",isbn:"978-1-83969-150-8",printIsbn:"978-1-83969-149-2",pdfIsbn:"978-1-83969-151-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"7409b2acd5150a93004300800918b736",bookSignature:"Prof. Karmen Pažek",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10548.jpg",keywords:"Lean Manufacturing, Agriculture, Production and Process, Costs Reduction, Lean Principles, Industry, Tools, Implementation, Sustainability, Modeling, Environment, Planning",numberOfDownloads:7,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 20th 2020",dateEndSecondStepPublish:"November 17th 2020",dateEndThirdStepPublish:"January 16th 2021",dateEndFourthStepPublish:"April 6th 2021",dateEndFifthStepPublish:"June 5th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Pažek is Head of the undergraduate study program Agricultural economics and rural development and Vice-dean for education. She is the author or co-author of 61 scientific papers, 6 scientific books, and 24 book chapters.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"179642",title:"Prof.",name:"Karmen",middleName:null,surname:"Pažek",slug:"karmen-pazek",fullName:"Karmen Pažek",profilePictureURL:"https://mts.intechopen.com/storage/users/179642/images/system/179642.jpg",biography:"Karmen Pažek achieved her Ph.D. at University of Maribor, Faculty of Agriculture in 2006. She is active as Full Professor for Farm management in the Department for Agriculture Economics and Rural Development on Faculty of Agriculture and Life Sciences, University of Maribor. Her research includes development of decision support tools and systems for farm management (simulation modeling, multi-criteria decision analysis, option models, investment analysis) and economics of agricultural production. 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\n\t\t\t
1. Introduction
\n\t\t\t
Biometric systems are based on the use of certain distinctive human traits, be they behavioral, physicial, biological, physiological, psychological or any combination of them. As reflected in the literature, some of the most frequently used biometric modalities include fingerprint, face, hand geometry, iris, retina, signature, palm print, voice, ear, hand vein, body odor and DNA. While these traits may be used in an isolated manner by biometric recognition systems, experience has shown that results from biometric systems analyzing a single trait are often insufficiently reliable, precise and stable to meet specific performance demands (Ross et al. 2006). In order to move system performance closer to the level expected by the general public, therefore, novel biometric recognition systems have been designed to take advantaje from taking multiple traits into account.
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Biometric fusion represents an attempt to take fuller advantage of the varied and diverse data obtainable from individuals. Just as is the case with human recognition activities in which decisions based on the opinions of multiple observers are superior to those made by only one, automatic recognition may also be expected to improve in both precision and accuracy when final decisions are made according to data obtained from multiple sources.
\n\t\t\t
its discussion of data fusion in biometric systems, the present chapter will analyze distinct types of fusion, as well as particular aspects related to the normalization process directly preceding data fusion.
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2. Biometrics
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Biometric recognition involves the determination of the identity of an individual according to his/her personal qualities in opposition to the classical identification systems which depend on the users’ knowledge of a particular type of information (e.g., passwords) or possession of a particular type of object (e.g., ID cards).
\n\t\t\t
In biometrics, ‘recognition’ may be used to refer to two distinct tasks. In the first one, that is called verification, an individual claims to be certain user who has been previously registered(enrolled) into the system. It is also possible that the individual does not indicate his/her identity but there exist some additional information that allow to suppose it. In this case system operation is reduced to confirm or reject that a biometric sample belongs to the claimed identity. In the second task, identification, it is not available such prior information about individual’s identity, the system must determine which among all of the enrollees the subject is, if any. In the present chapter, only verification will be discussed insofar as identification may be understood as the verification of multiple entities.
\n\t\t\t
In both cases (verification and identification), a sample of a predetermined biometric trait (e.g., face, voice or fingerprint) is captured (e.g., via photo, recording or impression) from a subject under scrutiny (donor), this is done using an adequate sensor for the task (e.g., camera, microphone or reader/scanner). A sample is called genuine when its donor identity and the identity of the claimed user are the same and it is called impostor when they are not. Following its capture, the sample is processed (feature extraction) in order to obtain the values of certain predefined aspects of the sample. This set of values constitute the feature vector. The feature vector is then matched against the biometric model corresponding to the individual whose identity has being claimed[1] -. This model has been created at the time of that user enrols into the system. As a result of the matching, an evaluation of the degree of similarity between the biometric sample and the model is obtained, it is called score[1] -. With this information, the decision is taken either to accept the subject as a genuine user or to reject the subject as an impostor (see Fig. 1).
