Near-Infrared Reflectance Spectroscopy: Different Strategies for Local Calibrations in Analyses of Forage Quality

1993 ◽  
Vol 47 (4) ◽  
pp. 463-469 ◽  
Author(s):  
Are Halvor Aastveit ◽  
Petter Marum
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
Calibration Method ◽  
Neutral Detergent Fiber ◽  
Optimum Number ◽  
Local Regression ◽  
Near Infrared Reflectance ◽  
Data Set ◽  
Positive Effects

This paper deals with the problem of how to utilize a large calibration set with 10 different analytes in order to make the best predictions possible on a routine basis. Ten different strategies of using the data set were studied with the use of numbers of principal components ranging from 4 to 12. We found positive effects of scatter correction for most of the analytes. On average, the local regression methods were superior to the others. The optimum number of samples for local regression seems to be between 50 and 100. The largest reduction in root mean square error of prediction (RMSEP), in comparison to results for the traditional method, was found on scatter-corrected spectra and a proposed local calibration with 50 calibration samples. The gain in RMSEP for neutral detergent fiber (NDF), acid detergent fiber (ADF), and crude fiber was about 25% and for protein and in vitro digestible dry matter digestibility (IVDMD) about 10%, compared to results for the traditional universal calibration method.

scholarly journals The Relative Performance of a Benchtop Scanning Monochromator and Handheld Fourier Transform Near-Infrared Reflectance Spectrometer in Predicting Forage Nutritive Value

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 658
Author(s):  
Matthew F. Digman ◽  
Jerry H. Cherney ◽  
Debbie J. R. Cherney
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Prediction Models ◽  
Low Cost ◽  
Neutral Detergent Fiber ◽  
In Vitro Digestibility ◽  
Near Infrared Reflectance ◽  
Average Decrease ◽  
Manufacturing Techniques

Advanced manufacturing techniques have enabled low-cost, on-chip spectrometers. Little research exists, however, on their performance relative to the state of technology systems. The present study compares the utility of a benchtop FOSS NIRSystems 6500 (FOSS) to a handheld NeoSpectra-Scanner (NEO) to develop models that predict the composition of dried and ground grass, and alfalfa forages. Mixed-species prediction models were developed for several forage constituents, and performance was assessed using an independent dataset. Prediction models developed with spectra from the FOSS instrument had a standard error of prediction (SEP, % DM) of 1.4, 1.8, 3.3, 1.0, 0.42, and 1.3, for neutral detergent fiber (NDF), true in vitro digestibility (IVTD), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), and crude protein (CP), respectively. The R2P for these models ranged from 0.90 to 0.97. Models developed with the NEO resulted in an average increase in SEP of 0.14 and an average decrease in R2P of 0.002.

scholarly journals Use of near-infrared spectroscopy for prediction of chemical composition of Tifton 85 grass

2021 ◽  
Vol 42 (3) ◽  
pp. 1287-1302
Author(s):  
Camila Cano Serafim ◽  
◽  
Geisi Loures Guerra ◽  
Ivone Yurika Mizubuti ◽  
Filipe Alexandre Boscaro de Castro ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Chemical Composition ◽  
Spectral Data ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Neutral Detergent Fiber ◽  
Mineral Matter ◽  
Data Set

The reduction in the quality, consumption, and digestibility of forage can cause a decrease in animal performance, resulting in losses to the rural producer. Thus, it is important to monitor these characteristics in forage plants to devise strategies or practices that optimize production systems. The aim of this study was to develop and validate prediction models using near-infrared spectroscopy (NIRS) to determine the chemical composition of Tifton 85 grass. Samples of green grass, its morphological structures (whole plant, leaf blade, stem + sheath, and senescent material) and hay, totaling 105 samples were used. Conventional chemical analysis was performed to determine the content of oven-dried samples (ODS), mineral matter (MM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), cellulose (CEL), hemicellulose (HEM), and in vitro dry matter digestibility (IVDMD). Subsequently, all the samples were scanned using a Vis-NIR spectrometer to collect spectral data. Principal component analysis (PCA) was applied to the data set, and modified partial least squares was used to correlate reference values to spectral data. The coefficients of determination (R2) were 0.74, 0.85, 0.98, 0.75, 0.85, 0.71, 0.82, 0.77, and 0.93, and the ratio of performance deviations (RPD) obtained were 1.99, 2.71, 6.46, 2.05, 2.58, 3.84, 1.86, 2.35, 2.09, and 3.84 for ODS, MM, CP, NDF, ADF, ADL, CEL, HEM, and IVDMD, respectively. The prediction models obtained, in general, were considered to be of excellent quality, and demonstrated that the determination of the chemical composition of Tifton 85 grass can be performed using NIRS technology, replacing conventional analysis.

Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat

1985 ◽  
Vol 39 (3) ◽  
pp. 491-500 ◽  
Author(s):  
P. Geladi ◽  
D. MacDougall ◽  
H. Martens
Keyword(s):  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Scatter Correction ◽  
Calibration Method ◽  
Light Scatter ◽  
Infrared Reflectance ◽  
Linear Calibration ◽  
Near Infrared Reflectance ◽  
Good Correspondence ◽  
Linearization Methods

This paper is concerned with the quantitative analysis of multicomponent mixtures by diffuse reflectance spectroscopy. Near-infrared reflectance (NIRR) measurements are related to chemical composition but in a nonlinear way, and light scatter distorts the data. Various response linearizations of reflectance (R) are compared ( R with Saunderson correction for internal reflectance, log 1/ R, and Kubelka-Munk transformations and its inverse). A multi-wavelength concept for optical correction (Multiplicative Scatter Correction, MSC) is proposed for separating the chemical light absorption from the physical light scatter. Partial Least Squares (PLS) regression is used as the multivariate linear calibration method for predicting fat in meat from linearized and scatter-corrected NIRR data over a broad concentration range. All the response linearization methods improved fat prediction when used with the MSC; corrected log 1/ R and inverse Kubelka-Munk transformations yielded the best results. The MSC provided simpler calibration models with good correspondence to the expected physical model of meat. The scatter coefficients obtained from the MSC correlated with fat content, indicating that fat affects the NIRR of meat with an additive absorption component and a multiplicative scatter component.

EFFECT OF STRUCTURAL AND CHEMICAL FACTORS OF FORAGES ON POTENTIALLY DIGESTIBLE FIBER, INTAKE, AND TRUE DIGESTIBILITY BY RUMINANTS

1988 ◽  
Vol 68 (3) ◽  
pp. 787-799 ◽  
Author(s):  
V. GIRARD ◽  
G. DUPUIS
Keyword(s):  
Cell Wall ◽  
Dry Matter ◽  
Plant Cell Wall ◽  
Neutral Detergent Fiber ◽  
Optimum Number ◽  
Apparent Digestibility ◽  
Cool Season ◽  
Dry Matter Digestibility ◽  
True Digestibility

In view of the large variation found in plant cell wall digestibilities with ruminants, an attempt was made to group 124 feeds into different lignification classes (clusters) on the basis of chemical characteristics. Each feed cluster was described using a structural coefficient [Formula: see text] that related the potentially digestible fiber (PDF, %) to the ratio between lignin and cell wall volume. The optimum number of clusters was determined iteratively by performing a regression of the apparent digestibility of dry matter at maintenance level (DDM1, %) against the PDF and cell soluble (SOL, %) contents of feeds. The [Formula: see text] coefficients varied from 0.05 (grains, N = 13) to 1.85 (corn silage, N = 3) and increased with the maturity of the grasses from 0.88 (legumes, vegetative cool season grasses, N = 26) to 1.33 (mature, cool season grasses, N = 19). Predicted PDF were closely correlated (r > 0.9, P < 0.01) to in vitro cell wall disappearances (IVCWD). Apparently digestible cell wall in four grasses and four legumes increased linearly with 96-h IVCWD and standard error (SE) was similar to the SE of predicted apparent digestible SOL from SOL concentrations. Assuming that similarity between SE could be also observed in larger samples, PDF and SOL were used in summative equations to predict apparent dry matter digestibility. DDM1 discounted for intake (DDM1 – 4, %) was regressed against SOL and PDF concentrations of 87 feeds:[Formula: see text]with ds and df, the true digestibilities of SOL and PDF. Estimates of ds and df were 0.98 and 0.95 for a zero-production (maintenance) level of intake, and 0.91 and 0.79 for an intake level four times maintenance. Since the true digestibility of the PDF component was only 4% – 13% lower than that of the cell soluble component, the concentration of PDF in cell wall was the major determinant in the variation in apparent digestibility of forages. Key words: lignin, neutral detergent fiber, true digestibility, cluster analysis, feeds

