**4. Students and teachers' attitudes towards the change**

The attitude of the students and teachers towards technology integration in higher education is an important aspect to be analysed. Understanding such attitude provides further insights into students' and teachers' behaviour and perception towards e-learning and allows better planning for future development and policies. Generation Z, the members who use modern technologies, especially mobile applications, is not particularly attracted to e-learning platforms, but rather, is more interested in the participation and collaboration in the creation of its content and interacts with each other in ways they are used to with other social media platforms such as Facebook, Instagram, and YouTube [43]. As students have unlimited access to information online, their attitudes towards e-learning have reflected a participatory approach in learning. This has resulted in the change of the role of instructors to that of a consultant or a coach. Hence, instructor knowledge of learning technologies and student understanding of computer systems, and technical infrastructure are important factors for ensuring the success of online learning [44]. With the introduction to technology from early childhood, students in higher education have learned to accept it as an integral part of their education, with many perceiving it as an essential resource for effective learning. However, the attitudes of students may not be the same in all disciplines and may differ from region to region, as there are various factors that influence its acceptance. Regarding this, a recent study [45] during the COVID-19 pandemic with 111 nursing students in the Philippines observed that, in spite of their having intermediate computer competency and a stable internet connection, the majority of them had negative or ambivalent attitudes towards e-learning. They reported e-learning as being impersonal, thus resulting in poor student-teacher interaction, whereas a study [46] in the UK on medical students' attitudes towards the Mental Health First Aid eLearning course found that the online course helped them to improve their knowledge and confidence to help someone in need, which thus resulted in positive responses to the approach. Another study [47] in Ghana, with 472 distance learning students of the University of Cape Coast, revealed that there are regional differences regarding students' perceived usefulness, self-efficacy, and attitudes towards e-learning. Hence, it can be concluded that there are various factors that might influence students' attitudes towards e-learning positively or negatively. Computer self-efficacy, social influence, level of enjoyment, system interactivity, computer anxiety, technical support, perceived usefulness, perceived ease of use, and behavioural intention to use are some of the factors that can influence the students' attitudes towards e-learning [48]. However, increasing reliance on the e-learning options due to the COVID-19 pandemic has led to the introduction of e-learning and other innovative technologies such as virtual learning, which have increased students' satisfaction levels and helped in developing positive attitudes towards e-learning [49].

*Embracing Technological Change in Higher Education DOI: http://dx.doi.org/10.5772/intechopen.100431*

Similar to students, teachers' attitudes towards technology may be influenced by a range of factors, which can be categorized as being at the teacher-level, school-level, and system-level [50]. As discussed previously, teachers may exhibit resistance to learn new technologies that require them to change their instructional and pedagogic strategies or they may experience a lack of support in terms of training to learn these technologies, which can lead to them developing negative attitudes about technology interventions in higher education. Teachers' attitudes can vary across different regions, with their acceptance and adoption of e-learning being dependent on their level of computer proficiency, the available resources, and students' readiness to engage in new technology [51]. The subject knowledge and experience may have no influence on the teachers' attitudes towards technology intervention. A recent study [52] investigated teachers' attitudes towards the use of Microsoft Teams in education, finding that the usability of the platform was negatively associated with their years of experience, and their general anxiety and power and control of the platform negatively affected the time they spent on the platform. Hence, it can be seen that, while those teachers who are provided with sufficient training and support may develop positive attitudes towards the use of new technology in higher education, others, with poor support and lower computer proficiency, can develop negative attitudes, and both of these dispositions can significantly influence the process of learning.


**1. Introduction**

Analysis of gene expression levels is essential in studying and detecting genes functions. According to Chandra and Tripathi [1], genes that have similar gene expression levels are likely to involve similar biological functions. The authors showed that the clustering process was quite useful to identify co-expressed genes in a group of genes and, in addition, to detect unique genes in different groups. Therefore, clustering can be quite helpful to extract valuable knowledge from a large amount of biological data [2], which could lead to prevention, prognosis, and treatment in biomedical research.

Cai et al. [3] developed a random walk-based technique to cluster similar genes. The authors show that the proposed method was useful in strengthening the interaction between genes by considering the types of interactions that exist in the same group of genes. Many previous random walk-based methods managed to extract local information from a large graph without knowledge of the whole graph data [4]. In a random walk-based method, a gene is important if it interacts with many other genes [5–8]. As illustrated in Figure 1, gene 1 has a higher degree than gene 2 (two outgoing links) compared to one outgoing link from gene 3 to gene 4. In this case, gene 1 is the most important gene among the four genes shown in the hypothetical gene network.

**Figure 1.** A hypothetical gene network to illustrate the importance of genes in a random walk.

Several previous studies have noted the importance of clustering to identify co-expressed genes in a cluster and inactive genes in another cluster [1,9]. Clustering can also discover the fundamental hidden structure of biomedical data, which can be used for diagnosis and treatments [9]. In addition, clustering is extremely vital for identifying cancer subtyping and the detection of the tumor.

Researchers typically focus on clustering by assuming the number of clusters beforehand, which can be seen in [10,11]. This problem can lead to the inability of the clustering techniques to obtain an optimal number of centroids and hence results in poor quality of clusters [11,12]. In previous studies, several proposed approaches managed to discover the optimal number of clusters by simply tuning and optimizing the parameters of the clustering method. This can be done by repeating the process of analyzing the eigenvalues of the affinity matrix, which are equal to the number of desired clusters [13]. In addition, rotating normalized eigenvectors and squared-loss mutual information (SMI) can be employed in the clustering process to obtain an optimal number of clusters [14,15]. Besides, the elbow method and the average silhouette method are the other examples to identify the optimal number of clusters in previous studies [15,16]. The elbow method identifies the optimal number of clusters by calculating sum of squared error for each number of clusters (k) from a range of k values. The average silhouette method computes the average silhouette values of genes for different values of k (number of clusters). Then, this method selects the optimal number of clusters that has the maximum average silhouette values from the range of k values. Optimization of the objective function and validation of clustering can improve the quality of clusters [11]. The optimization for the objective function of clustering can identify the best solution among a set of solutions. On the other hand, clustering validation is used to determine clusters in the data using an appropriate measurement [17]. Clustering validation can also evaluate the goodness of the clustering structure based on the given class labels [18]. Thus, validation is an essential step because it assists in the identification of which cluster is more informative compared to other clusters [19].

This paper focuses on reviewing existing computational methods on genes clustering using the notion of optimizing the objective function and validation.

#### **2. Gene Network Clustering Techniques**

In general, clustering can be categorized into partitioning, hierarchical, grid-based, and density-based techniques [11,17,20–22]. In Table 1, we show differences among categories of clustering techniques. The table also provides some information such as time complexity, computing efficiency, convergence rate, scalability, and initialization of cluster number. Partitioning clustering assigns the data objects into a number of clusters fixed beforehand. This technique identifies the number of centroids and assigns the objects to the nearest centroid. Hierarchical clustering groups the data based on the distance of the objects to form clusters. This technique can be either started with large data and aggregated into a small group or started from a small group of data and merged until all the

data are in one large group. Grid-based clustering divides each dimension of data space to form a grid structure. Density-based clustering separates the data according to the density of the objects. Traditionally, hierarchical, grid-based, and density-based techniques do not require cluster number as an input parameter [20,23]. In the view of Jain [17], hierarchical clustering is more versatile than partitioning clustering. With the discovery of clusters with good robustness and flexibility, grid-based and density-based techniques have been particularly useful [24]. They are also helpful for dealing with large spatial data and the proper use of expert knowledge. Grid-based and density-based techniques also aim to identify data densities and to split the data space into grid structures when looking for groupings [25]. Grid-based clustering techniques are more efficient compared to density-based clustering techniques; however, the use of summarized information makes these techniques lose effectiveness in cases where the number of dimensions increases [26].

**Table 1.** Differences among categories of clustering techniques.

In Table 2, we present several examples of clustering techniques done by previous researchers. The table also summarizes the advantages and the disadvantages of the techniques. From this table, k-means clustering is the most popular technique, even though k-means suffers from the shortcoming of identifying the number of potential clusters before the clustering setup.