\n\t\t\t
Figure 1.
Monobiometric\n\t\t\t\t\t\t\tTerm derived from the Greek monos (one) + bios (life) + metron (measure) and preferred by the authors of the present chapter over the term “unibiometric”, also found in the literature but involving a mix of Greek and Latin morphological units. The same comment should be made about polybiometric and multibiometric terms.\n\t\t\t\t\t\t process.
\n\t\t\t
In the majority of biometric recognition systems currently in use only a single biometric trait is captured in order to confirm or reject the claimed users’s identity. Such systems are known as monobiometric. Nevertheless, at they heart is a pattern recognizer, which arrives at a final decision with the results obtained from a single sample processed according to a single algorithm.
\n\t\t\t
\n\t\t\t\tFig. 1 presents a simple representation of the biometric recognition process in monobiometric systems. After a subject presents the biometric trait which the system’s sensor is designed to process, in the first stage (i.e., capturing sample), a biometric sample is obtained by the sensor and processed by the system to eliminate noise, emphasize certain features of interest and, in general, prepare the sample for the following stage of the process. In the next step (i.e., feature extraction), characteristic parameters of the sample are quantified and a feature vector that groups them is obtained. Following quantification, the system proceeds to match the feature vector (i.e., model matching) against others captured during the training phase that correspond to the individual whose identity is being claimed. These latter vectors are often represented in biometric systems with models that summarizes their variability. As a result of the matching process, a score is obtained quantifying the similarity between the sample and the model. In the final stage (i.e., decision making) and as a result of the score generated, the biometric system makes a decision to accept the sample genuine or to reject it as impostor.
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\n\t\t
\n\t\t\t
3. Biometric fusion
\n\t\t\t
In polybiometric systems, various data sources are used and combined in order to arrive at the final decision about the donor’s identity. These systems are composed of a set of monobiometric parallel subprocesses that operate the data obtained from the distinct sources in order to finally combine them (i.e., fusing data). This fused data is then processed by system through a single subprocess until th final decision can be made regarding the truth of the claimed identity
\n\t\t\t
In the construction of polybiometric recognitions systems, certain parameters must be set in response to the following questions:
\n\t\t\t
What are the distinct sources of biometric data being analyzed?
At what point in the biometric process will the data be fused or, said another way, what intermediate data will be used for this fusion?
What algorithm is most adequate for bringing about a particular type of fusion?
\n\t\t\t
The following sections of the present chapter will analyze these different aspects of system design.
\n\t\t
\n\t\t
\n\t\t\t
4. Data sources
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In order to respond to the first question of the previous paragraph regarding multiple data sources, the following specific questions must also be considered (Ross 2007).
\n\t\t\t
How many sensors are to be utilized? In the construction of multi-sensor systems, different sensors (each with distinct performances) are used in order to capture multiple samples of a single biometric trait. In one example of this sort of polybiometric system, simultaneous photographs are captured of a subject’s face using both infrared and visible light cameras.
\n\t\t\t
Figure 2.
Multi-sensor system.
\n\t\t\t
How many instances of the same biometric trait are to be captured? Human beings can present multiple versions of particular biometric traits (e.g., fingerprints for different fingers, hand geometry and veins for each hand and irises for each eye). As a result and with a schema similar to that of multi-sensor systems, multi-instance systems are designed to capture various instances of the same biometric trait.
How many times is an instance of a particular trait to be captured? Using a single sensor and a single instance of a particular trait, it is nevertheless possible to obtain distinct samples of that instance under different conditions (e.g., video images taken of a trait instance from different angles or voice recordings taken at different moments and with different speech content). These multi-sample systems may also be represented by a schema similar to that of multi-sensor systems.
