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Measurement of chemical composition in wet whole maize silage by visible and near infrared reflectance spectroscopy
Animal Feed Science and Technology 129 (2006) 329–336
Short communication
Measurement of chemical composition in wet whole maize silage by visible and near infrared reflectance spectroscopy D. Cozzolino a,∗ , A. Fassio b , E. Fern´andez b , E. Restaino b , A. La Manna b a b
Instituto Nacional de Investigaci´on Agropecuaria, INIA La Estanzuela, Colonia, Uruguay Instituto Nacional de Investigaci´on Agropecuaria, INIA La Estanzuela, Ruta 50-km 12, CC 39173, Colonia, Uruguay
Received 10 June 2005; received in revised form 16 January 2006; accepted 25 January 2006
Abstract Visible (Vis) and near infrared reflectance (NIR) spectroscopy were used to predict dry matter (DM), acid and neutral detergent fibre (ADFom and aNDFom), ash, crude protein (CP) and pH in wet whole maize (WWM) silage samples. Samples were analysed by reference methods and spectra collected using a NIR spectrophotometer in reflectance (400–2500 nm). Predictive equations were developed using modified partial least squares (MPLS) with full cross validation using raw spectra or second derivative. Two scatter corrections were also used, namely standard normal variate and detrend (SNVD) and multiplicative scatter correction (MSC). Coefficient of determination in calibration R2cal and standard error of cross validation (SECV) were 0.91 (SECV: 6.5 g kg−1 DM) for CP, 0.85 (SECV: 27.4 g kg−1 ) for DM using SNVD, and 0.86 (SECV: 22.1 g kg−1 DM) Abbreviations: NIR, near infrared reflectance; OMD, organic matter digestibility; N, nitrogen; CP, crude protein; ADFom, acid detergent fibre; aNDFom, neutral detergent fibre; OMD, organic matter digestibility; Vis, visible; R, reflectance; PCA, principal component analysis; MPLS, modified partial least squares; R2cal , coefficient of determination in calibration; SECV, standard error of cross validation; CV, coefficient of variation; SNVD, standard normal variate and detrend; MSC, multiplicative scatter correction; S.D., standard deviation; RPD, SD/SECV; WWM, wet whole maize ∗ Correspondence to: The Australian Wine Research Institute, Waite Road, P.O. Box 197, Glen Osmond, SA 5064, Australia. Fax: +61 8 8303 6601. E-mail address: [email protected] (D. Cozzolino). 0377-8401/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.anifeedsci.2006.01.025
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D. Cozzolino et al. / Animal Feed Science and Technology 129 (2006) 329–336
for ADFom using raw spectra. Poor correlations between the reference methods and NIR were found for aNDFom, ash, OMD and pH. It was concluded that NIR spectroscopy might be a suitable method to predict DM, CP and ADFom on WWM silage samples with minimal sample preparation. © 2006 Elsevier B.V. All rights reserved. Keywords: Wet whole maize; Silage; NIR; Modified partial least squares; Sample presentation
1. Introduction Maize silage is an important forage crop in ruminant production systems in Uruguay. In the past 100 years it became apparent that nutrient content as well as yield measurements should be considered in deriving proper animal feeding systems (De Boever et al., 1996; Deaville and Flinn, 2000). Laboratory methods were developed and refined to provide nutrient information to the industry, farmers and researchers, however, they are expensive and time-consuming (Murray, 1993; Deaville and Flinn, 2000). Near infrared reflectance (NIR) spectroscopy represents a radical departure from conventional analytical methods, in that the entire sample is characterized in terms of its absorption properties in the NIR region and offers advantages of simplicity, speed of analysis, reduced chemical waste, and more cost-effective prediction of product functionality (Murray, 1993; Deaville and Flinn, 2000). Both silage and forages have been analyzed routinely by NIR spectroscopy in dry and ground presentations to the spectrophotometer in many laboratories around the world (Murray, 1993). Among others, the advantages of use of dry material are it that removes water from the spectra and improves signal to noise conditions during scanning; samples are more stable for storage at ambient temperature, and improve sample presentation to the spectrophotometer (Murray and Cowe, 2004). NIR spectroscopy has gained widespread acceptance for the analysis of forage quality constituents on dry material, however, little attention has been given to the use of wet and unground forages. Initial success of NIR spectroscopy analysis of coarse forages suggests a need to better understand the potential for analysis of minimally processed samples and its suitability to use in routine laboratory analysis (Murray, 1993; Griggs et al., 1999). The use of fresh material might reduce preparation costs and possible compositional alterations during NIR analysis; however, the accuracy of the prediction might be less than of those obtained using dry materials (Sinnaeve et al., 1994; Park et al., 1998; Griggs et al., 1999; Park et al., 2002). Advances in chemometric software, spectral data transformation (e.g. scatter correction), and spectrophotometers, have contributed to the ability to minimize the interference of water and particle size in the analyses of wet material by NIR spectroscopy (Griggs et al., 1999; Murray and Cowe, 2004). Relatively little is reported on the use of NIR spectroscopy to determine quality parameters on undried materials (Sinnaeve et al., 1994; Park et al., 1998; Griggs et al., 1999; Alomar et al., 1999; Cozzolino et al., 2000; Park et al., 2002) and in wet maize silage (Reeves and Blosser, 1991; Lovett et al., 2005). The objective of this study was to evaluate the potential use of visible (Vis) and NIR spectroscopy to predict chemical composition of whole maize silage.
