Green and efficient extraction of different types of

Microchemical Journal 145 (2019) 345–353

Contents lists available at ScienceDirect

Microchemical Journal journal homepage: www.elsevier.com/locate/microc

Green and efficient extraction of different types of bioactive alkaloids using deep eutectic solvents

T

Zheng-Meng Jianga, Lan-Jin Wanga, Zhao Gaoa, Bo Zhuanga, Qiang Yinb, E-Hu Liua,

⁎

a b

State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing, PR China Department of Management, Xinjiang Uygur Pharmaceutical Co., Ltd., Wulumuqi 830001, PR China

ARTICLE INFO

ABSTRACT

Keywords: Deep eutectic solvents Green extraction Alkaloids Response surface methodology Artificial neural network hybridized with genetic algorithm

Deep eutectic solvents (DESs) have attracted increasing attention as eco-friendly and efficient alternatives to conventional organic solvents. In this study, five herbal medicines (HMs) including Caulis sinomenii, Coptis chinensis, Stephania tetrandra, Tetradium ruticarpum, and Sophora flavescens were selected for the first time to comprehensively evaluate the potential and effectiveness of DESs on the extraction of Morphinane, Protoberberine, Bisbenzylisoquinoline, Indole and Quinolizidine alkaloids, respectively. A total of 75 types of binary or ternary DESs with different polarity, viscosity, pH, and solubilization abilities were tailored to screen a suitable DESs for extraction of morphinane alkaloids from Caulis sinomenii, and the extraction conditions were optimized using response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA) for the first time. The results demonstrated that DESs were excellent solvents for extraction of alkaloids with superior efficiency to conventional extraction solvents. Moreover, both ANOVA and sensitivity analysis demonstrated that the most critical parameter for alkaloids extraction was water content in DESs. This study reveals the potential of eco-friendly DESs for applications involving the efficient extraction of bioactive alkaloids from natural sources.

1. Introduction Alkaloids are the largest group of secondary metabolites from microbial, plant and animal sources [1], and they have been extensively studied because they may help in the prevention of various diseases as they possess important biological properties such as antidiabetic [2], antidepressant [3], anxiolytic [4,5], anti-endotoxin [6], anti-inflammatory [7], antioxidant [8], antiproliferative [9] and anti-parkinson [10]. Extraction of bioactive alkaloids from plant is one of the fundamental and crucial steps before any applications. Nowadays, conventional organic solvents (e.g., methanol, alcohol, ethyl acetate, acetone and chloroform) have been widely used in in the extraction of alkaloids from HMs. Nonetheless, most of these solvents show many intrinsic drawbacks, such as environmental pollution, high toxicity and cost [11]. Over the last decades, Deep eutectic solvents (DESs) have been dramatically expanding in popularity as promising alternatives to hazardous organic solvents. DESs is defined as mixtures of two or more hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD), when at a particular molar ratio, bound together by strong intermolecular interactions, mainly hydrogen bonding [12]. Since DESs consist of

⁎

simple, low-toxic, and naturally occurring compounds with advantageous features, such as sustainability, biodegradability and acceptable pharmaceutical toxicity profiles, as well as high solubility for polar and non-polar compounds [13], they are proved to be novel high-efficiency extraction media compared with organic solvents. DESs have been applied in extraction and separation for bioactive plant compounds, including flavonoids [14,15], phycocolloid [16], phenolic acids [17], terpenoids [18], and anthraquinones [19]. In our previous study, the carboxylic acid-based DESs showed to be efficient for extraction of protoberberine alkaloids from Berberidis Radix [20]. Till date, DESs extraction has scarcely been applied to alkaloids extraction from herbal medicines (HMs) [20,21]. To extend the applications of DESs in the extraction of natural products, it is of great interest to attempt DESs to extract different types of bioactive alkaloids. Caulis sinomenii (CS) has been traditionally used for the treatment of inflammatory and rheumatic diseases in China. Pharmacological researches have demonstrated that sinomenine (SIN) and magnoflorine (MAN) are the major anti-inflammatory and anti-rheumatic isoquinoline alkaloids in CS [22]. The protoberberine-type alkaloids berberine hydrochloride (BER), epiberberine (EPI), coptisine (COP), palmatine hydrochloride (PAL), are the main beneficial components in Coptis

Corresponding author. E-mail address: [email protected] (E.-H. Liu).

https://doi.org/10.1016/j.microc.2018.10.057 Received 7 October 2018; Received in revised form 26 October 2018; Accepted 26 October 2018 Available online 28 October 2018 0026-265X/ © 2018 Elsevier B.V. All rights reserved.


Z.-M. Jiang et al.

Table 1 Different types of natural ionic liquids or DESs initially tested for extraction. No.

