Robust near-infra-red spectroscopic probe for dynamic monitoring of critical nutrient ratio in microbial fermentation processes
| Autor: | Sanjay Tiwari, Arun Chandavarkar, G. K. Suraishkumar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Near Infrared
Linoleic acid Process analytical technology Analytical chemistry Critical nutrient ratio ammonia chemistry.chemical_compound Nutrient Pichia pastoris Models lipstatin Wave numbers fermentation optimization Dynamic monitoring Off-line analysis Standard errors Streptomyces unclassified drug priority journal Direct use Calibration performance measurement system Aspergillus niger Spectroscopic probes Biotechnology molecular probe Environmental Engineering Mean percentage productivity near infrared spectroscopy Streptomyces toxitricini Biomedical Engineering Bioengineering Low concentrations Lipstatin Ammonia process monitoring Bioprocess monitoring Control Escherichia coli Batch cell culture controlled study Bioprocess analytical error Fed-batch cultures nutrient concentration Chromatography nonhuman Bacteria concentration (parameters) batch fermentation Microbial fermentation process Nutrients prediction Oleic acid esterase inhibitor Fed-batch culture chemistry Fermentation Process control Probes |
| Zdroj: | IndraStra Global. |
| ISSN: | 2381-3652 |
| Popis: | Near infra-red spectroscopy (NIRS) measurements for bioprocess monitoring and control, are integral to process analytical technology (PAT) initiatives by EMEA and US-FDA. Yet, NIRS is not widely practiced in challenging microbial fermentation processes. We present a practical approach to develop NIRS models for linoleic acid (LA), oleic acid (OA) and ammonia which are critical nutrients in lipstatin fermentation by Streptomyces toxitricini . The lipstatin productivity was enhanced and steadied by dynamic monitoring and control of critical nutrient ratio (CNR) of LA to ammonia. The NIRS models were used to develop a novel, soft probe for CNR as an alternative to laborious, hourly, off-line analyses. The calibration was designed with typical data for four industrially useful microbes. The approach enabled direct use of spectra for a generally applicable model with distinct wave number optima of 6250–5555 cm −1 (LA), 6666–5882 cm −1 (OA), 6800–6300 cm −1 (ammonia). The standard errors of calibration and prediction were 1.5 × 10 −3 g L −1 , 1.6 × 10 −3 g L −1 , 1.1 ppm, and 8.9 × 10 −4 g L −1 , 1.8 × 10 −2 g L −1 , 3.6 ppm, respectively, for the respective nutrients. The robustness of probe is evident from the low mean percentage error of 2.3% for prediction of CNR at low concentration ranges of 0.02–0.24 g L −1 and 0.21–0.56 g g −1 for LA and CNR, respectively. |
| Databáze: | OpenAIRE |
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