Objective quality results of different methods on ALR images in terms of PSNR (dB) (highest PSNR is highlighted by bold-face).
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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Whether feasible or futuristic, this book is a great read for researchers, practitioners, or just about anyone with an enquiring mind on this subject.",isbn:null,printIsbn:"978-953-51-0277-9",pdfIsbn:"978-953-51-6149-3",doi:"10.5772/2315",price:139,priceEur:155,priceUsd:179,slug:"fossil-fuel-and-the-environment",numberOfPages:318,isOpenForSubmission:!1,hash:"65fc2d4b7fcf8142f419dc0956d1ebf8",bookSignature:"Shahriar Khan",publishedDate:"March 14th 2012",coverURL:"https://cdn.intechopen.com/books/images_new/1893.jpg",keywords:null,numberOfDownloads:54787,numberOfWosCitations:35,numberOfCrossrefCitations:20,numberOfDimensionsCitations:56,numberOfTotalCitations:111,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 18th 2011",dateEndSecondStepPublish:"June 15th 2011",dateEndThirdStepPublish:"October 20th 2011",dateEndFourthStepPublish:"November 19th 2011",dateEndFifthStepPublish:"March 18th 2012",remainingDaysToSecondStep:"10 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:"Edited by",kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"107794",title:"Dr.",name:"Shahriar",middleName:null,surname:"Khan",slug:"shahriar-khan",fullName:"Shahriar Khan",profilePictureURL:"https://mts.intechopen.com/storage/users/107794/images/2376_n.jpg",biography:"Prof. Shahriar Khan has been an Associate Professor at Independent University Bangladesh (IUB) since 2005. He received his M.S. and Ph.D (1997) from Texas A&M University, where he also served as Assistant Lecturer. He received his B.Sc. in Electrical Engineering from Bangladesh University of Engineering and Technology (BUET). He was active as adjunct professor at Bronte College, Canada. Dr. Khan has had industrial experience in Schlumberger Oilfield Services, where he designed radio frequency and magnetic resonance electronics for drilling equipment. \nDr. Khan is a member of IEEE, and a Life Fellow of the Institute of Engineers, Bangladesh. He is a speaker and instructor for the Astronomy Association of Bangladesh. Besides fossil fuel and renewable energy, Dr. Khan has cross-disciplinary interests in power systems, power electronics, communications, control systems, and river basin dynamics. 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With generative adversarial networks (GAN) such as improvements of objective quality metric have been extended to the visual quality of super-resolved images [2]. However, most of the existing deep learning algorithms for solving SISR problems are categorized as supervised; in that they rely on paired high-resolution (HR) and low-resolution (LR) images to optimize the neural network weights. The HR images are downsampled using algorithms (like bicubic downsampling) to create the corresponding LR ones. These artificially created LR data deviate significantly from the complex real word degradation model and with that a rapid decrease in performance is observed when neural networks trained on artificial LR that are tested on real-world LR images [3].
In this chapter, we will focus on solving the problem of image superresolution for face images. Super-resolving low resolution face images can help solve crucial tasks such as person identification and recognition in the real world. To make our solution work for real-world LR face images, we borrow ideas from recent advances in style transfer [4] and image synthesis [5]. Style transfer refers to the task of transforming one image from one style to another (e.g., photo to painting, daytime to nighttime, and summer to winter). An important motivation behind our approach is to treat SISR as a style transfer problem which does not require pairing the HR-LR training data. In our unsupervised learning approach, we only assume two uncorrelated datasets: one is a collection of real-world LR images and the other HR images.
In the next sections, we will first review some related works including convolutional neural network (CNN), generative adversarial networks (GAN), image synthesis, and style transfer. We will then present an unsupervised approach that works for real-world LR face images. The key idea is to combine style-based generator [5] with relativistic discriminator [6] within a recently developed cycle-consistent GAN (CycleGAN) framework [4]. We will show that both our approaches outperform previous state-of-the-art ones.
In this section, we will first present to the reader the convolution neural networks and go through the different types of functions used in such networks. Our focus will be on the main convolution operation. We will talk about the first paper that showed that convolution neural networks can outperform model-based approaches in the task of image super-resolution [7]. We will talk about generative adversarial networks (GAN) and show that adding a discriminator can significantly improve the visual quality of the superresolved image [2]. We will define image synthesis task and present the latest advancements in the field. Finally, we will talk about the style transfer problem and different architectures used to solve it; our focus will be on the most popular one: CycleGAN [4].
Neural networks are a class of machine learning algorithms that are modeled loosely on the mechanism of human brain (e.g., neocognitron [8]). It consists of thousands or even millions of simple processing units that are densely interconnected. Most existing neural networks are organized into layers of nodes (simple processing units). They usually feed-forward information from input data to the output in one direction. An individual node might be connected to several nodes in the layer beneath it from which it receives data and several nodes in the layer above it for which it sends data. Figure 1 shows an example of feed-forward neural network. To each of its incoming connections, some nodes will assign a number called “weight,” multiply the input coming from the connection with its corresponding weights; other nodes will sum the results and add a value called bias. In other nodes, a non-linearity called activation function is included which models the biological firing of neurons in human brains [9].
