning Agency (1973). ††Meteorological data at OFR was from Mizoguchi et al. (2002). ‡‡Bedrock information at OFR was from Yoshinaga et al. (2002).

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and oak (Quercus crispula) that were dominant in theforest at each of the three sites. Ninety-two samplesprepared for NIR determination were selected toencompass as wide a decomposition range as possiblewith respect to the senescence, decomposition and humi-fication stages of the litter (see Table 2 for detailedinformation). All samples were dried in an oven at 40°Cfor 48 h. After being dried, contaminated soil particleswere carefully removed from the decomposed litterfragments as much as possible. The remnant weight ofthe decomposed leaves was used to calculate the accu-mulated litter mass loss. These air-dried samples weresufficiently ground to allow them to pass through

200-mesh (75-μm mesh pass) sieves using a high-speedvibrating sample mill (TI-200; CMT, Tokyo, Japan).These powdered samples from the three sites wereused for NIR determination and chemical analyses.These samples were divided into two with regard to thedecomposition and humification stages of the litter.One was a dataset for developing the NIR calibrationequation (Table 3), and the other was a dataset forexternal validation of the calibration equation (Table 4).The range of the calibration dataset for lignin-derivedcompounds is shown in Table 3, and the range of theexternal validation dataset for lignin-derived compoundsis shown in Table 4.

Table 2 Chemical reference values of PLS determination samples

Experimental site Species

Litterfall or leaf (needle)

Incubation period (yr) N

Remaining mass Lignin Ash

% of initial weight SD mg g–1 SD mg g–1 SD

TKB Cryptomeria japonica

litterfall 0 1 100.0 – 397 – 77 –0.3 3 76.0 10.7 417 44 160 741 3 62.9 5.9 358 101 283 1902 3 51.5 5.8 358 81 310 1433 3 33.9 3.7 335 20 335 76

fresh needle 0 1 100.0 – 292 – 57 –0.3 3 70.4 2.6 419 14 114 471 3 51.8 3.5 407 29 183 562 3 37.8 7.7 431 83 194 1273 3 46.4 6.9 509 26 111 52

TGR Chamaecyparis obtusa

litterfall 0 1 100.0 – 342 – 44 –0.3 3 75.3 5.1 458 1 61 91 3 51.8 3.5 466 44 51 142 3 45.5 6.5 476 68 120 1143 3 37.5 7.3 480 62 93 69

fresh needle 0 1 100 – 313 – 38 –0.3 3 91.4 10.9 468 14 50 81 3 36.7 7.7 476 18 41 132 3 34.7 8.2 492 18 47 223 3 35.7 16.7 508 13 48 15

OFR Fagus crenata litterfall 0 1 100.0 – 286 – 53 –0.3 3 106.1 1.7 467 14 51 51 3 75.4 2.5 433 11 58 152 3 48.2 7.0 427 15 114 373 3 33.9 6.8 395 105 228 200

Fagus crenata Quercus crispula

fresh leaf litterfall

0 1 100.0 – 325 – 31 –0 1 100.0 – 417 – 42 –0.3 3 93.0 5.4 453 93 174 1541 3 68.1 6.8 438 74 144 812 3 41.0 3.9 352 108 339 2053 3 26.3 4.9 419 118 271 205

Quercus crispula fresh leaf 0 1 100.0 – 348 – 43 –0.3 3 72.0 14.1 500 5 63 171 3 51.5 1.4 427 10 89 312 3 46.5 6.2 396 45 173 973 3 35.0 5.4 457 12 165 53

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Spectral measurementsSpectral measurements of 92 samples were made usinga near-infrared reflectance spectrophotometer (modelNIRS 6500; FOSS-NIRSystem, Silverspring, MD, USA)that was connected to a personal computer. Thecomputer was equipped with NSAS software (FOSS-NIRSystem). This device covered a spectral range of1,100–2,500 nm in 2-nm intervals with a bandwidth of10 nm. Reflectance (R) was converted to absorbance (A)using the following equation:

A = log (1/R).

The obtained spectra were described as the secondderivative spectra because of the influences of bothnoise elimination and light scattering and because of theseparation of overlapping peaks in the normal absorptionspectra (Iwamoto et al. 2002). The segment size andgap size in the wavelength data over which the secondderivative was taken were 20 nm and 0 nm, respectively.The downward peak in the second derivative spectrumcorresponds to the upward peak in the normal spectrum.

All samples were left in the laboratory overnightto allow them to become acclimatized to air conditionsand then thoroughly mixed before analysis. Eachsample was scanned once, rotated 90° within the samplecell holder and scanned a second time. Spectra weregenerated on the average of two scans per sample.

Chemical reference analysesStepwise extraction and digestion procedures accordingto the method described by Ono et al. (2003, 2007) wereused as compositional analyses. The concentrationsof lignin-derived compounds in 92 samples were analyzedusing the sulfate-hydrolysis method (TechnicalAssociation of the Pulp and Paper Industry 1997, 1998).The results were corrected by the addition of acid-soluble lignin as determined by photocolorimetry (Kondoand Sakai 1982). The ash content of the original sampleand the lignin fraction were also determined by ashingin a muffle furnace at 500°C for 2 h, to avoid overesti-mation of the lignin content as a result of soil particlecontamination, according to the method described byHarmon and Lajtha (1999).

