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.

scholarly journals Rapid determination of the content of digestible energy and metabolizable energy in sorghum fed to growing pigs by near-infrared reflectance spectroscopy1

2019 ◽  
Vol 97 (12) ◽  
pp. 4855-4864 ◽  
Author(s):  
Jie Hu ◽  
Juntao Li ◽  
Long Pan ◽  
Xiangshu Piao ◽  
Li Sui ◽  
...  
Keyword(s):  
Near Infrared ◽  
External Validation ◽  
Growing Pigs ◽  
Metabolizable Energy ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Calibration Models

Abstract The object of this study was to establish a new method to predict the content of DE and ME in sorghum fed to growing pigs by using near-infrared reflectance spectroscopy (NIRS). A total of 33 sorghum samples from all over China were used in this study. The samples were scanned for their spectra in the range of 12,000 to 4,000 cm−1. Based on principal components analysis of the spectra, the samples were split into a calibration set (n = 24) and a validation set (n = 9) according to the ratio of 3:1. With animal experiment values as calibration reference, the calibration models of DE and ME were established using partial least squares regression algorithm. Different spectral pretreatments were applied on the spectra to reduce the noise level. The best wavenumber ranges were also investigated. Results showed that DE and ME content in sorghum fed to growing pigs ranged from 14.57 to 16.70 MJ/kg DM and 14.31 to 16.35 MJ/kg DM, respectively. The optimal spectral preprocessing method for DE and ME was the combination of first derivative and multiplicative scatter correction. The most informative near-infrared spectral regions were 9,403.9 to 6,094.4 cm−1 and 4,605.5 to 4,242.9 cm−1 for both DE and ME. The best performance for DE and ME calibration models was the coefficient of determination of calibration (R2c) of 0.94 and 0.93, coefficient of determination of cross-external validation (R2cv) of 0.88 and 0.86, residual predictive deviation of cross-external validation (RPDcv) of 2.86 and 2.64, coefficient of determination of external validation (R2v) of 0.90 and 0.81, and residual predictive deviation of external validation (RPDv) of 3.15 and 2.35, respectively. There were no significant differences between the measured and NIRS predicted values for DE and ME (P = 0.895 for DE and P = 0.644 for ME). As the number of calibration samples increased from 24 to 33, the calibration performance of DE and ME models was improved, indicated by increased R2c, R2cv, and RPDcv values. In conclusion, NIRS quantitative models of the available energy in sorghum were established in this study. The results demonstrated that the content of DE and ME in sorghum could be predicted with relatively high accuracy based on NIRS and NIRS showed the superiority of speediness and practicality when compared with previous research methods including animal experiments, regression equations, and computer-controlled simulated digestion system.

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.

Short Communication: Near infrared reflectance spectroscopy accurately predicts the digestible energy content of barley for pigs

2011 ◽  
Vol 91 (2) ◽  
pp. 301-304 ◽  
Author(s):  
R. T. Zijlstra ◽  
M. L. Swift ◽  
L. F. Wang ◽  
T. A. Scott ◽  
M. J. Edney
Keyword(s):  
Near Infrared ◽  
Energy Content ◽  
External Validation ◽  
Reflectance Spectroscopy ◽  
Commercial Application ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Digestible Energy ◽  
Infrared Reflectance Spectroscopy

Zijlstra, R. T., Swift, M. L., Wang, L. F., Scott, T. A. and Edney, M. J. 2011. Short Communication:Near infrared reflectance spectroscopy accurately predicts the digestible energy content of barley for pigs. Can. J. Anim. Sci. 91: 301–304. Density, chicken apparent metabolizable energy (AME), and near infrared reflectance spectroscopy (NIRS) were tested to predict the widely varying swine digestible energy (DE) content of barley. The DE content of 39 barley samples ranged from 2686 to 3163 kcal kg−1 (90% DM) in grower pigs. The R2 between DE content and density (0.14) and broiler chicken AME content (0.18 and 0.56, without and with enzyme, respectively) was low. In contrast, the coefficient of determination to predict swine DE content for ground barley samples using NIRS was respectable for external validation (R2=0.74) and internal cross validation (1-VR=0.79), but more robust calibrations should be developed for commercial application.

scholarly journals One leaf for all: Chemical traits of single leaves measured at the leaf surface using Near infrared-reflectance spectroscopy (NIRS)

2020 ◽  
Author(s):  
Matteo Petit Bon ◽  
Hanna Böhner ◽  
Sissel Kaino ◽  
Torunn Moe ◽  
Kari Anne Bråthen
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
The Arctic ◽  
Infrared Reflectance ◽  
Growth Forms ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Calibration Models ◽  
Chemical Traits