Graphical representation of feed-forward neural network.
The Convolutional Neural Network (CNN) are a type of neural networks that are often designed to work on two-dimensional data such as image signals. In CNN, the most basic operation is called “convolution” implementing a linear filter and modeling simple cells in human brains [10]. In the context of convolutional neural networks, a convolution is a linear filtering operation that involves multiplying the input with the weights similar to the traditional neural network. Given the nature of 2D inputs, the multiplication is done between an array of data and 2D array of weights (often called filter or kernel).
The filter is smaller than the data and the multiplication operation between the filter and the data is the dot product. The dot product is the element-wise multiplication and summation resulting in one value. Having a filter or a kernel smaller than the input data enables the sliding of the kernel over the whole input, therefore giving the trained weights of the filter the ability to detect features anywhere in the image. Convolutional neural networks might also consist of max-pooling operations, used to down-sample the input (modeling complex cells in human brain [10]). Figure 2 shows a graphical representation of neural network.
Example of convolution neural network.
In [7], the authors present the first convolutional neural network called SRCNN that outperforms traditional model-based approaches for the task of single image super-resolution (SISR). The key idea underlying SRCNN is to learn a nonlinear mapping from the space of LR images to that of HR ones. The work of SRCNN is under the framework of supervised learning with the assumption of paired training data (artificial LR images are generated by down-sampling of HR images). Their convolution neural network as shown in Figure 3 consists of two layers. Figure 4 shows the comparison between [7] and traditional model-based SR methods such as sparse coding [11].
Convolution neural network architecture for [
Comparisons between [
With advancements in deep neural networks, deeper architectures powered by residual learning [12] has led to FSRCNN [13], DRCN [14], VDSR [15], EDSR [16] and LapSRN [17], RDN [18], and RCAN [19]. With deeper and densely connected networks, the performance of SISR has increased steadily at the price of higher computational complexity. With millions of parameters, EDSR and RCAN have advanced the state-of-the-art in supervised learning-based SISR. For face images, SISR has also been studied in recent works (e.g., Super-FAN [20] and FSRNet [21]).
In [22], Ian Goodfellow presented a novel system for the task of data generation. This system is called generative adversarial network (GAN) consists of two interacting subnetworks (generator and discriminator) as shown in Figure 5. A generator subnetwork is responsible for generating synthetic data capturing the data distribution and a discriminator subnetwork for estimating the probability that a sample comes from the real training data rather than synthetic. Through the interplay between two subnetworks, the generator and discriminator networks can be trained together by a minimax two-player game. The invention of GAN opens the door to construct a whole new class of powerful generative models which have found numerous applications in low-level vision including SISR, face image synthesis, and style transfer.
Architecture of GANs.
In SRGAN [2], the authors showed that using a GAN-based architecture for the task of single image super-resolution leads to noticeable improvements in terms of subjective visual quality despite the sacrifice on traditional objective quality metric such as PSNR. In the construction of SRGAN, residue network for SR-called SRResnet is used as the generator; a separated discriminator inspired by Deep Convolutional GAN (DCGAN) [23] is constructed to tell apart real SR from fake SR. Figure 6 shows the visual quality improvement when using a GAN-based architecture compared to using a generator without a discriminator.
Comparison between SRGAN and SRResnet.
Another successful application of GAN [2] is to generate high-fidelity face images that do not even exist in the real world. Radford et al. designed a variation of GAN architecture called Deep Convolutional GAN (DCGAN) [23] to generate face images; however, their results suffered from noticeable artifacts in synthesized images. More recently, self-attention (SAGAN) [24] used an attention mechanism to help minimize undesirable artifacts in generated images. Cleverly, designing loss functions for both discriminator and generator has shown impressive improvements in terms of convergence and artifacts suppression for the GAN networks.
Before 2017, most generated faces were still of low resolution, with the highest resolution equal to
Example of the progression made in GAN-based face synthesis from 2014 to 2017 (cited from [
Recently, GAN-based architectures were used for the task of style transfer, that is, translating one image from one style to another style (e.g., from sketch to real image). In [28], another GAN-based architecture called pix2pix was developed to transfer images from one style into another. Figure 8 shows the example of translating sketch images of hand bags into real images. These works are based on an architecture called conditional GAN (cGAN) [30] in which the data instead of random noise are fed to (provided as the condition) both the generator and discriminator. However, pix2pix [28] is a supervised learning technique, and it requires the existence of groundtruth data.
Results from pix2pix [
To extend the style transfer to the domain where groundtruth is unavailable, an unsupervised architecture called cycle-consistent GAN (CycleGAN) was proposed in [29]. In CycleGAN [29], two parallel GAN architectures are trained concurrently: the first one to map from source domain to target domain and the second one to map from target domain back to source domain. The new insight brought by CycleGAN [29] is the enforcement of cycle-consistency, that is, when an image
CycleGAN [
We are interested in solving the problem of image face super-resolution for real world LR data. Unlike artificial LR data, the ground-truth is unavailable for real-world LR data. For such blind SR problem, we propose to tackle it as style transfer, that is, the transfer between LR and HR image data. We mainly focus on an asymmetrical formulation of style transfer problem in one direction: from LR to HR. Based on this observation, we do not need to enforce the cycle consistency for the direction of low-to-high transfer. Our overall network architecture is shown in Figure 10, consisting of two generative adversarial networks called high-to-low and low-to-high, respectively. The high-to-low GAN takes a HR face as the input and project it into the style of the real world LR faces. The low-to-high GAN takes the output of high-to-low generator as the input and try to reconstruct the original HR faces.
