Biomedical subjects

Yan-Lu Yan

Publications and source records attributed to Yan-Lu Yan.

6 recordsLinked to original sources

PubMed2006

[Prediction of IVDMD with near infrared reflectance spectroscopy (NIRS) in maize stalk].

The in vitro dry matter digestion (IVDMD) in maize stalk was analyzed with 161 samples selected from 600 samples of different eco-environments, hybrids and inbred lines, development stages, and various parts of the plants in two years. The technique of near infrared reflectance spectroscopy (NIRS) and partial least square regression (PLS) were used to establish the models by comparing several preprocessing procedures and wavelength ranges. The optimal models could be obtained in the range of 6 101. 7-5 773. 8 cm(-1) and 4 601. 3-4 246. 5 cm(-1) by the spectral data preprocessing of the Max-Min normalization. The model is suitable for measuring various sample IVDMD. The determination coefficients of the modes were 0.907 3 and 0.906 6 for cross and external validation, respectively. The root mean square error of prediction was 2.08%, and the coefficient of correlation(r) was 0.956 between NIRS predicted and actual IVDMD in these materials. The results showed that NIRS is a simple effective means for measuring IVDMD in maize stalk. The results are of great value of application in screening and evaluating quality constituents of silage maize.

Animal Feed↗

PubMed2006

[The PLS calibration model optimization and determination of rice protein content by near-infrared reflectance spectroscopy].

A hundred and ninety one representative brown rice samples from the Chinese Rice Genebank and the DH population derived from the cross of japonica upland rice IRAT109 with paddy rice Yuefu were selected for this study. Their protein content range was 5.90%-14.50%. Near-infrared diffusive spectroscopy (NIDRS) and partial least square (PLS) were used to determine protein content with different wavelength ranges and data preprocessing methods for regression and information extraction. The object function [R/(1+RMSECV)] of quantitative model was defined, and the samples of calibration and validation tests were classified by projective distribution of PLS loadings. These methods were applied to the optimization of the calibration model. It is demonstrated that the calibration model developed by the spectral data pretreatment of the first derivative + standard vector normalization with the same spectral region (5 000-9 000 cm(-1) resulted in the best determination of protein content in brown rice when the maximum values of the object function were reached. The maximum values of the object functions of calibration and validation sets were 0.701 and 0.687, respectively. Projective distributions of PLS loadings were used to validate the models, and the result was the same as that of validating model by object function [R/(1+RMSECV)].

Least-Squares Analysis↗

PubMed2006

[The effects of noise on NIR analysis and related mathematic pretreatments and models].

The feasibility of using a relatively high noise NIR spectrometer for analysis was examined by using maize powder samples. The results showed that with four-time averaged NIR spectrum data without more pretreatments, PLS mathematic models and CAU-NIR software, the relative high noise scan NIR spectrometer could be used to get satisfied prediction results compared with other low noise NIR spectrometers. The prediction coefficient could reach 98% and the CV (variation coefficient) was 6.2%. It was proved that when the S/N of NIR spectrometer was lower than 10(5), it still could be used for quantity analysis with the help of some mathematic pretreatments and models.

Models, Theoretical↗

PubMed2005

[An applied study on Fourier transform near-infrared whole spectroscopy regression analysis].

In the present paper, 66 wheat samples were used as experimental materials, 33 of them were used for building the quantitative analysis model of protein content, and the rest composed the prediction set. Using Moore-Penrose matrix, we estimated directly the regression coefficients of the regression analysis model with Fourier transform near-infrared (FTNIR) whole spectroscopy. The samples of prediction set were analyzed, and the correlation coefficient is 0.979 9 between the prediction values of the near-infrared model and the standard chemical ones by Kjeldahl's method, and the average relative error is 1.76%. Using Moore-Penrose matrix, we can not only get the near-infrared spectroscopy analysis model's regression coefficients, but also know their contribution at every wavelength point. Consequently we can understand and explain the physical and chemical significance of the FTNIR whole spectroscopy regression model.

Plant Proteins↗

PubMed2005

[Study on the robust NIR calibration models for moisture].

The differences in sample moisture affect the robustness of NIR model obviously. In the present paper, three approaches, including preprocessing spectra, selecting wavelength, and setting up global calibration, were investigated to analyze the feasibility of setting up robust calibrations based on the protein content of wheat with different moistures. It has been found that with only spectral pretreatment method it fails to obtain satisfactory results, which can not remove the effects caused by moisture difference. Both selecting wavelengths and developing global calibration model proved to be good approaches for developing robust NIR calibration, yet developing global calibration is better. The mechanisms of the three different methods were also analyzed theoretically.

Calibration↗

PubMed2004

[A basic research to share Fourier transform near-infrared spectrum information resource].

A method to share the information resource in the database of Fourier transform near-infrared(FTNIR) spectrum information of agricultural products and utilize the spectrum information sufficiently is explored in this paper. Mapping spectrum information from one instrument to another is studied to express the spectrum information accurately between the instruments. Then mapping spectrum information is used to establish a mathematical model of quantitative analysis without including standard samples. The analysis result is that the relative coefficient r is 0.941 and the relative error is 3.28% between the model estimate values and the Kjeldahl's value for the protein content of twenty-two wheat samples, while the relative coefficient r is 0.963 and the relative error is 2.4% for the other model, which is established by using standard samples. It is shown that the spectrum information can be shared by using the mapping spectrum information. So it can be concluded that the spectrum information in one FTNIR spectrum information database can be transformed to another instrument's mapping spectrum information, which makes full use of the information resource in the database of FTNIR spectrum information to realize the resource sharing between different instruments.

Algorithms↗