peer-reviewedPerennial ryegrass (n = 1,836), Italian ryegrass (n = 137) and hybrid ryegrass (n =
103) herbage was taken from harvested plots from the Irish national variety evaluation
scheme and analysed for in vitro dry matter digestibility, water soluble carbohydrate
concentration, crude protein concentration and buffering capacity. Spectral data were
obtained using near infrared reflectance spectroscopy and three calibration strategies
(global, species-specific or local) were utilised to relate the reference values to the
spectral data. The local strategy generally provided the poorest estimation of herbage
composition, with global and species-specific calibration strategies producing similarly
accurate estimates of each quality trait. The higher accuracy and easier maintenance of
the global strategy make it the recommended calibration method for analysing quality
of ryegrass.Department of Agriculture, Food and the
Marine Research Stimulus Fund (07 526

Burns, G. A.

Gilliland, T. J.

Grogan, D.

O'Kiely, Padraig

Irish Journal of Agricultural and Food Research

English

T-Stór

199Irish Journal of Agricultural and Food Research 53: 199–204, 2014†Corresponding author: Gareth Burns, Agri-Food and Biosciences Institute, Plant Testing & Agronomy Unit, Crossnacreevy, Co. Antrim, Northern Ireland; Tel.: +00 44 28 905 48028; E-mail: gareth.burns@afbini.gov.ukA note on the comparison of three  near infrared reflectance spectroscopy  calibration strategies for assessing herbage quality of ryegrassG.A. Burns1,2,4†, P. O’Kiely2, D. Grogan3 and T.J. Gilliland2,41Animal & Grassland Research and Innovation Centre, Teagasc, Grange, Dunsany, Co. Meath, Ireland2School of Biological Sciences, Queen’s University Belfast, Co. Antrim, Northern Ireland3Crop Evaluation and Certification, Department of Agriculture, Food and the Marine,  Leixlip, Co. Kildare, Ireland4Sustainable Agri-Food and Sciences Division, Agri-Food and Biosciences Institute, Crossnacreevy,  Co. Down, Northern IrelandPerennial ryegrass (n = 1,836), Italian ryegrass (n = 137) and hybrid ryegrass (n = 103) herbage was taken from harvested plots from the Irish national variety evaluation scheme and analysed for in vitro dry matter digestibility, water soluble carbohydrate concentration, crude protein concentration and buffering capacity. Spectral data were obtained using near infrared reflectance spectroscopy and three calibration strategies (global, species-specific or local) were utilised to relate the reference values to the spectral data. The local strategy generally provided the poorest estimation of herbage composition, with global and species-specific calibration strategies producing similarly accurate estimates of each quality trait. The higher accuracy and easier maintenance of the global strategy make it the recommended calibration method for analysing quality of ryegrass.Keywords: calibration strategy; near infrared reflectance spectroscopy; quality; ryegrass200     IRISH JOURNAL OF AGRICULTURAL AND FOOD RESEARCH, VOL. 53, NO. 2, 2014IntroductionNear infrared reflectance spectroscopy (NIRS) is a secondary technique for ana-lysing herbage quality traits that uses predictive calibration models to relate spectral data to values obtained by stan-dard laboratory procedures. Applying a combination of chemometric techniques and calibration methodologies form the basis of a calibration strategy, with no one approach being universally optimal (Givens, DeBoever and Deaville 1997; Stuth, Jama and Tolleson 2003). A global calibration strategy involves all samples within the calibration set being utilised, with multiple linear regression models relating spectral data to reference values (Burns et al. 2013). The local calibration strategy of Shenk, Westerhaus and Berzaghi (1997) is potentially more accurate in calibration sets that contain several forage species, as these could introduce non-linear rela-tionships. The current study contains three species; perennial ryegrass (Lolium perenne L.), Italian ryegrass (Lolium multiflorum Lam.) and hybrid ryegrass (Lolium boucheanum Kunth), that may potentially introduce a bias between spe-cies within the calibration models. A third strategy, species-specific, develops individual calibration models for each species from within the total calibra-tion set in order to remove the bias between species. These smaller datasets can potentially lower the range within quality traits for the individual species-specific calibration models.The objective was to ascertain the opti-mal NIRS calibration approach from three calibration strategies (global, species-spe-cific and local) for assessing the in vitro dry matter digestibility (DMD), water soluble carbohydrate (WSC) concentration, crude protein (CP) concentration and buffering capacity of ryegrasses.Material and MethodsVariety trials were carried out at the DAFM Variety Evaluation Centre, Backweston, Co. Kildare. These were sown in separate trials for each of the species perennial ryegrass, Italian ryegrass and hybrid ryegrass. Varietal monocul-tures were harvested under a six cut com-bined simulated grazing and conservation management schedule as described by Burns et al. (2013). Ryegrass samples (n = 2,076) were collected from harvested plots and selected to ensure the calibration models encompassed the range in climatic conditions, species, developmental stage, ploidy, genotype and age of sward occur-ring in the recommended list trials (Burns 2012). Reference laboratory methods were carried out on each sample as described by Burns et al. (2013).Absorbance (log 1/reflectance) of each sample was measured from 400 to 2,500 nm at 2 nm intervals using a NIRsystems 6,500 or a standardised NIRsystems XDS (Foss UK Ltd., Warrington, UK). Prior to development of calibrations each spectrum was trimmed to 1,108–2,498 nm at 2 nm intervals. Burns et al. (2013) established a standard normal variate and detrend (Barnes, Dhanoa and Lister 1989) and a 1,4,4 derivation as spectral pre-treatments that produced the most accurate calibra-tion models and these were applied to each spectrum using WinISI v. 4.00 (Infrasoft International, Port Matilda, PA, USA). Three calibration strategies were applied to relate spectral data to the ref-erence values: (a) Global strategy – The global strategy of Burns et al. (2013) was used, in which the full calibration set (n = 2,076) was uti-lised to form calibration models for each of the four quality traits. (b) Species-specific strategy – For the species-specific strategy the full calibration  BURNS ET AL.: NIRS CALIBRATIONS FOR ASSESSING RYEGRASS QUALITY 201set (n = 2,076) was divided into a subset for each individual species: perennial rye-grass (n = 1,836), Italian ryegrass (n = 137) and hybrid ryegrass (n = 103). The same approach for the global calibration was applied to each of these individual subsets. A weighted mean, as weighted by the number of samples from each species-specific strategy, was calculated for the coefficient of determination (R2) and the standard error of calibration (s.e.c.) for each of the four quality traits for both the calibration and cross-validation models.(c) Local calibration strategy – The local algorithm of Shenk et al. (1997) was used in which principle component analysis was initially applied to the cali-bration set. Following this, an individual calibration was formed for each sample and quality trait whereby either 10, 25, 50, 100, 150, 200 or 250 samples (k) were selected based on the closeness in hyper dimensional space. Subsequently a multi-ple linear regression model was developed using the k samples to predict a value for the sample in question. This process was repeated for each individual sample so effectively an individual calibration set was generated for each sample.For each of the calibration models a cross-validation was carried out in which a random four-fifths of the full calibration set was used to form the calibration mod-els and the remaining samples were used as a validation set. The cross-validation procedure was carried out five times and the mean of the cross-validation proce-dures calculated.Results The calibration sample set displayed a wide range in each of the herbage quality traits when assessed by standard labora-tory techniques. Samples ranged from in vitro DMD 576–890 g/kg; 50–430 g WSC/kg DM; 57–267 g CP/kg DM and buffering capacity 188–736 mEq/kg DM (Table 1).Overall the calibration models pro-duced were accurate, with cross-valida-tion coefficient of determination (R2cv) above 0.94 in all cases, with the exception of in vitro DMD for a global strategy (R2cv = 0.86), local strategy (R2cv = 0.80, Table 1. Summary of the values obtained by standard laboratory techniques for four quality  traits of three ryegrass speciesn Mean Minimum Maximum s.d.In vitro dry matter digestibility (g/kg)Perennial ryegrass 1,836 801 629 890 36.9Italian ryegrass 137 748 576 870 73.7Hybrid ryegrass 103 773 587 886 70.7Water-soluble carbohydrate (g/kg DM)Perennial ryegrass 1,836 180 50 376 51.9Italian ryegrass 137 207 92 430 74.4Hybrid ryegrass 103 212 71 413 70.4Crude protein (g/kg DM)Perennial ryegrass 1,836 150 57 266 36.8Italian ryegrass 137 128 73 267 