Advances in Embryo Transfer",title:"胚胎移植新进展",subtitle:"Advances in Embryo Transfer",reviewType:"peer-reviewed",abstract:"本书阐述了生殖医学相关的技术知识,以21世纪最新进展和发展趋势为重点,注重创新性、实用性。 其内容从最佳的卵巢刺激方案、授精技术新进展,到胚胎移植操作技巧、胚胎冷冻保存以及子宫内膜容受性的最新研究成果等都做了详尽的描 述。本书旨在帮助更多从事辅助生殖技术的人员了解本领域最新进展,更新此领域中科学研究和临床诊治观念,以提高诊疗水平达到最佳活产 率。 Embryo transfer has become one of the prominent high businesses worldwide. This book updates and reviews some new developed theories and technologies in the human embryo transfer and mainly focus on discussing some encountered problems during embryo transfer, which gives some examples how to improve pregnancy rate by innovated techniques so that readers, especially embryologists and physicians for human IVF programs, may acquire some new and usable information as well as some key practice techniques. Major contents include the optimal stimulation scheme for ovaries, advance in insemination technology, improved embryo transfer technology and endometrial receptivity and embryo implantation mechanism. Thus, this book will greatly add new information for readers to improve human embryo transfer pregnancy rate. Please note that this is the official Chinese translation of the book originally published in English.",isbn:null,printIsbn:"978-953-51-1727-8",pdfIsbn:null,doi:"10.5772/59247",price:119,priceEur:129,priceUsd:155,slug:"advances-in-embryo-transfer-translation-chinese",numberOfPages:216,isOpenForSubmission:!1,isInWos:null,hash:"32b738c0d0cbce7a61a3ea63b5d43ed0",bookSignature:"Bin Wu",publishedDate:"October 23rd 2014",coverURL:"https://cdn.intechopen.com/books/images_new/4594.jpg",numberOfDownloads:4564,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,hasAltmetrics:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 16th 2014",dateEndSecondStepPublish:"October 7th 2014",dateEndThirdStepPublish:"January 11th 2015",dateEndFourthStepPublish:"April 11th 2015",dateEndFifthStepPublish:"May 11th 2015",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,editors:[{id:"108807",title:"Ph.D.",name:"Bin",middleName:null,surname:"Wu",slug:"bin-wu",fullName:"Bin Wu",profilePictureURL:"https://mts.intechopen.com/storage/users/108807/images/system/108807.jfif",biography:"Bin Wu, Ph.D., HCLD is currently a scientific laboratory director at Arizona Center for Reproductive Endocrinology and Infertility, USA. 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1. Introduction
Coffee is a tropical perennial plant from the Coffea genus of the Rubiaceae family. Although there are more than 103 species recognized nowadays, only 2 are responsible for world trade (arabica and canephora) [1]. The arabica variety constitutes more than 60% of the coffee that is commercialized in the international market and is cataloged by the consumers as the best coffee for its exceptional organoleptic characteristics [2]. This is due to the great variety of chemical compounds, which are responsible for granting the sensory quality and stimuli to the nervous system [3].
Coffee beverage is the result of the preparation of a drink by infusion from roasted and ground beans, with characteristic aroma and flavor, which have made it the second most consumed product in the world [3]. In the case of Colombia, coffee has been cataloged as one of the country’s main export products. For the above statement, coffee continues to be an activity of great importance. In this agricultural value chain, the by-products correspond to 80% of the total volume; the coffee industry generates about 2 billion tons of agro-waste, which represent a great pollution hazard [4]. Coffee pulp, husks, silverskin, peel, and spent coffee grounds are common coffee by-products [5].
Generally, coffee is internationally traded as green coffee [6], and it is obtained either by the wet, semi-wet, or dry methods. Typically, wet-processed coffee beans have a higher consumer acceptance than the dry-processed ones [7]. Wet coffee process consists of several steps, namely, de-pulping, fermentation, washing, de-hulling, and drying [8]. Depending on the processing method, either wet or dry, coffee pulp and husk are the first by-products and account for 29 and 12% of the overall coffee cherry [5]. Pulp and husk are rich in carbohydrates (35–85%), soluble fibers (30.8%), mineral (3–11%), proteins (5–11%), and bioactive compounds such as tannins, cyanidins, chlorogenic acid, caffeine and polyphenols [5, 6].
The disposal of agro-waste is a growing issue that can cause phytosanitary problems and cross-contamination in food industries [9]. As a consequence, new strategies to manage or benefit from agro-waste are urgently needed. One of the most promising options is to valorize the bioactive components present in the by-products [10]. In this sense, a growing field of studies highlights the presence of various bioactive compounds in agro-waste with potential applications in functional foods and nutraceutical developments [4, 9, 10]. The recovery of bioactive compounds improves the economic feasibility of the main processes, by producing secondary streams of value-added compounds.
This chapter assesses the usability of SUPRAS and water extraction for the recovery of high-added-value compounds from coffee peel. The method is simple and rapid and could be a sustainable strategy for coffee waste valorization.
2. Origin of coffee
Today, there are countless legends that talk about the origin and discovery of coffee. One of the most accurate ones mentions that coffee originated in the high plateau of Abyssinia and occurred in a wild form known as arabica. It was accidentally discovered by an Ethiopian shepherd named Kaldi. He noticed a strange behavior in his goats when eating fruits and leaves from a certain shrub, so he collected a sample and took it to a monastery [3], where possibly the cherries were mixed in the infusions or thrown into the fire allowing to feel a greater aroma and a better flavor [3].
The Arabs were the first to regularly consume coffee and give a primary role to its cultivation; hence, they are considered the pioneers in the establishment of coffee crops. Subsequently, coffee spreads to Mecca, Medina, and Syria and next to Aden and Cairo, covering the entire Muslim world around 1510 and Turkey in the year 1554 (www.cafedecolombia.com). The introduction of coffee in America was approximately in 1718 starting with the Dutch colony of Suriname, followed by plantations in French Guiana. In 1730, it was the British who introduced coffee into Jamaica and later spread to the rest of the continent [11].
Historically, it has been recognized that coffee was introduced to Colombia via the Venezuelan border by a traveler who came from French Guiana and carried a coffee plant. Thus, the first crops were in the North Santander and, later, in the departments of Antioquia, Tolima, Caldas, Valle del Cauca, Risaralda, Quindío, Cundinamarca, and Nariño, among others. The variety that was initially cultivated in Colombia was the Typica variety. At the end of the 1920s, a second variety was introduced, known as Bourbon, due to its higher yields; however, since the 1980s, the “Colombia” variety has been cultivated, coming from the Caturra variety and the Timor Hybrid, which is resistant to rust [12].
In Colombia, mainly arabica coffee is cultivated, due to this species produce a soft drink and of greater acceptance in the national and international market. The varieties of arabica are low or tall, and have red or yellow fruits. Some varieties of Arabica species are the Maragogype, Bourbon, Tabi, Typica, Castillo, Caturra, and Colombia, being these last three varieties the ones that are cultivated in greater proportion (see Table 1).
Variety
Description
Typica
Arabica, pajarito or national Coffee trees are fairly tall Its new leaves or bud are bronzed or reddish. The leaves are elongated Susceptible to rust Greater percentage of large beans than the varieties Caturra and Bourbon Planting density, 2500 trees per hectare
Bourbon
More branches than the Typica variety Lighter green buds than the other leaves Leaves are rounded Produces 30% more than Typica Susceptible to rust Planting density, 2500 trees per hectare
Tabi
Derived from crossing the Timor Hybrid with the Typical and Bourbon varieties Large bean, more than 80% supreme coffee Excellent quality ideal for obtaining specialty coffees Planting density, 3000 trees per hectare Susceptible to rust
Caturra
Lighter green buds than the other leaves Leaves are rounder than Bourbon’s Low-to-medium body Produces less than Bourbon and more than Typica Behaves well in the coffee zone Susceptible to rust Planting density, up to 10,000 trees per hectare
Colombia variety
The bud of the plants is bronzed Durable resistance to coffee rust attack Production equal to or greater than Caturra Type of bean and quality of beverage are similar to other varieties of arabica coffee
Table 1.
Colombian coffee varieties.
Internationally, 80% of the world’s production corresponds to the arabica species, which is cultivated mainly in Colombia, Brazil, and in some Asian countries such as India or in Africa such as Kenya and Ethiopia [12]. The remaining 20% corresponds to the species canephora and is generally cultivated in Africa, Brazil, and Indonesia, with differentiating factors such as resistance to rust and a higher caffeine content [13].
The first commercial production of coffee was made in 1835. In this opportunity, 2.560 sack bags were exported from Cucuta. In 1927, the National Federation of Coffee Growers was founded to promote the development of Colombian coffee culture. This organization makes the process of purchasing, storing, and exporting coffee, as well as accompanying and advising coffee growers from different regions of the country. In this way, coffee cultivation was consolidated as one of the country’s main agricultural activities [3]. Today, the sector continues to be an important articulating axis in the country’s rural development and providing economic stability despite the coffee crises represented by high production costs and low harvest levels. To date, the National Federation of Coffee Growers has reported a participation of 560,000 farms dedicated to coffee cultivation, which translates to 948,000 hectares of which 27% are harvested with the variety Colombia. The rest corresponds mainly to the varieties Typica, Caturra, and Bourbon [12], with 66% of the cultivated area in the country, being cataloged as the product with greater participation among the other registered crops [14], providing approximately 785 thousand rural jobs directly and 1.5 million indirectly (www.federaciondecafeteros.org).