Abbreviation

HBA Component 1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72

Chcl-Oa Chcl-Ca Chcl-LA Chcl-Ta Chcl-Ma Chcl-Laa Chcl-DLMa Chcl-DLMa Chcl-Glu Chcl-Su Chcl-Fru Chcl-Mal Chcl-Man Chcl-Eg Chcl-Eg Chcl-Gly Chcl-Gly Chcl-Gly Chcl-Gly Chcl-Gly Chcl-Gly Chcl-Gly Chcl-Sor Chcl-Xy Chcl-Ur Chcl-Met Chcl-Dme Chcl-Ace Chcl-La-Eg Chcl-La-Gly Chcl-Laa-Eg Chcl-Laa-Gly Chcl-Oa-Eg Chcl-Oa-Gly Chcl-Ca-Eg Chcl-Ca-Gly Chcl-Ma-Eg Chcl-Ma-Gly Chcl-DLMa-Gly Chcl-Pro-DLMa Chcl-Xy-DLMa Bet-Oa Bet-Ca Bet-La Bet-Laa Bet-Gly Bet-Eg Bet-Am Bet-Su Bet-Mal Bet-Oa-Glu Bet-La-Glu Bet-DLMa-Glu Bet-DLMa-Pro Pro-Oa Pro-Cc Pro-La Pro-Laa Pro-DLMa Pro-Gly Pro-Glu Pro-Eg Pro-Su Pro-Am Pro-DLMa-Glu Pro-DLMa-Gly Pro-DLMa-Ca DLMa-Ca DLMa-Laa DLMa-Laa DLMa-La-Glu DLMa-La-Gly

Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Choline chloride Betaine Betaine Betaine Betaine Betaine Betaine Betaine Betaine Betaine Betaine Betaine Betaine Betaine L-Proline L-Proline L-Proline L-Proline L-Proline L-Proline L-Proline L-Proline L-Proline L-Proline L-Proline L-Proline L-Proline

HBD Component 2

Component 1 Oxalic acid Citric acid Levulinic acid L-Tartaric acid Malonate Lactic acid DL-Malic acid DL-Malic acid D-Glucose Sucrose D-Fructose Maltose Mannitol Ethylene glycol Ethylene glycol Glycerol Glycerol Glycerol Glycerol Glycerol Glycerol Glycerol D-Sorbitol Xylitol Urea Methylurea N,N′-dimethylurea Acetamide Levulinic acid Levulinic acid Lactic acid Lactic acid Oxalic acid Oxalic acid Citric acid Citric acid Malonate Malonate DL-Malic acid DL-Malic acid DL-Malic acid Oxalic acid Citric acid Levulinic acid Lactic acid Glycerol Ethylene glycol Acetamide Sucrose Maltose Oxalic acid Levulinic acid DL-Malic acid DL-Malic acid Oxalic acid Citric acid Levulinic acid Lactic acid DL-Malic acid Glycerol D-Glucose Ethylene glycol Sucrose Acetamide DL-Malic acid DL-Malic acid DL-Malic acid DL-Malic acid DL-malic acid DL-Malic acid DL-Malic acid DL-Malic acid

L-Proline

L-Proline

Mole ratio Component 2

Component 3

Ethylene glycol Glycerol Ethylene glycol Glycerol Ethylene glycol Glycerol Ethylene glycol Glycerol Ethylene glycol Glycerol Glycerol Xylitol

D-Glucose D-Glucose D-Glucose

D-Glucose

Glycerol Citric acid Citric acid Lactic acid Lactic acid Levulinic acid Levulinic acid

D-Glucose

Glycerol

1:1 2:1 1:2 1:1 1:1 1:2 1:1 5:1 2:1 4:1 1:2 4:1 5:2 1:1 1:2 2:1 3:1 4:1 1:1 1:2 1:3 1:4 3:1 5:2 1:2 1:1 1:1 1:1 1:1:2 1:1:2 1:1:2 1:1:2 1:1:2 1:1:2 1:1:2 1:1:2 1:1:2 1:1:2 1:1:2 1:1:1 1:1:1 2:1 2:1 1:2 1:2 1:2 1:2 1:2 1:2 2:5 1:1:1 1:1:1 1:1:1 1:1:1 2:1 1:1 1:1 1:2 1:1 1:2 1:1 1:2 1:4 1:2 1:1:2 1:1:2 1:1 1:1 2:1 1:1 1:1:1 1:1:2

(continued on next page) 346


Z.-M. Jiang et al.

Table 1 (continued) No.

Abbreviation

HBA Component 1

73 74 75

HBD Component 2

Component 1

DLMa-Glu-Gly Ca-Glu Ca-Fru

DL-Malic

acid Citric acid Citric acid

chinensis (CC). Tetrandrine (TET) and fangchinolinee (FAN), the major bisbenzylisoquinoline alkaloids isolated from Stephania tetrandra (ST), have recently been found to exert inhibiting release of histamine, and reducing myocardial oxygen consumption [23]. Indole alkaloids (evodiamine (EVO), rutaecarpine (RUT)) are the main bioactive compounds in Tetradium ruticarpum (TR), which exhibit modulation of drug-metabolizing enzymes, platelet aggregation inhibition and anti-proliferative activity [24]. Sophora flavescens (SF), is one of the most frequently used HMs for the treatment of viral hepatitis, cancer, viral myocarditis, gastrointestinal hemorrhage and skin diseases. The main bioactive constituents in SF are quinolizidine alkaloids matrine (MAT) and oxymatrine (OMA), which have been reported to exhibit sedative, depressant, anti-tumor, antipyretic, cardiotonic activities and anti-hepatitis B virus activity [25]. Therefore, in this study, for the first time, the above five HMs were selected to comprehensively evaluate the efficiency of DESs on extraction of Morphinane alkaloids, Protoberberine alkaloids, Bisbenzylisoquinoline alkaloids, Indole alkaloids, and quinolizidine alkaloids, respectively. Initially, a series of DESs were rationally designed and prepared based on natural renewable products (Table 1). Screening of eight different DESs as potential extraction solvents was performed to evaluate the extraction efficiency of alkaloids. In order to improve the DESs extraction methods, the response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA) were applied to statistically optimize the operation conditions [26].

