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Scale-Up and Design of Chemometric Models for the Production of MCL-PHAs by Pseudomonas putida
KT2442
Vânia Filipa Carvalho Macário
Dissertação para obtenção do Grau de Mestre em
Engenharia Biológica
Júri Presidente: Prof. Luis Joaquim Pina da Fonseca
Orientadores: Prof. José Monteiro Cardoso de Menezes
Dr. Kuang-kuk Cho
Vogal: Prof. Helena Maria Rodrigues Vasconcelos Pinheiro
Setembro, 2009
ii
AABBSSTTRRAACCTT
PHAs are linear polyesters produced in nature by bacterial fermentation of sugars or lipids.
Their biocompatibility and biodegradability make them really desirable in many areas such as
packaging, agriculture, electronic and medicine. In order to PHAs can be significantly used in
those fields, their production has to be maximized, which can be done by finding out the best
producer organism and fermentation conditions.
In this work, different media and feeding strategies with several carbon sources were tested in
the production of PHAs by Pseudomonas putida KT2442. Once controlled in the laboratory,
the more successful fermentation, with octanoic acid as the carbon source, was scaled-up
and the utilization of two online monitoring equipments was included. The evolution of air
composition inside the fermenter was observed by Mass-spec gas analyser, while the
components in the medium were studied with NIRS. The spectra obtained by NIRS were used
to built models to predict the concentration of biomass, PHA and octanoic acid during the
fermentation.
Keywords: Polyhydroxyalkanoates, Pseudomonas putida, Chemometrics, Near Infrared
Spectroscopy, Online Monitoring.
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RREESSUUMMOO
Os PHAs são poliésteres lineares produzidos naturalmente por fermentação bacteriana de
açúcares ou lípidos. As suas biocompatibilidade e biodegradabilidade fazem com que estes
compostos sejam bastante desejáveis em muitas áreas, como o empacotamento, a
agricultura, a electrónica e a medicina. De modo a que a utilização de PHAs nestes campos
possa ser significativa, a sua produção tem que ser maximizada através da descoberta do
melhor organismo produtor e das melhores condições de fermentação.
Neste trabalho, diferentes meios e estratégias de alimentação com várias fontes de carbono
foram testados na produção de PHAs por Pseudomonas putida KT2442. Uma vez controlada
em laboratório, a fermentação mais bem sucedida, com ácido octanóico como fonte de
carbono, foi aumentada de escala, sendo incluídos nesta etapa dois equipamentos de
monitorização online. A evolução da composição do ar no fermentador foi observada por
espectrometria de massa, enquanto os componentes presentes no meio de cultura foram
estudados com espectroscopia de infravermelho próximo (NIRS). Os espectros obtidos por
NIRS foram usados para construir modelos de previsão da concentração de biomassa, PHA
e ácido octanóico durante a fermentação.
Palavras-chave: Polihidroxialcanoatos, Pseudomonas putida, Quimiometria, Espectroscopia
de Infravermelho Próximo, Monitorização Online.
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TTAABBLLEE OOFF CCOONNTTEENNTTSS
CHAPTER 1 INTRODUCTION ..................................................................................................................................... 1
CHAPTER 2 LITERATURE OVERVIEW .................................................................................................................... 2 2.1. PHAs: General Approach ............................................................................................... 3
2.1.1. PHAs properties .................................................................................................................... 4 2.1.2. PHAs applications ................................................................................................................. 7
2.2. Biosynthesis of PHAs ..................................................................................................... 8 2.2.1. PHA producer organisms .................................................................................................... 10 2.2.2. Optimal conditions for the production of PHAs .................................................................... 11 2.2.3. Carbon substrates ............................................................................................................... 12 2.2.4. Summary of MCL-PHA production by various methods ...................................................... 13
2.3. PHAs Recovery ............................................................................................................ 17
2.4. Development of Chemometric Models ......................................................................... 18 2.4.1. NIRS – Near Infrared Spectroscopy .................................................................................... 19 2.4.2. QUANT – Quantitative Analysis Software ........................................................................... 20
CHAPTER 3 MATERIALS AND METHODS ............................................................................................................ 23
CHAPTER 4 RESULTS AND DISCUSSION ............................................................................................................ 24
CHAPTER 5 CONCLUSIONS AND FUTURE WORKS ........................................................................................... 25
CHAPTER 6 REFERENCES ..................................................................................................................................... 26
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LLIISSTT OOFF TTAABBLLEESS
Table 1 – Summary of MCL-PHA production by various methods ..................................... 14
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LLIISSTT OOFF FFIIGGUURREESS
Figure 1 – Chemical structure of a generic PHA monomer ................................................ 3
Figure 2 – Life cycle of PHAs ............................................................................................. 7
Figure 3 – Metabolic pathways for PHA biosynthesis ......................................................... 9
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LLIISSTT OOFF AABBBBRREEVVIIAATTIIOONNSS
DCW Dry Cell Weight
HB Hydroxybutyrate
HHx Hydroxyhexanoate
HV Hydroxyvalerate
LPS Lipopolysaccharide
MCL-PHA Medium Chain Length Polyhydroxyalkanoate
MIR Middle Infra-Red
NIR Near Infra-Red
NIRS Near Infra-Red Spectroscopy
PC Principal Component
PHA Polyhydroxyalkanoate
PHB Polyhydroxybutyrate
PHBHx Poly(hydroxybutyrate-co-hexanoate)
PHBV Poly(hydroxybutyrate-co-valerate)
PHO Poly(hydroxyoctanoate-co-hydroxyhexanoate)
PLS Partial Least Squares
PP Polypropylene
RMSECV Root Mean Square Error of Cross Validation
RMSEE Root Mean Square Error of Estimation
RMSEP Root Mean Square Error of Prediction
RPD Residual Prediction Deviation
SCL-PHA Short Chain Length Polyhydroxyalkanoate
SDS Sodium Dodecyl Sulfate
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CCHHAAPPTTEERR 11 IINNTTRROODDUUCCTTIIOONN
Polyhydroxyalkanoates (PHAs) are linear polyesters produced in nature by bacterial
fermentation of sugars or lipids, being stored in inclusion bodies within the cytoplasm of the
microbial cells (Williamson and Wilkinson, 1958; Steinbuchel and Valentin, 1995).
Many studies have been carried out in order to find the role of PHAs in the producer bacteria.
Most of these studies have been done with polyhydroxybutyrate (PHB), as it is the most
common type of PHA produced and was the first to be discovered. Stanier et al. (1959)
determined that the main propose of PHB granules in bacteria is to serve as a carbon and
energy reserve and that they are produced by the cell in response to a nutrient limitation in
the environment in order to prevent starvation if an essential element becomes unavailable.