3. Characteristics of coffee
Coffees are berries obtained from a perennial and topical plant (cafeto) [3]. The coffee beans are morphologically very variable and have different shapes, colors, and sizes. Internally, seeds are found (usually two per fruit), which are processed and used to prepare infusions [3].
Plants have a cleft in the central part of the seed. Depending on the species, it is possible to find small shrubs or trees larger than 10 m. The leaves are simple, opposite, and with stipules and present variability both in size and texture. The plant has white and tubular flowers, which are complete, i.e., all organs are in the same flower [2]. The root is a vital organ for the coffee plant, because through it, the plant takes the water and nutrients for its growth and also is an anchor to the soil [3]. The coffee plant has a main root that can reach a depth of up to 50 cm, from which other thick roots are available to support the thinner ones in charge of absorbing nutrients. The stem forms the skeleton of the coffee tree along with the branches with leaves, flowers, and finally, the fruits (www.cafedecolombia.com).
Because of the union of the grain of pollen with the ovule, the fruit and seeds are formed. The coffee fruit is a cherry that is divided into three layers: epicarp or skin, which is the outermost layer; mesocarp or pulp, which forms a sweet and aromatic pulp of mucilaginous nature, protected by a yellow cellulose layer called parchment or endocarp; and finally a silvery layer, which covers the two oval-shaped grains called endosperm [11].
3.1 Chemical composition of coffee
Coffee has a number of chemical components, mainly water and dry matter, such as minerals, organic substances (carbohydrates, lipids, proteins), alkaloids (caffeine and trigonelline), carboxylic and phenolic acids, and volatile compounds responsible for the aroma. All together result in a great diversity and complexity of structures; however, these may have modifications in any of their stages, either from the crop or the mill [15].
The chemical composition varies depending on the species [16]. In the case of Coffea arabica, it has a higher lipid and sucrose content than Coffea canephora. The robusta differs by its higher content of polysaccharides, caffeine, chlorogenic acids, and ashes. Table 2 shows the most representative chemical components in arabica and robusta species.
Chemical composition of the coffee beans of the arabica and robusta species.
Expressed in percentage, on a dry basis.
N.D. Non-determined.
Additionally, within the varieties cultivated in Colombia are differences (see Table 3), due to the intrinsic factors, soil fertilization, atmospheric conditions, sowing density, and planting age, among others [15].
Coffee variety
Fiber (%)
Lipids (%)
Proteins (%)
Caffeine (%)
Chlorogenic acids (%)
Ash (%)
Bourbon
21.75
15.27
13.90
1.15
7.37
3.78
Caturra
18.85
13.98
14.79
1.13
6.97
3.39
Colombia yellow fruit
18.45
13.07
14.45
1.16
7.55
3.49
Colombia red fruit
16.69
14.27
13.92
1.19
7.42
3.52
Typica
18.71
13.99
14.59
1.20
6.66
3.43
Robusta
15.53
11.42
15.66
2.10
8.08
3.96
Table 3.
Chemical composition of coffee bean in the different varieties sown in Colombia.
Water: The water content of the bean is one of the most relevant factors in all coffee processes, from germination to roasting. In the fresh fruit, the water content is between 70 and 80% [25]. After the dry process, the water content is reduced up to 10–12% to improve the stability and avoid microbial proliferation, prolonging its shelf life [16].
Carbohydrates: Among the main polysaccharides in coffee are mannan or galactomannan (polymer of mannose and galactose), constituting 50% of the polysaccharides, 30% of arabinogalactan (polymer of galactose and arabinose), 15% of cellulose (polymer of glucose), and 5% of peptic substances [16]. The beans in an optimum ripening stage have a higher sucrose content than defective and immature beans. In the arabica species, the sucrose content ranged between 6 and 9%, while robusta contains 3–7% of sucrose [16]. Monosaccharides and some disaccharides such as lactose and maltose may oxidize to form alcohols and acids in the fermentation process or may react with the amino acids in roasting to form melanoidins, which are responsible for the coloration (enzymatic browning) of the roasted coffee [16].
Lipids: Triglycerides, linoleic, and palmitic acid are mainly presents (~ 75% of coffee lipids). The unsaponifiable matter constitutes 20 to 25% of the lipids of coffee. Sterols are 2.2% of coffee lipids and contain β-sitosterol, stigmasterol, campesterol, and ∆5 avenasterol. Cholesterol constitutes 0.11% of the dry weight of coffee beans (0.044% in robusta coffee) [16].
Nitrogen compounds: Nitrogen constitutes between 1.30 and 3.23% of the dry weight of the green coffee beans, after the roasted decreased up to 1.51 and 2.14% [16].
Alkaloids: Alkaloids are the substances responsible for giving the bitter taste of coffee, the most representative are caffeine, trigonelline, paraxanthine, theobromine, and theophylline [16]. Caffeine is a methylxanthine, which have attributed health benefits, such as improve the central nervous, cardiovascular, respiratory, renal, and muscular system [19]. For the above statement, caffeine is important in the pharmaceutical industry. Also it has important bioactive properties, so it may be cataloged as a functional ingredient, which can be used in different food matrices [20].
Chlorogenic acids: They are a series of phenolic esters derived from the union of an ester between caffeic acid and quinic acid [3]. The chlorogenic acid content in green coffee is 7% and reaches 4% after roasting [3]. A volume of 200 mL of roasted and ground coffee could provide between 70 and 350 mg of chlorogenic acid [16]. Coffee beans contain more than 40 chlorogenic acids, especially esters of quinic acid such as CQA, di-CQA, and FQA [16]. This compound has a significant antioxidant capacity and also a stimulant, expectorant, diuretic, choleretic, and antihepatotoxic effects [21].
3.2 By-products of coffee processing
The coffee bean is picked after reach the commercial ripening stage; it next must be quickly transformed into dry parchment coffee, to avoid accelerated fermentation because the entire bean includes high water and sugar content [22]. For these purposes the external layer is removed from the coffee bean and only 5% of the biomass is used to produce a coffee crop, the rest remains in a residual form as leaves, branches, green fruits, pulp, mucilage, parchment, and silverskin, among others [22]. There are two primary methods for processing coffee, to obtain green coffee (traded coffee beans): wet and dry. In the dry process, no layers are removed, and coffee cherries are laid out in the sun to dry. In the wet process, the fruit covering the layer is removed before they are dried. Approximately 40% of all coffee around the world is wet processed [23], because it is considered to produce superior tasting offers [8, 24]. In the wet process, it has been estimated that 40–45 L of wastewater are produced per kilogram of coffee [25].
In Colombia, the wet process has been implemented for decades, which generates a contamination of 115 g of COD per kilogram of cherry coffee [22]. To overcome this problem, new methods were developed; one of these is the Belcosub technology, in which the fruit is de-pulped. The external layer is transported without water, and the organic residues are reused; however, these do not generate a significant value. This system avoids up to 74% of the contamination of water resources, since less than 5 l/kg of dry parchment are used [22]. The most recent technology suggested by the National Federation of Coffee Growers is the ecological mill without dumping (Ecomil) that reduces the amount of water to 0.5 l of water per kilogram of dry parchment, implementing tanks generally in stainless steel that do not need water for coffee emptying. In addition, the water resulting from this process goes directly to purifying tanks with microorganisms and a series of filters that allow the water that falls to water sources to be clean and do not generate any pollution [22].
As mentioned above, a large amount of coffee bean components are removed. It is important to highlight that approximately 43.58% of the weight of the dried fruit are these by-products [22]. The valorization of these by-products through the recovery of bioactives has increasingly become of interest for food, pharmaceutical, and cosmetic industries [26, 27, 28, 29, 30].
A promising option to recover these bioactive compounds is the coffee pulp, which involves the epicarp and part of the mesocarp of the fruit. This by-product contains significant amounts of caffeine and another component [31]. The reported chemical composition (expressed in dry mass) includes polyphenols (1.5–2.9%), total sugars (4.1%), protein (4–13.3%), lignin (17.5–19.3%), lipids (1.7–2.5%), cellulose (18–63%), total fiber (18–60.5%), ash (6–10%), tannins (1.8–9%), carbohydrates (44–89%), reducing sugars (12.4%), nonreducing sugars (2%), caffeine (1.2–1.5%), and chlorogenic acid (1.6%) [5, 31, 32, 33].
3.3 Nowadays uses of coffee by-products
Coffee consumption has increased significantly, which generates an increase in waste amount [32]. These wastes have pollution problems. Discharges of wastewater from industrial activities have become a global issue of concern [34]. Different alternatives to both mitigate the negative effects in the discharges of coffee by-products and generate added-value alternatives have been evaluated. For this purpose, different studies have been carried out to evaluate alternative uses and reduce the toxic effect on the environment [35].
According to CONAMA Resolution No. 430 from 05/13/2011, the concentration of phenols should be lower than 0.5 mg L−1 [36], due to when phenolic compounds are discharged into the environment will lead to the degradation process of organic materials difficult to degrade [37]. Coffee by-products are the polyphenols and also carbohydrates, proteins, and pectins, making them potential sources of agro-industrialization for various industries, as well as renewable economic resources that can be given a high added value [32]. Table 4 presented alternatives for coffee by-product reuse.
Among the alternatives previously proposed for the food and nonfood industry, emphasis will be placed on the production of extraction of caffeine both from the beans and from the different by-products obtained.