Mole ratio Component 2 D-Glucose D-glucose

Component 3 Glycerol

D-fructose

1:1:1 1:1 1:1

magnetic agitation until a homogeneous colorless liquid formed, and a known amount of water was added when necessary [12,20]. Table 1 shows the different types of DESs prepared, their abbreviated names, along with the molar ratios of respective components. Additionally, different DESs solutions in water were prepared by dilution of a certain volume of DESs in deionized water (water solution of DESs containing 10%, 30% and 50% of water (v/v) were prepared). 2.3. Extraction procedure In each extraction process, dried HM sample was crushed to a fine powder (50 mesh), and 25 mg of powdered plant material was soaked in 1 mL of different solvent (MeOH, 30%DESs-water solvent) in 2 mL centrifuge tube, and extracted by ultrasound-assisted (UAE). The mixture was ultrasounded (KQ5200B, 200 W, 40 kHz, Kunshan, China) at 50 °C for 30 min and then centrifuged at 13000 rpm for 10 min. The suspension was diluted five times with water prior to HPLC analysis. Each extraction was performed in triplicate. 2.4. HPLC-UV analysis for quantification of extracted alkaloids

2. Materials and methods

Chromatographic analyses were performed on the Agilent 1290 Series HPLC system (Agilent, San Jose, CA, USA) equipped with a diode array detector. Separation was performed on an Agilent ZORBAX Extend C18 column (250 mm × 4.6 mm, 5.0 μm). The detailed chromatographic conditions for five HMs were shown in Supplemental material.

2.1. Reagents and materials

2.5. RSM modeling and optimization

The alkaloids standards (Fig. 1) SIN, MAN, BER, EPI, COP, PAL, EVO and RUT were purchased from Chengdu Must Biotechnology Co., Ltd. (Chengdu, China). Tetrandrine (TET), FAN, MAT and OMT were obtained from the National Institute for the Control of Pharmaceutical and Biological Products (Beijing, China). The purity of all reference compounds was determined to be > 98% by HPLC-DAD. Compounds for DESs preparation including choline chloride (ChCl), betaine (Bet), Lproline (Pro), levulinic acid (La), lactic acid (Laa), citric acid (Ca), oxalic acid (Oa), L-tartaric acid (Ta), malonate (Ma), DL-malic acid (DLMa), D-glucose (Glu), sucrose (Su), maltose (Mal), urea (Ur), methylurea (Met), N,N′-dimethylurea (Dme), acetamide (Am), mannitol (Man) ethylene glycol (Eg), glycerol (Gly), D-sorbitol (Sor), xylitol (Xy) were supplied by Aladdin Reagent Company (Shanghai, China). Ultrapure water was obtained from a Milli-Q system (Millipore, Billerica, MA, USA). Methanol (MeOH) and acetonitrile (ACN) of the chromatographic grade were purchased from Tedia Co., Inc. (Fairfield, OH, USA.). All other reagents and chemicals used were of analytical grade. CS, CC, ST, TR, and SF samples were purchased from a local pharmacy (Nanjing, China).

To optimize the extraction method with DESs, RSM was performed using the Design-Exper Ver. 10.0 (State-Ease Inc., Minneapolis, MN, USA). In the present study, the 3-level 4-factor Box-Behnken experimental design was applied to validate process parameters influencing the extraction amounts of alkaloids by DESs and the detailed optimization methods was shown in the supplementary materials.

2.2. Synthesis of DESs

3. Results and discussion

DESs were fundamentally synthesized by heating proper molar ratios of hydrogen bond acceptor (Chcl, Bet, Pro) with different groups of hydrogen donors (organic acid, alcohols, sugars and amides). These components were placed in a capped bottle and heated to 80 °C with

3.1. Preparation of DESs

2.6. ANN-GA modeling and optimization ANN is a more accurate modeling technique compared to RSM because it represents the nonlinear relationships including quadratic equations in a much better way [26]. As detailed in the supplementary materials, a feed-forward multilayer perceptron (MLP) type architecture of ANN model was used with back propagation (BP) algorithm to establish the predictive mathematical model with the condition of the four parameters (e.g., water content, temperature, time and solidliquid ratio) and one output as the extraction amounts of alkaloids. The ANN-GA based modeling studies were performed using MATLAB (Version 2015a).

As being elaborated by Dai et al. [27], DESs might play an important role as a liquid phase for solubilizing, storing, and transporting non347


Z.-M. Jiang et al.

Fig. 1. Chemical structures of different type of natural alkaloids. (1) sinomenine; (2) magnoflorine; (3) berberine; (4) rutaecarpine; (5) evodiamine; (6) tetrandrine; (7) fangchinolinee; (8) matrine; (9) ammothamnine; (10) epiberberine; (11) palmatine; (12) coptisine.

