**7.7 Development of best practice models**

As pedagogy transformed, the impact of different online teaching methodologies were studied and reported. This lead to development of learning models and various frameworks were proposed to structure meaningful online interactions. One such example is the "Community of Inquiry" model which is based on the interplay of teaching, social, and cognitive presence in an online environment, for an effective learning experience [66]. Based on experience, educationists also proposed solutions and recommendations for synchronous virtual learning sessions and strategies to optimize transition from face–to-face to online medical education [67, 68]. Moreover, as online medical education evolved, based on need, prediction models

for effectiveness of online clinical clerkship curricula were also developed [69]. The experience and insight of health profession education experts therefore promoted best practices in e-learning, improving the quality of online medical education and clerkship.


*2.3. Optimization of Extraction Conditions for Flavonoids by RSM*

Four factors were considered for optimizing the extraction conditions of the DES prepared using GA and CC: temperature, extraction time, DES content, and stirring speed. According to the results of the single-factor experiment (Figure S2), the four factors were subjected to the Box–Behnken design (BBD) analysis. The three levels of extraction temperature were 30, 60, and 90 ◦C, while those of the extraction time were 1, 24.5, and 48 h. The three levels of DES content were 10, 50, and 90%, and those of the stirring speed were 450, 850, and 1250 rpm. The design parameters were determined using the Design-Expert software and the results are listed in Table 2. The Design-Expert software was used to simulate the results of the BBD analysis. The obtained quadratic polynomial regression equation for the extraction efficiency Y, as the sum of the contents of rutin, hyperoside, and isoquercitrin, versus the temperature (A), extraction time (B), DES content (C), and stirring speed (D) was as follows:

*Y (content of flavonoids) =* 147.43 − 16.44*A* − 10.20*B* − 28.63*C* + 25.72*D* − 32.66*AB* + 1.18*AC* − 15.59*AD* + 10.62*BC* − 0.6583*BD* − 9.24*CD* + 20.68*A*2 + 10.77*B*2 − 48.72*C*2 + 13.77*D*2

**Table 2.** Design and experimental response values based on BBD analysis.

The results of the variance analysis of the above equation are listed in Table 3. According to the analysis of variance (ANOVA), the *p*-value (0.0441) of the four-factor global model was significant. The model F-value of 2.57 implied that the model was significant. The quadratic coefficients (A2, B2, C2, and D2) also had a significant effect, while the linear coefficients (A, B, C, and D) and terms representing the interaction between two factors had a negligible effect. Therefore, the above model can be employed to predict the extraction efficiency of the DES toward PUL. In the DES extraction, the content of DES was found to be the variable with the highest influence on the content of flavonoids. It also affected the order of the stirring speed, extraction temperature, and extraction time. The results confirmed that all the interactions, except that between the extraction temperature and extraction time, were insignificant. Figure 3 depicts a three-dimensional (3D) response surface curve based on the regression polynomial. It represents the range of three independent variables, while the fourth variable is held at level 0. As can be observed in Figure 3A, the extraction efficiency increased as the extraction temperature and time increased. On the other hand, it tended to increase initially and then decrease, depending on the DES content. The maximum extraction efficiency was achieved at a DES content of 30.85%. At low stirring speeds, the extraction efficiency was found to increase with temperature. However, when the stirring speed is fast, the extraction efficiency decreases as the temperature increases, and then increases again when the temperature reaches 60 ◦C. The optimal extraction conditions predicted from the three-dimensional response surface curves were as follows: extraction temperature: 30◦C, extraction time: 1 h, DES content: 30.85%, and stirring speed: 1113.1 rpm. The optimized conditions corresponded to a predicted flavonoid content of 217.515 μg/mL. Three confirmatory tests, performed under the predicted conditions, showed high significance, with error rates within 5%, and flavonoid yields of 223.37, 219.10, and 227.11 μg/mL.

**Table 3.** ANOVA statistical results.

**Figure 3.** Response surface in Box–Behnken experimental design for the four factors influencing the extraction of flavonoids. (**A**) Effect of temperature and extraction time; (**B**) effect of temperature and DES content; (**C**) effect of temperature and stirring speed; (**D**) effect of extraction time and DES content; (**E**) effect of extraction time and stirring speed; and (**F**) effect of DES content and stirring speed.

