NIRS determination of non-structural carbohydrates, water soluble carbohydrates and other nutritive quality traits in whole plant maize with wide range variability

Abstract

The aim of this work was to study the potential of near-infrared reflectance spectroscopy (NIRS) to predict non-structural carbohydrates (NSC), water soluble carbohydrates (WSC), in vitro organic dry matter digestibility (IVOMD), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and starch in samples of whole plant maize with a wide range of variability. The samples were analyzed in reflectance mode by a spectrophotometer FOSS NIRSystems 6500. Four hundred and fifty samples of wide spectrum from different origin were selected out of 3000 scanned for the calibration set, whereas 87 independent random samples were used in the external validation. The goodness of the calibration models was evaluated using the following statistics: coefficient of determination (R2), standard error of cross-validation (SECV), standard error of prediction for external validation (SEP) and the RPDCV and RPDP indexes [ratios of standard deviation (SD) of reference analysis data to SECV and SEP, respectively]. The smaller the SECV and SEP and the greater the RPDCV and RPDP, the predictions are better. Trait measurement units were g/100g of dry matter (DM), except for IVOMD (g/100g OM). The SECV and RPDCV statistics of the calibration set were 1.34 and 3.2 for WSC, 2.57 and 3 for NSC and 2.3 and 2.2 for IVOMD, respectively. The SEP and RPDP statistics for external validation were 0.74 and 4.7 for WSC, 2.14 and 2.5 for NSC and 1.68 and 1.6 for IVOMD respectively. It can be concluded that the NIRS technique can be used to predict WSC and NSC with good accuracy, whereas prediction of IVOMD showed a lesser accuracy. NIRS predictions of OM, CP, NDF, ADF and starch also showed good accuracy.

Carbohydrate Analysis by NIRS-Chemometrics

“…However, and particularly to specific sugar content, NIRS in combination with PLSR models has been used in sorghum stalks [40] and sweet sorghum (cellulose, lignin, and hemicellulose) [41], fruit juices [42,43], rice (amylose) [44], whey (lactose) [45], grasses (fructans) [46,47], maize (nonstructural and water soluble carbohydrates) [48], intact apple fruit to determinate fructose, glucose, and sucrose [49], orange [50], apricot [51], sugar beet [52], cherries [53], and other fruits ( Table 2). All these studies accorded that the performance of NIR spectroscopy is comparable to the reference chromatographic method, but the former is much faster and easier to carry out.…”

“…Grain sorghum stalks Sucrose, glucose PLSR [41] Fruit juices Glucose, fructose, sucrose PLSR, PCA [43,44] Rice Amylose mPLSs [45] Whey Lactose PLS [46] Grasses Fructan PLSR [47,48] Apple fruit Glucose, fructose, sucrose PLS [50] Sugar beet Sucrose SEPs [53,55] Cherries pared two commercial portable spectrometers (Vis/NIR spectrometer versus OTF-NIR) for four orange varieties quality: soluble solids content, acidity, titratable acidity, maturity index, flesh firmness, juice volume, fruit weight, rind weight, juice volume to fruit weight ratio, fruit color index, and juice color index, and they found relevant the prediction of maturity index. The Lab spec spectrometer showed better predictive performance than the laminar instrument.…”

Options for improving the yield and nutritive value of maize and grain legume residues for ruminants in East African farming systems

“…The poor prediction of the total N concentration in soybean in the present study may have been associated with the small population size (n=25) and that this material tends to be spectrally different to the other forages and crop residues. Except for total N prediction of soybean, the RPDv values obtained in the present study were higher than the values obtained in whole plant maize (Campo et al, 2013) and barley straw (Mathison et al, 1999) and comparable with the RPDv values in temperate forages (Norman et al, 2015). In general, the RPDv values obtained in the validation data set of the present study for CR quality attributes measured indicates that the better NIRS predictions of the parameters with good accuracy and further confirmed the improved predictive ability and robustness of the of chemometric models developed for DMD, Total N, NDF and ADF based on the NA dataset.…”

“…In the present study the RPDc for all the calibrations were > 3.4 supporting the validity of the NIRS calibration models for routine analysis of the forage attributes measured. These RPDc values for the measured attributes were higher than the values reported previously (Alomar et al, 2009;Asekova et al, 2016;Campo et al, 2013;Cozzolino et al, 2006;Norman et al, 2015;Park et al, 1998) but lower than the RPDc for DMD and total N reported by Norman et al (2015) in temperate forages.…”

Rapid Estimation of Wheat Straw Decomposition Constituents Using Near-Infrared Spectroscopy

“…Excellent NIRS prediction models were developed by Jin and Chen [45] for cellulose of rice straw and NDF and ADF of barley straw were successfully predicted by Mathison et al [22]. Campo et al [46] reported high R 2 pred values for NDF (0.91) and ADF (0.91) in maize whereas Wittkop et al [47] reported an R 2 pred value for NDF of oilseed rape as 0.62. Several other studies that used NIRS to predict ADL in numerous grain crops reported R 2 pred values higher than ours [27,48].…”