Considering that caffeine is an alkaloid that possesses antioxidant capacity and increases energy availability, cognitive performance, and neuromuscular coordination, among others [38], one of the alternative uses of coffee and its by-products has focused on the extraction of this compound of functional interest, both for the food and nonfood sectors. However, in recent years, new extraction techniques have been sought in order to reduce the generation of waste, with a lower consumption of chemical reagents and therefore improve the efficiency of the process, reducing process times [39].
4. SUPRAS extraction
Two amphiphiles (decanoic acid and hexanol) and two dispersion solvents (THF:water and ethanol:water) were selected for the study to generate a variety of SUPRAS based on previous promising results [73, 74, 75]. Hexanol has the advantage over decanoic acid for the possibility of removal by evaporation if further steps are required after the extraction of bioactives, while decanoic acid is a more biocompatible and renewable option. Thus, this offers different strategies for amphiphile recovery and reutilization in industrial purposes, being usually easier the operation with liquid phases. Ethanol was tested together with THF, the first considered biocompatible and authorized for use in food and the second easily removable by evaporation due to its high vapor pressure (143 mm Hg at 20°C) and relatively low boiling point (66°C). The coacervating agent (external stimuli driving the self-assembly synthesis of SUPRAS) is water in both cases, as a poor solvent for the amphiphiles promoting the aggregation as described before [76]. The type of amphiphile and of the dispersion solvent and the composition of the ternary mixture in the bulk solution (amphiphile, organic solvent, water) give rise to SUPRAS with different final composition and microstructure and volumes which can influence the extraction efficiency [73]. Thus, SUPRAS binding interactions and restricted access properties (conferred by the size of the aggregates) can be tuned depending on the amphiphile functional groups (OH, COOH) providing hydrogen bonds for extraction and dipole–dipole interactions, the alkyl chain length (C6, C10) giving dispersion interactions and the dispersion medium composition (providing hydrogen bonds, dipole–dipole interactions, and dispersion forces under different ratios of ethanol:water or THF:water mixtures).
Figures 1 and 2 show the caffeine and chlorogenic acid extraction with SUPRAS. As expected, recoveries were influenced by the amphiphile nature and bulk solution composition of the ternary mixture (amphiphile, water, organic solvent). Under all the tested SUPRAS, the caffeine extraction efficiency was in the range 31–68% ± 2.8%, while the chlorogenic acid extraction efficiency was between 0 and 26 ± 2.5%.
Figure 1.
Surface response plots for caffeine extraction from coffee peel with (a) hexanol, THF; (b) hexanol, ethanol; (c) decanoic acid, THF; and (d) decanoic acid, ethanol.
Figure 2.
Surface response for chlorogenic acid extraction from coffee peel with (a) hexanol, THF; (b) hexanol, ethanol; (c) decanoic acid, THF; and (d) decanoic acid, ethanol.
The highest caffeine extraction was obtained with hexanol as amphiphile and ethanol:water as dispersion solvent for the design of the SUPRAS (maximum at 69 ± 0.9% with 7% of hexanol and 15% of ethanol). The range obtained for the other SUPRAS was 45–56 ± 1.1%, 31–56 ± 2%, and 39–65 ± 7.5% with hexanol-THF, decanoic acid-THF, and decanoic acid-ethanol, respectively.
SUPRAS based on ethanol:water were indeed more suitable than those based on THF:water to extract caffeine with both amphiphiles. A possible explanation is that ethanol as protic solvent can extract caffeine more efficiently than THF (aprotic solvent), acting as hydrogen bond donor for caffeine, which contains hydrogen bond acceptors’ groups only. Additionally, the dielectric constant of ethanol is higher than that of THF (24 and 7.5, respectively). This parameter is a relative measure of the chemical polarity and could enhance the extraction of the polar bioactives by dipole–dipole interactions. With respect to the amphiphile, hexanol was the best choice, and the highest efficiency rates (69 ± 0,9%) were obtained in SUPRASs formed with this organic alcohol. The higher polarity of hexanol over decanoic acid could be the reason for the higher extraction efficiency of the polar bioactive compounds. Furthermore, the smaller size of hexanol aggregates, due to its shorter alkyl chain length, could generate SUPRAS with greater surface area, and, consequently, it will provide more available binding interactions for the bioactive components.
Chlorogenic acid extraction rates were lower than caffeine rates, and no clear correlation was found with the SUPRAS synthetic conditions. Its higher polarity could lead to losses in the equilibrium competing phase (i.e., calculated log P −0.4 and −0.1 for chlorogenic acid and caffeine, respectively). Furthermore, chlorogenic acid is a bigger molecule than caffeine; its molar mass is 354.31 g/mol, and its topological polar surface area is 165 A2, while for caffeine, values of 194.19 g/mol and 58.4 A2 are calculated. The higher contact polar area of caffeine could enhance the recoveries too. Caffeine is the most routinely ingested bioactive substance. Its consumption possesses health benefits, including lower risks of Parkinson’s and Alzheimer’s disease, a favorable effect on liver function, energy expenditure, and a decreased risk of developing certain cancers (endometrial, prostatic, colorectal, liver) [77]; it can stimulate fat oxidation, thermogenesis, and energy expenditure subsequently, which reduces body weight [78]. Caffeine is consumed daily, in the United States 89% of the population 19 years of age or older consumes some form of caffeine daily, but the primary source of caffeine is coffee (64%) [79].
Chlorogenic acid is the major polyphenol in edible plants with many health-promoting properties [80]. It has a strong antioxidant activity, anti-lipid peroxidation, anticancer effects [81], anti-inflammatory activity, inhibition of α-amylase, and α-glucosidase linked to type 2 diabetes and anti-obesity properties [82]; it also has antimicrobial properties [83]. Due to the beneficial effects of this bioactive component, it has been used for the preparation of functional materials in food and pharmaceutical areas [80].
5. Water extraction
The processing of every 60,000 tons of dried coffee beans produces approximately 218,400 tons of fresh pulp and mucilage or mesocarp [84]. Generally, the pulp is removed with mechanical movements generated by pulping and constitutes about 29–43% (w/w) of the fruit [6, 85]; the pulp a potential use has been identified by the compounds present such as anthocyanins, caffeine, and phenolic compounds with which an important added value can be generated [46, 86, 87]. In this study dried pulp was employed for the biocomponents extractions, using hot water as solvent, the dried pulp of arabica variety was selected with 10–12% of humidity, the response surface methodology was used to determine the effect of solvent temperature (water) (60–90°C) and extraction time (1–8 min) on the functional characteristics of the infusions obtained.
For the preparation of the infusions, dried pulp was taken and placed in infusers. Each sample was deposited in a beaker with 250 mL of the solvent (water) at a different time and temperature conditions. The samples were quantified polyphenol content by the Folin–Ciocalteu method reported by [44, 46]; the quantification of caffeine and chlorogenic acid was done by high-performance liquid chromatography (HPLC).
The chromatographic separation was performed in a Shimadzu Prominence with a UV detector and quaternary pump system (Shimadzu, Japan); the samples were filtrated in a cellulose filter of 25 μm, and the filtrated sample (20 μL) was conducted using a C8 Restek column (Restek Corporation, USA). The mobile phase consisted of 0.1% acetic acid and 30% methanol in water v/v; the injection volume was 20 μL. The mobile phase flow rate was 0.5 mL/min (35°C). The reference standards were used for identification, and calibration curves were obtained for quantification chlorogenic acid and caffeine.
The peak of caffeine was observed at the elution time of 11.59 min. The caffeine extracted from 3.3 g of coffee pulp ranged between 21–51 mg/L and did not depend on the extraction temperature from 65 to 90°C, the time has an effect in time upper 4.5 min [47], and the values of caffeine were higher (Figure 3a). The chlorogenic acid had a similar behavior of caffeine (Figure 3b) with range values 5–9 mg/L; this indicates that those substances are stable during extraction and heat treatment and storage of the beverage [84].
Figure 3.
Surface response for (a) caffeine and (b) chlorogenic acid using water as solvent.
In the extraction process, this type of biocomponents is the solvent, since the type of compound to be extracted depends on the type of solvent used for the capacity they possess which is directly related to their polarity. Extractions using water improved the extraction of phenolic compounds, caffeine and chlorogenic acid due to it polarity [84, 88].
Therefore, coffee pulp can be a raw material with a high content of compounds, and its consumption (e.g., in infusions or extracts) can help prevent degenerative diseases, taking into account that a relationship has been established between consumption of biocomponents such as polyphenols, caffeine, and chlorogenic acid and the reduction of risks of chronic diseases, including obesity and diabetes [5, 6, 32, 89]. The coffee pulp has potential for use in the food, pharmaceutical, and cosmetic industry, becoming an alternative for products to generate added value and reduce negative effects on the environment and improve the profitability of producers within of circular economy and biorefineries.
6. Conclusion
This study shows the ability of SUPRAS, nanostructured solvents made up of assembled amphiphile aggregates and water, for valorization of coffee waste. The results proved that these solvents offer excellent extraction capacity of high-added-value compounds with interest for the food, pharmaceutical, and cosmetic industry.
