J Sci Food Agric 1997, 75, 263È267

Predicting Condensed Tannin Concentrations inuliginosus using Near-InfraredLotus Schkuhr

Reýectance SpectroscopyKevin F Smith1,2* and Walter M Kelman21 Agriculture Victoria, Pastoral and Veterinary Institute, Private Bag 105, Hamilton, Victoria 3300,Australia2 CSIRO, Division of Plant Industry, GPO Box 1600, Canberra, ACT 2601, Australia

(Received 10 October 1996 ; revised version received 13 March 1997 ; accepted 26 March 1997)

Abstract : Near-infrared reÑectance spectroscopy (NIRS) was used to developequations to predict condensed tannins (CT) in greater lotus (L otus uliginosusSchkuhr) with a precision satisfactory for the screening of genotypes in a plantbreeding programme. NIRS equations were developed using both partial leastsquares and stepwise multiple linear regression techniques, and a wide range ofmathematical treatments of the log 1/reÑectance NIRS data. In general, equa-tions developed using partial least squares regression techniques had 10È15%lower standard errors than those developed using stepwise multiple linear regres-sion, during both calibration and prediction. Standard errors of calibrationranged from 10 to 17 g kg~1, standard errors of prediction from 12 to 17 g kg~1.These errors equate to coefficients of variation in the order of 20% The use ofNIRS to predict CT in greater lotus will allow more rapid evaluation and selec-tion of genotypes than could be achieved using butan-1-ol/HCl hydrolysis.

J Sci Food Agric 75, 263È267 (1997)No. of Figures : 1. No. of Tables : 2. No. of References : 23

Key words : near-infrared, condensed tannins, measurement, selection, L otus uli-ginosus

INTRODUCTION

Greater lotus (L otus uliginosus Schkuhr) is a perennialforage legume suitable for use on acid, infertile soils(Schachtman and Kelman 1991). The New Zealand bredcultivar Grasslands Maku is available commercially inAustralia, and e†orts are under way to select cultivarsspeciÐcally for Australian conditions (Kelman 1995).

L otus uliginosus herbage contains variable amountsof condensed tannins (CT) at concentrations generallyhigher than other lotus species (Kelman and Tanner1990). The presence of CT in greater lotus herbage is ofpractical importance in ruminant nutrition.

When present at low concentrations, CT are associ-ated with the prevention of bloat in grazing ruminants(Reid et al 1974), reduced degradation of protein in the

* To whom correspondence should be addressed at : Agricul-ture Victoria.

rumen (Barry and Duncan 1984) and increased avail-ability and absorption of essential amino acids in theintestine (Waghorn et al 1987). However, at high con-centrations CT have been associated with decreasedintake (Barry and Duncan 1984) and decreased digest-ibility (Barry and Manley 1984 ; Khazaal et al 1996). Aconcentration of CT less than 40 g kg~1 dry matter hasbeen suggested by Barry et al (1986) as optimal for theprevention of bloat and improved nitrogen utilisation inruminants. A major goal of the current Australiangreater lotus breeding programme is to reduce the CTconcentrations of elite germplasm to this level. Kelman(1995) reported signiÐcant additive gene action for CTconcentrations in and backcross generations ofF2high ] low CT crosses in greater lotus, suggesting thatselection for lower CT should be e†ective in this species.

Near-infrared reÑectance spectroscopy (NIRS) hasbeen used as a rapid method for estimating variousnutritive value components in forage species (Murray

2631997 SCI. J Sci Food Agric 0022-5142/97/$17.50. Printed in Great Britain(

264 K F Smith, W M Kelman

1993). The NIRS technique is based on the absorptionof electro-magnetic radiation in the near-infrared region(1100È2500 nm) by speciÐc chemical bonds in thesample. A computerised optical device is used to collectthis information for a set of samples of known composi-tion. The derived relationship between NIRS and refer-ence values is then used to predict the composition ofsimilar samples.