Architecture of our unsupervised approach for real-world face superresolution.
In this section, we go through the detailed of the convolution neural networks that form our unsupervised architecture in Figure 10. We have two generators and two discriminators.
Our generators are made up of a number of blocks that we call residual + attention [37] (Figure 11). We use self attention layers as defined in [38] as part of both low-to-high and high-to-low discriminators. The details of self-attention layer are shown in Figure 12.
Residual + attention block.
Self-attention layer.
High-to-low generator has an encoder-decoder type architecture [39]: with the encoder consisting of five residual + attention blocks each followed by average pooling layer and the decoder consisting of four residual + attention blocks where the first two are followed by bilinear upsampling layer. Therefore, the input is downsampled by a factor of 32 and upsampled by a factor of 4, which produces a down-sampled image by a factor of 8 but with more flexibility of modeling degradation (e.g., unknown blur [40]). We also concatenate a noise vector to the input image of the network using a fully connected layer, which contributes to the robustness of the proposed degradation modeling. The details of the high-to-low generator architecture is shown in Figure 13.
Architecture of the high to low generator.
Low to high generator subnetwork consists of four sections of 6, 3, 2, and 1 successive residual attention blocks separated by bilinear upsampling of 2 (similar to the strategy of progressive growing GAN [25]); overall the input
Architecture of the low to high generator.
High-to-low discriminator subnetwork consists of three convolution layers followed by a leaky relu layer and a last convolutional layer. We have added two self-attention layers at the end of the network (refer to Figure 15).
Architecture of the high to low discriminator.
Low-to-high discriminator consists of four convolution layers followed by a leaky relu layer and a last convolution layer. Similarly, we have also added two self-attention layers. The details of the architecture are in Figure 16.
Architecture of the low-to-high discriminator.
The generator loss, in both high-to-low and low-to-high GANs, is the weighted sum of the content loss and the GAN loss, as shown in Eq. (1) where
The GAN losses and the pixel loss function follow the formula in Eqs. (2) and (3).
It is worth mentioning that we have not augmented the data during training by standard techniques such as image flipping, scaling, and rotation. Our experience suggests that for unsupervised learning, data augmentation does not help improve the accuracy of face SR reconstruction but increase the computational burden as well as the risk of introducing artifacts (due to unpaired LR-HR training data). We have also found that the popular normalization tricks (e.g., batch normalization [41] and spectral normalization [42]) do not help in the unsupervised scenario but have the tendency of introducing artifacts to super-resolved images.
We have used a batch of size 32, and the total training requires about 20 epochs or
In this section, we present a comparison between our style-based approaches toward SISR of face images and state-of-the-art supervised (FSRNET [21]) and unsupervised ones (Bulat’s [3]). First, we use extensive ablation studies to show the effect of removing the low-to-high discriminator (Figure 16) from the architecture and demonstrate the output of our degradation model. As an extension of our ablation study, we also present a supervised approach for ALR face images based on using GAN composed of our low-to-high generator (Figure 14) and low-to-high discriminator (Figure 16) but without any cycle involved. Finally, we will report our unsupervised learning results on real-world LR face images and compare them against other competing approaches.
We have compared the outputs of our unsupervised architecture with and without the high-to-low discriminator. The image comparison results are shown in Figure 17. It is obvious that discriminator plays a significant role in improving the visual quality of super-resolved images.
Comparison between our unsupervised method with and without the discriminator on Widerface dataset [
We next show the capability of high-to-low network on learning real-world degradation models. Figure 18 includes several typical examples of learned LR images from HR inputs. It is important to note that our high-to-low network has managed to learn a variety of degradation models including varyimg poses and severe blurs.
Effectiveness of degradation model learning: exemplar synthetic LR images from the high-to-low network (note the rich variability and similarity to the real-world Widerface dataset [
We also visualize the feature maps of the low-to-high generator in Figure 14. In this visualization experiment, we have plotted the output from Sections 2, 3, and 4 as shown in Figure 19. It can be observed that as section/layer number increases, the learned feature representations have a larger field of view as well as more sophisticated semantic information related to faces.
Feature maps for low-to-high generator. From left to right:
We have experimented with a GAN-based supervised approach toward SISR as an extension of our ablation study. Such experiment is included to demonstrate a degeneration of network architecture from unsupervised (Figure 10) to supervised (Figure 20) setting. We have used the same architecture for the low-to-high generator and discriminator as in Figures 14 and 16. To get paired LR and HR images; we downsample the original high resolution faces by a scaling factor of 8 to create artificial low resolution (ALR) images as explained in Section 3.2. The overall architecture of this reduced supervised approach is detailed in Figure 20.
