Bioreactor monitoring with spectroscopy and chemometrics ... · handling techniques, which include transport to sensors or analyzers for ex-situ on-line bioreactor monitoring (e.g

REVIEW

Bioreactor monitoring with spectroscopy and chemometrics:a review

N. D. Lourenço & J. A. Lopes & C. F. Almeida &

M. C. Sarraguça & H. M. Pinheiro

Received: 15 March 2012 /Accepted: 21 April 2012 /Published online: 30 May 2012# Springer-Verlag 2012

Abstract Biotechnological processes are crucial to the de-velopment of any economy striving to ensure a relevantposition in future markets. The cultivation of microorgan-isms in bioreactors is one of the most important unit oper-ations of biotechnological processes, and real-timemonitoring of bioreactors is essential for effective biopro-cess control. In this review, published material on the po-tential application of different spectroscopic techniques forbioreactor monitoring is critically discussed, with particularemphasis on optical fiber technology, reported for in situbioprocess monitoring. Application examples are presentedby spectroscopy type, specifically focusing on ultraviolet–visible, near-infrared, mid-infrared, Raman, and fluores-cence spectroscopy. The spectra acquisition devices avail-able and the major advantages and disadvantages of eachspectroscopy are discussed. The type of informationcontained in the spectra and the available chemometricmethods for extracting that information are also addressed,including wavelength selection, spectra pre-processing,principal component analysis, and partial least-squares.Sample handling techniques (flow and sequential injection

analysis) that include transport to spectroscopic sensors forex-situ on-line monitoring are not covered in this review.

Keywords In situ monitoring . Bioreactors . Spectroscopy .

Chemometrics

AbbreviationsANN Artificial neural networksAOTF Acousto-optical tunable filterAPI Active pharmaceutical ingredientATR Attenuated total reflectionCCD Charge-coupled deviceCHO Chinese hamster ovaryCOD Chemical oxygen demandCPFs Cloud photonic crystal fibersDTGS Deuterated triglycine sulfateFIA Flow-injection analysisFT Fourier transformFT-IR Fourier transform infraredFT-NIR Fourier transform near infraredFT-Raman

Fourier transform Raman

Ge GermaniumHPLC High-performance liquid chromatographyInAs Indium arsenideInGaAs Indium–gallium arsenideInSb Indium antimonideIR InfraredKBr Potassium bromideKNN K-nearest neighborsLDA Linear discriminant analysisLWR Linear weighted regressionMCR Multivariate curve resolutionMCT Mercury–cadmium tellurideMIR Mid-infrared

N. D. Lourenço (*) :C. F. Almeida :H. M. PinheiroIBB—Institute for Biotechnology and Bioengineering;Centre for Biological and Chemical Engineering,Department of Bioengineering, Instituto Superior Técnico,Universidade Técnica de Lisboa,Av. Rovisco Pais,1049-001 Lisboa, Portugale-mail: [email protected]

J. A. Lopes :M. C. SarraguçaREQUIMTE, Departamento de Ciências Químicas,Faculdade de Farmácia, Universidade do Porto,Rua de Jorge Viterbo Ferreira, 228,4050-313 Porto, Portugal

Anal Bioanal Chem (2012) 404:1211–1237DOI 10.1007/s00216-012-6073-9

MLR Multiple linear regressionMNCN Mean centeringMSC Multiplicative scatter correctionMSPC Multivariate statistical process controlMSW Municipal solid wasteNADH Reduced nicotinamide adenine dinucleotideNADPH Reduced nicotinamide adenine dinucleotide

phosphateNIR Near infraredN-NO3

− Nitrate-nitrogenPAS Photoacoustic spectroscopyPAT Process analytical technologyPbS Lead sulfidePbSe Lead selenidePC Principal componentPCA Principal component analysisPCR Principal component regressionPDA Photodiode arrayPLS Partial least-squaresPLS-DA Partial least-squares discriminant analysisPP Pre-processingPTS Pulsed terahertz spectroscopyRNA Ribonucleic acidSBR Sequential batch reactorSER Surface-enhanced RamanSG Savitzky–GolaySi SiliconSIA Sequential injection analysisSIMCA Soft independent modeling of class analogySNV Standard normal variateSPC Statistical process controlSPE Square prediction errorSVR Support vector regressionTHz TerahertzTOC Total organic carbonTSS Total suspended solidsUV UltravioletUV–Vis Ultraviolet–visibleVFA Volatile fatty acids

Introduction

Bioreactor monitoring

Biotechnological processes are crucial to the developmentof any economy striving to ensure a relevant position infuture markets. They include a wide range of sectors, frompharmaceutical, agro-food, and beverage production towastewater and biowaste treatment. Interconnection of thesedifferent biotechnological areas is increasing with aware-ness of sustainability issues, because the receiving soils,

water bodies, and aquifers are the common sources fulfillingboth industrial and domestic needs [1].

Despite different main objectives and price ranges of theend products, all biotechnological processes include, atsome point, cultivation of microorganisms under controlledconditions corresponding to a complex combination of bio-logical, chemical, and physical (equilibrium and transport)phenomena [2]. The cultivation of microorganisms in bio-reactors is one of the most important unit processes ofmodern biotechnology and also the most challenging forprocess monitoring and control. In fact, bioreactors arecomplex multi-variable monitoring systems, because duringtheir operation, substrates are consumed and products andintermediate metabolites are formed in the presence of livecells. Furthermore, these cells are generally suspended in thebioreactor medium in amounts that can range from very lowup to constituting a major solid phase (absence or near-absence of free water), and their presence can stronglyinterfere with the most commonly used analytical methods[3]. In other cases, the cell fraction itself, i.e. the amount,composition, or physiological condition, is the object ofmonitoring [4, 5].

Bioreactor monitoring techniques and their relatedsensors and analyzers can be classified as off-line, at-line, and on-line, according to the location of the ana-lytical system in relation to the bioreactor (Fig. 1). Off-line measurements include manual or automatic sam-pling, sample transfer to the laboratory, and time-delayed laboratory analysis. At-line measurements alsoinvolve manual or automatic sampling, but the removedsample is analyzed in close proximity to the bioreactor.For on-line measurements, in-situ and ex-situ configu-rations are available. In typical in-situ measurements thesample is not removed from the bioreactor and a sensoris directly inserted in it (invasive) or separated from itby a glass wall (non-invasive). Another possible in-situconfiguration involves a sampling loop with an in-linesensor or a non-destructive in-line analyzer and returnof the bypassed sample to the bioreactor after the anal-ysis. In on-line ex-situ measurements the sample istransported to an in-line analyzer and does not returnto the bioreactor after the analysis.

The chemical components of bioreactor media (sub-strates, products and intermediates) are mainly monitoredoff-line by methods that require a biomass separation stepand other sample preconditioning stages [6]. High-performance liquid chromatography (HPLC) is an exampleof a well-established technique, widely used for the analysisof complex mixtures, in which the components are separatedby adsorption, hydrophobic interaction, ion exchange, liq-uid–liquid partition, ion-pair interaction, size exclusion, oraffinity separation [7]. However, in addition to sampling andsample preconditioning problems, the off-line analytical

1212 N.D. Lourenço et al.

methods currently used are retrospective and in many casestime-consuming, and, therefore, do not enable real-timeknowledge of the conditions affecting bioprocess performance.

Real-time monitoring of bioreactors is now regarded asan essential part of effective bioprocess control that can leadto increased efficiency, productivity, and reproducibility andalso to improved quality control and environmental pollu-tion reduction, thus optimizing overall costs [8].

Effective real-time process understanding can beachieved by process analytical technology (PAT), which isa system for designing, analyzing, and controlling processesby measurement of critical quality and performance attrib-utes along time with the objective of ensuring final productquality [9]. However, only a small number of variables arecommonly measured on-line in bioreactors, namely temper-ature, pressure, dissolved oxygen, pH, stirring speed, andgas and liquid flow rates, which are generally referred to asprocess variables or engineering data [10]. For fermentationprocesses, specifically, additional probes are used for varia-bles such as dissolved carbon dioxide, oxidation–reductionpotential, and optical density or turbidity.

In recent decades an effort has been made to conceivemore efficient bioprocess-monitoring strategies, includingthe development of optical sensors, chemosensors, and bio-sensors (depending upon the detection principle) [6] andtheir application in flow and sequential-injection analysis

(FIA and SIA, respectively) [10]. Reviews of fiber-opticchemosensors and biosensors are available [11, 12] andthese devices will not be discussed in this paper. Sample-handling techniques, which include transport to sensors oranalyzers for ex-situ on-line bioreactor monitoring (e.g. FIAand SIA), have also been reviewed by several authors [10,13] and, again, will not be covered in this text.

Despite of the improved performance and reliability ofthe on-line sensors developed for bioprocess monitoring,most are still high-priced, require frequent maintenance,and are normally limited to single-property analysis, andso are of limited applicability in bioreactor monitoring.Furthermore, the direct in-situ measurement of several keyvariables, for example concentrations of biomass and ofprimary carbon sources and products, is difficult to achievein complex, multiphase bioreactor media [14], and interestin developing inferential measurements that correlate withor predict those key variables is rapidly growing [15, 16].

Ideal sensors for real-time monitoring of bioreactorsshould be fast, sensitive, non-destructive and robust. Theyshould also generate multi-analyte data without analyte con-sumption, should not require sampling, should not interferewith culture metabolism and should be resistant to the harsh,mixed, sometimes aerated, and often sterile environmentsencountered in bioreactors [17]. In this context, spectrosco-py can be very useful in bioprocess monitoring and control

Fig. 1 Classification ofbioreactor monitoringtechniques and their relatedsensors and analyzers accordingto the location of the analyticalsystem in relation to thebioreactor

Bioreactor monitoring with spectroscopy and chemometrics 1213

and several spectroscopic techniques, generally coupled tooptical fibers, have been investigated for different in-situapplications, covering different wavelength ranges, includ-ing ultraviolet–visible (UV–Vis), near infrared (NIR), midinfrared (MIR), Raman, and fluorescence spectroscopy [6].In this work, the potential application of these differentspectroscopic techniques to bioreactor monitoring will becritically reviewed, with particular emphasis on optical fibertechnology, which enables in-situ bioprocess monitoring[12]. This review is intended to assist not only scientistsand professionals working on the design, operation, andcontrol of biotechnological processes, but also those devel-oping optical sensors for on-line inference of biotechnolog-ical process conditions.

Spectroscopy

Spectroscopy was, originally, the study of the interactionbetween electromagnetic radiation and matter as a functionof wavelength and was later expanded to include the mea-surement of any property that is a function of wavelength orfrequency [18]. Despite of this expansion, this review willnot deal with electron, acoustic, dielectric, or mechanicalspectroscopy, being centered on electromagnetic spectros-copy only.

All electromagnetic spectroscopic techniques work onthe common principle that, under certain conditions of in-teraction with radiation, materials absorb or emit energy. Inabsorption spectroscopy the measured radiation is emittedby the light source but attenuated by the sample, whereas inemission spectroscopy the measured radiation is producedby the sample on excitation, and has properties differentfrom those of the source radiation used for excitation. Agood description of the fundamental concepts of absorptionand emission spectroscopy can be found elsewhere [18].Some materials can also reflect electromagnetic radiation.Reflected radiation can be distinguished as specular anddiffuse reflection. The specular component is the radiationthat is reflected at the same angle as the incident radiation.Diffuse reflection occurs when the regular trajectory of theincident radiation is altered, and the radiation is reflected atmany different angles, because of the presence of non-uniformities, for example particles, bubbles, or surfaceroughness. This deviation of radiation as a result of samplenon-uniformities is also known as scattering.

