processes Article
Systematic Design and Evaluation of an Extraction Process for Traditionally Used Herbal Medicine on the Example of Hawthorn (Crataegus monogyna JACQ.) Maximilian Sixt and Jochen Strube * Institute for Separation and Process Technology, Clausthal University of Technology, 38678 Clausthal-Zellerfeld, Germany;
[email protected] * Correspondence:
[email protected]; Tel.: +49-05-323722200
Received: 29 May 2018; Accepted: 19 June 2018; Published: 21 June 2018
Abstract: Traditionally used herbal medicines are deep in the consciousness of patients for the treatment of only minor diseases by self-medication. However, manufacturers of herbal medicinal products suffer from major problems such as increasing market pressure by e.g., the food supplement sector, increasing regulations, and costs of production. Moreover, due to more stringent regulation and approval processes, innovation is hardly observed, and the methods used in process development are outdated. Therefore, this study aims to provide an approach based on modern process engineering concepts and including predictive process modelling and simulation for the extraction of traditional herbal medicines as complex extracts. The commonly used solvent-based percolation is critically assessed and compared to the so-called pressurized hot water extraction (PHWE) as a new possible alternative to replace organic solvents. In the study a systematic process design for the extraction of hawthorn (Crataegus monogyna JACQ.) is shown. While for traditional percolation the solvent is optimized to a mixture of ethanol and water (70/30 v/v), the PHWE is run at a temperature of 90 ◦ C. The extracts of various harvest batches are compared to a commercially available product based on a chromatographic fingerprint. For the first time, natural batch variability was successfully incorporated into the physico-chemical process modelling concept. An economic feasibility study reveals that the PHWE is the best choice not only from a technical point of view but also from economic aspects. Keywords: pressurized hot water extraction; modelling; simulation; harvest; variety
1. Introduction Herbal medicine is widely used to cure a myriad of diseases such as gastrointestinal complaints, high blood pressure, sore throat, colds, skin disorders and other sicknesses. They are considered to be natural, compatible and healthy. On account of its long tradition, the use and production of herbal medicine must not be overtaken by modern engineering technologies for production and quality management. Assessing small-molecule pharmaceuticals approved between 1981 and 2002 shows that approximately half are either natural products or natural product derivatives [1,2]. In the future, natural products will continue to play a major role as active substances, model molecules for the discovery and validation of drug targets [1]. Therefore, this paper highlights the systematic process development, simulation and cost assessment for a commonly used herbal drug represented by hawthorn (Crataegus monogyna JACQ.). The conventional solvent extraction is compared with the so-called pressurized hot water extraction (PHWE) [3–6] which shows significant potential for the eco-friendly and solvent-free extraction of Processes 2018, 6, 73; doi:10.3390/pr6070073
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plant matrices [3,4,6]. Hawthorn leaves are a representative system for a medical extract. It is used to treat cardiac insufficiency [7]. The flavonoid hyperoside is chosen as a marker substance. The extracts will be characterized by their content of hyperoside and the drug-extract ratio (DER). The DER is defined as the ratio between the plant material used for extraction and the amount of dry matter remaining in the fluid extract after the extraction process has taken place, according to Equation (1). DER =
mPlant mDry matter
: 1
(1)
Corresponding monographs in the European Pharmacopoeia define no specific DER for hawthorn extracts [8]. An overview of the drug market shows that common products are extracted within a range of DER values between 4–7:1 [9–13]. This range is therefore taken as a benchmark. 2. Material and Methods 2.1. Raw Material Raw hawthorn leaves (Crataegus monogyna JACQ. folium cum flore) were purchased from CfM Oskar Tropitzsch (Markredwitz, Germany). The herb was ground to approximately 1 mm size prior to extraction in a knife mill Retsch® Grindomix® 200 (Haan, Germany). The plant material was stored in the refrigerator at −20 ◦ C to minimize decomposition. Pure hyperoside was purchased from CfM Oskar Tropitzsch (Markredwitz, Germany), as well. The purity was >98%. 2.2. Solvent Screening Solvent screening is performed in 50 mL Falcons® (VWR® , Darmstadt, Germany) using 3 g of ground plant material and 40 mL of solvent. The mixtures are agitated on a tilting table for 24 h before a sample is taken and analyzed. All solvents are in analytical grade. 2.3. Solid-Liquid Extraction The conventional extraction is performed as maceration for the equilibrium measurements and as percolation for the determination of the extraction kinetics as well as the overall amount. The maceration device is a stirred tank of approximately 1.5 L. It is equipped with a three-way top were the joint in the middle is used to insert a stirrer, driven by a lab stirrer motor from IKA® (Staufen, Germany). A PTFE seal prevents solvent from leaking through the gap between the top and the vessel. The device can be tempered. Percolation is performed in a stainless steel tube equipped with frits to ensure proper solid retention during extraction. A P110 preparative HPLC pump from VWR® as well as a Foxy Jr. fraction collector are used. Both are controlled by the EZChrom Elite® HPLC Software (Agilent® , Santa Clara, CA, USA). The experimental setups have already been described by the authors [14,15]. 2.4. Pressurized Hot Water Extraction (PHWE) The flow diagram of the PHWE equipment for batch extraction experiments is shown in Figure 1. The water is pumped from a storage vessel by a HPLC pump P130 from VWR® (Darmstadt, Germany) to the extraction column filled with ground plant material. The extraction column is made from stainless steel with 10 mm i.d. × 100 mm (approx. 7.85 mL). The column itself and a coil made of a steel capillary (length = 3 m, i.d. = 1 mm) are placed in a GC oven HP 5890 Series II Plus (Palo Alto, CA, USA) serving as heating device. The water is preheated in the coil before reaching the extraction column. The extract leaves the extraction column at the back-end where a filter frit serves for solid-liquid separation. The extract is transferred into a second coil for effective cooling (length = 3 m, i.d. = 1 mm). The pressure drop is adjusted by a valve to keep the water in liquid state.
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Data DataLogger Logger
TT
TT
PP PP Valve Valve
Pump Pump
Heater Heater
Extraction ExtractionVessel Vessel GC-Oven GC-Oven
Cooler Cooler
Storage StorageVessel Vessel
Extract Extract
Figure 1. Flow diagram ofofthe milli-scale PHWE equipment for for batchextraction extraction experiments. Figure Figure1. 1.Flow Flowdiagram diagram ofthe themilli-scale milli-scalePHWE PHWEequipment equipment forbatch batch extractionexperiments. experiments.
determine the thermal investigated components during extraction, the ToTo determine degradation the investigated components during extraction, To determinethe thethermal thermal degradation degradation of ofofthe the investigated components during extraction, the is modified to in mode. This is in 2. themilli-scale milli-scaledevice device modified to operate in recycling mode. This is shown in Figure milli-scale device is is modified to operate operate in recycling recycling mode. This setup setupsetup is shown shown in Figure Figure 2. The The 2. is into the vessel (max. 100 and is during whole process. extract is transferred transferred intointo the storage storage vessel (max.(max. 100 mL) mL) and is recycled recycled duringthe the wholethe process. Theextract extract is transferred the storage vessel 100 mL) and is recycled during whole Thus, the target component accumulates in the system and occurring degradation effects be Thus, Thus, the target component accumulates in the system and occurring degradation effects can can be process. the target component accumulates in the system and occurring degradation effects studied. The storage vessel itself is in an ultrasonic bath for mixing and avoiding possible The The storage vessel itselfitself is in an an ultrasonic bath forformixing canstudied. be studied. storage vessel is in ultrasonic bath mixingand andavoiding avoidingpossible possible agglomerates as well as concentration gradients inside the vessel. agglomerates well concentrationgradients gradientsinside insidethe the vessel. vessel. agglomerates as as well as as concentration Data DataLogger Logger
TT
TT
PP PP Valve Valve
Pump Pump
Heater Heater
Extraction ExtractionVessel Vessel GC-Oven GC-Oven
Cooler Cooler
Storage StorageVessel Vessel Ultrasonic UltrasonicBath Bath
Figure Flow diagram the milli-scale Figure 2. 2.2. Flow diagram equipment inrecycling recyclingmode. mode. Figure Flow diagramofof ofthe themilli-scale milli-scalePHWE PHWEequipment equipmentin in recycling mode.