\n',keywords:"agri-food waste, by-products, coffee husks, coffee peel, spent coffee grounds",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/69900.pdf",chapterXML:"https://mts.intechopen.com/source/xml/69900.xml",downloadPdfUrl:"/chapter/pdf-download/69900",previewPdfUrl:"/chapter/pdf-preview/69900",totalDownloads:449,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,dateSubmitted:"June 21st 2019",dateReviewed:"September 3rd 2019",datePrePublished:"November 5th 2019",datePublished:null,dateFinished:null,readingETA:"0",abstract:"Coffee is one of the most consumed products around the world; 2.25 billions of coffee cup are consumed everyday in the world. For coffee crop production, different by-products are produced, such as coffee peel, coffee husk, parchment, and spent coffee grounds. These by-products have several problems associated at the final disposition. In this book chapter, we study the main coffee varieties produced in the world, the by-products produced, and its composition and finally assess the potential of supramolecular solvents (SUPRAS) and water as green solvents for high-added-value compound extractions. Bioactive compounds were extracted from fresh and dried coffee peel in an acceptable rate for industrial applications. SUPRAS offer advantages in terms of rapidity (5 min) and simplicity (stirring and centrifugation at room temperature), thus avoiding costly processes based on high pressure and temperature. Extractions carried out using water as solvent is another technique of extraction mixing temperature (above 60°C) and time (4.5 min) obtained a beverage or solution with presence a bioactive compounds how caffeine, chlorogenic acid and polyphenols.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/69900",risUrl:"/chapter/ris/69900",book:{slug:"coffee-production-and-research"},signatures:"Laura Sofía Torres-Valenzuela, Johanna Andrea Serna-Jiménez and Katherine Martínez",authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Origin of coffee",level:"1"},{id:"sec_3",title:"3. Characteristics of coffee",level:"1"},{id:"sec_3_2",title:"3.1 Chemical composition of coffee",level:"2"},{id:"sec_4_2",title:"3.2 By-products of coffee processing",level:"2"},{id:"sec_5_2",title:"3.3 Nowadays uses of coffee by-products",level:"2"},{id:"sec_7",title:"4. SUPRAS extraction",level:"1"},{id:"sec_8",title:"5. Water extraction",level:"1"},{id:"sec_9",title:"6. 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Chinchiná, Colombia: Cenicafé; 2015. 35 p'},{id:"B23",body:'Garde WK, Buchberger SG, Wendell D, Kupferle MJ. Application of Moringa oleifera seed extract to treat coffee fermentation wastewater. Journal of Hazardous Materials. 2017;329:102-109'},{id:"B24",body:'Aguiar LL, Andrade-Vieira LF, de Oliveira David JA. Evaluation of the toxic potential of coffee wastewater on seeds, roots and meristematic cells of Lactuca sativa L. Ecotoxicology and Environmental Safety. 2016;133:366-372'},{id:"B25",body:'Zayas Péerez T, Geissler G, Hernandez F. Chemical oxygen demand reduction in coffee wastewater through chemical flocculation and advanced oxidation processes. Journal of Environmental Sciences. 2007;19(3):300-305'},{id:"B26",body:'Campos-Vega R, Loarca-Piña G, Vergara-Castañeda HA, Oomah BD. Spent coffee grounds: A review on current research and future prospects. Trends in Food Science and Technology. 2015;45(1):24-36'},{id:"B27",body:'Hall S, Desbrow B, Anoopkumar-Dukie S, Davey AK, Arora D, McDermott C, et al. A review of the bioactivity of coffee, caffeine and key coffee constituents on inflammatory responses linked to depression. Food Research International. 2015;76(3):626-636'},{id:"B28",body:'Kovalcik A, Obruca S, Marova I. Valorization of spent coffee grounds: A review. Food and Bioproducts Processing. 2018;110:104-119'},{id:"B29",body:'McNutt J, He QS. Spent coffee grounds: A review on current utilization. Journal of Industrial and Engineering Chemistry. 2019;71:78-88'},{id:"B30",body:'Torres-Valenzuela LS, Ballesteros-Gómez A, Sanin A, Rubio S. Valorization of spent coffee grounds by supramolecular solvent extraction. Separation and Purification Technology. 2019;228:115759'},{id:"B31",body:'Armas Flores EA, Cornejo Mazariego NC, Murcia Zamora KM. Propuesta para el aprovechamiento de los subproductos del beneficiado del café como una alternativa para la diversificación de la actividad cafetera y aporte de valor a la cadena productiva. Buenos Aires, Argentina: Universidad del Salvador; 2008'},{id:"B32",body:'Murthy PS, Madhava Naidu M. Sustainable management of coffee industry by-products and value addition—A review. Resources, Conservation and Recycling. 2012;66:45-58'},{id:"B33",body:'Preedy VR. Coffee in Health and Disease Prevention [Internet]. Elsevier; 2015 [citado 9 de julio de 2019]. 1080 p. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/C20120069591'},{id:"B34",body:'Anastopoulos I, Karamesouti M, Mitropoulos AC, Kyzas GZ. A review for coffee adsorbents. Journal of Molecular Liquids. 2017;229:555-565'},{id:"B35",body:'Noriega Salazar A, Silva Acuña R, García de Salcedo M. Composición química de la pulpa de café a diferentes tiempos de ensilaje para su uso potencial en la alimentación animal. Zootecnia Tropical. 2009;27:135-141'},{id:"B36",body:'Chagas PMB, Torres JA, Silva MC, Corrêa AD. Immobilized soybean hull peroxidase for the oxidation of phenolic compounds in coffee processing wastewater. International Journal of Biological Macromolecules. 2015;81:568-575'},{id:"B37",body:'Novita E. Biodegradability simulation of coffee wastewater using instant coffee. Agriculture and Agricultural Science Procedia. 2016;9:217-229'},{id:"B38",body:'Guglielmetti A, D’Ignoti V, Ghirardello D, Belviso S, Zeppa G. Optimisation of ultrasound and microwave-assisted extraction of caffeoylquinic acids and caffeine from coffee silverskin using response surface methodology. Italian Journal of Food Science. 2017;29(3). Disponible en: https://www.chiriottieditori.it/ojs/index.php/ijfs/article/view/727'},{id:"B39",body:'De Marco I, Riemma S, Iannone R. Life cycle assessment of supercritical CO2 extraction of caffeine from coffee beans. Journal of Supercritical Fluids. 2018;133:393-400'},{id:"B40",body:'Torres-Valenzuela LS, Martínez KG, Serna-Jimenez JA, Hernández MC. Secado de pulpa de café: Condiciones de proceso, modelación matemática y efecto sobre propiedades fisicoquímicas. Información Tecnológica. 2019;30(2):189-200'},{id:"B41",body:'Pedraza-Beltrán P, Estrada-Flores JG, Martínez-Campos AR, Estrada-López I, Rayas-Amor AA, Yong-Angel G, et al. On-farm evaluation of the effect of coffee pulp supplementation on milk yield and dry matter intake of dairy cows grazing tropical grasses in central Mexico. Tropical Animal Health and Production. 2012;44(2):329-336'},{id:"B42",body:'Didanna HL. A critical review on feed value of coffee waste for livestock feeding. World Journal of Biology and Biological Sciences. 2014;2(5):72-086'},{id:"B43",body:'Rathinavelu R, Graziosi G. Potential Alternative Use of Coffee Wastes and by-Products. 2005:1-4'},{id:"B44",body:'Duangjai A, Suphrom N, Wungrath J, Ontawong A, Nuengchamnong N, Yosboonruang A. Comparison of antioxidant, antimicrobial activities and chemical profiles of three coffee (Coffea arabica L.) pulp aqueous extracts. Integrative Medicine Research. 2016;5(4):324-331'},{id:"B45",body:'Tello J, Viguera M, Calvo L. Extraction of caffeine from Robusta coffee (Coffea canephora var. Robusta) husks using supercritical carbon dioxide. Journal of Supercritical Fluids. 2011;59:53-60'},{id:"B46",body:'Magoni C, Bruni I, Guzzetti L, Dell’Agli M, Sangiovanni E, Piazza S, et al. Valorizing coffee pulp by-products as anti-inflammatory ingredient of food supplements acting on IL-8 release. Food Research International. 2018;112:129-135'},{id:"B47",body:'Narita Y, Inouye K. High antioxidant activity of coffee silverskin extracts obtained by the treatment of coffee silverskin with subcritical water. Food Chemistry. 2012;135(3):943-949'},{id:"B48",body:'Borrelli RC, Esposito F, Napolitano A, Ritieni A, Vicenzo F. Characterization of a new potential functional ingredient: Coffee silverskin. Journal of Agricultural and Food Chemistry. 2004;52(5):1338-1343'},{id:"B49",body:'Furusawa M, Narita Y, Iwai K, Fukunaga T, Nakagiri O. Inhibitory effect of a hot water extract of coffee “silverskin” on hyaluronidase. Bioscience, Biotechnology, and Biochemistry. 2011;75(6):1205-1207'},{id:"B50",body:'Mirón-Mérida VA, Yáñez-Fernández J, Montañez-Barragán B, Barragán Huerta BE. Valorization of coffee parchment waste (Coffea arabica) as a source of caffeine and phenolic compounds in antifungal gellan gum films. LWT-Food Science and Technology. 2019;101:167-174'},{id:"B51",body:'Gouvea BM, Torres C, Franca AS, Oliveira LS, Oliveira ES. Feasibility of ethanol production from coffee husks. Biotechnology Letters. 