Table 3 Range of the calibration dataset for lignin

Component MeanStandard

errorStandard deviation Variance Range Minimum Median Maximum

TKB (n = 17)Lignin (mg g–1) 376 17 70 496 224 240 403 464Ash (mg g–1) 236 35 143 2,047 478 57 227 535

TGR (n = 17)Lignin (mg g–1) 471 12 49 235 183 342 493 525Ash (mg g–1) 61 12 51 259 223 29 52 252

OFR (n = 26)Lignin (mg g–1) 425 14 74 545 249 269 439 518Ash (mg g–1) 147 28 144 2,066 476 31 81 507

OFR, Ogawa Forest Reserve; TGR, Tengakura Experimental Site; TKB, Tsukuba Experimental Site.

Table 4 Range of the external validation dataset for lignin

Component MeanStandard

errorStandard deviation Variance Range Minimum Median Maximum

TKB (n = 9)Lignin (mg g–1) 385 21 63 399 226 274 382 500Ash (mg g–1) 225 44 132 1,754 378 77 203 455

TGR (n = 9)Lignin (mg g–1) 457 21 63 396 205 313 472 518Ash (mg g–1) 64 14 41 171 132 38 47 170

OFR (n = 14)Lignin (mg g–1) 416 17 65 425 214 286 432 500Ash (mg g–1) 140 27 102 1,031 384 43 125 427

OFR, Ogawa Forest Reserve; TGR, Tengakura Experimental Site; TKB, Tsukuba Experimental Site.

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Partial least squares regression and external validationDetermination equations of lignin-derived compoundsfor each sample were developed by PLS regression usingNSAS software (FOSS-NIRSystem) using the quantitativeand spectrophotometrical data of the calibration sampleset. The PLS regression was run with a maximum of 15factors set by the software based on the number of samplesin the dataset. Final equations were used to optimize theroot mean squared error of cross validation (RMSECV)according to leave-four-out cross validation results.Leave-four-out cross validation groups are alternatelyexcluded from the calibration dataset, and using thosedata over leave-four-out cross validation produces fouriterations of calibrations and predictions so that eachsample is predicted once during equation development(Bolster et al. 1996). To further evaluate the calibrationresults, we predicted the lignin-derived compoundsusing external independent sample sets (validationdataset) determined by the NIR determination equationsdeveloped in this study.

RMSECV was expressed using the following equation:

where y is the measured value and y the predicted valueof sample i, and I is the total number of samples. Forexternal predictions, we also determined the bias:

and the standard error of prediction (SEP) corrected forbias:

To evaluate the calibration parameters we calculatedthe ratios of the standard deviation of the sample set toRMSECV (RPD), which is an indicator of the usefulnessof the calibration results (Williams 1996).

RESULTS AND DISCUSSION

Dataset characteristicsThe samples’ compositional ranges and diversity regardingthe litter decomposition stage were very wide at everysite examined (Tables 2–4). This study might have a greatadvantage in that NIR calibration sample sets such asthese can be prepared for the determination of decomposedlitter quality in typical forests of the northern KantoDistrict. The NIR determination dataset contained

markedly decomposed and humified litter samples withhigh ash contents over the 3-year incubation (Table 2).Therefore, we expected that the NIR determinationequations of the three respective sites might be usefulfor quantifying the components of surface soil rich insoil organic matter (SOM) in the forests of the northernKanto District of central Japan.

NIR spectral changes of litter during the decomposition and humification of organic materialsFigure 1 shows examples of the original and second-derivative NIR spectra of decomposed litter of Japanesecedar and oak. The absorbance baselines of the originalspectra in both litters increased gradually as thedecomposition proceeded (Fig. 1a,c). These baseline shiftsmight be caused by contamination of the litter with soilduring the decomposition and humification processesof organic matter (Paul 1988; Windham et al. 1991).Therefore, the absorptional fluctuation ranges in theentire near-infrared region decreased over time. A seconddifferential treatment could eliminate the baselineeffects caused by particle arrangement and sampleconditions (e.g. moisture contents), and could alsoclearly separate the absorption band peculiar to eachorganic component (Fig. 1b,d). The downward peak inthe second-derivative spectrum corresponds to the upwardpeak in the original spectrum. Many absorption peakswith various absorption intensities were observed in thesecond-derivative spectra (Fig. 1b,d). These peak positionswere common mostly among the decomposition stages.An absorptional increment at approximately 1,670 nm,specific to lignin compounds (Ono et al. 2007; Shenket al. 2001), and decrements at approximately 2,350and 2,270 nm, related to protein and polysaccharide(Ono et al. 2007; Shenk et al. 2001), were observed. Thesespectral changes were also observed for decomposedlitter of other species (Japanese cypress and beech), althoughthe absorptional intensities were different.