40.5Hybrid ryegrass 103 133 71 241 43.4Buffering capacity (mEq/kg DM)Perennial ryegrass 1,836 437 215 736 90.8Italian ryegrass 137 360 188 559 87.5Hybrid ryegrass 103 368 207 616 83.6202     IRISH JOURNAL OF AGRICULTURAL AND FOOD RESEARCH, VOL. 53, NO. 2, 2014k = 200) and species-specific strategy for perennial ryegrass (R2cv = 0.78), and the weighted mean for the species- specific strategy (R2cv = 0.80) (Table 2). The global and species-specific calibration strategies produced similarly accurate estimates of each quality trait. Comparing individual species sets within the species-specific strategy, the accuracy of estima-tions was relatively similar except for the in vitro DMD estimation in perennial ryegrass, which was the weakest estima-tion. Applying the local strategy generally provided the poorest estimation of the three strategies, with in vitro DMD having the weakest estimates (R2cv = 0.80). The R2cv of the local strategy for WSC, CP and buffering capacity accuracy approached the levels of the other two strategies, but never exceeded them. DiscussionA global strategy uses all samples from the calibration set and should encompass as much of the variability as possible that the model will likely face in practice (Shenk and Westerhaus 1991). The current study had a large calibration set (n = 2,076) that contained three species and additional sources of variation (e.g., age of sward, seasonal developmental stage, genotype) that encompassed much of the variation the model will likely face in practise.Berzaghi, Shenk and Westerhaus (2000) and Andueza et al. (2011) reported the increased accuracy of a local calibration strategy in comparison to a global strat-egy for predicting herbage quality traits in calibration sets that encompassed several forage species. The greater accuracy of the global strategy in comparison to the local strategy in the current study may be attributed to the lack of bias and non-linear relationships that existed between monocultures of three species within the same genus (Lolium), whereas Berzaghi et al. (2000) and Andueza et al. (2011) had a larger range of diverse forages (including ryegrass, red clover, lucerne, corn silage, haylage, small grain silage and total mixed ration) which could have potentially limit-ed the performance of their global calibra-tion models. The use of a global strategy may therefore be more appropriate when most variation is encompassed with little bias between factors in the model. The accuracy of the species-specific strategy for Italian and hybrid ryegrasses was higher than the species-specific strat-egy for perennial ryegrass or the global strategy, and both Berzaghi et al. (2000) and Andueza et al. (2011) also found that a species-specific strategy produced the most accurate calibration models for some individual forage species. It is evident that the inclusion of the Italian and hybrid rye-grass (i.e., global strategy) to the species specific approach for perennial ryegrass expands the range and distribution of in vitro DMD values (Table 1). This could potentially account for the increased accu-racy of the global strategy in comparison to the perennial ryegrass species-specific calibration model. A comparable outcome occurred for the standard deviation of reference values with WSC, whereby there was a lower standard deviation for peren-nial ryegrass samples than for both other species; however the resultant global and species-specific strategies were of similar accuracy. The range and distribution of reference values for CP and buffering capacity between species was quite similar (Table 1) and this may have led to the similar accuracy of the species-specific and global calibration strategies.When considered across all species, a global or species-specific calibration strategy produced the most accurate cali-bration models for WSC and CP, while for in vitro DMD the global strategy was the  BURNS ET AL.: NIRS CALIBRATIONS FOR ASSESSING RYEGRASS QUALITY 203Table 2. Comparison of three calibration strategies (Global; Local; Species-specific) for the development of NIRS predictive models for the  analysis of four quality traitsIn vitro DMD (g/kg)WSC (g/kg DM)Crude protein (g/kg DM)Buffering capacity (mEq/kg DM)CalibrationCross-validationCalibrationCross-validationCalibrationCross-validationCalibrationCross-validationCalibration strategyNR2s.e.c.R2 cvs.e.c.v.R2s.e.c.R2 cvs.e.c.v.R2s.e.c.R2 cvs.e.c.v.R2s.e.c.R2 cvs.e.c.v.