Different spectroscopic techniques operate over different,limited wavelength ranges within the electromagnetic spec-trum (Fig. 2), depending on the processes examined andmagnitudes of the energy changes involved.

Several types of spectrophotometer have been developedfor particular wavelength ranges, using different light sour-ces, detectors, and wavelength-selection devices. The new-est detectors, based on use of photodiode arrays (PDA) and

charge-coupled devices (CCDs) to replace the photomulti-pliers and avalanche photodiodes used in conventionalinstruments, afford excellent performance for a wide rangeof spectroscopic applications [19]. CCDs are more sensitivethan PDAs and are ideal for low level light detection, forexample in Raman and fluorescence spectroscopy. PDAs,on the other hand, have a larger detection range than CCDsand lower noise, which makes them more suited to applica-tions for which high output accuracy is required and thelight level is relatively high, for example UV–Vis spectros-copy. The great benefits of these multichannel detectors aretheir compact geometry and simultaneous multi-wavelengthdata acquisition with high sensitivity, high speed, low noise,and low cost. Furthermore, for CCDs the dark current (theelectric current that flows through the photosensitive detec-tor when no photons are entering the device) is low, andPDAs can be used in a reversed optic configuration thatavoids outer stray light (light at wavelengths other than thatintended), enabling minimization of overall stray lightinterference.

Optical fibers enable guidance of light from one point toanother without line-of-sight propagation, so their use withspectroscopic techniques is highly attractive for in-situ bio-process monitoring. In fact, optical fibers enable analyses tobe conducted remotely from the sampling point and use of asingle analyzer with a multiplexer (multiplexing instrument)to perform measurements at several points in a process.

Standard optical fibers are mainly composed of a corewith a high refractive index surrounded by a cladding withlow refractive index. Light is guided through the core by atotal internal reflection mechanism.

One of the main problems associated with use of opticalfibers is light attenuation (reduction of light intensity) withdistance traveled through the fiber mainly because of ab-sorption and scattering effects. However, for the typicalfiber length used in process analyzers (a few meters) lightattenuation because of absorption can be regarded as negli-gible [20]. In the last few years photonic crystal fibers(PCFs), a novel type of optical fiber, have been the focusof attention because of properties that provide new degreesof freedom with regard to light guidance, fabrication tech-niques, and fiber materials and structures [21]. Whereaslight guidance in conventional fibers is based on two con-centric regions with different refractive indices (core andcladding), in PCFs it is based on subtle variations in therefractive index caused by corralling the light within amicroscopic and periodic array of air holes. This propertymakes the cladding refractive index more stronglywavelength-dependent, which provides a number of unusualand tailor-made optical properties. PCFs can transporthigher power than traditional optical fibers and can be madefrom a large number of materials, for example pure silica(SiO2), doped silica, air, quantum dots, other glasses,


liquids, and even gases. State-of-the-art conventionaloptical fibers maintain a careful equilibrium betweenoptical losses, optical nonlinearity and dispersion, andpolarization effects. PCFs can have an enormous rangeof effective refractive indices, which can be efficientlyused to develop fibers with extremely low or highnonlinearity. By proper tailoring of the fiber photoniccrystals it is possible to efficiently manage the chromat-ic dispersion of the fibers by changing their waveguidedispersion. Hollow-core PCFs enable light guidancewith low attenuation over kilometer distances, which isimpossible to achieve with conventional optical fibers.This characteristic is particularly important for mid in-frared spectroscopy because negligible light attenuationin conventional fibers is achievable only with cablelengths under 2 m.

PCFs can also be used in the mid infrared region withoutthe light losses observed with standard silica fibers, which areunusable for wavelengths above 3 μm. The lack of suitablefibers for guided propagation of terahertz (THz) radiation(wavelength range 10 μm–3 mm) has been a barrier to appli-cation of pulsed THz spectroscopy (PTS) in bioprocess mon-itoring. Some recently developed PCFs have proved effectivefor THz radiation guidance and the number of THz monitor-ing applications is now expected to increase [21].

The recent advances in detector technology and in thelight guidance efficiency of optical fibers is bringing aboutthe development of miniaturized fiber-optics spectropho-tometers [22] that enable use of a large variety of light-source, probe, and detector configurations for enhancedon-line and in situ bioreactor monitoring.

The process variables that are easily measured on-line,namely temperature, pressure, liquid level, impeller speed,pH, and dissolved oxygen, present different units and a highlevel of non-systematic variation and include mainlyphysical-state information. In contrast, on-line spectroscopicprocess data include chemical-state information related tosubstrates, products, and biomass composition and concen-trations, contain far more systematic variation and all vari-ables (i.e. wavelengths) are measured in the same units [23].Furthermore, in many situations spectra provide a finger-print of the monitored bioprocess that can be used forprocess supervision and diagnosis. Combination of on-linespectroscopic process data with on-line process variabledata can greatly improve the ability to fully characterizebioprocesses [6].

Potential difficulties in the use of in-situ spectroscopy areits sensitivity to changes in process conditions, includingtemperature fluctuations and variations in the backgroundmedium, and the occurrence of light-scattering effects andbaseline or wavelength shifts. Wavelength selection canovercome some of these difficulties by focusing on regionsthat are less prone to interferent variation. Mathematicalpreprocessing techniques, for example derivatives and mul-tiplicative scatter correction (MSC), can also be used toremove unwanted spectral variation [24].

Chemometrics in spectroscopy

General

In a broad sense, chemometrics can be defined as the use ofmathematical or statistical methods to analyze data from achemical system, to extract information on system state, tocharacterize system behavior, and to assist in the identifica-tion of processes occurring in the analyzed system.

Spectroscopic bioprocess monitoring provides series ofspectra that constitute large data sets with much lowerinformation content than data volume and from whichmeaningful information should be rapidly extracted [24].The non-selectivity of spectroscopic methods, associatedwith the high collinearity of many of the measured variablesand with the need to understand the relevant relationshipsamong those variables, has led to the application of multi-variate data analysis and other mathematical tools, i.e. che-mometrics, to spectral data to reduce the large number ofvariables and to support process analysis, understanding,and troubleshooting. These tools can be applied at samplelevel (i.e., process monitoring), batch level (i.e., processsupervision), and overall process or plant level (i.e., PAT)[25].

The chemometric methods most widely used for spectraldata analysis in bioprocess monitoring are dimension reduc-tion, latent variable methods, for example principalcomponent analysis (PCA), principal component regression(PCR), and partial least-squares (PLS). The reasons for theirextensive acceptance and use by the scientific communityare their efficiency in retrieving the information sought,their simplicity, and the stability over time and interpretabil-ity of their models. In addition, these methods are oftenavailable in the commercial software packages providedfor spectrometer operation.

Fig. 2 Electromagneticspectrum with the differenttypes of radiation identifiedaccording to their approximatewavelength ranges


In this section, the chemometric methods currently usedin bioreactor monitoring will be briefly described, coveringaspects of mathematical pretreatment of spectral data, qual-itative and quantitative spectral analysis, and multivariatestatistical process control. A detailed description of thesemethods can be found in several books and papers [26–28].

Mathematical pretreatment of spectral data

The classical spectrum pretreatment methods include nor-malization, derivatives, and smoothing, and they are usedwith the objective of enhancing the relevant information inthe data [29].

Because spectral data variables (wavelengths) are allmeasured in the same units, mean-centering is a widely usednormalization pretreatment that removes the absolute inten-sity information from each of the variables, thus enhancingthe response variations. This operation involves subtractingthe response of each variable from the mean response of thatvariable over all the samples in the data set.

Derivatives can be used to eliminate offset and back-ground slope variations among spectra. The first derivativeeffectively removes baseline offset variations in spectralprofiles and the second derivative removes both baselineoffset differences and differences in baseline slopes betweenspectra. Because spectra are a series of measurements atequally spaced discrete points and not continuous curves,the easiest way to obtain a derivative is to use differencesbetween adjacent points in the spectrum for the first deriv-ative and to calculate the differences between the adjacentpoints of the first derivative to determine the second. As thisapproach decreases the signal-to-noise ratio, smoothing hasto be incorporated when applying derivatives and the mostcommon approach is the use of the Savitzky–Golay (SG)algorithm.

When scatter effects are the dominating sources of spec-tra variability, for example in diffuse reflectance spectros-copy, a multiplicative scatter correction (MSC) or a standardnormal variate (SNV) transformation can be used to removethose effects. The scatter effect has a multiplicative and anadditive component. MSC removes both effects from thespectra, reducing the number of components needed in aregression model. It may also improve linearity. This trans-formation should be performed only on the parts of thespectrum that are most affected by light dispersion. SNV isconceptually and empirically related to the MSC method. Itperforms both additive and a multiplicative data adjustment,but spectra are centered variable-wise and scaled individu-ally, i.e., SNV standardizes each spectrum using only datafrom that spectrum. Martens and Stark developed a pre-processing method called extended multiplicative signalcorrection (EMSC) that enables separation of physical light

scattering effects from chemical light absorbance effects inspectral data [30].

Qualitative spectral analysis

Principal component analysis (PCA) is a data-reductionmethod extensively used for qualitative spectral analysis.This method reduces the initial dimension of the systemby creating new variables, called principal components(PCs), that are linear combinations of the original var-iables and that reflect the inherent structure of the data[31, 32]. The PCA model can be described in matrixnotation as:

X ¼ TPT þ E ð1Þ

whereX is the spectral data matrix,T is the matrix of scores, Pis the loadings matrix and PT corresponds to the transposed Pmatrix. Matrix E contains the model residuals and representsthe noise or irrelevant variability in X. The scores in T arelinear combinations of the original variables of X (wave-lengths). The loadings in P are estimated by regressing X onto T and the residual matrix E is calculated by subtracting theestimated TPT from X.

For the particular case of spectroscopic data, PCA iden-tifies the major sources of correlated variance in a collectionof spectra by defining a series of ranked components (orfactors) and assigning each spectrum a score based on therelative contribution of each factor. The sources of variance,once identified, can aid in the visualization of the major datatrends. In this way, the data can be reduced from a compli-cated multidimensional representation to a more easily vi-sualized two or three-dimensional space (score plots)describing the main information present in the data [33].

Because PCA is regarded as a dimension-reduction tech-nique, it is often applied as a precursor to classification (e.g.K-near neighbors), clustering (e.g. Gustaf- Kesselson clus-tering [34]), and regression methods.

The purpose of classification methods is to find or identifytendencies of samples to cluster in sub-groups. They can beunsupervised (samples are classified without prior knowledgeof group structures in the data) or supervised (the categorymembership of samples is required in advance).

Clustering methods are unsupervised pattern-recognitiontools used to detect natural groupings in data in a way suchthat the degree of association between two objects is max-imum if they belong to the same cluster and minimumotherwise. The definition of the clusters is based on thesimilarity of multiple variables measured for each object.This similarity is normally derived from distance measure-ments and the main outcome of cluster analysis is a dendro-gram, also called a tree diagram, which is a visual aid fordeciding when to stop a clustering process.


For supervised classification, the methods can becorrelation-based, distance-based, linear discriminant analysis(LDA), and K-nearest neighbors (KNN). Modeling methodssuch as soft independent modeling of class analogy (SIMCA)and partial least-squares discriminant analysis (PLS-DA) arealso commonly used [26]. Non-linear classification methods,for example artificial neural networks (ANN, especially Koho-nen networks), are used with non-linear spectroscopy data.