**Table 2.** Examples of popular clustering techniques along with their advantages and disadvantages.

**Table 2.** *Cont.*

According to the reviewed clustering techniques in Table 2, this experimental work aims to investigate which category of clustering techniques would perform better in clustering genes. Gene expression data from the leukemia microarray study by Golub et al. [49] are used in this study. These data consist of 3051 genes, 38 tumor mRNA samples [27 acute lymphoblastic leukemia (ALL) and 11 acute myeloid leukemia (AML)] [50]. The clustering techniques investigated in this experimental work are k-means clustering (partitioning), agglomerative nesting (AGNES) (hierarchical), clustering in quest (CLIQUE) (grid-based), and density-based spatial clustering of applications with noise (DBSCAN) (density-based). The results in terms of percentage of accuracy are shown in Table 3. The experimental work was carried out using stratified ten-fold cross-validation and a support vector machine as a classifier. The selected clusters in Table 3 were validated based on silhouette width. According to Table 3, the CLIQUE was able to achieve the highest classification accuracy when applied on the leukemia dataset compared to other clustering techniques. In addition, Table 3 also shows several genes were biologically validated as prognostic markers for leukemia when PubMed text mining was used. Prognostic marker was commonly used to differentiate between good or poor disease outcomes [51]. This validation was done to show the relationship between genes and prognostic markers of leukemia [52]. Although CLIQUE achieved the best classification accuracy, the technique identified 67 genes as prognostic markers of leukemia out of 919 genes in the selected cluster. On the other hand, k-means had the best performance in identifying prognostic markers of leukemia (8%). The remaining techniques were able to achieve between 6% and 8% in determining the prognostic markers of leukemia over the number of genes in the selected clusters.

Note: k is the number of clusters to be selected; dimensions are divided into several equal-width intervals; density is the density threshold; minPts is the minimum size of clusters.

#### *2.1. Category 1: Partitioning Clustering*

Detection of clusters using partitioning clustering has low time complexity and high computational cost [53]. However, there are specific problems related to this technique. One of these problems is detecting clusters inappropriate for non-convex data. This could be because clustering techniques cannot spatially separate the data [54]. Other disadvantages are the need to initialize the number of clusters beforehand, and that the clustering result is sensitive to the intended number of possible clusters. Fuzzy C Means (FCM), k-means clustering, Partitioning Around Medoids (PAM), and Self-Organizing Maps (SOM) are all examples of partitioning clustering [9–12,27–38]. PAM is a variation of k-means clustering [55], and it is more robust in terms of accuracy compared to k-means clustering, for instance, when applied to classify cancer types [56,57].

#### *2.2. Category 2: Hierarchical Clustering*

Hierarchical clustering's scalability is relatively high in cluster detection [53]. One benefit of the method is that it can detect the hierarchical relationship among clusters easily. However, the major drawback associated with hierarchical clustering is the high computational cost. Agglomerative (bottom-up) and divisive (top-down) are the categories of hierarchical clustering [2,35,58]. The way of merging clusters and identification of the node levels can di fferentiate between agglomerative and divisive hierarchical clustering [58]. Agglomerative hierarchical clustering (AHC) combines the most adjacent pair of clusters, forming a group from bottom to top [59]. Several strategies of AHC are used to identify the distance between clusters, which are single linkage, complete linkage, centroid linkage, average linkage, Ward's method, and the probability-based method [25,58,59]. On the other hand, divisive hierarchical clustering is useful to identify clusters with di fferent densities and shapes [58,59]. The method starts from all samples in a group and then splits the samples into two sub-clusters, which are then divided into further sub-clusters and so on [58]. For AHC, node-level is the diameter of a new cluster formed at the splitting step. The node-level of divisive methods is to divide the groups based on their diameters. Agglomerative nesting (AGNES), EISEN clustering, and divisive analysis (DIANA) are examples of hierarchical clustering [19,34]. Garzón and González [19] used these clustering techniques to group similar genes before the step of the gene selection.

#### *2.3. Category 3: Grid-Based Clustering*

The design of grid-based clustering divides the entire data space into multiple, non-overlapping grid structures [24,59]. This method performs faster than density-based clustering. Grid-based clustering can benefit from dividing the data space into grids to reduce its time complexity [22,60]. CLIQUE, grid-clustering technique for high-dimensional very large spatial databases (GCHL), and statistical information grid (STING) are examples of grid-based clustering [39–43]. The GCHL technique can discover concave (deeper) and convex (higher) regions when applied in medical and geographical fields and by using the average eight direction (AED) technique [26,41]. However, both techniques struggle to identify complex clusters from high dimensional data. CLIQUE partitions the data space into cells and searches subspaces by counting the number of points in each cell [61]. Searching a suitable set of dimensions for each cluster can form the candidate subspace for the centroid of the cluster. Di fferent groups of points are clustered in di fferent subspaces [62].

#### *2.4. Category 4: Density-Based Clustering*

Usually, the regions contain points with high density in the data space, which makes density-based clustering mistake them as clusters [59]. Mechanisms of aggregation in density can characterize the clustering [45]. A significant advantage of density-based clustering is that it can discover di fferently shaped clusters and noise from data [22,24,63]. However, density-based clustering has a high runtime analysis to detect clusters [64]. DBSCAN, random walk, and Relative Core Merge (RECOME) are examples of density-based clustering [44–48]. Historically, a random walk uses the theory of Markov chain [48,65]. In most studies, the random walk has been used to infer and to optimize the structural properties of networks [65,66]. Much of the current literature on the random walk is on ranking the genes concerning their specific probabilities from high to low [67,68]. In literature, a random walk mostly uses the topological similarity in networks to identify genes with a similar disease.

#### **3. Optimization for Objective Function of Partitioning Clustering Techniques**

Optimization for objective function can improve the e fficiency of partitioning clustering techniques during initialization of the intended cluster number [11,33]. Swarm intelligence is widely used as the objective function for a clustering problem. The number of intended clusters can be predicted based on the typical search of the patterns [69,70]. Swarm intelligence can also be applied through maximizing or minimizing the objective function of clustering [69,71,72]. In most studies, swarm intelligence has been mostly used in the field of optimization [73,74].

Swarm intelligence refers to the collective behavior of decentralized, self-organized systems of living creatures. The swarm intelligence systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems [75,76].

For modeling the behavior of a swarm, the techniques are made up of animals and insects, such as bees, ants, birds, fishes, and so on [74,77]. Most recent studies used swarm intelligence to solve problematic real-world problems such as networking, tra ffic routing, robotics, economics, industry, games, etc. [73,74]. Hence, clustering techniques can benefit from swarm intelligence [74].

Swarm intelligence can optimize the objective function of clustering based on population and evolution strategies [11,33]. This function is usually used to determine the fitness of each particle since the community has a set of particles (known as a swarm), and each particle represents a solution. Table 4 compares the use of optimization in population and evolution strategies. Both optimization strategies are designed to imitate the best features in nature and produce a better quality of solution efficiently [78,79]. Previous studies have explored the use of optimization in a generation with more than 1000 populations before the convergence step, but it was not computationally e fficient [80].

**Table 4.** Comparison of the use of optimization between population and evolution strategies.

Table 5 summarizes existing techniques of swarm intelligence based on the strategies together with their usages. Xu et al. [81] found particle swarm optimization (PSO) is faster than both artificial bee colony (ABC) and genetic algorithm (GA) because PSO can perform without any complicated evolution. Previous studies have also shown some drawbacks of ABC, which are the limited ability of exploitation, slow convergence speed, and low-quality solutions [82]. In the review of GA and PSO algorithms, Gandomi et al. [79] identified the main purposes of these techniques, which solved significant problems faster.

#### *3.1. Strategy 1: Population-Based Optimization*

Population-based optimization is performed in terms of exploration and exploitation [69,100]. Exploration is the technique able to reach the best solution within the search space, while exploitation expresses the ability of the technique to reach a global optimum solution. Metaheuristic search can apply in this optimization for global optimal solutions using informative parameters. However, the optimization still has difficultly avoiding the problems of local minima and early convergence [11,33,101]. Several examples of population-based optimization are reviewed, which are ant colony optimization (ACO), ant lion optimization (ALO), firefly algorithm (FA), and particle swarm optimization (PSO) [11,33,70,71,77,81,83,86,89].