2009;31(9):1315-1319'},{id:"B52",body:'Bonilla VA. Reaproveitamento de resíduos da indústria do café como matéria prima para a produção de etanol. Universidad Federal de Lavras; 2014'},{id:"B53",body:'Harsono SS, Salahuddin, Fauzi M, Purwono GS, Soemarno D, Kissinger. Second generation bioethanol from Arabica coffee waste processing at smallholder plantation in Ijen plateau region of East Java. Procedia Chemistry. 2015;14:408-413'},{id:"B54",body:'Mussatto SI, Machado EMS, Carneiro LM, Teixeira JA. Sugars metabolism and ethanol production by different yeast strains from coffee industry wastes hydrolysates. Applied Energy. 2012;92:763-768'},{id:"B55",body:'Jayachandra T, Venugopal C, Anu Appaiah KA. Utilization of phytotoxic agro waste—Coffee cherry husk through pretreatment by the ascomycetes fungi Mycotypha for biomethanation. Energy for Sustainable Development. 2011;15(1):104-108'},{id:"B56",body:'Orozco AL, Pérez MI, Guevara O, Rodríguez J, Hernández M, González-Vila FJ, et al. Biotechnological enhancement of coffee pulp residues by solid-state fermentation with Streptomyces. Py–GC/MS analysis. Journal of Analytical and Applied Pyrolysis. 2008;81(2):247-252'},{id:"B57",body:'Kivaisi A. Pretreatment of robusta coffee hulls and co-digestion with cow-dung for enhanced anaerobic digestion. Tanzania Journal of Science. 2002;28(2):1-10'},{id:"B58",body:'Battista F, Fino D, Mancini G. Optimization of biogas production from coffee production waste. Bioresource Technology. 2016;200:884-890'},{id:"B59",body:'Corro G, Paniagua L, Pal U, Bañuelos F, Rosas M. Generation of biogas from coffee-pulp and cow-dung co-digestion: Infrared studies of postcombustion emissions. Energy Conversion and Management. 2013;74:471-481'},{id:"B60",body:'Houbron E, Larrinaga A, Rustrian E. Liquefaction and methanization of solid and liquid coffee wastes by two phase anaerobic digestion process. Water Science and Technology. 2003;48(6):255-262'},{id:"B61",body:'Chala B, Oechsner H, Latif S, Müller J. Biogas potential of coffee processing waste in Ethiopia. Sustainable Switzerland. 2018;10(8):1-14'},{id:"B62",body:'Neves L, Oliveira R, Alves MM. Anaerobic co-digestion of coffee waste and sewage sludge. Waste Management. 2006;26(2):176-181'},{id:"B63",body:'Juliastuti SR, Widjaja T, Altway A, Iswanto T. Biogas production from pretreated coffee-pulp waste by mixture of cow dung and rumen fluid in co-digestion. AIP Conference Proceedings. 2017;1840(May)'},{id:"B64",body:'Menéndez JA, Domínguez A, Fernández Y, Pis JJ. Evidence of self-gasification during the microwave-induced pyrolysis of coffee hulls. Energy & Fuels. 2007;21(1):373-378'},{id:"B65",body:'Saenger M, Hartge E-U, Werther J, Ogada T, Siagi Z. Combustion of coffee husks. Renewable Energy. 2001;23(1):103-121'},{id:"B66",body:'Sathianarayanan A, Khan AB. An eco-biological approach for resource recycling and pathogen (Rhizoctonia solani Kuhn.) suppression. Journal of Environmental Protection Science. 2008;2(October):36-39'},{id:"B67",body:'Kassa H, Workayehu T. Evaluation of some additives on coffee residue (coffee husk and pulp) quality as compost, southern Ethiopia. International Invention Journal of Agricultural and Soil Science. 2014;2'},{id:"B68",body:'Dzung NA, Dzung TT, Khanh VT. Evaluation of coffee husk compost for improving soil fertility and sustainable coffee production in rural Central Highland of Vietnam. Resource Enviroment. 2013;3(4):77-82'},{id:"B69",body:'Brand D, Pandey A, Roussos S, Soccol CR. Biological detoxification of coffee husk by filamentous fungi using a solid state fermentation system. Enzyme and Microbial Technology. 2000;27(1-2):127-133'},{id:"B70",body:'Murthy P, Manonmani M. Bioconversion of coffee industry wastes with white rot fungus Pleurotus florida. Research Journal of Environmental Sciences. 2008;2(2):145-150'},{id:"B71",body:'Santos da Silveira J, Durand N, Lacour S, Belleville M-P, Perez A, Loiseau G, et al. Solid-state fermentation as a sustainable method for coffee pulp treatment and production of an extract rich in chlorogenic acids. Food and Bioproducts Processing. 2019;115:175-184'},{id:"B72",body:'Moreira MD, Melo MM, Coimbra JM, dos Reis KC, Schwan RF, Silva CF. Solid coffee waste as alternative to produce carotenoids with antioxidant and antimicrobial activities. Waste Management. 2018;82:93-99'},{id:"B73",body:'Ballesteros-Gómez A, Rubio S. Environment-responsive alkanol-based supramolecular solvents: Characterization and potential as restricted access property and mixed-mode extractants. Analytical Chemistry. 2012;84(1):342-349'},{id:"B74",body:'García-Fonseca S, Ballesteros-Gómez A, Rubio S. Restricted access supramolecular solvents for sample treatment in enzyme-linked immuno-sorbent assay of mycotoxins in food. Analytica Chimica Acta. 2016;935:129-135'},{id:"B75",body:'Accioni F, García-Gómez D, Girela-López E, Rubio S. SUPRAS extraction approach for matrix-independent determination of amphetamine-type stimulants by LC-MS/MS. Talanta. 2018;182:574'},{id:"B76",body:'Ruiz F-J, Rubio S, Pérez-Bendito D. Water-induced coacervation of alkyl carboxylic acid reverse micelles: Phenomenon description and potential for the extraction of organic compounds. Analytical Chemistry. 2007;79(19):7473-7484'},{id:"B77",body:'Mejia EG, Ramirez-Mares MV. Impact of caffeine and coffee on our health. Trends in Endocrinology and Metabolism. 2014;25(10):489-492'},{id:"B78",body:'Golzarand M, Toolabi K, Aghasi M. Effect of green tea, caffeine and capsaicin supplements on the anthropometric indices: A meta-analysis of randomized clinical trials. Journal of Functional Foods. 2018;46:320-328'},{id:"B79",body:'Beyer LA, Hixon ML. Review of animal studies on the cardiovascular effects of caffeine. Food and Chemical Toxicology. 2018;118:566-571'},{id:"B80",body:'Santana-Gálvez J, Cisneros-Zevallos L, Jacobo-Velázquez D, Santana-Gálvez J, Cisneros-Zevallos L, Jacobo-Velázquez DA. Chlorogenic acid: Recent advances on its dual role as a food additive and a nutraceutical against metabolic syndrome. Molecules. 2017;22(3):358'},{id:"B81",body:'Nam S-H, Ko J-A, Jun W, Wee Y-J, Walsh MK, Yang K-Y, et al. Enzymatic synthesis of chlorogenic acid glucoside using dextransucrase and its physical and functional properties. Enzyme and Microbial Technology. 2017;107:15-21'},{id:"B82",body:'Hwang SH, Zuo G, Wang Z, Lim SS. Novel aldose reductase inhibitory and antioxidant chlorogenic acid derivatives obtained by heat treatment of chlorogenic acid and amino acids. Food Chemistry. 2018;266:449-457'},{id:"B83",body:'Muthuswamy S, Rupasinghe HPV. Fruit phenolics as natural antimicrobial agents: Selective antimicrobial activity of catechin, chlorogenic acid and phloridzin. Journal of Food, Agriculture and Environment. 2007:5'},{id:"B84",body:'Heeger A, Kosińska-Cagnazzo A, Cantergiani E, Andlauer W. Bioactives of coffee cherry pulp and its utilisation for production of cascara beverage. Food Chemistry. 2017;221:969-975'},{id:"B85",body:'Bressani R. Potential uses of coffee-berry by-products. Braham JE, Bressani R, editores. Coffee Pulp: Composition, Technology and Utilization. Ottawa; 1979'},{id:"B86",body:'de Melo Pereira GV, de Carvalho Neto DP, Magalhães Júnior AI, Vásquez ZS, Medeiros ABP, Vandenberghe LPS, et al. Exploring the impacts of postharvest processing on the aroma formation of coffee beans—A review. Food Chemistry. 2019;272:441-452'},{id:"B87",body:'Komes D, Bušić A. Antioxidants in coffee. In: Processing and Impact on Antioxidants in Beverages. Amsterdam: Elservier; 2014. pp. 25-32'},{id:"B88",body:'Muñoz W, Chavez W, Pabón LC, Rendón MR, Patricia-Chaparro M, Otálvaro-Álvarez ÁM. Extracción de compuestos fenólicos con actividad antioxidante a partir de Champa (Campomanesia lineatifolia). Revista CENIC Ciencias Químicas. 2015;46'},{id:"B89",body:'Echeverria MC, Nuti M. Valorisation of the residues of coffee agro-industry: Perspectives and limitations. The Open Waste Management Journal. 2017;10(1):13-22'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Laura Sofía Torres-Valenzuela",address:"torresvallaura@miugca.edu.co",affiliation:'
University of Cordoba, Cordoba, Spain
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1. Introduction
Stating that statistical methods are useful in machine learning is analogous to saying that wood working methods are helpful for a carpenter. Statistics is the foundation of machine learning. However not all machine learning methods have been said to have derived from statistics. To begin with let us take a look at what statistics and machine learning means.
Statistics is extensively used in areas of science and finance and in the industry. Statistics is known to be mathematical science and not just mathematics. It is said to have been originated in seventeenth century. It consists of data collection, organizing the data, analyzing the data, interpretation and presentation of data. Statistical methods are being used since a long time in various fields to understand the data efficiently and to gain an in-depth analysis of the data [1].
