Effects of moisture content and chemical composition on the near infrared spectra of forest foliage

1990 ◽  
Author(s):  
Mary E. Martin ◽  
John D. Aber
Keyword(s):  
Chemical Composition ◽  
Moisture Content ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra

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.

Dynamic behavior of wood chemical components under drying process measured by near infrared spectroscopy

2017 ◽  
Vol 25 (6) ◽  
pp. 400-406 ◽  
Author(s):  
Shiho Sugii ◽  
Takaaki Fujimoto ◽  
Harusa Tsutsumi ◽  
Tetsuya Inagaki ◽  
Satoru Tsuchikawa
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Moisture Content ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Principal Component ◽  
Chemical Components ◽  
Drying Process ◽  
Near Infrared Spectra ◽  
Content Range

This study examined the dynamic behavior of wood chemical components during the drying process using near infrared spectroscopy. Principal component analysis and generalized two-dimensional correlation spectroscopy were applied to identify significant absorption bands from the heavily overlapping near infrared spectra. The near infrared spectra were successively acquired over the moisture content range of 60–11%. The principal component analysis scores indicated that the wood–water interaction in the moisture content range of 60–46% significantly differed from that in the range of 45–11%. The synchronous 2D correlation spectrum constructed from the spectra in the moisture content range of 60–46% revealed that the cell wall components and water molecules responded to the drying process even though the wood exceeded the fiber saturation point. In the moisture content range of 45–11%, the H-bonded OH groups in the crystalline region of cellulose clearly increased with the decrease in bound water. Moreover, the sequential order of events was also clarified from the asynchronous spectrum.

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.

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.

Temperature and moisture insensitive prediction of biomass calorific value from near infrared spectra using external parameter orthogonalization

2019 ◽  
Vol 27 (4) ◽  
pp. 259-269 ◽  
Author(s):  
Guillaume Hans ◽  
Bruce Allison
Keyword(s):  
Moisture Content ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Calorific Value ◽  
Partial Least Square ◽  
External Parameter ◽  
Calibration Model ◽  
Gross Calorific Value ◽  
Influence Of Temperature ◽  
Near Infrared Spectra

In the pulp and paper and biofuel industries, real-time online characterization of biomass gross calorific value is of critical importance to determine its quality and price and for process optimization. Near infrared spectroscopy is a relatively low-cost technology that could potentially be used for such an application. However, the near infrared spectra are also influenced by biomass temperature and moisture content. In this study, external parameter orthogonalization is employed to remove simultaneously the influence of temperature and moisture content on the spectra before predicting gross calorific value. External parameter orthogonalization is of particular interest when one desires to transfer information from one modeling experiment to another, such as when developing a calibration model for a new property from the same material, or when it would be more efficient to divide the experimental effort. External parameter orthogonalization (EPO) was found to be an effective method for desensitizing a partial least squares calibration model to the influence of temperature and moisture content, enabling robust and accurate prediction of biomass gross calorific value. Partial least square models developed with external parameter orthogonalization always provided equal or better performance than models developed without external parameter orthogonalization. The paper shows that experimental efforts and costs can be reduced by approximately one half while maintaining prediction accuracy and model robustness.

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.

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