Both setups have already been introduced by the authors [5,6].
Both setups have already beenintroduced introducedby bythe theauthors authors [5,6]. [5,6]. Both setups have already been 2.5. Analytics
Analytics 2.5.2.5. Analytics The analytics for hyperoside in the extract is performed by HPLC using an Elite LaChrom ®®
analytics hyperosideininthe theextract extract isis performed performed by by HPLC TheThe analytics forfor hyperoside HPLC using using an anElite EliteLaChrom LaChrom® (Agilent® ,, Santa Clara, CA, USA) device equipped with aa diode array detector DAD-L 2455 from (Agilent® Santa Clara, CA, USA) device equipped with diode array detector DAD-L 2455 (Agilent®,® Santa Clara, CA, USA) device equipped with a diode® array detector DAD-L 2455from from Hitachi (Tokyo, Japan). The eluents are in analytical grade (VWR , Darmstadt, Germany) and water ® ® Hitachi (Tokyo, Japan). The eluents are in analytical grade (VWR® , Darmstadt, Germany) and water ® Hitachi (Tokyo, Japan). The eluents are in analytical grade (VWR , Darmstadt, Germany) and water ®® arium®® pro (Göttingen, Germany). All samples are passed through a 0.2 is is taken taken from from aa Sartorius Sartorius arium® pro (Göttingen, Germany). All samples are passed through a 0.2 ® arium is taken from a Sartorius pro (Göttingen,performed Germany). All samples are passed through a µµm m syringe syringe filter filter prior prior to to analysis. analysis. Calibration Calibration was was performed with with external external standards standards ordered ordered from from 0.2CfM µm syringe filter prior to analysis. Calibration was performed with external standards ordered CfM Oskar Oskar Tropitzsch Tropitzsch (Marktredwitz, (Marktredwitz, Germany). Germany). The The purity purity was was greater greater 98%. 98%. The The column column was was aa from CfM Oskar Tropitzsch (Marktredwitz, Germany).Germany), The purity was greater 98%. The column ® 250-4 250-4 mm mm PharmPrep PharmPrep RP18 RP18 from from Merck Merck® (Darmstadt, (Darmstadt, Germany), the the column column temperature temperature is is 25 25 °C °C ® (Darmstadt, Germany), the column temperature is was a 250-4 mm PharmPrep RP18 from Merck the flow rate is 1 mL/min and the isocratic eluent consists of methanol/acetonitrile/water (pH 2.5 the flow rate is 1 mL/min and the isocratic eluent consists of methanol/acetonitrile/water (pH 2.5 ◦ C the flow rate is 1 mL/min and the isocratic eluent consists of methanol/acetonitrile/water 25 with with ortho-phosphoric ortho-phosphoric acid) acid) 16/21/63 16/21/63 v/v/v. v/v/v. The The detection detection wavelength wavelength was was 355 355 nm nm and and the the injection injection (pH 2.5 with ortho-phosphoric acid) 16/21/63 v/v/v. The detection wavelength was 355 nmof and the volume 5 µ L. The chromatogram is shown in Figure 3. The coefficient of determination the volume 5 µ L. The chromatogram is shown in Figure 3. The coefficient of determination of the injection volume 5 µL. The chromatogram is shown in Figure 3. The coefficient of determination of the
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calibration was 0.99 and the limit of quantification 0.02 g/L. The side components (SCs) SC1 to SC3 are calibration was 0.99 and the limit of quantification 0.02 g/L. The side components (SCs) SC1 to SC3 referred to the calibration of hyperoside and expressed as pseudo-concentrations. IR-spectroscopy are referred to the calibration of hyperoside and expressed as pseudo-concentrations. was performed with a FTIR Bruker®with alphaa (Billerica, MA,® USA). was IR-spectroscopy was performed FTIR Bruker alpha Principal (Billerica,Component MA, USA).Analysis Principal ® X from Camo® (Oslo, Norway). calculated using Unscrambler ® ® Component Analysis was calculated using Unscrambler X from Camo (Oslo, Norway).
Absorption (mAU)
300 250 200 SC1
150
Hyperoside
100
SC3
50 SC2
0 0
1
2
3
4
5
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8
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10
Time (min) Figure3.3.Chromatogram Chromatogram of Figure of the the hawthorn hawthornextract. extract.
3. Results and Discussion 3. Results and Discussion Remark. Every experiment is carried out in triplicates. The error bars represent the standard deviation of the
Remark. Every experiment is carried out they in triplicates. error bars the standard deviation of the measurements. If no error bars are visible are withinThe the symbol sizerepresent of the individual data points. measurements. If no error bars are visible they are within the symbol size of the individual data points. The scope of this article is to introduce the reader to a modern process engineering attempt for theThe manufacturing of traditionally used the herbal extracts including physico-chemical processfor scope of this article is to introduce reader to a modern process engineering attempt and experimental parameter determination on lab-scale. The benefits of the modelling themodelling manufacturing of traditionally used herbal extracts including physico-chemical process modelling and simulation parameter approach for phytoextraction are documented in detail various studies of the and experimental determination on lab-scale. The benefits of thebymodelling and simulation authors [5,6,14–23]. The cited articles focused mostly on the extraction of valuable compounds for approach for phytoextraction are documented in detail by various studies of the authors [5,6,14–23]. plants in order to perform a further purification until a pure substance in pharma-grad is obtained, The cited articles focused mostly on the extraction of valuable compounds for plants in order to perform e.g., forpurification 10-deacetylbaccatin III substance from yew in [6,18] or artemisinin from annual [5]. In the III a further until a pure pharma-grad is obtained, e.g., formugworth 10-deacetylbaccatin present study,or the engineering toolbox will be broadened tothe thepresent processing of complex extractstoolbox that from yew [6,18] artemisinin from annual mugworth [5]. In study, the engineering are characterized by marker substances and the DER in a regulated environment, within the area of will be broadened to the processing of complex extracts that are characterized by marker substances conflict of being economical on the one side and fulfilling approvals on the other side. Therefore, and the DER in a regulated environment, within the area of conflict of being economical on the one modern and in-silico-based approaches are needed, especially if there is a new approval of existing side and fulfilling approvals on the other side. Therefore, modern and in-silico-based approaches are processes considered to broaden the area of application or to just modernize the production needed, especially if there is a new approval of existing processes considered to broaden the area of procedure. application or to just modernize the production procedure. In general, solid-liquid extraction is described by several partial models. The corresponding In general, solid-liquid can extraction is in described by literature several partial models. Thebalance corresponding equations and assumptions be found detail in the [14,15,17]. The mass in the equations and assumptions can be found in detail in the literature [14,15,17]. The mass balance in the liquid phase is described by the so-called distributed plug flow model (DPF). It serves for modeling liquid phase is described by the so-called distributedcolumn. plug flow (DPF). It serves modeling the macroscopic mass transport in the percolation Formodel the solid phase a porefordiffusion themodel macroscopic mass transport in the percolation column. For the solid phase a pore describes the mass transport in the plant material’s pores. The raw material’s residual diffusion load of model describes the mass transport in the plant material’s pores. is The raw material’s residual load target components and the corresponding extract’s concentration accounted for by equilibrium of curves. target components the corresponding extract’s is accounted by equilibrium The target and component’s mass balance in concentration the fluid phase (Equationfor(2)) considers curves. The targetaxial component’s mass balance inmass the fluid phase (Equation considers accumulation, dispersion, convective transport and mass (2)) transfer fromaccumulation, the plant material’s poresconvective into the bulk phase. axial dispersion, mass transport and mass transfer from the plant material’s pores into the
bulk phase.
