scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible-near infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Author(s):  
Elise Ai Hwee Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Diagnostic Methods ◽  
Calibration Model ◽  
Good Prediction ◽  
Blood Concentrations ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background: Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, preventing production and welfare loss in the flock. We previously demonstrated the ability of visible-near infrared (vis-NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we investigate whether variation in sheep type and environment affect the prediction accuracy of vis-NIR spectroscopy in quantifying blood in faeces.Methods: Vis-NIR spectra were obtained from worm-free sheep faeces from different environments in South Australia (SA) and New South Wales (NSW), Australia and spiked with various sheep blood concentrations collected. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387 – 609 nm) using partial least squares (PLS) regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected Queensland (QLD) faeces. Naturally occurring blood in QLD samples was quantified using Hemastix® and FAMACHA© scores.Results: PCA showed that location, class of sheep and pooled/individual samples were factors affecting the Hb predictions in sheep faeces. The calibration models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity: 57 – 94%, specificity: 44 – 79%). The models were not predictive for naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of QLD samples, however, identified a difference between samples containing high and low quantities of blood.Conclusion: This study demonstrates the potential of vis-NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture enough environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic for the accurate prediction of H. contortus infections in these regions.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible-near infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Author(s):  
Elise Ai Hwee Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Diagnostic Methods ◽  
Calibration Model ◽  
Good Prediction ◽  
Blood Concentrations ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background: Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, preventing production and welfare loss in the flock. We previously demonstrated the ability of visible-near infrared (vis-NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we investigate whether variation in sheep type and environment affect the prediction accuracy of vis-NIR spectroscopy in quantifying blood in faeces. Methods: Vis-NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales (NSW), Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387 – 609 nm) using partial least squares (PLS) regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). QLD samples were quantified using Hemastix® and FAMACHA © scores. Results: PCA showed that location, class of sheep and pooled/individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity: 57 – 94%, specificity: 44 – 79%). The models were not predictive for blood in naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion: This study demonstrates the potential of vis-NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture enough environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible–near-infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Elise A. Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Diagnostic Methods ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces. Methods Visible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores. Results Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion This study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

Noninvasive Alcohol Detection Using Near-Infrared Spectroscopy Based on Partial Least Squares and Monte-Carlo

2012 ◽  
Vol 229-231 ◽  
pp. 1308-1311
Author(s):  
Si Te Luo ◽  
Guo Qiang Chen ◽  
Ruo Fei Cui ◽  
Wei Wei Zhou ◽  
Li Qian Lu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Blood Alcohol Concentration ◽  
Test Method ◽  
Calibration Model ◽  
Good Prediction ◽  
Good Prediction Accuracy

The objective of this study was to assess the feasibility of noninvasive alcohol testing in vivo with near-infrared (NIR) spectroscopy. The suitable distance between light source and detector was determined by Monte-Carlo simulation. The NIR spectra signals of alcohol in vitro and in vivo were measured, and the blood alcohol concentration (BAC) was measured with breath test method. Wavelet de-noising and partial least squares (PLS) method were used to establish the quantitative calibration model of alcohol. The results indicate that alcohol spectra had two absorption peaks at range of 2200nm～2400nm. The optimal principal component number of PLS model is 3, RMSEP=9.29, MREP=3.5%，R=0.9802. The model has good prediction accuracy. NIRS might provide a new method to the measurement of alcohol in vivo.

Near-Infrared Spectroscopy for Monitoring Starch Hydrolysis

2000 ◽  
Vol 54 (2) ◽  
pp. 277-283 ◽  
Author(s):  
Hoeil Chung ◽  
Mark A. Arnold
Keyword(s):  
Near Infrared ◽  
Physical Property ◽  
Nir Spectroscopy ◽  
Starch Hydrolysis ◽  
Calibration Model ◽  
Calibration Models ◽  
Fourier Filtering ◽  
On Line ◽  
Acid Catalyzed ◽  
Better Than

