Carbohydrates in alfalfa-timothy mixtures predicted with near infrared reflectance spectroscopy equations developed for single species

Nie, Z.; Tremblay, Gaëtan F.; Bélanger, Gilles; Berthiaume, R.; Castonguay, Yves; Bertrand, Annick; Michaud, R.; Allard, G.; Han, Jing

doi:10.4141/cjas08128

Cited by 5 publications

(4 citation statements)

References 3 publications

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“…Near infrared regions had considerable influence on the spectra due to the strong relationship between minerals and other constituents, mainly with O-H overtones (water) and with C-H combination tones (organic functional groups) (Cozzolino and Moron 2004;Garnsworthy et al, 2001;Huang et al 2009;Ko et al, 2004). However, compared with determining organics, the correlation coefficient, RSQ values, of measuring minerals are lower (in general, compare larger than 0.9 to less than 0.8) because low concentrations and a narrow range are generally observed for mineral concentrations, which could render RSQ values misleading (Clark et al, 1989;Nie et al, 2009). Some authors suggest evaluating the NIRS prediction of minerals by using the ratio of SD/SECV rather than RSQ (Wu and Shi, 2007).…”

Section: Calibration and Validationmentioning

confidence: 95%

Rapid Diagnosis of Nutrient Elements in Fingered Citron Leaf Using Near Infrared Reflectance Spectroscopy

Liao

Chen

et al. 2012

Journal of Plant Nutrition

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2 In order to provide references for leaf nutrition diagnosis of fingered citron, the technique of near infrared reflectance spectroscopy (NIRS) was introduced to analyze nitrogen (N), phosphorus (P), potassium (K), iron (Fe), manganese (Mn), zinc (Zn), and copper (Cu) in the dry-leaf samples of fingered citron. The best calibration model for N was developed with high RSQ CAL (0.90), SD/SECV (2.73) and low SEC (1.06 mg g −1 ), good calibration models were obtained for P, K, Fe and Mn, and no significant correlations were found between the spectra and the individual amounts of Zn and Cu. When tested using a validation set (n = 38), N was well predicted with low values of SEP (1.21 mg g −1 ) and high RPD (2.5). The values of SEP and RPD were also acceptable for the external validation of P, Fe and Mn. Near-infrared spectroscopy analysis technique shows potential of diagnosing minerals in fingered citron, particularly for N, P, Fe and Mn.

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Section: Calibration and Validationmentioning

confidence: 95%

Rapid Diagnosis of Nutrient Elements in Fingered Citron Leaf Using Near Infrared Reflectance Spectroscopy

Liao

Chen

et al. 2012

Journal of Plant Nutrition

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“…Partial least squares regression (PLSR) can include all of the NIR spectral information by utilizing singular value decomposition of either the correlation matrix of the NIR spectral data or the covariance matrix between NIR and the soluble carbohydrate data to summarize the variation into latent variables. Partial least squares regression methods have been applied with success to many NIR calibrations of soluble carbohydrates in wheat and other grasses (Brink and Marten, 1986; Centner et al, 2000; Shetty et al, 2012), in which both high levels of accuracy ( r 2 = 0.92–0.98, residual prediction deviance [RPD] = 3.3) (Jafari et al, 2003; Chen and Wang, 2004; Decruyenaere et al, 2012) and moderate levels of accuracy ( r 2 = 0.71–0.78, RPD = 1.56–1.97) (Nie et al, 2009) have been reported. Partial least squares regression methods can summarize variation effectively, but they are computationally intensive.…”

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confidence: 99%

Near‐Infrared Calibration of Soluble Stem Carbohydrates for Predicting Drought Tolerance in Spring Wheat

