Application of near-infrared spectroscopy technology in the complex fermentation system to achieve high-efficiency production

Chen Yang , Chen Lingli , Guo Meijin , Li Xu , Liu jinsong , Liu Xiaofeng , Chen Zhongbing , Tian Xiaojun , Zheng Haoyue , Tian Xiwei , Chu Ju , Zhuang Yingping

Bioresources and Bioprocessing ›› 2021, Vol. 8 ›› Issue (1) : 96

PDF
Bioresources and Bioprocessing ›› 2021, Vol. 8 ›› Issue (1) : 96 DOI: 10.1186/s40643-021-00452-9
Research

Application of near-infrared spectroscopy technology in the complex fermentation system to achieve high-efficiency production

Author information +
History +
PDF

Abstract

The fermentation process is dynamically changing, and the metabolic status can be grasped through real-time monitoring of environmental parameters. In this study, a real-time and on-line monitoring experiment platform for substrates and products detection was developed based on non-contact type near-infrared (NIR) spectroscopy technology. The prediction models for monitoring the fermentation process of lactic acid, sophorolipids (SLs) and sodium gluconate (SG) were established based on partial least-squares regression and internal cross-validation methods. Through fermentation verification, the accuracy and precision of the NIR model for the complex fermentation environments, different rheological properties (uniform system and multi-phase inhomogeneous system) and different parameter types (substrate, product and nutrients) have good applicability, and R 2 was greater than 0.98, exhibiting a good linear relationship. The root mean square error of prediction shows that the model has high credibility. Through the control of appropriate glucose concentration in SG fermentation as well as glucose and oil concentrations SLs fermentation by NIR model, the titers of SG and SLs were increased to 11.8% and 26.8%, respectively. Although high cost of NIR spectrometer is a key issue for its wide application in an industrial scale. This work provides a basis for the application of NIR spectroscopy in complex fermentation systems.

Keywords

Near-infrared spectroscopy / On-line / Lactic acid / Sophorolipids / Sodium gluconate

Cite this article

Download citation ▾
Chen Yang, Chen Lingli, Guo Meijin, Li Xu, Liu jinsong, Liu Xiaofeng, Chen Zhongbing, Tian Xiaojun, Zheng Haoyue, Tian Xiwei, Chu Ju, Zhuang Yingping. Application of near-infrared spectroscopy technology in the complex fermentation system to achieve high-efficiency production. Bioresources and Bioprocessing, 2021, 8(1): 96 DOI:10.1186/s40643-021-00452-9

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Afendras G, Markatou M. Optimality of training/test size and resampling effectiveness in cross-validation. J Stat Plan Inference, 2019, 199: 286-301.

[2]

Bence K, Andrs S, Szilveszter G. On-line glucose monitoring by near infrared spectroscopy during the scale up steps of mammalian cell cultivation process development. Bioprocess Biosyst Eng, 2019, 42(6): 921-932.

[3]

Broderick GA, Kang JH. Automated simultaneous determination of ammonia and total amino acids in ruminal fluid and in vitro media. J Dairy Sci, 1980, 63(1): 64-75.

[4]

Cai X, Chen S, Chu J, Zhuang YP, Zhang SL, Wang H, Liu Y. The optimization of guanosine fermentation based on process parameter correlation analysis. Acta Microbiol Sin, 2002, 42(2): 232-235.

[5]

Cervera AE, Petersen N, Lantz AE, Larsen A, Gernaey KV. Application of near-infrared spectroscopy for monitoring and control of cell culture and fermentation. Biotechnol Prog, 2010, 25(6): 1561-1581.

[6]

Chen H, Lin Z, Tan C. Classification of different animal fibers by near infrared spectroscopy and chemometric models. Microchem J, 2019, 144: 489-494.