How many different biometric traits are to be captured? Biometric recognition systems may be designed to analyze a single biometric trait (i.e., unimodal systems) or various traits (i.e., multimodal systems). The particularities of the latter type of system are represented by the schema below.
\n\t\t\t
Figure 3.
Multimodal systems.
\n\t\t\t
How many distinct feature extraction algorithms are to be utilized in the processing of the biometric samples? Multi-algorithm systems are designed to use various algorithms for the feature extraction from biometric samples. In this case, the use of different extraction algorithms may allow the system to emphasize different biometric features of interest (e.g., spectral or prosodic features of a voice sample) and produce different feature vectors for each.
\n\t\t\t
Figure 4.
Multi-algorithm systems.
\n\t\t\t
Against how many types of patterns and using how many methods are the feature vectors to be matched? Multi-matching systems are biometric recognition systems that allow match the feature vectors against various types of models or/and. using multiple techniques.
Finally, it is also possible to construct hybrid systems systems of an even greater complexity that incorporate more than one type of the multiple data source discussed above.
\n\t\t\t
Figure 5.
Multi-matching schema.
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\n\t\t
\n\t\t\t
5. Fusion level
\n\t\t\t
As discussed earlier, biometric fusion is composed by a set of monobiometric subprocesses that work in parallel to operate the data obtained from distinct sources. Once this different data has been operated and fused, it is then handled by the system through a single subprocess until the point where the donor’s identity final decision can be made. This process is represented in Fig. 6 below.
\n\t\t\t
Figure 6.
Biometric fusion process.
\n\t\t\t
Having considered the biometric fusion schema, it is time to return to the questions articulated earlier in the chapter and analyze now at what level of the process the fusion should be carried out or, in other words, what type of data the system should fuse. The possible responses in the literature to these points allow to establish diverse characterizations of data fusion systems defined as fusion levels(Ross 2007) (Joshi et al. 2009) (Kumar et al. 2010).
\n\t\t\t
The first point at which data fusion may be carried out is at the sample level, that means immediately following sample capture by system sensors. This type of fusion is possible in multi-sensor, multi-instance and multi-sample systems and may be obtained by following a particular sample fusion method. The form that this method takes in each case depends on the type of biometric trait being utilized. While fusion may range from a simple concatenation of the digitalized sample data sequence to more complex operations between multiple sequences, but it is almost always carried out for the same reason: to eliminate as many negative effects as possible associated with the noise encrusted in the data samples during capture. Once the fused sample has been generated, it may be used by the system for feature extraction.
\n\t\t\t
The second point at which data may be combined is immediately following the feature extraction. At the feature level, vectors derived from the different sources are combined, yielding a single, fused vector.
\n\t\t\t
Figure 7.
Sample level fusion.
\n\t\t\t
Figure 8.
Feature level fusion.
\n\t\t\t
Another alternative is the fusion of scores obtained following the matching of different sample data against corresponding models. The new score resulting from this fusion is then used by the system to reach the final decision. This sort of fusion is normally carried out according to mathematic classification algorithms ranging in type from decision trees to techniques from the field of artificial intelligence, the latter of which offering learning capabilities and requiring prior training. The present chapter focuses particularly on this latter type of fusion which will be developed in much greater detail in sections below.
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Figure 9.
Score level fusion.
\n\t\t\t
Fusion may also be carried out on the final decisions obtained for each monobiometric process through the use of some kind of Boolean function. The most frequent algorithms used in this type of fusion are AND, OR and VOTING. With the first type, the final, combined decision is GENUINE if and only if each monobiometric process decision is also GENUINE. For the second type, the final, combined decision is IMPOSTOR if and only if each monobiometric process decision is also IMPOSTOR. Finally, for the third type combined decision is that of the majority of monobiometric process decisions which may or may not have been previously weighted.
\n\t\t\t
Figure 10.
Decision level fusion.
\n\t\t\t
Finally, dynamic classifier selection schema uses scores generated at the data matching level in order to determine what classifier offers the highest degree of confidence. The system then arrives at a final decision through the application of solely the selected classifier. This is represented in Fig. 11 below.