∂cL (z,t) ∂2 cL (z,t) uz ∂cL (z,t) 1−ε = Dax ⋅ − ⋅ − ⋅ kf ⋅ aP ⋅ [cL (z,t) − cP (r,z,t)] 2 ∂t ∂z ε ∂z ε 2
(2)
∂cL (z, t) ∂ cL (z, t) uz ∂c (z, t) 1−ε = Dax · − · L − · kf · aP · [cL (z, t) − cP (r, z, t)] ∂t ε ∂z ε ∂z2
(2)
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Under the assumption of spherical particulates, the following equation can be derived as a mass balance for the target component in the plant material. The effective diffusion coefficient Deff must be measured by real extraction experiments. Processes 2018, 6, x FOR PEER REVIEW
5 of 19 ∂q(z, r, t) ∂2 cP (z, r, t) 2 ∂cP (z, r, t) ∂Deff (r) ∂cP (z, r, t) = Deff (r) · + · + · ∂tthe assumption of spherical r the following ∂r ∂r as a mass ∂r2 particulates, Under equation∂r can be derived
(3)
balance for the target component in the plant material. The effective diffusion coefficient D eff must be
The adsorption/desorption equilibrium inside the pores is described through equilibrium curves. measured by real extraction experiments. Herein q is the plant material’s residual2 load linking the equilibrium curve with the pore diffusion ∂q(z,r,t) ∂ cP (z,r,t) 2 ∂cP (z,r,t) ∂Deff (r) ∂cP (z,r,t) (r)⋅ (the Langmuir-equilibrium. = Diseffe.g., + ⋅ ) + ⋅ model. A well-known approach The KL and q(3) max can be ∂t ∂r2 r ∂r ∂r parameters ∂r derived from measurements. The maximum load qmax is the total amount of the regarded target and The adsorption/desorption equilibrium inside the pores is described through equilibrium side components. The equilibrium curves are measured with multistep macerations. The approach is curves. Herein q is the plant material’s residual load linking the equilibrium curve with the pore described in detail in the literature approach [14,15]. is e.g., the Langmuir-equilibrium. The parameters KL and diffusion model. A well-known qmax can be derived from measurements. The maximum load qmax is the total amount of the regarded KL · c target and side components. The equilibrium curves measured with multistep macerations. The q = qmax · are 1 + approach is described in detail in the literature [14,15]. KL · c
3.1. Solvent Screening
q = qmax ⋅
KL ⋅ c 1 + KL ⋅ c
(4)
(3)
To achieve a high extraction performance, solvent selection is crucial. Because hawthorn extract is used3.1. as Solvent a traditional herbal medicine, not every solvent that is ideal from an engineering point of Screening view is suitable. Commonly solvents or their aqueous solutions which are described in monographs To achieve a high extraction performance, solvent selection is crucial. Because hawthorn extract are used. According to theherbal European Pharmacopeia, water ethanol water solutions with is used as a traditional medicine, not every solvent that isorideal from an engineering point of more than 45view vol.is % of ethanol are used fororthe production of a siccum that is afterwards processed to suitable. Commonly solvents their aqueous solutions which are described in monographs are used. According to thea European Pharmacopeia, or solutions ethanol water solutions with more a dry extract [8]. To provide realistic study, ethanolwater water ranging from pure water to than 45 vol. % of ethanol are used for the production of a siccum that is afterwards processed to a section pure ethanol in 10 vol. % steps are screened. The results are presented in Figure 4. The grey dry extract [8]. To provide a realistic study, ethanol water solutions ranging from pure water to pure contains ethanol water solutions that are not reported in the European Pharmacopeia, as depicted ethanol in 10 vol. % steps are screened. The results are presented in Figure 4. The grey section earlier. Besides hyperoside three other side components are regarded in the screening. They are contains ethanol water solutions that are not reported in the European Pharmacopeia, as depicted referred to hyperoside by pseudo-concentrations. A solution of 70 vol. % ethanol rest water earlier. Besides hyperoside three other side components are regarded in the screening. They are shows the highest capacity towards the target molecule hyperoside. Therefore, it is chosen as solvent referred to hyperoside by pseudo-concentrations. A solution of 70 vol. % ethanol rest water shows for the the highest capacity towards the target hyperoside. Therefore, it issignificantly chosen as solvent for capacities the conventional process design. Both, puremolecule water and pure ethanol have lower conventional process design.mixtures Both, pure waterMoreover, and pure ethanol have lowerthe capacities towards hyperoside than their have. they are at significantly approximately same level of hyperoside than their mixtures have. Moreover, they are at approximately the same level of relativetowards solubility. This indicates that screening for completely different pure solvents, such as e.g., relative solubility. This indicates that screening for completely different pure solvents, such as e.g., n-propanol or methanol, is not sufficient but aqueous solutions have to be considered. n-propanol or methanol, is not sufficient but aqueous solutions have to be considered.
Figure 4. Solvent screening for hyperoside from hawthorn in ethanol water solutions.
Figure 4. Solvent screening for hyperoside from hawthorn in ethanol water solutions.
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According to the screening following solvent compositions are preferred: • Hyperoside, % ethanol, rest water According to 70 thevol. screening following solvent compositions are preferred: • SC1, 30 vol. % ethanol, rest water •• Hyperoside, 70 vol. % ethanol, rest water SC2, 30–40 vol. % ethanol, rest water •• SC1, 30 vol. % ethanol, rest water SC3, 70 vol. % ethanol, rest water • SC2, 30–40 vol. % ethanol, rest water Probably SC1 to SC3 are flavonoids as well, because their adsorption maxima in • SC3, 70 vol. % ethanol, rest water UV/VIS-spectra show significant bands at 207–215 nm, 257–270 nm and 332–359 nm [24]. Probably SC1 to SC3 flavonoids as well,with because their%adsorption maxima UV/VIS-spectra Established products are are commonly extracted 45 vol. ethanol, rest waterin[9–13]. Assuming show bands at 207–215 257–270 and 332–359 nmto [24]. Established products are SC1 tosignificant SC3 are also flavonoids, thisnm, solvent is anm preferable mixture achieve high total flavonoid commonly extracted with 45 vol. % ethanol, rest water [9–13]. Assuming SC1 to SC3 are also flavonoids, content in the extract. this solvent is a preferable mixture to achieve high total flavonoid content in the extract. 3.2. Model Parameter Determination for Conventional Extraction 3.2. Model Parameter Determination for Conventional Extraction For proper modeling and simulation, the model parameters equilibrium and overall amount modeling andoverall simulation, the model parameters overall amount must mustFor beproper determined. The amount of hyperoside is equilibrium derived byand exhaustive percolation be determined. Theethanol/water overall amount of hyperoside derived by exhaustive percolation experiments using (70/30 v/v). It is is 0.35% ± 0.02% referred to dry mass.experiments The overall using ethanol/water (70/30 v/v). It is 0.35% ± 0.02% referred to dry mass. The overall In amounts of amounts of SC1 to SC3 regarding hyperoside are 0.82%, 0.19% and 0.35%, respectively. literature SC1 to SC3 regarding are 0.82%, 0.19%1–2% and 0.35%, respectively. In literature values for the values for the overall hyperoside flavonoid amount between are given [7]. overall flavonoid amount between 1–2% are given [7]. The equilibrium data was measured by multi step maceration experiments and is shown in The data was measured equilibrium by multi step shownflat, in Figure 5. equilibrium The Langmuir-type distribution formaceration hyperosideexperiments and SC3 areand bothisrather Figure 5. The equilibrium for hyperoside and SC3 are both rather flat, confirming theLangmuir-type chosen ethanol distribution water mixture as well-chosen solvent. The equilibrium curves for SC1 confirming the chosen ethanol water mixture as well-chosen solvent. The equilibrium curves for SC1 and SC2 are almost approaching to their specific overall amount, on the contrary. There is a and SC2 are almost approaching their specificremaining overall amount, the matrix, contrary.soThere is a relatively relatively high amount of thesetocomponents in the on plant the solvent is not high amount of these components remaining in the plant matrix, so the solvent is not ideally chosen ideally chosen for these components. This finding is consistent to the solvent screening of Section 3.1. for these5 components. is consistent to the of solvent screening Section 3.1. Figure 5 also Figure also containsThis the finding equilibrium relationship the whole dry of matter extracted from the contains equilibrium relationship the whole dry matter extracted from the hawthorn leaves. hawthorntheleaves. This parameter is offurther needed for proper modeling of the DER during This parameter is further needed for matter properwas modeling of thetoDER extraction. The overall amount of dry determined 34%,during thus theextraction. minimum The DERoverall would amount of dry matter was determined to 34%, thus the minimum DER would be 2.9:1. be 2.9:1. 300
A
B
Residue load (g/l)
250
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0 0
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Figure 5. (A) Equilibrium data for the system hawthorn-hyperoside/SC1/SC2/SC3-EtOH/Water Figure 5. (A) Equilibrium data for the system hawthorn-hyperoside/SC1/SC2/SC3-EtOH/Water (70/30 v/v); (B) equilibrium data for the system hawthorn-dry matter-EtOH/Water (70/30 v/v). (70/30 v/v); (B) equilibrium data for the system hawthorn-dry matter-EtOH/Water (70/30 v/v).
