scholarly journals Agronomic characteristics of annual Trifolium legumes and nutritive values as predicted by near-infrared reflectance (NIR) spectroscopy

2011 ◽  
Vol 62 (12) ◽  
pp. 1078 ◽  
Author(s):  
X. Li ◽  
R. L. Ison ◽  
R. C. Kellaway ◽  
C. Stimson ◽  
G. Annison ◽  
...  
Keyword(s):  
Organic Matter ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Nir Spectroscopy ◽  
Subterranean Clover ◽  
Trifolium Subterraneum ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Stem Leaf

A range of annual legume genotypes comprising one line of Trifolium subterraneum, four lines of T. michelianum, 11 of T. resupinatum var. resupinatum, and one line of T. resupinatum var. majus were grown in glasshouses under temperature regimes of 10−15°C and 16−21°C. Dry matter (DM) weights of stem, leaf, and flower tissues were measured when plants had six nodes, at first flower appearance, and at senescence. All samples were scanned by near-infrared reflectance spectroscopy (NIRS). One-third of the samples, covering the range of spectral characteristics, were analysed for in vitro digestible organic matter (DOMD), organic matter, crude protein (CP), neutral detergent fibre (NDF), lignin, cellulose, and the hemicellulosic polysaccharide monomers arabinose, xylose, mannose, galactose, and rhamnose. These data were used to develop calibration equations from which the composition of the remaining samples was predicted by NIRS. The higher temperature resulted in plants reaching respective phenological stages earlier, but did not affect either DM yields of total plant, stem, leaf, and petiole tissues or the proportions of each fraction. In vitro DOMD and arabinose and galactose levels decreased, while lignin, cellulose, NDF, xylose, mannose, and rhamnose levels increased with advancing maturity. In vitro DOMD was positively associated with contents of CP, arabinose, galactose, and the arabinose/xylose ratio and was negatively associated with contents of lignin, cellulose, NDF, xylose, mannose, and rhamnose. Lignin contents were highly correlated with levels of both xylose and mannose. Stems were more digestible than leaves in subterranean clover and T. resupinatum var. majus. The study also demonstrated that NIRS can be used routinely as a quick, inexpensive, and reliable laboratory technique to predict feed components of annual Trifolium legumes.

Monitoring the performance of a broad-based calibration for measuring the nutritive value of two independent populations of pasture using near infrared reflectance (NIR) spectroscopy

1991 ◽  
Vol 31 (2) ◽  
pp. 205 ◽  
Author(s):  
KF Smith ◽  
PC Flinn
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Linear Regression Analysis ◽  
Spectral Characteristics ◽  
Nir Spectroscopy ◽  
Cost Effective ◽  
Multiple Linear Regression Analysis ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance

Near infrared reflectance (NIR) spectroscopy is a rapid and cost-effective method for the measurement of organic constituents of agricultural products. NIR is widely used to measure feed quality around the world and is gaining acceptance in Australia. This study describes the development of an NIR calibration to measure crude protein (CP), predicted in vivo dry matter digestibility (IVDMD) and neutral detergent fibre (NDF) in temperate pasture species grown in south-western Victoria. A subset of 116 samples was selected on the basis of spectral characteristics from 461 pasture samples grown in 1987-89. Several grass and legume species were present in the population. Stepwise multiple linear regression analysis was used on the 116 samples to develop calibration equations with standard errors of 0.8,2.3 and 2.2% for CP, NDF and IVDMD, respectively. When these equations were tested on 2 independent pasture populations, a significant bias existed between NIR and reference values for 2 constituents in each population, indicating that the calibration samples did not adequately represent the new populations for these constituents. The results also showed that the H statistic alone was inadequate as an indicator of equation performance. It was confirmed that it was possible to develop a broad-based calibration to measure accurately the nutritive value of closed populations of temperate pasture species. For the resulting equations to be used for analysis of other populations, however, they must be monitored by comparing reference and NIR analyses on a small number of samples to check for the presence of bias or a significant increase in unexplained error.