In the literature related to PSO, most previous studies used PSO because it does not have any complex evolution [81]. Fister et al. [70] found that FA is suitable for multi-modal optimization and fast convergence.

#### *3.2. Strategy 2: Evolution-Based Optimization*

Evolution-based optimization is involved in the processes of selection, recombination, and mutation [102]. The selection of evolution strategy fails to deal with changing environments, and it threatens the self-adaptation with its control parameters (internal model) [103,104]. For recombination processes (in terms of discrete and intermediate processes), it performs with control parameters on object variables, standard deviations, and rotation angles. The mutation mechanism makes the techniques evolve its control parameters (standard deviations and covariances). Evolution-based optimization can optimize the mathematical functions of the technique with continuously changeable parameters and extend to solve discrete optimization problems. This strategy can deliver a high quality of solutions and allows the technique to move toward better solutions in the search space with a population [105,106]. GA is one of the techniques using evolution strategy, which is commonly used for clustering based on selection, crossover, and mutation. In previous studies, most algorithms were derived from GA, such as evolution strategy (ES) and evolutionary programming (EP) [92]. The memetic algorithm is the extension of GA and includes local search optimization for problem-solving [97–99]. Genetic programming (GP), on the other hand, is the extension of GA that has been successfully applied and used to solve many problems [95,96]. Moreover, gene expression programming (GEP) uses the character of linear chromosomes and has been applied in symbolic regression and block stacking [93,94].

#### **4. Clustering Validation in Measurements**

Previous studies have evaluated the identified gene clustering in terms of distance [1]. If they are not within a distance regarding a specified gene in each experimental condition, then the specified gene is classified as an inactive gene. Otherwise, the specified gene is co-expressed.

Clustering validation can be measured in terms of internal and external criteria [17,18,100,107]. Table 6 summarizes the differences between internal and external validations. In general, internal criteria can assess the fitness between clustering structure and data. External criteria can measure the performance by matching cluster structure to prior information. As mentioned by Handl et al. [23], internal validation suffers from bias regarding clusters number and partitioning structure from data. The goal of internal validation is measured based on compactness and separation [18,107]. Compactness is defined as a measure of how close the objects are in a cluster based on variance. Separation measures either how a cluster is distinct or how well separated it is from other clusters. Handl et al. [23] held the view that external validation can suffer from biases in a partitioning according to cluster number and distribution of groups with class sizes.

Table 7 sets out examples of measurements to validate the quality of clusters. As can be seen from the table, previous studies commonly used Euclidean distance and silhouette width. In general, silhouette width can validate the clustering performance in terms of pairwise difference between and within cluster distances [18,107]. The maximum values of the silhouette width can identify an optimal number of clusters.

**Table 6.** The di fference in measurements between internal and external validations.

**Table 7.** Examples of previous studies in clustering validation.


**3. Modeling and vehicle routing problem**

Vehicle Routing Problem has been used in different applications and practices but with many constraints. The major constraints according to [16] are the network [25], demand and customers, depot locations, the type of vehicle used, and sometimes drivers. it is not possible to satisfy all the constraints in one model. In this case, some of the constraints can be reduced without loss of generality. When some customers left unserved due to this reason, in VRP, it called route failure [7]. In some cases, by introducing different penalties or priorities, the situations can be handled [6, 16].

The routing operations are performed to serve customers to start and end at one or more locations, located at the road [5]. Each location or depot is identified by the number and types of vehicles associated with it and by the number of goods it can deal with [23]. Transportation of goods is carried out by using a fleet of vehicles with fixed composition and size according to the necessities of the customers [25, 26].

The vehicle used in VRP is also one constraint in the model assumptions. According to [16], the vehicles may be characterized by the capacity of the vehicle, expressed as the maximum weight, or volume, or the number of pallets, the vehicle can carry. In most application areas, the least practice constraint is the drivers [16].

VRP is one of the most studied combinatorial optimization problems and is concerned with the optimal design of routes to be used by a fleet of vehicles to serve a set of customers [26]. VRP is not only focusing on the delivery and collection of goods but involves also in different application areas arising from the transportation and logistics system [16].

Different VRP models have been developed for different applications to study the Routing Problem [1, 22]. The most recent is the one that has been devoted to more complex variants of the VRP occasionally called "rich" VRPs. These are closer to the VRP models [23] and are used to design an optimum allocation system that can improve the level of their services [27]. Toth Paolo and Vigo Daniele [16] have reported that the use of computerized methods in the VRP problems has significantly saved the computational effort ranging from 5 to 20%.

VRP can be designed as a directed or undirected graph subject to the problem environment [21, 28]. In the case of classical VRP where customers or customer's demands are known in advance and the driving time between the customer and the *Linear Programming Optimization Techniques for Addis Ababa Public Bus Transport DOI: http://dx.doi.org/10.5772/intechopen.93629*

service time at each customer are also known, it can be defined and formulated in this section as a general VRP model [28].

It is designated in graph theory. To define the general model of VRP, let *G* ¼ ð Þ *v*, *A* be an asymmetric graph where *V* ¼ f0, 1, … , *n*Þ is a set of vertices representing cities or depot situated at the vertex 0, and *A* is the set of arcs. In every arcð Þ *i*, *j* ; *i* ¼ *j* is related to a nonnegative distance matrix *C* ¼ *Cij* � ). In some cases, *Cij* can be understood as a travel cost or as a travel time [21, 29, 30], it is often appropriate to substitute *A* by *E*. Where *E* is a set of undirected edges in the graph to represent asymmetric or undirected graph.

The common VRP comprises a set of at most *K* delivery or collection routes plan such that each route starts and ends at the depot, each customer is visited exactly once by exactly one vehicle, the total demand of each route does not exceed the vehicle capacity and the total routing cost is minimized. With all these assumptions, according to Stewart and Golden [31], a compact representation of VRP can be presented as follow:

$$\text{Minimize} = \sum\_{k=1}^{n} c\_{\vec{\eta}} \mathbf{x}\_{\vec{\eta}}^{k} \tag{1}$$

Subject to

**3. Modeling and vehicle routing problem**

*Concepts, Applications and Emerging Opportunities in Industrial Engineering*

handled [6, 16].

**Figure 2.** *VRP outputs.*

drivers [16].

**140**

and logistics system [16].

Vehicle Routing Problem has been used in different applications and practices but with many constraints. The major constraints according to [16] are the network [25], demand and customers, depot locations, the type of vehicle used, and sometimes drivers. it is not possible to satisfy all the constraints in one model. In this case, some of the constraints can be reduced without loss of generality. When some customers left unserved due to this reason, in VRP, it called route failure [7]. In some cases, by introducing different penalties or priorities, the situations can be

The routing operations are performed to serve customers to start and end at one or more locations, located at the road [5]. Each location or depot is identified by the number and types of vehicles associated with it and by the number of goods it can deal with [23]. Transportation of goods is carried out by using a fleet of vehicles with fixed composition and size according to the necessities of the customers [25, 26]. The vehicle used in VRP is also one constraint in the model assumptions. According to [16], the vehicles may be characterized by the capacity of the vehicle, expressed as the maximum weight, or volume, or the number of pallets, the vehicle can carry. In most application areas, the least practice constraint is the

VRP is one of the most studied combinatorial optimization problems and is concerned with the optimal design of routes to be used by a fleet of vehicles to serve a set of customers [26]. VRP is not only focusing on the delivery and collection of goods but involves also in different application areas arising from the transportation

Different VRP models have been developed for different applications to study the Routing Problem [1, 22]. The most recent is the one that has been devoted to more complex variants of the VRP occasionally called "rich" VRPs. These are closer to the VRP models [23] and are used to design an optimum allocation system that can improve the level of their services [27]. Toth Paolo and Vigo Daniele [16] have reported that the use of computerized methods in the VRP problems has signifi-

VRP can be designed as a directed or undirected graph subject to the problem environment [21, 28]. In the case of classical VRP where customers or customer's demands are known in advance and the driving time between the customer and the

cantly saved the computational effort ranging from 5 to 20%.

$$\sum\_{i,j}^{n} q\_i \mathbf{x}\_{ij}^k \le \mathbf{Q}, k = 1, 2, \dots \\ n \tag{2}$$

Where:

*cij* = The cost/distance of traveling, from *i* to *j:*

*xk ij* = 1 if vehicle k travels from *i* to *j*; 0 otherwise

*m* = The number of vehicles available

*Sm* = The set of all feasible solutions in the m-traveling salesman problem (m-TSP)

*qi* = The amount demanded at location *i*

*Q* = The vehicle capacity.