Quantitative spectral analysis

Regression methods are used to establish a relationshipbetween spectra and the quantifiable properties of sam-ples. The multi-linear regression (MLR) method is suitedfor systems with a reduced number of variables [29].Principal component regression (PCR) and partial least-squares (PLS) regression are used when the degrees offreedom are a limitation (too many variables comparedwith the number of experiments). The PLS method, alsoknown as projection to latent structures, is the mostwidely used method for multivariate calibration and cor-responds to a guided decomposition model in which thedependent variables (sample properties) intervene directlyin the decomposition of the independent variables (e.g.,spectra) [35]. The purpose of this method is to determinea small number of latent factors (or latent variables) thatcan predict sample properties by using the spectral dataas efficiently as possible [36]. There are several ways ofcalculating the PLS model terms. The most widely usedmethod is known as NIPALS (non-iterative partial least-squares); it is described in Eqs. (2) and (3):

X ¼ TPT þ Ex ð2Þ

y ¼ TqT þ ey ð3Þwhere X is the spectral data matrix and y is a column vectorcontaining the values of one sample property; T is the scoresmatrix; P and q are the loadings matrix and column vector,respectively; and Ex and ey are the X and y residuals,respectively. Similarly to PCA, the loadings in P and q aredetermined by maximizing the correlation between thescores in T. The PLS algorithm can be extended to handleseveral sample properties simultaneously, i.e., the y columnvector can be replaced by a matrix Y with several columns,one for each property. This algorithm is commonly calledPLS2 and is very similar to that described by the formerequations. The main difference is that instead of maximizingthe covariance between y and the linear function of X, thePLS2 algorithm maximizes the covariance between twolinear functions: one in X and one in Y [37].

Non-linear regression methods applied to spectral datainclude ANN and support vector regression (SVR) [28]. A

detailed description of non-linear regression methods can befound elsewhere [38, 39].

Multivariate statistical process control

The main idea behind statistical process control (SPC) is thepossibility of capturing the nominal state of the operationprocess by the use of historical process information. Multi-variate statistical process control (MSPC) methods are pow-erful tools that can use massive amounts of historical datafrom continuous or batch processes for efficient processmonitoring, supervision, and diagnosis [40].

The most practical approaches to MSPC are based onmultivariate statistical projection methods, for example PCAand PLS, that can handle a large number of highly-correlated variables. This way, the information containedon the data is projected on to a lower dimensional spaceand the process can be monitored and supervised by obser-vation of two charts: the Hotelling T2 chart, calculated fromthe selected latent variables, and the SPE (squared predic-tion error) chart of the residuals. The Hotelling T2 statisticmeasures the variability explained by the MSPC model,whereas the SPE statistic measures the unexplained vari-ance. These two multivariate control charts enable the de-tection of deviations (faults) from normal operation and thediagnosis of possible causes. Diagnosis of a fault is based onestimation of the contributions of each original variable tothe scores statistic (Hotelling T2 in the case of a linearmodel) and error statistic (SPE) [23]. These contributionsare compared with the average contributions obtained forthe nominal data to detect the origin of the problem.

The extension of MSPC to batch processes is possible bythe use of multiway PCA (MPCA) and multiway PLS(MPLS) [41]. These methods can be further extended bymultiblock techniques to incorporate data on initial processconditions and other available process information [42].

In-situ spectral data have a high potential to becomeextremely useful for bioreactor monitoring and supervisionvia MSPC model development. In fact, spectra can providea fingerprint of the bioprocess that can be used in an unsu-pervised manner to diagnose the process state in real-time.However, the use of in-situ spectroscopic data for MSPCmodel development for bioreactor monitoring, supervisionand diagnosis has scarcely been reported.

Ultraviolet–visible spectroscopy

General aspects

UV–Vis spectroscopy corresponds to the interaction be-tween samples and radiation in the 200–780 nm wavelengthrange. Measured absorption regions correspond to electron


transitions between orbitals, involving higher energy thaninfrared spectroscopy. Consequently, the vibrational androtational levels of molecular orbitals are superimposed onthe electronic levels, resulting in absorption spectra withbroader bands [43]. However, principal component analysis(PCA) of UV–Vis spectra of wastewater samples has al-ready revealed that these spectra contain information thatcan be extracted and used for quality monitoring in thecontext of biological wastewater treatment [44].

Selective absorption of UV–Vis radiation has been usedfor analysis of sample composition for over 60 years, buton-line applications constitute a recent development thatwas achieved mainly because of the availability of UV-grade optical fibers, sensitive and cheaper array detectors,and powerful chemometric techniques [45].

Most liquid and gas UV–Vis measurements rely on thewell-known Beer’s Law that relates the amount of lightabsorbed by a sample to the amount of some chemicalspecies present in that sample. The basic principles ofUV–Vis spectroscopy can be found in the literature [43].

Instrumentation

As opposed to infrared spectroscopy, in which the use ofFourier transform (FT) techniques predominates, in UV–Visspectroscopy dispersive instruments are used almost exclu-sively. Among these, four different classes can be distin-guished, namely, photometer, scanning, diode-array, andfiber-optic (diode array or CCD) instruments.

Photometers are simple instruments that include a lightsource, one or more wavelength filters and a photodetector,enabling analysis at up to three wavelengths only. Conven-tional scanning UV–Vis spectrophotometers measure theintensity of transmitted light of a narrow bandpass and scanthe wavelength domain in time in order to collect a spec-trum. These instruments can feature single or double beams.The latter type of instrument has a separate reference chan-nel which enables elimination of lamp drift and other low-frequency fluctuations, thus generally resulting in betterperformance than single-beam instruments. Scanning instru-ments are not suitable for on-line measurements, because oftheir delicate, moving optics and of the time required foracquisition of each spectrum.

Recent fiber-optic spectrophotometers use photodiodearrays (PDAs) or charge-coupled devices (CCDs) as detec-tors. The performance of these is excellent in a wide rangeof applications in the UV–Vis region; they combine highsensitivity, high speed, low noise, and low cost with com-pact geometry and instantaneous capture of full spectra [28].These instruments have no moving parts, are generally usedin single-beam configuration, and are suitable for on-lineapplications. Furthermore, high-quality solarization resistant(i.e., resistant to color change induced by the incidence of

high-energy electromagnetic radiation, for example UVlight) silica fibers that enable analyses at wavelengthsshorter than 280 nm have recently become available. Thisand the development of robust submersible instruments withauto-cleaning systems [46–48] are fast converting UV–Visspectroscopy into a technology particularly suitable for in-situ measurements.

With regard to light sources, deuterium lamps are nor-mally used for the ultraviolet region and tungsten/halogenlamps for the visible region. Xenon light sources are high-power sources that supply a continuous spectrum from theUV through to the NIR wavelength range (185–2,200 nm).

Several sample interfaces are available for UV–Vis spec-troscopy, including the fiber-optic transmission, transflec-tion, reflection, and attenuated total reflectance (ATR)probes. In transmission probes the light is sent through asample hole and makes only a single pass through thesample. In transflection probes the light is sent through asample hole but reflects back from a mirror placed at theopposite end of the probe, making a double pass through thesample. Diffuse reflectance probes are mainly used to mea-sure the light reflected from solid surfaces or powders anddo not measure specular reflections. In contrast, backscatterprobes detect both specular and diffuse reflectance light andare adequate for spectra acquisition from slurries or solu-tions with high concentrations of suspended solids [45]. InATR probes, light passes from a material with high refrac-tive index (ATR crystal) to a material with lower refractiveindex (the sample). The light partially enters the samplewhere it can be absorbed, resulting in attenuated reflectedlight. These probes are particularly suited to measurementsin highly absorbent samples, for which standard transmis-sion probes cannot be used.

Advantages and disadvantages

An advantage of UV–Vis spectroscopy is the possibility ofdetecting individual electron transfers between orbitals oratomic, ionic, and molecular bands. It is most suited to thedetection of changes in lower energy electronic levels, as-sociated with the more reactive chemical functions (e.g.,aromatic, multiple bonds); this enables mapping of molecu-lar conformation changes, particularly important in the con-text of bioprocesses.

On the other hand, higher energy electronic levels inmolecules such as saturated hydrocarbons and sugars arenot detected by UV–Vis spectroscopy. Also, UV–Vis spec-tra of aqueous samples are highly affected by the presenceof a second, dispersed phase (immiscible liquids or sus-pended particles distributed over the aqueous phase), be-cause of light scattering effects. Another disadvantage ofUV–Vis spectroscopy is that simple visual inspection ofspectral data is often not sufficient to extract the relevant


information on sample composition because of the broadand unspecific bands obtained. Thus, use of chemometricmethods is generally required for this [6].

Applications

The availability of compact and relatively low-cost UV–Visspectrophotometers led to new applications of UV–Visspectroscopy. The use of UV–Vis spectroscopic data forreal-time multivariate monitoring of wastewater treatmentprocesses is now a very active field of research [49]. Partialleast-squares (PLS) calibration models for prediction ofchemical oxygen demand (COD), total organic carbon(TOC), and nitrate have been successfully derived fromUV–Vis spectra of wastewater samples [46, 50]. However,the actual number of on-line in-situ applications remainsrather limited [47].

A papermill wastewater treatment plant has beenmonitoredwith a submersible UV–Vis spectrometer directly inserted inthe activated sludge bioreactor for in-situ spectra acquisition inthe range 200–750 nm. Simultaneous prediction of COD,filtered COD, total suspended solids (TSS), and nitrate weresuccessfully achieved by an established PLS model [51]. Asimilar submersible spectrophotometer has been used for in-situ prediction of nitrate, TSS, and filtered COD levels in anactivated sludge sequential batch reactor (SBR) [46].

A method of deriving alarm conditions for waterquality management from UV–Vis spectra, using theinformation contained in UV–Vis spectra and their evo-lution over time, has been presented by Langergraber etal. [52]. Sarraguça et al. [53] recently used an immer-sion transflectance probe for in-situ acquisition of UV–Vis spectra to monitor a laboratory-scale activated-sludge bioreactor. PLS models have been developedfor prediction of the key properties COD, nitrate, andTSS. Models for prediction of the same values weredeveloped using spectra acquired off-line with a con-ventional bench spectrophotomer. Comparison of theoff-line and the in-situ UV-visible monitoring methodsshowed that the in-situ technique yielded the most ac-curate results [53]. However, apart from wastewatertreatment bioprocesses, the use of UV–Vis spectroscopyfor in-situ bioreactor monitoring has been scarcelyaddressed in the published literature. The main featuresof these applications are summarized in Table 1.

Near infrared spectroscopy

General aspects

The NIR region (13,000–4,000 cm−1 or 780–2,500 nm) issituated between the red band of visible light and the mid-

infrared region. The functional groups almost exclusivelyinvolved in NIR spectroscopy are those containing the hydro-gen atom (C–H, N–H, O–H, and S–H). These groups furnishthe overtones and combination bands of the fundamentalinfrared frequencies in the NIR region [54, 55]. It was notuntil the mid to late 1960s that NIR spectroscopy was used asan analytical technique by Norris and co-workers to quantifythe moisture content of methanol extracts from seeds [56, 57].The development of equipment with improved electronic andoptical components and the advent of computers capable ofeffectively processing the information contained in NIR spec-tra facilitated the expansion of this technique to an increasingnumber of fields [58]. In recent years, NIR spectroscopy hasgained wide acceptance within the pharmaceutical industryfor raw material testing, product quality control, and processmonitoring. This growing interest in NIR spectroscopy isprobably a direct result of its major advantages over otheranalytical techniques, i.e., no sample pre-treatment require-ments and no sample alteration (which enable the sample to bere-used after the measurement), fast spectra acquisition (aspectrum can be obtained in a second or less), and the possi-bility of keeping the samples separate from the spectrometerby use of optical fiber probes (decisive for in-situ applica-tions). Moreover, with a single spectrum it is possible tosimultaneously predict physical and chemical properties ofthe samples [20].