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2. Materials and methods 2.1. Silage samples and preparation Wet whole maize (WWM) silage samples (n = 90), collected from commercial farms during 1998–2001 harvests were used. Silage samples represent most of the commercial systems of beef and dairy production in Uruguay, where different types of silo structure, harvest machinery as well as maize plant materials (hybrids, varieties, different maturities) were used. Samples were collected directly from the farms, placed in plastic bags, frozen (−20 ◦ C) and delivered immediately to the laboratory for chemical and NIR analysis. Uruguay (34◦ S, 57◦ W) (South West) is located entirely within the temperate zone where average high and low temperatures in summer (January) are 28 ◦ C and 17 ◦ C, and in winter (July) are 14 ◦ C and 6 ◦ C, respectively. Rainfall is relatively evenly distributed throughout the year with mean annual precipitation of 950 mm. 2.2. Reference analysis Samples were dried in oven at 60 ◦ C for 48 h and ground in a Wiley forage mill to pass a 1 mm screen (Arthur H. Thomas, Philadelphia, PA, USA). Nitrogen (N) was determined on the dried samples using a semi-micro automated Kjeldhal method (Tecator, Sweden) and converted to crude protein (CP = N × 6.25) (AOAC, 2.057, 1984). Acid detergent fibre (ADFom) and neutral detergent fibre (aNDFom) were estimated using the procedures reported elsewhere (Robertson and Van Soest, 1981; Van Soest et al., 1991). Organic matter digestibility (OMD) was estimated using in vitro two-stage rumen–pepsin technique with rumen fluid (48 h) followed by HCl–pepsin digestion (48 h) (Tilley and Terry, 1963). Ash was determined by incinerating the dry sample at 500 ◦ C for 4 h (AOAC, 7.009, 1984). Sample pH was determined on the liquid phase using a glass-electrode pH meter (Orion Model 230 A, USA). All chemical analysis were expressed on a dry weight basis and samples analysed in duplicate. 2.3. Visible and near infrared analysis Spectra were collected in the visible (Vis) and near infrared (NIR) regions in reflectance (400–2500 nm) at 2 nm intervals using a scanning monochromator NIRSystems 6500 (NIRSystems, Silver Spring, MD, USA). Wet samples were wrapped in PVC bags and scanned in a course rectangular cell (200 mm×30 mm×20 mm) (Part number IH-0395 or NR 7080, NIRSystems, USA). Samples were scanned once and reflectance data were stored as reciprocal of the logarithm of reflectance log(1/R). Samples were not rotated when spectra collection was made. The spectrum of each sample was the average of 32 successive scans (1050 data points per scan). 2.4. Chemometrics Spectra collection, data manipulation and calibrations were performed using Infrasoft International software (ISI, version 3.01, Infrasoft International, NIRSystems, Silver Spring,
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MD, USA). The mathematical treatments used in the transformation of the spectra were 1, 4, 4 and 2, 4, 4; where the first number indicates the order of derivative (one is first derivative of log 1/R), the second number is the gap in nm over which the derivative is calculated; the third number is the number of nm used in the first smoothing and the fourth number refers to the number of nm over which the second smoothing is applied (1 = no smoothing) (Shenk and Westerhaus, 1993). The regression method used to build the calibration equations was modified partial least squares (MPLS) regression, where one variable was estimated at a time, with full cross validation. Full cross validation was used to estimate the optimal number of terms in the calibration to avoid overfitting and the optimal number of terms to be included in the calibration models was determined as having the lowest standard error of cross validation (SECV) (Shenk and Westerhaus, 1993). Due to the limited number of samples available, calibration models were developed and evaluated using full cross validation (Dardenne et al., 2000). It is well known that in spectroscopy, scatter correction can be used to correct the whole spectrum for particle size variation. In the present study two scatter correction methods were used, namely standard normal variate and detrend (SNVD) (Barnes et al., 1989) and multiplicative scatter correction (MSC) (Martens and Naes, 1992; Naes et al., 2002). The resulting calibration equations between the chemical reference values and the NIR data were evaluated based on the coefficient of determination in calibration (R2cal ) and SECV. The ratio of standard deviation (S.D.) and SECV, namely residual predictive value (RPD) was used to test the accuracy of the calibration models (Williams, 2001). The RPD for the NIR calibration for the parameters evaluated demonstrated how well the calibration models performed in predicting the reference data. If a product shows a narrow range in composition, or if the error in estimation is large compared with the spread (as S.D.) in composition, then regression finds increasing difficulty in finding stable NIR calibrations. An RPD value greater than three was considered adequate for analytical purposes in most of the NIR applications for agricultural products (Williams, 2001).