NIR calibration and leave-four-out cross validation for lignin-derivatives as determined by PLS calibrationFigure 2 shows the relationships between the root meansquared error of cross validation (RMSECV), the ratiosof the standard deviation of the sample set to RMSECV(RPD), and the numbers of factors (F) for lignin-derivedcompounds in PLS calibration. As a result of PLScalibration, the Fs normally recommended by NSASsoftware were 1 for TKB, 7 for TGR and 5 for OFR inthe calibration sample sets of lignin-derived compounds(grayish arrows in Fig. 2). According to Williams (1996),an RPD value of 2.5–3.0 is regarded as adequate for

RMSECV ( )= −=∑1 2

1Iyi i

i

I

y

bias ( )= −=∑1

1Iyi i

i

I

y

SEP

( )=−

− −=∑1

12

1Iy biasi i

i

I

y

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rough use in certain applications, whereas RPD valuesabove 8 are excellent and can be used in any analyticalsituation. In the present study, a value of approximately8 is regarded as optimal for NIR determination. The Fthat the NSAS software normally recommended wassuitable only for the calibration sample sets of TGR(Fig. 2). In addition, the RMSECV for both sites hadgradually approached the fixed values at more thanthe recommended F (Fig. 2). Thus, we adopted these Fsas calibration sample sets for TGR. In contrast, weadjusted the Fs for TKB and OFR because we consid-ered that the RPDs were still too low to obtain moreprecise NIR equations for TKB and OFR at the recom-mended F (Fig. 2). We selected 6 for TKB and 7 for OFRas optimal Fs for the calibrations of lignin-derived

compounds so that the RPD is more than 8 and theRMSECVs approached the fixed values.

The PLS calibration results for lignin-derived compoundsat the three respective sites agreed very closely withthe chemical analysis data determined by F adjustmentsusing an RPD limit of 8 (Fig. 3). Regression coefficientsshowed high values, 0.99 for every component, and thedata were very close to the 1:1 line (Fig. 3).

Figure 4 shows the results of independent validationusing an external validation dataset. This figure demon-strates the close correlation of the values estimatedby the NIR determination equation and those determinedby chemical analysis regarding lignin-derived compounds.The NIR determination equations developed in thisstudy showed a close fit as well (Fig. 4). The accuracy of

Figure 1 Spectral changes during 3-year litter decomposition of (A,B) Japanese cedar (Cryptomeria japonica) and (C,D) oak(Quercus crispula). The downward peak in the second derivative spectrum corresponds to the upward peak in the originalspectrum. Black arrows in the figure show the peculiar absorption of aromatics (lignin compounds) and the grayish arrows in thefigure show the specified absorption of protein and polysaccharide compounds.

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Figure 2 Relationships between rootmean squared error of cross validation(RMSECV; left axis, solid line), ratios ofthe standard deviation of the sample setto RMSECV (RPD; right axis, brokenline) and F (the number of factors) forlignin-derived compounds in the partialleast square (PLS) calibration. Thegrayish arrows in the figure show thenumbers of factors normally recommendedby NSAS software. The black arrows inthe figure are the numbers of factorsshowing best fit by PLS.

Figure 3 Near-infrared reflectance spectroscopy calibration versus the chemical analysis values for lignin contents in thedecomposing litter. TKB, Tsukuba Experimental Site; TGR, Tengakura Experimental Site; OFR, Ogawa Forest Reserve; RMSECV, rootmean squared error of cross validation.

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the prediction model was much higher in comparisonwith the results of SMLR reported by Ono et al.(2003). The correlation between measured and PLScalibrated values for the components of agriculturalcrops and soils (e.g. lignin [ratio of carbon content tonitrogen content] CN, cellulose CN, clay content,organic carbon and pH) have been shown previouslyby Stenberg et al. (2004) and McCarty et al. (2002) usingnear-infrared spectra. The results of the present studysuggest that the equations obtained by PLS regressioncould be successfully applied to the quantification of lignin-derived compounds in humified organic materials in topsoils with rich SOM as well as to those in decomposedorganic materials in litter layers.

ACKNOWLEDGMENTS

We are grateful to Drs Masamichi Takahashi, YojiroMatsuura, Makoto Araki, Eriko Ito and Koji Shichi fortheir valuable advice and comments, and we thank MsYumiko Okazaki, Ms Teru Notsukidaira and Mr IsaoKarube for their help in sample preparation, laboratoryanalysis and experimental site maintenance. We wish toexpress our appreciation to the staff of the Departmentof Forest Site Environment and the Arboretum and NurseryOffice, the Forestry and Forest Products Research Institute.Their advice and assistance were extremely helpfulduring our fieldwork and experiments. This study wassupported in part by the program of the Japanese Ministryof the Environment entitled “Evaluation, Adaptationand Mitigation of Global Warming in Agriculture, Forestry,and Fisheries: Research and Development (A1120)”.

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