Subsequent studies by Dawes and Senior (1973), Dawes (1976; 1985; 1989), Merrick (1978),
Preiss (1989), Karr et al. (1984) and McDermott et al. (1989) provided detailed references
about the role of PHB in the bacteria. One of the major advantages for an organism to
accumulate these polyesters is the ability to store large quantities of reduced carbon without
significantly affect the osmotic pressure of the cell. The possession of PHB usually retards the
degradation of cellular components during the starvation phase, enhances the survival of
some of the producer bacteria and serves as a carbon and energy source for spore formation
in some Bacillus species. Likewise, PHB has been shown to serve as a carbon and energy
source for the encystment of azotobacters and it has also been implicated as an energy
source in the symbiotic nitrogen fixation process between the bacterial genera Rhizobium and
Bradyrhizobium and leguminous plants (Anderson and Dawes, 1990). Like the PHB, the
PHAs in general are used by the producer microorganisms as carbon and energy reserves.
In addition to their great advantages for the bacteria, these types of polyesters can be very
useful in several different ways, due to their mechanical properties, biodegradability and
biocompatibility. These special properties make these materials very suitable in various
industries, where they can substitute the conventional non-biodegradable plastics or even be
used in several applications in medicine.
Due to the valuable properties of PHAs in several fields, their production has to be maximized
in order to compete with the huge utilization of synthetic plastics, which do not present either
the biodegradability or the biocompatibility.
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CCHHAAPPTTEERR 22 LLIITTEERRAATTUURREE OOVVEERRVVIIEEWW
As early as in the year 1927, Maurice Lemoigne managed to isolate the first of the PHAs
(PHB) from the Bacillus megaterium bacterium. He became involved with PHB while he was
trying to determine the cause of the acidification of aqueous suspensions of this bacterium
when it was kept under an oxygen-free atmosphere. In 1923, he reported that the acid
produced by the bacteria was 3-hydroxybutyric acid, and in 1927, he described the isolation
of a solid material obtained from the cell which he characterized as a polymer of that acid.
Despite these and several further discoveries of Lemoigne and his co-workers for 30 years,
organic and polymer chemists didn’t seem to be aware of Lemoigne’s work, even though PHB
was described in biochemistry textbooks, where, however, it was referred to as a lipid, not a
polyester.
The recognition of Lemoigne’s work happened around 30 years after the first studies with two
groups of microbiologists, Williamson and Wilkinson (1958) in Great Britain and Doudoroff
and Stanier (1959) in the United States. After these contributions, several scientists became
interested in this matter and the intensive study of PHAs was launched.
In 1976, the British company Imperial Chemical Industries (ICI) recognized the potential of
PHB to replace the synthetic oil-originated polymers. However, the production of this
polyester was much more expensive than the conventional synthetic plastics, so, this didn’t
seem to be a very clever business. On the other hand, the use of synthetic plastics kept
raising more and more problems, like the deleterious effects on wildlife and on the visual
qualities of cities and forest, as well as the potential hazards from waste incineration, such as
dioxin emission from polyvinyl chloride (PVC). These ecological aspects are the main driving
force of people trying to find the solution to the waste problem. There are mainly three
possible strategies: The first is to prepare materials of such resistance and quality that can be
used for a very long time, not creating waste in high amount; the second is to prepare plastics
that are recyclable; and the third way is to produce biodegradable plastics.
The third way can be achieved with the production of PHAs by bacterial fermentation.
However, in order to make the process economically attractive, many improvements have to
be done. The discovery of new strains and the development of recombinant ones, as well as
3
more efficient fermentation processes, better recovery techniques and the use of inexpensive
substrates can all contribute to increase the overall production and, therefore, reduce the
production cost.
2.1. PHAS: GENERAL APPROACH
PHAs are polymers of hydroxyacids, which possess the formula in Figure 1. Monomers are
linked in ester bonds between the carboxyl group of monomer I and the hydroxyl group of
monomer I+1.
Figure 1 – Chemical structure of a generic PHA monomer.
These bacterial polyesters have been divided into two groups according to their monomer
structures (Kim, 2002):
• Short chain length PHAs (SCL-PHAs): monomers with 3 to 5 carbon atoms;
• Medium chain length PHAs (MCL-PHAs): monomers with 6 to 14 carbon atoms.
Despite most of the PHAs being constituted by short chain lengthed monomers, to date, more
than 140 different hydroxyacids have been identified as MCL-PHA constituents (Kim et al.,
2007). The combination of these monomers can generate materials with extremely different
properties.
There is an enormous variation possible in the composition and length of the chains of these
natural polyesters. Although the great majority of PHA-synthesizing bacteria accumulate
either SCL-PHAs or MCL-PHAs, microbial copolyesters consisting of SCL- and MCL-
hydroxyalkanoates have been reported in some bacterial strains (Steinbüchel and Hein,
2001). The number of monomers is typically between 100 and 30000 (Lee, 1996) and it
depends on the R-group and the microorganisms in which the polymer is produced.
The most common PHA is PHB, which is constituted by short chain lengthed monomers and
can accumulate up to 90% of the dry cell weight (DCW) in some bacteria, whereas MCL-
PHAs are often produced by Pseudomonas species and their content in the cell is much less
than this value (Wang et al., 2007).
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2.1.1. PHAs properties
There are many different types of PHAs, distinctly characterized by chain length, type of
functional group and degree of unsaturated bonds. The majority of PHAs are composed of 3-
hydroxyalkanoic acid monomers with variety of saturated or unsaturated and straight or
branched chain containing aliphatic or aromatic side groups (Doi et al., 1992; DeSmet et al.,
1983).
Bacteria produce PHAs with average molecular weight of up to 4.0 x 106 Da with a
polydispersity of around 2.0 (Agus et al., 2006a) and accumulate them as discrete granules,
which size and number per cell varies depending on the different species.
PHB has material characteristics which are similar to those of conventional plastics such as
polypropylene (PP), however, it is stiffer and more brittle. The properties of PHB can vary
from sample to sample due to different producer organisms, extraction techniques and
sample preparation methods. The differences in the measured properties can be attributed to
the variation in the molecular weight and polydispersity index of the extracted polymers (Misra
et al., 2006).
In general, PHB is a highly crystalline thermoplastic with a high melting point, which makes its
processing by injection molding difficult. Due to its crystallinity, this polyester forms large
spherulitic structures that report poor mechanical properties in molded plastics and films
(Lenz and Marchessault, 2005). However, PHB has also several useful properties such as
good oxygen impermeability, moisture resistance, water insolubility and optical purity, which
differentiate it from other currently available biodegradable plastics which are either water
soluble or moisture sensitive (Lindsay, 1992; Holmes, 1988). The mechanical properties of
PHB including Young’s modulus (3.5GPa) and tensile strength are similar to those of PP, but
the elongation to break of PHB is significantly lower.
