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Published on: November 8, 2019
The effect of analyte concentration range on measurement errors obtained by NIR spectroscopy
1Room 403, Shantz Building, Department of Agricultural and Biosystems Engineering, University of Arizona, Tucson, AZ 85721, USA.
Talanta
|October 31, 2008
Summary
Near infrared spectroscopy (NIRS) can quantify key cell culture analytes, but accuracy decreases at low concentrations (0-1 mM). Wider ranges (0-30 mM) offer better precision for noninvasive monitoring.
Area of Science:
- Biotechnology
- Analytical Chemistry
- Bioprocess Engineering
Background:
- Accurate monitoring of nutrients and wastes is crucial for animal cell cultivation control.
- Near infrared spectroscopy (NIRS) offers a noninvasive method for analyte quantification.
- Challenges exist in accurately measuring analytes at low concentrations (e.g., 1 mM).
Purpose of the Study:
- To evaluate the accuracy of NIRS for quantifying ammonium, glucose, glutamate, glutamine, and lactate in cell culture conditions.
- To determine the impact of analyte concentration ranges on NIRS measurement accuracy.
- To explore predictive relationships for measurement errors across different concentration ranges.
Main Methods:
- NIRS was used to quantify five key analytes (ammonium, glucose, glutamate, glutamine, lactate).
- Measurements were performed across varying concentration ranges, including narrow low ranges (0-1 mM) and wider ranges (0-30 mM).
- Partial least squares regression analysis was applied to spectral data.
Main Results:
- NIRS measurements in narrow low ranges (0-1 mM) yielded ~11% error.
- Wider concentration ranges (0-30 mM) resulted in significantly lower errors (~1.6%).
- A consistent ratio (approx. 6.6) was observed for SEP/concentration range across analytes, aiding error prediction.
Conclusions:
- NIRS can quantify key cell culture analytes, but precision is concentration-dependent.
- Wider concentration ranges are preferable for achieving higher accuracy in NIRS measurements.
- The established ratio provides a valuable tool for estimating NIRS prediction errors in similar applications.
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