In many realistic cases, the cost or the distance matrix satisfies the triangular inequality such that Eq. (3):

$$c\_{ik} + c\_{kj} \ge c\_{ij}; \forall\_{i,j,k} \in V. \tag{3}$$

In the VRP models, a differentiation has to be made between symmetric Eq. (4) and asymmetric Eq. (5). Solution approaches can vary significantly between these two cases [30].

$$A = \{(i, j) \mid i \in V, j \in V, i = j\} \tag{4}$$

$$A = \{(i, j) \mid i \in V, j \in V, i < j\} \tag{5}$$

In the real world, however, the general VRP model is enhanced by various constraints or side-constraints, [5]. The constraints can be such as vehicle capacity or time interval in which each customer has to be served [16], revealing the Capacitated Vehicle Routing Problem (CVRP) [20] and the Vehicle Routing Problem with Time Windows (VRPTW) [18, 21].

VRP models, whether they are used for public transport or transit, as well as distribution and logistics, they share certain mutual features. That is, they focus on the optimization of cost (working cost), distance covered, waiting time, etc.

occurs after the morning peak and the evening peak shift in which 20% and 5% for

**Shift Time interval Duration (minute) Demand proportion (%)**

Morning peak hours 6:15–9:30 195 40% First off-peak hours 9:30–15:30 360 20% Evening peak hours 15:30–19:30 240 35% Second off-peak hours 19:30–21:00 90 5% **Total** 870 100

*Linear Programming Optimization Techniques for Addis Ababa Public Bus Transport*

The researchers have investigated the existing operating systems of the ACBSE, and a linear programming (LP) model was developed. The model helps to achieve a solution for the scheduling problems of the enterprise. The LP model developed in this study is a new approach in the literature of VRP that considers the trips made by two different types of busses to address the demand distribution of passengers in

The LP model was fitted with data. To achieve its object, the model was coded and run using the General Algebraic Modeling System (GAMS) optimization software. The GAMS code was running within 0.15 seconds on 3.10 GHz, Window7 Home Premium, 4GB RAM, and core (i5) Dell Personal computer (Optiplex 790-Model). The resulting solution of the LP-model was first the number of trips per route per shift for each type of bus. After obtaining the solution, then, the number of trips was

translated into the number of busses per route per shift for each type of bus.

total distance traveled, and the different operating costs.

to the ACBSE problem with data collected from the enterprise.

The LP model was investigated and validated to identify potential areas of improvement in the scheduling and assignment problem of ACBSE. It is also used to determine the number of busses to be assigned in a given route at a given shift that can address the demand distribution of passengers each shift for the 93 routes. The results of the LP-model were validated by comparing the current schedule and performances of the enterprise. The validation was made using four performance measuring parameters namely bus utilization, the total number of trips made, the

In order to develop the LP mode, leti be the number of trips per route per shiftj made by bus type-I is represented by "*xij*" and by that of bus type-II is marked by "*yij*". The model is developed as a general model, at this section, then later is fitted

the demand proportion is allocated respectively.

*Demand proportion and duration of each shift.*

*DOI: http://dx.doi.org/10.5772/intechopen.93629*

**6. Model development**

**Table 2.**

93 routes in four working shifts.

**6.1 Running the model**

**6.2 Model validation**

**7. Develop the LP model**

**143**

**Table 1.**

*The fixed number of busses assigned on selected routes (as of 2011).*


**3. Results**

#### *3.1. Impact of TWSC on ET Estimate*

#### 3.1.1. ET Estimated by Ignoring TWSC

Figure 3 shows the monthly mean of ETWB following Equation 1, ETPQ, *P*, *Q*, and TWSC for 2003–2015 in the nine catchments. The negative ETWB values in January and February in the SRB and December in the Haihe River Basin (HRB) may result from the uncertainties of in situ precipitation and TWSC [13,40]. The TWSC has a significant impact on ET estimates in most catchments. The deviation of the monthly mean of ETWB and ETPQ reaches 34.2 mm/month in June (accounting for 52.9% of ETWB) in the Upper Yangtze River Basin (UYRB). The deviations between ETWB and ETPQ range from 6.7 to 37.2 mm/month for twelve months in the Middle Yangtze River Basin (MYRB), and its RMSE accounts for 37.1% of variations of ETWB. The RMSEs are computed following Equation 2. In the YeRB and SRB, the deviations between ETWB and ETPQ are small, with their RMSEs between ETWB and ETPQ reaching only 6.4 and 8.7 mm/month, respectively. In the MRB, the RMSE between ETWB and ETPQ shows the maximum value, i.e., 27.2 mm/month, accounts for 33.5% of variations of monthly mean ETWB.

**Figure 3.** Monthly mean of ETWB (blue curves), ETPQ (red lines), P, Q (purple lines), and TWSC (green lines) for 2003–2015 in the nine exorheic catchments. The histograms represent the monthly precipitation (P). The error bars show the uncertainties of monthly mean ETWB. (**a**)–(**i**) corresponding to the nine exorheic catchments in China. (**a**): UYRB; (**b**): MYRB; (**c**) YeRB; (**d**): SRB; (**e**): PRB; (**f**): LRB; (**g**) HRB; (**h**): HuRB; (**i**): MRB.

The annual ETWB and ETPQ estimates are shown in Figure 4; the mean annual PCMDC, ETWB, and ETPQ results are shown in Table 3. In the UYRB, the largest deviation of annual ET between ETWB and ETPQ is only 27.5 mm/yr in 2014, and the RMSE between ETWB and ETPQ only makes up 2.6% of the mean annual ET. In the SRB, the TWSC has a large impact on annual ET, large deviations between ETWB and ETPQ occur almost all the years, and the proportion of the RMSE accounting for the mean annual ETWB reaches 11.5%. In the Minjiang River Basin (MRB), the RMSE represents 12.7% of the mean annual ET, with the largest deviation (291.8 mm/yr, 39.1% of total ETWB in this year) occurring in 2003.

**Figure 4.** Annual ETWB and ETPQ from 2003–2015. (**a**)–(**i**) corresponding to the nine exorheic catchments in China.

**Table 3.** The mean annual precipitation from CMDC, mean annual ETWB and ETPQ estimation from GRACE in the exorheic catchments of China from 2003–2015, with one standard deviation (unit: mm/yr).

#### 3.1.2. Impact of Different GRACE Solutions on ET Estimate

The monthly mean TWSC from different GRACE solutions is shown in Figure A1, where their mean TWSC is the arithmetical mean from all TWSC estimates for the corresponding calendar month. Note that the TWSC derived from CSR-M compare favorably with the mean TWSC (Figure A1), thus the CSR-M TWSC is used for the estimate of ETWB in this study. The TWSC from CSRT-GSH.sf show significant differences among the four TWSC results, as they exaggerate the monthly mean TWSC in the UYRB, MYRB, YeRB, SRB, and PRB, and the differences may result from the scaling factor derived from CLM4.5 [43]. The spatial distribution of scaling factors is checked in our study (not shown), and the spatial variability of scaling factors varies greatly in the basins, indicating exaggerated TWSC. The maximum deviations are calculated between each two monthly mean TWSC estimates, which range from 10.7 to 35.6 mm/month, the deviations occur in the YeRB (10.7 mm/month) and MRB (35.6 mm/month) (Figure A1c,i), respectively. As the area of MRB is the smallest, and with the most abundant precipitation, it is understandable that the MRB shows the largest deviation of TWSC. The large deviation of TWSC between JPL-M.dsf and other GRACE solutions in the HRB and MRB (Figure A1g,i) may result from the processing strategy and coarse resolution in the spatial of JPL-M since the areas of the two basins are small [54].