Unlike PHB (and the SCL-PHAs in general), MCL-PHAs are elastomeric materials with a low
crystallinity and a low melting temperature. Therefore, MCL-PHAs have attracted interest
because they have flexible properties for a wide range of applications that cannot be obtained
with SCL-PHAs (Preusting et al., 1990). The most attractive feature of MCL-PHAs is that
various MCL-PHAs bearing different functional groups in the side chains can be synthesized
by some organisms when they are grown with substrates containing the corresponding
chemical structures (Kim et al., 2000a). These groups can improve the physical properties of
the polymers and some of them can even be modified by chemical reactions to obtain more
useful polymers and extend the potential application of MCL-PHAs as environmentally
biodegradable polymers and functional biomaterials for biomedical uses (Hazer and
Steinbüchel, 2007). However, the final MCL-PHA concentration and PHA content obtained
have been at relatively low levels compared with those of SCL-PHAs, which has hampered
the development of MCL-PHA applications.
5
The issue good properties/high production of PHAs has been tried to solve by two strategies:
one of them is focused on the discovery of more efficient production processes of MCL-PHAs;
the other is related with the production of heteropolymers with improved qualities.
Poly(hydroxybutyrate-co-valerate) (PHBV) is an example of an improved heteropolymer and it
is less stiff, tougher and easier to process than PHB, making it more suitable for commercial
production. PHBV is water insoluble and is not affected by moisture, does not degrade under
normal conditions of storage, and is stable indefinitely in air (Mergaert et al., 1993; Mergaert
et al., 1994; Lee, 1996). Another example of a copolymer with great properties is
Poly(hydroxybutyrate-co-hexanoate) (PHBHx). Copolymers like PHBV and PHBHx or MCL-
PHAs have much more suitable properties than PHB, while retaining most of its mechanical
properties (Padermshoke et al., 2004).
When copolymer formation occurs with hydroxybutyrate (HB) and hydroxyvalerate (HV) or
hydroxyhexanoate (HHx) monomer units, the properties of the material alter as a
consequence of decreased crystallinity and melting temperature. It results, in mechanical
terms, in a decrease in stiffness (Young's modulus) and an increase in toughness, producing
more desirable properties for commercial application. The ability to control the crystallization
conditions, which influence the physical and mechanical properties of the material and hence
its commercial applications, is obviously of paramount importance (Anderson and Dawes,
1990).
Biocompatibility
Various in vitro and in vivo tests have shown polymers from the PHA family to be compatible
with bone and cartilage tissue (Shishatskaya et al., 2004; Deng et al., 2003), blood, collagen
and various cell lines, like fibroblasts, endothelium cells and isolated hepatocytes
(Shishatskaya et al., 2004; Luklinska and Schluckwerder, 2003).
PHB has been shown to have excellent biocompatibility (Saito et al., 1991) and lack of toxicity
toward mouse fibroblast cell lines, chondrocytes (Zhao et al., 2003), osteoblasts (Torun et al.,
2003), and gastrointestinal regions of rats (Freier et al., 2002). This polyester has an ideal
biocompatibility as it is a product of cell metabolism and because the product of its
degradation (3-hydroxybutyric acid) is normally present in blood at concentrations between
0.3 and 1.3 mmol/L (Zinn et al. 2001).
Prior experiments have shown that the PHBHx-based materials have also a good
biocompatibility for chondrocyte (Deng et al., 2003), nerve cells, osteoblasts and fibroblast
cells (Kai et al., 2003; Wang et al., 2004).
6
Biodegradability
One of the commercially attractive features of PHAs is their degradability in natural
environment. In order to use PHAs advantages in their full potential, various investigators
have studied the environmental factors that may influence biodegradation, as well as the
enzymology of the process and the importance of polymer composition (Anderson and
Dawes, 1990).
Because PHAs are stored by bacteria for eventual breakdown and utilization as a carbon
source, when extracellular carbon is no longer available, there must be an effective and quick
mechanism within the cell for the degradation of these polyesters into simple organic
compounds (Lenz and Marchessault, 2005). In fact, when there is no more extracellular
carbon, the bacteria secrete specific extracellular PHA depolymerases that hydrolyze the
polymer to water-soluble products. These depolymerases are divided into two groups, SCL-
PHA depolymerises and MCL-PHA depolymerases, which differ with respect to substrate
specificity for SCL-PHAs or MCL-PHAs and to producer organism (Kim et al., 2007). The end
products of PHA degradation in aerobic environments are carbon dioxide and water, while
methane is also produced in anaerobic conditions (Ojumu et al., 2004).
The activities of these enzymes depend on the composition of the polymer and the
environmental conditions. The degradation rate of a piece of PHB is typically in the order of a
few months (in anaerobic sewage) to years (in seawater) (Madison and Huisman, 1999).
PHBV biodegrades in microbially active environments (Luzier, 1992; Poirier et al., 1995; Lee,
1996). Microorganisms colonize on the surface of the polymer and secrete enzymes which
degrade PHBV into HB and HV units. These units are then used up by the cell as a carbon
source for biomass growth (Ojumu et al., 2004).
When used in a biomedical implant, the PHA-derived material has also to be degradable in
the body environment. Some studies have been made within mammals, and in these
conditions the polymer is hydrolysed only slowly. After a 6-month period of implantation in
mice, the mass loss was less than 1.6% (w ⁄ w) (Pouton and Akhtar, 1996).
A wide range of PHA-degrading microorganisms have been isolated and most are SCL-PHA
degraders. SCL-PHA-degrading microorganisms of many taxa are widely distributed in
various ecosystems such as soil, sewage sludge, compost, and marine water (Mergaert and
Swings, 1996; Jendrossek, 2001; Kim and Rhee, 2003). However, few reports concerning the
abundance and diversity of MCL-PHA-degrading microorganisms in natural environments
have been documented, most likely due to the less common occurrence of MCL-PHAs in
these environments (Kim et al., 2007).
7
Renewable nature and life cycle
Maybe even more important than biodegradability of PHAs is the fact that their production is
biological and based on renewable resources (Braunegg et al., 2004). Fermentative
production of PHAs uses agricultural feeds such as sugars and fatty acids as carbon and
energy sources (Kadouri et al., 2005). Consequently, the synthesis and biodegradation of
PHAs are totally compatible to the carbon-cycle as depicted in Figure 2.
Figure 2 – Life cycle of PHAs (Verlinden et al., 2007).
