Jordi Ortuño1, Sokratis Stergiadis2, Anastasios Koidis1, Jo Smith3,4, Chris Humphrey5, Lindsay Whistance6, Katerina Theodoridou7. 1. Institute for Global Food Security, Queen's University Belfast, Belfast, BT9 5DL, Northern Ireland, UK. 2. Department of Animal Sciences, School of Agriculture, Policy and Development, University of Reading, New Agriculture Building, Earley Gate, P.O. Box 237, Reading, RG6 6EU, UK. s.stergiadis@reading.ac.uk. 3. Organic Research Centre, Trent Lodge, Stroud Road, Cirencester, Gloucestershire, GL7 6JN, UK. 4. MV Agroecological Research Centre, Moinhos de Vento, 7750-217, Espirito Santo, Portugal. 5. Department of Animal Sciences, School of Agriculture, Policy and Development, University of Reading, New Agriculture Building, Earley Gate, P.O. Box 237, Reading, RG6 6EU, UK. 6. Organic Research Centre, Trent Lodge, Stroud Road, Cirencester, Gloucestershire, GL7 6JN, UK. lindsay.w@organicresearchcentre.com. 7. Institute for Global Food Security, Queen's University Belfast, Belfast, BT9 5DL, Northern Ireland, UK. k.theodoridou@qub.ac.uk.
Abstract
BACKGROUND: The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT's full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000-550 cm-1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization-mDP, procyanidins:prodelphidins ratio-PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. RESULTS: The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). CONCLUSIONS: MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.
BACKGROUND: The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT's full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000-550 cm-1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization-mDP, procyanidins:prodelphidins ratio-PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. RESULTS: The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). CONCLUSIONS: MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.