The appropriate NIR measurement mode is dictated bythe optical properties of the samples. Three measurementmodes are used in NIR spectroscopy: transmission, reflec-tion, and transflection. Transmittance (T) measurementsgenerally apply to liquids (diluted or undiluted) and to solidsin liquid suspension, if the liquid medium is transparent.The radiation passes through the sample and what is mea-sured is the decrease in radiation intensity because of ab-sorption or scattering by the sample. Reflectance (R)spectroscopy measures the ratio of the intensity of lightreflected by a sample to the light reflected by a backgroundor reference reflective surface. The reflected light contains aspecular component and a diffuse component. Specularreflectance contains little information about composition.On the other hand, diffuse reflectance is the most usedmeasurement mode in NIR spectroscopy and is useful forturbid liquids, slurries, and solids. Transflectance (T*) com-bines the transmittance and reflectance measurements. Inthis case, light is transmitted through a sample and theunabsorbed radiation is reflected back from a mirror or adiffuse reflectance surface placed at the end of the probe,thus doubling the pathlength. Transflectance can be used toacquire spectra from turbid or transparent liquids. In thesemeasurement modes, absorbance (A) values relative to astandard reference material are calculated as log (1/R), log(1/T), and log (1/T*) for reflectance, transmittance and trans-flectance, respectively [20].


Instrumentation

The first commercially available NIR instruments werescanning grating monochromators [20]. They were basedon a concave holographic grating, controlled via anindustrial motor drive and an optical encoder for precisepositioning. These instruments were robust but with lowsignal-to-noise ratio and disadvantages associated withwavelength registration and the slit arrangements. Thealternative scanning grating polychromators were fittedwith a photodiode array as exit slit, but still required anentrance slit to the concave grating. The development ofphotodiode-array instruments enabled the simultaneousmeasurement of several detection channels with a shortresponse time. Another option is the acousto-opticaltunable filter monochromator (AOTF) based on use ofa carefully orientated bi-refringent crystal which is op-tically transparent across the NIR region (tellurium ox-ide is a typical example). The AOTF configuration doesnot require slits and provides full spectral coverage, butis still subject to interference from stray light. The mostpopular NIR instruments, Fourier-transform NIR (FT-NIR) analyzers, are based on an interferometer. Theyare equipped with an optical modulator which enableswavelength encoding. The input beam to the interferom-eter is un-modulated broadband NIR radiation; the exitbeam is still NIR broadband radiation, but with eachoptical frequency uniquely amplitude-modulated in theacoustic frequency range. This enables the detector sig-nal (which is a combination of signals from all theincident broadband NIR frequencies) to be decomposed

by use of a Fourier transform and the individual ampli-tudes of each optical frequency in the broadband inputsignal to be obtained. Most practical devices use avisible frequency laser to define the optical retardationsampling interval. This is converted directly into thefrequency axis of the recorded spectrum. FT-NIR instru-ments have four conclusive advantages [54]:

1. the Jacquinot advantage or throughput advantage result-ing from the lack of significant optical throughputrestrictions on the optical layout;

2. the Fellgett advantage or multiplex advantage whichenables the analyzer to measure all wavelengthssimultaneously;

3. the Connes advantage or the wavelength advantagewhich arises from the fact that the modulation frequencyenables very high wavelength precision and accuracy;and

4. the advantage associated with analyzer immunity tostray light.

Selection of the appropriate NIR analyzer depends on theequipment reliability and ease of use required, analyte sensi-tivity, calibration transferability, and implementation needs.For these reasons, laboratory and process analyzers have to bedifferentiated. Laboratory analyzers are normally used off-lineor at-line in quality-control or in research measurements. Theymust be sensitive to the analyte, reliable, and have optimumsample presentation and high signal-to-noise ratio. Gratingand interferometer-based instruments are the most recommen-ded. On the other hand, process analyzers are intended for in-line or on-line measurements which provide real-time data

Table 1 Summary of applications of in-situ bioreactor monitoring with UV–Vis spectroscopy and chemometrics

Area Application Bioreactor Spectroscopic system Chemometricmethodsa

Qualitative monitoring/quantitative monitoring/control

Ref.

Environmental Batch wastewatertreatment

Full-scaleactivatedsludge SBR

Submersiblespectrophotometer

Light source: xenon lampIntegrated cleaning system(pressurized air)

PLS No/Yes/NoQuantitative: TSS, filteredCOD, N-NO3

−

[46]

Continuouswastewatertreatment

Lab-scale 25 L,aerated

Portable spectrometerDetector: linear CCDRange: 230–700 nmLight source: deuterium/tungsten halogen

Probe: transflection(immersion)

PLSPP: SG, MSC, SNV,MNCN

Wavelength selection(bootstrapping)

No/Yes/NoQuantitative: TSS, COD,N-NO3

−

[53]

Paper millwastewater

Full-scaleactivatedsludge

Submersiblespectrophotometer

Light source: xenon lampIntegrated cleaning system(pressurized air)

PLS No/Yes/NoQuantitative: COD dissolvedCOD, TSS, N-NO3

−

[51]

aPP pre-processing


while operating in industrial, often harsh, conditions. Robustand rapid analyzers with no moving parts are necessary underthose conditions. The AOTF and the dispersive instrumentscomply with all these requirements [59].

With regard to light sources, tungsten–halogen lamps arealmost universally used for NIR spectroscopy. They havebroadband, pseudo-blackbody emission spectra with no sig-nificant structure and are inexpensive and durable (approx-imately one year’s continuous use) if operated at appropriatefilament temperature and lamp wattage. The peak spectralradiance of a lamp operating at a filament temperature of2,800 K is located at approximately 10,000 cm−1

(1,000 nm).Two main basic types of detector are available for NIR

spectroscopy: thermal and photon detectors. Photon detec-tors can be divided into photoconductive and photodiodedetectors. Thermal detectors are not common in NIR ana-lyzers, having unique and useful application as pyroelectricdetectors in FT-NIR instruments. Both types of photondetector are based on semiconductor materials, normallyintrinsic for photoconductive detectors and doped for pho-todiode detectors. Materials typically used in photoconduc-tive detectors are lead sulfide (PbS), lead selenide (PbSe),indium antimonide (InSb), and indium arsenide (InAs)whereas silicon (Si), germanium (Ge), and indium–galliumarsenide (InGaAs) are used for photodiode detectors. Silicondetectors are low-noise, rapid, small, and highly sensitivefrom the visible region to 9,000 cm−1. PbS detectors areslower but with good signal-to-noise ratio and sensitivitybetween 9,000 and 4,000 cm−1. The InGaAs detector is themost expensive because it combines the speed and sizecharacteristics of silicon-based detectors with the wave-length range of the PbS detector. The InGaAs detector alsohas the significant advantage that the band-gap can be tunedfor different wavelength cut-offs, by varying the alloy com-position [54, 59].


Since the introduction of NIR spectroscopy in industrialapplications in the mid 70s (protein and moisture analysisof grain) [56], this technique has been described as havingundoubted advantages over other methods. Its major advan-tage is the fact that samples can be analyzed without pre-processing. Samples can also be analyzed by differentmodes (e.g., diffuse reflectance, transmittance, transflec-tance). This is a direct consequence of the intrinsic featuresof NIR radiation. NIR spectroscopy is also a multipurposemethod, because it can be used to estimate different com-pounds by a single measurement. Applications related todetecting and quantifying bio-macromolecules and moistureare especially interesting.

NIR light can be very easily transported using opticalfibers. This led to a large range of applications, namely theuse of multiplexing instruments. NIR spectra are typicallyprocessed by the use of multivariate methods (chemomet-rics). This is a consequence of matrix effects and the com-plexity of NIR spectra, which is affected by the chemicaland physical properties of samples [60]. Compounds activein the NIR region can often mask variation arising from theanalyte(s) of interest. Resolution methods must therefore beused to identify the variations of interest. Many methods cando this. For example, PLS or multiple curve resolution(MCR) are among the most used. In this sense, the non-selectivity of these methods can be also a disadvantage,because interferents can often mask the signals. Specifically,water absorbs strongly in the NIR region (e.g., 5,200 cm−1

and 6,900 cm−1). Therefore, the use of NIR spectroscopy toanalyze aqueous solutions (aquaphotonics) is often impos-sible. The high detection limits of this technique are also adisadvantage. Components in low concentrations (e.g., ac-tive ingredients in pharmaceutical solid forms at concentra-tions below 0.1–1 ppm) may not be visible and,consequently, this technique is not suitable for trace-component analysis. Another disadvantage is that onlybonds to hydrogen produce intense bands in the NIR region(overtones and combination regions). Therefore, a widerange of compounds cannot be directly analyzed by use ofNIR radiation.

Applications

Despite the potential of NIR spectroscopy for bioprocesscontrol the technique has not been widely applied [61–63].There are two main reasons for this: skeptical views ofregulators (e.g., pharmaceutical industry applications) anddifficulties in monitoring highly complex systems (e.g.,fermentations). With regard to the type of sampling, mostof the applications are off-line or at-line, with very few in-situ applications being reported. In a recent review Teixeiraet al. [63] published a survey of on-line monitoring ofmammalian cell cultures in the context of PAT, by use ofdifferent high-throughput methods, including NIRspectroscopy.

Undoubtedly, most NIR applications for in-situ monitor-ing of bioprocesses have been developed in the context offermentation in the pharmaceutical industry (primary phar-maceutical manufacture). Non-invasive monitoring of thefermentation process can be performed by placing the fiberoptic probe outside the bioreactor [64, 65]. However, idealbioprocess monitoring is considered to be that which uses aprobe located inside the bioreactor fluid, and is, therefore,capable of coping with the high agitation/aeration rateswhile generating real-time data, and having the ability tomultiplex [66]. Some authors reported difficulties in


fermentation monitoring because of interferences from stir-ring and gas bubbles [65], from temperature changes, aera-tion, and feeding [66], and from water and other sources ofvariability [67]. It is therefore important to provide robustinstrumentation and mathematical models to cope with themain sources of variation in these systems. For example, insome situations appropriate wavelength selection can im-prove the results by eliminating irrelevant spectral regions[68].

There have been several reports of use of immersion(invasive) probes to quantify different analytes in fermenta-tion processes, with good results compared with the well-established monitoring techniques [66, 67, 69–74]. Theconcept of multiplexing was investigated by monitoringseveral vessels in parallel, using the same spectrometer butdifferent probes, to monitor glucose and lactate in processeswith Chinese hamster ovary (CHO) cell lines [70]. Slightdegradation of multiplexed models was experienced com-pared with single-probe results, because of probe-to-probevariability. A different type of multiplexing was studied byuse of a distributed multi-analyzer to monitor and controlbioprocesses, on the basis of a real-time expert system. Theproposed system revealed the usefulness of different probesbased on different spectroscopic methods (including NIRspectroscopy) for monitoring a single process [75].

Among the main analytes followed in fermentation pro-cesses are biomass, glucose, lactic acid, and acetic acid.With regard to chemometric methods, PLS is the multivar-iate method most used to relate analyte levels to NIR spectra[66–68, 71, 72]. MLR [64], locally weighted regression(LWR) [65] and univariate approaches [76, 77] have alsobeen used, but the resulting models were not as robust asthose obtained by use of PLS.