(a) Global2,0760.86415.90.85816.20.96110.40.95910.80.9825.00.981 5.10.95220.30.95020.7(b) Species-specificPerennial 1,8360.78816.00.78216.30.96110.20.95910.50.9805.10.979 5.30.95319.70.95020.3Italian 1370.97211.80.96313.60.983 9.00.97411.20.9894.20.984 5.00.96217.10.95219.1Hybrid 1030.981 9.50.96912.20.982 9.20.96911.90.9923.90.988 4.60.94519.90.91224.4Weighted mean0.81015.40.80315.90.96310.10.96010.60.9815.00.979 5.20.95319.50.94820.4(c) Localk = 100.71024.80.70025.90.89318.10.85821.00.9468.80.92410.50.86734.10.84534.1250.73923.40.71525.20.91716.00.90417.40.9607.60.945 8.90.91726.90.89826.9500.75922.50.76322.90.92914.80.92315.50.9667.00.955 8.00.93523.80.93123.81000.78521.30.79621.30.93414.30.93114.60.9696.70.959 7.60.94921.00.94321.11500.79121.00.79921.10.93414.30.93414.30.9706.60.958 7.60.95220.40.94020.42000.79221.00.80320.80.93314.40.93314.50.9686.80.958 7.60.95320.40.94320.22500.78921.10.80521.10.93114.60.93114.70.9667.00.958 7.70.95220.40.94420.4s.e.c.: Standard error of calibration; s.e.c.v.; Standard error of cross-validation.204     IRISH JOURNAL OF AGRICULTURAL AND FOOD RESEARCH, VOL. 53, NO. 2, 2014most accurate strategy and for buffering capacity all three strategies were similarity accurate. It is thus recommended that due to the higher accuracy and easier mainte-nance of the global strategy it would be the preferred approach when evaluating herbage quality of ryegrass samples. Acknowledgements The Department of Agriculture, Food and the Marine research stimulus fund (07 526) is acknowl-edged for providing the resources to carry out this research; the laboratory staff at Teagasc, Grange for carrying out the laboratory analyses and the field staff at the Variety Evaluation Unit, Backweston for the harvesting and management of ryegrass plots. ReferencesAndueza, D., Picard, F., Jestin, M., Andrieu J. and Baumont R. 2011. NIRS prediction of the feed value of temperate forages: Efficacy of four cali-bration strategies. Animal 5: 11–17.Barnes, R.J., Dhanoa, M.S. and Lister, S.J. 1989. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spec-tra. Applied Spectroscopy 43: 772–777.Berzaghi, P., Shenk, J.S. and Westerhaus, M.O. 2000. Local prediction with near infrared multi-product databases. Journal of Near Infrared Spectroscopy 8: 1–9. Burns, G.A. 2012. Variation in the nutritive quality of Lolium as assessed by near infrared reflectance spectroscopy. PhD Thesis. Queen’s Univeristy Belfast.Burns, G.A., Gilliland, T.J., Grogan, D., Watson, S. and O’Kiely, P. 2013. Assessment of herbage yield and quality traits of perennial ryegrasses from a national variety evaluation scheme. Journal of Agricultural Science 151: 331–346.Givens, D.I., DeBoever, J.L. and Deaville, E.R. 1997. The principles, practices and some future applica-tions of near infrared spectroscopy for predict-ing the nutritive value of foods for animals and humans. Nutrition Research Reviews 10: 83–114.Shenk, J.S. and Westerhaus, M.O. 1991. Population definition, sample selection, and calibration pro-cedures for near-infrared reflectance spectros-copy. Crop Science 31: 469–474.Shenk, J.S., Westerhaus, M.O. and Berzaghi, P. 1997. Investigation of a local calibration procedure for near infrared instruments. Journal of Near Infrared Spectroscopy 5: 223–232. Stuth, J., Jama, A. and Tolleson, D. 2003. Direct and indirect means of predicting forage quality through near infrared reflectance spectroscopy. Field Crops Research 84: 45–56.Received 8 January 2013

A note on the comparison of three near infrared reflectance spectroscopy calibration strategies for assessing herbage quality of ryegrass

Perennial ryegrass (n = 1,836), Italian ryegrass (n = 137) and hybrid ryegrass (n =
103) herbage was taken from harvested plots from the Irish national variety evaluation
scheme and analysed for in vitro dry matter digestibility, water soluble carbohydrate
concentration, crude protein concentration and buffering capacity. Spectral data were
obtained using near infrared reflectance spectroscopy and three calibration strategies
(global, species-specific or local) were utilised to relate the reference values to the
spectral data. The local strategy generally provided the poorest estimation of herbage
composition, with global and species-specific calibration strategies producing similarly
accurate estimates of each quality trait. The higher accuracy and easier maintenance of
the global strategy make it the recommended calibration method for analysing quality
of ryegrass.Department of Agriculture, Food and the
Marine Research Stimulus Fund (07 526

A note on the comparison of three near infrared reflectance spectroscopy calibration strategies for assessing herbage quality of ryegrass

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