3. Results and discussion Table 1 shows the descriptive statistics for dry matter (DM), ADFom, aNDFom, CP, OMD, pH and ash on WWM samples analysed. The WWM samples analysed showed a Table 1 Descriptive statistics for chemical parameters of wet whole maize silage (WWM) samples used to develop the NIR calibration models DM (g kg−1 ) CP (g kg−1 DM) ADFom (g kg−1 DM) aNDFom (g kg−1 DM) OMD (g kg−1 DM) pH
Mean
Min.
Max.
S.D.
CV%
345.4 76.6 295.9 522.6 640 3.6
227.8 37.6 197.5 393.7 510 3.0
528.0 243.2 434.5 715.4 740 4.4
65.0 31.6 45.9 83.7 40 0.25
18.8 41.3 15.5 16.0 6.2 6.9
DM: dry matter; CP; crude protein; OMD: in vitro organic matter digestibility; ADFom: acid detergent fibre; aNDFom: neutral detergent fibre; Max.: maximum; Min.: minimum; S.D.: standard deviation, CV coefficient of variation calculated as (S.D.×100)/mean.
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Table 2 Summary of the NIR cross validation statistics to measure chemical parameters in wet whole maize silage (WWM) (g kg−1 )
DM CP (g kg−1 DM) OMD (g kg−1 DM) ADFom (g kg−1 DM) aNDFom (g kg−1 DM) pH
Scatter correction
Derivative
R2cal
SECV
T
RPD
SNVD SNVD SNVD Raw MSC SNVD
2nd 2nd 2nd – – 2nd
0.85 0.91 0.53 0.86 0.60 0.51
27.4 6.5 30 22.1 67.1 0.18
6 7 10 10 10 10
2.4 4.8 1.3 2.1 1.2 1.4
DM: dry matter; CP; crude protein; OMD: in vitro organic matter digestibility; ADFom: acid detergent fibre; aNDFom: neutral detergent fibre; R2cal : coefficient of determination in cross validation; SECV: standard error of cross validation, T: number of PLS terms used to develop the calibration models, RPD: SD/SECV; SNVD: standard normal variate; MSC: multiplicative scatter correction.
wide range in composition. Overall, the ensiling characteristics were considered acceptable in most of the samples analysed based on the DM content and pH values obtained (Wilkinson et al., 1998). The variability in chemical composition was considered suitable to develop NIR calibrations. The Vis region of the spectra (data not presented) showed an absorption band around 666 nm, related with the absorption of chlorophylls (Cozzolino et al., 2000). In the NIR region (data not presented), absorption bands around 980 nm, 1200 nm, 1450 nm and 1970 nm were observed related with O–H overtones (water) (Murray, 1986; Cozzolino et al., 2000). Absorption bands around 1726 nm and 1762 nm were related with CH stretch first overtone (oil content and PVC bag), absorption bands at 2312 nm and at 2352 nm were related with C–H overtones and combination bands (Murray, 1986). Table 2 summarises the cross validation statistics for the measurement of chemical composition in WWM samples by NIR. The R2cal and SECV were for DM 0.85 (SECV: 27.5 g kg−1 ) and for CP 0.91 (SECV: 6.5 g kg−1 DM) using SNVD; and for ADFom 0.86 (SECV: 22.1 g kg−1 DM) using raw spectra. The use of MSC did not improve the NIR calibrations and appeared to have inconsistent effect on the SECV. These results agreed with those reported by other authors (Gordon et al., 1998; Griggs et al., 1999). Both DM and CP gave the best accuracy in prediction, in the different combinations of mathematical and scatter corrections applied (see Table 2). The RPD values obtained for the chemical parameters analysed were 4.8, 2.4, 2.1, 1.3, 1.2, and 1.4 for CP, DM, ADFom, OMD, aNDFom and pH respectively. In the present study CP gave the best RPD, while intermediate RPD values were obtained for DM and ADFom. Natural plant tissues contain physiological concentrations of water around 70% of their fresh mass, filling pore space with water rather than air, which leads to less refraction at boundaries and deeper light penetration (Murray and Cowe, 2004). Using wet silage is causing serious sampling and sample presentation problems at wavelengths longer than 1400 nm (Murray and Cowe, 2004). Either wet samples must be presented as thin films at those wavelengths or alternatively longer wavelengths must be abandoned in favour of the Herschel infrared region (780–1100 nm) (Murray and Cowe, 2004). Beyond 1400 nm, water in wet tissue will be the largest absorber compared with other major constituents (Murray and Cowe, 2004). In this study, additionally NIR calibrations were developed using shorter wavelength in the NIR region (Table 3). Improved calibration statistics in most of the
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Table 3 Summary of the NIR cross validation statistics to measure chemical parameters in wet whole maize silage (WWM) (500–1100 nm) using second derivative and SNVD (g kg−1 )
DM CP (g kg−1 DM) OMD (g kg−1 DM) ADFom (g kg−1 DM) aNDFom (g kg−1 DM) pH
R2cal
SECV
T
RPD
0.90 0.90 0.40 0.81 0.84 0.60
25.5 7.7 40 27.1 54.1 0.14
4 4 10 6 6 10
2.5 4.1 1.0 2.0 1.6 1.8
DM: dry matter; CP; crude protein; OMD: in vitro organic matter digestibility; ADFom: acid detergent fibre; aNDFom: neutral detergent fibre; R2cal : coefficient of determination in cross validation; SECV: standard error of cross validation, T: number of PLS terms used to develop the calibration models, RPD: SD/SECV; SNVD: standard normal variate; MSC: multiplicative scatter correction.