NIRS calibrations to predict CT have been reportedfor the perennial legumes, sericea lespedeza (L espedezacuneata (Dum-Cours) G Don) (Windham et al 1988 ;Petersen et al 1991) and birdsfoot trefoil (L otus cor-niculatus L) (Roberts et al 1993). The success of thesecalibrations suggests that it should be possible todevelop NIRS calibrations to predict CT concentrationsin greater lotus. The preceding reports all use stepwisemultiple linear regression (SMLR) techniques to derivethe relationship between reÑectance data and CT con-centrations. While SMLR was once the most commonmethod of developing NIRS calibrations to predictforage quality, modiÐed partial least squares (PLS) tech-niques (Shenk and Westerhaus 1991a,b) are nowbecoming the method of choice (Windham and Flinn1992). PLS derived equations have been shown to bemore accurate at predicting a wide range of nutritivevalue parameters than equations developed usingSMLR (Shenk and Westerhaus 1991a,b ; Windham andFlinn 1992 ; Herrero et al 1996).

This paper describes the development of NIRS cali-bration equations to predict CT concentrations ingreater lotus. NIRS equations were developed usingboth PLS and SMLR regression techniques, and withseveral di†erent derivative and smoothing treatments ofthe NIRS absorbance data.

MATERIAL AND METHODS

Lotus samples

The calibrations were derived using a subset of samplesfrom a total of 1438 L uliginosus herbage samples har-vested from three di†erent trials during the 1995È1996growing seasons. The samples were harvested by handand immediately dried at 70¡C in a forced draughtoven. After drying samples were ground through a

cyclone mill (Tecator Cyclotec) Ðtted with a 1 mmscreen.

Chemical analyses

The calibration samples were analysed, in duplicate, forCT concentration using the butan-1-ol/HCl hydrolysistechnique, as described by Kelman and Tanner (1990).

NIRS scanning and calibration

Samples were scanned using an NIRSystems model5000 scanning monochromator connected to an IBMcompatible personal computer. Infrasoft International(Port Matilda, PA, USA) software was used duringNIRS data collection and manipulation.

NIRS data were collected at 2 nm intervals through-out the region 1100È2500 nm, to provide 700 NIRSdata points for each sample. Data were recorded asabsorbance (log 1/reÑectance ; log 1/R) values at eachdata point.

The software program SELECT was used to choose asubset of samples for reference analysis and calibrationdevelopment. The SELECT algorithm identiÐed asubset of samples with spectral characteristics represen-tative of the entire set of samples on the basis of stan-dardised H (Mahalanobis) distances (Shenk andWesterhaus 1991a). A subset of 212 samples was identi-Ðed using this procedure.

It is common to apply one, or more, of a range ofmathematical treatments to log 1/R NIRS data prior tocalibration development (Windham et al 1989). Thesetreatments involve the derivation and smoothing of log1/R data. The 26 mathematical treatments used in thisstudy were the same as those used by Herrero et al(1996) and are listed in Table 1.

Mathematical treatments of NIRS data are generallysummarised as a sequence of four numbers, eg 1, 5, 5, 1.The Ðrst number is the order of the derivative, thesecond is the segment gap (in data points) over whichthe derivative was calculated, and the third and fourthare the number of data points used during smoothing(Williams 1987).

A total of four NIRS equations were developed foreach mathematical treatment :

TABLE 1Mathematical treatments of NIRS data used prior to calibrationa

0, 0, 1, 1 1, 4, 4, 1 1, 10, 5, 1 1, 10, 10, 1 1, 20, 10, 1 1, 30, 10, 1 1, 40, 10, 11, 50, 10, 1 2, 4, 1, 1 2, 4, 2, 1 2, 6, 2, 1 2, 8, 2, 1 2, 8, 8, 2 2, 10, 1, 12, 12, 2, 2 2, 20, 10, 1 2, 30, 10, 1 2, 40, 10, 1 2, 30, 20, 1 3, 10, 1, 1 3, 10, 10, 13, 20, 10, 1 3, 30, 10, 1 3, 30, 20, 1 4, 4, 2, 2 4, 10, 10, 1

a Treatments are listed with Ðrst number as derivative ; second number as gap size, in data points, over whichderivative was taken ; third and fourth numbers are the number of data points used for the running averagesmoothing of the NIRS spectrum.

Predicting tannins with NIRS 265

(i) SMLR with scatter correction ;(ii) SMLR without scatter correction ;(iii) PLS with scatter correction ; and(iv) PLS without scatter correction.

Scatter correction utilised the standard normal variateand detrending procedure of Barnes et al (1989) todevelop calibration equations with adjustment for thee†ect of particle size variation on the NIRS data.

A total of 104 NIRS equations to predict CT weredeveloped. Critical F, T and H values were set at 7, 2and 3, respectively (Murray 1993 ; Herrero et al 1996).The H test is a test for atypical spectra, the T test iden-tiÐes samples with large di†erences between referenceand predicted values and the F test is a test for the sig-niÐcance of added terms during regression.