Architecture of the supervised approach.
Similar to Section 3.4, the loss functions for our supervised approach are defined as follows:
We have used a batch of size 32 and trained for 20 epochs or
We compare our unsupervised face super-resolution approach with two supervised approaches; FSRNET [21] and our own approach outlined in Section 4.2. FSRNET [21] uses geometric priors to estimate (e.g., facial landmark heat maps and parsing maps) to facilitate the procedure of supervised learning.
We report our experimental results for ALR data and compare them against the current state-of-the-art FSRNet/FSRGAN [22]. Despite being synthetic, ALR images are still useful because they have ground-truth (HR) available and appropriate for gauging the performance of supervised learning (with paired HR-LR training data).
Visual quality comparisons among competing methods on artificial low resolution face images. Rows top-down: bicubic, FSRGAN [
Method | PSNR |
---|---|
FSRGAN [21] | 22.840 |
Ours (Supervised) | |
Ours (Unsupervised) | 21.97 |
Objective quality results of different methods on ALR images in terms of PSNR (dB) (highest PSNR is highlighted by bold-face).
We tested both our supervised and unsupervised methods on the popular real-world LR dataset Widerface [36]. This dataset is particularly challenging for face detection and SR because its 393,703 faces contain a high degree of variability in scale, pose, and occlusion. Due to lack of groundtruth images (HR counterparts) for this dataset, we have to count on visual quality comparison alone for performance evaluation (without PSNR comparisons). We report our visual quality comparison between our methods and current state-of-the-art supervised (FSRGAN [22]). Figure 22 shows the results; we can see that the unsupervised approach outperforms supervised ones in case of real world low resolution images.
Qualitative comparisons with FSRGAN on Widerface test data. Rows are respectively: Bicubic, FSRGAN, ours (supervised), and ours (unsupervised). Please zoom in for better visualization.
We compare our unsupervised method with Bulat’s [45]. We show that our methods are able to better preserve facial features Figure 23.
Qualitative comparisons with Bulat’s method [
One can observe that the SR image produced by Bulat’s [45] method has the following problems: age variation (third), gender swapping (fourth and seventh), and artifacts (second and seventh).
Using Openface [46] matching algorithm, we plot the ROC curve for three types of degradation models: artificial degradation (or ALR), jpeg compression, and our high-to-low degradation model. We show that our proposed supervised approach outperform all previous ones in case of artificial degradation and our unsupervised approach performs the best when it comes to the other two degradation models.
We have compared our supervised and unsupervised approach with FSRNET [21] and Bulat’s [45] in terms of ROC curve results. We use our Artificial Low Resolution (ALR) test data. Figure 24 shows that our supervised method outperforms all other methods. On the other hand, our unsupervised method performs worse than FSRNET [21] but still performs significantly better than previous unsupervised state-of-the-art approach Bulat’s [45].
ROC curve for ALR test data.
We compressed our Artificial Low Resolution (ALR) test data using JPEG lossy compression. We used this compressed data to plot the ROC curve for our supervised and unsupervised approach as well as Bulat’s [3]. Our unsupervised approach outperforms better than the other ones, as shown in Figure 25.
ROC curve for compressed test data.
We also plotted the ROC curve using the data generated by passing high resolution test data to our trained high-to-low generator. We show here that our unsupervised approach performs better than our supervised one; Figure 26.
ROC curve for our high-to-low degradation model.
As mentioned above, our approach intentionally skips the step of data augmentation. It turns out that our method is still sensitive to extreme variations of face pose such as as those shown in Figure 27. Due to severe occlusions and large pose variations, those LR examples are often rare even among Widerface dataset. This is within our expectation because high-to-low network simply does not have sufficient training data to learn the challenging degradation model. Note that similar findings have been reported for Bulat’s method in [3] (refer to Figure 9 in that paper).
Exemplar failure cases of our unsupervised method on Widerface test data. Top-row: Input LR; bottom-row: our SR result (unsupervised).
We have studied the problem of SISR for real-world face images in this chapter and presented an unsupervised learning approach toward such blind reconstruction of SR images. The challenging scenario of real-world LR low-quality images defies conventional approaches based on paired HR-LR training data because groundtruth HR images are generally unavailable for real-world LR images. By pairing style-based generator with relativistic discriminator, we demonstrate an unsupervised learning approach with GAN-based end-to-end optimization that is capable of advancing the state-of-the-art in blind SR reconstruction of real-world LR face images. We have compared our degradation modeling against previous Bulat’s method as well as their ROC performance on artificial LR dataset. Extensive experimental results have shown favorable performance for the proposed method over Bulat’s method.