parameters analysed were obtained using the so-called Herschel infrared region. The R2cal and SECV were 0.90 (SECV: 25.5 g kg−1 ) for DM, 0.90 (SECV: 7.7 g kg−1 DM) for CP, 0.81 (SECV; 27.2 g kg−1 DM) for ADFom, 0.84 (SECV: 54.0 g kg−1 DM) for aNDFom and 0.60 (SECV: 0.14) for pH. Poorer correlations were found for OMD. However, the predictive accuracy is far from the desirable if wet materials will be analysed by NIR spectroscopy for analytical purposes. Nevertheless, analysing fresh samples by NIR spectroscopy presents logistic advantages by minimising delays between sampling and laboratory scanning during routine analysis and providing greater flexibility in sample preparation and presentation to the spectrophotometer. However, problems will arise due to sample surface wetness or interferences of the plastic bags used to hold the sample during spectra collection. Water absorbs energy in the NIR region; consequently the moisture content of the sample has a significant effect on the accuracy of the NIR calibration models. Other factors such as particle size and particle size distribution might also influence the accuracy of the NIR calibrations when coarse materials are analysed. This is of particular importance in coarse samples such as whole maize plant. In this study, silages were highly heterogeneous in chop length, with particle sizes ranging from 2 cm to 20 cm, as a result of the different types of harvest machinery employed by the farmers. Similar findings were reported by other authors (Alomar et al., 1999; Gordon et al., 1998; Lovett et al., 2005).
4. Conclusions The results from this study suggested that WWM might be analysed by NIR spectroscopy to determine CP, DM and ADFom. The use of wet materials will enable rapid turnaround in both farm advisory systems and in breeding programmes involving large number of samples. Wet sample presentation offers considerable potential in order to increase the speed of analysis, by avoiding sample preparation before analysis (i.e. grinding or drying), however, the prediction accuracy is less than desirable for analytical purposes. Further work will be carried out in order to assess the effect of particle size on the calibration of wet silage samples.
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Acknowledgments The authors thank the technicians of the Animal Nutrition Lab at INIA La Estanzuela for the chemical analysis. The work was supported by the National Institute for Agricultural Research (INIA), Uruguay. References Alomar, D., Montero, R., Fuchslocher, R., 1999. Effect of freezing and grinding method on near infrared reflectance (NIR) spectra variation and chemical composition of fresh silage. Anim. Feed Sci. Technol. 78, 57–63. Association of Official Analytical Chemist, 1984. Official Methods of Analysis, 14th ed. AOAC, Washington, DC, USA. Barnes, R.J., Dhanoa, M.S., Lister, S.J., 1989. Standard Normal variate transformation and detrending of near infrared diffuse reflectance spectra. Appl. Spectrosc. 43, 772–777. Cozzolino, D., Fassio, A., Gimenez, A., 2000. The use of near infrared reflectance spectroscopy (NIRS) to predict the composition of whole maize plants. J. Sci. Food Agric. 81, 142–146. Dardenne, P., Sinnaeve, G., Baeten, V., 2000. Multivariate calibration and chemometrics for near infrared spectroscopy: which method? J. Near Infrared Spectrosc. 8, 229–237. Deaville, E.R., Flinn, P.C., 2000. Near infrared (NIR) spectroscopy: an alternative approach for the estimation of forage quality and voluntary intake. In: Givens, D.I., Owen, E., Axford, R.F.E., Omedi, H.M. (Eds.), Forage Evaluation in Ruminant Nutrition. CABI Publishing, Wallingford, UK, pp. 301–320. De Boever, J.L., Cottyn, B.G., De Brabander, D.L., Vanacker, J.M., Boucque, Ch.V., 1996. Prediction of the feeding value of grass silage by chemical parameters, in vitro digestibility and near infrared reflectance spectroscopy. Anim. Feed Sci. Technol. 60, 103–115. Gordon, F.J., Cooper, K.M., Park, R.S., Steen, R.W.J., 1998. The prediction of intake potential and organic matter digestibility of grass silages by near infrared spectroscopy analysis of undried samples. Anim. Feed Sci. Technol. 70, 339–351. Griggs, T.G., Lobos, K.B., Kingery, P.E., 1999. Digestibility analysis of undried, unground and dry ground herbage by near infrared reflectance spectroscopy. Crop Sci. 39, 1164–1170. Lovett, D.K., Deaville, E.R., Givens, D.I., Finlay, M., Owen, E., 2005. Near infrared reflectance spectroscopy (NIRS) to predict biological parameters of maize silage: effects of particle comminution, oven drying temperature and the presence of residual moisture. Anim. Feed Sci. Technol. 