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Figure 11.
Dynamic classifier selection.
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6. Biometric performances
\n\t\t\t
For the recognition of a individual by a classical recognition system, the data collected (e.g., passwords or ID cards information) from the subject must be identical to the previously recorded data into the system. In biometric recognition systems, however, almost never the data captured from a subject (nor the feature vectors obtained from them) are identical to the previous ones (Ross et al. 2006). The reasons for these variations are manifold and include the following:
\n\t\t\t
Imperfections in the capture process that create alterations (e.g., noise) in the data;
Physical changes in the capture environment (e.g., changes in lighting and degradation of the sensors used); and
Inevitable changes over time in individual’s biometric traits.
As a result of the unrepeatibility of biometric samples, the process of biometric recognition can not be deterministic and it must be based on the stochastic behaviour of samples. In this way, rather than flatly asserting correspondence between a biometric sample and the corresponding model, biometric systems only permit the assertion that this correspondence has a certain probability of being true.
\n\t\t\t
The differences observed among the distinct biometric samples taken of a single trait from a single donor are known as intra-class variations. On the other hand, inter-class variation refers to the difference existing between the samples captured by the system from one subject and those of others. The level of system confidence in the correctness of its final decision is determined according to these two types of variation. The lesser the intra-class variation and the greater the inter-class variation are, the greater the probability that the final decision is correct.
\n\t\t\t
In the matching model step, the system assigns a score to the sample feature vector reflecting the system’s level of confidence in the correspondence between sample and claimed identity. If this score (s) lies above a certain threshold (th) (i.e., if: s ≥ th), the system will decide that the sample is genuine. However, if the score lies below the threshold the system will decide that the sample is an impostor one.
\n\t\t\t
Insofar as score, as understood here, is a random variable, the probability that any particular score corresponds to a genuine sample can be defined by its probability density function (pdf) f\n\t\t\t\t\n\t\t\t\t\tg\n\t\t\t\t\n\t\t\t\t(s). Similarly, the probability that the score corresponds to an impostor sample can be defined by a pdf f\n\t\t\t\t\n\t\t\t\t\ti\n\t\t\t\t\n\t\t\t\t(s). As a result, the terms ‘false match rate’ (FMR) or ‘false acceptance rate’ (FAR) may be defined as the probability that an impostor sample be taken by the biometric system as genuine. Similarly, the terms ‘false not match rate’ (FNMR) or ‘false rejection rate’ (FRR) may be defined as the probability that a genuine sample be taken for as an impostor one.
\n\t\t\t
When the decision score threshold is established in a system (see Fig. 12), the level of system performance is therefore established, because FAR and FRR directly depend on its value. Wether threshold values increases, FAR will also increase while FRR will decrease [Stan et al. 2009]. The optimal value of th can be obtained by minimizing the cost function established for the concrete system use. This cost function defines the balance between the damage that can be done by a false acceptance (e.g., a subject is granted access by the system to a protected space in which he or she was not authorized to enter) and that done by a false rejection (e.g., a subject with authorization to enter a space is nevertheless denied entry by the system).
\n\t\t\t
Figure 12.
Error rates and pdfs (left). Error rates and cost functions (right).
The National Institute of Standards and Technology (NIST) proposes as a cost function the one shown in formula 2, which is a weighted sum of both error rates. CFR and CFA correspond to the estimated costs of a false rejection and false acceptance, respectively, and Pg and Pi indicate the probabilities that a sample is genuine or impostor. Is obviously true that Pi+Pg=1 (Przybocki et al. 2006):
In NIST recognition system evaluations, the costs of a false acceptance and a false rejection are quantified, respectively, at 10 and 1, whereas the probabilities that a sample is genuine or impostor are considered to be 99% and 1%, respectively. With these parameters and normalizing the resulting expression, the following equation is obtained (formula 3):
For reasons of expediency, however, the present chapter utilizes other criteria that nevertheless enjoy wide use in the field. According to these criteria, CFA = CFR and Pg = Pi, such that the resulting cost function may be defined as the following (formula 5):
Another value used in the characterization of biometric systems is the equal error rate (EER) which, as shown below, indicates the point at which the error rates are equal:
As a final concept to consider here, the receiver operating characteristic curve (ROC curve) is a two-dimensional measure of classification performance and is regularly used in the comparison of two biometric systems. The ROC curve represents the evolution of the true acceptance rate (TAR) with respect to that of the FAR (Martin 1997):
Through the analysis of the ROC curve, the evaluation of a recognition system may be carried out by considering the costs associated with errors even where the latter have not been previously established. In particular, using the area under the convex ROC curve (AUC), system performance may be expressed as a single numeric value and evaluated: the system considered the best being that with the greatest AUC (Villegas et al. 2009)(Marzban 2004).