The genus
Domain | Eukaryota |
Kingdom | Plantae |
Subkingdom | Viridaeplantae |
Phylum | Tracheophyta |
Subphylum | Euphyllopsidia |
Infra phylum | Radiatopses |
Division | Magnoliophyte |
Class | Magnoliopsida |
Subclass | Dilleniidae |
Superorder | Urticaneae |
Order | Urticales |
Family | |
Genus |
Since ancient times,
Phytochemicals are the bioactive components of plants having great importance in pharmaceutical and medicinal field. The genus
Ficus species | Plant parts | Extracting solvent | Class | Phytochemical components | References |
---|---|---|---|---|---|
Barks | Water, methanol, organic solvents, helium | Polysterols | Bergapten, bergaptol, lanosterol, β-sitosterol, stigmasterol, β-sitosterol-d-glucoside (Phytosterolin) | [24, 25] | |
Flavonoids | Leucocyanidin-3-O-β-glucopyranosid, leucopelargonidin-3-O-β-d-glucopyranoside, leucopelargonidin-3-O-α-l-rhamnopyranoside, lupeol, cetyl behenate, acetate and α-amyrin acetate | ||||
Polyphenols | Tannin, wax, saponin, leucoanthocyanidin, leucoanthocyanin | ||||
Fruit | Water | Flavonols | Kaempferol, quercetin, and myricetin | [26, 27] | |
Miscellaneous compounds | Undecane, tridecane, tetradecane, (e)-β-ocimene β-bourbonene, β-caryophyllene, α-trans bergamotene, α-thujene, α-pinene, β-pinene, α-terpinene, limonene, dendrolasine, dendrolasine α-ylangene, α-copaene, aromadendrene, α-humulene, alloaromadendrene, germacrene, bicycle-germacrene, γ-cadinene and δ-cadinene | ||||
Leaves | Ethanol | Polyphenols | Eugenol, 2-phenylethyl alcohol, and benzyl alcohol, hexenol, n-hexanol, phytol, benzyl alcohol | [28] | |
Miscellaneous compounds | Phenol, salicylaldehyde, phenylacetaldehyde, allyl caproate, linalool, n-nonanal, adipoin, methylcyclopentane, 2-dione, itaconic anhydride, 2-phenylethyl alcohol, benzeneacetonitrile, nonadienal, nonen-1-ol, nonadienol, linalool oxide, catechol, coumaran, cinnamyl alcohol, vinylguaiacol, hexenyl tiglate, eugenol, hexenyl hexenoate, β-ionone, dihydroactinidiolide, α-copaene, hexenyl benzoate, eudesmol, eudesmol, epi-α-cadinol, β-eudesmol, α-eudesmol, α-cadinol, pentadecanal, palmitic acid and itaconic anhydride, 3-methylcyclopentane-1, 2-dione | ||||
Leaves and fruits | Ether, chloroform and ethanol | Flavonols | Kaempferol, quercetin, myricetin | [29] | |
Phenolic acids | Betulinic acid, lupeol | ||||
Sterols | Stigmasterol, bergapten, scopoletin, β-sitosterol-3-O-β-d-glucopyranoside | ||||
Whole plant | N-butanol, ethanol and methanol | Flavonoids | Quercetin, quercetin 3-O-l-rhamnopyranosyl (1-6)-β-d-glucopyranoside, quercetin 3-O-β-d-glucopyranoside (isoquercitrin), quercetin 3,7-O-α-l-dirhamnoside, quercetin, 3-O-β-d-galactopyranosyl(1-6)-glucopyranoside | [30] | |
Sterol | β-Sitosterol-3-β-d-glucopyranoside | ||||
Phenolic acids | Gallic acid | ||||
Dried fruit | Water | Flavonoids | Alkaloids, flavonoids, coumarins, saponins, rennin, caoutchouc, resin, albumin, cerin, sugar and terpenes | [31] | |
Latex | Water | Enzymes | Proteolytic enzymes, diastase, esterase, lipase, catalase, and peroxidase | [32] | |
Phenolic acids | Malic acid | ||||
Leaves | Water | Coumarins | Psoralen and bergapten | [33] | |
Flavonoids | Rutin, quercetin, and luteolin | ||||
Phenolic acids | Ferulic acid | ||||
Phytosterols | Taraxasterol, psoralen and bergapten (5-methoxypsoralen) | ||||
Pulp | Water | Phenolic acids | Chlorgenic acid | ||
Peel | Water | Coumarins and sterol | Quercitin-3-O-rutinoside, psoralen | ||
Aerial roots | Water and methanol | Polyphenols | Saponins, tannins, glucoside and flavonoids | [14] | |
Sterol | β-Sitosterol-α-d-glucose and meso-inositol | ||||
Stem bark | Water | Polyphenols | Alkaloids, balsams, carbohydrates, flavonoids, free anthraquinones, tannins, glycosides, terpenes, resins, sterols and saponins, glycosides | [34] | |
Leaves | Water | Volatile compounds | Carvacrol, α-caryophyllene, caryophyllene oxide, linalool, 3-tetradecanone, geranylacetone, 3,7,11-trimethyl-3-hydroxy-6;10-dodecadiene-1-yl acetate, hexahydrofarnesyl acetone, α-caryophyllene, 2-methyl-3-hexyne and scytalone | [35] | |
Roots | Water | Phenolic acids | Betulinic acid and ursolic acid | [36] | |
Anthocyanins | Trihydroxy-stilbene-3, 5-O-β-d-diglucopyranoside, euphol-3-ocinnamate, lupeol, taraxar-14-ene | ||||
Aerial roots | Triterpenoids | Friedelin, lupeol, oleanolic acid, ursolic acids | [37] | ||
Leaves | Flavoinoids | Catechin, epicatechin and isovitexin | |||
Leaves | Methanol | Polyphenols | 1,2-Benzenedicarboxylic acid-dibutyl ester, phenol, 4-(2aminopropyl), butyrolactone | [38] | |