Comparisons of the prediction results of soil properties based on fuzzy c-means clustering and expert knowledge from laboratory Visible – Near-Infrared reflectance spectroscopy data

2020 ◽  
pp. 1-12
Author(s):  
Lingyun Peng ◽  
Hao Cheng ◽  
Liang-Jie Wang ◽  
Dianzhen Zhu
Keyword(s):  
Organic Matter ◽  
Soil Properties ◽  
Spectral Clustering ◽  
Near Infrared ◽  
Expert Knowledge ◽  
Nir Spectroscopy ◽  
Infrared Reflectance ◽  
Spectroscopy Data ◽  
Near Infrared Reflectance ◽  
Fuzzy C Means

Soil organic matter and soil particle composition play extremely important roles in soil fertility, environmental protection, and sustainable agricultural development. Visible – near-infrared reflectance (Vis–NIR) spectroscopy is a rapid, effective, and low-cost analytical method to predict soil properties. In this study, laboratory Vis–NIR spectroscopy data were used to compare the differences among partial least squares regression (PLSR), artificial neural network (ANN) and multivariate adaptive regression splines (MARSplines) based on fuzzy c-means spectral clustering and expert knowledge classification methods for soil prediction. The results showed that (1) the sand content (R2 = 0.69–0.77) had the best prediction, followed by the silt (R2 = 0.56–0.71) and organic matter (R2 = 0.54–0.69) contents, whereas the clay content (R2 = 0.29–0.65) had the poorest prediction, (2) the performance of the models followed the order of PLSR > ANN > MARSplines, and (3) the accuracies of the organic matter and sand contents were higher when applying expert knowledge classification, whereas the prediction of the clay and silt contents was better when applying spectral clustering. However, the overall accuracy of the spectral clustering method was slightly better than that of expert classification. Our findings showed that the spectral cluster-based models produced effective and interpretable prediction results for estimating soil properties. Therefore, this approach should be considered when dealing with large and heterogeneous soil samples.

scholarly journals COMPARISON OF NEAR INFRARED CALIBRATIONS FOR ESTIMATING IN VITRO DIGESTIBILITY IN WHOLE-PLANT CORN HYBRIDS

1987 ◽  
Vol 67 (2) ◽  
pp. 557-562 ◽  
Author(s):  
E. V. VALDES ◽  
R. B. HUNTER ◽  
G. E. JONES
Keyword(s):  
Dry Matter ◽  
Near Infrared ◽  
Mature Stage ◽  
In Vitro Digestibility ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Corn Hybrids ◽  
Dry Matter Digestibility ◽  
Whole Plant

A comparison of two near infrared (NIRA) calibrations (C1 and C2) for the prediction of in vitro dry matter digestibility (IVDM) in whole-plant corn (WPC) was conducted. C1 consisted of 40 WPC samples collected from four locations across Ontario (Brucefield, London, Guelph and Elora). C2 consisted of 90 samples and included the above locations plus Pakenham and Winchester. Nine wavelengths were used in both equations but only three were common in C1 and C2 equations. These wavelengths were 2139 nm, 2100 nm, and 1445 nm, respectively. The predictions of IVDM utilizing both C1 and C2 were good. Coefficients of determination (r2) and standard error of the estimate (SEE) for calibration and prediction sets were 0.91, 1.7; 0.85, 1.7 for C1 and 0.88, 1.6; 0.77, 1.6 for C2 respectively. Regression analysis within location, however, showed low r2 values for the prediction of IVDM for Pakenham and Winchester in both calibrations. The more mature stage of harvest at these locations might be the cause of the poorer predictions. Key words: In vitro digestibility, whole-plant corn, near infrared reflectance

On-line application of visible and near infrared reflectance spectroscopy to predict physical and sensory characteristics of beef quality

2009 ◽  
Vol 2009 ◽  
pp. 135-135
Author(s):  
N Prieto ◽  
D W Ross ◽  
E A Navajas ◽  
G Nute ◽  
R I Richardson ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Sensory Characteristics ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Beef Quality ◽  
Near Infrared Reflectance ◽  
On Line ◽  
Meat Samples