The number of applications of in-situ NIR spectroscopyfor environmental process monitoring is still quite limited.One explanation is the typical complexity of these processesand the fact that most environmental applications (e.g.,wastewater treatment) involve essentially water with someminor (but complex) contaminants. Nevertheless, recentpublications have proved that, in some circumstances, NIRspectroscopy can provide very useful results, especiallybecause real-time measurements can help in process control.An anaerobic unit for treatment of municipal solid wastewas monitored by use of NIR with PCA to observe changesin substrate composition during operation [78]. Páscoa et al.[79] reported one of the first applications of in-situ NIRmonitoring of a wastewater treatment process. A transflec-tance probe was used to monitor a one-liter stirred batchreactor, and process trends were followed by analyzingspectra by use of PCA in a multivariate statistical processcontrol (MSPC) strategy. A quantitative approach was alsoproposed for prediction of total suspended solids, total sol-ids and oxygen demand. Similar equipment (spectrometer

and probe) was used to monitor a 25-liter activated sludgetreatment unit by immersing the probe in the settler [80].Disturbances of feed and operating conditions were success-fully detected by use of PCA in an MSPC strategy. Thiswork was extended by using the same unit, equipment, andmonitoring strategy to produce quantitative predictions ofsome key values, specifically chemical oxygen demand,nitrates, and total suspended solids, by using PLS as regres-sion technique [53]. These examples show that MSPC strat-egies can be implemented with success in industrialapplications, e.g. wastewater treatment plants, with benefitsin terms of cost and time spent.

NIR spectroscopy has been also used in situ to monitorvariables such as acetate, methane, biomass, glycerol, vola-tile fatty acids, and propanoic acid levels in biogas produc-tion bioreactors [81–83]. A qualitative approach was alsoused to monitor process deviations when disturbances wereimposed [84].

Agro-food bioprocess monitoring applications are scarce—only a few have been reported, even though food-qualitymonitoring with NIR spectroscopy is an established tech-nique [85, 86]. Short-wave NIR has been used to monitorthe fat, protein, and casein content of milk during pasteur-ization, with PLS as regression technique. This work led tothe introduction of this type of technology in the dairyindustry [87]. NIR has also been applied in the context ofPAT for monitoring the beer brewing process; ethanol for-mation was accurately determined by use of a univariateapproach [88].

The main features of these applications are summarizedin Table 2.

Mid-infrared spectroscopy

General features

The mid-infrared (MIR) region covers the fundamentalvibrations of most of the common chemical bonds. Theoperational range is defined as that between 4,000 cm−1

and 400 cm−1 [89]. The vibration of a polyatomic moleculecan be regarded as a system of oscillators. If there are Natomic nuclei in the molecule, there will be a total of 3N − 6vibrational degrees of freedom for a non-linear moleculeand 3N − 5 vibrational degrees of freedom for linear mole-cules. These internal degrees of freedom correspond to thenumber of independent normal modes of vibration. A vibra-tional mode is only infrared-active if it alters the electricdipole moment of the molecule. The position and intensityof a vibrational band are characteristic of the underlyingmolecular motion and, consequently, of the atoms partici-pating in the chemical bond, their conformation, and theirimmediate environment. Thus, a specific sub-molecular


Tab

le2

Sum

maryof

applications

ofin-situ

bioreactor

mon

itoring

with

NIR

spectroscopy

andchem

ometrics

Area

App

lication

Bioreactor

Spectroscop

icsystem

Chemom

etric

metho

dsa

Qualitativemon

itoring

/quantitativ

emon

itoring

/con

trol

Ref.

Pharm

aceutical

Indu

strial

ferm

entatio

n:Streptom

yces

clavuligerus

Fed-batch

indu

strial

bioreactors0.5,

20,

40m

3

FT-NIR

PLS

No/Yes/Yes

[68]

Detector:InGaA

sPP:SG

(2nd

derivativ

e),SNV

Quantitativ

e:API,viscosity,nitrog

ensources,carbon

sources.

Range:11,000–

4,00

0cm

−1

Probe:transflection

Laboratory-scaleferm

entatio

n:Streptomyces

Cylindrical

glassvessel

12L

Reflectance

spectrom

eter

Detector:InGaA

sUnivariate:

Chang

esat

7,26

3cm

−1

Yes/No/No

[77]

Range:10

,650–

5,85

5cm

−1

PP:SG

(1st

derivativ

e)

Qualitative:carbon

evolutionrate,biomass.

Laboratory-scaleferm

entatio

n:CHO

cells

Con

tinuo

usprocess

ferm

enters

5L,12

LFT-NIR

Linearregression

Yes/Yes/No

[70]

Range:11,000–

4,50

0cm

−1

PCA

Quantitativ

e:glucose,lacticacid,acetic

acid,

biom

ass.

Probe:transflection

(multip

lexing

)

Pathleng

th:1.2mm

Fed-batch

cultivatio

n:Vibrio

cholerae

Laboratory-scale3L

Dispersivespectrom

eter

PCA

(outliers

detection)

Yes/Yes/Yes

[69]

Range:40

0–2,50

0nm

PLS

Quantitativ

e:biom

ass,glucose,acetate

Probe:transm

ission

Pathleng

th0.5mm

Fermentatio

n:Stap

hylococcus

xylosus,

Lactoba

cillu

sferm

entum,Streptococcus

thermop

hylus

Aerob

icferm

enter2L

Dispersivespectrom

eter

PLS

No/Yes/Yes

[72]

Range:70

0–1,80

0nm

PP:2n

dderivativ

eQuantitativ

e:biom

ass,glucose,lactic

acid,

acetic

acid

Probe:interactance

Pathleng

th:2mm

CHO-K

1anim

alcellcultu

reBioreactor2L

Dispersivespectrom

eter

Range:40

0–2,50

0nm

PLS

No/Yes/No

[67]

Probe:transm

ission

PP:SNV,2n

dderivativ

e

Quantitativ

e:glucose,lactate,glutam

ine,

ammon

iaPathleng

th:1.2mm

Cultures:Lactoba

cillu

sStap

hylococcus

Laboratory-scale

Dispersivespectrom

eter

PLS

No/Yes/No

[71]

Range:40

0–2,50

0nm

PP:SG

(2nd

derivativ

e)Quantitativ

e:glucose,lacticacid,acetic

acid,

biom

ass

Probe:interactance

Pathleng

th:2mm

Flocculation:

yeastho

mog

enates

Laboratory-scale

flocculatio

ntank

Dispersivespectrom

eter

PLS

Yes/Yes/Yes

[74]

Detector:PBS

Quantitativ

e:celldebris,protein,

RNA

Range:1,90

0–2,50

0nm

Mon

itoring

:po

lyethy

leneim

inefeed

Microbial

bioreactors

Laboratory-scale

bioreactor

Dispersivespectrom

eter

PLS,P

CA(M

SPC)

Yes/No/Yes

[75]

Cellcultu

rebioreactors

Range:40

0–2,50

0nm

Bioreactormon

itoring

usingdifferenttypes

ofspectroscopicprob

e(inc.NIR)

Probe:im

mersion


Tab

le2

(con

tinued)

Area

App

lication

Bioreactor

Spectroscop

icsystem

Chemom

etric

metho

dsa

Qualitativemon

itoring

/quantitativ

emon

itoring

/con

trol

Ref.

Fed-batch

cultivatio

n:Escherichia

coli

Bioreactor1.5L

Dispersivespectrom

eter

PLS

No/Yes/No

[66]

Range:40

0–2,50

0nm

PP:2n

dderivativ

eQuantitativ

e:biom

ass

Probe:transm

ission

Pathleng

th:0.5–

2mm

Ethanol

ferm

entatio

n:Sa

ccha

romyces

cerevisiae

Bioreactor2L

Dispersivespectrom

eter

MLR

No/Yes/No

[64]

Range:70

0–1,10

0nm

(SW-N

IRregion

)PP:2n

dderivativ

eQuantitativ

e:ethano

l

Fermentatio

n:Sa

ccha

romyces

cerevisiae

Bioreactor5L

Dispersivespectrom

eter

LWR

No/Yes/No

[65]

Range:70

0–1,10

0nm

(SW-N

IRregion

)PP:MNCN

Quantitativ

e:celldensity

Fermentatio

n:Lactoba

cillu

scasei

Laboratory-scale

ferm

enter

Dispersivespectrom

eter

PLS

No/Yes/No

[73]

Probe:transm

ission

Quantitativ

e:lactic

acid,glucose,biom

ass

Cultiv

ations:differentbacteria

andyeasts

Laboratory-scale

bioreactor

Spectrometer:sing

lewavelength

measurement

Correlatio

nanalysis

No/Yes/No

[76]

Quantitativ

e:biom

ass

Agro-Foo

dBeerbrew

ing

Laboratory-scalereactor

Dispersivespectrom

eter

Linearregression

No/Yes/No

[88]

Quantitativ

e:ethano

l

Dairy

indu

stry

Pasteurizationbioreactor

Dispersivespectrom

eter

PLS

No/Yes/No

Range:1,10

0–2,50

0PP:SNV

Quantitativ

e:fat,protein,

casein

Env

iron

mental

Mun

icipal

solid

wastewater

treatm

ent

Anaerob

icdigester

8L

Dispersiveinstrument

PCA

(MSPC)

Yes/Yes/Yes

[78]

Probe:transflectance

PLS

Qualitative:

Respo

nseto

changesin

the

digester

fluid.

Quantitativ

e:prop

ionate

(at-lin

e)

Activated

slud

gereactor

Laboratory-scale25

LDispersivespectrom

eter

PCA

(MSPC)

Yes/No/No

[80]

Range:90

0–1,70

0nm

Qualitative:

identificationof

perturbatio

nsoccurred


Pathleng

th.0.5mm

Activated

slud

gereactor,SBRforaerobic

treatm

entof

dairyresidu

esGlass

bioreactor

1.5L

Dispersivespectrom

eter

PCA

(MSPC)

Yes/Yes/No

[79]

Range:90

0–1,70

0nm

PLS

Qualitative:

processfollo

wing


PP:SNV,2n

dderivativ

eQuantitativ

e:TSS,TS,COD

Pathleng

th:0.5mm

Activated

slud

gereactorforaerobictreatm

ent

Laboratory-scale25

LDispersivespectrom

eter

PLS

No/Yes/No

[53]

Range:90

0–1,70

0nm

PP:SNV,SG

(2nd

derivativ

e)Quantitativ

e:COD,NO3−,TSS


Pathleng

th:0.5mm

Biofuels

Biogasprod

uctio

nprocess(anaerob

icdigestion)

Tank8L

Dispersivespectrom

eter

PLS

No/Yes/Yes

[81]

Range:40

0–2,50

0nm

PP:2n

dderivativ

eQuantitativ

e:acetate,methane,biom

ass

Biogasprod

uctio

nprocess(anaerob

icdigestion)

Biogasprod

uctio

nun

it(1

MW

power)

Dispersivespectrom

eter

PCA

(rem

oving

outliers)

No/Yes/No

[83]

Range:58

0–1,65

0nm

PLS

Quantitative:VFA

,acetic

acid,propionicacid


group produces bands in a characteristic spectral region. Thevibrational movements of atoms are very small (in the orderof 10−9 to 10−10 cm) and are confined within this narrowrange by a potential energy well. The potential well wallsare formed on one side by the binding force of the boundingelectrons, and on the other by the repulsive electrostaticforces generated between the atomic nuclei. The MIR fun-damental absorptions in the fingerprint region (below1,700 cm−1) arising from the molecular skeleton (e.g., C–C, C0C, and C0O) or functional groups containing heavieratoms (e.g., C–Cl and C–N) are into the second or thirdovertones above 4,000 cm−1.