120, 323–332. Martens, H., Naes, T., 1992. Multivariate Calibration. John Wiley and Sons, Chichester, UK. Murray, I., 1986. The NIR spectra of homologous series of organic compounds. In: Hollo, J., Kaffka, K.J., Gonczy, J.L., (Eds.), Proceedings of the International NIR/NIT Conference, Akademiai Kiado, Budapest, pp. 13–28. Murray, I., 1993. Forage analysis by near infrared spectroscopy. In: Davies, A., Baker, R.D., Grant, S.A., Laidlaw, A.S. (Eds.), Sward Management Handbook. British Grassland Society, UK, pp. 285–312. Murray, I., Cowe, I., 2004. Sample Preparation. In: Roberts, C.A., Workman, J., Reeves, IIIJ.B., (Eds.), Near Infrared Spectroscopy in Agriculture, ASA, CSSA, SSSA, Madison, USA, pp. 75–115, Chapter 5. Naes, T., Isaksson, T., Fearn, T., Davies, T., 2002. A User-friendly Guide to Multivariate Calibration and Classification. NIR Publications, Chichester, UK. Park, P.S., Agnew, R.E., Gordon, F.J., Steen, R.W.J., 1998. The use of near infrared reflectance spectroscopy (NIRS) on undried samples of grass silage to predict chemical composition and digestibility parameters. Anim. Feed Sci. Technol. 72, 155–167. Park, P.S., Agnew, R.E., Kilpatrick, D.J., 2002. The effect of freezing and thawing on grass silage quality predictions based on near infrared reflectance spectroscopy. Anim. Feed Sci. Technol. 102, 151–167. Reeves III, J.B., Blosser, T.H., 1991. Near infrared spectroscopy analysis of undried silages as influenced by sample grind, presentation and spectral region. J. Dairy Sci. 74, 882–895. Robertson, J.B., Van Soest, P.J., 1981. The detergent system of analysis. In: James, W.P.T., Theander, O. (Eds.), The Analysis of Dietary Fibre in Food. Marcel Dekker, N.Y. and Basel, pp. 123–158, Chapter 9. Shenk, J.S., Westerhaus, M.O., 1993. Analysis of Agriculture and food products by Near Infrared Reflectance Spectroscopy. Infrasoft International [ISI], MD, USA.
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Sinnaeve, G., Dardenne, P., Agneessens, R., Biston, R., 1994. The use of near infrared spectroscopy for the analysis of fresh grass silage. J. Near Infrared Spectrosc. 2, 79–84. Tilley, J.M.A., Terry, R.A., 1963. A two-stage technique for the in vitro digestion of forage crops. J. Brit. Grassland Soc. 18, 104–111. Van Soest, P.J., Robertson, J.B., Lewis, B.A., 1991. Methods for dietary fiber, neutral detergent fiber and non-starch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74, 3583–3597. Williams, P.C., 2001. Implementation of near-infrared technology. In: Williams, P.C., Norris, K.H. (Eds.), American Association of Cereal Chemist. St. Paul, Minnesota, USA, pp. 145–169. Wilkinson, J.M., Newman, G., Allen, D.M., 1998. Maize: Production and Feeding Maize Silage. Chalcombe Publications, UK.
Short communication
Measurement of chemical composition in wet whole maize silage by visible and near infrared reflectance spectroscopy D. Cozzolino a,∗ , A. Fassio b , E. Fern´andez b , E. Restaino b , A. La Manna b a b
Instituto Nacional de Investigaci´on Agropecuaria, INIA La Estanzuela, Colonia, Uruguay Instituto Nacional de Investigaci´on Agropecuaria, INIA La Estanzuela, Ruta 50-km 12, CC 39173, Colonia, Uruguay
Received 10 June 2005; received in revised form 16 January 2006; accepted 25 January 2006
Abstract Visible (Vis) and near infrared reflectance (NIR) spectroscopy were used to predict dry matter (DM), acid and neutral detergent fibre (ADFom and aNDFom), ash, crude protein (CP) and pH in wet whole maize (WWM) silage samples. Samples were analysed by reference methods and spectra collected using a NIR spectrophotometer in reflectance (400–2500 nm). Predictive equations were developed using modified partial least squares (MPLS) with full cross validation using raw spectra or second derivative. Two scatter corrections were also used, namely standard normal variate and detrend (SNVD) and multiplicative scatter correction (MSC). Coefficient of determination in calibration R2cal and standard error of cross validation (SECV) were 0.91 (SECV: 6.5 g kg−1 DM) for CP, 0.85 (SECV: 27.4 g kg−1 ) for DM using SNVD, and 0.86 (SECV: 22.1 g kg−1 DM) Abbreviations: NIR, near infrared reflectance; OMD, organic matter digestibility; N, nitrogen; CP, crude protein; ADFom, acid detergent fibre; aNDFom, neutral detergent fibre; OMD, organic matter digestibility; Vis, visible; R, reflectance; PCA, principal component analysis; MPLS, modified partial least squares; R2cal , coefficient of determination in calibration; SECV, standard error of cross validation; CV, coefficient of variation; SNVD, standard normal variate and detrend; MSC, multiplicative scatter correction; S.D., standard deviation; RPD, SD/SECV; WWM, wet whole maize ∗ Correspondence to: The Australian Wine Research Institute, Waite Road, P.O. Box 197, Glen Osmond, SA 5064, Australia. Fax: +61 8 8303 6601. E-mail address: [email protected] (D. Cozzolino). 0377-8401/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.anifeedsci.2006.01.025
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for ADFom using raw spectra. Poor correlations between the reference methods and NIR were found for aNDFom, ash, OMD and pH. It was concluded that NIR spectroscopy might be a suitable method to predict DM, CP and ADFom on WWM silage samples with minimal sample preparation. © 2006 Elsevier B.V. All rights reserved. Keywords: Wet whole maize; Silage; NIR; Modified partial least squares; Sample presentation
