摘要
小波变换(WT)具有很好的时频分离特征,信息处理能力强,已广泛用于分析化学领域;本文就小波变换在近红外光谱领域的应用进行简述。小波变换用于近红外预处理,提取有用信息,消除背景干扰,可以提高近红外的分析精度和模型稳健性;用于数据压缩可以减少数据库存储空间,提高建模速度;小波系数用于模型传递,具有传递速度快,稳健性强,所需标样少等特点;小波变换可以与神经网络、遗传算法等结合,在近红外分析领域呈现出良好的发展前景。
Wavelet transform (WT) has proven a powerful and efficient tool for dealing with chemical data due to its characteristic of dual localization and has been widely used in analytical chemistry. This paper reviews the application of wavelet transform in the field of near infrared spectroscopy, such as signal preprocessing, data compression and model transfer. The wavelet transform has shown great prospects in the signal preprocessing, and produces more robust model with little deviation. The standardization by transferring wavelet transform coefficients shows similar performance to the spectrum transfer case. In addition, it shows the possibility of using a smaller number of stable samples. The combination of wavelet transform and other chemometrics such as network and genetic analysis has shown great prospects in the NIR field.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2003年第6期1111-1114,共4页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金(20075035)
