柠檬酸发酵液化清液生产过程监控对整个柠檬酸生产至关重要,近红外光谱能够通过不同波长下分子的振动多方面地反映过程的运行状况,包含了大量的过程信息。但是,现有方法往往是建立近红外光谱与总糖总氮等质量变量的回归模型,通过判断质量变量是否超过阈值实现对过程运行状态的事后报警,忽略了近红外光谱内部的很多有用信息,监控效果较差。该文充分利用和分析近红外光谱的统计特性,提出一种基于近红外光谱生产过程的统计监控方法,首先建立近红外光谱和总糖总氮的概率偏最小二乘模型(probability partial least squares, PPLS),然后基于模型对不同的信息设计监控指标,能够充分利用近红外不同波长上的信息,实现故障的事前预警。结果表明,采用该方法得到漏报率为9.68%,错报率为25.81%,可以有效地对柠檬酸发酵液化清液生产过程进行监控。
The monitoring of liquefied process of clear solution for citric acid fermentation is crucial to the citric acid production. Near-infrared spectroscopy can reflect the operation status through the vibration of molecules at different wavelengths, and contains a lot of process information. However, the existing methods often develop regression models between near-infrared spectroscopy (NIR) and quality variables such as total sugar total nitrogen, then the process is monitored afterwards by judging whether the quality variable exceeds its threshold. These methods often ignore useful information in NIR, resulting in poor monitoring performance. In this paper, the statistical property of near-infrared spectroscopy was fully utilized and analyzed, and a statistical monitoring method was proposed based on NIR. Firstly, a probability partial least squares model was developed for the estimation of total sugar total nitrogen by NIR. Then, based on this model, monitoring indicators were designed to achieve advance warning of faults by taking full use of different information at different wavelengths of the near infrared. The results showed that this proposed method could effectively monitor the liquefied process of clear solution for citric acid fermentation, the false negative rate was 9.68% and the false positive rate was 25.81%.
[1] 王宝石,陈坚,孙福新,等.发酵法生产柠檬酸的研究进展[J].食品与发酵工业, 2016,42(9):251-256.
[2] 潘声龙.玉米清液发酵生产柠檬酸的工艺研究[J].安徽农业科学, 2010,38(29):16 700-16 701.
[3] 孙荣.柠檬酸新型清液发酵工艺研究[D].济南:山东轻工业学院, 2011.
[4] ALVE J C, POPPI R J.Biodiesel content determination in diesel fuel blends using near infrared (NIR) spectroscopy and support vector machines (SVM)[J].Talanta, 2013,104(2):155-161.
[5] SASKIA L, MANFRED S, ROSNER S.Hydraulic traits of Norway spruce sapwood estimated by Fourier transform near-infrared spectroscopy (FT-NIR)[J].Canadian Journal of Forest Research, 2015,45(6):625-631.
[6] 战皓,方婧,杨滨,等.近红外光谱法测定不同产地独活中蛇床子素和二氢欧山芹醇当归酸酯含量[J].光谱学与光谱分析, 2017,37(4):1 110-1 113.
[7] 狄天云,高晓娟,张霞,等.基于指标成分与近红外光谱对宁夏野生和栽培甘草的比较鉴别研究[J].中国中药杂志, 2017,42(3):536-541.
[8] 王元忠,赵艳丽,张霁,等.近红外光谱信息筛选在玛咖产地鉴别中的应用[J].光谱学与光谱分析, 2016,36(2):394-400.
[9] ESPINOSA F, ANTONIO J.Combining PLS regression with portable NIR spectroscopy to on-line monitor quality parameters in intact olives for determining optimal harvesting time[J].Talanta, 2016,148:216-228.
[10] MIRSCHEL G, HELMSTEDT U, SCHERZER T, et al.Monitoring of the degree of condensation in alkoxysiloxane layers by nir reflection spectroscopy[J].Industrial & Engineering Chemistry Research, 2016,53(43):16 813-16 819.
[11] WEI Luo,JIA Wu,WANG Xuekai,et al.Near infrared spectroscopy combination with PLS to monitor the parameters of naproxen tablet preparation process[J].Analytical Methods, 2013,5(5):1 337-1 345.
[12] KANG Qian,RU Qingguo,LIU Yan,et al.On-line monitoring the extract process of Fu-fang Shuanghua oral solution using near infrared spectroscopy and different PLS algorithms[J].Spectrochim Acta A, 2016,152:431-437.
[13] 王永香,郑伟然,米慧娟,等.热毒宁注射液青蒿金银花浓缩过程近红外快速定量检测方法的建立[J].中草药, 2017,48(1):102-108.
[14] REED J P, DEVLIN D, ESTEVES S R, et al.Integration of NIRS and PCA techniques for the process monitoring of a sewage sludge anaerobic digester[J].Bioresour Technol, 2013,133(4):398-404.
[15] ZHOU Bo,YE Hao,ZHANG Haifeng,et al.Process monitoring of iron-making process in a blast furnace with PCA-based methods[J].Control Engineering Practice, 2016,47:1-14.
[16] HUCHO F, OBERTHUR W, LOTTSPEICH F.PLS-based EWMA fault detection strategy for process monitoring[J].Journal of Loss Prevention in the Process Industries, 2015,36(1):108-119.
[17] LI S, GAO J, NYAGILO J O, et al. Probabilistic partial least square regression: A robust model for quantitative analysis of raman spectroscopy data[C]. San Diego: IEEE International Conference on Bioinformatics & Biomedicine. 2012.
[18] STOCIA P, XU L Z,LI J.A new type of parameter estimation algorithm for missing data problems[J].Statistics & Probability Letters, 2005,75(3):219-229.
[19] RADY A M, GUYER D E.Evaluation of sugar content in potatoes using NIR reflectance and wavelength selection techniques[J].Postharvest Biology & Technology, 2015,103:17-26.
[20] LI Yang,GUO Mingye,SHI Xinyuan,et al.Online near-infrared analysis coupled with MWPLS and SiPLS models for the multi-ingredient and multi-phase extraction of licorice (Gancao)[J].Chinese Medicine, 2015,10(1):1-10.
[21] 郭慧娴,朱思祁,黎远鹏,等.基于iPLS和SiPLS算法的人体血清胆红素含量的可见-近红外光谱建模[J].光电子·激光, 2016,27(10):1 136-1 144.
[22] NORGAARD L, SAUDLAND A, WAGNER J, et al.Interval partial least-squares regression(iPLS): A comparative chemometric study with an example from near-infrared spectroscopy[J].Applied Spectroscopy, 2000,54(3):413-419.