1. Introduction Maize silage is an important forage crop in ruminant production systems in Uruguay. In the past 100 years it became apparent that nutrient content as well as yield measurements should be considered in deriving proper animal feeding systems (De Boever et al., 1996; Deaville and Flinn, 2000). Laboratory methods were developed and refined to provide nutrient information to the industry, farmers and researchers, however, they are expensive and time-consuming (Murray, 1993; Deaville and Flinn, 2000). Near infrared reflectance (NIR) spectroscopy represents a radical departure from conventional analytical methods, in that the entire sample is characterized in terms of its absorption properties in the NIR region and offers advantages of simplicity, speed of analysis, reduced chemical waste, and more cost-effective prediction of product functionality (Murray, 1993; Deaville and Flinn, 2000). Both silage and forages have been analyzed routinely by NIR spectroscopy in dry and ground presentations to the spectrophotometer in many laboratories around the world (Murray, 1993). Among others, the advantages of use of dry material are it that removes water from the spectra and improves signal to noise conditions during scanning; samples are more stable for storage at ambient temperature, and improve sample presentation to the spectrophotometer (Murray and Cowe, 2004). NIR spectroscopy has gained widespread acceptance for the analysis of forage quality constituents on dry material, however, little attention has been given to the use of wet and unground forages. Initial success of NIR spectroscopy analysis of coarse forages suggests a need to better understand the potential for analysis of minimally processed samples and its suitability to use in routine laboratory analysis (Murray, 1993; Griggs et al., 1999). The use of fresh material might reduce preparation costs and possible compositional alterations during NIR analysis; however, the accuracy of the prediction might be less than of those obtained using dry materials (Sinnaeve et al., 1994; Park et al., 1998; Griggs et al., 1999; Park et al., 2002). Advances in chemometric software, spectral data transformation (e.g. scatter correction), and spectrophotometers, have contributed to the ability to minimize the interference of water and particle size in the analyses of wet material by NIR spectroscopy (Griggs et al., 1999; Murray and Cowe, 2004). Relatively little is reported on the use of NIR spectroscopy to determine quality parameters on undried materials (Sinnaeve et al., 1994; Park et al., 1998; Griggs et al., 1999; Alomar et al., 1999; Cozzolino et al., 2000; Park et al., 2002) and in wet maize silage (Reeves and Blosser, 1991; Lovett et al., 2005). The objective of this study was to evaluate the potential use of visible (Vis) and NIR spectroscopy to predict chemical composition of whole maize silage.
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2. Materials and methods 2.1. Silage samples and preparation Wet whole maize (WWM) silage samples (n = 90), collected from commercial farms during 1998–2001 harvests were used. Silage samples represent most of the commercial systems of beef and dairy production in Uruguay, where different types of silo structure, harvest machinery as well as maize plant materials (hybrids, varieties, different maturities) were used. Samples were collected directly from the farms, placed in plastic bags, frozen (−20 ◦ C) and delivered immediately to the laboratory for chemical and NIR analysis. Uruguay (34◦ S, 57◦ W) (South West) is located entirely within the temperate zone where average high and low temperatures in summer (January) are 28 ◦ C and 17 ◦ C, and in winter (July) are 14 ◦ C and 6 ◦ C, respectively. Rainfall is relatively evenly distributed throughout the year with mean annual precipitation of 950 mm. 2.2. Reference analysis Samples were dried in oven at 60 ◦ C for 48 h and ground in a Wiley forage mill to pass a 1 mm screen (Arthur H. Thomas, Philadelphia, PA, USA). Nitrogen (N) was determined on the dried samples using a semi-micro automated Kjeldhal method (Tecator, Sweden) and converted to crude protein (CP = N × 6.25) (AOAC, 2.057, 1984). Acid detergent fibre (ADFom) and neutral detergent fibre (aNDFom) were estimated using the procedures reported elsewhere (Robertson and Van Soest, 1981; Van Soest et al., 1991). Organic matter digestibility (OMD) was estimated using in vitro two-stage rumen–pepsin technique with rumen fluid (48 h) followed by HCl–pepsin digestion (48 h) (Tilley and Terry, 1963). Ash was determined by incinerating the dry sample at 500 ◦ C for 4 h (AOAC, 7.009, 1984). Sample pH was determined on the liquid phase using a glass-electrode pH meter (Orion Model 230 A, USA). All chemical analysis were expressed on a dry weight basis and samples analysed in duplicate. 