Given the symmetry of the functions, it can be held that the threshold value minimizing the cost function can be located at th=0, point at which FAR and FRR are equal, defining also the EER as shown in formula 9:
From an estimation, the value EER=15.85% is obtained. It is clear, then, that the farther apart the centroids or the smaller the deviations of the distribution functions are, the smaller the error rates.
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\n\t\t
\n\t\t\t
8. Multiple score fusion
\n\t\t\t
Let it be supposed that match score fusion is to be applied to the results of two processes having generated independent scores (s1 and s2) and with distribution functions identical to those described in the previous section of the present chapter. Thus, a match score vector is formed with Gaussian distribution functions for both genuine and impostor subject samples. This vector will have two components, each of which integrating the results from each of the monobiometric classifiers.
\n\t\t\t
Figure 15.
Representation of two-dimensional Gaussian distribution scores.
In Fig.15, the distribution functions are presented together for both genuine and impostor subject score vectors. Right image represents the contour lines of the distribution functions. Observing it, it seems intuitive that, just as was done in the previous section of the present chapter and applying the criteria for symmetry discussed therein, the best decision strategy is that which takes as a genuine subject score vector any vector found above and to the right of the dotted line which, in this particular case, corresponds to s1+s2 ≥ 0. This converts the threshold, for the one-dimension scores, to a boundary line decision in this two-dimension space (an hiperplane if n dimensions space).
Following this, the resulting estimation of the EER is shown in formula 12. In the specific case proposed here, the resulting EER is found to be 7.56% indicating an important improvement owing to the fact that the centroids of the distribution functions have been separated here by a factor of\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t.
Gaussian mixture model (GMM) classifiers are used in order to create a model of statistical behaviour represented by the weighted sum of the gaussian distributions estimated for the class of genuine training score vectors and another similar model to represent the class of impostor vectors. Using the two models, the vectors are classified using the quotient of the probabilities of belonging to each of the two classes. If this quotient is greater than a given threshold (established during the system training phase), the vector is classified as genuine. If the quotient is below the given threshold, the vector is classified as an impostor. Such a procedure is quite similar to that discussed in the previous section of the chapter.
\n\t\t\t
In a situation such as that described in the paragraph above, the following points indicate the expectations for a training process and test using GMMs:
\n\t\t\t
These models (fg’, fi’) of sums of Gaussian functions should maintain a certain similarity to the generative sample distribution ;
The established threshold may be equivalent to the theoretical decision boundary between genuine and impostor score vectors; and
Test results clearly approach the theoretic FAR and FRR.
\n\t\t\t
In order to test the fitness of these premises, 1000 two-dimensional random vectors (Vg) following the distribution function of the genuine vectors and another 1000 vectors (Vi) following the distribution function of the impostor vectors have been taken as training data. With these vectors, GMMs were created to approximate the distribution functions[1] -.
The models obtained in the training phase for 10 Gaussian models (10G) derived from the simulated data training are presented below in Table 1:
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t
Table 1.
Gaussian Mixture Model (GMM)
\n\t\t\t
In Fig.16 (left), genuine and impostor models are presented for the score s1 of the score vector. With red lines indicating the impostor model and black lines indicating the genuine sample model, each of the 10 individual Gaussian distributions with which the GMM classifier approximated the distribution of the training data are represented by the thin lines on the graph. The weighted sums of these Gaussian functions (see Formula 13) are represented by the thick lines on the graph. The result has an appearance similar to two Gaussian distributions around +1 and -1. Fig. 18 (right) shows the contour lines of the two-dimensional models.