natural-occurring alkaloids, and the HPLC profile was shown in Fig. S1. Totally 8 different types of Chcl-based DESs (Table 2) and control solvent were tested under the same condition and the results were elaborated in Fig. 2. As shown in Fig. 2A, the results demonstrated that the type of DESs has a great effect on extraction efficiencies of SIN and MAN. Clear superiority in the extracted amounts of SIN and MAN was observed with the evaluated DESs (except sugar-based) compared with methanol. The Fig. 2B–C clearly shows, the extraction yields for the four protoberberines have similar variation trend in the different types of DESs. It is worth noting that sugar-based DESs showed the lowest extraction efficiency for protoberberine alkaloids compared with other three types of HBD. Moreover, Chcl-La produced the highest extractability of protoberberine alkaloids from CC, which was in accordance with our previous work [20]. The results as Fig. 2D indicated that the extraction capacity of the tested DESs varied greatly, which depends on the types of HBD in DESs. Chcl-Ur and Chcl-Glu DESs exhibited relatively lower extraction capacity compared with methanol, whereas organic acid-based DESs, such as Chcl-La and Chcl-Laa, could not extract the bisbenzylisoquinoline alkaloids from ST. Since the bisbenzylisoquinoline compounds contain no phenolic hydroxyl groups, the DESs with carboxyl groups might not interact with the alkaloids through the formation of hydrogen bonds (Fig. 1). Unfortunately, the results showed that most DESs had inferior extraction efficiency, and only one synthesized DESs (Chcl-La) produced similar extraction efficiency for indole alkaloids with methanol (Fig. 2E). We speculate that the low extractability of DESs solvents may be attributed to the weaker alkaline of indole alkaloids. As displayed in Fig. 2F, the extraction efficiencies of DESs varied extremely with the different types of HBD. Several sugar-based DESs, such as Chcl-Glu, and ChCl-Mal presented relatively lower extraction capacity, whereas organic acid-, amides-, and alcohol-based DESs demonstrated higher extractability for quinolizidine alkaloids. Based on the data, six DESs showed comparable extraction yields with methanol, particularly Chcl-La, Chcl-Ace and ChclEg exhibited higher extraction amounts of alkaloids with a significant difference. In sum, the excellent properties of DESs highlight their

water soluble metabolites in living cells and organisms. In this study, we tested different mixtures of various abundant constituents such as sugars, alcohols, organic acids and amides. The exploration of different combinations of these common constituents, which abundantly present in all types of cells and organisms or have renewable, inexpensive and readily accessible resources provided over 70 combinations of DESs (Table 1). During the preparation process, we found that liquids could be formed when Chcl mixed with any HBD constituent. The DESs were classified into four main groups: ionic liquids with an acid or a base, alcohol-based DESs with a faintly acid, sugar-based DESs with neutral compounds and sugar-based DESs with bases or acids. It must be noted that different kinds of sugars, organic acids and alcohols mixtures can also form liquids, such as DLMa-Ca, DLMa-Glu-Gly, and Ca-Glu [23]. Other combinations of more than two components, like Chcl-La-Gly can also form clear liquids. 3.2. Comprehensive evaluation of DESs in extraction of bioactive natural alkaloids The DESs have certain characteristics, such as high extraction affinity to the analyte, low solubility in the aqueous solution and easy dispersion into water [28]. Moreover, the structure of DESs plays a crucial role in their physicochemical properties and consequently greatly influences extraction efficiency of biologically active compounds. Thus, we prepared two organic acid-based DESs (Chcl-Laa and Chcl-La), two amide-based DESs (Chcl-Ace and Chcl-Ur), two alcoholbased DESs (Chcl-Gly and Chcl-Eg), and two sugar-based DESs (ChclGlu and Chcl-Mal in this study, with different mixing ratios of various organic acids, amides, alcohols, and sugars to Chcl) (Table 2). The extraction efficiency was tested by extracting five different types of bioactive natural-occurring alkaloids (Morphinane, Protoberberine, Indole, quinolizidine and Bisbenzylisoquinoline) from HMs, and methanol was selected as the contrast. An external standard method was established to investigate the extraction efficiency of DESs for five different types of bioactive 348


Z.-M. Jiang et al.

Table 2 List of DESs initially tested for alkaloids extraction. No. 1 2 3 4 5 6 7 8

Abbreviation Chcl-LA Chcl-Laa Chcl-Glu Chcl-Mal Chcl-Eg Chcl-Gly Chcl-Ace Chcl-Ur

HBA Choline Choline Choline Choline Choline Choline Choline Choline

HBD chloride chloride chloride chloride chloride chloride chloride chloride

Levulinic acid Lactic acid D-Glucose Maltose Ethylene glycol Glycerol Glycerol Urea

Mole ratio

Water content (%)

1:2 1:2 2:1 4:1 1:2 1:2 1:3 1:2

30 30 30 30 30 30 30 30

Fig. 2. Extraction amount of different DESs for morphinane (sinomenine, magnoflorine), protoberberine (berberine, coptisine), bisbenzylisoquinoline (tetrandrine, fangchinoline), indole (evodiamine, rutaecarpin), and quinolizidine (matrine, oxymatrine) alkaloids. Extraction efficiencies of DESs compared with MeOH are indicated with * (P < 0.0001, ****, P < 0.001, ***, P < 0.01 **, P < 0.05, *), Error bars indicate the SD (n = 3). Significant difference DESs are indicated by dark color bars, no significant difference DESs are indicated by light color bars.

potential as promising green solvents for the extraction of quinolizidine alkaloids. Generally, DESs capacity to extract natural compounds varied considerably according to the target compounds and the DES. Taking into consideration all the solvents, the molecular structure of HBDs in Chcl- DESs has a significant effect on extraction efficiency of alkaloid compounds. The Chcl- DESs containing HBDs with carboxyl, polyphenols and amides groups interact with the target compounds more easily, that have phenolic hydroxyl groups by forming chemical bonds or hydrogen bonds. In the case of morphinane and quinolizidine, the

poorest extraction efficiency was obtained with sugar-based DESs. Morphinane and quinolizidine alkaloids are polar molecules, therefore, the difference of extraction efficiency among various DESs result from the distinction of polarity. For example, among the tested DESs, the organic acid-based ones are most polar, thus probably showed better extraction efficiency, whereas sugar-based DESs are least polar and showed the poorest results [27]. Another important parameter of DESs for alkaloids extraction is viscosity, since the addition of water led to a decrease in the viscosity of the reaction media, enhancing the mass transfer from plant matrices to solution. Moreover, the extractability of 349


Z.-M. Jiang et al.