On the other hand, machine learning is a branch of computer science which uses statistical abilities to learn from a particular dataset [2]. It was invented in the year 1959. It learns using algorithm and then has the ability to predict based on what it has been fed with. Machine learning gives out detailed information than statistics [3].
Most of the techniques of machine learning derive their behavior from statistics. However not many are familiar with this since both of them have their own jargons. For instance learning in statistics is called as fitting, supervised learning from machine learning is called as regression. Machine learning is a subfield of computer science and artificial intelligence. Machine learning is said to be a subdivision of computer science and artificial intelligence. It does use fewer assumptions than statistics. Machine learning unlike statistics deals with large amount of data and it also requires minimum human effort since most of its computation is done by the machine or the computer itself. Machine learning unlike statistics has a strong predicting power than statistics. Depending on the type of data machine learning can be categorized into supervised machine learning, unsupervised machine learning and reinforcement learning [4].
There seems to be analogy between machine learning and statistics. The following picture from textbook shows how statistics and machine learning visualize a model. Table 1 shows how terms of statistics have been coined in machine learning.
Machine learning
Statistics
Network, graphs
Model
Weights
Parameters
Learning
Fitting
Generalization
Tool set performance
Supervised learning
Regression/classification
Unsupervised learning
Density estimation, clustering
Table 1.
Machine learning jargons and corresponding statistics jargons.
To understand how machine learning and statistics come out with the results let’s look at Figure 1. In statistical modeling on the left half of the image, linear regression with two variables is fitting the best plane with fewer errors. In machine learning the right half of the image to fit the model in the best possible way the independent variables have been converted into the square of error terms. That is machine learning strives to get a better fit than the statistical model. In doing so, machine learning minimizes the errors and increases the prediction rates.
Figure 1.
Statistical and machine learning method.
Statistics methods are not just useful in training the machine learning model but they are helpful in many other stages of machine learning such as:
Data preparation—where statistics is used for data preprocessing which is later sent to the model. For instance when there are missing values in the dataset, we compute statistical mean or statistical median and fill it in the empty spaces of the dataset. It is recommended that machine learning model should never be fed with a dataset which has empty cells in it. It also used in preprocessing stage to scale the data by which the values are scaled to a particular range by which the mathematical computation becomes easy during the training of machine learning.
Model evaluation—no model is perfect in predicting when it is built for the first time. Simply building the model is not enough. It is vital to check how well is it performing and if not then by how much is it closer to being accurate enough. Hence, we evaluate the model by statistical methods, which tell by how much the result is accurate and a lot many things about the end result obtained. We make use of metrics such as confusion matrix, Kolmogorov Smirnov chart, AUC—ROC, root mean squared error and many metrics to enhance our model.
Model selection—we make use of many algorithms to train the algorithm and there is a chance of selecting only one which gives out accurate results when compared to others. The process of selecting the right solution for this is called model selection. Two of the statistical methods can be used to select the appropriate model such as statistical hypothesis test and estimation statistics [5].
Data selection—some datasets carry a lot of features with them. Of many features, it may happen so that only some contribute significantly in estimation of the result. Considering all the features becomes computationally expensive and as well as time consuming. By making use of statistics concepts we can eliminate the features which do not contribute significantly in producing the result. That is it helps in finding out the dependent variables or features for any result. But it is important to note that this method requires careful and skilled approach. Without which it may lead to wrong results.
In this chapter we take a look at how statistical methods such as, regression and classification are used in machine learning with their own merits and demerits.
2. Regression
Regression is a statistical measure used in finance, investing and many other areas which aims to determine relationship between the dependent variables and ‘n’ number of independent variables. Regression consists of two types:
Linear regression—where one independent variable is used to explain or predict the outcome of the dependent variable.
Multiple regression—where two or more independent variables are used to explain or predict the outcome of the dependent variable.
In statistical modeling, regression analysis consists of set of statistical methods to estimate how the variables are related to each other.
Linear and logistic are the types of regression which are used in predictive modeling [6].
Linear assumes that the relationship between the variables are linear that is they are linearly dependent. The input variables consist of variables X1, X2, …, Xn (where n is a natural number).
Linear models were developed long time ago but till date they are able to produce significant results. That is even in the modern computer’s era they are well off. They are widely used because they are not complex in nature. In prediction, they can even out perform complex nonlinear models.
There are ‘n’ number of regressions that can be performed. We look at the most widely used five types of regression techniques. They are:
Linear regression
Logistic regression
Polynomial regression
Stepwise regression
Ridge regression
Any regression method would involve the following:
The unknown variables is denoted by beta
The dependent variables also known as output variable
The independent variables also known as input variables
It is denoted in the form of function as:
Y≈fXβE1
2.1 Linear regression
It is the most widely used regression type by far. Linear regression establishes a relationship between the input variables (independent variables) and the output variable (dependent variable).
That isY=X1+X2+…+Xn
It assumes that the output variable is a combination of the input variables. A linear regression line is represented by Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is ‘b’, and ‘a’ is the intercept (the value of y when x = 0).
A line regression is represented by the equation:
Y=a+bX
where X indicates independent variables and ‘Y’ is the dependent variable [7]. This equation when plotted on a graph is a line as shown below in Figure 2.
Figure 2.
Linear regression on a dataset.
However, linear regression makes the following assumptions:
That there is a linear relationship
There exists multivariate normality
There exists no multi collinearity or little multicollinearity among the variables
There exists no auto-correlation between the variables
No presence of homoscedasticity
It is fast and easy to model and it is usually used when the relationship to be modeled is not complex. It is easy to understand. However linear regression is sensitive to outliners.
Note: In all of the usages stated in this chapter, we have assumed the following:
The dataset has been divided into training set (denoted by X) and test set (denoted by y_test)
The regression object “reg” has been created and exists.
We have used the following libraries:
Scipy and Numoy for numerical calculations
Pandas for dataset handling
Scikit-learn to implement the algorithm, to split the dataset and various other purposes.
Usage of linear regression in python:
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
#Declare the linear regression function
reg=linear_model.LinearRegression()
#call the method
reg.fit(height,weight)
#to check slope and intercept
m=reg.coef_[0]
b=reg.intercept_
print("slope=",m, "intercept=",b)
# check the accuracy on the training set
reg.score(X, y)
2.2 Logistic regression
Logistic regression is used when the dependent variable is binary (True/False) in nature. Similarly the value of y ranges from 0 to 1 (Figure 3) and it is represented by the equation:
Odds=p/1−p=probability that event will occur/probabilitythat the eventwill not occur
lnodds=lnp/1−pE2
logitp=lnp/1−p=b0+b1X1+b2X2+b3X3…+bkXk
Figure 3.
Standard logistic function.
Logistic regression is used in classification problems. For example to classify emails as spam or not and to predict whether the tumor is malignant or not. It is not mandatory that the input variables have linear relationship to the output variable [8]. The reason being that it makes us of nonlinear log transformation to the predicted odds. It is advised to make use of only the variables which are powerful predictors to increase the algorithms performance.
However, it is important to note the following while making use of logistic regression:
Doesn’t handle large number of categorical features.
The non-linear features should be transformed before using them.
Usage of logistic regression in python:
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
# instantiate a logistic regression model, and fit with X and y
reg = LogisticRegression()
reg = model.fit(X, y)
# check the accuracy on the training set
reg.score(X, y)
2.3 Polynomial regression
It is a type of regression where the independent variable power is greater than 1. Example:
Y=a+bX2+X3+…Xn.E3
The plotted graph is usually a curve in nature as shown in Figure 4.
Figure 4.
Plotted graph is looks as curve in nature.
If the degree of the equation is 2 then it is called quadratic. If 3 then it is called cubic and if it is 4 it is called quartic. Polynomial regressions are fit with the method of least squares. Since the least squares minimizes the variance of the unbiased estimators of all the coefficients which are done under the conditions of Gauss-Markov theorem. Although we may get tempted to fit a higher degree polynomial so that we could get a low error, it may cause over-fitting [9].
Some guidelines which are to be followed are:
The model is more accurate when it fed with large number of observations.
Not a good thing to extrapolate beyond the limits of the observed values.
Values for the predictor shouldn’t be large else they will cause overflow with higher degree.
Usage of polynomial regression in python:
from sklearn.preprocessing import PolynomialFeatures
import numpy as np
#makes use of a pre-processor called degree for the function
reg = PolynomialFeatures(degree=2)
reg.fit_transform(X)
reg.score(X, y)
2.4 Step-wise regression
This type of regression is used when we have multiple independent variables. To select the variables which are independent an automatic process is used. If used in the right way it puts more power and presents us ton of information. It can be used when the number of variables is too many. However if it is used haphazardly it may affect the models performance.
We make use of the following scores to help us find out the independent variables which contribute to the output variable significantly—R-squared, Adj. R-squared, F-statistic, Prob (F-statistic), Log-Likelihood, AIC, BIC and many more.
It can be performed by any of the following ways:
Forward selection—where we start by adding the variables to the set and check how affects the scores.
Backward selection—we start by taking all the variables to the set and start eliminating them one by one by looking at the score after each elimination.
Bidirectional selection—a combination of both the methods mentioned above.
The greatest limitation of using step-wise regression is that the each instance or sample must have at least five attributes. Below which it has been observed that the algorithm doesn’t perform well [10].
