Zhao Li-li, Zhao Long-lian, Li Jun-hui, Zhang Lu-da, Yan Yan-lu
Information College of China Agriculture University, Beijing 100094, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2004 Jan;24(1):41-4.
This study is based on the agriculture product near infrared spectra database, which is a foundation database. The database has very important effects on agriculture products quality analysis and agriculture breeding. What the NIR researchers and NIR users care about is how to utilize information of the foundation database fully. To share the NIR resource, unifying the scanning term to get high quality spectra is the first step. This article uses wheat powder as sample to study the influence of different resolution, different He-Ne frequency and sample granularity on the wheat powder protein model. The results show that scanning sample by 4, 8 or 16 cm(-1) resolution has little influence on the wheat powder protein math model. The change in He-Ne frequency has influence to wavenumber accuracy, but when the change is within 1 cm(-1), the influence is indistinctive. For FT-NIR instruments with He-Ne to have better stability, we needn't often adjust the He-Ne wavenumber. Sample granularity has more distinctive influence on the NIR math models.
本研究基于农产品近红外光谱数据库，这是一个基础数据库。该数据库对农产品质量分析和农业育种具有非常重要的作用。近红外研究人员和近红外用户所关心的是如何充分利用基础数据库的信息。为了共享近红外资源，统一扫描条件以获取高质量光谱是第一步。本文以小麦粉为样品，研究了不同分辨率、不同氦氖频率和样品粒度对小麦粉蛋白质模型的影响。结果表明，以4、8或16 cm⁻¹分辨率扫描样品对小麦粉蛋白质数学模型影响不大。氦氖频率的变化对波数精度有影响，但当变化在1 cm⁻¹以内时，影响不明显。对于配备氦氖的傅里叶变换近红外仪器，为使其具有更好的稳定性，无需经常调整氦氖波数。样品粒度对近红外数学模型有更显著的影响。