DESs may be affected by acidity and basicity of alkaloids and DESs. The extraction yields of organic acid-, amides- and alcohol-based DESs for strong alkaline alkaloids (Protoberberine alkaloids) are significantly higher than sugar-based DESs, most likely due to the acid-base neutralization reaction. Given that pH has great importance in the equilibrium forms and stability of alkaloids, it was no surprise that among the DESs tested, acidic Chcl-La or Chcl-Ace enabled higher concentrations of the analyzed alkaloids. On the contrary, the extraction efficiency of alkaloids decreased with neutral solvents.

Table 3 Independent variables and Levels chosen for Box-Behnken design. No.

A B C D

3.3. DESs selection for extraction of alkaloid compounds in CS

Variable

Level

Water content (%) Temperature (°C) Time (min) Solid/liquid ratio (mg/mL)

Low (−1)

Middle (0)

High (+1)

10 40 10 20

30 50 20 40

50 60 30 60

of the water content (A) and time (C) were significant (P < 0.05). Among these factors, the F-value of the water content was 62.13 and 58.98, which demonstrated that the extraction efficiency of SIN and MAN was more related to the water content. The two models also gave the coefficient of determination (R2) value of 0.928 and 0.908, which showed that the models were accurate and reliable for extract SIN and MAN from CS. The model was expressed as second-order polynomial quadratic equations for the extraction yield (Y) and coded factors (A, B, C, and D) as follows:

Till now, no systematic study has reported for DESs extraction of alkaloid compounds in CS. To get insight into the excellence of DESs, a total of 75 binary or ternary DESs (Table 1) were selected to evaluate their extraction efficiency on the main alkaloids (SIN and MAN) in CS, and the extraction method was performed according to the Section 2.3, and methanol was selected as the control. The structure of DESs determines their physicochemical properties and consequently greatly influences extraction efficiency of biologically active compounds. As shown in Fig. S2, the types of DESs had a profound effect on SIN and MAN extraction efficiencies. Among the 75 DESs, Chcl-La, Chcl-Gly and Chcl-Ace exhibited higher yields compared to other binary DESs for SIN and MAN extraction, and the ternary Chclorganic acid-alcohol DESs all presented higher extraction efficiency. Conversely, sugar-based DESs exhibited relatively lower yield than methanol. This discrepancy maybe probably due to the facts that sugarbased DESs have higher viscosity and lead to a decrease in diffusivity, which reduces the extraction efficiency. In addition, the neutral property of sugar-based DESs may be another important factor of lower yield since it affects the solubilizing ability. Thus, the DESs with carboxyl, polyphenols and amides groups may interact more strongly with alkaloids (SIN and MAN) by forming chemical bonds or hydrogen bonds. It is noteworthy that the results obtained in the initial screening studies demonstrated that the less viscous DESs (Pro-Eg and Pro-DLMaGly) shown higher extraction efficiency for SIN and MAN than other DESs. However, the high viscosity of DESs 60 (Pro-Gly) restricts its application as extraction solvents for CS. Therefore, the extraction capacity of DESs for bioactive natural products is correlated with their physical-chemical properties, such as Hansen solubility parameters, viscosity, and pH [29].

Y(SIN) = 1.88 + 0.247A + 0.054B + 0.115C + 0.050D 0.178AC

0.064 AD

0.278A^2

0.037 B^2

0.025BC 0.058C^2

0.021D^2

Y(MAN) = 1.52 + 0.204A + 0.049B + 0.098C + 0.048D 0.144AC 0.221A^2

0.050AD 0.025B^2

0.010AB

0.009 BD + 0.063CD

0.0113 BC 0.050 C^2

0.040AB

0.010BD + 0.040CD 0.023D^2

To better understand the effects of the independent variables and their interactions on the response, 3D response surface plots were employed (Figs. 3–4). The response surface plots showed the influence of two variables on the response at the center level of other variables. The nonlinear nature of 3D response surface plots demonstrates that there were interactions between each of the independent variables and dependent variable. 3.4.2. Validation of the optimized conditions Optimization of the process variables to maximize the extraction efficiency by the optimal DESs from CS was performed using the quadratic model within the investigated experimental range of various process variables. The process optimization modeling indicated that the optimal parameters (A = 29.94%, B = 53.92 °C, C = 30 min and D = 60.00 mg/mL) gave the 2.029 g/100 g for SIN and 1.639 g/100 g for MAN extraction amount. The six confirmation experiments were conducted at selected optimal levels of the process parameters. The relative error between average removal percentage obtained through confirmation experiments and the predicted value of the model was within 5% (SIN:3.40%; MAN:1.71%).

