基于近红外光谱特征信号的秸秆粒径校正方法研究

Straw Particle Size Correction Method Based on Near-infrared Spectral Characteristic Signals

摘要:

近红外光谱技术已被广泛用于秸秆理化特性的快速分析研究，具有检测速度快、效率高、无损等优点。然而实际应用时，秸秆的粒径不同会降低模型的检测精度及其稳定性。针对几何粒径水稻秸秆样本，分别获取其在10000~4000cm-1波段的近红外光谱。采用多项式拟合（Polynomial fitting, PF）消除秸秆样本光谱信号中的背景信号。使用单因素方差分析对多项式拟合的几何粒径秸秆样本光谱特征信号进行统计学检验（P<0.05）。运用主成分分析（Principal component analysis, PCA）及Biplot解析光谱特征信号对几何粒径秸秆样本光谱差异的贡献度。分别利用Pearson与Spearman相关性分析筛选关键光谱特征信号。根据二次多项式拟合数学模型，假设吸光度由粒径相关光谱信息（Particle size related spectrum information, PSRSI）与非粒径相关光谱信息（Non-particle-size-related spectrum information, nPSRSI）构成，提出了一种基于关键光谱特征信号吸光度与粒径的粒径回归校正方法（Particle size regression correction method, PSRCM）。结果表明：几何粒径秸秆样本5180cm-1光谱特征信号存在极显著差异性（P≤005，P≤0.01，P≤0.001）且与粒径呈现极显著相关性（Pearson’s r为0.96）。基于二次多项式数学模型的表现最优，决定系数R2与均方根误差（RMSE）的平均值分别为0.53和0.0021。PSRCM可有效校正6.824、5360、5180cm-1等特征信号引起的几何粒径秸秆样本光谱差异，校正效果明显优于标准正态变量变换（Standard normal variate transformation, SNV）与乘性散射校正（Multiplicative scatter correction, MSC）。本研究为秸秆近红外光谱无标样模型传递校正方法提供了理论依据。

Abstract:

Near-infrared spectroscopy has been widely used for the rapid analysis of the physical and chemical properties of straw, offering advantages such as fast detection speed, high efficiency, and non-destructive testing. However, in practical applications, differences in straw particle size can reduce the detection accuracy and stability of the model. For rice straw samples with varying geometric particle sizes, near-infrared spectra were obtained in the 10000~4000cm-1 wavelength range. Polynomial fitting (PF) was employed to remove background signals from the spectral data of the straw samples. A one-way analysis of variance (ANOVA) was used to perform statistical testing (P<0.05) on the spectral feature signals of the geometrically sized straw samples after polynomial fitting. Principal component analysis (PCA) and Biplot analysis were employed to assess the contribution of spectral feature signals to the spectral differences among straw samples with different geometric particle sizes. Pearson and Spearman correlation analyses were used to screen for key spectral feature signals. Based on the quadratic polynomial fitting mathematical model, it was assumed that absorbance was composed of particle size-related spectral information (PSRSI) and non-particle size-related spectral information (nPSRSI). A particle size regression correction method (PSRCM) was proposed based on the absorbance and particle size of key spectral feature signals. The results indicated that the spectral feature signals at 5180cm-1 of the geometric particle size straw samples exhibited extremely significant differences (P≤0.05, P≤0.01, P≤0.001) and showed a highly significant correlation with particle size (Pearson’s r was 0.96). The quadratic polynomial mathematical model performed optimally, with an average coefficient of determination R2 and root mean square error (RMSE) of 0.53 and 0.0021, respectively. PSRCM effectively corrected the spectral differences caused by characteristic signals at 6824cm-1, 5360cm-1, and 5180cm-1, with correction effects significantly superior to those of standard normal variate transformation (SNV) and multiplicative scatter correction (MSC). The research result can provide a theoretical basis for the transfer correction method of straw near-infrared spectroscopy models, which can be applied without the need for standard samples.