3.3. Model Parameter Determination for PHWE 3.3. Model Parameter Determination for PHWE For modeling and simulation of the PHWE process a suitable extraction temperature must be For modeling and simulation of the PHWE process a suitable extraction temperature must be defined. Therefore, a temperature screening was performed ranging from 80 ◦°C to 140 ◦°C in 20 ◦°C defined. Therefore, a temperature screening was performed ranging from 80 C to 140 C in 20 C
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steps. Afterwards degradation kinetics are determined. The pressure was keptwas constant Afterwardsthe theindividual individual degradation kinetics are determined. The pressure kept to 15 bars in all experiments, to ensure the best possible comparability. constant to 15 bars in all experiments, to ensure the best possible comparability. 3.3.1. Extraction Temperature Temperature 3.3.1. Extraction The The results results of of the the screening screening are are presented presented in in Figure Figure 6. 6.
100%
90%
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Hyperoside
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Figure 6. Temperature screening. Figure 6. Temperature screening.
• •
• • • •
Hyperoside: The screening shows that hyperoside is almost entirely extracted after 60 min at Hyperoside: The showsafter thatabout hyperoside after 60 min at 100 °C. A Yield ofscreening 95% is reached 20 minisatalmost 120 °Centirely and 140extracted °C. At that temperature, ◦ ◦ ◦ 100 C. A Yield ofis95% is reached after aboutto20100 min°Catand 120 700% C andhigher 140 C. At that temperature, the productivity 250% higher compared compared to 80 °C. A ◦ C and 700% higher compared to 80 ◦ C. the productivity is 250% higher compared to 100 decline in yield with rising temperature cannot be observed, indicating a high thermal stability A in yield with rising temperature cannot be observed, indicating a high thermal stability of decline hyperoside. of hyperoside. Side component 1: The productivity of extraction is nearly constant in the regarded The productivity nearly constant in the regardedastemperature Side component 1: from temperature range 80 °C to 140 of °C.extraction Thermal is decay does not seem to appear, well. ◦ C to 140 ◦ C. Thermal decay does not seem to appear, as well. range from 80 Side component 2: SC2 shows the greatest dependency on temperature among the regarded Side component shows the greatest dependency on temperature among regarded substances. At 80 2: °CSC2 a yield of only about 70% is reached after 120 min. This yield the is reached at substances. Atjust 80 ◦ C only about 70% is reached after min. This yield is reached at 140 °C after 15a yield min. of The increase in productivity is 120 approximately 800%. Thermal degradation cannot be traced based on this data.
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◦ C after just 15 min. The increase in productivity is approximately 800%. 1402018, Processes 6, x FOR PEER REVIEW
Thermal degradation 8 of 19 cannot be traced based on this data. Side component 3: 3: SC3 shows shows high productivity productivity among the whole temperature field. Besides ◦ C are under the level of 100 ◦ C (90% yield after 120 min). At a that, the yields at 80 ◦°C C and 140 °C °C temperature of 140 ◦°C C this can indicate thermal decomposition, which is investigated investigated in the temperature decomposition, which ◦ C the productivity is limited because the temperature is not well-chosen 80 80 following section. section.AtAt °C the productivity is limited because the temperature is not for this substance. well-chosen for this substance.
••
Already commercialized hawthorn hawthorn drugs drugsare areextracted extractedtotoa specific a specific DER ranging from to Already commercialized DER ranging from 4:1 4:1 to 7:1. 7:1. To ensure a good comparability towards established products, that parameter is investigated as To ensure a good comparability towards established products, that parameter is investigated as well. well. shows the extraction curves forand hyperoside the DER intemperature the investigated FigureFigure 7 shows7 the extraction curves for hyperoside the DER inand the investigated range. temperature range. 80 °C 100 °C 140 °C
10 9 8
90 °C 120 °C
100% 90% 80%
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DER
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40 60 80 100 Extraction time (min)
120
Figure 7. 7. (A) (A) DER DER during during the the PHWE PHWE of of hawthorn; hawthorn; (B) (B) Extraction curve of hyperoside during PHWE. Figure ◦ C aayield At regarding hyperoside is reached afterafter 120 120 min min of extraction. The At 80 80 °C yieldofof85% 85% regarding hyperoside is reached of extraction. corresponding DERDER is approximately 7.2:1.7.2:1. In that casecase onlyonly a small amount of dry extract is The corresponding is approximately In that a small amount of dry extract obtained that contains a relatively high quantity of hyperoside, so the extraction is rather selective. is obtained that contains a relatively high quantity of hyperoside, so the extraction is rather selective. By hyperoside is is achieved. At the same time,time, the By rising rising temperature temperaturealmost almostcomplete completeextraction extractionofof hyperoside achieved. At the same ◦ C, 2.7:1 ◦ C and DER decreases to values of 3.5:1 at 100 °C, 2.7:1 at 120 °C and 2:1 at °C, ◦being smaller thanthan the the DER decreases to values of 3.5:1 at 100 at 120 2:1140 at 140 C, being smaller minimum value obtained at conventional percolation with ethanol/water. None of the examined the minimum value obtained at conventional percolation with ethanol/water. None of the examined temperature temperature matches matches aa high high yield yield of of hyperoside hyperoside and and aa DER DER in in the the desired desired range range at at the the same same time. time. Therefore, an extraction at 90 °C was performed. A yield of hyperoside of 90% and a DER of 5.5:1 ◦ Therefore, an extraction at 90 C was performed. A yield of hyperoside of 90% and a DER of 5.5:1 after after 120ofmin of extraction havemeasured, been measured, this temperature for hot water 120 min extraction have been thus thisthus temperature is chosenisforchosen hot water extraction extraction of hyperoside from hawthorn. In that the process be named pressurized hot of hyperoside from hawthorn. In that case, thecase, process cannot cannot be named pressurized hot water water extraction by definition. From a process engineers point of view this is not decisive, because extraction by definition. From a process engineers point of view this is not decisive, because the shown the shown model determination parameter determination doesand not moreover, change and extraction is model parameter concept doesconcept not change themoreover, extractionthe is now possible now possible without additional reducing investment operating costs at the same without additional pressure, thuspressure, reducingthus investment and operatingand costs at the same time. time. 3.3.2. Thermal Degradation 3.3.2. Thermal Degradation To be aware of thermal degradation in hot water extraction is the key parameter for successful To be [5,6]. awareAs ofshown thermalindegradation in no hotclear waterthermal extraction is the keybeparameter extraction the screening, decay could traced byfor thesuccessful provided ◦ ◦ extraction shown in the experiments screening, nowere clear performed thermal decay could traced provided data. For [5,6]. that, As recycling-mode at 120 C be and 140 by C the to assess the data. Forofthat, experiments were performed 120given °C and 140 °C to thebe impact impact highrecycling-mode temperature toward hyperoside. The resultsatare in Figure 8. assess As it can seen, of high temperature toward hyperoside. The results are given in Figure 8. As it can be seen, all regarded components are quite stable at high temperatures. Only SC3 shows a rather small decline in concentration after 4 h of process time. At the chosen extraction temperature of 90 °C no thermal
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all regarded components are quite stable at high temperatures. Only SC3 shows a rather small decline in concentration after h of process time. At the extraction temperature of 90 ◦ C no thermal decay occurs, and the4 degradation kinetics canchosen be neglected in the physico-chemical extraction decay occurs, and the degradation kinetics can be neglected in the physico-chemical extraction model. model.