3.4. Modeling and optimization for the SIN and MAN extraction efficiency from CS by RSM 3.4.1. Optimization of the operational conditions for CS alkaloids extraction RSM is an empirical modeling technique used to evaluate the relationship between the experimental and the predicted results. In the present study, the Box-Behnken design was used to obtain a proper model for the optimization of the extraction efficiency with four process variables (water content, temperature, ultrasonic time and solid-liquid ratio) at three levels. All experiments were conducted in triplicate to verify the precision for the extraction of SIN and MAN by Chcl-Gly solvent, and all experimental ranges and levels of independent variables chosen were collected in Table 3, which were based on the single factor experiments (Fig. S3). The single factor experiments were carried out to provide a reasonable range for the independent variables of response surface experiments. The ANOVA for the response surface quadratic model for SIN and MAN (Table S1 and Table S2) gave an F-value of 9.84 and 9.29, which demonstrated that the terms have a significant effect on the response in the model. The lack of fit (LOF) was the variation of the data around the fitted model. The P-value for LOF was 0.0582 and 0.0781 (> 0.05), which showed that the model used to fit response variables is significant and adequate to represent the relationship between the response and the independent variables. In this model, effects

3.5. Modeling and optimization for the SIN and MAN extraction efficiency from CS by ANN-GA 3.5.1. Optimization of the operational conditions for CS alkaloids extraction The relationship between the input and output data was achieved by ANN to approximate any function with a finite number of discontinuities by learning their relationships. The MSE and R2 are the criterion to assess the quality of the optimum ANN structure. As shown in Figs. S3-1 and S4-1, the smallest MSE (SIN:0.00466; MAN:0.00404) was considered for using 7 and 5 hidden neurons, respectively. Figs. S32 and S4-2 show the predicted value of extraction data using the ANN model against experimental data (SIN: R2 = 0.9963; MAN: R2 = 0.9908). The results indicated that there is a good agreement between the predicted data using the ANN model and experimental data. Figs. S3-3 and S4-3 exhibit the MSE amounts against the number 350


Z.-M. Jiang et al.

Fig. 3. Response surface plots of the model for extraction of SIN in CS.

of epochs for optimal ANN models. The training stopped after about 1998 epochs with 0.001474 for SIN and 2000 epochs with 0.0035871 for MAN of MSE. The results demonstrated that there was a good agreement between experimental data and predicted data using the ANN model. In order to obtain an optimum solution, the optimization process was iterated at GA input conditions. Achievement of similar results for most of the GA inputs ensured that it can be the global optimal solution [30]. As depicted in Figs. S3-4 and S4-4, the fitness reached a minimum after 17 and 15 generations.

when the water content (46.72%), temperature (42.50 °C), time (29.84 min) and S/L (24.69 mg/mL) were employed, the extraction amount of MAN reached the maximum predicted date (1.619 g/100 g). Under these optimum conditions, the predicted extraction amount for SIN and MAN were 2.009 ± 0.032, 1.610 ± 0.026, respectively. The experimental values were close to the predicted value with 1.23% and 0.56% of the prediction error. 3.6. Modeling and optimization for the SIN and MAN extraction efficiency from CS by ANN-GA

3.5.2. Validation of the optimized conditions The maximum predicted extraction amount of 2.034 g/100 g for SIN was obtained when the water content, temperature, time and S/L were 41.41%, 58.59 °C, 27.50 min and 23.28 mg/mL, respectively. Also,

3.6.1. Comparison for the optimization of SIN extraction The comparison for predicted extraction efficiency of SIN via using RSM and ANN-GA was given in Table 4. It's worth noting that

Fig. 4. Response surface plots of the model for extraction of MAN in CS. 351


Z.-M. Jiang et al.

Table 4 Comparative results of confirmatory experiments for SIN model validation. Process parameters

RSM

ANN-GA

Optimized values

Experimental values

Optimized values

Experimental values

29.94 53.92 30.00 60.00 2.029

30.00 54 30.00 60.00 1.96 ± 0.035

41.41 58.59 27.50 23.28 2.034

41.00 59.00 28.00 24.00 2.009 ± 0.032

Water content (%) Temperature (°C) Time (min) S/L ratio (mg/mL) Extraction amount (g/100 g) Average predicted error (%) R2

3.40 0.928

1.23 0.9964

Table 5 Comparative results of confirmatory experiments for MAN model validation. Process parameters

Water content (%) Temperature (°C) Time (min) S/L ratio (mg/mL) Extraction amount (g/100 g) Average predicted error (%) R2

RSM

ANN-GA

Optimized values

Experimental values

Optimized values

Experimental values

29.94 53.92 30.00 60.00 1.639

30.00 54 30.00 60.00 1.611 ± 0.041

46.72 42.50 29.84 24.69 1.619

47.00 43.00 30.00 25.00 1.610 ± 0.026

1.71 0.908

optimization condition by RSM and ANN-GA showed significant differences in water content and S/L ration. The extraction amount of SIN predicted by RSM was 2.029 g/100 g, and the experimental verification had been proved as 1.960 ± 0.035 g/100 g. Similarly, the predicted and experimental extraction efficiencies for ANN-GA were 2.034 g/ 100 g and 2.009 ± 0.032 g/100 g, respectively. The prediction error in optimum removal efficiencies by RSM and ANN-GA were 3.40% and 1.23%. It was found that the average predicted error of RSM was higher than that of ANN-GA, indicating the superiority of ANN-GA.