#### *2.4. Evaluation of Antioxidant Activity*

The antioxidant activity of the flavonoids extracted from the PUL under the optimum conditions was evaluated at various concentrations. The flavonoid concentration corresponding to 50% free radical scavenging activity was calculated based on L-ascorbic acid, which constituted the positive control group. As can be observed in Figure 4, the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging activity increases as the concentration of the extract is increased. However, it does not increase when the maximum concentration is reached. The concentrations of L-ascorbic acid (positive group) and the eluted extract corresponding to 50% DPPH radical scavenging activity were 59.54 and 299.68 ppm, respectively. The antioxidant activity of the active component of the PUL extracted using the DES was approximately 1/6 that of L-ascorbic acid. It was, thus, confirmed that the active components of the PUL extracted using the DES had potential utility as antioxidants.

**Figure 4.** DPPH radical scavenging activity of *PUL* extract under optimal extraction conditions.


**4. Multivariate analysis applied to spectroscopic data analysis**

Multivariate analysis tools are used to extract information from spectral data set and to assist in biomedical interpretation. Chemometric methods are diverse and offer different approaches to extract specific information from the data. These methods used for data interpretation and to extract information from complex datasets were usually univariate and therefore they were not suitable to describe the sample variation or composition. A range of processing methods can be applied to a Raman data set; usually baseline corrections should be performed as a first approach, prior to normalization methods and then a wide range of multivariate analysis tolls can be applied [12].

**5. Raman spectroscopy in cancer diagnosis**

for diagnosis of different cancer types (**Figure 2**).

cases and cancer mortality.

from [13, 14].

**5.1. Skin cancers**

A preeminent application of Raman spectroscopy in health sciences is its use in cancer diagnosis. According to Cancer Research UK, in 2012, an estimated 14.1 million new cancer cases occurred worldwide, resulting in 8.2 million people died [15]. Despite the majority of new cancer cases was registered in less-developed countries, a significative number (more than 6 million cases) occurs in developed regions, with access to advanced medical care and treatments [16]. These data reveal the urgent need of reliable diagnostic tools to reduce cancer

**Figure 1.** Most frequently applied multivariate data analysis techniques in combination with spectroscopic methods. MVA: Multivariate data analysis; PCR: Principal component regression; PLS: Partial least-squares regression; PCA: Principal-component analysis; CA: Cluster analysis; LDA: Linear discriminant analysis; PLS-DA: Partial least-squares discriminant analysis; SIMCA: Soft independent modeling of class analogy; ANNS: Artificial neural networks. Adapted

Raman Spectroscopy Applied to Health Sciences http://dx.doi.org/10.5772/intechopen.73087 279

The common methodologies for cancer diagnosis are based on invasive histological analysis and biomedical imaging, which are expensive, time-consuming and can give rise to subjective diagnosis [17]. Raman spectroscopy has been extensively studied in an attempt to replace or complement current methods and increase sensitivity and specificity of the diagnosis [18]. In this section, we discuss the advances in Raman spectroscopy applied to biofluids and tissue

Raman has been widely investigated to discriminate benign skin lesions from malignant lesions, mainly basal cell carcinoma and squamous cell carcinoma (**Table 1**). Despite the high complexity of skin tissue, studies *in vivo* show huge potential as a routine procedure in hospitals for cancer screening [19]. In 2008, a preliminary report used 289 patients with different

Multivariate methods include techniques of multivariate classification (or pattern recognition techniques) and multivariate regression. Techniques of multivariate classification can be divided into unsupervised and supervised learning procedures. In unsupervised pattern recognition techniques such as principal-component analysis (PCA) and cluster analysis (CA), there is no need for *a priori* knowledge about the training set samples (spectra). These methods are used to consider differences and similarities between spectra. By contrast, supervised pattern recognition techniques such as linear discriminant analysis (LDA) and artificial neural networks (ANNS) require some *a priori* knowledge, for example, undoubtedly identifying samples from disease and samples from healthy cases. In this way, supervised procedures allow a more precise classification, while unsupervised methods are useful for an exploratory analysis of data.

On the other hand, multivariate regression techniques (or multivariate calibration methods) are usually applied to analyze one or multiple molecules of a complex sample that possess overlapping spectroscopic signals. These techniques include principal components regression (PCR) and partial least squares regression (PLS) (**Figure 1**).

**Figure 1.** Most frequently applied multivariate data analysis techniques in combination with spectroscopic methods. MVA: Multivariate data analysis; PCR: Principal component regression; PLS: Partial least-squares regression; PCA: Principal-component analysis; CA: Cluster analysis; LDA: Linear discriminant analysis; PLS-DA: Partial least-squares discriminant analysis; SIMCA: Soft independent modeling of class analogy; ANNS: Artificial neural networks. Adapted from [13, 14].