Code to implement Backward Elimination algorithm:
Assume that the dataset consists of 5 columns and 30 rows, which are present in the variable ‘X’ and let the expected results contain in the variable ‘y’. Let ‘X_opt’ contain the independent variables which are used to determine the value of ‘y’.
We are making use of a package called statsmodels, which is used to estimate the model and to perform statistical tests.
#import stats models package
import statsmodels.formula.api as sm
#since it is a polynomial add a column of 1s to the left
#Let X-opt contain the independent variables only and Let y contain the output variable
X_opt = X[:,[0,1,2,3,4,5]]
#assign y to endog and X_opt to exog
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()
The above code outputs the summary and based on it the variable which should be eliminated should be decided. Once decided remove the variable from ‘X-opt’.
It is used to handle high dimensionality of the dataset.
2.5 Ridge regression
It can be used to analyze the data in detail. It is a technique which is used to get rid of multi collinearly. That is the independent values may be highly correlated. It adds a degree of bias due to which it reduces the standard errors.
The multi collinearity of the data can be inspected by correlation matrix. Higher the values, more the multi collinearity. It can also be used when number of predictor variables in the dataset exceeds the number of instances or observations [11].
The equation for linear regression is
Y=A+bXE4
This equation also contains error. That is it can be expressed as
Y=A+bX+error
Error with mean zero and known variance.
Ridge regression is known to shrink the size by imposing penalty on the size. It is also used to control the variance.
In (Figure 5) how ridge regression looks geometrically.
Least absolute shrinkage and selection operator is also known as LASSO. Lasso is a linear regression that makes use of shrinkage. It does so by shrinking the data values toward the mean or a central point. This is used when there are high levels of multi collinearity [12].
It is similar to ridge regression and in addition it can reduce the variability and improves the accuracy of linear regression models.
It is used for prostate cancer data analysis and other cancer data analysis.
Important points about LASSO regression:
It helps in feature extraction by shrinking the co-efficient to zero.
It makes use of L1 regularization.
In the data if the predictors are have high correlation, the algorithm selects only one of the predictors discards the rest.
A classification task is when the output is of the type “category” such as segregating data with respect to some property. In machine learning and statistics, classification consists of categorizing the new data to a particular category where it fits in on the basis of the data which has been used to train the model. Examples of tasks which make use of classification techniques are classifying emails as spam or not, detecting a disease on plants, predicting whether it will rain on some particular day, predicting the house prices based on the area it is located.
In terms of machine learning classification techniques fall under supervised learning [13].
The categories may be either:
categorical (example: blood groups of humans—A, B, O)
ordinal (example: high, medium or low)
integer valued (example: occurrence of a letter in a sentence)
Real valued
The algorithms which make use of this concept in machine learning and classify the new data are called as “Classifiers.” Algorithms always return a probability score of belonging to the class of interest. That is considered an example where we are required to classify a gold ornament. Now when we input the image to the machine learning model the algorithms returns the probability value for each category, such as for if it is a ring the probability value may be higher than 0.8 if it not a necklace it may return less than 0.2, etc.
Higher the value more likely it is for it to belong to the particular group.
We make use of the following approach to build a machine learning classifier:
Pick a cut off probability above which we consider a record to belong to that class.
Estimate that a new observation belongs to a class.
If the obtained probability is above the cut off probability, assign the new observation to that class.
Classifiers are of two types: linear and nonlinear classifiers.
We now take a look at various classifiers are also statistical techniques:
Naive Bayes
stochastic gradient dissent (SGD)
K-nearest neighbors
decision trees
random forest
support vector machine
3.1 Naive Bayes
In machine learning, these classifiers belong to “probabilistic classifiers.” This algorithm makes use of Bayes’ theorem with strong independence assumptions between the features. Although Naive Bayes were introduced in the early 1950s, they are still being used today [14].
Given a problem instance to be classified, represented by a vector
X=x1x2x3…xn
Which represent ‘n’ features.
PCkx1x2…xn
We can observe that in the above formula that if the number of features is more or if a feature accommodates a large number of values, then it becomes infeasible. Therefore we rewrite the formula based on Bayes theorem as:
pCkx=pCkpxCk/pxE5
Makes two “naïve” assumptions over attributes:
All attributes are a priori equally important
All attributes are statistically independent (value of one attribute is
not related to a value of another attribute)
This classifier makes two assumptions:
All attributes are equally important
All attributes are not related to another attribute
There are three types of naive Bayes algorithms, which can be used: GaussianNB, BernoulliNB, and MultinomialNB.
Usage of naive Bayes in python:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
reg= GaussianNB()
reg.fit(X,y)
reg.predict(X_test)
reg.score()
3.2 Stochastic gradient dissent (SGD)
An example of linear classifier which implements regularized linear model (Figure 6) with stochastic gradient dissent. Stochastic gradient descent (often shortened to SGD), also known as incremental gradient descent, is an iterative method to optimize a differentiable objective function, a stochastic approximation of gradient descent optimization [15]. Although SGD has been a part of machine learning since ages it wasn’t extensively used until recently.
In linear regression algorithm, we make us of least squares to fit the line. To ensure that the error is low we use gradient descent. Although gradient descent does the job it can’t handle big tasks hence we use stochastic gradient classifier. SGD calculates the derivative of each training data and also calculates the update within no time.
The advantages of using SGD classifier are that they are efficient and they are easy to implement.
Also known as k-NN is a method used to classify as well as for regression. The input consists of k number of closest training examples. It is also referred as lazy learning since the training phase doesn’t require a lot of effort.
In k-NN an object’s classification is solely dependent on the majority vote of the object’s neighbors. That is the outcome is based on the presence of the neighbors. The object is assigned to the class most common among its k nearest neighbors. If the value of k is equal to 1 then it’s assigned to its nearest neighbor. Simply put, the k-NN algorithm is entirely dependent on the neighbors of the object to be classified. Greater the influence of a neighbor, the object is assigned to it. It is termed as simplest machine learning algorithm among all the algorithms [16].
Let us consider an example where the green circle is the object which is to be classified as shown in Figure 7. Let us assume that there are two circles—the solid circle and the dotted circle.
Figure 7.
K-Neighbors.
As we know that there are two classes class 1 (blue squares) and class 2 (red squares). If we consider only the inner circle that is the solid circle then there are two objects of red circle existing which dominates the number of blue squares due to which the new object is classified to Class 1. But if we consider the dotted circle, the number of blue circle dominates since there are more number of blue squares due to which the object is classified to Class 2 [17].
However, the cost of learning process is zero.
The algorithm may suffer from curse of dimensionality since the number of dimensions greatly affects its performance. When the dataset is very large the computation becomes very complex since the algorithm takes time to look out for its neighbors. If there are many dimensions then the samples nearest neighbors can be far away. To avoid curse of dimensionality dimension reduction is usually performed before applying k-NN algorithm to the data.
Also the algorithm may not perform well with categorical data since it is difficult to find the distance between the categorical features.
Usage in python:
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier (n_neighbors=5)
classifier.fit(X_train, y_train)
3.4 Decision trees
Decision trees are considered to be most popular classification algorithms while classifying data. Decision trees are a type of supervised algorithm where the data is split based on certain parameters. The trees consist of decision nodes and leaves [18].
The decision tree consists of a root tree from where the tree generates and this root tree doesn’t have any inputs. It is the point from which the tree originates. All the other nodes except the root node have exactly one incoming node. The other nodes except the root node are called leaves. Below is the example of a decision tree an illustration of how the decision tree looks like as shown in Figure 8.
Figure 8.
Typical decision tree.
“Is sex male” is the root node from where the tree originates. Depending on the condition the tree further bifurcates into subsequent leaf nodes. Few more conditions like “is Age >9.5?” are applied by which the depth of the node goes on increasing. As the number of leaf nodes increase the depth of the tree goes on increasing. The leaf can also hold a probability vector.
Decision tree algorithms implicitly construct a decision tree for any dataset.
The goal is to construct an optimal decision tree by minimalizing the generalization error. For any tree algorithm, it can be tuned by making changes to parameters such as “Depth of the tree,” “Number of nodes,” “Max features.” However construction of a tree by the algorithm can get complex for large problems since the number of nodes increase as well as the depth of the tree increases.
Advantages of this tree are that they are simple to understand and can be easily interpreted. It also requires little data preparation. The tree can handle both numerical and categorical data unlike many other algorithms. It also easy to validate the decision tree model using statistical testes. However, disadvantages of the trees are that they can be complex in nature for some cases which won’t generalize the data well. They are unstable in nature since if there are small variations in data they may change the structure of the tree completely.
Usage in python:
from sklearn.neighbors import tree
classifier = tree.DecisionTreeClassifier()
classifier.fit(X_train, y_train)
clf.predict(X_test)
3.5 Random forest
These are often referred as ensemble algorithms since these algorithms combine the use of two or more algorithms. They are improved version of bagged decision trees. They are used for classification, regression, etc.
Random forest creates n number of decision trees from a subset of the data. On creating the trees it aggregates the votes from the different trees and then decides the final class of the sample object. Random forest is used in recommendation engines, image classification and feature selection [19].
The process consists of four steps:
It selects random samples from the dataset.
For every dataset construct a dataset and then predict from every decision tree.
For every predicted result perform vote.
Select the prediction which has the highest number of votes.
Random forest’s default parameters often produce a good result in most of the cases. Additionally, one can make changes to achieve desired results. The parameters in Random Forest which can be used to tune the algorithm which can be used to give better and efficient results are:
Increasing the predictive power by increasing “n_estimators” by which the number of tress which will be built can be altered. “max_features” parameter can also be adjusted which is the number of features which are used to train the algorithm. Another parameter which can be adjusted is “min_sample_leaf” which is the number of leafs that are used to split the internal node.