2.1.2. PHAs applications
PHAs are materials with great potential because they have useful mechanical properties and
are biodegradable and biocompatible. The variation in the length and composition of the
PHAs makes these polymers suitable for an array of potential applications. The majority of
expected applications of PHAs is associated to the replacement of petrochemical polymers.
The primary application areas in which their features meet some market needs are on
packaging and coating, disposable personal hygiene, agriculture, electronic products and
medicine.
The plastics currently used for packaging and coating applications can be replaced partially or
entirely by PHAs. These polymers could be used for containers and films (Bucci et al., 2005)
or even in biodegradable personal hygiene articles such as diapers and their packaging
(Noda, 2001). PHAs have also been processed into toners for printing applications and
adhesives for coating applications (Madison and Huisman, 1999). Potential agricultural
8
applications include encapsulation of seeds, encapsulation of fertilizers for slow release,
biodegradable plastic films for crop protection and biodegradable containers for hothouse
facilities. Likewise, composites of bioplastics have already been used in electronic products,
like mobile phones (Verlinden et al., 2007).
Between all these important applications, the interest of PHAs in the biomedical field may be
the most revolutionary. Despite the fact that SCL-PHAs are too rigid and brittle to be used in
biomedical applications, elastomeric MCL-PHAs are a great promise for this end, particularly
in drug delivery (Pouton and Akhtar, 1996) and tissue engineering (Williams et al., 1999;
Chen and Wu, 2005a). Their main advantage in the medical field is that a biodegradable
plastic can be inserted into the human body and does not need to be removed again.
Therefore, they have been used to produce sutures, repair patches, orthopedic pins,
adhesion barriers, stents, nerve guides and bone marrow scaffolds. PHA together with
hydroxyapatite (HA) can find applications as a bioactive and biodegradable composite for
applications in hard tissue replacement and regeneration (Chen and Wu, 2005a).
2.2. BIOSYNTHESIS OF PHAS
During recent years, several scientists have investigated the bacterial production of PHAs and
a great effort is underway to improve the procedure. Nonetheless, PHA production cost is still
far above the cost of conventional plastics (Verlinden et al., 2007).
However, to date, the process development for the production of MCL-PHAs (the PHAs with
more useful properties) has been much less extensive than for SCL-PHAs. MCL-PHA
production costs considerations differ from those for SCL-PHA in several ways: the substrates
used are generally different and more expensive; the fermentation processes developed to
date are less productive, leading to higher capital and labour costs; the solubility of the
polymers in common solvents and the granule density are also different from SCL-PHAs,
which affect separation costs (Sun et al., 2007). More efficient fermentation processes, better
recovery of PHAs and the use of inexpensive substrates can substantially reduce the
production cost.
For a better understanding about the production process, it is useful to be aware of the
pathways that lead to the formation of PHAs from carbon substrates in the bacterial cell. The
three most-studied metabolic pathways are shown in Figure 3.
9
Figure 3 – Metabolic pathways for PHA biosynthesis (adapted from Tsuge, 2002).
Sugars such as glucose and fructose are mostly processed via pathway I, yielding PHB
homopolymer. If fatty acids or sugars are metabolized by pathway II, III or other pathways,
copolymers are produced (Aldor and Keasling, 2003; Steinbuchel and Lutke-Eversloh, 2003).
The current supposition is that the (S)-3-hydroxyacyl-CoA and 3-ketoacyl-CoA molecules are
subsequently converted to (R)-3-hydroxyacyl-CoA molecules by 3-hydroxyacyl-CoA
epimerase and 3-ketoacyl-ACP reductase, respectively (Steinbüchel and Hein, 2001). On the
other hand, the transacylase PhaG catalyses the formation of (R)-3-hydroxyacyl-CoA from
(R)-3-hydroxyacyl-ACP, which is formed from sugars through the pathway III. In addition, (R)-
specific enoyl-CoA hydratase (PhaJ), which catalyzes the (R)-specific hydration of β-oxidation
intermediate, 2-trans-enoyl-CoA, to (R)-3-hydroxyacyl-CoA, plays a critical role in supplying
monomer units from β-oxidation to PHA synthesis (Fiedler et al., 2002; Tsuge et al., 2003). In
the final step of PHA biosynthesis, PHA synthases (PhaC) catalyze the conversion of
percursor molecules into PHAs with the concomitant release of coenzyme A (CoA).
10
2.2.1. PHA producer organisms
PHAs are synthesized by many living organisms. The main candidates for the large-scale
production of PHAs are plants and bacteria. Plant cells can only cope with low yields (above
10% w/w of dry weight) of PHA production as higher levels of polymer inside the plant have a
negative effect on its growth and development. In contrast, within bacteria, PHAs are
accumulated to levels as high as 90% (w/w) of the dry cell mass (Steinbuchel and Lutke-
Eversloh, 2003).
Over 250 different bacteria, including gram-negative and gram-positive species, have been
reported to accumulate various PHAs (Steinbüchel, 1991; Lenz et al., 1992). Several factors
need to be considered in the selection of microorganisms for the industrial production of PHA,
such as the ability of the cell to utilize an inexpensive carbon source and to achieve good
rates of growth and polymer synthesis. The majority of the published research has been
concentrated in Alcaligenes eutrophus, Pseudomonas oleovorans and, more recently,
Pseudomonas putida.
In addition to the well-studied accumulation of polyesters of C3 and C4-hydroxyacids, A.
eutrophus is capable of producing PHAs containing 4-hydroxybutyrate and 5-hydroxyvalerate
monomers; Doi and colleagues (1987-1990) have demonstrated that these monomers are
incorporated into copolymers and terpolymers when A. eutrophus is supplied with various
single or mixed carbon sources. The composition of such polyesters is determined by the
relative concentrations of the carbon sources available during polymer accumulation, and the
monomer units are, in each case, derived from a substrate of the same carbon chain length.
Contrary to A. eutrophus, several Pseudomonas species can actually produce MCL-PHAs. As
it had already been discovered for P. oleovorans, Haywood et al. (1989) verified that P.
aeruginosa, P. putida, P. fluorescens, and P. testosteroni accumulate PHAs with C6 to C10
straight-chain alkanes, alcohols, and alkanoic acids as the sole carbon source. The carbon
chain length of the substrate determined the range of monomer units incorporated into PHA;
in fact, many reports have shown that P. oleovorans and P. putida could even produce PHA
with the same functional groups as the carbon substrates, only in the presence of a good
polymer-producing substrate, such as octanoic or nonanoic acid (Lenz et al., 1992; Kim et al.,
2000a). However, due to the formation of substantial proportions of 3-hydroxyacids that
differed in chain length from the carbon substrate by two carbon units, it could be concluded
that the removal or addition of C2 units is involved in the biosynthesis of PHA by these
Pseudomonas species (Anderson and Dawes, 1990).
