scholarly journals Measurements of Chemical Compositions in Corn Stover and Wheat Straw by Near-Infrared Reflectance Spectroscopy

Animals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 3328
Author(s):  
Tao Guo ◽  
Luming Dai ◽  
Baipeng Yan ◽  
Guisheng Lan ◽  
Fadi Li ◽  
...  
Keyword(s):  
Wheat Straw ◽  
Corn Stover ◽  
Near Infrared ◽  
Neutral Detergent Fiber ◽  
Chemical Compositions ◽  
Near Infrared Reflectance ◽  
Forage Crops ◽  
Acid Detergent Lignin ◽  
Calibration Models ◽  
Ruminant Diets

Rapid, non-destructive methods for determining the biochemical composition of straw are crucial in ruminant diets. In this work, ground samples of corn stover (n = 156) and wheat straw (n = 135) were scanned using near-infrared spectroscopy (instrument NIRS DS2500). Samples were divided into two sets, with one set used for calibration (corn stover, n = 126; wheat straw, n = 108) and the remaining set used for validation (corn stover, n = 30; wheat straw, n = 27). Calibration models were developed utilizing modified partial least squares (MPLS) regression with internal cross validation. Concentrations of moisture, crude protein (CP), and neutral detergent fiber (NDF) were successfully predicted in corn stover, and CP and moisture were in wheat straw, but other nutritional components were not predicted accurately when using single-crop samples. All samples were then combined to form new calibration (n = 233) and validation (n = 58) sets comprised of both corn stover and wheat straw. For these combined samples, the CP, NDF, and ADF were predicted successfully; the coefficients of determination for calibration (RSQC) were 0.9625, 0.8349, and 0.8745, with ratios of prediction to deviation (RPD) of 6.872, 2.210, and 2.751, respectively. The acid detergent lignin (ADL) and moisture were classified as moderately useful, with RSQC values of 0.7939 (RPD = 2.259) and 0.8342 (RPD = 1.868), respectively. Although the prediction of hemicellulose was only useful for screening purposes (RSQC = 0.4388, RPD = 1.085), it was concluded that NIRS is a suitable technique to rapidly evaluate the nutritional value of forage crops.

Prediction of neutral detergent fiber content in corn stover using near-infrared spectroscopy technique

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Xuyang Pan ◽  
Laijun Sun ◽  
Guobing Sun ◽  
Panxiang Rong ◽  
Yuncai Lu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Prediction Model ◽  
Spectral Data ◽  
Corn Stover ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Fiber Content ◽  
Partial Least Square ◽  
Neutral Detergent Fiber

AbstractNeutral detergent fiber (NDF) content was the critical indicator of fiber in corn stover. This study aimed to develop a prediction model to precisely measure NDF content in corn stover using near-infrared spectroscopy (NIRS) technique. Here, spectral data ranging from 400 to 2500 nm were obtained by scanning 530 samples, and Monte Carlo Cross Validation and the pretreatment were used to preprocess the original spectra. Moreover, the interval partial least square (iPLS) was employed to extract feature wavebands to reduce data computation. The PLSR model was built using two spectral regions, and it was evaluated with the coefficient of determination (R2) and root mean square error of cross validation (RMSECV) obtaining 0.97 and 0.65%, respectively. The overall results proved that the developed prediction model coupled with spectral data analysis provides a set of theoretical foundations for NIRS techniques application on measuring fiber content in corn stover.

scholarly journals Development of Calibration Models to Predict Mean Fibre Diameter in Llama (Lama glama) Fleeces with Near Infrared Spectroscopy

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1998
Author(s):  
José Ignacio Amorena ◽  
Dolores María Eugenia Álvarez ◽  
Elvira Fernández-Ahumada
Keyword(s):  
Textile Industry ◽  
Near Infrared ◽  
Prediction Models ◽  
External Validation ◽  
Fibre Diameter ◽  
Near Infrared Reflectance ◽  
Screening Programs ◽  
Spectral Signatures ◽  
Calibration Models ◽  
Cross Validation Error

Llama fibre has the potential to become the most valuable textile resource in the Puna region of Argentina. In this study near infrared reflectance spectroscopy was evaluated to predict the mean fibre diameter in llama fleeces. Analyses between sets of carded and non-carded samples in combination with spectral preprocessing techniques were carried out and a total of 169 spectral signatures of llama samples in Vis and NIR ranges (400–2500 nm) were obtained. Spectral preprocessing consisted in wavelength selection (Vis–NIR, NIR and discrete ranges) and multiplicative and derivative pretreatments; spectra without pretreatments were also included, while modified partial least squares (M-PLS) regression was used to develop prediction models. Predictability was evaluated through R2: standard cross validation error (SECV), external validation error (SEV) and residual predictive value (RPD). A total of 54 calibration models were developed in which the best model (R2 = 0.67; SECV = 1.965; SEV = 2.235 and RPD = 1.91) was obtained in the Vis–NIR range applying the first derivative pretreatment. ANOVA analysis showed differences between carded and non-carded sets and the models obtained could be used in screening programs and contribute to valorisation of llama fibre and sustainable development of textile industry in the Puna territory of Catamarca. The data presented in this paper are a contribution to enhance the scarce information on this subject.

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.

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.

Determination of dry matter (DM) and nitrogen (N) degradability in forages by near infrared reflectance spectroscopy (NIRS)

2003 ◽  
Vol 2003 ◽  
pp. 154-154
Author(s):  
C. Cajarville ◽  
J. P Repetto ◽  
A. Curbelo ◽  
C. Soto ◽  
D. Cozzolino
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Chemical Constituents ◽  
Climatic Conditions ◽  
Near Infrared Reflectance ◽  
Forage Crops ◽  
Optimum Planning ◽  
Plant Maturity ◽  
Animal Feeding

The nutritive value of forage crops is related mainly to climatic conditions and stage of plant maturity, and its determination for any given crop is essential for optimum planning and animal feeding (Berardo et al., 1993; Deaville and Flinn, 2000). Worldwide the nutritive value of forages is often estimated by chemical or physical methods and is expressed as the concentration of chemical constituents in the plant tissue. There is little information in the literature about the use of NIRS to determine degradability in pastures with different conditions, season, different places (Wilman et al., 2000). The aim of the work to explore the use of NIRS as rapid tool for estimate DM and N degradability in forages.

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.

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