AbstractThe leaf is an essential unit for measures of plant ecological traits. Yet, measures of plant chemical traits are often achieved by merging several leaves, masking potential foliar variation within and among plant individuals. This is also the case with cost-effective measures derived using Near-infrared reflectance spectroscopy (NIRS). The calibration models developed for converting NIRS spectral information to chemical traits are typically based on spectra from merged and milled leaves. In this study we ask if such calibration models can be applied to spectra derived from whole leaves, providing measures of chemical traits of single leaves.We sampled cohorts of single leaves from different biogeographic regions, growth forms, species and phenological stages in order to include variation in leaf and chemical traits. For each cohort we first sampled NIRS-spectra from each whole, single leaf, including leaf sizes down to Ø 4 mm (the minimum area of our NIRS application). Next, we merged, milled and tableted the leaves and sampled spectra from the cohort as a tablet. We applied arctic-alpine calibration models to all spectra and derived chemical traits. Finally, we evaluated the performance of the models in predicting chemical traits of whole, single leaves by comparing the traits derived at the level of leaves to that of the tablets.We found that the arctic-alpine calibration models can successfully be applied to single, whole leaves for measures of Nitrogen (R2=0.88, RMSE=0.824), Phosphorus (R2=0.65, RMSE=0.081), and Carbon (R2=0.78, RMSE=2.199) content. For Silicon content we found the method acceptable when applied to Silicon-rich growth forms (R2=0.67, RMSE=0.677). We found a considerable variation in chemical trait values among leaves within the cohorts.This time- and cost-efficient NIRS-application provides non-destructive measures of a set of chemical traits in single, whole leaves, including leaves of small sizes. The application can facilitate research into the scales of variability of chemical traits and include intraindividual variation. Potential trade-offs among chemical traits and other traits within the leaf unit can be identified and be related to ecological processes. In sum this NIRS-application can facilitate further ecological understanding of the role of leaf chemical traits.

Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef

2021 ◽  
pp. 096703352110495
Author(s):  
Cassius EO Coombs ◽  
Robert R Liddle ◽  
Luciano A González
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Consumer Confidence ◽  
Spectroscopic Methods ◽  
Marbling Score ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Beef Steak

The present study analysed the ability for portable near infrared reflectance (NIR) and Raman spectroscopy sensors to differentiate between grass-fed and grain-fed beef. Scans were made on lean and fat surfaces of 108 beef steak samples labelled as grass-fed ( n = 54) and grain-fed ( n = 54), with partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) used to develop discrimination models which were tested on independent datasets. Furthermore, PLS-DA was used to predict visual marbling score and days on feed (DOF). The NIR spectra accurately discriminated between grass- and grain-fed beef on both fat (91.7%, n = 92) and lean (88.5%, n = 96), as did Raman (fat 95.2%, n = 82; lean 69.6%, n = 68). Fat scanning using NIR spectroscopy moderately predicted DOF (r2val = 0.53), though Raman and NIR spectroscopy lean prediction models for DOF and marbling were less precise (r2val < 0.50). It can be concluded that portable NIR and Raman spectrometers can be used successfully to differentiate grass-fed from grain-fed beef and therefore aid retail and consumer confidence.

Prediction of Chemical Composition in Sunflower Whole Plant and Silage (Helianthus Annus L.) by near Infrared Reflectance Spectroscopy

2007 ◽  
Vol 15 (3) ◽  
pp. 201-207 ◽  
Author(s):  
A. Fassio ◽  
A. Gimenez ◽  
E. Fernandez ◽  
D. Vaz Martins ◽  
D. Cozzolino
Keyword(s):  
Chemical Composition ◽  
Near Infrared ◽  
Cross Validation ◽  
Reference Method ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Whole Plant ◽  
Calibration Models

The aim of this study was to investigate the potential use of near infrared (NIR) reflectance spectroscopy to predict chemical composition in both sunflower whole plant (WPSun) and sunflower silage (SunS). Samples of both WPSun ( n = 73) and SunS ( n = 50) were analysed by reference method and scanned in reflectance using a NIR monochromator instrument (400–2500 nm). Calibration models were developed between NIR data and reference values for dry matter (DM), crude protein (CP), ash, acid detergent fibre (ADFom), neutral detergent fibre (aNDFom), in vitro organic matter digestibility (OMD), ether extract (EE) and pH using partial least squares regression (PLS). Due to the limited number of samples full cross-validation was used to test the calibration models. The best correlations (R 2cal) and lowest standard errors in cross-validation (SECV) were obtained for DM (R 2cal > 0.82, SECV: 27.0 and 35.8 g kg−1), CP (R 2cal> 0.85, SECV: 9.9 and 10.1 g kg−1) and ash (R2cal> 0.85, SECV 11.2 and 8.2 g kg−1) in both WPSun and SunS samples, respectively. For ADFom, aNDFom and OMD the calibrations were considered to be poor (R 2cal < 0.85). In SunS samples a good correlation was found for EE (R 2cal = 0.94, SECV: 15.3 g kg−1).

