Near infrared calibration of chemical constituents of Cajanus cajan (pigeon pea) used as forage

ANIMALFEED SCIENCEAND TECHNOLOGY ELSEVIER

Animal Feed Science Technology 69 (1997) 201-206

Near infrared calibration of chemical constituents Cajanus cajan (pigeon pea) used as forage

of

N. Berardoa,*, B.H. Dzowelab, L. HoveC, M. Odoardi” “Isrituto Sperimentale Colture Foraggere, Viale Piacenza 29, 20075 Lodi, Italy bSADAC / ICRAF Agroforestry Project, PO Box CY594, Camewary, Harare, Zimbabwe ‘Makoholi Research Station, P. Bag 9182, Masvingo, Zimbabwe

Abstract

Near infrared reflectance spectroscopy (NIRS) was used to predict quality parameters in Cujunus cajun used as forage for animals. Crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL), crude lipid (CL) and organic matter (OM) were determined by chemical methods. The set of samples for analysis was selected using the software SELECT on the basis of the NIR spectra of samples from field plots and consisted of branches and leaves of Cajunus cujan, harvested in two contrasting locations in Zimbabwe. The samples were scanned and screened using an NIR Systems Model 5000 monochromator; 48 samples were used to calibrate and to cross-validate the equations derived. Equations for predicting chemical composition of the species under study were calculated using scores from partial least squares (PLS) as independent variables. Cross-validation procedures indicated good correlations between laboratory values and NIRS estimates. NIRS calibrations obtained from this study could be utilised in current and future programmes for evaluating the quality of Cujunus cajun forage for animal nutrition. 0 1997 Elsevier Science B.V. Keywords:

Cajunus

cajun;

Near infrared reflectance

spectroscopy; Chemical composition

*Corresponding author. Present address: Istituto Sperimentale, Per La Cerealicoltura, Via Stezzano 24, 24126 Bergano, Italy. Tel.: +39 35 313132; fax: +39 35 316054. 0377-8401/97/$17.00 0 1997 Elsevier Science B.V. All rights reserved PII SO377-8401(97)00108-9

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1. Introduction NIR spectroscopy is widely used in the evaluation of many forages and good calibrations have been obtained (Abreau et al., 1992; Berardo, 1992). Many shrubs and legumes grow well in Zimbabwe and other semi-arid countries when they have been established and many wild and domestic animals need to consume indigenous species of plants during the dry season. Deeper knowledge is required as quickly as possible to ascertain their suitability as a source of protein and other nutrients for feeding ruminants. The NIRS technique could be a useful tool for helping to solve this problem. Major advantages of NIRS are speed, simplicity of sample preparation, multiplicity of analyses in one operation, non-consumption of samples and reduced costs for chemical reagents (Norris, 1989). The technique is based on a correlation between chemical properties, as determined by defined methods and absorption of light at different wavelengths in the NIR region, as measured by reflectance. Some of the chemical methods used for analyses of feed are not specific. Constituent values are often dependent on the method and the composition of the substances included in the analysis can differ widely. The NIRS technique, on the other hand, is based on the amount of certain functional groups or chemical bonds and the reflectance properties are very sensitive to differences between constituents even in closely related materials (Watson et al., 1977). Although NIRS calibrations are based upon comparisons with wet chemistry, Shenk et al. (1979) concluded that NIRS is capable of predicting the nutrient composition of forages with errors lower then those resulting from wet chemical methods. Templeton et al. (1983) reported lower standard errors for analysis of ADF, NDF and ADL with NIRS. After initial work in applying NIRS to determine the percentage of CP, ash, crude fibre (CF), NDF, ADF, ADL, CL in four forage species (Berardo et al., 1988), we broadened our investigation to other forage species (Berardo et al., 1995). The work presented here was designed to investigate the effectiveness of NIR spectroscopy in predicting the chemical characteristics of Cujunus cajun used as forage in tropical areas.

2. Materials and methods The set of pigeon pea samples utilised in this study varied widely in chemical composition, representing different parts of the plant (leaves and branches) and different varieties and locations (Domboshawa and Makoholi, representing high and low potential ecozones, respectively). The distribution symmetry of the population used in this study is represented in Fig. 1 utilizing three selected principal component scores (2, 3 and 4). During the 1993-1994 growing season, the plants were sampled twice for chemical analyses. The first sampling was done in April and the second one in October, representing the beginning of winter and summer, respectively, at Domboshawa. The samples at Makoholi were sampled in April only. Samples were separated into leaves and branches (unpublished) and dried at 65S”C in a forced air oven until they reached constant weight. After cooling and