[7]

Chen Y, Lin YM, Tian XW, Li QH, Chu J. Real-time dynamic analysis with low-field nuclear magnetic resonance of residual oil and sophorolipids concentrations in the fermentation process of Starmerella bombicola. J Microbiol Methods, 2019, 157: 9-15.

[8]

Costa MCA, Morgano MA, Ferreira MMC, Milani RF. Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression. Food Chem, 2019, 273: 85-90.

[9]

Do Nascimento RJA, De Macedo GR, Dos Santos ES. Real time and in situ near-infrared spectroscopy (NIRS) for quantitative monitoring of biomass, glucose, ethanol and glycerine concentrations in an alcoholic fermentation. Braz J Chem Eng, 2017, 34(2): 459-468.

[10]

Dong CW, Li J, Wang JJ, Liang GZ, Jiang YW, Yuan HB, Yang YQ, Meng HW. Rapid determination by near infrared spectroscopy of theaflavins-to-thearubigins ratio during Congou black tea fermentation process. Spectrochim Acta A Mol Biomol Spectrosc, 2018, 205: 227-234.

[11]

Durge AS, Paliwal KV. Some aspects of ascorbic acid as a reductant in the estimation of phosphorus. Plant Soil, 1967, 27(3): 460-462.

[12]

Feng Y, Tian XW, Chen Y, Wang ZY, Xia JY, Qian JC, Zhuang YP, Chu J. Real-time and on-line monitoring of ethanol fermentation process by viable cell sensor and electronic nose. Bioresour Bioprocess, 2021

[13]

Lorena V, Miguel G, Patricia RM. Monitoring in real time the cytotoxic effect of Clostridiumdifficile upon the intestinal epithelial cell line HT29. J Microbiol Methods, 2015, 119: 66-73.

[14]

Navrátil M, Norberg A, Lembrén L, Mandenius CF. On-line multi-analyzer monitoring of biomass, glucose and acetate for growth rate control of a Vibriocholerae fed-batch cultivation. J Biotechnol, 2005, 115(1): 67-79.

[15]

Olarewaju OO, Magwaza LS, Nieuwoudt H, Poblete-Echeverría C, Fawole OA, Tesfay SZ, Opara UL. Model development for non-destructive determination of rind biochemical properties of ‘Marsh’ grapefruit using visible to near-infrared spectroscopy and chemometrics. Spectrochim Acta A Mol Biomol Spectrosc, 2019, 209: 62-69.

[16]

Peng R, He ZF, Gou TT, Du JY, Li HJ. Detection of parameters in solid state fermentation of Monascus by near infrared spectroscopy. Infrared Phys Techn, 2019, 96: 244-250.

[17]

Pinto ASS, Pereira SC, Ribeiro MPA, Farinas CS. Monitoring of the cellulosic ethanol fermentation process by near-infrared spectroscopy. Bioresource Technol, 2015, 203: 334-340.

[18]

Puvendran K, Anupama K, Jayaraman G. Real-time monitoring of hyaluronic acid fermentation by in situ transflectance spectroscopy. Appl Microbiol Biot, 2018, 102(6): 2659-2669.

[19]

Quintelas C, Mesquita DP, Ferreira EC, Amaral AL. Quantification of pharmaceutical compounds in wastewater samples by Near Infrared Spectroscopy (NIR). Talanta, 2018, 75(5): 1356-1361.

[20]

Rehman NU, Ali L, Al-Harrasi A, Mabood F, Al-Broumi M, Khan AL, Hussain H, Hussain J, Csuk R. Quantification of AKBA in Boswelliasacra using NIRS coupled with PLSR as an alternative method and cross-validation by HPLC. Phytochem Anal, 2018, 29(2): 137-143.

[21]

Rodrigues LO, Vieira L, Cardoso JP, Menezes JC. The use of NIR as a multi-parametric in situ monitoring technique in filamentous fermentation systems. Talanta, 2008, 75(5): 1356-1361.