Near-infrared (NIR) spectroscopy has been evaluated for monitoring the acid-catalyzed hydrolysis (thinning) of starch. In practice, the extent of starch hydrolysis is measured in fluidity units, which correspond to a physical property of the hydrolyzed starch material. NIR spectra of samples taken periodically during a series of starch-thinning reactions were used to predict fluidity. The standard error of prediction (SEP) was 1.06 mL with the use of partial least-squares (PLS) regression in conjunction with digital Fourier filtering. This SEP was significantly better than that reported before with a univariate calibration model based on the integrated area of the 4400 cm−1 (2272 nm) absorption band for carbohydrates. The improved SEP meets the industry demands for real-time monitoring. Although these calibration models were developed from samples prepared in the laboratory, no spectroscopic differences were apparent between spectra collected from these laboratory samples and spectra collected from samples taken directly from plant starch slurries during actual thinning reactions. This similarity in spectral features, and hence chemical matrix, supports the potential of NIR spectroscopy for on-line monitoring of industrial starch-thinning processes.

scholarly journals Prediction of Soil Organic Carbon in a New Target Area by Near-Infrared Spectroscopy: Comparison of the Effects of Spiking in Different Scale Soil Spectral Libraries

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4357
Author(s):  
Hongyang Li ◽  
Shengyao Jia ◽  
Zichun Le
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Zhejiang Province ◽  
Small Scale ◽  
Calibration Model ◽  
Target Area ◽  
Good Prediction ◽  
Spiked Models

Near-infrared (NIR) spectroscopy is widely used to predict soil organic carbon (SOC) because it is rapid and accurate under proper calibration. However, the prediction accuracy of the calibration model may be greatly reduced if the soil characteristics of some new target areas are different from the existing soil spectral library (SSL), which greatly limits the application potential of the technology. We attempted to solve the problem by building a large-scale SSL or using the spiking method. A total of 983 soil samples were collected from Zhejiang Province, and three SSLs were built according to geographic scope, representing the provincial, municipal, and district scales. The partial least squares (PLS) algorithm was applied to establish the calibration models based on the three SSLs, and the models were used to predict the SOC of two target areas in Zhejiang Province. The results show that the prediction accuracy of each model was relatively poor regardless of the scale of the SSL (residual predictive deviation (RPD) < 2.5). Then, the Kennard-Stone (KS) algorithm was applied to select 5 or 10 spiking samples from each target area. According to different SSLs and numbers of spiking samples, different spiked models were established by the PLS. The results show that the predictive ability of each model was improved by the spiking method, and the improvement effect was inversely proportional to the scale of the SSL. The spiked models built by combining the district scale SSL and a few spiking samples achieved good prediction of the SOC of two target areas (RPD = 2.72 and 3.13). Therefore, it is possible to accurately measure the SOC of new target areas by building a small-scale SSL with a few spiking samples.

scholarly journals Shining a Light on Haemonchus contortus in Sheep

Proceedings ◽  
2020 ◽  
Vol 36 (1) ◽  
pp. 138
Author(s):  
Elise Kho ◽  
Jill Fernandes ◽  
Andrew Kotze ◽  
Maggy Lord ◽  
Glen Fox ◽  
...  
Keyword(s):  
Haemonchus Contortus ◽  
Near Infrared ◽  
Wavelength Region ◽  
Nir Spectroscopy ◽  
Detection Methods ◽  
Absorption Bands ◽  
Minimal Sample ◽  
Blood Sucking ◽  
Calibration Models ◽  
On Farm

Heavy infestations of the Barber’s pole worm, Haemonchus contortus, can cause severe wasting, morbidity and mortality in animals if not promptly treated. The current detection methods for this blood-sucking parasite involve faecal worm egg counts and diagnosis of anaemia, both of which are time consuming and require expertise. As blood is detected in sheep faeces sooner during infection than worm eggs, quantitative evaluation of blood may serve as a sensitive indicator of H. contortus infection. Here we investigated the feasibility of rapid estimation of haemoglobin (Hb) in sheep faeces using visible near-infrared spectroscopy. Haemoglobin (Hb) was assessed at various concentrations in moist sheep faeces using portable visible near-infrared (vis-NIR) spectrometers. Calibration models were developed for the region of 400–600 nm, where Hb absorption bands can be found. Within this wavelength region, Hb in sheep faeces can be estimated with minimal interference from background moisture (970 nm) or chlorophyll (670 nm), suggesting that difference in diets in sheep will have minimal effect on prediction accuracy. Predictions for sheep faeces based on a drenching threshold of 3 µg Hb/mg faeces showed high levels of accuracy with minimal sample preparation (Sensitivity = 89%, specificity = 80%). The success in detecting Hb in sheep faeces indicates the potential of vis-NIR spectroscopy as a rapid, on-farm diagnostic method for predicting blood in sheep faeces, and timing treatment of H. contortus infections.

