scholarly journals The Effect of Time and Method of Storage on the Chemical Composition, Pepsin-Cellulase Digestibility, and Near-Infrared Spectra of Whole-Maize Forage

2019 ◽  
Vol 9 (24) ◽  
pp. 5390 ◽  
Author(s):  
Donato Andueza ◽  
Fabienne Picard ◽  
Charlène Barotin ◽  
Véronique Menanteau ◽  
Corentin Gervais ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Infrared Spectra ◽  
Nutritive Value ◽  
Near Infrared ◽  
Storage Conditions ◽  
Soluble Carbohydrates ◽  
Dry Matter Digestibility ◽  
Near Infrared Spectra ◽  
Maize Varieties ◽  
Storage Methods

This study examined the effects of long-term storage conditions on the chemical composition, pepsin-cellulase dry matter digestibility (PCDMD), and visible (VIS)/near infrared spectra (NIR) of forage. Eighteen samples of different whole-crop maize varieties originally harvested in 1987 were used. After drying, these samples were analyzed in the laboratory for ash, crude protein (CP), structural carbohydrates, total soluble carbohydrates (TSC), starch and PCDMD, and the remaining samples were stored frozen (at −20°C) or at barn temperature (ambient temperatures ranged from −8.5 °C to 27.1 °C). In 2016, the samples were analyzed for ash, CP, structural carbohydrates, TSC, starch and PCDMD. The visible/NIR spectra of both storage methods were obtained. Chemical composition and PCDMD analyses revealed significant differences (p < 0.05) between the storage methods for TSC but not for the other parameters (p > 0.05). After sample harvesting in 1987, the analyses were compared with those in 2016. It was found that the post-harvest TSC and ash content were higher (p < 0.05) and lower (p < 0.05), respectively, during 2016. No significant differences were found for starch and PCDMD. Important differences between the VIS/NIR spectra of both storage methods were obtained in the VIS segment, particularly in the area between 630 and 760 nm. We concluded that storing dry forage samples at ambient temperature for a very long time (29 years) did not change their nutritive value compared to the values obtained before storage.

Rapid evaluation of pasture quality for a critically endangered mammal, the northern hairy-nosed wombat (Lasiorhinus krefftii)

2002 ◽  
Vol 29 (1) ◽  
pp. 91 ◽  
Author(s):  
Andrew P. Woolnough ◽  
William J. Foley
Keyword(s):  
Large Scale ◽  
Nutritive Value ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Water Soluble ◽  
Critically Endangered ◽  
Soluble Carbohydrates ◽  
Dry Matter Digestibility ◽  
Acid Detergent Fibre

Near-infrared spectroscopy (NIRS) was used to predict the nutritive value of forage species available to the critically endangered northern hairy-nosed wombat (Lasiorhinus krefftii). Nutritive attributes of the forage successfully estimated included total nitrogen concentration, fibre (including neutral detergent fibre, acid detergent fibre and acid lignin), organic matter, water soluble carbohydrates and in vitro dry matter digestibility. The reported results demonstrate the seasonal variability of the forage resource available to L. krefftii in its tropical savanna habitat. Multivariate modelling of the spectra enabled the nutritive value of forage samples to be estimated with coefficients of determination (r2) of 0.770–0.995 and standard errors of the cross-validation of 0.070–2.850 using a modified partial least-squares analysis technique. The standard error of the laboratory was 0.02–1.42. This study demonstrates that broad-based NIRS predictive equations can be used to predict the nutritive value of a number of plant types available to a herbivore over time. By using NIRS the analyst can rapidly analyse large numbers of samples with limited reduction of precision, thereby enabling large-scale ecological applications that may have previously been impeded by time and costs.