The annual ET estimates based on different GRACE solutions are shown in Figure A2. The RMSEs among ETCSR–M (=ETWB), ETJPL–M.dsf, ETCSRT–GSH.sf, and ETCSR–DDK4 are understandably less than those RMSEs between ETWB and ETPQ, and their interannual fluctuations are more consistent than that of ETPQ. We compute the standard deviations (STDs) between the four ET estimates from di fferent GRACE solutions for every single year, and the results show that the max STD is only 51.2 mm/yr, occurring in the MRB. The mean STD for the years from 2003–2015 in the corresponding catchment is also computed, ranging from 9.7 to 27.1 mm/yr (accounting for 1.8–3.9% of annual ETWB), with the least occurring in the YeRB and the largest occurring in the MRB. In three catchments, the max STDs occur in 2003 in the PRB, the Huaihe River Basin (HuRB), and the MRB, which are located in Southeast China. In the other three catchments, the max STDs appear in 2011, which are the YeRB, SRB, and Liaohe River Basin (LRB), in North China.

#### *3.2. Comparison of Di*ff*erent ET Products*

Figure 5 shows the monthly mean of ET estimates from di fferent ET products. Their mean annual cycles are similar among all the catchments. In the humid catchments: in the UYRB, the other three ET products overestimate the ET compared with ETWB for all months except December, the maximum deviation exists in July, which has the most precipitation (Figure 5a). In the MYRB, other ET estimates are bigger than ETWB estimates for all months except November when the ETWB increases to respond to increased precipitation. In the PRB, the mean of ETWB in July is less than that in June and August, and the mean of ETWB in October is also less than September and November (Figure 5e). In the HuRB, ETWB shows a rapid increase response for sharply increased precipitation in July (Figure 5h), while the three other ET results do not catch it. The ETWB also can capture the irregular monthly mean precipitation changes from June to December in the MRB (Figure 5i). In the semihumid and semiarid catchments: the two versions of GLDAS both show the maximum deviation in September with ETWB in the YeRB. Most months of ETGLDAS–2.1 are more than other ET estimates in the LRB. During the intense irrigation period of April and May, the ETWB is significantly greater than other ET estimates in the HRB. From the above, these ET results all show similar annual cycles, while ETWB can capture some irregular variations in monthly precipitation.

**Figure 5.** Monthly mean of ET estimates in the exorheic catchments of China. The error bars show the uncertainties of monthly mean ETWB. (**a**)–(**i**) corresponding to the nine exorheic catchments in China.

The maximum RMSEs between the monthly mean of ETWB and other ET results are in the MRB, which are 27.5 (vs ETGLDAS–1), 26.3 (vs ETGLDAS–2.1), and 24.7 (vs ETGLEAM) mm/month (Table A1). In the YeRB, the RMSEs are the least, which are 7.2, 7.5, and 13.0 mm/month. We also compute the

proportion of the RMSEs accounting for an average of the monthly mean of ETWB. The proportions in the UYRB show the maximum values, which are 50.5% (vs ETGLDAS–1), 43.3% (vs ETGLDAS–2.1), and 43.1% (vs ETGLEAM). The HuRB experienced the minimum proportions, which are 20.4%, 22.3%, and 25.9%, respectively.

The annual ET from different sources is illustrated in Figure 6, which shows huge gaps among different ET estimates. In terms of the humid catchments: In the UYRB and MYRB, it is obvious that other annual ET estimates are all larger than ETWB. Their mean deviations between ETWB and ETGLEAM reaching 144.7 mm/yr (31.3% in mean annual ETWB) and 88.0 mm/yr (12.8% in mean annual ETWB) in the two catchments (Figure 6a,b). In the PRB, ETGLDAS–1 and ETGLEAM both overestimate the annual ET, and ETGLDAS–2.1 shows different interannual variations with respect to ETWB (Figure 6e). In the HuRB, all the ET estimates capture the drop of ET in 2011 due to reduced precipitation (Figure A3h), but there is some discrepancy among mean annual ET. In the MRB, the ET results show large differences in the interannual variations. The ETGLDAS–1 even verges on 1100 mm/yr after 2012. Concerning the semihumid and semiarid catchments: In the YeRB, the ETWB is consistent with ETGLDAS–2.1 except for the years 2006 and 2009, while ETGLEAM underestimates the annual ET for all the years. In the SRB, the ETGLDAS–2.1 is significantly greater than the other annual ET. Nevertheless, the ETGLEAM is close to ETWB. In the LRB, the ETGLDAS–1 is close to ETWB in mean annual ET, and their interannual variations are similar. In the HRB, ETGLDAS–2.1 is closest to ETWB. ETGLEAM somewhat underestimates the annual ET for the other three results. For the two catchments in Northeast China (i.e., SRB and LRB, Figure 6d,f), both ETGLDAS–2.1 results overestimate the annual ET. Additionally, the four ET results show consistent interannual changes in most catchments.

**Figure 6.** Annual ET from different products in the exorheic catchments of China. The dotted lines with different colors corresponding to their mean annual ET. (**a**)–(**i**) corresponding to the nine exorheic catchments in China.

#### *3.3. Comparison of Di*ff*erent Precipitation and Runo*ff *Inputs for ET Estimation*

In all the catchments, the interannual fluctuations of precipitation from different sources show similar patterns (Figure A3), while the mean annual precipitation shows some differences (Figure 7). In the MYRB and YeRB, the PCMDC is higher than PGLDAS–1, which is similar to the comparison from Lv et al. [55] in corresponding regions. In the SRB and LRB (Northeast China), PGLDAS–2.1 is prominently larger than the other three precipitation sources (Figure A3d,f, and Figure 7b). Caution should be taken when using the PGLDAS–2.1 in the two catchments. It should be noted that the annual

PMSWEP is the least for all the catchments (Figure A3), and mean PGLDAS–1 are all less than those of PCMDC (Figure 7a).

**Figure 7.** Deviations of annual ET, precipitation, and runoff between (**a**): WB and GLDAS–1; (**b**): WB and GLDAS–2.1; (**c**): WB and GLEAM. The blue curves with triangles represent the deviations between annual ET and other ET estimates. The red curves with dots represent the deviations between PCMDC minus QRSBC and PGLDAS minus the QGLDAS. The purple lines with rhombus points represent the deviations between PCMDC and other precipitation forcing data. The green lines with square points represent the deviations of runoff between QRSBC and two versions of QGLDAS.

QRSBC, QGLDAS–1, and QGLDAS–2.1 show larger discrepancies than that for precipitation. The comparison of annual runoff from 2003–2015 can be found in Figure A4. Since the runoff is modeled results, it faces more uncertainties than precipitation. As the similar results showed in YRB (UYRB and MYRB) and YeRB in Lv et al. [55], the in situ runoff was significantly larger than that from QGLDAS–1. The interannual variations of runoff from different sources show similar patterns in most catchments except in the YeRB and HRB, which experienced small amounts of runoff. In all the catchments, QGLDAS–2.1 is larger than that from QGLDAS–1, and they are closer to QRSBC in most catchments, presumably due to some modification for GLDAS-2.1 [56].

To explore the impact of precipitation and modeled runoff (GLDAS Noah LSM outputs) on ET estimates, we analyze the difference in both sides of the water balance equation. We first compute the deviation between mean annual ET, precipitation, runoff, and precipitation minus runoff for 2003–2015 (Figure 7). In the UYRB, the mean annual deviation between ETWB and ETGLDAS–1 reaches 200.9 mm/yr, the mean annual deviation between PCMDC minus QRSBC (expressed as P-Q) and PGLDAS–1 minus QGLDAS–1 are close to the deviation of ET (Figure 7a). This deviation is mostly contributed by the deviation of runoff (−241.6 mm/yr). As Figure 7a shows, the deviations of P-Q are close to the deviations of ET in all the catchments. For the water balance (WB) with GLDAS-2.1 (Figure 7b), the deviations of P-Q are close to the deviations of ET in all the catchments except PRB. In the PRB, the deviation of mean annual ET is only 5.3 mm/yr, while the deviation of mean annual precipitation reaches −27.9 mm/yr, and the deviation of mean annual runoff is −4.34 mm/yr (Figure 7b). Based on the water balance method, we only assess the precipitation forcing data variable for GLEAM ET. As Figure 7c shows, in the YeRB, LRB, and HRB, the deviation of precipitation may explain the most differences between annual ETWB and ETGLEAM. In other catchments, it is somewhat opposite between the deviation of annual precipitation and ET.

