聚谷氨酸发酵过程中ATR-FTIR光谱信号的分数阶基线校正

采用衰减全反射傅里叶变换红外光谱法（ATR-FTIR），结合多元校正模型对γ-聚谷氨酸（γ-PGA）发酵过程中两种主要底物葡萄糖和谷氨酸钠的浓度进行间接测量，为优化发酵系统控制提供重要的反馈信息。光谱测量中经常出现的基线漂移会严重影响后续多元校正模型的性能，需要采用基线校正算法对光谱进行预处理。现有流行的基线校正算法多数是基于Whittaker Smoother（WS）平滑算法，这些算法均采用整数阶微分对拟合基线进行约束，表达能力有限。针对现有基线校正算法中的整数阶微分自适应性差的问题，利用更加灵活的分数阶微分对基线进行约束，提出了一种基于分数阶的基线校正算法，实现对整数阶基线校正的扩展。总共进行了5个批次的γ-PGA发酵实验，并对不同批次和全部批次的ATR-FTIR光谱数据分别进行了分数阶基线校正，模型的预测精度均得到不同程度的提升。实验结果表明，只有在批次2时，基于整数阶的基线校正效果最好；其他批次的基线校正效果最好时的阶次均为分数阶。这也表明了分数阶微分（包含整数阶微分）对基线的约束更加合理。同时发现全部批次的整体基线校正效果远远差于单一批次的效果，原因可能是各批次发酵光谱的基线是不同的，对不同的批次需要选用不同的阶次以获得最佳的基线校正。此外，γ-PGA发酵样品的ATR-FTIR光谱测量是以蒸馏水为背景，会在3 100~3 600 cm-1波数范围内出现负水峰，形成有害的干扰信号；分数阶基线校正后的光谱表明，分数阶基线校正算法将负的水峰当作基线，在一定程度上进行了消除。综上分析，分数阶基线校正算法不仅扩展了传统整数阶基线校正算法的应用范围，也为消除ATR光谱中负的水峰提供了新的解决思路。

Study on the Fractional Baseline Correction Method of ATR-FTIR Spectral Signal in the Fermentation Process of Sodium Glutamate

In this paper, Attenuated Total Reflection Fourier Transformed Infrared Spectroscopy (ATR-FTIR) combined with the multivariate calibration model was used to realize the indirect measurement of the concentration of two main substrates (glucose and sodium glutamate) during the fermentation process of γ-polyglutamic acid (γ-PGA), which could provide feedback information for the fermentation process. The frequent baseline drift phenomenon in the spectrum measurement will seriously affect the performance of the subsequent multivariate calibration model, and it is necessary to use the baseline calibration algorithm to preprocess the spectrum. Most of the popular baseline correction algorithms are based on the Whittaker Smoother (WS) smoothing algorithm. And use integer-order differentials with limited expressive power to constrain the fitted baseline. Because of the poor adaptability of integer-order differential in the existing baseline correction algorithms, we use more flexible fractional-order differentials to constrain the baseline and then propose a baseline correction algorithm based on fractional-order, which realizes the extension of the integral order baseline correction. 5 batches of γ-PGA fermentation experiments were carried out, and the ATR-FTIR spectra of different batches and all batches were subjected to fractional baseline correction respectively; subsequently, the prediction accuracy of each model was improved to some extent. The experimental results show that only in batch 2 the baseline correction effect based on the integer-order is the best; the orders to obtain the best baseline correction effect for other batches were all fractional-order. Italso reflects that the constraint of the fractional-order derivative (including the integer-order derivative) on the baseline is reasonable. At the same time, it is found that the overall baseline correction effect of all batches is far worse than that of a single batch. The reason may be that the baseline of the spectra for each fermentation batch is different. Different orders need to be selected for different batches to achieve the best effect of baseline correction. In addition, the background spectrum was acquired with distilled water as the reference before measuring each γ-PGA fermentation sample. Anegative water peak thus inevitably appears in the wavenumber range of 3 100~3 600 cm-1 and forms harmful interference signals; the fractional baseline-corrected spectra show that the fractional-order baseline correction algorithm regards the negative water peak as the baseline and eliminates it to a certain extent. In summary, the fractional-order baseline correction algorithm expands the application range of the traditional integer-order baseline correction algorithm and provides a new solution to eliminate negative water peaks in the ATR spectra with water as the background spectrum.