Real-time estimation of glucose concentration in algae cultivation system using Raman spectroscopy

Oh, Se-Kyu; Yoo, Sung Jin; Jeong, Dong Hwi; Lee, Jong Min

doi:10.1016/j.biortech.2013.05.008

Cited by 34 publications

(19 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In nonalgal bioprocesses, gas analyzers, fluorescence sensors and DO sensors are often used as hardware sensor inputs [249]. In a wider sense, practically all advanced monitoring methods used for bioprocesses could be called software sensors because all measurement methods producing complex signals such as Raman spectroscopy must be accompanied by complex multistep evaluation algorithms to provide meaningful results [227]. Software sensors sensu stricto are based either on a mathematical process model with mass and energy balances and process kinetics implemented in an observer or a statistical filter, or employ ANNs (which in itself is a type of correlation machine) trained on process data to map combined signals of several physical sensors to the value of the desired process variable without having to formulate a mathematical model or to recognize and quantify the underlying causal connection.…”

Section: Software Sensors and Other Computer-aided Monitoring Methodsmentioning

confidence: 99%

“…Raman spectroscopy has also been employed for rapid determination of dissolved compounds and nutrients. In a mixotrophic cultivation of Chlorella, the glucose concentration was successfully estimated from the Raman spectra of unprocessed microalgal samples using a partial least squares model with pre-and post-processing of spectral data [227]. The latter method could be adapted for online monitoring.…”

Section: Raman Spectroscopymentioning

confidence: 99%

See 1 more Smart Citation

Monitoring of Microalgal Processes

Havlík

Scheper

Reardon

2015

Microalgae Biotechnology

View full text Add to dashboard Cite

Process monitoring, which can be defined as the measurement of process variables with the smallest possible delay, is combined with process models to form the basis for successful process control. Minimizing the measurement delay leads inevitably to employing online, in situ sensors where possible, preferably using noninvasive measurement methods with stable, low-cost sensors. Microalgal processes have similarities to traditional bioprocesses but also have unique monitoring requirements. In general, variables to be monitored in microalgal processes can be categorized as physical, chemical, and biological, and they are measured in gaseous, liquid, and solid (biological) phases. Physical and chemical process variables can be usually monitored online using standard industrial sensors. The monitoring of biological process variables, however, relies mostly on sensors developed and validated using laboratory-scale systems or uses offline methods because of difficulties in developing suitable online sensors. Here, we review current technologies for online, in situ monitoring of all types of process parameters of microalgal cultivations, with a focus on monitoring of biological parameters. We discuss newly introduced methods for measuring biological parameters that could be possibly adapted for routine online use, should be preferably noninvasive, and are based on approaches that have been proven in other bioprocesses. New sensor types for measuring physicochemical parameters using optical methods or ion-specific field effect transistor (ISFET) sensors are also discussed. Reviewed methods with online implementation or online potential include measurement of irradiance, biomass concentration by optical density and image analysis, cell count, chlorophyll fluorescence, growth rate, lipid concentration by infrared spectrophotometry, dielectric scattering, and nuclear magnetic resonance. Future perspectives are discussed, especially in the field of image analysis using in situ microscopy, infrared spectrophotometry, and software sensor systems.

show abstract

Section: Software Sensors and Other Computer-aided Monitoring Methodsmentioning

confidence: 99%

Section: Raman Spectroscopymentioning

confidence: 99%

Monitoring of Microalgal Processes

Havlík

Scheper

Reardon

2015

Microalgae Biotechnology

View full text Add to dashboard Cite

show abstract

“…1, it can be found that the RCF method eliminated the fluorescence background and increased the signal-to-noise ratio 13 of 25 of spectra, this may be the reason why the RCF pretreatment can improve the performance of the spectral determination models. Oh et al [28] used RCF pretreatment in real-time estimation of glucose concentration in algae by Raman spectroscopy, and the result was also improved. Table 2).…”

Section: Selecting Of Characteristic Bands For Quantitative Determinamentioning

confidence: 99%

Quantitative visualization of photosynthetic pigments in tea leaves based on Raman spectroscopy and calibration model transfer

