Prediction of diet quality for sheep from faecal characteristics: comparison of near-infrared spectroscopy and conventional chemistry predictive models

2015 ◽  
Vol 55 (1) ◽  
pp. 1 ◽  
Author(s):  
D. G. Kneebone ◽  
G. McL. Dryden

This study evaluated the ability of equations developed from the analysis of faecal material by conventional chemistry (F.CHEM), and by near-infrared spectroscopy (F.NIRS), to predict intake and digestibility of forages fed with or without supplements. In vivo datasets were obtained using 30 sheep and 25 diets to provide 124 diet–faecal pairs, with each sheep fed four or five of the diets. The diets were five forages fed alone or with urea, molasses, cottonseed meal or sorghum grain supplements. Ninety-nine diet–faecal pairs were selected at random, but ensuring that all diets were represented and both the F.CHEM and F.NIRS prediction equations were developed from this dataset. The remaining 25 diet–faecal pairs were used as a validation dataset. Regressions for F.CHEM were developed by stepwise regression, and F.NIRS prediction equations were developed by partial least-squares regression. Prediction equations based solely on faecal analyte concentrations (F.CHEMc) had poor predictive ability, and models incorporating faecal constituent excretion rates (F.CHEMe) were the best at predicting feed constituent intakes. These models had slightly lower standard errors of prediction (SEP) for organic matter (OM) intake and digestible OM intake compared with the F.NIRS models that did not include faecal excretion rates. However, F.NIRS models had lower SEP for protein intake and OM digestibility. Good agreement between the F.CHEMe and F.NIRS methods was evident (according to the 95% limits-of-agreement test), and both predicted the reference values precisely and with small bias. Equations derived from a dataset that included representatives of all diets used in the experiment gave much better prediction of diet characteristics than those developed from a dataset constructed entirely at random. Equations for F.NIRS developed in this way successfully predicted the characteristics of diets that included forages fed alone and with the type of supplements used in tropical Australia.

2019 ◽  
Vol 9 (11) ◽  
pp. 2366 ◽  
Author(s):  
Laura Di Sieno ◽  
Alberto Dalla Mora ◽  
Alessandro Torricelli ◽  
Lorenzo Spinelli ◽  
Rebecca Re ◽  
...  

In this paper, a time-domain fast gated near-infrared spectroscopy system is presented. The system is composed of a fiber-based laser providing two pulsed sources and two fast gated detectors. The system is characterized on phantoms and was tested in vivo, showing how the gating approach can improve the contrast and contrast-to-noise-ratio for detection of absorption perturbation inside a diffusive medium, regardless of source-detector separation.


2019 ◽  
Vol 59 (6) ◽  
pp. 1190 ◽  
Author(s):  
A. Bahri ◽  
S. Nawar ◽  
H. Selmi ◽  
M. Amraoui ◽  
H. Rouissi ◽  
...  

Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.


2017 ◽  
Vol 25 (5) ◽  
pp. 301-310 ◽  
Author(s):  
Jetsada Posom ◽  
Panmanas Sirisomboon

This research aimed to determine the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using Fourier transform near infrared spectroscopy as an alternative to bomb calorimetry and thermogravimetry. Bamboo culms used in this study had circumferences ranging from 16 to 40 cm. Model development was performed using partial least squares regression. The higher heating value, volatile matter, fixed carbon and ash content were predicted with coefficients of determination (r2) of 0.92, 0.82, 0.85 and 0.51; root mean square error of prediction (RMSEP) of 122 J g−1, 1.15%, 1.00% and 0.77%; ratio of the standard deviation to standard error of validation (RPD) of 3.66, 2.55, 2.62 and 1.44; and bias of 14.4 J g−1, −0.43%, 0.03% and −0.11%, respectively. This report shows that near infrared spectroscopy is quite successful in predicting the higher heating value, and is usable with screening for the determination of fixed carbon and volatile matter. For ash content, the method is not recommended. The models should be able to predict the properties of bamboo samples which are suitable for achieving higher efficiency for the biomass conversion process.


2021 ◽  
Vol 271 ◽  
pp. 03067
Author(s):  
Xiaohong He ◽  
Zhihong Song ◽  
Haifei Shang ◽  
Silang Yang ◽  
Lujing Wu ◽  
...  

Currently, the laboratory diagnostic tests available for HIV-1 viral infection are mainly based on serological testing which relies on enzyme-linked immunosorbent assay (ELISA) for blood HIV antigen detection and reverse transcription polymerase chain reaction (RT-PCR) for HIV specific RNA sequence identification. However, these methods are expensive and time-consuming, and suffer from false positive and/or false negative results. Thus, there is an urgent need for developing a cost effective, rapid and accurate diagnostic method for HIV-1 infection. In order to reduce the barriers for effective diagnosis, a near-infrared spectroscopy (NIR) method was used to detect the HIV-1 virus in human serum, specifically, three absorption peaks with dose-dependent at 1582nm, 1810nm and 2363nm were found by multiple FBiPLSR test analysis for HIV-nano and HIV-EGFP, but not for MLV. Therefore, we recommend the use of 1582nm, 1810nm and 2363nm as the characteristic spectrum peak, for early screening and rapid diagnosis of serum HIV.


2018 ◽  
Vol 11 (7) ◽  
pp. e201700365 ◽  
Author(s):  
Raphael Henn ◽  
Christian G. Kirchler ◽  
Zora L. Schirmeister ◽  
Andreas Roth ◽  
Werner Mäntele ◽  
...  

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