Aerial parts | Ethanol | Flavonols | Luteolin, afzelechin, catechin, vitexin, β-sitosterol acetate, β-amyrin acetate, moretenone, β-amyrin | [39] | |
Sterols | β-Sitosterol, friedelenol | ||||
Stem bark | Water | Anthocyanins | Cetyl behenate, lupeol, α-amyrin acetate | [40] | |
Leaves and bark | Water | Sterols | β-Sitosterol and a new tetracyclic tritepene-glaunol acetate | ||
Fresh leaves and stem | Methanol | Anthocyanins | Amyrin acetate, α-amyrin acetate, lupeol, β-amyrin, α-amyrin, rhoiptelenol, 3α-hydroxyisohop-22(29)-en-24-oic acid, lupenyl acetate | [41] | |
Phenolic acids | Ursolic acid, betulinic acid | ||||
Stem bark | Water | Terpenes | Pentacyclic triterpenes 8,26-cyclo-urs-21-en3β, 20β-diol and 3β-acetoxy-8, 26-cyclo-ursan-20β-ol and also 3-friedelanone | [42] | |
Phenolic acids | Oleanolic acid, betulinic acid | ||||
Anthocyanins | Lupeol acetate, α and β amyrine, 3,5,7,4′-tetra hydroxyl flavones | ||||
Leaves | Hot and cold water | Flavonols | Triterpene, conrauidienol, and dihydroflavonol, conrauiflavonol, 3,4’,5-trihydroxy-6’’,6’’-dimethylpyrano[2,3-g]flavone | [43, 44, 45] | |
Anthocyanin | β-amyrin acetate, 6β-hydroxystigmasta-4,22-dien-3-one, 8-prenylapigenin | ||||
Phenolic acid | Betulinic acid, ursolic acid | ||||
Flavonoids | Luteolin, catechin, epigallocatechin, orientin | ||||
Sterol | β-Sitosterol glucoside | ||||
Whole plant | Water | Phenolic acid | Gallic acid | [46] | |
Anthocyanin | 3, β-hydroksilup-20(29)-en, (lupeol) | ||||
Polyphenols | Carbohydrates, glycosides, saponins, resins, fat, flavonoids, tannins, and phenolic compounds. Alkaloids and steroid were absent | [47] |
Phytochemical quality of various parts of commonly used species of
Plant parts | ES | TPC | TFC | TF | AAC | TAC | TSC | TA | References | |
---|---|---|---|---|---|---|---|---|---|---|
Roots | Ethanol | 70 mg/g extract | 5 mg QE/g extract | 3 mg QE/g extract | [48] | |||||
Pulp | Water | 0.49–0.88 mg GAE/g | [49] | |||||||
Leaves | Hexane | 6.6–9.5 M/TE | [50] | |||||||
Dried leaves | Hexane | 17.44 mg/g | 3.87 mg/g | [51] | ||||||
Dried leaves | Methanol | 7.83 mg/g | 1.05 mg/g | [51] | ||||||
Fruit | Ethanol | 28.6–211.19 mg GAE/100 g FW, 11.9 mg/g of DM | 2.75 μg CE/mg sample | 9.6% | 0.59% | 0.0–298.6 μg cy-3-rutinoside/g FW | [52, 53] | |||
Fruit | Hexane | 259.2 mg GAE/g | [54] | |||||||
Methanol | 245.2 mg GAE/g | |||||||||
Chloroform | 159.2 mg GAE/g | |||||||||
Pulp | Methanol | 28–30 mg/100 g extract | [55] |
Phytochemical content of various parts of commonly used species of
ES: extracting solvents, TPC: total phenolic content, TFC: total flavonoid content, TF: total flavonols, AAC: ascorbic acid content, TAC: total alkaloid content, TSC: total saponin content, TA: total anthocyanins, DM: dried material, QE: quercetin equivalent, TE: trolox equivalent, ep: edible pulp, GAE: gallic acid equivalent, FW: fresh weight.
Antioxidants are the substances which can scavenge free radicals and reduce the oxidative stress in the living and nonliving systems. The antioxidants possess electron donating ability and inhibit the free radical-mediated oxidative reactions by various mechanisms, such as, hydrogen donation, metal chelation, metal and lipid reduction, inhibition of lipid peroxidation and free radical inhibition [56, 57, 58, 59, 60]. Free radicals are the reactive oxygen and nitrogen species which are produced during various biochemical reactions particularly redox reactions. If not controlled properly, these free radicals may initiate the chain reactions in the biomolecules particularly the lipids and protein, cause the oxidative stress, and finally lead to the oxidative damage to the cell organelles, cells and tissues [24]. The oxidative damage to the cells and tissues may further lead to various health problems including cardiovascular, neurological, hepatic, and musculoskeletal abnormalities and aging. In nonliving system, the free radicals cause oxidative stress and rancidity in the food stuff for human [25]. The naturally occurring antioxidant compounds have been proved to be effective in preventing the oxidative damage to the living and nonliving systems [26]. These substances are either synthesized endogenously or taken from exogenous natural sources such as plants. The naturally occurring antioxidants include some enzymes such as glutathione peroxidase, catalase, superoxide dismutase and some non-enzymatic phytochemicals compounds including phenolic acids, polyphenols, flavonoids, anthocyanins, ascorbic acid, tocopherols, and β-carotenes [27, 28]. Some synthetic antioxidant compounds have been also reported to be effective against free radical-induced oxidative damage [29].