2.3. Visible and near infrared analysis Spectra were collected in the visible (Vis) and near infrared (NIR) regions in reflectance (400–2500 nm) at 2 nm intervals using a scanning monochromator NIRSystems 6500 (NIRSystems, Silver Spring, MD, USA). Wet samples were wrapped in PVC bags and scanned in a course rectangular cell (200 mm×30 mm×20 mm) (Part number IH-0395 or NR 7080, NIRSystems, USA). Samples were scanned once and reflectance data were stored as reciprocal of the logarithm of reflectance log(1/R). Samples were not rotated when spectra collection was made. The spectrum of each sample was the average of 32 successive scans (1050 data points per scan). 2.4. Chemometrics Spectra collection, data manipulation and calibrations were performed using Infrasoft International software (ISI, version 3.01, Infrasoft International, NIRSystems, Silver Spring,
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MD, USA). The mathematical treatments used in the transformation of the spectra were 1, 4, 4 and 2, 4, 4; where the first number indicates the order of derivative (one is first derivative of log 1/R), the second number is the gap in nm over which the derivative is calculated; the third number is the number of nm used in the first smoothing and the fourth number refers to the number of nm over which the second smoothing is applied (1 = no smoothing) (Shenk and Westerhaus, 1993). The regression method used to build the calibration equations was modified partial least squares (MPLS) regression, where one variable was estimated at a time, with full cross validation. Full cross validation was used to estimate the optimal number of terms in the calibration to avoid overfitting and the optimal number of terms to be included in the calibration models was determined as having the lowest standard error of cross validation (SECV) (Shenk and Westerhaus, 1993). Due to the limited number of samples available, calibration models were developed and evaluated using full cross validation (Dardenne et al., 2000). It is well known that in spectroscopy, scatter correction can be used to correct the whole spectrum for particle size variation. In the present study two scatter correction methods were used, namely standard normal variate and detrend (SNVD) (Barnes et al., 1989) and multiplicative scatter correction (MSC) (Martens and Naes, 1992; Naes et al., 2002). The resulting calibration equations between the chemical reference values and the NIR data were evaluated based on the coefficient of determination in calibration (R2cal ) and SECV. The ratio of standard deviation (S.D.) and SECV, namely residual predictive value (RPD) was used to test the accuracy of the calibration models (Williams, 2001). The RPD for the NIR calibration for the parameters evaluated demonstrated how well the calibration models performed in predicting the reference data. If a product shows a narrow range in composition, or if the error in estimation is large compared with the spread (as S.D.) in composition, then regression finds increasing difficulty in finding stable NIR calibrations. An RPD value greater than three was considered adequate for analytical purposes in most of the NIR applications for agricultural products (Williams, 2001).
3. Results and discussion Table 1 shows the descriptive statistics for dry matter (DM), ADFom, aNDFom, CP, OMD, pH and ash on WWM samples analysed. The WWM samples analysed showed a Table 1 Descriptive statistics for chemical parameters of wet whole maize silage (WWM) samples used to develop the NIR calibration models DM (g kg−1 ) CP (g kg−1 DM) ADFom (g kg−1 DM) aNDFom (g kg−1 DM) OMD (g kg−1 DM) pH
Mean
Min.
Max.
S.D.
CV%
345.4 76.6 295.9 522.6 640 3.6
227.8 37.6 197.5 393.7 510 3.0
528.0 243.2 434.5 715.4 740 4.4
65.0 31.6 45.9 83.7 40 0.25
18.8 41.3 15.5 16.0 6.2 6.9
DM: dry matter; CP; crude protein; OMD: in vitro organic matter digestibility; ADFom: acid detergent fibre; aNDFom: neutral detergent fibre; Max.: maximum; Min.: minimum; S.D.: standard deviation, CV coefficient of variation calculated as (S.D.×100)/mean.
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Table 2 Summary of the NIR cross validation statistics to measure chemical parameters in wet whole maize silage (WWM) (g kg−1 )
DM CP (g kg−1 DM) OMD (g kg−1 DM) ADFom (g kg−1 DM) aNDFom (g kg−1 DM) pH
Scatter correction
Derivative
R2cal
SECV
T
RPD
SNVD SNVD SNVD Raw MSC SNVD
2nd 2nd 2nd – – 2nd
0.85 0.91 0.53 0.86 0.60 0.51
27.4 6.5 30 22.1 67.1 0.18
6 7 10 10 10 10
2.4 4.8 1.3 2.1 1.2 1.4
DM: dry matter; CP; crude protein; OMD: in vitro organic matter digestibility; ADFom: acid detergent fibre; aNDFom: neutral detergent fibre; R2cal : coefficient of determination in cross validation; SECV: standard error of cross validation, T: number of PLS terms used to develop the calibration models, RPD: SD/SECV; SNVD: standard normal variate; MSC: multiplicative scatter correction.