Instrumentation

Fourier transform infrared (FT-IR) spectroscopy has almostentirely replaced dispersive instruments because of its betterperformance in nearly all respects [43]. In photoacousticspectroscopy (PAS), intensity-modulated infrared (IR) radi-ation hitting the sample is absorbed and generates heatwaves within the sample. When these heat waves reach thesurface of the sample they generate sound in the surround-ing transfer gas, normally helium. This technique enablescontrollable depth profiling of the sample. By applying asequence of several modulation frequencies in a repeatedmanner, it is possible to obtain data from various probingdepths [54]. The attenuated total reflection mode (ATR) isbased on light reflection through a crystal. The sample isplaced in optical contact with a special crystal, termed anATR crystal, which is composed of a material of highrefraction index (e.g. zinc selenide) [89]. The IR beam fromthe spectrometer is focused on to the edge of the ATRelement by a set of mirrors, reflected through the crystal,and then directed to the detector by means of another set ofmirrors. Complete internal reflection occurs between thesample and the crystal interface and radiation penetrates justa short distance into the sample. The sample interacts withthe penetration wave (evanescent wave), resulting in absorp-tion of radiation which closely resembles a transmissionspectrum. Samples analyzed by FT-IR with an ATR acces-sory do not require sample preparation. Nevertheless, thesample must be in close contact with the crystal [90].

Most of the radiation sources intended for IR are thermalsources, and feature some form of electrically heated ele-ment providing a characteristic blackbody or near black-body emission spectrum. Two types of broadband sourceare used in IR instrumentation, the open-element source andthe closed-element source. The latter is essentially a lightbulb with a tungsten lamp enclosed in a quartz envelope.Because this source does not transmit energy below2,000 cm−1, it is more suitable for NIR instruments. Theformer type is a variant of a light bulb with an enclosedelectrically heated element within a standard package. TheT

able

2(con

tinued)

Area

App

lication

Bioreactor

Spectroscop

icsystem

Chemom

etric

metho

dsa

Qualitativemon

itoring

/quantitativ

emon

itoring

/con

trol

Ref.

Glycerol-bo

ostedthermop

hilic

anaerobic

digestionprocess

Laboratorybiotank5L

Dispersivespectrom

eter

PLS

No/Yes/Yes

[82]

Quantitativ

e:glycerol,VFA

Biogasprod

uctio

nprocess

Laboratory-scaleMSW

biog

astank

8L

Dispersivespectrom

eter

PCA

Yes/Yes/No

[84]

Range:80

0–2,00

0nm

PLS

Qualitative:detectionofprocessdisturbances

PP:2n

dderivativ

eQuantitativ

e:prop

ionate

aPPpre-processing


unit is sealed with a low-pressure atmosphere of a protectiveinert gas, for example nitrogen, by an IR-transmitting win-dow of a material such as potassium bromide, zinc selenide,sapphire, or germanium. The thin filament used in bulbs andbulb-like sources is of low thermal mass, which provides theopportunity to pulse the source radiation.

Two main types of detector are used for MIR measure-ments: thermal and photon detectors. Thermal detectors areused for most routine applications. They have the crucialadvantage of operating at room temperature. The most com-mon thermal type detectors are the pyroelectric detectors,for example deuterated triglycin sulfate (DTGS) and lithiumtantalete. Most FT-IR instruments contain the more expen-sive and more sensitive DTGS detector. These detectors arenot very fast, so for applications requiring high-speed ac-quisition, the mercury–cadmium telluride (MCT) detector isused [54].


Transmittance was the most used mode in MIR instrumentsfor a long time. The samples were diluted in an IR radiation-transparent material, for example potassium bromide (KBr),and pressed into very thin pellets. Nowadays, reflectanceand ATR are widely used in MIR applications. These sam-pling modes enable direct measurement of IR spectra ofsolids in their native state, thus eliminating the need forsample pretreatments such as the KBr pellet technique,which was one of the major disadvantages of the use ofMIR spectroscopy for on-line applications.

An important disadvantage of the use of infrared spec-troscopy is the large absorbance of water in the infraredregion. The band produced by water absorption can maskimportant information present in the spectra [89].

Applications

MIR spectroscopy has been less used than NIR, because ofthe specificity of the technique. However, by use of opticalfiber probes with attenuated total reflectance (ATR), thetechnique can nowadays be easily applied in situ. As withNIR, reported MIR applications are mainly in monitoringfermentation processes. In all reports, an ATR probe wasused. Properties monitored include fructose, glucose, etha-nol, ammonium, acetate, glycerol, methanol, phosphate, andgluconaceten content, optical density, and pH [91–97]. Thechemometric methods PLS [91, 96] and PCR [96] have beenused for quantification.

A different approach has been reported in which a spec-tral library was used to predict glucose, ethanol, ammonium,acetate, and glycerol levels in an aerobic Saccharomycescerevisiae fermentation [92]. However, the last two were not

accurately predicted by this approach, possibly because ofthe small number of available standards.

A data reconciliation method based on continuous ele-mental balances has also been used to quantify severalproperties to improve bioprocess monitoring [94]. Glucoseand lactate levels were monitored in CHO cell cultures; PLSwas used to establish the regression models [98]. The ro-bustness of the technique was demonstrated by applicationof the developed models without modification after2.3 years.

Trichloroethylene, tetrachloroethylene, and carbon tetra-chloride were monitored by use of MIR spectroscopy withan ATR sensor in the aqueous effluent of a dechlorinatingbioreactor [99]. The sensor was based on an ATR elementcoated with an extracting hydrophobic polymer which pre-vented water molecules from interacting with the infraredradiation.

The main features of these applications are summarizedin Table 3.

Raman spectroscopy

General aspects

Raman spectroscopy is a technique based on inelastic scat-tering of monochromatic light upon interaction with a sam-ple. It provides information about vibrational, rotational,and other low-frequency transitions in molecules for whichthe polarizability changes on excitation. When the incidentlight (usually from a laser source) interacts with the vibra-tional frequencies of molecules, most of it is scattered with-out frequency change (Rayleigh scattering) and only a verysmall fraction is shifted from its original wavelength to oneor more different wavelengths (Raman scattering). Thewavelength shift between the laser light and the scatteredlight depends on the chemical bonds that caused the Ramanscattering. Therefore, Raman spectra can provide qualitativeand quantitative information about the samples analyzed. AStokes shift occurs when energy is transferred from the lightto a molecule, resulting in scattered light with a longerwavelength and lower energy. On the other hand, an anti-Stokes shift occurs when energy is transferred to the lightfrom a molecule, resulting in a scattered light with a shorterwavelength and higher energy. Raman spectra are symmet-ric around the wavelength of the incident light, with theStokes shifts typically on the left and the anti-Stokes shiftson the right on a left-to-right increasing wavenumber (cm−1)axis. Near-IR Raman spectroscopy has been widely used forqualitative analysis of lipids and carbohydrates, because ithas the advantage of overcoming the fluorescence interfer-ences occurring with other excitation wavelengths.


Resonance Raman is a type of Raman spectroscopy inwhich the excitation light energy is coincident with that ofan electronic transition of a molecule (i.e. with a molecularabsorption band), resulting in intense Raman scattering. Inthis spectroscopy, vibrational modes related to the excitedstate of the molecule can be enhanced by a factor of 106.This form of Raman spectroscopy is highly selective and iswidely used to study biological components that absorbdeep-UV radiation (between 220 and 280 nm) and thatresonate with UVexcitation, for example nucleic and aminoacids. In addition to resonance enhancement, deep-UVRaman spectroscopy, with excitation wavelengths below

250 nm, eliminates fluorescence interference, providingcomplete spectral separation between Raman and fluores-cence emission bands, resulting in high signal-to-noisemeasurements and low detection limits. However, thedeep-UV lasers available for UV Raman spectroscopy arestill expensive to install and operate (water cooling and highelectric power are required) and portability is poor (becausethey are bulky and heavy); they are, thus, not yet adequatefor in-situ bioreactor monitoring.

In addition to resonance Raman, several improved tech-niques have recently been developed—Fourier transform(FT-Raman), surface enhanced (SER), and two-

Table 3 Summary of applications of in-situ bioreactor monitoring with MIR spectroscopy and chemometrics

Area Application Bioreactor Spectroscopicsystem

Chemometricmethodsa


Ref.

Pharmaceutical CHO cultures Industrialbioreactor15 L

Dispersive spectrometer PLS No/Yes/No [91]Range: 1,820–1,000 cm−1 PP: baseline

correction,MNCN

Quantitative: fructose, glucoseethanol, organic acidsProbe: ATR

CHO cultures Stirred tankbioreactor2 L

Spectrometer PLS No/Yes/Yes [98]Detector: MCT PP: MNCN Quantitative: glucose, acetate.

Range: 1,800–800 cm−1 Monitoring: analytes concentrationover the time.Probe: ATR

Fermentation:Saccharomycescerevisiae

Bioreactor3.6 L

FT-IR Libraryapproach

No/Yes/No [92]Range: 1,800–900 cm−1 Quantitative: glucose, ethanol,

ammoniumProbe: ATR

Fermentations:Saccharomycescerevisiae

Laboratory-bioreactor3.6 L

FT-IR PLS No/Yes/Yes [94]Range: 4,000–650 cm−1 PP: MNCN Quantitative: glucose, ethanol,

ammonium, phosphates, glycerol,acetic acid.

Detector: MCT

Monitoring: analytes concentrationover the time

Probe: ATR

Fermentation:Pachiapastoris

Bioreactorfullyautomated2 L

FT-IR Linear two-pointcalibration

No/Yes/Yes [93]Probe: ATR Quantitative: methanol

Monitoring: concentration ofmethanol over time

Agro-food andbeverages

Fermentation:S. cerevisiae

Bioreactor1.5 L

FT-IR PLS No/Yes/Yes [96]Detector: DTGS PCR Quantitative: ethanol, glucose,

optical density (OD).Range: 4,000–400 cm−1 PP: 1stderivative Monitoring: analytes

concentration and OD over time

Probe: ATR

Fed-batchcultivation:Gluconacetobacterxylinus

Bioreactor15 L

Spectrometer PLS No/Yes/Yes [95]Detector: MCT PP: MNCN Quantitative: fructose, ethanol,

acetate, gluconacetan, ammonium,phosphate

Probe: ATR

Monitoring: analytes concentrationover time

Environmental Dechlorinationprocess

Elongatedcylindricalglass tube4.4 L

FT-IR Univariate No/Yes/Yes [99]Probe: ATR(coated withan extractinghydrophobicpolymer)

PLS Quantitative: trichloroethylene,tetrachloroethylene, carbontetrachloride

Monitoring:analytes concentration over time

aPP pre-processing


dimensional (2D) Raman spectroscopy. SER spectroscopyis not suitable for in-situ measurements because it requiressample adsorption on the surface of specific metals and 2DRaman spectroscopy is limited to a small class of simplemolecules and still lacks theoretical and experimental de-velopment [100].

Instrumentation

Technological advances have enabled the development of newRaman instruments that are smaller, more efficient, and moresensitive, and that have higher signal-to-noise ratio than theirpredecessors. Any Raman spectrometer includes a laser asmonochromatic excitation light source, an interface betweenthe sample and the instrument, a filter to remove Rayleighscatter, a spectrograph to separate the scattered light by wave-length, and a detector.

From an experimental perspective Raman spectrometerscan be categorized as dispersive and Fourier-transform (FT).In dispersive instruments, the Raman signal is dispersedthrough a grating spectrograph and the separated Ramanbands are detected simultaneously across the width of a mul-tichannel detector, for example a CCD. CCD detectors arevery sensitive, low noise, and enable simultaneous measure-ment at several wavelengths of a particular spectral region. Adisadvantage of these detectors is that they are designed todetect visible light, so the Raman spectra have to be generatedwith excitation in the visible region. This can lead to problemsof signal obscurity because of fluorescence. One of the mostimportant advances in Raman dispersive instrumentation isholographic notch filters, which attenuate Rayleigh scatter andare essential in reducing the size of these instruments [101].

