Azizul Azri Mustaffa

Herbal Processing and Extraction Technologies

Herbs are widely utilized in food and health industries. Their beneficial effects to the human body have been attributed to the presence of active phytochemical ingredients with some efficiency for disease treatment as well as for beauty and health enhancement. Public awareness on the adverse effects of synthetic chemical products also increased the demand for herbal products. Highly efficient herbal processing and extraction technologies have been developed to obtain the optimal amounts of active ingredients from herbs and cope with the rising demands for herbal products. This article reviews the state-of-the-art development in herbal processing and extraction methods from the year 1991 until 2015. We start with a brief history of herbal usage, followed by descriptions of 10 types of extraction processes and critical analysis of their relative advantages and disadvantages. Scale-up considerations of the extraction methods are shared, and a highlight of the current and future challenges facing the herbal industry is presented.

Tailor-Made Green Diesel Blends Design using a Decomposition-Based Computer-Aided Approach

In this study, the tailor-made green diesel blend design problem is mathematically formulated and solved by a decomposition-based computer-aided approach. The green diesel design problem is solved in three main stages to identify the feasible green diesel blend candidates that meet the product property constraints (density, kinematic viscosity, cetane number, higher heating value and flash point) with the desired performance criteria. An optional additives identification step is introduced to enhance the blends. The shortlisted green diesel blends are evaluated on the basis of cost, cetane number and higher heating value. To ensure that the shortlisted candidates have acceptable functional reliability, their compatibility with the engine compartment, engine performance, and emission requirements should be addressed in future works.

Selection of optimum ionic liquid solvents for flavonoid and phenolic acids extraction

Phytochemicals are important in improving human health with their functions as antioxidants, antimicrobials and anticancer agents. However, the quality of phytochemicals extract relies on the efficiency of extraction process. Ionic liquids (ILs) have become a research phenomenal as extraction solvent due to their unique properties such as unlimited range of ILs, non-volatile, strongly solvating and may become either polarity. In phytochemical extraction, the determination of the best solvent that can extract highest yield of solute (phytochemical) is very important. Therefore, this study is conducted to determine the best IL solvent to extract flavonoids and phenolic acids through a property prediction modeling approach. ILs were selected from the imidazolium-based anion for alkyl chains ranging from ethyl > octyl and cations consisting of Br, Cl, [PF6], BF4], [H2PO4], [SO4], [CF3SO3], [TF2N] and [HSO4]. This work are divided into several stages. In Stage 1, a Microsoft Excel-based database containing available solubility parameter values of phytochemicals and ILs including its prediction models and their parameters has been established. The database also includes available solubility data of phytochemicals in IL, and activity coefficient models, for solid-liquid phase equilibrium (SLE) calculations. In Stage 2, the solubility parameter values of the flavonoids (e.g. kaempferol, quercetin and myricetin) and phenolic acids (e.g. gallic acid and caffeic acid) are determined either directly from database or predicted using Stefanis and Marrero-Gani group contribution model for the phytochemicals. A cation-anion contribution model is used for IL. In Stage 3, the amount of phytochemicals extracted can be determined by using SLE relationship involving UNIFAC-IL model. For missing parameters (UNIFAC-IL), they are regressed using available solubility data. Finally, in Stage 4, the solvent candidates are ranked and five ILs, ([OMIM] [TF2N], [HeMIM] [TF2N], [HMIM] [TF2N], [HeMIM] [CF3SO3] and [HMIM] [CF3SO3]) were identified and selected.

Solubility Parameter Prediction for Kacip Fatimah Herb using Group Contribution-Based Models

Abstract This study is focusing on determining the most suitable solubility parameter prediction model for Kacip Fatimah herb based on Group Contribution (GC) approach. Stefanis,Van Krevelen, and Marrero and Gani GC models are used to predict Hansen Solubility Parameters (HSP) property of selected Kacip Fatimah active ingredients extracted using methanol solvent. From the results, solubility parameters predicted using Stefanis and Van Krevelen GC methods show high deviations with the experimental data. On the other hand, the HSP predictions using Marrero and Gani GC method is the best from the three GC Methods because it uses the data from organic compound and take into account the contribution of the third order groups. The variation in solubility parameter concludes that Van Krevelen and Stefanis GC parameter is not suitable for computing the parameter for herbs.

Ionic liquid screening for solvent design of herbal phytochemical extraction

Ionic liquids (ILs) have been used in many applications and currently have been a favourable solvent in separation technology due to their solvation power compared to organic solvent. A development of IL solvent design approach is necessary in order to apply the most optimal solvents in herbal phytochemical extraction. In this study, solvent design utilised a systematic approach combined with property predictive model rather than trial-and-error experimental approach to reduce the amount of solvent waste and extraction time. This work focus on the screening of ILs as phytochemical extraction solvents of phenolic acids (e.g. gallic acid and caffeic acid) where we used property models of solubility and toxicity as part of solvent design. The methodology consisted of several stages. Stage 1 specifies the user needs of an extraction solvent, problems and constraints of new solvent design. Stage 2 involved in the development of a comprehensive Excel-based database of ionic liquid properties (e.g. solubility, heat capacity etc.) and factors that affect phytochemical extraction (e.g. extraction time, particle size etc.). In Stage 3, property library was assembled by collecting property models relevance for ILs from other studies to identify the most suitable models and estimate property values for solvent design. In Stage 4, ILs available in the database were first screened based on four factors: toxicity, heat capacity, density and viscosity (properties which represent characteristics of solvent and which affect the extraction). Only those ILs which have acceptable value range of each properties were selected. Finally, the ILs candidates were further screened down based on their solvation performance by using a solubility parameter-solid-liquid equilibrium approach involving UNIFAC-IL models to select most optimal solvent that can extract highest amount of phytochemicals. From the screening process, 16 best IL solvent candidates for the phenolic acid extraction were obtained from a database of 880 imidazolium-based IL.