\n\t\t\t
For a value of th = 0.9045 (calculated to minimize) it was found that FAR = 8.22% and FRR = 7.46%.
\n\t\t\t
Figure 16.
GMM with 10G. Single axe (left). Contours for two-dimension model (right).
In Fig.16. the decision boundary line, at which the quotient of pdfs is equal to the threshold and which separates genuine and impostor decisions, presented as a dotted line. This line is quite near to the proposed boundary. Then the formula 14 represents a transformation from a two-dimension criterion to a one-dimension threshold, which, of course, is easier to manage.
\n\t\t\t
If the same exercise is repeated for a model with 3 Gaussians (3G) and for another with only 1 Gaussian (1G), the following results are obtained:
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
N Gaussian
\n\t\t\t\t\t\t
FAR
\n\t\t\t\t\t\t
FRR
\n\t\t\t\t\t\t
MER\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
th
\n\t\t\t\t\t\t
AUC
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
10
\n\t\t\t\t\t\t
8.22%
\n\t\t\t\t\t\t
7.46%
\n\t\t\t\t\t\t
7.84%
\n\t\t\t\t\t\t
0.9045
\n\t\t\t\t\t\t
97.12%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
3
\n\t\t\t\t\t\t
7.97%
\n\t\t\t\t\t\t
7.52 %
\n\t\t\t\t\t\t
7.75%
\n\t\t\t\t\t\t
0.9627
\n\t\t\t\t\t\t
97.05%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1
\n\t\t\t\t\t\t
7.99%
\n\t\t\t\t\t\t
7.56%
\n\t\t\t\t\t\t
7.77%
\n\t\t\t\t\t\t
0.9907
\n\t\t\t\t\t\t
97.10%
\n\t\t\t\t\t
\n\t\t\t\t
Table 2.
Gaussian Mixture Model (GMM).
\n\t\t\t
Figure 17.
Single axe GMM with 3G (left) and 1G (right).
\n\t\t\t
Figure 18.
Contour lines for two-dimension GMM with 3G (left) and 1G (right).
\n\t\t\t
Changing the threshold value (see Fig.19), distinct decision boundaries and their corresponding error rates may be obtained. With these values, a ROC curve may be drawn and its AUC estimated.
\n\t\t\t
Figure 19.
Various decision boundaries (left) and ROC Curve (right) for GMM 10G.
\n\t\t
\n\t\t
\n\t\t\t
10. Using support vector machine classifiers
\n\t\t\t
A support vector machine (SVM) is a classifier that estimates a decision boundary between two vector sets (genuine and impostor ones) such that maximizes the classification margin. In the training phase of a SVM, a model is constructed that defines this boundary in terms of a subset of data known as support vectors (SV), a set of weights (w) and an offset (b).
The equation above defines the distance of a vector (v) to the boundary, where positive distances indicate genuine samples and negative distances indicate impostor samples[1] -. For other kind of boundary lines is possible to select between different kernel functions. Then the general decision function is shown in formula 16, where K(sv,v) represents the adequate kernel function. The kernel implied in formula 15 is called “linear kernel”.
Given the data distribution and the fact that the expected separation boundary is a straight line, it may be assumed that the linear kernel is the most adequate kernel function here.
\n\t\t\t
\n\t\t\t\tFig.20 shows the distribution of genuine samples (in blue) and impostor samples (in red). Points indicated with circles correspond to the support vectors generated in the training phase. The central black line crossing the figure diagonally represents the set of points along the boundary line, which is also quite close to the theoretical boundary.
\n\t\t\t
Figure 20.
SVM with linear kernel.
\n\t\t\t
The results of the test data classification demonstrate the performance indicated below for
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Kernel
\n\t\t\t\t\t\t
FAR
\n\t\t\t\t\t\t
FRR
\n\t\t\t\t\t\t
MER
\n\t\t\t\t\t\t
nSV\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
AUC
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Linear
\n\t\t\t\t\t\t
8.01%
\n\t\t\t\t\t\t
7.56%
\n\t\t\t\t\t\t
7.78%
\n\t\t\t\t\t\t
1956
\n\t\t\t\t\t\t
95.06%
\n\t\t\t\t\t
\n\t\t\t\t
Table 3.