FT-Raman is based on use of an interferometer thatenables detection of all wavelengths simultaneously, sub-stantially reducing the time needed to record a Ramanspectrum. The excitation laser provides near infrared radia-tion at 780 or 1,064 nm, which greatly reduces signalintensity but also avoids a significant fluorescent back-ground [90]. The FT-Raman detectors are normally of indi-um–gallium arsenide (InGaAs) or liquid nitrogen-cooledgermanium [55], because the photoelectric response ofCCDs is negligible at the wavelengths used.

Raman scattering can be measured in three ways: backscat-tering (the most used), spatial offset, and transmission [102]. Inbackscattering and spatial offset modes, the scattered radiationis collected on the same side of the sample. However, in spatialoffset the signal is collected from spatial regions offset from thepoint of illumination on the sample surface, providing resolvedspectra of each layer of a stratified sample. In transmissiongeometry, the laser beam is incident on one side of the probedsample and the light is collected from the opposite side [103].

Raman spectroscopy is particularly suitable for in-situbioreactor monitoring, because of incorporation of fiber

optics as the delivery and collection system. Fiber scatterand fluorescence are the main difficulties of silica probeRaman spectroscopy, but use of new fiber materials andmathematical subtraction of fiber spectra can reduce theseeffects [101]. The probes may be in direct contact with thesample or placed at a specific distance, depending on theapplication.


A major advantages of Raman spectroscopy is its flexibility.In fact, it can be used for a wide variety of types of sample,from solids, liquids, and gases to any combination thereof(slurries, gels), without sample preparation [2]. Further-more, the sample interface can be non-invasive (non-con-tact) or in direct contact with the sample via an immersionprobe. The spectra are not sensitive to water, which is a greatadvantage in the monitoring of bioreactors, and can providedata about the composition, chemical environment, andstructure of the sample, enabling structural, qualitative,and quantitative analysis.

The Raman signal tends to be weak, however, and somebiological molecules fluoresce in the Raman scattering bandregion, resulting in significant spectral interference. In fact,fluorescence is one of the biggest challenges in the acquisitionof quality Raman spectra. Another major disadvantage ofRaman spectroscopy is use of high power lasers to generateenough Raman scattered photons for detection. These lasersincrease the complexity and cost of the instruments, not allwavelengths are available, and high laser power can destroydelicate or dark samples [2]. Selection of a laser wavelength tomaximize Raman signal and minimize fluorescence is a crit-ical decision in the selection of a Raman instrument.

Applications

Raman spectroscopy is an important analytical and researchtool with a wide range of potential applications, including thefood, pharmaceutical, and medical sciences, but the use ofRaman spectroscopy for on-line monitoring of bioprocesses isstill very limited. In-situ Raman spectroscopy has been usedfor monitoring carotenoid (food and pharmaceutical grades)production from the yeast Phaffia rhodozyma [104]. It hasalso been used for determination of glucose, acetate, formate,lactate, and phenylalanine concentrations in E. coli growthbioreactors that were modified to enable spectra collectionthrough a viewing port window [105]. Resonance Ramanspectroscopy has also been used for nitrate and nitrite concen-tration monitoring in a wastewater treatment bioreactor [106]and also for in-situ monitoring of alcoholic fermentation bySaccharomyces cerevisiae [107]. The main features of theseapplications are summarized in Table 4.


Fluorescence spectroscopy

General aspects

Fluorescence spectroscopy is a technique specific for chem-ical species with this property. Upon irradiation, a fluores-cent compound absorbs a photon and is excited from itsground electronic state to a higher electronic (excited) state.The energy of the molecule then drops to one of the differentvibrational levels of the ground electronic state (relaxation),and the molecule emits a photon in the process. Because theenergy of molecules may drop into any of several vibration-al levels in the ground state, the emitted photons will havedifferent energy, and are thus of different frequency. There-fore, by analyzing the different frequencies of light emittedin fluorescent spectroscopy, with their relative intensities,the structure of the different vibrational levels can bedetermined.

Fluorescence is short-lived (~10−8 s) and, therefore, inmany molecules it can compete favorably with collisionaldeactivation, intersystem crossing, and phosphorescence.Fluorescence energy is always lower than its associatedabsorption energy for a given molecule, thus the emittedlight is observed at longer wavelengths than those used forexcitation.

In comparison with light absorption measurements, fluo-rescence spectroscopy is more sensitive, because the mea-sured signal has, in principle, zero background.

Many biological compounds fluoresce on excitation byvisible or near-UV light. Proteins, enzymes, coenzymes,

vitamins, and primary or secondary metabolites from micro-bial growth have this property, rendering fluorescence spec-troscopy an objective tool for bioprocess monitoring.

In early publications dealing with bioprocesses, fluores-cence was measured only at one excitation/emission wave-length pair, limiting the signal to a single fluorophore—thereduced form of the cofactors nicotinamide adenine dinu-cleotide (NADH) and nicotinamide adenine dinucleotidephosphate (NADPH) [108, 109]. These nucleotides are ofcentral importance in cellular metabolism and have beenused as a way of inferring biomass concentration duringgrowth of different microorganisms in bioreactors [110].

Such applications to a single fluorophore are sensitive butnot specific. The use of single-pair emission–excitationwavelengths can be severely limited by overlap of spectraand inner filter effects (in which non-fluorescent compoundsabsorb the excitation radiation or that emitted by the targetfluorophore). In the turbid, complex media common in bio-reactors, other effects are also common, for example emis-sion from one fluorophore exciting another (cascade effects)or one of the compounds present quenching the fluorescencefrom the target compound (quenching effects) [111].

Modern fluorimeters can use several excitation and emis-sion wavelengths, enabling simultaneous monitoring of dif-ferent fluorescent constituents over the course of thebioprocess [112, 113]. With this multi-dimensional fluores-cence technique it is possible to measure several intra andextracellular fluorophores, resulting in an emission versusexcitation contour plot [114] characterizing the biochemicalstate of the process.

Table 4 Summary of applications of in-situ bioreactor monitoring with Raman spectroscopy and chemometrics

Area Application Bioreactor Spectroscopic system Chemometricmethodsa


Ref.

Food andbeverages /Pharmaceutical

Fermentation:Phaffiarhodozyma

Fed-batchbioreactor 7.0 L

Dispersive spectrometer PLS No/Yes/No [104]Detector: CCD PP: SG (1st,

2ndderivatives)

Quantitative: carotenoidsLaser excitation: 285 nm

Probe: 12.5 mm

Food andbeverages

Fermentation:Escherichia coli

Stirred tankbioreactor 7.0 L

Dispersive spectrometer(backscattering)

PLS No/Yes/No [105]

Detector: CCDQuantitative: glucose,phenylalanine, acetate,formate, lactateLaser excitation: 785 nm

Fermentation:Saccharomycescerevisiae

Laboratory-scalefermenters

Dispersive spectrometer Univariate No/Yes/No [107]Detector: CCD Quantitative: ethanolLaser excitation:514.5 nm

Environmental Wastewatertreatment

Full-scaleactivated sludgebioreactors

UV resonance Ramanspectrometer(backscattering)

None Yes/No/No [106]

Detector: CCD

Qualitative: nitrate, nitrite

Laser excitation: 204 nm

aPP pre-processing


Instrumentation

A typical fluorimeter contains an excitation light source, asample holding cell, and a fluorescence detector. Moleculesin solution are usually excited by UV–Vis light and theexcitation source is currently a deuterium or xenon lamp.Broad-band excitation light from a lamp passes through amonochromator, allowing a selected wavelength through.The fluorescence is dispersed by another monochromatorand detected by use of a photomultiplier tube. Scanning theexcitation monochromator gives the excitation spectrum andscanning the fluorescence monochromator results in thefluorescence spectrum. Simple instruments sometimes useonly a bandpass filter to select the excitation wavelength.

To obtain fluorescence measurements from the contentsof a bioreactor it is necessary to use a light conductor(optical fiber) to guide the excitation light into the bioreactorand collect the backward fluorescent light via a second fiber[114]. In several applications this light conductor is com-monly placed in an optical well in the bioreactor [115, 116],or attached to the outer glass wall via a holder [117, 118]and neither the glass wall nor the curvature of the glassmodule has a detrimental effect on the fluorescence spectra.

Examples of optical fibers mentioned in the literature arethe bifurcated liquid light conductor (Lumatech, Germany)[115, 119–121] coupled to the BioView fluorescence spec-trometer (Delta Light and Optics, Denmark). Another ex-ample is the R400-7 fiberoptic reflection probe with sixillumination fibers and one read fiber [122], coupled to theUSB2000 CCD array fiber optic spectrophotometer (OceanOptics, USA).


As previously emphasized, this spectroscopy is specific forfluorescent chemical species, thus direct data determinationon other compound properties is not possible. However,some properties can be indirectly assessed by fluorescencestudies. This possibility was greatly enhanced by instrumen-tal advances enabling application of an excitation spectrumand acquisition of an emission spectrum. By analyzing thecomplex data obtained from these spectrophotometers byuse of chemometric methods, several researchers have beenable to obtain very interesting and useful results in biopro-cess monitoring. Such cases have been reviewed by Pons etal. [2] for bioprocess microbiology and by Teixeira et al.[63] with regard to cell culture aspects.

Chemometric methods are of utmost importance for in-terpretation of the large amounts of data generated by on-line measurements of 2D fluorescence. Data yielding avariable plot of excitation, emission, and intensity are usu-ally collected every minute [115, 119], or every 5 to 10 min[120, 121]. Qualitative exploratory analysis of bioprocess

states and quantitative variable prediction can be computedfrom this information.

An advantage of fluorescence spectroscopy comparedwith UV–Vis absorption spectroscopy is that it can bemeasured in the presence of high biomass concentrations.The fiber optic probe design, with side by side emission andexcitation, inserted perpendicular to the bioreactor or bio-process stream, has reportedly enabled fluorescence meas-urements in the presence of biomass [122–124].

Applications

The cultivation of the fungus Claviceps purpurea for pro-duction of ergot alkaloids with pharmaceutical applicationswas the first bioprocess monitored on-line by chemometricevaluation of fluorescence spectra [119]. Later, the cell massconcentration of Saccharomyces cerevisiae was estimatedby on-line monitoring of culture fluorescence [125], andfurther use of multi-wavelength fluorescence also enabledprediction of glucose and ethanol levels [124] in the sametype of fermentation. Fermentation processes with recombi-nant E. coli were monitored by use of 2D fluorescencespectral data, with PCA, principal component regression(PCR), and PLS being used for process modeling [123].Furthermore a multi-wavelength on-line fluorescence sensorwas combined with unfolding-PLS regression to predict theconcentrations of biomass, glycerol, and methanol in theheterologous production of lipase by Rhizopus oryzae [116].

In biological wastewater treatment, a correlation wasdeveloped between COD removal and fluorescence signalsin activated sludge SBR [113]. On-line scanning fluores-cence spectroscopy has also been used to monitor anaerobic[122] and aerobic sludge digestion bioreactors in wastewatertreatment processes [126]. The potential of fluorescencespectroscopy as a monitoring tool for recycled water sys-tems has recently been reviewed [127]. The main features ofthese applications are summarized in Table 5.

Terahertz spectroscopy

Terahertz spectroscopy, or far-infrared spectroscopy, is atechnique that uses electromagnetic radiation in the regionbetween microwave and infrared wavelengths (from 0.1 to1 mm or from 10 to 100 cm−1). The resulting spectra aredetermined by the molecular structures present, i.e., arehighly dependent on intermolecular interactions, for exam-ple hydrogen torsions and librations, and whole-moleculestretches [128]. The interpretation of terahertz spectra issimilar to that of infrared and Raman spectra, and providesstructural information that complements that contained inmid-infrared spectra.