scholarly journals Methods for predicting the energy value of pet foods

2009 ◽  
Vol 38 (spe) ◽  
pp. 1-14 ◽  
Author(s):  
Carlos Castrillo ◽  
Marta Hervera ◽  
Maria Dolores Baucells
Keyword(s):  
Near Infrared ◽  
Metabolizable Energy ◽  
Apparent Digestibility ◽  
Crude Fibre ◽  
Near Infrared Reflectance ◽  
Energy Value ◽  
Close Relationship ◽  
Accuracy Of Prediction

The energy value of foods as well as energy requirements of dogs and cats is currently expressed in terms of metabolizable energy (ME). The determination of ME content of foods requires experimental animals and is too expensive and time consuming to be used routinely. Consequently, different indirect methods have been proposed in order to estimate as reliably an accurately as possible the ME content of pet food. This work analyses the main approaches proposed to date to estimate the ME content of foods for cats and dogs. The former method proposed by the NRC estimates the ME content of pet foods from proximal chemical analysis using the modified Atwater factors, assuming constant apparent digestibility coefficients for each analytical fraction. Modified Atwater factors systematically underestimate the ME content of low-fibre foods whereas they overestimate those that are high in fibre. Recently, different equations have been proposed for dogs and cats based in the estimation of apparent digestibility of energy by the crude fibre content, which improve the accuracy of prediction. In any case, whatever the method of analysis used, differences in energy digestibility related with food processing and fibre digestibility are unlikely to be accounted for. A simple in vitro enzymatic method has been recently proposed based in the close relationship that exist between energy digestibility and organic matter disappearance after two consecutive enzymatic (pepsin-pancreatin) incubation of food sample. Nutrient composition and energy value of pet foods can be also accurately and simultaneously predicted using near infrared reflectance spectroscopy (NIRS).

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

Determination of Particle Size in Powders by Scatter Correction in Diffuse Near-Infrared Reflectance

1988 ◽  
Vol 42 (5) ◽  
pp. 722-728 ◽  
Author(s):  
J. L. Ilari ◽  
H. Martens ◽  
T. Isaksson
Keyword(s):  
Particle Size ◽  
Near Infrared ◽  
Scatter Correction ◽  
Large Degree ◽  
Chemical Compositions ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Analytical Technique ◽  
Scattering Effects ◽  
Reflectance Data

Diffuse near-infrared reflectance spectroscopy has traditionally been an analytical technique for determining chemical compositions in a sample. We will, in this paper, focus on light scattering effects and their ability to determine the mean particle sizes of powders. The reflectance data of NaCl, broken glass, and sorbitol powders are linearized and submitted to the Multiplicative Scatter Correction (MSC), and the ensuing parameters are used in subsequent multivariate calibrations. The results indicate that particle size can, to a large degree, be determined from NIR reflectance data for a given type of powder. Up to 99% of the partical size variance was explained by the regression.

On the Scales Associated with Near-Infrared Reflectance Difference Spectra

1995 ◽  
Vol 49 (6) ◽  
pp. 765-772 ◽  
Author(s):  
M. S. Dhanoa ◽  
S. J. Lister ◽  
R. J. Barnes
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
The Other ◽  
Difference Spectra ◽  
Near Infrared Reflectance ◽  
Standard Normal Variate ◽  
Near Infrared Spectra ◽  
Standard Normal ◽  
Relationship Of ◽  
The Relationship

Scale differences of individual near-infrared spectra are identified when set-independent standard normal variate (SNV) and de-trend (DT) transformations are applied in either SNV followed by DT or DT then SNV order. The relationship of set-dependent multiplicative scatter correction (MSC) to SNV is also referred to. A simple correction factor is proposed to convert derived spectra from one order to the other. It is suggested that the suitable order for the study of changes using difference spectra (when removing baselines) should be DT followed by SNV, which leads to all derived spectra on the scale of mean zero and variance equal to one. If baselines are identical, then SNV scale spectra can be used to calculate differences.

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