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weighing, the samples were ground to pass a l-mm screen in a forage mill. The samples were subsequently re-ground in a Cyclotec mill through a l-mm sieve. 2.1. NIRS analyses Samples of commercial varieties, up to 136 samples, were collected from experimental trials. All spectral data were recorded in the range 1100-2500 nm using an NIR Systems Model 5000 scanning monochromator and were stored on an IBM compatible computer. An example of NIRS spectra leaves and branches as second order derivative in one of the major significant absorption ranges of the functional groups is showed in Fig. 2. The software for scanning, mathematical processing and statistical analysis was supplied with the spectrophotometer by Infrasoft International (Port Matilda, PA, USA). The spectra data were mathematically transformed (1, 10, 5) before derivation of regression models. The first number (1) indicates the

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204

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and branches (---_) displayed as Fig. 2. NIRS absorption spectra of Cajanus cajan leaves ( -) second order derivative, and some generic functional groups which are typical of agricultural products.

derivative used, the second (10) the length of the segment for data points and the last (5) the length of the smoothing segment. 2.2. Chemical analyses All samples were analyzed in duplicate. Determinations of CP were carried out according to the Dumas method (Kirsten, 1983); NDF, ADF and ADL by the method of Goering and Van Soest (1970); CL by extraction with petroleum ether and OM by ashing the samples in an oven at 550°C.

3. Results and discussion The range and mean values for CP, NDF, ADF, ADL, CL and OM in the samples utilised in the calibration set are shown in Table 1. In the sample set, there was a wide variation in chemical composition and the samples covered a good portion of the variability reported in the literature for this species (Norton, 1994). Table 1 shows the statistics, including standard error of calibration (S.E.C.) and coefficient of determination CR’) for the equations obtained for each of the dry matter quality constituents. Performance on cross-validation, expressed as squared

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Table 1 Range of chemical composition (g/kg DM), standard deviation (SD.) and standard error of laboratory analysis (S.E.L.) of the calibration set of Cajanus cajan samples composed of branches and leaves from two different locations, i.e. Domboshawa and Makoholi in Zimbabwe and statistics of calibration and cross-validation, including standard error of calibration (S.E.C.) and coefficient of determination (I?‘), squared coefficient of correlation (r’) and standard error of cross-validation (S.E.C.V.) Variable

n

Mean

S.D.

Minimum

Maximum

S.E.L.

S.E.C.

R2

S.E.C.V.

r2

CP NDF ADF ADL CL OM

48 48 48 48 48 47

152 631 465 190 23 955

86.2 95.8 93.1 38.3 14.1 16.7

52 453 293 120 6 913

302 778 605 298 54 981

6.9 7.7 7.5 3.1 1.1 1.3

12.4 10.6 15.7 7.4 2.5 2.9

0.98 0.99 0.98 0.96 0.97 0.97

15.9 19.4 22.2 15.2 3.4 5.0

0.97 0.96 0.95 0.81 0.95 0.93

Abbreviations: n, number of samples used; CP, crude protein; NDF, neutral detergent fibre; ADF, acid

detergent fibre; ADL, acid detergent lignin; CL, crude lipid; OM, organic matter.

coefficient of correlation (r’) and standard error of cross validation (S.E.C.V.) are also shown in Table 1. S.E.C. ranged from 2.5 for CL to 15.7 for ADF; R2 from 0.96 for ADL to 0.99 for NDF; r2 from 0.81 for ADL to 0.97 for CP; and S.E.C.V. from 3.4 for CL to 22.2 for ADF. The precision with which NIRS predicted laboratory analyses confirmed the results obtained by other authors and are similar to those found for other species (Abreau et al., 1992). The S.E.C.V. generally reflected the accuracy of the chemical determinations, and the results shown in Table 1 were in agreement with results obtained with conventional wet analysis, where the level of accuracy varies with the chemical parameter measured. A good correlation was also obtained for ADL, which generally gives poorer results compared to the other chemical parameters analyzed. From the preliminary results reported here on this plant species, it appears that NIRS can accurately predict the composition of Cujanus cajun (pigeon pea) used as forage. A further development of this work will be to validate the calibration equation with a separate set of pigeon pea forage material. This methodology appears to be potentially extremely useful in screening large number of samples of this forage for its chemical composition and consequently, its nutritional value for ruminants. However, further studies are warranted to assess the effects of growing season on the composition and accuracy of prediction by NIRS to detect the interactions between growing season and geographical locations in quality parameters of this forage species. Further work is also required to widen the usage of the NIRS technique as a tool for predicting the chemical composition of other tropical species and to extend research for detecting the presence of anti-nutrients in plant material for feeding ruminants.

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Acknowledgements This work was funded by EUC grant number TS3*-CT93-0211. The authors wish to express their appreciation to Agata Ursino and Emiliana Piccinini for their technical assistance.

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