Determination of potentially mineralizable nitrogen and nitrogen in particulate organic matter fractions in soil by visible and near-infrared reflectance spectroscopy

2004 ◽  
Vol 142 (3) ◽  
pp. 335-343 ◽  
Author(s):  
A. MORON ◽  
D. COZZOLINO
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Calibration Models ◽  
Potentially Mineralizable Nitrogen ◽  
Mineralizable Nitrogen ◽  
Organic Matter Fractions

Visible (VIS) and near-infrared reflectance spectroscopy (NIRS) combined with multivariate data analysis was used to predict potentially mineralizable nitrogen (PMN) and nitrogen in particulate organic matter fractions (PSOM-N). Soil samples from a long-term experiment (n=24) as well as soils under commercial management (n=160) in Uruguay (South America) were analysed. Samples were scanned in a NIRS 6500 monochromator instrument by reflectance (400–2500 nm). Modified partial least square regression (MPLS) and cross validation were used to develop the calibration models between NIRS data and reference values. NIRS calibration models gave a coefficient of determination for the calibration (R2CAL)>0·80 and the standard deviation of reference data to standard error in cross validation (RPD) ratio ranging from 2 to 5·5 for the variables evaluated. The results obtained in the study showed that NIRS could have the potential to determine PMN and PSOM-N fractions in soils under different agronomic conditions. However, the relatively limited number of samples led us to be cautious in terms of conclusions and to extend the results of this work to similar conditions.

Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy

2006 ◽  
Vol 82 (1) ◽  
pp. 111-116 ◽  
Author(s):  
N. Barlocco ◽  
A. Vadell ◽  
F. Ballesteros ◽  
G. Galietta ◽  
D. Cozzolino
Keyword(s):  
Shear Force ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Intramuscular Fat ◽  
Coefficient Of Determination ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Calibration Models ◽  
Intact Muscle

AbstractPartial least-squares (PLS) models based on visible (Vis) and near infrared reflectance (NIR) spectroscopy data were explored to predict intramuscular fat (IMF), moisture and Warner Bratzler shear force (WBSF) in pork muscles (m. longissimus thoracis) using two sample presentations, namely intact and homogenized. Samples were scanned using a NIR monochromator instrument (NIRSystems 6500, 400 to 2500 nm). Due to the limited number of samples available, calibration models were developed and evaluated using full cross validation. The PLS calibration models developed using homogenized samples and raw spectra yielded a coefficient of determination in calibration (R2) and standard error of cross validation (SECV) for IMF (R2=0·87; SECV=1·8 g/kg), for moisture (R2=0·90; SECV=1·1 g/kg) and for WBSF (R2=0·38; SECV=9·0 N/cm). Intact muscle presentation gave poorer PLS calibration models for IMF and moisture (R2<0·70), however moderate good correlation was found for WBSF (R2=0·64; SECV=8·5 N/cm). Although few samples were used, the results showed the potential of Vis-NIR to predict moisture and IMF using homogenized pork muscles and WBSF in intact samples.

Partial Least-Squares Modeling of Near-Infrared Reflectance Data for Noninvasive in Vivo Determination of Deep-Tissue pH

1998 ◽  
Vol 52 (3) ◽  
pp. 400-406 ◽  
Author(s):  
Songbiao Zhang ◽  
Babs R. Soller ◽  
Ronald H. Micheels
Keyword(s):  
Blood Flow ◽  
Least Squares ◽  
Near Infrared ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Deep Tissue ◽  
Tissue Ph ◽  
Calibration Models ◽  
The Subject

Noninvasive monitoring of deep-tissue pH has been demonstrated with the use of near-infrared spectroscopic measurements and the partial least-squares (PLS) multivariate calibration technique. The near-infrared reflectance spectra (700 to 1100 nm) of the teres major muscle in five New Zealand rabbits were obtained in vivo, along with reference pH values in the muscle measured by microelectrodes. The muscle pH was varied by controlling the blood supply to the muscle. PLS analysis with cross-validation techniques, along with several data preprocessing methods, was used to relate the tissue pH to spectra. When multi-subject PLS calibration models were used to predict a new independent subject, a subject-dependent offset was observed. Several strategies for minimizing the subject-dependent offset were discussed. With a baseline subtraction procedure, the subject-dependent offset was minimized to less than 0.1 pH units while the average standard error of prediction (SEP) was close to 0.05 pH units. This result suggests that it is possible to build a single robust calibration model for all new independent subjects. Tissue chemistry during ischemia (blood flow reduction) is different from the chemistry of reperfusion (blood flow restoration), and it was found that separate calibration models permit more accurate prediction of pH.

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