Measuring The Tensile Strain of Wood By Visible And Near-Infrared Spatially Resolved Spectroscopy

2021 ◽  
Author(s):  
Ma Te ◽  
Tetsuya Inagaki ◽  
Masato Yoshida ◽  
Mayumi Ichino ◽  
Satoru Tsuchikawa
Keyword(s):  
Light Scattering ◽  
Prediction Accuracy ◽  
Tensile Strain ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Calibration Model ◽  
Spatially Resolved ◽  
Squared Error ◽  
Stiffness Evaluation ◽  
Spatially Resolved Spectroscopy

Abstract Wood has various mechanical properties, so stiffness evaluation is critical for quality management. Using conventional strain gauges constantly is high cost, also challenging to measure precious wood materials due to the use of strong adhesive. This study demonstrates the correlation between light scattering changes inside the wood cell walls and tensile strain. A multifiber-based visible-near-infrared (Vis–NIR) spatially resolved spectroscopy (SRS) system was designed to rapidly and conventiently acquire such light scattering changes. For the preliminary experiment, samples with different thicknesses were measured to evaluate the influence of thickness. The differences in Vis–NIR SRS spectral data diminish with an increase in sample thickness, which suggests that the SRS method can successfully measure the whole strain (i.e., surface and inside) of wood samples. Then, for the primary experiment, 18 wood samples with the same thickness (2 mm) were tested to construct a strain calibration model. The prediction accuracy was characterized by a determination coefficient (R2) of 0.86 with a root mean squared error (RMSE) of 297.89 με for five-fold cross-validation; for test validation, The prediction accuracy was characterized by an R2 of 0.82 and an RMSE of 345.44 με.

scholarly journals Determination of glomalin in agriculture and forest soils by near-infrared spectroscopy

2017 ◽  
Vol 63 (No. 5) ◽  
pp. 226-230 ◽  
Author(s):  
Zbíral Jiří ◽  
Čižmár David ◽  
Malý Stanislav ◽  
Obdržálková Elena
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Forest Soils ◽  
Near Infrared ◽  
Arable Land ◽  
Calibration Model ◽  
Sustainable Land Management ◽  
Chemical Methods ◽  
Calibration Models

Determining and characterizing soil organic matter (SOM) cheaply and reliably can help to support decisions concerning sustainable land management and climate policy. Glomalin was recommended as one of possible indicators of SOM quality. Extracting glomalin from and determining it in soils using classical chemical methods is too complicated and therefore near-infrared spectroscopy (NIRS) was studied as a method of choice for the determination of glomalin. Representative sets of 84 different soil samples from arable land and grasslands and 75 forest soils were used to develop NIRS calibration models. The parameters of the NIRS calibration model (R = 0.90 for soils from arable land and grasslands and R = 0.94 for forest soils) proved that glomalin can be determined in air-dried soils by NIRS with adequate trueness and precision simultaneously with determination of nitrogen and oxidizable carbon.

A New Fuzzy Support Vector Machine for Credit Scoring

2011 ◽  
Vol 109 ◽  
pp. 636-640
Author(s):  
Bo Tang ◽  
Min Xia
Keyword(s):  
Support Vector Machine ◽  
Economic Development ◽  
Prediction Accuracy ◽  
Credit Scoring ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Good Prediction ◽  
Support Vector Machine Algorithm ◽  
Fuzzy Support Vector Machine ◽  
Good Prediction Accuracy

With China's rapid economic development, credit scoring has become very important. This paper presents a new fuzzy support vector machine algorithm used to solve the problems of credit scoring. The empirical results show that the proposed fuzzy membership model is valid ,the algorithm has good prediction accuracy and anti-noise ability.

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