0.56 0.9908

4. Conclusions In the present study, the tailor-made DESs (Chcl-LA) proved to be a green and efficient solvent for extraction of Morphinane Protoberberine, Indole, and Quinolizidine alkaloids, but presented poorer extractability for Bisbenzylisoquinoline alkaloids. As for SIN and MAN, the results showed that the ANN model which had a higher R2 value and a lower MSE value offered more accurate predictions than the RSM model. Moreover, both ANOVA and sensitivity analysis demonstrated that the most critical parameter for alkaloids extraction was water content. To our knowledge, this is the first attempt to comprehensively evaluate the efficiency of DESs on the extraction of different types of bioactive natural alkaloids from HMs. The DESs, with their compositional flexibility and low cost and environmental impacts, have the potential to be utilized for possible industrial applications involving, as demonstrated here, the extraction of bioactive alkaloids from HMs.

3.6.2. Comparison for the optimization of MAN extraction In the present study, 29 experiments were designed by the RSM mode and the data were used to establish the ANN-GA model. Sextuplicate experiments were carried out under the RSM and ANN-GA optimized conditions and mean values of experimental results were compared with the predicted values, respectively. The validation results and the prediction errors between the RSM and ANN-GA model were present in Table 5. The results showed that the prediction errors of RSM and ANN-GA models were 1.23% and 0.56%, respectively. Moreover, the R2 of ANN-GA (0.9908) was higher than that of RSM (0.908). Therefore, ANN is an optimal model for CS Alkaloids Extraction, with higher precision and accuracy compared with RSM. Although ANN is superior to RSM, the two models complement each other in explaining the experimental extraction efficiency. RSM is a commonly adopted statistical method for building quadratic models and optimizing the process parameters, and its important advantage is that it requires lesser number of experiments to be performed. ANNs inspired by biological neurons belong to artificial intelligence (AI) techniques, which have recently experienced a tremendous advance in various applications, e.g., intelligent search, autonomous driving, big data, pattern recognition and robotics. ANNs (the black box models) can model ill-defined and non-linear problems because they have the capacity to modeling from the input and output data without any detailed knowledge of the extraction processes. ANNs combined with RSM can be considered as an effective approach when the extraction processes are complex and require a large number of experiments to study [31].

Acknowledgement This work was supported by the National Key Research and Development Program of China (2017YFC1701105), the National Natural Science Foundation of China (No. 81673569, 81473343), and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions. Conflict of interest The authors have declared no conflict of interest. We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.microc.2018.10.057. 352


Z.-M. Jiang et al.

References

[16] A.K. Das, M. Sharma, D. Mondal, K. Prasad, Deep eutectic solvents as efficient solvent system for the extraction of kappa-carrageenan from Kappaphycus alvarezii, Carbohydr. Polym. 136 (2016) 930–935. [17] M.L.A. Fernandez, M. Espino, F.J.V. Gomez, M.F. Silva, Novel approaches mediated by tailor-made green solvents for the extraction of phenolic compounds from agrofood industrial by-products, Food Chem. 239 (2018) 671–678. [18] X. Liu, S. Ahlgren, H.A.A.J. Korthout, L.F. Salomé-Abarca, L.M. Bayona, R. Verpoorte, Y.H. Choi, Broad range chemical profiling of natural deep eutectic solvent extracts using a high performance thin layer chromatography–based method, J. Chromatogr. A 1532 (2018) 198–207. [19] Y.C. Wu, P. Wu, Y.B. Li, T.C. Liu, L. Zhang, Y.H. Zhou, Natural deep eutectic solvents as new green solvents to extract anthraquinones from Rheum palmatum L, RSC Adv. 8 (2018) 15069–15077. [20] L. Duan, L.-L. Dou, L. Guo, P. Li, E.H. Liu, Comprehensive evaluation of deep eutectic solvents in extraction of bioactive natural products, ACS Sustain. Chem. Eng. 4 (2016) 2405–2411. [21] M.H. Zainal-Abidin, M. Hayyan, A. Hayyan, N.S. Jayakumar, New horizons in the extraction of bioactive compounds using deep eutectic solvents: a review, Anal. Chim. Acta 979 (2017) 1–23. [22] Z.Z. Zhao, Z.T. Liang, H. Zhou, Z.H. Jiang, Z.Q. Liu, Y.F. Wong, H.X. Xu, L. Liu, Quantification of Sinomenine in Caulis sinomenii collected from different Growg regions and wholesale herbal markets by a modified HPLC method, Biol. Pharm. Bull. 28 (2005) 105. [23] L. Liu, S. Li, Z. Chen, Simultaneous determination of tetrandrine and fangchinoline in herbal medicine Stephania tetrandra S. Moore by liquid chromatography with electrochemical detection, J. Pharm. Biomed. Anal. 61 (2012) 252–255. [24] Y. Zhao, Z. Li, X. Zhou, Z. Cai, X. Gong, C. Zhou, Quality evaluation of Evodia rutaecarpa (Juss.) Benth by high performance liquid chromatography with photodiode-array detection, J. Pharm. Biomed. Anal. 48 (2008) 1230–1236. [25] J.Y. Ling, G.Y. Zhang, Z.J. Cui, C.K. Zhang, Supercritical fluid extraction of quinolizidine alkaloids from Sophora flavescens Ait. and purification by high-speed counter-current chromatography, J. Chromatogr. A 1145 (2007) 123–127. [26] M. Fan, T. Li, J. Hu, R. Cao, Q. Wu, X. Wei, L. Li, X. Shi, W. Ruan, Synthesis and characterization of reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites used for Pb(II) removal, Materials 9 (2016). [27] Y. Dai, J. van Spronsen, G.J. Witkamp, R. Verpoorte, Y.H. Choi, Natural deep eutectic solvents as new potential media for green technology, Anal. Chim. Acta 766 (2013) 61–68. [28] N. Lamei, M. Ezoddin, K. Abdi, Air assisted emulsification liquid-liquid microextraction based on deep eutectic solvent for preconcentration of methadone in water and biological samples, Talanta 165 (2017) 176–181. [29] A. Shishov, A. Bulatov, M. Locatelli, S. Carradori, V. Andruch, Application of deep eutectic solvents in analytical chemistry. A review, Microchem. J. 135 (2017) 33–38. [30] Y. Singh, S.K. Srivastav, Statistical and evolutionary optimization for enhanced production of an antileukemic enzyme, L-asparaginase, in a protease-deficient Bacillus aryabhattai ITBHU02 isolated from the soil contaminated with hospital waste, Indian J. Exp. Biol. 51 (2013) 322. [31] M. Fan, J. Hu, R. Cao, K. Xiong, X. Wei, Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO, Sci. Rep. 7 (2017) 18040.