wide range in composition. Overall, the ensiling characteristics were considered acceptable in most of the samples analysed based on the DM content and pH values obtained (Wilkinson et al., 1998). The variability in chemical composition was considered suitable to develop NIR calibrations. The Vis region of the spectra (data not presented) showed an absorption band around 666 nm, related with the absorption of chlorophylls (Cozzolino et al., 2000). In the NIR region (data not presented), absorption bands around 980 nm, 1200 nm, 1450 nm and 1970 nm were observed related with O–H overtones (water) (Murray, 1986; Cozzolino et al., 2000). Absorption bands around 1726 nm and 1762 nm were related with CH stretch first overtone (oil content and PVC bag), absorption bands at 2312 nm and at 2352 nm were related with C–H overtones and combination bands (Murray, 1986). Table 2 summarises the cross validation statistics for the measurement of chemical composition in WWM samples by NIR. The R2cal and SECV were for DM 0.85 (SECV: 27.5 g kg−1 ) and for CP 0.91 (SECV: 6.5 g kg−1 DM) using SNVD; and for ADFom 0.86 (SECV: 22.1 g kg−1 DM) using raw spectra. The use of MSC did not improve the NIR calibrations and appeared to have inconsistent effect on the SECV. These results agreed with those reported by other authors (Gordon et al., 1998; Griggs et al., 1999). Both DM and CP gave the best accuracy in prediction, in the different combinations of mathematical and scatter corrections applied (see Table 2). The RPD values obtained for the chemical parameters analysed were 4.8, 2.4, 2.1, 1.3, 1.2, and 1.4 for CP, DM, ADFom, OMD, aNDFom and pH respectively. In the present study CP gave the best RPD, while intermediate RPD values were obtained for DM and ADFom. Natural plant tissues contain physiological concentrations of water around 70% of their fresh mass, filling pore space with water rather than air, which leads to less refraction at boundaries and deeper light penetration (Murray and Cowe, 2004). Using wet silage is causing serious sampling and sample presentation problems at wavelengths longer than 1400 nm (Murray and Cowe, 2004). Either wet samples must be presented as thin films at those wavelengths or alternatively longer wavelengths must be abandoned in favour of the Herschel infrared region (780–1100 nm) (Murray and Cowe, 2004). Beyond 1400 nm, water in wet tissue will be the largest absorber compared with other major constituents (Murray and Cowe, 2004). In this study, additionally NIR calibrations were developed using shorter wavelength in the NIR region (Table 3). Improved calibration statistics in most of the
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Table 3 Summary of the NIR cross validation statistics to measure chemical parameters in wet whole maize silage (WWM) (500–1100 nm) using second derivative and SNVD (g kg−1 )
DM CP (g kg−1 DM) OMD (g kg−1 DM) ADFom (g kg−1 DM) aNDFom (g kg−1 DM) pH
R2cal
SECV
T
RPD
0.90 0.90 0.40 0.81 0.84 0.60
25.5 7.7 40 27.1 54.1 0.14
4 4 10 6 6 10
2.5 4.1 1.0 2.0 1.6 1.8
DM: dry matter; CP; crude protein; OMD: in vitro organic matter digestibility; ADFom: acid detergent fibre; aNDFom: neutral detergent fibre; R2cal : coefficient of determination in cross validation; SECV: standard error of cross validation, T: number of PLS terms used to develop the calibration models, RPD: SD/SECV; SNVD: standard normal variate; MSC: multiplicative scatter correction.
parameters analysed were obtained using the so-called Herschel infrared region. The R2cal and SECV were 0.90 (SECV: 25.5 g kg−1 ) for DM, 0.90 (SECV: 7.7 g kg−1 DM) for CP, 0.81 (SECV; 27.2 g kg−1 DM) for ADFom, 0.84 (SECV: 54.0 g kg−1 DM) for aNDFom and 0.60 (SECV: 0.14) for pH. Poorer correlations were found for OMD. However, the predictive accuracy is far from the desirable if wet materials will be analysed by NIR spectroscopy for analytical purposes. Nevertheless, analysing fresh samples by NIR spectroscopy presents logistic advantages by minimising delays between sampling and laboratory scanning during routine analysis and providing greater flexibility in sample preparation and presentation to the spectrophotometer. However, problems will arise due to sample surface wetness or interferences of the plastic bags used to hold the sample during spectra collection. Water absorbs energy in the NIR region; consequently the moisture content of the sample has a significant effect on the accuracy of the NIR calibration models. Other factors such as particle size and particle size distribution might also influence the accuracy of the NIR calibrations when coarse materials are analysed. This is of particular importance in coarse samples such as whole maize plant. In this study, silages were highly heterogeneous in chop length, with particle sizes ranging from 2 cm to 20 cm, as a result of the different types of harvest machinery employed by the farmers. Similar findings were reported by other authors (Alomar et al., 1999; Gordon et al., 1998; Lovett et al., 2005).
4. Conclusions The results from this study suggested that WWM might be analysed by NIR spectroscopy to determine CP, DM and ADFom. The use of wet materials will enable rapid turnaround in both farm advisory systems and in breeding programmes involving large number of samples. Wet sample presentation offers considerable potential in order to increase the speed of analysis, by avoiding sample preparation before analysis (i.e. grinding or drying), however, the prediction accuracy is less than desirable for analytical purposes. Further work will be carried out in order to assess the effect of particle size on the calibration of wet silage samples.
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