Studies On The Determining Of Maize Quality By Near Infrared Spectroscopy

Determining maize component is an important procedure in maize quality breeding. As usual, sample component was determined by normal chemical method that destroyed sample and very slow in some content. Advanced instrument can avoid the side effects.Near infrared spectroscopy is a kind of technique that can determine sample component quickly. But every instrument must calibrate singly. So this thesis is about the feasibility of an NIR system HN1100 for determining the quality of maize. 129 varieties which had different contents were collected and were divided into two groups, one used for determining chemical values and collecting NIR spectrum of powdered samples, the other used for collecting NIR spectrum of intact samples. The content of crude protein, crude fat, and crude starch was determined by normal chemical method, regarding the chemical values as true values. Reflectance reading were collected from 1100 to 1750 nm every 2nm. After approaching the effect of different spectra treatment and mathematical treatment to calibration, the method of partial least squares (PLS) analysis was used to model development for intact and powdered samples, respectively. The other samples were used to validation. The results showed as fallows: 1. The absorbing band of samples showed that maize had different absorbance in different wavelength region. There were direct correlation between absorbance and content of samples. So the spectra of maize can apply to quantitative analysis of maize.2. There were remarkable correlations between prediction values and chemical values of maize quality. The correlation coefficiencies were 0.937, 0.945, 0.964 (powdered samples) and 0.961, 0.957, 0.982 (intact samples) in validation set of crude protein, crude fat and crude starch respectively. The standard error validation (SEP) was 0.271, 0.745, 0.666 (intact samples) and 0.499, 0.820 and 0.883 (powdered samples) of crude protein, crude fat and crude starch respectively. This showed that it is feasible to measure quality of maize by NIR.3. Compared with control experiment, all the parameter were improved after scattering correction for spectra of samples by SNV, Mean Center, Basic Offset, Detrending, SNV+ Mean Center, SNV+ Basic Offset, SNV+ Detrending. This showed that scattering correction had significant effects on calibration and validation. Derivative treatments also affected the results. The optimal scatter correction condition of crude protein, crude fat, crude starch was SNV+ Detrending, SNV+Mean center, SNV+Mean center(intact samples)and SNV+Mean center, SNV+Basic offset, SNV+Mean center(powered samples)ï¼›The optimal derivative condition was 2,2,1(intact)and 1,1,2(powered),respectively.