In addition to the experiences with wild type strains, efforts have been made to express MCL-
PHAs in recombinant Escherichia coli (Langenbach et al. 1997; Qi et al. 1998; Ren et al.,
2000) or to improve PHA synthesis with recombinant Pseudomonas (Kraak et al. 1997).
However, few fermentation processes have been based on these strains (Prieto et al. 1999).
11
2.2.2. Optimal conditions for the production of PHAs
Early studies attempted to understand the physiological and kinetic fundamentals of MCL-
PHA production. The most interesting aspects for process development are the effects of
carbon sources and their concentration, specific growth rate (μ), and the ratio of carbon to
other major nutrients on cell growth and MCL-PHA synthesis. Physiological conditions directly
affect subunit composition, cellular PHA content (%), specific PHA synthesis rate (qPHA, g
PHA. g−1 rest biomassα. h−1), and overall volumetric productivity (g. l−1. h−1) (Sun et al., 2007).
It has been reported that PHAs are synthesized during an imbalanced growth in several
microorganisms, i.e., when these microorganisms grow in an environment with excess of
carbon source and limitation of one nutrient (nitrogen, phosphorus, oxygen, magnesium or
sulphur). One explanation for the increased PHA content that often results from nutrient
depletion or limitation is that the growth of rest biomass is limited while PHA synthesis
continues unabated (Ramsay et al. 1991).
The metabolic regulation of SCL-PHAs was well-studied (Senior and Dawes 1971;
Steinbuchel and Lutke-Eversloh, 2003), but less is known about the factors controlling MCL-
PHA synthesis. Nitrogen or phosphate limitation stimulates rapid PHA synthesis in most of the
well-studied SCL-PHA-synthesizing bacteria. Therefore, most SCL-PHA production
processes employ a stage of rapid cell growth followed by a PHA accumulation stage, which
is almost always N- or P-limited. Possibly due to an assumption that MCL-PHA production
physiology is similar to that of most SCL-PHA accumulating bacteria, almost all publications
dealing with MCL-PHA production have incorporated a nutrient limitation (see Table 1).
While the rate of MCL-PHA production in P. resinovorans was greatly stimulated by N-
limitation (Ramsay et al., 1992), this did not occur in P. oleovorans GPo1 ATCC 29347
(Ramsay et al., 1991) now recognized to be a strain of P. putida. Significant MCL-PHA
accumulation during the exponential growth phase of P. putida KT2442 (Huisman et al.,
1992), P. putida U (Carnicero et al., 1997) and P. putida GPo1 (Durner et al., 2001) was
reported, indicating little need for N or P limitation, contrary to what happens with the
production of SCL-PHA. By this means, the link between MCL-PHA accumulation and growth
limitation by key nutrients such as N or P seems to differ depending on the strain, carbon
sources, the cultivation conditions, or possibly a combination of these factors.
Sun et al. (2007) have concluded that carbon-limited exponential feeding of nonanoic acid, or
related substrates, to cultures of P. putida KT2440, without any nutrient limitation, is a simple
and highly effective method of producing MCL-PHA. There are many possible explanations
for the production of substantial amounts of PHA during carbon-limited growth; some
deficiency in the tricarboxylic acid (TCA) cycle, blockage in β-oxidation or a detoxification
mechanism may redirect substrate and energy toward PHA synthesis to cause this effect. α Rest biomass is defined as dry biomass minus PHA
12
Regarding fermentation mode, continuous fermentation is not only an appropriate method to
study bioprocess physiology, but also a good way to achieve high productivity. However, this
type of processes is impractical at a commercial scale, which makes the fed-batch
fermentation the best method of achieving a high density of biomass containing the highest
possible amount of PHA. Because the rate of substrate demand is difficult to measure or
predict, the design and control of a N- or P-limited accumulation phase in a fed-batch process
is difficult when it involves the feeding of inhibitory substrates such as SCL and MCL
carboxylic acids. Simple exponential feeding of substrate is reliable and productive; however,
exponential feeding is only possible when N or P limitation is not imposed (Sun et al., 2007).
Processes involving complete nutrient depletion are usually two-stage fed-batch processes
with PHA accumulation occurring mainly during the nutrient-depleted stage (Kim et al. 1997;
Lee et al. 2000; Diniz et al. 2004). In other studies, all nutrients are provided continuously
while ensuring that a key nutrient (most often N) is the growth-limiting factor (Sun et al.,
2007).
Chemostat studies of P. putida GPo1 on octane (Preusting et al., 1991) and octanoate
(Durner et al., 2000; Ramsay et al., 1991), and P. putida KT2442 on oleic acid (Huijberts and
Eggink, 1996) have shown that cellular PHA content decreases with increasing specific
growth rate. Thus, a compromise between PHA content, concentration, and productivity is
required in a single-stage continuous process if a reasonable percent of PHA is to be attained
in the biomass.
Based on the results reported, it can be concluded that the optimal conditions are not the
same for all the processes. Thus, it is necessary to assess the optimal PHA-synthesis
conditions when any new bacterial strain, carbon source, or nutrient composition is employed.
2.2.3. Carbon substrates
One of the difficulties in the production of MCL-PHAs is the nature of the carbon source.
PHAs are usually produced most efficiently from structurally related carbon sources such as
alkanes and alkanoic acids. Alkanes exhibit little toxicity but their low aqueous solubility limits
their use in high density culture. On the other hand, alkanoic acids pose little mass transfer
difficulty, but their toxicity requires their concentration to be well controlled (Sun et al., 2007).
For example, in experiences made with P. oleovorans, Ramsay et al., (1991) verified that
these bacteria were inhibited at 4 g/L octanoic acid and the PHA content in the cells
decreased after the carbon source was exhausted (Lageveen et al., 1988). This suggests that
the octanoate concentration in the culture broth has to be maintained at 0 – 4 g/L for optimum
cell growth and PHA production.
13
To produce MCL-PHAs from cheaper and more widely available resources, vegetable oils
and animal fats are of interest, as many Pseudomonas are able to utilize such carbon
sources. Oleic acid, a common carboxylic acid in vegetable oils, has been used in the efficient
production of MCL-PHAs by P. putida KT2442 (Huijberts and Eggink, 1996; Lee et al., 1999).