#### *3.4. Uncertainty Estimation Results*

Uncertainties of TWSC, PCMDC, QRSBC, and ETWB are shown in Table 4. Large uncertainties of TWSC appear in the PRB, HuRB, and MRB, which are more than 30 mm/month. The large uncertainties of TWSC may result from the small study area (HuRB and MRB) and large variations of TWSC caused by abundant precipitation (PRB and MRB). In three catchments (MYRB, PRB, and MRB), their annual precipitation is more than 1300 mm/yr, and their uncertainties of precipitation are also more than 11 mm/month. The uncertainties of runoff are similar to those of precipitation except the YeRB, LRB, and HRB, where the water use is intense and the runoff is little.

**Table 4.** The uncertainties of monthly TWSC, precipitation, runoff, ET, monthly mean ET and annual ET from 2003–2015 (unit: mm/month and mm/yr).

From Table 4, we can conclude that the uncertainties of monthly ET are mainly from TWSC, which is similar to the conclusions in Long et al. [1] and Pan et al. [13]. Almost in all the catchments, the uncertainties of TWSC are two times or even three times larger than the uncertainties of PCMDC, and they are also much larger than the uncertainties of QRSBC. The uncertainties of annual ET are all larger than 45 mm/yr, while the uncertainties are mainly from the uncertainties of annual precipitation.


**2.2 Solid-phase microextraction (SPME)**

Miniaturization of analytical processes into microchip platforms designed for micro total analytical systems is a new and rapidly developing field. Solid-phase microextraction (SPME) is a modern technique that consists in direct extraction of the analytes with the use of a small-diameter fused silica fiber coated with an adequate polymeric stationary phase [10]. On the other hand, in two designs of SPME, a thin layer of sorbent is coated on the outer surface of fibers (fiber design), and the inner surface of a capillary tube (in-tube design) is covered. The fiber design can be used as an interface in both GC and HPLC, but in-tube design has been just applied as an easier approaching interface with HPLC. In fiber design, a thin film of liquid polymer or mixture of a solid sorbent with a liquid polymer has covered on the surface of a fused silica core fiber.

The properties of extraction process, by SPME, are as follows [2]:

SPME can be conducted in three modes [2]:

The SPME procedure is performed through two separate steps:

As discussed before, SPME has been introduced as a solventless extraction method, in which a fused silica fiber has been coated with a *thin* film of sorbent, to separate volatile analytes of interest from a matrix sample. Usually, the fiber is placed into a syringe needle which is protected for easy penetration into the sample and GC vial septa. Analyte extraction and analysis depend on the fiber type and its thermal desorption into a GC inlet. There are two approaches to SPME sampling of volatile organics:

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*Sample Preparation Techniques for Gas Chromatography DOI: http://dx.doi.org/10.5772/intechopen.84259*

immersion process.

Choosing between direct immersion and headspace SPME is relatively straightforward. Direct immersion SPME is warranted for liquid and solution samples which are used in solid-phase and liquid-liquid extraction methods. Headspace SPME is considered for extraction of volatile specious, with normal boiling point less than 200°C, from solid and liquid samples. For higher boiling point analytes, direct immersion SPME is probably necessary. Headspace is more preferred for especially complex or dirty samples due to fouling the fiber coating in a direct

SPME fibers have different coatings, and there is no single coating for extraction and separation of all volatile organics from a sample. Therefore, different types of coatings with different polarities are applied on SPME fibers. Currently, three types of fiber coatings are commercially available: (1) nonpolar, (2) semipolar, and (3) polar coatings. There are several SPME fiber coatings commercially available. These range in polarity from polydimethylsiloxane (PDMS), which is nonpolar, to "*carbowax-divinylbenzene*" (CW-DVB), which is highly polar. The nonpolar fibers are more commonly used for headspace SPME as the majority of volatile analytes tend to be nonpolar or slightly polar. The advantage of using different fiber polarities is that using a matched polarity fiber, as polar coated for a polar analyte, makes extraction selectivity be enhanced. On the other hand, there is less of a chance of extracting interfering compounds along with the analyte of interest, and an organic matrix is not a problem.

Fiber coating thickness is a second parameter that should be considered to select a fiber for both direct immersion and headspace SPME. The PDMS coating is commercially in hand in three thicknesses: 100, 30, and 7 μm. The 100-μm-thick fiber is generally applied for highly volatile compounds or when a larger organic matrix

Once the fiber is chosen, extraction conditions must be optimized. There are many variables such as (1) extraction time, (2) sample volume, (3) agitation,

1.As extraction time is increased, a plateau in peak area is reached. So, this

represents the time required for the system to reach the equilibrium and is the optimized extraction time. Most headspace SPME methods are completed in less than 5 min, while direct immersion SPME may require more than 30 min.

2.The sensitivity of a SPME method is proportional to the number of moles of analyte recovered from the sample. As the sample volume increases, analyte recovery increases too. But in very dilute samples, larger sample volume results

3.In many extraction methods, the agitation method affects both the extraction time and efficiency. In direct immersion SPME, agitation is often accomplished with a magnet and a stirrer. So, the stirring rate should be optimized and constant during the extraction process. Also, the fiber should be off-centered in the vial so that liquid is moving quickly around it. Agitation can also be achieved by physical movement of the fiber or by movement of the sample vial and/or sonication.

4.Extraction temperature can also be an important factor, especially in headspace SPME analyses. Despite of GC headspace analysis, increasing the

5.By modifying the sample matrix, the extraction recovery can be improved. There are two ways to modify the sample matrix: (a) adjusting the sample pH

temperature in SPME makes the extraction sensitivity decrease.

volume is used. The 7-μm-thick fiber is used for less volatile compounds.

Also, direct immersion SPME is highly matrix dependent.

in slower kinetics and higher analyte recovery.

(4) temperature, and (5) sample matrix.

### *Sample Preparation Techniques for Gas Chromatography DOI: http://dx.doi.org/10.5772/intechopen.84259*

*Gas Chromatography - Derivatization, Sample Preparation, Application*

is easily automated [11].

samples (i.e., single cells).

sample headspace for SPME sampling [13].

SPME can be conducted in three modes [2]:

desorption processes, respectively.

(disadvantage).

8.SPME is compatible with chromatographic analytical systems, and the process

9.In conventional SPE, the analyte can be extracted exhaustively (>90%) into the solid phase from a sample medium, while small amount of sample (1–2%) has been introduced into the analytical equipment. But in SPME, although the analyte extraction is non-exhaustive and its small portion has been extracted into the solid phase (about 2–20%), all sample can be injected into the analytical instrument. So, besides high concentration ability and selectivity, SPME possesses another advantage in the ability of using trace analyses [12].

10. SPME facilitates unique investigations, such as extraction from very small

11. In SPME, changes in the sample matrix may affect quantitative results

12. SPME can be used to extract semivolatile organics from environmental

waters and biological matrices as long as the sample is relatively clean. Since extraction of semivolatile organics by SPME from dirty matrices is difficult, one strategy for doing it is to heat the sample to drive the compound into the

1.Direct extraction, in which the coated fiber is immersed in the aqueous sample.

2.Headspace configuration, for sampling air or the volatiles from the headspace above an aqueous sample. However, headspace techniques are more applicable

3.Membrane protection configuration, in which the coated fiber is protected with a membrane, for analyzing the analytes in too much dirty samples.

a.At first, the solid sorbent is immersed into the sample medium for a specific period of time. This step is used for both of fiber and in-tube designs.

b.Then, the solid sorbent, either fiber or in-tube design, is interfaced with GC and HPLC (or capillary electrophoresis) instruments for thermal and solvent

As discussed before, SPME has been introduced as a solventless extraction method,

1.Direct sampling. In this approach, the fiber is placed directly into the sample

2.Headspace sampling. In this approach, the fiber is placed in the headspace of

in which a fused silica fiber has been coated with a *thin* film of sorbent, to separate volatile analytes of interest from a matrix sample. Usually, the fiber is placed into a syringe needle which is protected for easy penetration into the sample and GC vial septa. Analyte extraction and analysis depend on the fiber type and its thermal desorption into a GC inlet. There are two approaches to SPME sampling of volatile organics:

to volatile organics than to the semivolatile organic compounds.