Zeng

Zhang

Luo

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Photosynthetic pigments participating in the absorption, transformation and transfer of light energy play a very important role in plant growth. While, the spatial distribution of foliar pigments is an important indicator of environmental stress, such as pests, diseases and heavy metal stress. Results: In this paper, in situ quantitative visualization of chlorophyll and carotenoid was realized by combining the Raman spectroscopy with calibration model transfer, and a laboratory Raman spectral model was successfully extended to a portable field spectral measurement. Firstly, a nondestructive and fast model for determination of chlorophyll and carotenoid in tea leaf was established based on confocal micro-Raman spectrometer in the laboratory. Then the spectral model was extended to a real-time foliar map scanning spectra of a field portable Raman spectrometer through calibration model transfer, and the spectral variation between the confocal micro-Raman spectrometer in the laboratory and the portable Raman spectrometer were effectively corrected by the direct standardization (DS) algorithm. The portable map scanning Raman spectra of the tea leaves after the model transfer were got into the established quantitative determination model to predict the concentration of photosynthetic pigments at each pixel of the tea leaves. The predicted photosynthetic pigments concentration of each pixel was imaged to illustrate the distribution map of foliar pigments. Statistical analysis showed that the predicted pigment contents were highly correlated with the real contents. Conclusions: It can be concluded that the Raman spectroscopy was applicable for in situ, non-destructive and rapid quantitative detecting and imaging of photosynthetic pigment concentration in tea leaves, and the spectral detection model established based on the laboratory Raman spectrometer can be applied to a portable field spectrometer for quantitatively imaging of the foliar pigments.

show abstract

“…The strain used in this study was an Escherichia coli B2032/82 serotype K1. It is an original clinical wildtype isolate (Rode et al, 2008) and is used for polysialic acid production (Vries et al, 2017). The starter culture was prepared by inoculating a glycerol stock into 100 mL lysogeny broth (LB) medium and subsequently incubating at 37 • C, 150 rpm for 7 h. The culture was diluted (1/1000) in 500 mL of defined medium (pH 7.5), which consists of 0.15 g L −1 MgSO 4 q 7H 2 O, 1 mg L −1 FeSO 4 q 7H 2 O, CuSO 4 q 7H 2 O, 9.3 g L −1 K 2 HPO 4 , 2.03 g L −1 KH 2 PO 4 , 10 g L −1 (NH 4 ) 2 SO 4 , 1.2 g L −1 NaCl, 1.1 g L −1 K 2 SO 4 , 13 mg L −1 CaCl 2 and 22 g L −1 glucose.…”

Section: Bacterial Strain and Cultivation Conditionsmentioning

confidence: 99%

Development and characterisation of a new fluorescence sensor for online monitoring of bioprocesses

et al. 2018

View full text Add to dashboard Cite

Fluorescence spectroscopy is a highly sensitive and non-invasive technique for the identification of characteristic process states and for the online monitoring of substrate and product concentrations. Nevertheless, fluorescence sensors are mainly used in academic studies and are not well implemented for monitoring of industrial production processes. In this work, we present a newly developed robust online fluorescence sensor that facilitates the analysis of fluorescence measurements. The set-up of the sensor was miniaturised and realised without any moveable part to be robust enough for application in technical environments. It was constructed to measure only the three most important biologic fluorophores (tryptophan, NADH and FAD/FMN), resulting in a significant data reduction compared to conventional a 2-D fluorescence spectrometer. The sensor performance was evaluated by calibration curves and selectivity tests. The measuring ranges were determined as 0.5-50 µmol L −1 for NADH and 0.0025-7.5 µmol L −1 for BSA and riboflavin. Online monitoring of batch cultivations of wild-type Escherichia coli K1 in a 10 L bioreactor scale were performed. The data sets were analysed using principal component analysis and partial least square regression. The recorded fluorescence data were successfully used to predict the biomass of an independent cultivation (RMSEP 4.6 %).

show abstract

Real-time estimation of glucose concentration in algae cultivation system using Raman spectroscopy

Cited by 34 publications

References 30 publications

Monitoring of Microalgal Processes

Monitoring of Microalgal Processes

Quantitative visualization of photosynthetic pigments in tea leaves based on Raman spectroscopy and calibration model transfer

Development and characterisation of a new fluorescence sensor for online monitoring of bioprocesses

Contact Info

Product

Resources

About