Other complex substrates, such as coconut oil and tallow (Solaiman et al., 1999; Thakor et
al., 2005), co-products of soy-based biodiesel production (Ashby et al., 2004), soy molasses
(Solaiman et al., 2006), and waste food oils have been reported to support MCL-PHA
synthesis by various Pseudomonas strains. Although MCL-PHA production would be much
simpler and cheaper using processes based on these unrelated carbon sources, the use of
related carbon sources allows tailoring of the final product to fit commercial demands. In
addition, these cheaper carbon sources lead to much lower yields than pure carboxylic acids,
besides that they were just studied in shake-flask culture or lab-scale batch fermentations and
thus their economic potential was not really evaluated (Sun et al., 2007).
In order to take advantage of both kinds of carbon source, fermentation processes based on
mixtures of two carbon sources (one cheaper and other related to the product of interest) are
ideal. In these processes, the first carbon source would be mainly directed to the biomass
growth, while the other would be added later to induce the polymer production. Lenz et al.
(1992) divided the various carbon substrates for PHA biosynthesis into three groups: Group A
corresponds to alkanoic acids that support both cell growth and PHA production, group B
represents alkanoic acids that support cell growth, but not PHA production, and group C
contains alkanoic acids that do not support cell growth.
In addition to MCL-PHAs containing solely hydroxyalkanoates subunits, MCL-PHAs
containing various functional groups, such as olefins (Fritzsche et al., 1990), branched alkyls
(Hazer et al., 1994), halogens (Kim et al., 1996), phenyls (Hazer et al., 1996), and esters
(Scholz et al., 1994), can often be incorporated when grown on suitable carbon substrates
with such groups. This is a great advantage for some commercial fields, where it is essential
to have specific types of PHAs for the most distinct applications.
2.2.4. Summary of MCL-PHA production by various methods
Over the years, many studies have been done to discover the best way to produce PHAs.
From the theory of nutrient or carbon limitation to the fermentation mode, there have been lots
of suppositions. However, the numbers talk for themselves; Table 1 points up the results of
some of the studies developed till now.
14
Table 1 – Summary of MCL-PHA production by various methods.
Culture Mode Organism Carbon Source Limitation condition CDW (g.L-1)
[PHA] (g.L-1)
PHA content (%)
Overall PHA productivity (g.L-1.h-1) Reference
Single-stage Fed-batch P. putida Octanoic acid - 63 39 62 1 Kim (2002)
Single-stage Fed-batch P. putida Octanoic acid - 55 41 75 0.63 Kim (2002)
Single-stage Fed-batch P. putida Octanoic acid - 9.5 6.4 67 0.16 Kim (2002)
Two-stage Fed-batch P. putida Glucose & Octanoate Nitrogen & Oxygen (2nd stage) 54.8 35.9 65.5 0.92 Kim et al.
(1997)
Continuous P. putida Octanoic acid Ammonium (2nd stage) 4.3 0.7 16 0.16 Ramsay et al.
(1991)
Continuous P. putida Octane Ammonium 11.6 2.9 25 0.58 Preusting et al. (1993a)
Single-stage Fed-batch P. putida Octanoic acid Nitrogen 51.5 18.0 35.8 0.41 Kellerhals et al. (2000)
Single-stage Fed-batch P. putida Octanoic acid Nitrogen 42.0 9.9 23.6 0.22 Kellerhals et al. (2000)
Single-stage Fed-batch P. putida Oleic acid Nitrogen 67.5 18.9 28.0 0.49 Kellerhals et al. (2000)
Single-stage Fed-batch P. putida Oleic acid Nitrogen 89.8 18.0 20.0 0.57 Kellerhals et al. (2000)
Single-stage Fed-batch P. putida Vegetable fatty acids Nitrogen 73.0 25.0 34.2 0.56 Kellerhals et al. (2000)
Single-stage Fed-batch P. putida Animal fatty acids Nitrogen 28.5 15.4 54.0 0.33 Kellerhals et al. (2000)
15
Culture Mode Organism Carbon Source Limitation condition CDW (g.L-1)
[PHA] (g.L-1)
PHA content (%)
Overall PHA productivity (g.L-1.h-1) Reference
Two-stage Fed-batch P. putida Glucose & nonanoic acid Nitrogen (2nd stage) 46.1 12.4 26.8 0.25 Sun et al.
(2007)
Single-stage Fed-batch P. putida Nonanoic acid Nonanoic acid 70.2 52.9 75.4 1.11 Sun et al. (2007)
Single-stage Fed-batch P. putida Nonanoic acid Nonanoic acid 56 37.5 66.9 1.44 Sun et al. (2007)
Two-stage Fed-batch P. putida Nonanoic acid Nonanoic acid (1st stage) & Nitrogen (2nd stage) 88 52.8 60 0.91 Sun et al.
(2007)
Single-stage Fed-batch P. putida Oleic acid - 173 32.3 18.7 1.13 Lee et al. (1999)
Single-stage Fed-batch P. putida Oleic acid Phosphorus 141 72.6 51.4 1.91 Lee et al. (1999)
Two-stage Continuous P. putida Octane Nitrogen (2nd stage) 18 11.34 63 1.06 Jung et al. (2001)
Continuous P. putida Oleic acid Oxygen 30 6.9 23 0.69 Huijberts and
Eggink (1996)
Single-stage Fed-batch P. putida Octane Ammonium 37.1 12.1 33 0.25 Preusting et al. (1993b)
Single-stage Fed-batch P. putida Octane - 112 11.2 10 0.19 Kellerhals et al. (1999a)
Single-stage Fed-batch P. putida Amonium octanoate & Octanoic acid Nitrogen 47 25.9 55 0.53
Dufresne and Samain (1998)
Two-stage Fed-batch P. putida Octanoate Nitrogen (1st stage) & Ammonium (2nd stage) 51.5 17.4 34 0.40 Kellerhals et al.
(1999b)
Single-stage Fed-batch P. putida Glucose & Fructose Ammonium 40 8.4 21 0.25 Diniz et al. (2004)
16
Culture Mode Organism Carbon Source Limitation condition CDW (g.L-1)
[PHA] (g.L-1)
PHA content (%)
Overall PHA productivity (g.L-1.h-1) Reference
Single-stage Fed-batch P. putida Glucose & Fructose Phosphate 50 33.6 63 0.8 Diniz et al. (2004)
Continuous P. putida Octanoic acid - 1.67 0.89 53 0.016 Prieto et al. (1999)
Single-stage Fed-batch E. coli (recombinant) Glycerol - 3.55 0.43 12 0.011 Prieto et al.
(1999)
Single-stage Fed-batch P. putida 2-octanone & octane Nitrogen 9.5 2.52 26.5 0.063 Jung et al. (2000)
Single-stage Fed-batch P. putida n-octylacetate & octane Nitrogen 11.0 2.10 19.1 0.053 Jung et al.