Diurnal variation in the composition of duodenal digesta

1997 ◽  
Vol 1997 ◽  
pp. 207-207
Author(s):  
S.J. Lister ◽  
R. Sanderson ◽  
A. Sargeant
Keyword(s):  
Chemical Composition ◽  
Chemical Analysis ◽  
Infrared Spectra ◽  
Chemical Structure ◽  
Near Infrared ◽  
Diurnal Changes ◽  
Difference Spectra ◽  
Near Infrared Spectra ◽  
Source Of Information ◽  
Detailed Chemical

The size of biological samples is often, by necessity, small and precludes a full and detailed chemical analysis of the material. Near infrared spectra are comprehensive records of the chemical structure and content of a substrate and are thus a rich source of information. To investigate diurnal changes in the chemical composition of duodenal digesta, NIR spectra and difference spectra were used to examine samples collected over a 24h period.

Application of Wavelet Transform in the Prediction of Chemical Composition of Sunflower Seeds by Near Infrared Reflectance Spectroscopy

2011 ◽  
Vol 460-461 ◽  
pp. 599-604
Author(s):  
Rui Zhen Han ◽  
Shu Xi Cheng ◽  
Yong He
Keyword(s):  
Wavelet Transform ◽  
Chemical Composition ◽  
Infrared Spectra ◽  
Crude Protein ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Sunflower Seeds ◽  
Near Infrared Reflectance ◽  
Near Infrared Spectra

In this paper, a method based on wavelet transform, which is used to analyze near infrared spectra, is discussed with the purpose of prediction of the content of oil, crude protein(CP) and moisture in sunflower seeds. By using different decomposing levels of Daubechies 2 wavelet transform, the near infrared spectra signals obtained from 105 intact sunflower seed samples were de-noised. Calibration equations were developed by partial least square regression (PLS) using the reconstructed spectra data with internal cross validation. It was indicated that the prediction effects varied when different wavelet decomposing level were employed. At the wavelet decomposing level 5, the best prediction effect was obtained, with the coefficient of correlation(R)and root mean square error prediction (RMSEP) being 0.953 and 0.466% for moisture;0.963 and 1.259% for crude protein; 0.801 and 1.874% for oil on a dry weight basis. It was concluded that the near infrared spectral model de-noised by means of wavelet transform can be used for the prediction of chemical composition in sunflower seeds for rapid pre-screening of quality characteristics on breeding programs.

Near Infrared Investigation of Some Lipid Extracts in Order to Ascertain Their Quality

1998 ◽  
Vol 6 (A) ◽  
pp. A285-A290 ◽  
Author(s):  
Gabriela Blagoi ◽  
Anca Bleotu ◽  
Madalina Puica ◽  
Mihaela Vasilescu ◽  
Mihaela Ilie
Keyword(s):  
Pattern Recognition ◽  
Chemical Composition ◽  
Olive Oil ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Quantitative Composition ◽  
Pattern Recognition Techniques ◽  
Qualitative And Quantitative ◽  
Olive Oils ◽  
Near Infrared Spectra

Lipid extracts that exhibit similar near infrared spectra due to their chemical composition were investigated to generate their fingerprint by using pattern recognition techniques. Sunflower, buckthorn, wheatgerm, chrysalis, and olive oils were checked for qualitative and quantitative composition by GC-MS techniques and then analysed by using an NIRSystems Pharma device. Good results were obtained for wheatgerm, chrysalis and buckthorn oil (the “quality areas” do not overlay), but contradictory results were obtained for sunflower and olive oil.

Temperature Robust Multivariate Calibration: An Overview of Methods for Dealing with Temperature Influences on near Infrared Spectra

2005 ◽  
Vol 13 (2) ◽  
pp. 53-62 ◽  
Author(s):  
J.A. Hageman ◽  
J.A. Westerhuis ◽  
A.K. Smilde
Keyword(s):  
Chemical Composition ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Data Sets ◽  
Near Infrared Spectra ◽  
Sample Composition ◽  
Spectral Intensities

Multivariate calibration is a powerful tool for establishing a relationship between spectral variables and properties of interest. Usually, changes in spectral variables are ascribed to changes in the chemical composition of the sample. However, spectral intensities that are measured at varying temperatures do not only change because of changes in sample composition but also respond to the change in temperature. In these cases, multivariate calibration can be (severely) hindered, resulting in a loss of prediction capabilities. This paper provides an overview of the characteristics and possibilities of (most) methods for temperature robust multivariate calibration. The methods are discussed by using two data sets.

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

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