[1] J.P. Michael, Indolizidine and quinolizidine alkaloids, Nat. Prod. Rep. 19 (2002) 719–741. [2] O.P. Patel, A. Mishra, R. Maurya, D. Saini, J. Pandey, I. Taneja, K.S. Raju, S. Kanojiya, S.K. Shukla, M.N. Srivastava, M. Wahajuddin, A.K. Tamrakar, A.K. Srivastava, P.P. Yadav, Naturally occurring carbazole alkaloids from Murraya koenigii as potential antidiabetic agents, J. Nat. Prod. 79 (2016) 1276–1284. [3] M.T.H. Anna, J. Kochanowska-Karamyan, Marine indole alkaloids: potential new drug leads for the control of depression and anxiety, Chem. Rev. 110 (2010) 4489–4497. [4] F. Cicero Bezerra Felipe, J. Trajano Sousa Filho, L.E. de Oliveira Souza, J. Alexandre Silveira, D. Esdras de Andrade Uchoa, E. Rocha Silveira, O. Deusdenia Loiola Pessoa, G.S. de Barros Viana, Piplartine, an amide alkaloid from Piper tuberculatum, presents anxiolytic and antidepressant effects in mice, Phytomedicine 14 (2007) 605–612. [5] L.B. Pagliosa, S.C. Monteiro, K.B. Silva, J.P. de Andrade, J. Dutilh, J. Bastida, M. Cammarota, J.A. Zuanazzi, Effect of isoquinoline alkaloids from two Hippeastrum species on in vitro acetylcholinesterase activity, Phytomedicine 17 (2010) 698–701. [6] Y. Hu, B. Li, L. Wen, K. He, Study on the anti-endotoxin effect of sinomenine using an Agilent genome array, QJM 111 (2018) 171–178. [7] L. Qian, Z. Xu, W. Zhang, B. Wilson, J.S. Hong, P.M. Flood, Sinomenine, a natural dextrorotatory morphinan analog, is anti-inflammatory and neuroprotective through inhibition of microglial NADPH oxidase, J. Neuroinflammation 4 (2007) 23. [8] H.F. Zhang, L.J. Shi, G.Y. Song, Z.G. Cai, C. Wang, R.J. An, Protective effects of matrine against progression of high-fructose diet-induced steatohepatitis by enhancing antioxidant and anti-inflammatory defences involving Nrf2 translocation, Food Chem. Toxicol. 55 (2013) 70–77. [9] Y. Takada, Y. Kobayashi, B.B. Aggarwal, Evodiamine abolishes constitutive and inducible NF-kappaB activation by inhibiting IkappaBalpha kinase activation, thereby suppressing NF-kappaB-regulated antiapoptotic and metastatic gene expression, up-regulating apoptosis, and inhibiting invasion, J. Biol. Chem. 280 (2005) 17203–17212. [10] G. Ribaudo, E. Zanforlin, M. Canton, S. Bova, G. Zagotto, Preliminary studies of berberine and its semi-synthetic derivatives as a promising class of multi-target anti-parkinson agents, Nat. Prod. Res. 32 (2018) 1395–1401. [11] L. Duan, W.-H. Zhang, Z.-H. Zhang, E.H. Liu, L. Guo, Evaluation of natural deep eutectic solvents for the extraction of bioactive flavone C-glycosides from Flos Trollii, Microchem. J. 145 (2019) 180–186. [12] M. Ruesgas-Ramon, M.C. Figueroa-Espinoza, E. Durand, Application of deep eutectic solvents (DES) for phenolic compounds extraction: overview, challenges, and opportunities, J. Agric. Food Chem. 65 (2017) 3591–3601. [13] X. Li, K.H. Row, Development of deep eutectic solvents applied in extraction and separation, J. Sep. Sci. 39 (2016) 3505–3520. [14] S. Bajkacz, J. Adamek, Development of a method based on natural deep eutectic solvents for extraction of flavonoids from food samples, Food Anal. Methods 11 (2017) 1330–1344. [15] B. Zhuang, L.L. Dou, P. Li, E.H. Liu, Deep eutectic solvents as green media for extraction of flavonoid glycosides and aglycones from Platycladi cacumen, J. Pharm. Biomed. Anal. 134 (2017) 214–219.

353