The antioxidant profile of various parts of
Part | ES | TEAC* | FRAC | DPPH-RSC | ABT-RSC | ILP | LRA | References | |
---|---|---|---|---|---|---|---|---|---|
Stem | Methanol | 16.2% | 8615.3 mmol/g DM | [61] | |||||
Bark | Ethanol | 79% | 10884.6 μmol/g DM | ||||||
Roots | Water | 0.5–0.26 mg/ml | |||||||
Leaves | Water | 0.13–0.66 mg/ml | IC50: 0.34 mg/ml | IC50: 0.23 mg/ml | 83.30% | [51] | |||
Leaves | Methanol | 0.07–0.26 mg/ml | IC50: 0.69 mg/ml | IC50: 0.97 mg/ml | [51] | ||||
Mouse liver | Normal saline | 4.20–5.31 μmol TE/g ep | EC50: 313.3 μg/ml | [55, 62] | |||||
Chicken liver | Normal saline | EC50: 333.8 μg/ml | |||||||
Fruit | Methanol | 0.08–0.33 mg/ml | IC50: 0.95 mg/ml | IC50: 0.35 mg/ml | 41–83% | [51] | |||
Fruit | Methanol | 77.6 mg AC/100 g FW | 104.9 mg CE/100 g FW | 577.09 mg BH/100 g FW | [63, 64] | ||||
Ethanol | 146.67 mg AC/100 g FW | 146.9 mg CE/100 g FW | 729.45 mg BH/100 g F W | ||||||
Roots | Acetone | 0.1–0.45 mg/ml | IC50: 0.29 mg/ml | IC50: 0.25 mg/ml | 41–83% | [51] | |||
Bark | Water | 0.06–0.32 mg/ml | IC50: 1.03 mg/ml | IC50: 0.48 mg/ml | [51] | ||||
Leaves | Methanol | SC50 (74.00 μg/ml) | [65] | ||||||
Leaves | Acetone | 0.04–0.22 mg/ml | IC50: 2.54 mg/ml | IC50: 0.86 mg/ml | 41.40% | [51] | |||
Aerial roots | Methanol | 71% | 6096.1 μmol/g DM | [61, 66] | |||||
Acetone, Water | 0.1–1.0 mg/ml | 96.07% | 6182.7 μmol/g DM | ||||||
Stem bark | Methanol | 84.088% | [67] | ||||||
Stem bark | Chloroform | 83.864% | |||||||
Stem bark | Hexane | 42% | |||||||
Leaves | Acetone | 2.32%, 4.73 mg GAE/g DW | [68] | ||||||
Leaves | Hexane, water | 14.04%, 23.50 acetate/g DW | 7.9–16.1 mmol/kg FW | 11.42 mmol/100 g DW | 6.48 mmol/100 g DW | [52, 69, 70] | |||
Fruit | Dichloromethane | IC50: 0.02 mg/ml | [71] | ||||||
N hexane | IC50: 1.64 mg/ml | ||||||||
Root, Bark | Water | IC50: 1.62–47.50 μg/ml | IC50: 0.91–6.48 μg/ml | 86.13% | [72] | ||||
Leaves | Acetone | 2.65%, 8.23 mg GAE/g DW | [68] | ||||||
Leaves | Ethanol | SC50 > 0.4 mmol/100 g DW | [73] | ||||||
Bark | Water | 489.4 mg GAE/g DW | 104.57 μmol FSE/mg DE | 56.50 QE/mg DE | [74] | ||||
Unripe fruit | 62.34 GAE/g DW | 19.61 μmol FSE/mg DW | 7.3 QE/mg DE | ||||||
Leaves | Acetone | 2.60%, 9.80 mg GAE/g DW | [68] | ||||||
Fruit | Methanol | 55.9% | 93.91% | [75] | |||||
Fruit | Water | 5.89 mg GAE/g DW | 1.82 mmol FSE/g DE | IC50 = 111.20 μg/ml | 1.01–1.04 mmol TE/g DE | [76] | |||
Leaves | Acetone | 2.60%, 19.24 mg GAE/g DW | [68] | ||||||
Bark | Ethyl acetate | 436 mg GAE/g DW | 63.2 μg/ml | 1.2 μg/ml | 4.83 μg/ml | [71] | |||
Leaves | Ethanol | 86.13% | |||||||
Hexane | 86.76% | ||||||||
Leaves | Ethanol | 90.70% | [71] | ||||||
Hexane | 88.97% | ||||||||
Leaves | Ethanol | 90.13% | [71] | ||||||
Hexane | 94.38% | ||||||||
Fruit | Water organic solvents | 17.9 g GAE/g DW | [22] | ||||||
Leaves | Ethanol | SC50 (8.27, 12.14 μg/ml) | 80.41% | [65] | |||||
Methanol | SC50 (38.37 mg/ml) | [65] | |||||||
Dried leaves | Methanol | SC50 (61.67 μg/ml) | [65] | ||||||
Pulp | Methanol | SC50 (60.22 μg/ml) | [65] | ||||||
Leaves | Methanol | SC50 (81.62 μg/ml) | [65] | ||||||
Leaves | Acetone | 3.70%, 56.85 mg GAE/g DW | [68] | ||||||
Leaves | Acetone | 2.35%, 4.75 mg GAE/g DW | [68] | ||||||
Leaves | Acetone | 3.15%, 8.04 mg GAE/g DW | [68] | ||||||
Leaves | Acetone | 2.45%,5.40 mg GAE/g DW | [68] | ||||||
Leaves | Acetone, hexane and methanol | 2.60%, 12.33 mg GAE/g DW | SC50 (79.50 μg/ml) | 82.35% | [65, 68] | ||||
Leaves | Acetone | 2.40%, 4.64 mg GAE/g DW | [68] | ||||||
Leaves | Ethanol | 86.40% | [71] |