The SPME procedure is performed through two separate steps:

**50**

matrix.

the sample.

Choosing between direct immersion and headspace SPME is relatively straightforward. Direct immersion SPME is warranted for liquid and solution samples which are used in solid-phase and liquid-liquid extraction methods. Headspace SPME is considered for extraction of volatile specious, with normal boiling point less than 200°C, from solid and liquid samples. For higher boiling point analytes, direct immersion SPME is probably necessary. Headspace is more preferred for especially complex or dirty samples due to fouling the fiber coating in a direct immersion process.

SPME fibers have different coatings, and there is no single coating for extraction and separation of all volatile organics from a sample. Therefore, different types of coatings with different polarities are applied on SPME fibers. Currently, three types of fiber coatings are commercially available: (1) nonpolar, (2) semipolar, and (3) polar coatings. There are several SPME fiber coatings commercially available. These range in polarity from polydimethylsiloxane (PDMS), which is nonpolar, to "*carbowax-divinylbenzene*" (CW-DVB), which is highly polar. The nonpolar fibers are more commonly used for headspace SPME as the majority of volatile analytes tend to be nonpolar or slightly polar. The advantage of using different fiber polarities is that using a matched polarity fiber, as polar coated for a polar analyte, makes extraction selectivity be enhanced. On the other hand, there is less of a chance of extracting interfering compounds along with the analyte of interest, and an organic matrix is not a problem.

Fiber coating thickness is a second parameter that should be considered to select a fiber for both direct immersion and headspace SPME. The PDMS coating is commercially in hand in three thicknesses: 100, 30, and 7 μm. The 100-μm-thick fiber is generally applied for highly volatile compounds or when a larger organic matrix volume is used. The 7-μm-thick fiber is used for less volatile compounds.

Once the fiber is chosen, extraction conditions must be optimized. There are many variables such as (1) extraction time, (2) sample volume, (3) agitation, (4) temperature, and (5) sample matrix.

or its salt content and (b) dissolving the solid sample in a proper solvent like water or a strongly aqueous solution. In similarity with classical liquid-liquid extractions, modifying the pH can change the extraction behavior.

Also, the SPME-GC injection system must be optimized. When the SPME interfacing GC is used, the GC injection system is typically done under splitless conditions. Since there is not any solvent and accommodation of the sample solvent, there is no need of specific small internal diameter glass liners, which are often used [2]. The main consideration is to transfer the analytes in the shortest possible time out of the fiber coating and in focusing the analytes into the sharpest bands possible. For semivolatile compounds, inlet optimization is very simple, and classical splitless inlet conditions can be used. A typical condition would be a temperature of about 250°C; a sufficient head pressure can maintain optimum GC column flow and an initial column temperature at least 100°C below the normal boiling point of the analyte. For volatile analytes, the optimization of the inlet is more difficult. So, keeping the initial column temperature at enough low level to refocus these analytes is often not possible, without cryogenics. The inlet must therefore be optimized to provide the fastest possible desorption and transfer to the GC column, while the GC column is maintained as cool as possible to achieve any focusing that is possible.

There are some examples for applying SPME as sample preparation step before GC detection of different analytes in a variety of samples. Some of them are pointed below.

Goncalves et al. studied solid-phase microextraction-gas chromatography- (tandem) mass spectrometry as a tool for pesticide residue analysis in water samples at high sensitivity and selectivity with confirmation capabilities. In this study, for SPME extraction a *"poly(dimethylsiloxane)-poly(divinylbenzene)"*-coated fiber was selected [14].

Yonamine et al. studied solid-phase microextraction-gas chromatography-mass spectrometry and headspace-gas chromatography of tetrahydrocannabinol (THC), amphetamine, methamphetamine, cocaine, and ethanol in saliva samples. In this study, at first saliva samples were submitted to an initial headspace procedure for ethanol determination by a GC-flame ionization detector. Then, two consecutive fiber solid-phase microextractions were carried out: THC was extracted by submersing a polymeric fiber, and amphetamine, methamphetamine, and cocaine were subsequently extracted after alkalinization [15].

### **2.3 Molecularly imprinted polymer (MIP) adsorbent in SPE and SPME**

Molecularly imprinted polymer (MIP) is an alternative kind of sorbent which can be applied for *solid-phase extractions* and solid-phase microextractions. MIP is a polymeric sorbent which is produced in the presence of a target analyte, as a molecular template. Once the template is washed and removed through the polymer, some selective recognition sites has been remained in the polymeric sorbent for selective extraction of the analyte target. By using MIP as the sorbent, the surface contact area between the sorbent and the sample is much greater than in the coated fiber or coated inner surface tubing SPME procedures described earlier [2]. MIP inherent advantages include reusability, simplicity, low cost, high affinity and selectivity for target molecule, and physical and chemical stability over a wide range of experimental conditions and solvents [16].

Some of those applications are discussed below.

Djozan et al. studied preparation and evaluation of solid-phase microextraction fibers based on monolithic molecularly imprinted polymers for selective extraction of diacetylmorphine and analogous compounds. The main purpose of this research

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*Sample Preparation Techniques for Gas Chromatography DOI: http://dx.doi.org/10.5772/intechopen.84259*

was to develop a technique for fabrication of a monolithic and robust solid-phase microextraction on the basis of MIP interfacing with GC and GC-MS analysis for selective extraction and structural analysis of diacetylmorphine, respectively. On the other hand, a very simple approach has been developed for the fabrication of SPME fiber from diacetylmorphine-imprinted polymers which were subsequently used for

extraction of diacetylmorphine and then analyzed with GC and GC-MS [16].

syringe can be applied several times for different samples [17].

tative extractions can be achieved than those of SPME procedures.

Some of SBSE applications with GC analysis are discussed below.

(PDMS) prepared by sol-gel method was used as extraction phase [19].

**2.4 Stir bar sorptive extraction (SBSE)**

acid anhydride as acetylation reagent [18].

the potential to prepare thermally stable coatings [19].

approaching equilibrium.

extraction (HSSE) [2].

**2.5 Soxhlet extraction**

Rehim studied new trend in sample preparation [17]: online microextraction in packed syringe for liquid and gas chromatography applications and determination of local anesthetics in human plasma samples using gas chromatography-mass spectrometry. In this study, local anesthetics in plasma samples were used as model substances, and the method was developed and validated for microextraction in packed syringe (MEPS) online with GC-MS. MEPS and SPE procedures have some differences. In MEPS method, the sorbent packing is placed directly into the syringe, not into a separate column, as it is done in SPE. So, a separate robot does not need for applying the sample into the separation phase, as it is done in SPE. Also, the packed

Stir bar sorptive extraction (SBSE) is used for the extraction of trace amounts of organics from aqueous food, environmental, and biological samples. A stir bar has been covered with a sorbent phase and placed into the sample solution to separate the analyte of interest. Although SBSE procedures are not exhaustive, more quanti-

The coated stir bar is usually used to stir the sample solution for a specialized period of time, depending on the sample volume and stirring speed, until

SBSE improves on the low concentration capability of in-sample solid-phase microextraction (IS-SPME). Also, SBSE can be applied to headspace sorptive

Nakamura et al. studied simultaneous determination of alkylphenols and bisphenol A in river water by stir bar sorptive extraction with in situ acetylation and thermal desorption-gas chromatography-mass spectrometry. In this study, SBSE was used for the sample enrichment of seven alkylphenols and bisphenol A in river water. Also, in situ derivatization in aqueous samples was performed with acetic

The extraction phase on the stir bar in SBSE is critical for the performance of both extraction and thermal desorption. The sol-gel coating technology possesses

Guan et al. studied determination of organophosphorus pesticides in cucumber and potato by stir bar sorptive extraction. In this study, organophosphorus pesticides (OPPs) in vegetables were determined by SBSE and capillary GC with thermionic specific detection (GC-TSD). Hydroxy-terminated polydimethylsioxane

Soxhlet extraction was accepted as a standard method for the extraction of semivolatile and nonvolatile organics by the US Environmental Protection Agency (EPA 3540C0) and also the extraction of fat in cacao products by the Association of Official Analytical Chemists (AOAC 963.15). Soxhlet extraction was *introduced by Franz Ritter von Soxhlet in 1879*. It had been the most extensive applied technique

#### *Sample Preparation Techniques for Gas Chromatography DOI: http://dx.doi.org/10.5772/intechopen.84259*

*Gas Chromatography - Derivatization, Sample Preparation, Application*

or its salt content and (b) dissolving the solid sample in a proper solvent like water or a strongly aqueous solution. In similarity with classical liquid-liquid

extractions, modifying the pH can change the extraction behavior.