Assessing the NIRS calibration equations

The adequacy of the NIRS calibration was Ðrstly assess-ed on the goodness of Ðt between reference data andNIRS predictions of CT on the 212 samples used duringthe calibration process. For SMLR equations theassessment of accuracy was based on the standard errorof calibration (SEC) and the coefficient of determination(R2) (Windham et al 1989). For PLS equations theinternal cross validation procedure (Shenk and West-erhaus 1991b) was used, and the accuracy of the equa-tions was assessed according to the standard error ofcross-validation (SECV) and the coefficient of determi-nation for cross-validation (1-VR) as well as SEC andR2.

The two most important wavelengths, ie those withthe highest F statistics, were also noted for the SMLRequations.

A further estimate of the accuracy of the NIRS equa-tions was obtained by using the equations to predict CTconcentrations for another 134 randomly selectedgreater lotus samples from the same set. The per-formance of the equations was then monitored accord-ing to the protocol of Shenk et al (1989). The protocol isbased on two tests to determine the existence of a sig-niÐcant bias or a signiÐcant increase in unexplainederror (SEP(C)) when an NIRS equation is used topredict the composition of new samples. Bias is esti-mated as the mean di†erence between reference andNIRS values for the new samples.

RESULTS AND DISCUSSION

Reference analyses

CT concentrations (measured using butan-1-ol/HClhydrolysis) in the 212 calibration samples ranged from1É10 to 179É9 g kg~1 DM, with a mean 61 g kg~1 DM.The wide range of CT concentrations present in geno-

types of greater lotus has been noted previously(Kelman 1995). In the 134 samples selected to test equa-tion performance CT ranged from 1É80 to 122É3 g kg~1DM (mean 60É9 g kg~1 DM).

NIRS equations

The statistics describing the performance of the NIRSequations are listed in Table 2, and are grouped accord-ing to the regression technique (SMLR or PLS) andthen the derivative mathematical treatment.

In no instance was the observed bias or SEP(C)outside conÐdence limits set using the procedure ofShenk et al (1989). Expressing the standard error of anNIRS calibration relative to the mean value of the refer-ence analyses (CV) is a method of describing the accu-racy of the NIRS calibration. Clark et al (1987) suggestthat a CV less than 20% is acceptable for most analyti-cal purposes. The CV of the equations we developed topredict CT in greater lotus were around 20% and weresimilar to those reported for the NIRS prediction of CTin the legume species sericea lespedeza (L espedezacuneata (Dum-Cours) G Don) (Windham et al 1988 ;Petersen et al 1991) and birdsfoot trefoil (L otus cor-niculatus L) (Roberts et al 1993). The standard error ofthe NIRS calibration may alternatively be expressedrelative to the standard deviation of the reference popu-lation, ie SD/SEP, (RPD) (Williams and Sobering 1993)our RPD values were of the order of 3É5 which is con-sidered satisfactory for screening genotypes in a breed-ing programme (Williams and Sobering 1993).

The relationship between NIRS prediction of CT andCT concentrations obtained using butan-1-ol/HClhydrolysis is represented graphically in Fig 1. The NIRSdata were obtained using an NIRS equation developed

Fig 1. Relationship between NIRS and butan-1-ol/HCl con-densed tannin (CT) values for 134 greater lotus herbagesamples. (R2\ 0É818).CTbutanv1vol@HCl\ 3É390 ] 0É952 CTNIRS

266 K F Smith, W M Kelman

TABLE 2Calibration and performance statistics of the NIRS equations for condensed tanninsa

0D 1D 2D 3D 4D

SML RCalibration

SEC (g kg~1) 13È14(14) 12È17(14É1) 13È15(13É2) 12È16(13É8) 13È14(13É3)R2 0É83È0É86 0É76È0É88 0É82È0É86 0É80È0É88 0É84È0É87

PredictionBias (g kg~1) [2É2È1É2 [0É4È1É1 [0É3È0É8 [1È1 [0É5È0É6SEP(C) (g kg~1) 14È15(14É4) 13È17(14É2) 13È15(13É4) 12È14(13É2) 14È15(14É8)Slope 0É91È0É93 0É84È0É95 0É91È0É95 0É92È0É95 0É89È0É95r2 0É73È0É76 0É65È0É80 0É74È0É80 0É77È0É81 0É72È0É78