0.10 0.09
SC1
Concentration (g/l)
0.08 0.07 140°C 120°C
0.06 Hyperoside
0.05 0.04
SC3
0.03 0.02
SC2
0.01 0.00 0
25
50
75
100 125 150 175 Process time (min)
200
225
250
Figure 8. Concentration during the recycling experiment for all respective substances. Figure 8. Concentration during the recycling experiment for all respective substances.
3.4. Evaluation of Hot Water Extraction and Solvent-Based Percolation 3.4. Evaluation of Hot Water Extraction and Solvent-Based Percolation Experimental data (points) as well as the simulation results (drawn lines) of the conventional Experimental data (points) as well as the simulation results (drawn lines) of the conventional percolation of hawthorn are shown in Figure 9. Between 80 and 180 min of extraction, the acceptable percolation of hawthorn are shown in Figure 9. Between 80 and 180 min of extraction, the acceptable range of the DER between 7:1 and 4:1 is exceeded. The extraction curves of the components show a range of the DER between 7:1 and 4:1 is exceeded. The extraction curves of the components show strong bend in this timespan and according to economic considerations, the extraction should be a strong bend in this timespan and according to economic considerations, the extraction should be aborted after 250 min at the latest. The DER shows a strong correlation with the individual aborted after 250 min at the latest. The DER shows a strong correlation with the individual substances, substances, as expected. In that case the DER is a suitable tool for a basic process control, besides as expected. In that case the DER is a suitable tool for a basic process control, besides being technically being technically overtaken by various procedures of in-line monitoring ranging from overtaken by various procedures of in-line monitoring ranging from Raman-spectroscopy [20,25], Raman-spectroscopy [20,25], IR-spectroscopy [25] or simple conductivity measurement. Because the IR-spectroscopy [25] or simple conductivity measurement. Because the simulation of the pressurized simulation of the pressurized hot water extraction simplifies to a percolation, due to the thermal hot water extraction simplifies to a percolation, due to the thermal decay not being considered, decay not being considered, the approach of modeling and model parameter determination is the approach of modeling and model parameter determination is similar to that already shown and similar to that already shown and therefore is not shown in detail once again. therefore is not shown in detail once again.
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10 0.12 DER
9 SC1
0.1
8
7 Hyperoside
6 0.06
DER
Mass (g)
0.08
5
SC3
0.04 4 0.02
3 SC2
0 0
100
2 200
300 400 500 Extraction time (min)
600
700
800
Figure 9. Conventional extraction of hawthorn, experiments and simulation for hyperoside and side Figure 9. Conventional extraction of hawthorn, experiments and simulation for hyperoside and side components SC1 to SC3. components SC1 to SC3.
Besides process engineering aspects, the extract composition plays an important role. For an Besides process aspects, the an were important role. to Fora an evaluation extracts engineering of both, percolation andextract PHWEcomposition with a DERplays of 6:1 compared evaluation extracts of both,product percolation and45 PHWE with a DER of 6:1 were compared to a commercially commercially available (ethanol vol. %, DER 4–7:1). Assessed are the ratios of the side components relative to hyperoside. The chromatograms are shown in Figure are normalized available product (ethanol 45 vol. %, DER 4–7:1). Assessed are the ratios 10 of and the side components to the peak of hyperoside. relative to hyperoside. The chromatograms are shown in Figure 10 and are normalized to the
peak of hyperoside.
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Adsorption (mAU)
1200 1000 800
SC1
PHWE 90°C Percolation
600
Dry extract
Hyperosid e
400
SC3
SC2
200 0 1
2
3
4
5
6
7
8
Time (min) Figure 10. Chromatogram of the hawthorn extract and exemplified finger print region. Figure 10. Chromatogram of the hawthorn extract and exemplified finger print region.
•
Side component 1: SC1 shows a minor content in percolation compared to the commercially
•
SC1 showstoathe minor content in percolation compared thesolvent commercially Side component 1: According available product. solvent screening, SC1 dissolves better intothe of the available product. According to the solvent dissolves betterduring in the solvent of the reference product. A significantly higher screening, amount ofSC1 SC1 is extracted hot water extraction. reference product. A significantly higher amount of SC1 is extracted during hot water extraction. Side component2:2:SC2 SC2shows shows the the same According to the solvent screening, it • • Side component samebehavior behaviorasasSC1. SC1. According to the solvent screening, dissolves better the reference extract. it dissolves betterin inthe thesolvent solventused usedtotoproduce produce the reference extract. • Side component 3: The relative amount of SC3 in the reference than in in thethe • Side component 3: The relative amount of SC3 in the referenceextract extractisissmaller smaller than percolation performed in this study. According to the solvent screening the used mixture of 70 percolation performed in this study. According to the solvent screening the used mixture of vol. % ethanol and 30 vol. % water has a much higher capacity towards this substance 70 vol. % ethanol and 30 vol. % water has a much higher capacity towards this substance compared to the solution of the reference product. Again, the PHWE delivers an even higher compared to the solution of the reference product. Again, the PHWE delivers an even amount. higher amount. The chromatograms match the observations of the solvent screening. The PHWE reaches the The chromatograms the observations the screening. The PHWE reachestothe highest values of all sidematch components, therefore itof can besolvent considered as less selective, compared the percolation. It must be consideredtherefore that the reference was not obtained fromcompared the same to highest values of all side components, it can be extract considered as less selective, of plant material for the shown experiments of this but the place thebatch percolation. It must used be considered that extraction the reference extract was notstudy, obtained from theand same yearofofplant harvest are the used same.for Moreover, it isextraction not the objective of the shown batch material the shown experiments of thisfingerprint study, butapproach, the placeto and evaluate the quality or efficacy of the extracts, besides it is not yet known, which substances or group year of harvest are the same. Moreover, it is not the objective of the shown fingerprint approach, substances responsible for itof [26]. are only the DER, commonly pass or to of evaluate the are quality or efficacy theProducts extracts,that besides it isspecified not yet by known, which substances an internal qualityare management where fingerprint methods are widely [8]. With the group of substances responsibleprocess, for it [26]. Products that are only specified by used the DER, commonly shown approach of experimental model parameter determination on lab scale and physico-chemical pass an internal quality management process, where fingerprint methods are widely used [8]. With the modeling, it is now possible to predictively simulate markers, which are monitored in fingerprint shown approach of experimental model parameter determination on lab scale and physico-chemical methods, anyway, while monitoring the DER at the same time. Advanced process control and a modeling, it is now possible to predictively simulate markers, which are monitored in fingerprint model-based endpoint decision are possible [20]. This requires a process development with regards methods, anyway, while monitoring the DER at the same time. Advanced process control and a to the principles of Quality-by-Design (QbD) in a new approval of the product [19].
model-based endpoint decision are possible [20]. This requires a process development with regards to the3.5. principles of Quality-by-Design (QbD) in a new approval of the product [19]. Batch Variablity A fundamental 3.5. Batch Variablity challenge in extracting plants is the content of the ingredients, which fluctuates due to natural growth and site conditions. In terms of a model description of the solid-liquid A fundamental challenge in extracting plants is the content of the ingredients, which fluctuates extraction, the total content and the equilibrium per batch would have to be re-measured, if no
due to natural growth and site conditions. In terms of a model description of the solid-liquid extraction,
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the total content and the equilibrium per batch would have to be re-measured, if no systematics can be derived between two parameters. determine a possible dependency, a total of nine different systematics can bethe derived between theTo two parameters. To determine a possible dependency, a total batches, which differ by crop year (and thus storage life) and location, were examined. The equilibrium of nine different batches, which differ by crop year (and thus storage life) and location, were data are shown in Figure 11data and are as Langmuir-isotherms. examined. The equilibrium are interpreted shown in Figure 11 and are interpreted as Langmuir-isotherms.