Solubility Prediction of Flavonoids Using New Developed Unifac-based Model

Flavonoids are phytochemicals extensively used in the pharmaceutical, food, and pigment industries. They have many important biological properties including antioxidant, anti-inflammatory, antifungal, and anti-viral. The importance of flavonoids has motivated the development of many processes for the manufacture of flavonoids derivative products. The aim of this study is to develop a new set of Universal Functional Activity Coefficient (UNIFAC) parameters for solubility prediction of flavonoids in organic solvents. In this study, group interaction parameters of the UNIFAC have been regressed and improved from the solubility experimental data of flavonoids based on the activity coefficient model through the thermodynamic modelling of Solid- Liquid Equilibrium (SLE) relationship which involves an iterative step. The results showed that a more accurate prediction (lower prediction error) could be obtained using the new parameters. By using our developed parameter for flavonoids, better agreements were obtained between the experimental and the predicted values by the UNIFAC model with less than 5.57 % deviation. The results indicated that the newly developed UNIFAC-based model can adequately be used to represent the measured data with good accuracy and can be useful for the purpose of solubility estimation for flavonoids in various solvents.

Decomposition-Based Optimization of Tailor-Made Green Diesel Blends

This chapter presents a tailor-made green diesel blends design algorithm to solve the tailor-made green diesel blends design problem. The design algorithm obtains the optimum solutions of tailor-made green diesel blends by incorporate experimental work with the decomposition-based computer-aided approach. There are four main phases in the algorithm: (1) formulate the design problem into a mathematical form; (2) optimize green diesel blend using a decomposition-based computer-aided approach; (3) further enhance the shortlisted blends using fuel additives and (4) experimental validation for the physicochemical properties and engine performance of the shortlisted green diesel blends. B5 palm oil biodiesel-biochemical blends is taken as a case study to demonstrates the application of the presented algorithm.

Computer-Aided Approach for Designing Solvents Blend for Herbal Phytochemical Extraction

There are many methods to obtain herbs phytochemicals such as the use of solvents as a phytochemical transfer medium in the extraction process. Currently, solvent is preliminarily selected based on the target phytochemicals and solvent polarities. This is followed by performing experiments to determine the potential solvents blends which give the highest desired phytochemicals content. The method uses a trial-and-error approach and is time consuming as well as resource intensive. The combination of property predictive models with computer-assisted search is one way to reduce the needs for huge amount of experiments needed to be conducted. Thus, the main objective of this work is to design solvent blends for the extraction of herbal phytochemicals using computer-aided approach. The methodology is divided into four levels which are pure component constraints (level 1), linear constraints (level 2), non-linear constraints (level 3) and stability checks (level 4). The proposed method has been applied to design a solvent mixture for the extraction of kaempferol from Kacip Fatimah herb as a case study. From the analysis, 12 feasible binary solvents mixture have been identified to be suitable for the extraction as it was within range of the design target. Thus, the optimal search has been performed to find the mixture solvents that can produce the highest kaempferol extraction yield with the lowest cost. Here, five binary solvents showed the highest kaempferol yield with the lowest solvent cost.

An Evaluation of Thermodynamic Models for the Prediction of Solubility of Phytochemicals from Orthosiphon Staminues in Ethanol

Orthosiphon Staminues (OS) is a species of herbs locally known in Malaysia as Misai Kucing. This study aims to analyse the performance of existing group contribution models in predicting the solubilities of phytochemical compounds from OS in ethanol. The compounds are oleanolic acid, ursolic acid and betulinic acid which have properties of pharmaceutical importance. The solubility experimental data of the compounds in ethanol were derived from the literature and were compared with the predictions made by the original UNIFAC, Modified UNIFAC (Dortmund), and Pharma Modified UNIFAC. The very high ranges of ARD values of between 40.86% to 98.6% calculated in this work shows that the models presented in this paper are not able to accurately predict the solubility of the studied phytochemical compounds in ethanol.

Flash point prediction of tailor-made green diesel blends using UNIFAC-based models

Flash point of tailor-made green diesel is an important property for safety regulation. Based on the previous analysis, the prediction accuracy of the Liaw model through UNIFAC-type models is found to be satisfactory for the mixtures of B5 palm oil biodiesel with ester and ether, except for B5-alcohol blends. To fill up the research gap, the aim of this study is to improve the prediction efficiency of the model for green diesel blends containing alcohol. The improvement is done by adjusting the group interaction parameters for Original-UNIFAC and NIST-UNIFAC model according to the experimental flash point data. A significant improvement of prediction results were obtained with a reduction of the prediction errors (calculated using the average absolute relative deviation - AARD) from about 7.32 and 6.39 % for Original-UNIFAC and NIST-UNIFAC to around 1.2 % for both models using the revised group interaction parameter set that containing the revised parameters of alcohol and alkyl chains group. Overall, the prediction accuracies obtained by using Original-UNIFAC and NIST-UNIFAC model are similar when revised group interaction parameters are used