Visible and near infrared reflectance spectroscopy (Vis-NIR) has been widely used by the industry research-base for large-scale meat quality evaluation to predict the chemical composition of meat quickly and accurately. Meat tenderness is measured by means of slow and destructive methods (e.g. Warner-Bratzler shear force). Similarly, sensory analysis, using trained panellists, requires large meat samples and is a complex, expensive and time-consuming technique. Nevertheless, these characteristics are important criteria that affect consumers’ evaluation of beef quality. Vis-NIR technique provides information about the molecular bonds (chemical constituents) and tissue ultra-structure in a scanned sample and thus can indirectly predict physical or sensory parameters of meat samples. Applications of Vis-NIR spectroscopy in an abattoir for prediction of physical and sensory characteristics have been less developed than in other fields. Therefore, the aim of this study was to test the on-line Vis-NIR spectroscopy for the prediction of beef quality characteristics such as colour, instrumental texture, water holding capacity (WHC) and sensory traits, by direct application of a fibre-optic probe to the M. longissimus thoracis with no prior sample treatment.

Determination of Fat, Protein, and Total Solids in Cheese by Near-Infrared Reflectance Spectroscopy

1995 ◽  
Vol 78 (3) ◽  
pp. 802-806 ◽  
Author(s):  
José Louis Rodriguez-Otero ◽  
Maria Hermida ◽  
Alberto Cepeda
Keyword(s):  
Near Infrared ◽  
Independent Set ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sample Treatment ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Total Solids

Abstract Near-infrared reflectance (NIR) spectroscopy was used to analyze fat, protein, and total solids in cheese without any sample treatment. A set of 92 samples of cow’s milk cheese was used for instrument calibration by principal components analysis and modified partial least-square regression. The following statistical values were obtained: standard error of calibration (SEC) = 0.388 and squared correlation coefficient (R2) = 0.99 for fat, SEC = 0.397 and R2 = 0.98 for protein, and SEC = 0.412 and R2 = 0.99 for total solids. To validate the calibration, an independent set of 25 cheese samples of the same type was used. Standard errors of validation were 0.47,0.50, and 0.61 for fat, protein, and total solids, respectively, and hf for the regression of measurements by reference methods versus measurements by NIR spectroscopy was 0.98 for the 3 components.

scholarly journals Use of Visible and Near-Infrared Reflectance Spectroscopy Models to Determine Soil Erodibility Factor (K) in an Ecologically Restored Watershed

2020 ◽  
Vol 12 (18) ◽  
pp. 3103
Author(s):  
Qinghu Jiang ◽  
Yiyun Chen ◽  
Jialiang Hu ◽  
Feng Liu
Keyword(s):  
Land Use ◽  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soil Erodibility ◽  
Infrared Reflectance ◽  
Cultivated Land ◽  
Near Infrared Reflectance ◽  
Natural Land ◽  
Soil Erodibility Factor

This study aimed to assess the ability of using visible and near-infrared reflectance (Vis–NIR) spectroscopy to quantify soil erodibility factor (K) rapidly in an ecologically restored watershed. To achieve this goal, we explored the performance and transferability of the developed spectral models in multiple land-use types: woodland, shrubland, terrace, and slope farmland (the first two types are natural land and the latter two are cultivated land). Subsequently, we developed an improved approach by combining spectral data with related topographic variables (i.e., elevation, watershed location, slope height, and normalized height) to estimate K. The results indicate that the calibrated spectral model using total samples could estimate K factor effectively (R2CV = 0.71, RMSECV = 0.0030 Mg h Mj−1 mm−1, and RPDCV = 1.84). When predicting K in the new samples, models performed well in natural land soils (R2P = 0.74, RPDP = 1.93) but failed in cultivated land soils (R2P = 0.24, RPDP = 0.99). Furthermore, the developed models showed low transferability between the natural and cultivated land datasets. The results also indicate that the combination of spectral data with topographic variables could slightly increase the accuracies of K estimation in total and natural land datasets but did not work for cultivated land samples. This study demonstrated that the Vis–NIR spectroscopy could be used as an effective method in predicting K. However, the predictability and transferability of the calibrated models were land-use type dependent. Our study also revealed that the coupling of spectrum and environmental variable is an effective improvement of K estimation in natural landscape region.

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