0.50 0.45
Residue load (g/l)
0.40 0.35 0.30
A
0.25 C E D I B
0.20 0.15 0.10
G H F
0.05 0.00
0.0
0.1
0.2 0.3 0.4 Concentration (g/l)
0.5
Figure Equilibria of harvest different batches of hawthorn-hyperoside-EtOH/Water hawthorn in the system Figure 11.11. Equilibria of different batches harvest of hawthorn in the system hawthorn-hyperoside-EtOH/Water (70/30 v/v). (70/30 v/v).
The Langmuir coefficient is considered constant for all curves. The maximum load q max is The Langmuir coefficient is considered constant for all curves. The maximum load q is basically basically determined by the total content of the respective component. Experimentally,max however, it is determined by the total content of the respective component. Experimentally, however, it is observed observed that the equilibrium practically never reaches this final value. The deviation is described that the equilibrium practically never reaches this final value. The deviation is described based on a based on a model parameter a, which is referred to below as the capacity factor according to model parameter a, which is referred to below as the capacity factor according to Equation (5). Equation (5).
q =q q=max q · ⋅aa · ⋅ max
KLL ⋅ ·c c K 11 + + KKL L⋅ c· c
(5) (4)
Obviously, the equilibrium relationships, the maximum loading is not reached, which Obviously, ininthe equilibrium relationships, the maximum loading is not reached, which indicates indicates is no ad- orequilibrium desorption in equilibrium in the extraction of plants. that there that is nothere pure ador pure desorption the extraction of plants. A high partitionAofhigh the partition of the valuable substance is dissolved e.g., in the vacuole or in cell interspaces. This valuable substance is dissolved e.g., in the vacuole or in cell interspaces. This corresponds to the model corresponds to the model concept and[27]. Intact Cells’ by Sovová Kassing etconcept al. have in taken concept ‘Broken and Intact Cells’ ‘Broken by Sovová Kassing et al. have[27]. taken up this the up conceptmodel in theand pore diffusion model and implemented it using adistribution radial target[14,15]. component porethis diffusion implemented it using a radial target component If the distribution [14,15]. If the target distribution be as measured by methods as target component distribution cancomponent be measured by methodscan such Raman mapping [25], such this can Raman mapping If[25], this can be implemented. If nothecorresponding are available, thea be implemented. no corresponding data are available, introduction ofdata the capacity factor as introduction of the capacity factor as a macroscopic parameter reflects the basic idea of the ”Broken macroscopic parameter reflects the basic idea of the ”Broken and Intact Cells” model, which is based and Intact Cells” model, which is based on discontinuously [28,29]. If on discontinuously differentiable equilibrium lines [28,29]. differentiable If the solvent equilibrium can dissolvelines and transport the solvent dissolve andtarget transport away aorlarge amount of the target substances, if it is away a largecan amount of the substances, if it is accessible, only a small fraction,or which accessible, only a small fraction, which is actually adsorbed in the cell or between the cells, remains. actually adsorbed in the cell or between the cells, remains. An extreme example is the fennel fruit, An extreme fennel fruit, in whicharound the essential oil is localized channels around in which theexample essential is oilthe is localized in channels the actual fruit and in in principle only has tothe be actual fruitbyand principle onlyleads has to displaced by themeasurable solvent, which leads tolines very[30]. flat or displaced thein solvent, which to be very flat or barely equilibrium If, barely on the measurable equilibrium lines [30]. If, oncapacity, the other hand, theofsolvent has only a small capacity, other hand, the solvent has only a small then much the dissolved substance remains inthen the much of the dissolved substance remains in the cell or a large amount of solvent must be made available. In this case, clearly pronounced equilibrium lines result. The resulting equilibrium lines for the respective systems hyperoside-hawthorn-ethanol/water (70/30 v/v) are shown in Figure 11. The Langmuir coefficient was chosen to be the same for all datasets (75). There are differences only in the measured total content and in the capacity factor. The
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cell or a large amount of solvent must be made available. In this case, clearly pronounced equilibrium lines result. The resulting equilibrium lines for the respective systems hyperoside-hawthorn-ethanol/water (70/30 shown inREVIEW Figure 11. The Langmuir coefficient was chosen to be the same for all datasets Processesv/v) 2018, are 6, x FOR PEER 13 of 19 (75). There are differences only in the measured total content and in the capacity factor. The total content, year and of harvest and the capacity factor factor a are summarized in Tablein1.Table Batch1.I Batch is the total content, yearcountry and country of harvest and the capacity a are summarized reference batch used inused the previous experiments. I is the reference batch in the previous experiments. Table 1. Overview of the hawthorn batches.
Batch Batch
Harvest Harvest Year
A A BB CC DD EE FF G G H H I (Reference) I (Reference)
2017 2016 2016 2017 2017 2017 2017 2014 2014 2017 2017 2017 2017 2017 2017 2017
Year
2017
2017
Overall
Overall Amount Amount
Origin Origin Southeast Europe
Southeast Europe Macedonia Macedonia Bulgaria Bulgaria Romania Romania Bulgaria Bulgaria Albania Albania Southeast Europe Southeast Europe Serbia Serbia Germany
Germany
Deviation to
Deviation to Reference Reference
0.87%
0.87% 0.35% 0.35% 0.58% 0.58% 0.41% 0.41% 0.60% 0.60% 0.42% 0.42% 0.57% 0.57% 0.51% 0.51% 0.35%
0.35%
146%
146% −1% −1% 64% 64% 16% 16% 71% 71% 20% 20% 62% 62% 43% -43%
-
a a
0.11
0.11 0.10 0.10 0.12 0.12 0.13 0.13 0.10 0.10 0.02 0.02 0.03 0.03 0.015 0.015 0.13
0.13
Deviation to
Deviation to Reference Reference
−15% −15% −−23% 23% −−8% 8% 0% 0% −−23% 23% −−84% 84% −77% −77% −88% −88% -
The 1% to The capacity capacity The overall overall amount amount with with respect respect to to the the reference referencebatch batchIIvaries variesfrom from−−1% to +146%. +146%. The factor assumes values between 0.10 and 0.13 for all batches except F to G. Batches F to G factor assumes values between 0.10 and 0.13 for all batches except F to G. Batches F to G showed showed significantly fewerflowers flowersininthe the extraction material; capacity factor for these samples was significantly fewer extraction material; thethe capacity factor for these samples was 0.021 0.021 ± The 0.006. The results are shown graphically in Figure ± 0.006. results are shown graphically in Figure 12. 12.
0.9 Overall amount (%)
0.8
0.2 Overall amount Capacity factor
0.18 0.16
0.7
0.14
0.6
0.12
0.5
0.1
0.4
0.08
0.3
0.06
0.2
0.04
0.1
0.02
0.0
0
a [-]
1.0
A B C D E F G H I Batch Figure 12. Evaluation of the batch screening regarding the overall amount and the capacity factor. Figure 12. Evaluation of the batch screening regarding the overall amount and the capacity factor.