Also, the SPME-GC injection system must be optimized. When the SPME interfacing GC is used, the GC injection system is typically done under splitless conditions. Since there is not any solvent and accommodation of the sample solvent, there is no need of specific small internal diameter glass liners, which are often used [2]. The main consideration is to transfer the analytes in the shortest possible time out of the fiber coating and in focusing the analytes into the sharpest bands possible. For semivolatile compounds, inlet optimization is very simple, and classical splitless inlet conditions can be used. A typical condition would be a temperature of about 250°C; a sufficient head pressure can maintain optimum GC column flow and an initial column temperature at least 100°C below the normal boiling point of the analyte. For volatile analytes, the optimization of the inlet is more difficult. So, keeping the initial column temperature at enough low level to refocus these analytes is often not possible, without cryogenics. The inlet must therefore be optimized to provide the fastest possible desorption and transfer to the GC column, while the GC column is maintained as cool as possible to achieve any focusing that is possible. There are some examples for applying SPME as sample preparation step before GC detection of different analytes in a variety of samples. Some of them are pointed

Goncalves et al. studied solid-phase microextraction-gas chromatography- (tandem) mass spectrometry as a tool for pesticide residue analysis in water samples at high sensitivity and selectivity with confirmation capabilities. In this study, for SPME extraction a *"poly(dimethylsiloxane)-poly(divinylbenzene)"*-coated fiber was

Yonamine et al. studied solid-phase microextraction-gas chromatography-mass spectrometry and headspace-gas chromatography of tetrahydrocannabinol (THC), amphetamine, methamphetamine, cocaine, and ethanol in saliva samples. In this study, at first saliva samples were submitted to an initial headspace procedure for ethanol determination by a GC-flame ionization detector. Then, two consecutive fiber solid-phase microextractions were carried out: THC was extracted by submersing a polymeric fiber, and amphetamine, methamphetamine, and cocaine were

**2.3 Molecularly imprinted polymer (MIP) adsorbent in SPE and SPME**

Molecularly imprinted polymer (MIP) is an alternative kind of sorbent which can be applied for *solid-phase extractions* and solid-phase microextractions. MIP is a polymeric sorbent which is produced in the presence of a target analyte, as a molecular template. Once the template is washed and removed through the polymer, some selective recognition sites has been remained in the polymeric sorbent for selective extraction of the analyte target. By using MIP as the sorbent, the surface contact area between the sorbent and the sample is much greater than in the coated fiber or coated inner surface tubing SPME procedures described earlier [2]. MIP inherent advantages include reusability, simplicity, low cost, high affinity and selectivity for target molecule, and physical and chemical stability over a wide range

Djozan et al. studied preparation and evaluation of solid-phase microextraction fibers based on monolithic molecularly imprinted polymers for selective extraction of diacetylmorphine and analogous compounds. The main purpose of this research

**52**

below.

selected [14].

subsequently extracted after alkalinization [15].

of experimental conditions and solvents [16].

Some of those applications are discussed below.

was to develop a technique for fabrication of a monolithic and robust solid-phase microextraction on the basis of MIP interfacing with GC and GC-MS analysis for selective extraction and structural analysis of diacetylmorphine, respectively. On the other hand, a very simple approach has been developed for the fabrication of SPME fiber from diacetylmorphine-imprinted polymers which were subsequently used for extraction of diacetylmorphine and then analyzed with GC and GC-MS [16].

Rehim studied new trend in sample preparation [17]: online microextraction in packed syringe for liquid and gas chromatography applications and determination of local anesthetics in human plasma samples using gas chromatography-mass spectrometry. In this study, local anesthetics in plasma samples were used as model substances, and the method was developed and validated for microextraction in packed syringe (MEPS) online with GC-MS. MEPS and SPE procedures have some differences. In MEPS method, the sorbent packing is placed directly into the syringe, not into a separate column, as it is done in SPE. So, a separate robot does not need for applying the sample into the separation phase, as it is done in SPE. Also, the packed syringe can be applied several times for different samples [17].


**6. Conclusions**

In Spain, the goat sector has the potential to continue growing. At a national level the development of several certified products with quality trades/labels on meat or on elaborated products such as cheeses would allow increases and improvements to production, the value of the products and globally breeders recognition, profitability, and income. Also exportation of Spanish goat milk or meat to other countries together with higher and more common or traditional goat consumption would be a possible growth strategy.

The high genetic variability of breeds has been identified and characterized in the Spanish goat population. Each one has been adapted to the conditions of the rearing region and specific production systems according to their main aptitude (meat, dairy, dual purpose) and environmental resources.

**39**

provided the original work is properly cited.

\*Address all correspondence to<EMAIL_ADDRESS>
Ana Guerrero1,2\*, José Alfonso Abecia1,3 and Carlos Sañudo1,2

2 Instituto Agroalimentario de Aragón (IA2), Zaragoza, Spain

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

1 Department of Animal Production and Food Science, Universidad de Zaragoza,

3 Instituto Universitario de Investigación en Ciencias Ambientales de Aragón

*The Goat Sector in Spain: Situation, Local Breeds, and Production Systems*

Versatility of the species lets them adapt to different orographic and technical conditions, presenting adequate performance since extensive rural areas to on the

Institutional support from the government, from the small ruminant sectors and breeders' associations is essential for the progress of the species. Also, the increase in studies of the characteristics, possibilities of production of different breeds, and goodness of their products would be desirable. It is essential to transmit such knowledge to the population, which needs to be sensitized, informed, and educated about nutritional, environmental, social, and economic virtues of

The authors thank the Spanish Goat Breeders' Associations and individual breeders that have collaborated on the diverse departmental projects developed over

*DOI: http://dx.doi.org/10.5772/intechopen.82539*

newest specialized and intensified farms.

this species.

**Acknowledgements**

the last few decades.

**Author details**

Zaragoza, Spain

(IUCA), Zaragoza, Spain

*The Goat Sector in Spain: Situation, Local Breeds, and Production Systems DOI: http://dx.doi.org/10.5772/intechopen.82539*

Versatility of the species lets them adapt to different orographic and technical conditions, presenting adequate performance since extensive rural areas to on the newest specialized and intensified farms.

Institutional support from the government, from the small ruminant sectors and breeders' associations is essential for the progress of the species. Also, the increase in studies of the characteristics, possibilities of production of different breeds, and goodness of their products would be desirable. It is essential to transmit such knowledge to the population, which needs to be sensitized, informed, and educated about nutritional, environmental, social, and economic virtues of this species.


**6. Conclusion**

Quinazoline and quinazolinone compounds which have a lot of considerable pharmacological interests can be synthesized by different methods, and the most attractive method was carried out starting from benzoxazinone derivatives.


*Edited by Kaan Kirali*

Heart valves are the basic functional structure of the heart to obtain, sustain, and maintain blood circulations in the body without energy loss, allowing blood regurgitation or obstructing blood flow. There are two basic designs of heart valves: actively protected via subvalvular apparatus (atrioventricular valves) and passively strengthened (ventriculoarterial valves). The embryologic and morphologic development of heart valves is the key stage to continue normofunctional structure and physiological behavior. Fetal anomalies, embryologic defects, or non-development pathologies may damage the structure of heart valves and cause functional deterioration at different grades. In this book, we have focused on specific pathologies, which require special knowledge and treatment modalities.

ISBN 978-1-78923-766-5

Structural Insufficiency Anomalies in Cardiac Valves

Published in London, UK © 2018 IntechOpen

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