To increase the model’s speed, “n_jobs” parameter can be adjusted which is the number of processors it can use. To use as many as needed “−1” can be specified which signifies that there is no limit.
Due to large number of decision trees random forest is highly accurate. Since it takes the average of all the predictions which are computed the algorithm doesn’t suffer from over fitting. Also it does handle missing values from the dataset. However, the algorithm is takes time to compute since it takes time to build trees and take the average of the predictions and so on.
One of the real time examples where random forest algorithm can be used is predicting a person’s systolic blood pressure based on the person’s height, age, weight, gender, etc.
Random forests require very little tuning when compared to other algorithms. The main disadvantage of random forest algorithm is that increased number of tress can make the process computationally expensive and lead to inaccurate results.
Usage in python:
from sklearn.ensemble import RandomForestClassifier
Support vector machines also known as SVMs or support vector networks fall under supervised learning. They are used for classification as well as regression purposes. Support vectors are the data points which lie close to the hyper plane. When the data is fed to the algorithm the algorithm builds a classifier which can be used to assign new examples to one class or the other [20]. A SVM consists of points in space separated by a gap which is as wide as possible. When a new sample is encountered it maps it to the corresponding category.
Perhaps when the data is unlabeled it becomes difficult for the supervised SVM to perform and this is where unsupervised method of classifying is required.
A SVM constructs a hyper plane which can be used for classification, regression and many other purposes. A good separation can be achieved when the hyper plane has the largest distance to the nearest training point of a class.
In (Figure 9) H1 line doesn’t separate, while H2 separates but the margin is very small whereas H3 separates such as the distance between the margin and the nearest point is maximum when compared to H1 and H2.
Figure 9.
Hyper plane construction and H1, H2 and H3 line separation.
SVMs can be used in a variety of applications such as:
They are used to categorize text, to classify images, handwritten images can be recognized, and they are also used in the field of biology.
SVMs can be used with the following kernels:
Polynomial kernel SVM
Linear kernel SVM
Gaussian kernel SVM
Gaussian radial basis function SVM (RBF)
The advantages of SVM are:
Effective in high dimensional data
It is memory efficient
It is versatile
It may be difficult for SVM to classify at times due to which the decision boundary is not optimal. For example, when we want to plot the points randomly distributed on a number line.
It is almost impossible to separate them. So in such cases we transform the dataset by applying 2D or 3D transformations by using a polynomial function or any other appropriate function. By doing so it becomes easier to draw a hyper plane.
When the number of features is much greater than number of samples it doesn’t perform well with the default parameters.
Usage of SVM in python:
from sklearn import svm
clf = svm.SVC()
clf.fit(X,y)
clf.predict(X_test)
4. Conclusion
It is evident from the above regression and classification techniques are strongly influenced by statistics. The methods have been derived from statistical methods which existed since a long time. Statistical methods also consist of building models which consists of parameters and then fitting it. However not all the methods which are being used derive their nature from statistics. Not all statistical methods are being used in machine learning. Extensive research in the field of statistical methods may give out new set methods which can be used in machine learning apart from the existing statistical methods which are being used today. It can also be stated that machine learning to some extent is a form of ‘Applied Statistics.’
\n',keywords:"machine learning, statistics, classification, regression, algorithms",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/67545.pdf",chapterXML:"https://mts.intechopen.com/source/xml/67545.xml",downloadPdfUrl:"/chapter/pdf-download/67545",previewPdfUrl:"/chapter/pdf-preview/67545",totalDownloads:49,totalViews:0,totalCrossrefCites:0,dateSubmitted:"July 5th 2018",dateReviewed:"February 4th 2019",datePrePublished:"December 7th 2020",datePublished:"January 20th 2021",dateFinished:"June 6th 2019",readingETA:"0",abstract:"This chapter aims to introduce the common methods and practices of statistical machine learning techniques. It contains the development of algorithms, applications of algorithms and also the ways by which they learn from the observed data by building models. In turn, these models can be used to predict. Although one assumes that machine learning and statistics are not quite related to each other, it is evident that machine learning and statistics go hand in hand. We observe how the methods used in statistics such as linear regression and classification are made use of in machine learning. We also take a look at the implementation techniques of classification and regression techniques. Although machine learning provides standard libraries to implement tons of algorithms, we take a look on how to tune the algorithms and what parameters of the algorithm or the features of the algorithm affect the performance of the algorithm based on the statistical methods.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/67545",risUrl:"/chapter/ris/67545",signatures:"Pramod Kumar, Sameer Ambekar, Manish Kumar and Subarna Roy",book:{id:"9961",title:"Data Mining",subtitle:"Methods, Applications and Systems",fullTitle:"Data Mining - Methods, Applications and Systems",slug:"data-mining-methods-applications-and-systems",publishedDate:"January 20th 2021",bookSignature:"Derya Birant",coverURL:"https://cdn.intechopen.com/books/images_new/9961.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"15609",title:"Dr.",name:"Derya",middleName:null,surname:"Birant",slug:"derya-birant",fullName:"Derya Birant"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"266304",title:"Mr.",name:"Pramod",middleName:null,surname:"Kumar",fullName:"Pramod Kumar",slug:"pramod-kumar",email:"pramodbiotech@gmail.com",position:null,institution:null},{id:"266836",title:"Mr.",name:"Sameer",middleName:null,surname:"Ambekar",fullName:"Sameer Ambekar",slug:"sameer-ambekar",email:"ambekarsameer@gmail.com",position:null,institution:null},{id:"266837",title:"Dr.",name:"Subarna",middleName:null,surname:"Roy",fullName:"Subarna Roy",slug:"subarna-roy",email:"roys@icmr.gov.in",position:null,institution:null},{id:"282335",title:"Mr.",name:"Manish",middleName:null,surname:"Kumar",fullName:"Manish Kumar",slug:"manish-kumar",email:"manishkumarlps41@gmail.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Regression",level:"1"},{id:"sec_2_2",title:"2.1 Linear regression",level:"2"},{id:"sec_3_2",title:"2.2 Logistic regression",level:"2"},{id:"sec_4_2",title:"2.3 Polynomial regression",level:"2"},{id:"sec_5_2",title:"2.4 Step-wise regression",level:"2"},{id:"sec_6_2",title:"2.5 Ridge regression",level:"2"},{id:"sec_7_2",title:"2.6 Lasso regression",level:"2"},{id:"sec_9",title:"3. Classification",level:"1"},{id:"sec_9_2",title:"3.1 Naive Bayes",level:"2"},{id:"sec_10_2",title:"3.2 Stochastic gradient dissent (SGD)",level:"2"},{id:"sec_11_2",title:"3.3 K-nearest neighbors",level:"2"},{id:"sec_12_2",title:"3.4 Decision trees",level:"2"},{id:"sec_13_2",title:"3.5 Random forest",level:"2"},{id:"sec_14_2",title:"3.6 Support vector machine",level:"2"},{id:"sec_16",title:"4. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Hawkins DM. On the investigation of alternative regressions by principal component analysis. Journal of the Royal Statistical Society Series. 1973;22:275-286. https://www.jstor.org/stable/i316057'},{id:"B2",body:'Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal. 2015;13:8-17. DOI: 10.1016/j.csbj.2014.11.005'},{id:"B3",body:'Machine Learning [Internet]. Available from: https://en.wikipedia.org/wiki/Machine_learning'},{id:"B4",body:'Trevor H, Robert T. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer; 2009. pp. 485-586. DOI: 10.1007/978-0-387-84858-7_14'},{id:"B5",body:'Aho K, Derryberry DW, Peterson T. Model selection for ecologists: The worldviews of AIC and BIC. Ecology. 2014;95(3):631-636. DOI: 10.1890/13-1452.1'},{id:"B6",body:'Freedman DA. Statistical Models: Theory and Practice. USA: Cambridge University Press; 2005. ISBN: 978-0-521-85483-2'},{id:"B7",body:'sklearn.linear_model.LinearRegression—scikit-learn 0.19.2 documentation [Internet]. Available from: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html'},{id:"B8",body:'Linear Regression—Wikipedia [Internet]. Available from: https://en.wikipedia.org/wiki/Linear_regression'},{id:"B9",body:'Shaw P et al. Gergonne’s 1815 paper on the design and analysis of polynomial regression experiments. Historia Mathematica; 2006;1(4):431-439. DOI: 10.1016/0315-0860(74)90033-0'},{id:"B10",body:'Stepwise Regression—Wikipedia [Internet]. Available from: https://en.wikipedia.org/wiki/Stepwise_regression'},{id:"B11",body:'Tikhonov Regularization—Wikipedia [Internet]. Available from: https://en.wikipedia.org/wiki/Tikhonov_regularization'},{id:"B12",body:'sklearn.linear_model.LogisticRegression—scikit-learn 0.19.2 documentation [Internet]. 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Available from: https://en.wikipedia.org/wiki/Lasso_(statistics)'},{id:"B19",body:'sklearn.linear_model.Lasso—Scikit-Learn 0.19.2 Documentation [Internet]. Available from: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html'},{id:"B20",body:'Corinna C, Vapnik Vladimir N. Support-vector networks. Machine Learning. 1995;20(3):273-297. DOI: 10.1007/BF00994018'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Pramod Kumar",address:"pramodbiotech@gmail.com",affiliation:'
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