Obviously, the capacity factor does not correlate with the total content. It is, therefore, not Obviously,Since the itcapacity factor in does correlate total content. It is, therefore, batch-specific. is understood the not model conceptwith as a the macroscopic implementation of the not batch-specific. Since it is understood in the model concept as a macroscopic implementation Broken and Intact Cell model, the data support this hypothesis. In addition, batches F, G and H show of the Brokenwhich and Intact Cell model, thefrom data the support hypothesis. In addition, batches F, G a parameter is clearly different otherthis batches and is very probably related to and the H show a parameter which is clearly different from the other batches and is very probably related to lower share of flowers in the extraction material. The comminution of the extraction material here the lower share of flowers in the extraction material. The comminution of the extraction material here apparently causes a different, reproducible cell disruption, which consequently results in a different apparently causes a different, reproducible cell disruption, which consequently results in capacity factor. With the same pre-treatment and a visual inspection to determine thea different share of capacity factor. With the same pre-treatment and a visual inspection to determine the share of flowers flowers in each batch, so only the total content must be measured. The capacity factor can then be assumed to be constant. This data is the basis to simulate the individual percolations of the different batches, as shown in Figure 13. The capacity factor is constant at 0.115 for batches A to E and I and 0.021 for batches F, G and H. Apart from the individual total content of the respective batch, no further value is changed in the simulation.
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in each batch, so only the total content must be measured. The capacity factor can then be assumed to be constant. This data is the basis to simulate the individual percolations of the different batches, as shown in Figure 13. The capacity factor is constant at 0.115 for batches A to E and I and 0.021 for batches F, G and H. Apart from the individual total content of the respective batch, no further value is changed in the simulation. Processes 2018, 6, x FOR PEER REVIEW 14 of 19 0.2
Batch A Batch D Batch G
0.18 0.16
Batch B Batch E Batch H
Batch C Batch F Batch I
Mass (g)
0.14 0.12 0.1 0.08 0.06 0.04
0.02 0 0
100
200 Time (min)
300
400
Figure 13. Simulation of the extraction of different hawthorn batches. Figure 13. Simulation of the extraction of different hawthorn batches.
The deviations between simulation and experiment relative to the experimentally determined between simulation and experiment relative to the A experimentally determined total The massdeviations of hyperoside are listed in Table 2. The simulations of batches and B are underestimated total mass hyperoside are listed Table 2. batches, The simulations of batches Asize and was B areprobably underestimated by 16% andof10%, respectively. For in these two the average particle smaller by 16% and 10%, respectively. For these two batches, the average particle size was probably smaller than expected, which limits the diffusion in the simulation more than was the case in the experiment. than expected, which limits the diffusion in the simulation more than was the case in the experiment. The remaining batches have maximum deviations of +5% and 4%, respectively. The remaining batches have maximum deviations of +5% and 4%, respectively. Table 2. Overview of the simulation results. Table 2. Overview of the simulation results. Batch Mass Experiment (g) Mass Simulation (g) Difference (g) A Batch 0.135 0.161 −0.026 (g) Mass Experiment (g) Mass Simulation (g) Difference B 0.054 0.060 −0.006 A 0.135 0.161 −0.026 C 0.090 0.091 −0.001 B 0.054 0.060 −0.006 D C 0.0610.090 0.063 −0.002 0.091 −0.001 E D 0.0940.061 0.090 0.004 0.063 − 0.002 F E 0.0740.094 0.077 −0.003 0.090 0.004 G F 0.0970.074 0.093 0.004 0.077 − 0.003 0.093 0.004 H G 0.0870.097 0.086 0.000 H 0.086 0.000 I (Reference) 0.0560.087 0.054 0.002 I (Reference) 0.056 0.054 0.002
Deviation −16% Deviation −10% −16% −1% −10% −−3% 1% 5% −3% −4% 5% 4% −4% 4% 0% 0% 3% 3%
Examination of individual hawthorn batches showed large fluctuations of the hyperoside content. Nevertheless, the equilibrium be showed described uniformly across Using this Examination of individual hawthorncould batches large fluctuations of all the batches. hyperoside content. database, onlythe theequilibrium total content of abe new batch must be measured and a predictive simulation is Nevertheless, could described uniformly across all batches. Using this database, possible. In addition, the capacity factor can be correlated with complex matrix IR-spectra of the only the total content of a new batch must be measured and a predictive simulation is possible. powdered material.factor Thesecan fingerprint spectra, which are determined as triplicates, shown In addition,plant the capacity be correlated with complex matrix IR-spectra of the are powdered in Figure 14A. A principal component analysis of the second derivative ofshown these spectra shows plant material. These fingerprint spectra, which (PCA) are determined as triplicates, are in Figure 14A. a detailed clustering of the different batches, as depicted in Figure 14B. The batches F, G and H with A principal component analysis (PCA) of the second derivative of these spectra shows a detailed the lower share of flowers are clearly separated from the batches with high share of flowers. Therefore, the fast track data acquisition for predictive simulation of the lot variety can be performed with IR-spectroscopy using the raw material.
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clustering of the different batches, as depicted in Figure 14B. The batches F, G and H with the lower share of flowers are clearly separated from the batches with high share of flowers. Therefore, the fast track data acquisition for predictive simulation of the lot variety can be performed with IR-spectroscopy using raw Processesthe 2018, 6, xmaterial. FOR PEER REVIEW 15 of 19 0.002
0.06
A
High Low
B
0.05 0.001
PC-4
0.03
Intensity
0.04 0
0.02 -0.001
0.01 0 1400
1200
1000 800 Wavenumber (cm-1)
600
400
-0.002 -0.003
-0.002
-0.001
0 PC-1
0.001
0.002
0.003
Figure 14. (A) Spectra of the different harvest batches, black: batches F, G, H (low share of flowers), Figure 14. (A) Spectra of the different harvest batches, black: batches F, G, H (low share of flowers), grey: grey: rest (high share of flowers); (B) PCA of the second derivative of the hawthorn harvest batches rest (high share of flowers); (B) PCA of the second derivative of the hawthorn harvest batches spectra. spectra.
4. Economic Feasibility Study 4. Economic Feasibility Study Both hot water extraction and conventional percolation have their own specific advantages and Both hot water extraction and conventional percolation have their own specific advantages and disadvantages. Based on a profitability analysis, economic aspects should be considered in addition to disadvantages. Based on a profitability analysis, economic aspects should be considered in addition the purely procedural aspects. To make the cost study, the following assumptions were made: to the purely procedural aspects. To make the cost study, the following assumptions were made: ••
30 of hawthorn hawthorn leaves leaves are are to to be be extracted extracted annually annually in in 60 60 batches batches on on aa multi-product multi-product device. device. 30 tons tons of The of of thethe system with thisthis product should be 25%. The purchase purchaseprice priceisis3 3€/kg. €/kg.The Theannual annualoccupancy occupancy system with product should be 3 empty volume is available. • For the SLE as well as for the PHWE, an extractor of 2 m 25%. The €. The equipment is 100% more • For investment the SLE as costs well for as the for SLE the amount PHWE, to an200,000 extractor of 2high-pressure m3 empty volume is available. The expensive the technology and €. amounts to 400,000 €. equipment is 100% more investmentthan costs forconventional the SLE amount to 200,000 The high-pressure • Operating parameters and process times and up amounts to 95% to yield are €.directly taken from the expensive than the conventional technology 400,000 laboratory studies. • Operating parameters and process times up to 95% yield are directly taken from the laboratory • The energy costs are split into solvent preheating (here PHWE only) and evaporation. To operate studies. • Theevaporators energy costs are into solvent (here PHWE only) To the and thesplit preheating steam preheating (120 ◦ C, 5 bar, 2.7 MJ/kg) is usedand at aevaporation. price of 13 €/t. operate the evaporators and the preheating steam (120 °C, 5 bar, 2.7 MJ/kg) is used at a price of • 10% of the solvent quantity per extraction is lost due to losses and must be renewed continuously. 3 13 addition, €/t. In once a year, the entire pre-existing solvent, which is assumed to be 20 m , is replaced. • 10%the of ethanol, the solvent is lost due to losses and must be renewed For a pricequantity of 3 €/L per and extraction for water 0.08 €/L is assumed. continuously. In addition, once a year, the entire pre-existing solvent, which is assumed to be€/a 20 • A worker is scheduled for the operation of the plant. The labor costs amount to 100,000 3 m , isare replaced. For according the ethanol, price of 3process €/L and time for water 0.08 €/L is assumed. and weighted toa the total of the extraction of hawthorn added • A worker is scheduled for the operation of the plant. The labor costs amount to 100,000 €/a and (multi-product plant!). are costs weighted according and to the total process time of theamount extraction of of hawthorn added • The for depreciation maintenance for this product to 2.5% the total system (multi-product plant!). costs (multi-product investment!). • The costs for depreciation and maintenance for this product amount to 2.5% of the total system The of the costinvestment!). study is shown in Figure 15. The total annual PHWE costs are about 50% costsresult (multi-product less than conventional extraction. The biggest difference is the annual solvent change. Hot water The result of the cost study is shown in Figure 15. The total annual PHWE costs are about 50% extraction can save about 99% of the costs compared to SLE. If the solvent is not renewed annually, less than conventional extraction. The biggest difference is the annual solvent change. Hot water the PHWE is still about 30% cheaper. However, the evaporation of the aqueous extract takes up about extraction can save about 99% of the costs compared to SLE. If the solvent is not renewed annually, 10 times the amount of energy. The significantly shorter process time for hot water extraction means the PHWE is still about 30% cheaper. However, the evaporation of the aqueous extract takes up about 10 times the amount of energy. The significantly shorter process time for hot water extraction means that staff can be used much more efficiently, which means that the personnel costs of this product are around 20% lower than in the comparative process.