(2000)
17
2.3. PHAS RECOVERY
The last stage of PHA manufacture involves separating the polymer from the cells. Although
most of the studies pay more attention to the production processes, recovery of PHA should
also be considered because it significantly affects the overall process economics. There are
several methods which have been tried in order to find the best to allow a simple and efficient
extraction of the polymers.
The solvent extraction is one of the simplest methods to recover PHA. The solvent employed
include chloroform, dichloromethane, dichloroethane, propylene carbonate, ethylene
carbonate and acetone (Baptist, 1962; Lafferty et al., 1988; Ramsay et al., 1994; Jiang et al.,
2006). It involves the reflux with one of these solvents to remove the non-dissolved cell
debris. The polymer is then concentrated and precipitated with methanol or ethanol, leaving
low-molecular-weight lipids in solution. For medical applications this is a good method,
because the resulting PHAs have a high purity (Chen and Wu, 2005a). However, a large
amount of solvent is required due to high viscosity of PHA, which makes the procedure
economically and environmentally unattractive (Lee, 1996; Braunegg et al., 1998).
As an alternative to the unfavourable extraction with organic solvents, aqueous enzymatic
procedures (Holmes and Lim, 1984; Kapritchkoff et al., 2006; Lakshman and Shamala, 2006),
treatments with ammonia (Page and Cornish, 1993) or digestion with sodium hypochlorite and
surfactants (Ramsay et al., 1991; Ryu et al., 2000) have been proposed. Recently,
supercritical fluid disruption (Hejazi et al. 2003; Khosravi-Darani et al. 2004), dissolved-air
flotation (van Hee et al., 2006) and selective dissolution of cell mass (Yu and Chen, 2006) for
the recovery of PHAs were studied.
Kellerhals et al. (2000) have processed the biomass by selective enzymolysis to an aqueous
PHA latex. Cells were centrifuged and resuspended in deionized water and the rest biomass
was solubilized by addition of Alcalase, ethylenediaminetetraacetic acid (EDTA), and sodium
dodecyl sulfate (SDS) and incubated. The resulting PHA suspension was washed by cross-
flow diafiltration with water and the latex was further concentrated by filtration. The washing
step was carried out at in a CSTR (continuous stirred-tank reactor) operated in diafiltration
mode and equipped with a recirculation loop.
The aqueous extraction method with sodium hypochloride has been extensively used despite
the fact that this reagent significantly increases PHA degradation, reducing the polymer
molecular weight. This method uses the hydrophobicity of PHAs to recover them, by
dissolving the rest of the cellular components. In addition to the great level of purity achieved
with this method, it is also less expensive than the solvent extraction method (Ojumu et al.,
2004).
18
In biomedical field, more important than the cost of the purification process is its efficiency.
Along with cell growth, all of the Gram negative production strains produce
lipopolysaccharides (LPSs) as an integral part of the outer membrane (Petsch and Anspach,
2000). LPSs are pyrogenic and their concentration in a medical product is strictly regulated.
Inappropriate extraction of PHA from bacterial biomass results in contamination by pyrogenic
compounds and thus influences medical testing. This problem was solved by Furrer et al.
(2007) with a temperature-controlled method for the recovery of poly(hydroxyoctanoate-co-
hydroxyhexanoate) (PHO) from Pseudomonas putida GPo1. In contrast to other methods, the
dissolution steps took place at high temperatures, while precipitation of PHO was triggered by
cooling the hot solution. After the dissolution and precipitation with n-hexane, the product was
re-dissolved and re-precipitated with propanol, which resulted in a purity of close to 100% and
the minimal endotoxicity of 2 EU/g PHO.
The separation and purification step is extremely important because it affects not just the
overall process economics, but also the purity of the final product and thus development of
processes that allow a suitable extraction of polymers will be well rewarded.
2.4. DEVELOPMENT OF CHEMOMETRIC MODELS:
The use of NIRS and QUANT Software
Fermentation processes are very useful in various fields, however, they are still very difficult
to control and optimize through typical manual methods. In fact, quantifying the biomass, the
product or even the nutrient sources in the fermenter in a certain time involves time-
consuming separation methods; in addition, taking the samples to be analysed decreases the
fermentation volume and, therefore, the quantity of the product obtained and so, they are not
very frequently taken. In order to automate these measurements and increase the amount of
data obtained (which improve their interpretation), quantitative analytical models can be built.
Generally speaking, every quantitative analytical method aims to determine a system property
Y quantitatively from a measured system parameter X. This determination normally requires
two steps: the calibration and the analysis (prediction). During calibration, a correlation of the
measured quantity X and the system property Y is searched for. After that, the analysis is
performed: by connecting the calibration model to the measured parameter X, the system
property Y of an unknown sample is determined (Conzen, 2006). There are two types of
calibration that can be done, the univariate and the multivariate calibration.
In case of a univariate calibration of the system, the X values (like the absorption of radiation)
are only referred to the peak maximum (wavelength with maximum absorption) of the spectra.
The analysis of a new, unknown sample is carried out by measuring the correspondent X
value at the peak maximum and the Y value (for example, the concentration of one
19
component) is determined by correlation of this value with the calibration function. Although
being very used in analytical laboratory work, this calibration method suffers from several
disadvantages (Bruker Optik GmbH, 2006; Conzen, 2006):
• Outliers or perturbations caused by additional unknown components are not
recognized because the concentration of the analyte is determined in one spectral
point only.
• Statistical fluctuations caused by detector noise are directly incorporated into the
concentration data. The resulting uncertainty usually has to be minimized by multiple
sample measurements and subsequent averaging of the results.
• Peaks used for the analysis of multicomponent systems must be different and well
separated, which is a severe drawback in Near Infrared Spectroscopy (NIRS) (section 2.4.1.).
• The analysis of multicomponent systems assumes the validity of the Lambert Beer’s
law, i.e., a linear correlation between the concentration and the spectral response.
This does not account for temperature fluctuations or intermolecular interactions.
The appearance of modern analytical chemistry has changed fundamentally over the last few
years, due to the introduction of so-called chemometric evaluation techniques. The term
“Chemometrics” nowadays represents all multivariate calibration methods in analytical
chemistry. Compared to the classical univariate calibration, this technique uses not only one
spectral data point for the calibration, but the whole spectral structure. The advantage of this
type of calibration is the large amount of spectral information used, which leads to higher
precision and error stability (Conzen, 2006).
The spectra needed for analysing bioprocesses can be obtained by NIRS and the models can
be built using the QUANT software.