Table 5 Summary of applications of in-situ bioreactor monitoring with fluorescence spectroscopy and chemometrics


Chemometricmethods

Qualitativemonitoring/quantitativemonitoring/control

Ref.

Agro-food andbeverages

Cultivation:Saccharomycescerevisiae

Batch reactor10 L

BioView sensorbifurcated liquidlight conductor

None No/No/No [114]


Batch reactor4 L


PARAFAC Yes/Yes/No [125]PLS Qualitative: of cell

mass


Fed- batch reactor2 L


PLS No/Yes/Yes [115]Qualitative:glucose

Monitor: feed

Cultivation:Pichia pastoris

Batch culture2 L


PLS No/Yes/No [116]Qualitative:biomass,glycerol,methanol

Fermentation:Escherichia coli

Bioreactor 7.5 L

Hitachi F-4500bifurcated liquidlight conductor

PCA Yes/Yes/No [123]PCR In situ prediction

of opticaldensity and5-aminolevulinicacid

PLS

Cultivation:Pichia pastoris

Batch reactor2 L


PARAFACMSPC

Yes/No/Yes [121]


Batch reactor3 L


PCA Yes/Yes/No [124]PLS Quantitative:

biomass,glucose, ethanol

Pharmaceuticaland agro-foodand beverages

Cultivation:SaccharomycescerevisiaeClavicepspurpureaSphingomonasyanoikuyae

Bioreactor1.5–10 L


None No/No/No [112]

Pharmaceutical Cultivation: Clavicepspurpurea

Bioreactor2 L


PLS No/Yes/No [119]Quantitative:biomass, protein,alkaloids

Cultivation:Escherichia coli

Fed-batch reactor20 L


PLS No/Yes/No [120]Radial basisfunctionnetwork

Quantitative:biomass, totalcell number,percentage ofdead cells,plasmid copynumber, product,productformation rate

Environmental Degradation Extractive membranebioreactor0.6 - 1.3 L

Perkin Elmer LS50B(optical fiber cable)

ANN Yes/No/No [118]Qualitative:degradation ofchlorinatedorganiccompounds


The slower development of terahertz spectroscopy com-pared with the other infrared techniques can be attributed todifficulties in generating and detecting terahertz radiation.However, novel semiconductor-based terahertz sources andcompact terahertz detectors have been developed in the last15 years and terahertz spectroscopy is now expected tobecome a very useful tool in bioprocess monitoring forexamination of the low-frequency collective vibrationalmodes of biomolecules [6].

Critical overview of spectroscopic techniquesfor bioreactor monitoring

The most relevant properties for in-situ bioprocess monitor-ing of the spectroscopic techniques reviewed in this paperare compared in Table 6.

Response time is very difficult to quantify, because it ishighly variable within the same spectroscopic technique,depending largely on the type of instrument used. For examplein NIR spectroscopy, dispersive instruments can measure aspectrum in milliseconds whereas an FT instrument can re-quire a second to measure a spectrum. Nevertheless, responsetimes are clearly lowest in UV, Vis, and NIR spectroscopy.

The detection limits or sensitivity to trace components inmixtures are commonly regarded as indicators of instrumentperformance. Detection limits are, however, highly depen-dent on the instrumentation, on measurement conditions, onthe analyte to be measured, and on the characteristics of thesolvent or matrix containing the analyte. So, if sensitivity isa critical factor when choosing a technique for spectroscopicmonitoring, all aspects must be considered and evaluated. Ingeneral terms, detection limits can be improved by usinglonger measurement times and these can be achieved byincreasing the detector integration time and/or by increasingthe number of scans from individual integrations. This, ofcourse, lengthens response time, eventually masking impor-tant variations in measured or inferred system variables.

Because the signal to noise ratio (S/N) normally increasesin proportion to the square root of the measurement time, acompromise must be sought between reasonable measure-ment time and achievable detection limit.

Selectivity is always an issue when dealing with spectro-scopic techniques. High chemical selectivity can beobtained by use of Raman spectroscopy. Normal Ramanspectroscopy, however, is a low-selectivity method com-pared with fluorescence spectroscopy. All other spectro-scopic techniques can also be regarded as non-selective.

Chemometric methods must be used when dealing withvibrational spectroscopy-based techniques. Nowadays, severaluser-friendly and ready-to-use chemometric software packagesare available, often being provided with the spectroscopyequipment. Basic use of these packages requires little training,but critical observation of the data by specialized personnel isalways recommended. In addition to the theoretical bases forspectral data analysis that include band assignment and quan-tification, multivariate statistical methods, for example factoranalysis, PCA, and PLS associated with classification methods,are very useful for extraction of knowledge from spectroscopicdata. The spectroscopic data can also be used to establish andquantify trends or drifts, and to diagnose the cause of abnormalevents, i.e., to monitor the process state in real-time. This is thebasis of multivariate statistical process control (MSPC). Veryfew applications use this concept, but it can be extremely usefulin several areas. For example, cultivation of bacteria can beeasily followed by building a model for the average trajectoryof the variable to be analyzed. In this way, an ideal processtrajectory can be defined as the ideal path from which newcultures can be evaluated. The real-time and in-situ availabilityof multivariate data provided by spectroscopic methods furtherenhances knowledge of a process, enabling the reduction ofprocess variability and batch-to-batch variation. Wastewatertreatment is another application in which MSPC can be veryuseful. By the use of an MSPC strategy, perturbations in theprocess can be easily followed, diagnosed, and corrected

Table 5 (continued)


Chemometricmethods

Qualitativemonitoring/quantitativemonitoring/control

Ref.

Wastewatertreatment

Upflow anaerobic sludgebed 3.5 L

Array fiber opticspectrophotometer

PLS No/Yes/No [122]PCR Quantitative: VSS,

COD,propionate, VFA

Step-wise PCR

Aerobic sludgedigestion

Bioreactor2 L

BioView sensor None – [126]

Degradation Extractive membranebioreactor 0.6–1.3 L

Perkin Elmer LS50B(optical fiber cable)

PCA No/Yes/No [117]ANN Quantitative: seven

inferred processperformanceparameters


in a timely way to prevent non-compliant wastewaterdischarges.

For in-situ monitoring, the possibility of using opticalfiber probes with the spectroscopy equipment is an extreme-ly important feature. In this way, the process can be moni-tored with a probe in contact or in close contact with thebioreactor media, providing real-time, undisturbed informa-tion. The length of the optical fiber is also relevant, becausethe possibility of having the spectrometer at several metersfrom the process unit or stream may be advantageous. Thisleads to another useful feature, multiplexing, e.g., the possi-bility of having several probes connected at the same time tothe spectrometer. For example, in a conventional activatedsludge line, the bioreactor and the settler units may be mon-itored at the same time using two different probes connected toa single spectrophotometer, avoiding the purchase and opera-tion of a second instrument. Another useful possibility is themonitoring of different locations in the same full-scale biore-actor in which properties may be inadvertently or deliberatelydistinct. MIR spectroscopy is the least versatile of the spec-troscopic techniques which use optical fibers, because it isonly possible to use a probe (ATR probe) with internal reflec-tion for acquisition. Moreover, the length of the fiber must be

low to avoid MIR light attenuation and a short fiber is usuallyinadequate for multiplexing.

Although, in bioreactor monitoring, most of the samples arein either the liquid or solid form, gas analysis can often beimportant, for example the need to measure the CO2 content offermenter off-gases. Normally CO2 detectors are based on(mid)infrared sensors. With the exception of NIR and fluores-cence spectroscopy, all the techniques can be used to analyzegaseous samples.

Besides chemical information (e.g., the presence andconcentration of a chemical component), physical propertiescan also be important in bioreactor monitoring. The possi-bility of measuring suspended solids content and particle-size ranges can be an advantage in some processes. NIR andRaman spectroscopy are highly affected by physical factorsand can, therefore, be easily used to predict them.

The robustness of an analytical procedure is a measureof its capacity to remain unaffected by variations inmethod conditions and provides an indication of its reli-ability during normal usage. Temperature fluctuations,sample turbidity, and the water masking effect are theconditions which most usually impair the reliability ofspectroscopic measurements.

Table 6 Overview of spectroscopic techniques for in-situ bioreactor monitoring

Properties UV–Vis NIR MIR Raman Fluorescence

Response time (s)a <1 <1 1–60 1–60 1–60

Detection limit (ppm)b 0.3 0.1–1 100–2,000 25–150 <0.1

Selectivity Low Low Low Low High

Fiber optics availability Yes Yes Yesc Yes Yes

Fiber optic length (m) Several Several <2 Several Several

Multiplexing availability Yes Yes No Yes Yes

Acquisition mode Reflectance Yes Yes No No No

Transmittance Yes Yes No Yes No

Transflectance Yes Yes No No No

Internal reflection Yes No Yes No No

Emission No No No No Yes

Sample type Solid Yes Yes Yes Yes No

Liquid Yes Yes Yes Yes Yes

Gas Yes No Yes Yes No

Spectral information Chemical properties Yes Yes Yes Yes Yes(fluorescence)

Physical properties Yes (turbidity) Yes (turbidity,particle size)

No Yes(particle size)

No

Robustness To temperature fluctuations Very high Low Very low Low Low

To sample turbidity Low Highd Very low Very high Very low

To water masking effects Very high Low Very low Very high Very high

a The response time depends on the type of equipmentb The limit of detection depends on the type of equipment and applicationcWith ATR probed In transmittance mode


UV–Vis spectroscopy is mostly used for wastewater treat-ment bioreactor monitoring, but the spectra obtained are highlydependent on medium turbidity. MIR and NIR techniques arewidely used for fermentation monitoring but are affectedby the presence of water, especially MIR spectroscopy.Conversely, Raman spectroscopy is not sensitive to wa-ter, but can be strongly affected by sample fluorescence.Temperature fluctuations can be a problem in most ofthe techniques, affecting mainly the infrared spectrosco-py ranges (MIR and NIR).

Overall, when choosing a spectroscopic technique forbioreactor monitoring it is always necessary to ponder sev-eral aspects. The nature of the bioprocess, the monitoringobjectives (qualification, quantification, supervision, con-trol) and the output data must be carefully assessed toadequately select the spectroscopic technique and, whenrelevant, the associated acquisition mode. As technologyprogresses and wider experience is gathered, further broad-ening of spectroscopic applications for bioreactor monitor-ing is likely to occur.

Conclusion

The advantages of real-time bioprocess monitoring arewidely recognized. However, the number of applicationshas been somewhat limited, mainly because of the lim-itations of the instruments available. Recently, spectro-scopic techniques have attracted increasing interest forin-situ bioreactor monitoring, because of crucial advan-ces in the development of robust spectrometers andhigh-quality probes and optical fibers. From severalrevealing research reports it has become apparent thatin-situ bioreactor monitoring with spectroscopy coupledto chemometrics is a new means of fundamental under-standing of bioprocesses. The widening of the amountand quality of measurements that can be taken insidethe process, and in real time, promises better under-standing of biotechnological process dynamics and com-plexity. Multivariate and real-time data-handlingcapabilities have also been shown to be powerful tech-niques for fault detection and diagnosis, enabling morecontrolled running of bioprocess operations. Investmentin spectroscopy equipment and personnel training thushave a good return potential in resources conservationand product quality assurance. These demonstratedadvantages are driving an increasing number of studiesinto applications of the resources of spectroscopy in themonitoring of bioreactors.

Acknowledgments ND Lourenço, CF Almeida, and MC Sarraguçaacknowledge financial support from Fundação para a Ciência e aTecnologia (FCT, Portugal) through a post-doctoral research grant

(SFRH / BPD / 31497 / 2006, Portugal) a PhD grant (SFRH / BD /30445 / 2006), and a post-doctoral research grant (SFRH/BPD/74788/2010, Portugal), respectively.

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