Antioxidant potential of extracts from various parts of
ES: extracting solvent, ABTS-RSC: azino-bis-tetrazolium sulfate radical scavenging capacity, DE: dry extract, DM: dry matter, DPPH-RSC: 2,2-diphenyl-1-picrylhydrazyl radical scavenging capacity, DW: dry weight, FRAC: ferric-reducing antioxidant capacity, FSE: ferrous sulfate equivalent, FW: fresh weight, GAE: gallic acid equivalent, IC50: inhibitory concentration required for 50% inhibition, QE: quercetin equivalent, SC50: scavenging concentration for required for 50% scavenging, TEAC: trolox equivalent antioxidant capacity, TE: trolox equivalent, BH: butylated hydroxyanisole, FW: fruit weight, CE: catechin equivalents, ILP: inhibition of lipid peroxidation, LRA: lipid reducing ability.
On the basis of their phytochemical composition and antioxidant profile,
Plant part | Extracting solvent | Activity | References | |
---|---|---|---|---|
Whole | Ethanol | Anticancer activity by reduction of lipid peroxidation, γ-glutamyl transpeptidase and xanthine oxidase and by generation of hydrogen peroxide | [77] | |
Bark | Methanol | Hepatoprotective activity by reducing the activities of ALT, AST and ALP | [4] | |
Whole | Ethanol | Hypoglycemic activity by decreasing blood glucose level | [4] | |
Fruit | Water | Antitumor activity due to blockage of calcium uptake in pituitary cells | [13] | |
Whole | Water | Antioxidant and antidiabetic activity with lowering the superoxide dismutase exaggerated activity | [78] | |
Whole | Methanol | Anthelmintic activity with 100% effectiveness | [79] | |
Whole | Water | Antimicrobial activity with inhibition zone against | [4] | |
Bark | Methanol | Anti-parasitic effect with 100% lethality for | [79] | |
Bark | Water | Antioxidant and hypolipidemic activity by reduction in lipid peroxidation, cholesterol level and triacylglycerol | [80] | |
Fruit | Water | Anticancer and antibacterial activity but no antifungal activity | [81] | |
Roots | Various polarity solvents | Anti-inflammatory and analgesic activity | [82] | |
Whole | Methanol | Anti-inflammatory and analgesic activity due to inhibition of malanodialdehyde formation | [82] | |
Roots | Methanol | Antiulcerogenic activity with cytoprotective nature of constituents | [83] | |
Leafs | Methanol | Mucoprotective activity and gastric antisecretory | [23] | |
Leaves | Methanol | Hepatoprotective activity with decrease in lipid peroxides with cytochrome p450 complex inhibition | ||
Fruit | Ethanol | Gastroprotective effect | [84] | |
Fruit | Phenol | Anti ulcerogenic, antimutagenic and anti cancerogenic compounds | [84] | |
Whole | Water | Antiviral activity due to inhibition of reverse transcriptase activity of HIV-1 | [85] | |
Leaves | Water | Antimalarial action against | [86] | |
Leaves | Water, ethanol | Significant antibacterial activity | [35] | |
Leaves | Water | Activate against standard human pathogenic yeasts strains | [87] | |
Leaves | Diethyl ether | Anti-pneumonia activity | [88] | |
Leaves | Water | Significant antibacterial activity but no antifungal activity | [35] | |
Leaves and fruits | Alcohol | Antifungal and antibacterial activities | [89] | |
Stem bark | Water | Antimicrobial activities against | [65] | |
Leaf | Water | Significant antimicrobial effect | [90] | |
Leaves | Acetone | Act as potent inhibitor of α-amylase | [68] |
Biological activities of extracts from various parts of
Almost all of the
All species of
The authors have no conflict of interest regarding this chapter.
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