PL SCalibration

SEC (g kg~1) 12È13(13) 10È13(11É2) 10È13(11É7) 11È13(11É8) 12È12(12É2)R2 0É87È0É88 0É87È0É91 0É85È0É91 0É87È0É90 0É87È0É88SECV (g kg~1) 15 14 14È15 13È14 14È151-VR 0É81È0É82 0É83È0É84 0É82È0É85 0É83È0É85 0É81È0É85

PredictionBias (g kg~1) [0É2È0É4 [0É1È0É5 [0É6È0É9 [0É3È0É4 [1É0È0É1SEP(C) (g kg~1) 13(13É1) 12È14(12É6) 12È14(12É7) 12È14(12É3) 12È15(13É2)Slope 0É93 0É93È0É95 0É93È0É97 0É91È0É96 0É90È0É98r2 0É78È0É80 0É78È0É82 0É78È0É83 0É77È0É83 0É75È0É83

a Ranges are presented for groups of mathematical treatments, mean in parentheses. 0D, log 1/R ; 1D, Ðrst deriv-ative log 1/R ; 2D, second derivative log 1/R ; 3D, third derivative log 1/R ; 4D, fourth derivative log 1/R ; SMLR,stepwise multiple linear regression ; PLS, partial least squares ; SEC, standard error of calibration ; R2, squaredmultiple correlation coefficient ; Bias, mean di†erence between chemical and NIRS values ; SEP(C), standard errorof prediction corrected for bias ; slope of linear regression of chemical and NIRS values ; r2, squared simple corre-lation coefficient of NIRS and chemical values ; SECV, standard error of cross validation ; 1-VR, coefficient ofdetermination for cross-validation

with PLS and the mathematical treatment 2, 8, 2, 1(Table 1).

Several wavelength regions were used during SMLR.Wavelengths in the ranges 2100È2200 nm, 1660È1670 nm and 1720È1730 nm were most common and allhave been reported previously to be associated with CT(Windham et al 1988 ; Petersen et al 1991 ; Roberts et al1993). The exact chemical signiÐcance of these wave-lengths is unknown, absorbances in the region around1660 nm may be associated with CÈH stretching in aro-matic compounds in CT (Windham et al 1988). Theregion around 2100 nm may be important in CT pre-diction due to complexing of CT with proteins whichabsorb in this region or through a negative correlationbetween CT and protein (Windham et al 1988). Thissuggests that the major e†ects of CT on the NIRS spec-trum are consistent across species.

NIRS calibration methods

PLS derived equations had SEC and SEP(C) onaverage 10È15% lower than equations developed usingSMLR (Table 2). When PLS and SMLR derived NIRSequations have been compared previously for the pre-diction of forage quality traits, PLS derived equations

have usually had lower standard errors (Shenk andWesterhaus 1991a,b ; Windham and Flinn 1992 ;Herrero et al 1996). The di†erence in standard errorbetween regression techniques was greater than thee†ect of mathematical treatment within a regressiontechnique. The lack of e†ect of mathematical treatmenton NIRS estimates of composition was also observedfor the prediction of crude protein and neutral detergentÐbre (Herrero et al 1996). Herrero et al (1996) attributedsome of the consistency across mathematical treatmentto the efficient selection of calibration samples on thebasis of spectral characteristics when using the SELECTprogram.

There was no increase in accuracy due to the use ofscatter correction during the calibration process (datanot shown). This may have been due to a lack of sys-tematic variation for particle size within the calibrationset.

CONCLUSIONS

NIRS was used to develop equations to predict CT ingreater lotus with an accuracy satisfactory for thescreening of genotypes in a breeding program aimed atselecting genotypes with levels of CT no greater than

Predicting tannins with NIRS 267

40 g kg~1 DM. Using the NIRS predictions for CTdeveloped here for L uliginosus there would only be asmall probability of overlooking the desired genotypeswith low CT concentrations. Also because of the rap-idity of the NIRS technique, large populations can bescreened to improve the chance of recovering rarerecombinants. The use of NIRS to predict CT in greaterlotus will allow more rapid evaluation and selection ofgenotypes than can be achieved using butan-1-ol/HClhydrolysis.

In general, equations developed using PLS had lowerstandard errors than those developed using SMLR.This indicates that PLS regression techniques should beused when estimating CT concentrations in greaterlotus, especially if the di†erences between genotypes aresmall.

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