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.

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.

Sign in / Sign up

Export Citation Format

Share Document