[22]

Ryan TE, Southern WM, Reynolds MA, McCully KK. A cross-validation of near-infrared spectroscopy measurements of skeletal muscle oxidative capacity with phosphorus magnetic resonance spectroscopy. J Appl Physiol, 2013, 115(12): 1757-1766.

[23]

Sandor M, Rudinger F, Solle D, Bienert R, Grimm C, Grop S, Scheper T. NIR-spectroscopy for bioprocess monitoring and control. BMC Proc, 2013, 7: 29.

[24]

Scarff M, Arrnold SA, Harvey LM, McNeil B. Near infrared spectroscopy for bioprocess monitoring and control: current status and future trends. Crit Rev Biotechnol, 2007, 26(1): 17-39.

[25]

Schalk R, Heintz A, Braun F, Iacono G, Radle M, Gretz N, Methner FJ, Beuermann T. Comparison of raman and mid-infrared spectroscopy for real-time monitoring of yeast fermentations: a proof-of-concept for multi-channel photometric sensors. Appl Sci, 2019, 9(12): 2472.

[26]

Svendsen C, Cieplak T, Frans VDB. Exploring process dynamics by near infrared spectroscopy in lactic fermentations. J Near Infrared Spectrosc, 2016, 24(5): 443-451.

[27]

Tian XW, Shen YT, Zhuang YP, Wei Z, Hang HF, Chu J. Kinetic analysis of sodium gluconate production by Aspergillusniger with different inlet oxygen concentrations. Bioprocess Biosyst Eng, 2018, 41(11): 1697-1706.

[28]

Tian XW, Zhou G, Wang WF, Zhang M, Hang HF, Mohsin A, Chu J, Zhuang YP, Zhang SL. Application of 8-parallel micro-bioreactor system with non-invasive optical pH and DO biosensor in high-throughput screening of L-lactic acid producing strain. Bioresour Bioprocess, 2018

[29]

Wang XY. Strategy for improving L-isoleucine production efficiency in Corynebacteriumglutamicum. Appl Microbiol Biotechol, 2019, 107(5): 2101-2111.

[30]

Wang T, Liu T, Wang Z, Tian X, Yang Y, Guo M, Chu J, Zhuang Y. A rapid and accurate quantification method for real-time dynamic analysis of cellular lipids during microalgal fermentation processes in Chlorellaprotothecoides with low field nuclear magnetic resonance. J Microbiol Methods, 2016, 124: 13-20.

[31]

Wang SY, Zhang J, Wang CC, Yu XM, Cai WS, Shao XG. Determination of triglycerides in human serum by near-infrared diffuse reflectance spectroscopy using silver mirror as a substrate. Chin Chem Lett, 2019, 30(1): 111-114.

[32]

Wang S, Tamura T, Kyouno N, . Rapid detection of quality of Japanese fermented soy sauce using near-infrared spectroscopy. Anal Methods, 2020, 12(18): 2347-2354.

[33]

Zhang SL, Chu J, Zhuang YP. A multi-scale study of industrial fermentation processes and their optimization. Adv Biochem Eng Biotechnol, 2004, 87: 97-150.

[34]

Zhang Q, Chen Y, Hong M, Gao Y, Chu J, Zhuang YP, Zhang SL. The dynamic regulation of nitrogen and phosphorus in the early phase of fermentation improves the erythromycin production by recombinant Saccharopolyspora erythraea strain. Bioresour Bioprocess, 2014

[35]

Zhao HT, Pang KY, Lin WL, Wang ZJ, Gao DQ, Guo MJ, Zhuang YP. Optimization of the n-propanol concentration and feedback control strategy with electronic nose in erythromycin fermentation processes. Process Biochem, 2016, 51(2): 195-203.

Funding

the National Key Research and Development Program(2017YFB0309302)

the National Science Foundation for Young Scientists of China(31700038)

the Fundamental Research Funds for the Central Universities(WF01211708)

AI Summary AI Mindmap
PDF

183

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/