2.4.1. NIRS – Near Infrared Spectroscopy
The application of NIRS in many industries has undergone explosive growth in recent years
(Arnold et al., 2003; Hall and Pollard, 1992; Finn et al., 2006; Blanco et al., 1998). This has
been particularly apparent in the area of microbial and cell culture system monitoring and
control. Potentially, NIRS offers the prospect of real-time control of the physiology of cultured
cells in fermenters, leading to marked improvements in authenticity, purity and production
efficiency (Scarff et al., 2006).
NIRS is a spectroscopic method which uses the near infrared region of electromagnetic
spectrum to study the interaction of electromagnetic waves and matter. The NIR region of the
20
spectrum is situated between 700–2500 nm, where the functional groups in molecules have
characteristic vibration frequencies (Scarff et al., 2006).
Near infrared spectroscopy is based on molecular overtone and combination vibrations. In
order to a stretch or bending event be IR active and allow the absorption, there must be a
change in the polarity of the molecule. The molar absorptivity in the NIR region is typically
quite small, smaller than in the middle infrared (MIR) region; however, contrary to MIR, this
radiation can penetrate much farther into a sample, which can be very useful in probing bulk
material with little or no sample preparation (Scarff et al., 2006).
Vibrations involving X-H bonding (i.e. covalent bonding) form the basis of most NIR
spectroscopic information because the dipole moment is high. Stretching and bending
vibrations of C-H, N-H, O-H and S-H bonds are common contributors to NIR spectra, and
since X-H bonding is found in almost all biological molecules, NIRS provides a theoretical
means of measuring the majority of fundamental components in a bioprocess (Scarff et al.,
2006).
2.4.2. QUANT – Quantitative Analysis Software
The molecular overtone and combination bands seen in the NIR are typically very broad,
leading to complex spectra; it can be difficult to assign specific features to specific chemical
components. Multivariate (multiple wavelength) calibration techniques like principal
components analysis (PCA) or partial least squares (PLS) are often employed to extract the
desired chemical information. Careful development of a set of calibration samples and
application of multivariate calibration techniques is essential for near infrared analytical
methods.
The OPUS/QUANT software is designed for the quantitative analysis of spectra consisting of
bands showing considerable overlap. Usually, they originate from samples containing one or
several components in a matrix. The software allows determining the concentration of more
than one component in each sample simultaneously. For this purpose, QUANT uses a PLS fit
method (Bruker Optik GmbH, 2006).
In order to carry out a PLS regression, the information of the substance spectra must be
compared to the corresponding concentration values. To facilitate the handling of the data,
they are written in the form of matrices, where each row in the spectral data matrix represents
a sample spectrum and each point of the concentration data matrix is the correspondent
concentration values of the samples. The matrices are broken down into their Eigenvectors
which are called factors or principal components (PCs) and that can be used for the prediction
of concentrations instead of the original spectra. The advantage of this approach is that not all
21
of the PCs are necessary to describe the relevant spectral features; for example some of
these vectors simply represent the spectral noise of the measurement. This leads to a
considerable reduction of the amount of data, as only the relevant PCs are used (Bruker Optik
GmbH, 2006).
The determination of the number of PCs is a crucial point for the quality of the calibration
model. Using an insufficient number of PCs (“underfitting”) leads to a poor reproduction of the
spectral data and therefore the model will not be able to recognize changes in the spectral
features. On the other hand, including too many PCs just adds spectral noise to the
regression and does not increase the amount of valuable information (“overfitting”) (Bruker
Optik GmbH, 2006).
A PLS regression algorithm is deployed to find the best correlation function between spectral
and concentration data matrix. Usually, six steps are necessary for the setting up a model
(Conzen, 2006):
1. Entering Spectral Data and Concentration Data: Before calculating the model, the spectra must be loaded and the correspondent
concentration values for the individual components must be entered. Furthermore, it
is necessary to define the calibration and validation spectra sets.
For sufficiently large data sets, it is advisable to assign two independent sets of
samples with about the same number of spectra (Test Set Validation). The advantage
of the test set method is the speed of calculation when dealing with a very large
number of samples.
If only a small amount of data is available, all of the samples are used to calibrate and
validate the method (Cross Validation). Before starting the calibration, one sample is
excluded from the entity of samples and used for the validation while the remaining
samples are used to calibrate the system. QUANT reiterates this cycle, starting with
the first sample, until all samples have been used for validation.
2. Data Preprocessing: In this step, one or more methods for the preprocessing of the spectral data are
selected. The optimum method depends strongly on the system to be analysed, so
that normally all possible data preprocessing variations should be tried.
3. Definition of an Appropriate Frequency Range: The PLS regression method is a “full spectrum method”; the chemometric model
should improve with an increasing number of data points. However, in some cases
spectral noise or additional components in the samples may cause the PLS algorithm
to interpret these features, which can degrade the model. In these cases it is
advisable to limit the frequency region used for the PLS regression and choose the
range of the spectrum where a good correlation between the changes in the spectral
and the concentration data can be found.
22
4. Validation and Optimization of the Method: After the calibration with the calibration data set, the model is validated with the
validation data set. The suitability of the chosen data preprocessing methods and of
the frequency range for the given measurement task is evaluated during the
validation. In this step, important parameters like the coefficient of determination R2
(that evaluates the linearity of the model), the number of PCs (that reflects the
amount of captured variance) and the Root Mean Square Errors (which measure the
standard deviation of NIR predictions from the true values) are calculated. Depending
on the data set used for prediction, different errors are defined:
• RMSEE (Root Mean Square Error of Estimation): Quantitative measure for
the difference between the true and the fitted values in calibration.
• RMSEP (Root Mean Square Error of Prediction): Quantitative measure for
the preciseness of the analysis of Test Set samples in Test Set Validation.
• RMSECV (Root Mean Square Error of Cross Validation): Quantitative
measure for the preciseness with which the samples are predicted during the
Cross Validation.
• RPD (Residual Prediction Deviation): Qualitative measure for the assessment
of the validation results. It is represented by the quotient of the standard
deviation (SD) of the reference values and the bias-corrected mean error of
prediction of the validation. The larger the RPD, the better is the calibration.
The settings which lead to a high R2 value and a corresponding low mean error of
prediction should be used for the calibration. Moreover, in many cases, it is sensible
to choose a setting that delivers a smaller number of PCs with similar results.
During the validation, potential outliers can be detected, but only if an independent
check confirms it, they should be removed from the data set.
5. The Calibration: Only after all outliers have been removed from the calibration data set, and after the
optimum system parameters have been found, the final version of the model is
constructed.
6. The Quantitative Analysis: In this last step, the optimized chemometric model is used to analyse new samples.
Here, the spectral structures of the calibration data set are compared to the structure
of the analyte spectrum and the desired value is determined.
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CCHHAAPPTTEERR 66 RREEFFEERREENNCCEESS
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