Trends in Biotechnology
Volume 17, Issue 1, 1 January 1999, Pages 30-34
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Reviews
Optical sensor systems for bioprocess monitoring

https://doi.org/10.1016/S0167-7799(98)01247-5Get rights and content

Abstract

Optical sensor systems are of increasing interest in bioprocess monitoring, because they are very sensitive, specific and noninvasive. Recent developments in optical-density probes, in situ microscopy, optical biosensors, optical-fiber sensors and infrared and fluorescence sensors demonstrate the growing importance of optical sensor systems. The possibility of continuous optical bioprocess monitoring in situ, which offers an insight into a bioprocess without disturbing it, is coming closer to reality.

Section snippets

Optical-density probes

For measuring cell growth and biomass concentration, the on-line or off-line optical density (OD) are often used as the reference method. Commercially available in situ optical sensors for the on-line determination of microbial growth measure light absorption (turbidity) or scattering (nephelometry) continuously in the visible and near-infrared (NIR) ranges.

These devices allow accurate on-line determination of cell density with no need for intermittent calibration procedures. Commercially

In situ microscopy

On-line characterization of cell populations in bioreactors can also be performed by in situ microscopy and, indeed, cell-concentration measurements are already performed in this way. A microscope can be mounted directly in a port of a bioreactor to generate in situ images from the agitated broth using pulsed illumination. This technique was successfully tested during yeast fermentations and gave results that correlated well with those obtained from a hemocytometer. This technique also allows

Optical biosensors

In an optical biosensor, a biological ‘receptor’ (e.g. an enzyme, microorganism or antibody) produces an optical signal (e.g. chemiluminescence or NADH fluorescence); this signal is converted by an electronic transducer into an electrical signal12. Bioluminescent sensors have also been developed, which consist of a bioluminescent enzyme and an optical transducer. Optical biosensors provide a rapid and highly selective detection system.

In most cases, the analysis must be performed outside the

Fiber-optic sensors

Fiber-optic sensors (known as optodes) are based on a change in the optical properties (such as absorption or luminescence) of particular indicator. Fiber-optic oxygen sensors are produced by the immobilization of suitable oxygen-sensitive dyes at the tip of an optical fiber.

The principle of these measurements is the decrease in fluorescence intensity of an organometallic dye, caused by the interactions with oxygen. The light of a blue light-emitting-diode (LED) is guided through an optical

Near-infrared sensors

NIR spectroscopy can be used to measure the concentration of certain organic species, even in complex media. Biologically important bonds (aliphatic CH, aromatic or alkene CH, amine NH and OH) absorb in the NIR range, at 2.0 to 2.5 μm. Each chemical structure is related to a specific position, shape and size of the analytebsorption bands. Because the absorption bands are very similar, advanced data-analysis algorithms are required to extract the analytical information in a reliable manner.

Fluorescence sensors

The most common fluorescence sensors are based on the fluorescence measurement of the reduced form of nicotinamide adenine dinucleotide (phosphate) [NAD(P)H], which was first used by Duysenz and Amesz for in vivo measurements27. NAD(P)H-dependent fluorescence at 450 nm is measured after excitation at 360 nm. During the growth phase of a cultivation process, the NAD(P)H-fluorescence signal has often been found to show a good correlation with biomass concentration. This correlation is only found

Future prospects

Optical sensor systems are currently of increasing interest because of their rapid response, high sensitivity and easy maintenance. Their potential for combining with modern data-evaluation systems such as chemometrics enables continuous monitoring during cultivation or downstream processing. Through the use of intelligent data-processing models, problems of interactions can be excluded and non-invasive, flexible and detailed monitoring for improved processing, documentation and control becomes

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