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that staff can be used much more efficiently, which means that the personnel costs of this product are around 20% lower than in the comparative process. Processes 2018, 6, x FOR PEER REVIEW 16 of 19 €300,000 SLE
€250,000
PHWE
Costs / a
€200,000 €150,000 €100,000 €50,000 €-
Figure 15. Comparison of the annual costs of the hawthorn extraction for conventional solid-liquid Figure 15. Comparison of the annual costs of the hawthorn extraction for conventional solid-liquid extraction and PHWE. extraction and PHWE.
The cost calculation shows that the PHWE is competitive, even if the equipment is much more The cost PHWE is competitive, even extraction if the equipment is muchbymore expensive. In calculation addition, itshows can bethat seenthe that the cost of conventional is dominated the expensive. In addition, it can be seen that the cost of conventional extraction is dominated by the solvent. This means that often procedurally sensible operating points, here 70% ethanol and 30% solvent. This means that often procedurally sensible operating points, here 70% ethanol and 30% water water as solvent, are not feasible for economic reasons. In this case, this results in the as solvent, are not feasible for economic In thiswhich case, drastically this resultsreduces in the industry-preferred industry-preferred solvent mixture of onlyreasons. 45% ethanol, the cost of solvent solvent mixture of only 45% ethanol, which drastically reduces the cost of solvent renewal but is not renewal but is not the optimum according to solvent screening. Here, PHWE offers the opportunity the optimum according to solvent screening. Here, PHWE offers the opportunity to implement the to implement the optimum process technology at reasonable costs. optimum process technology at reasonable costs. 5. Effort Analysis 5. Effort Analysis The evaluation of hot water extraction and conventional percolation is based on a valid model The evaluation of hot water extraction and conventional percolation is based on a valid model parameter determination which can be handled under sensible experimental effort. For the parameter determination which can be handled under sensible experimental effort. For the modelling modelling and simulation of extraction processes to become routine industrial processes, the path and simulation of extraction processes to become routine industrial processes, the path shown must be shown must be superior to a purely empirical design using statistical experimental design. For this superior to a purely empirical design using statistical experimental design. For this purpose, Figure 16 purpose, Figure 16 shows a schedule for a complete parameter determination which must be shows a schedule for a complete parameter determination which must be handled by a person with handled by a person with the corresponding equipment. Black is the actual working time, dark gray the corresponding equipment. Black is the actual working time, dark gray are the times in which the are the times in which the experiment does not have to be supervised and light gray is the time in experiment does not have to be supervised and light gray is the time in which the analysis takes place. which the analysis takes place. Day 1
Day 2
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Experiment Solvent Screening Equilibrium Overall amount Temperature Screening Degradation Kinetics Figure 16. Time schedule of the experimental setup.
Day 8
Day 9 Day 10
modelling and simulation of extraction processes to become routine industrial processes, the path shown must be superior to a purely empirical design using statistical experimental design. For this purpose, Figure 16 shows a schedule for a complete parameter determination which must be handled by a person with the corresponding equipment. Black is the actual working time, dark gray Processes 2018, 6, 73 in which the experiment does not have to be supervised and light gray is the time17in of 19 are the times which the analysis takes place. Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Day 7
Day 8
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Experiment Solvent Screening Equilibrium Overall amount Temperature Screening Degradation Kinetics Figure 16. Time schedule of the experimental setup. Figure 16. Time schedule of the experimental setup.
For a complete parameter determination, with each assay being run as a triple determination, appr. 10 days are required. The classical extraction experiments can be left unattended most of the time, which is why the more labor-intensive model parameter determination for hot water extraction can take place in parallel. To determine all parameters, only appr. 300 g of plant material and 8 L of solvent are consumed. If a statistical test plan is used instead of model parameter determination and simulation, one percolation can be carried out daily with the same equipment. Even with only three parameters and one center point, a full-factorial experimental design results in 11 experiments, which, taken together, have a much lower information density than the shown path of model parameter determination and a predictive process simulation. The model-based approach is therefore to be preferred in any case, especially since with increasing experience the results can be very quickly checked for plausibility or can be transferred to other material systems. 6. Conclusions The study showed a systematic and model-based comparison of two different ways of manufacturing a traditionally used herbal extract. Both a percolation using a mixture of ethanol and water (70/30 v/v) as solvent as well as an extraction with water at 90 ◦ C show high productivities and yields. A high yield of the main flavonoid hyperoside as well as the desired range of the DER is reached. The chromatographic fingerprints revealed that all extracts are comparable to a commercially available one. The combination of experimental model parameter determination and rigorous process model is an efficient method of predictive process simulation, not only for the extraction of substances which are afterwards purified to pharma-grade, but also for the processing of traditionally used complex extracts. For the first time, natural batch variability was successfully incorporated into the physico-chemical process modelling concept. These generated data sets are required by regulatory authorities demanding Quality-by-Design (QbD) and Process Analytical Technology (PAT) approaches as modern tools with data-driven decisions documented for filing due to technological change, entering of markets of other countries as well as changes of regional regulations and authority inquiry. An economic feasibility study showed that the PHWE can overcome the financial drawbacks of solvent storage and renewal efficiently, thereby justifying the higher investment costs for the necessary high-pressure equipment. Author Contributions: M.S. did the conceptuation, experiments, simulations and validation. J.S. did the supervision and the reviewing of the manuscript. Funding: We also acknowledge the financial support obtained from the Deutsche Forschungsgemeinschaft (DFG) in Bonn, Germany (project Str 586/4-2). The authors want to thank the Bundesministerium für Wirtschaft und Energie (BMWi), especially M. Gahr (Projektträger FZ Jülich), for funding this scientific work. Acknowledgments: We gratefully acknowledge the support of the ITVP lab-team. Special thank is also addressed to M. Tegtmeier (Schaper and Brümmer) for providing the different harvest batches of hawthorn and fruitful discussions. Conflicts of Interest: The authors declare no conflict of interest.
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Abbreviations aP cL cP Dax Deff KL Pe q qmax Re R Sc SC Sh t uz z ε ε DPF HPLC i.d. PHWE PTFE SLE
Specific surface area, 1/m Concentration in the liquid phase, kg/m3 Concentration in the porous particle, kg/m3 Axial dispersion coefficient, m/s2 Effective diffusion coefficient, m2 /s Equilibrium constant, m3 /kg Péclet number Loading, kg/m3 Maximum Loading, kg/m3 Reynolds number Radius, m Schmidt number Side Component Sherwood number Time, s Superficial velocity, m/s Coordinate in axial direction, m Dielectric constant, Voids fraction, Distributed plug flow High performance liquid chromatography Inner diameter Pressurized hot water extraction Polytetrafluoroethylene Solid-liquid extraction
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