2016

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Soluble stem carbohydrates are a component of drought response in wheat (Triticum aestivum L.) and other grasses. Near‐infrared spectroscopy (NIR) can rapidly assay for soluble carbohydrates indirectly, but this requires a statistical model for calibration. The objectives of this study were: (i) to build a robust calibration between the NIR spectra and soluble carbohydrate concentration of ground wheat stems; and (ii) to determine whether soluble stem carbohydrates are correlated with yield rankings of drought‐stricken wheat grown in the northwestern United States. Five spring wheat cultivars were grown in field trials conducted at six environments in the state of Washington varying in annual precipitation from 212 to 474 mm. Wheat stems were harvested from all plots at the onset of grain fill and assayed for NIR reflectance. Soluble stem carbohydrates were determined on a subset of the samples. The NIR data were calibrated to soluble stem carbohydrates using multiple linear regression, partial least squares regression, ridge regression with best linear unbiased prediction, random forest, least absolute shrinkage and selection operator (lasso), elastic net, and Bayesian lasso regression. Partial least squares regression provided the most accurate and reliable predictions for soluble carbohydrates. Correlations between soluble stem carbohydrates and grain yield were consistent across environments (r = 0.904 and ρ = 0.80). The effect of environment on the variation in response variables was lower for soluble carbohydrates than yield (5.93 and 71.7%, respectively) across environments. These data provide evidence that stem carbohydrates can aid in selecting cultivars with enhanced drought resilience.

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“…GT = grass tetany = K/(Ca + Mg) (Kemp and 't Hart, 1957). Murray and Cowe, 2004;Nie et al, 2009b), some authors suggest evaluating the NIRS prediction of minerals by using the coefficient of variation rather than R 2 . As proposed by Clark et al (1989), the coefficient of variation of prediction {CV P = [SEP(C)/mean] × 100} between chemically analyzed and NIRS-predicted values is considered a useful tool in evaluating NIRS performance across minerals.…”

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confidence: 99%

Predicting timothy mineral concentrations, dietary cation-anion difference, and grass tetany index by near-infrared reflectance spectroscopy

Tremblay

Nie

Bélanger

et al. 2009

Journal of Dairy Science

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The mineral concentration of forage grasses plays a significant role in 2 metabolic disorders in dairy cattle production, namely, hypocalcemia (milk fever) and hypomagnesemia (grass tetany). Risks of occurrence of these 2 metabolic disorders can be evaluated by determining the dietary cation-anion difference (DCAD) and the grass tetany (GT) index of forages and specific rations. The objective of this study was to evaluate the feasibility of predicting timothy (Phleum pratense L.) mineral concentrations of Na, K, Ca, Mg, Cl, S, and P, the DCAD, and the GT index by near-infrared reflectance spectroscopy (NIRS). Timothy samples (n = 1,108) were scanned using NIRS and analyzed for the concentration of 7 mineral elements. Calculations of the DCAD were made using 3 different formulas, and the GT index was also calculated. Samples were divided into calibration (n = 240) and validation (n = 868) sets. The calibration, cross-validation, and prediction for mineral concentrations, the DCAD, and the GT index were performed using modified partial least squares regression. Concentrations of K, Ca, Mg, Cl, and P were successfully predicted with coefficients of determination of prediction (R(P)2) of 0.69 to 0.92 and coefficients of variation of prediction (CV(P)) ranging from 6.6 to 11.4%. The prediction of Na and S concentrations failed, with respective R(P)2 of 0.58 and 0.53 and CV(P) of 82.2 and 12.9%. The 3 calculated DCAD and the GT index were predicted successfully, with R(P)2 >0.90 and CV(P) <20%. Our results confirm the feasibility of using NIRS to predict K, Ca, Mg, and Cl concentrations, as well as the DCAD and the GT index, in timothy.

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Carbohydrates in alfalfa-timothy mixtures predicted with near infrared reflectance spectroscopy equations developed for single species

Cited by 5 publications

References 3 publications

Rapid Diagnosis of Nutrient Elements in Fingered Citron Leaf Using Near Infrared Reflectance Spectroscopy

Rapid Diagnosis of Nutrient Elements in Fingered Citron Leaf Using Near Infrared Reflectance Spectroscopy

Near‐Infrared Calibration of Soluble Stem Carbohydrates for Predicting Drought Tolerance in Spring Wheat

Predicting timothy mineral concentrations, dietary cation-anion difference, and grass tetany index by near-infrared reflectance spectroscopy

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