Results of SVM test
\n\t\t\t
The classifier establishes a transformation of the vector space into a real value whose module is the distance from the boundary, calculated such that the system be optimized to establish the decision threshold at the distance of 0. Just as in the case of GMMs, system behavior can be analyzed using the ROC curve and, more specifically, the AUC through the adjustment of this threshold value (see Table 3).
\n\t\t
\n\t\t
\n\t\t\t
11. Using neural network classifiers
\n\t\t\t
An artificial neural network (ANN) simulates an interconnected group of artificial neurons using a computational model. In this context, a neuron is a computational element that operates n-inputs in order to obtain just one output following a transfer function like the one shown at formula 16. Where s\n\t\t\t\t\tk\n\t\t\t\t is the k-esime neuron input, wk is the weigth of k-esime input w0 represent the offset and finaly represents a function (typically sigmoid or tanh) that performs the transference between neurons.
A typical ANN groups its neurons in a few layers, so that, the certain layer neuron outputs are only connected to the next layer neuron inputs.
\n\t\t\t
The neural network training step gets as a result the weight for every neuron input that minimizes the error rates.
\n\t\t\t
Then the simplest network is one which has only one neuron with two input and one output (2-1-1). This way, the transfer function has no effect on the system and at the end decision function becomes a linear combination of the inputs and therefore the training estimates a linear separator similar to the one seem before for SVM with linear kernel.
\n\t\t\t
Applying neural networks to above described data, is possible to obtain the following results:[1] -\n\t\t\t
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Struct
\n\t\t\t\t\t\t
FAR
\n\t\t\t\t\t\t
FRR
\n\t\t\t\t\t\t
MER
\n\t\t\t\t\t\t
AUC
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
2-1-1
\n\t\t\t\t\t\t
7.94%
\n\t\t\t\t\t\t
7.62 %
\n\t\t\t\t\t\t
7.78%
\n\t\t\t\t\t\t
95.06%
\n\t\t\t\t\t
\n\t\t\t\t
Table 4.
Results of 3 ANN test
\n\t\t
\n\t\t
\n\t\t\t
12. Beta distributions
\n\t\t\t
One common way in which monobiometric systems present their scores is through likelihood estimates (the probability that the sample is genuine). In such cases, the score rangeis limited to 0-1 (0-100%). Ideally, instances of genuine subject scores would be grouped together around 1 or a point close to 1, while impostor subject scores would be grouped together around zero or near it. Both would demonstrate beta distributions. An example of this ideal situation is plotted in Fig.23 with the pdf for genuine samples follows Beta(5,1) and the pdf for impostor samples follows Beta(1,5). Because the symmetrical properties of these functions, the equilibrium point can be clearly located at s = 0.5 with an solving the integral in formula 1 the EER = 3.12%.
\n\t\t\t
As it was done for the Gaussians, identical distribution functions are established for both dimensions of the two-dimension score space, then a theoretical value of EER= 0.396% would be obtained. Also is possible the same routine and evaluate system performances for GMM, SVM and NN classifiers[1] -.
\n\t\t\t
\n\t\t\t\tFig. 21 shows the pdf’s used in this example and de model obtained for them, while table 5 display test results.
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Classifier.
\n\t\t\t\t\t\t
FAR
\n\t\t\t\t\t\t
FRR
\n\t\t\t\t\t\t
MER
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 10G
\n\t\t\t\t\t\t
0.55%
\n\t\t\t\t\t\t
0.31%
\n\t\t\t\t\t\t
0.43%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 3G
\n\t\t\t\t\t\t
0.56%
\n\t\t\t\t\t\t
0.28%
\n\t\t\t\t\t\t
0.42%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 1G
\n\t\t\t\t\t\t
0.54%
\n\t\t\t\t\t\t
0.28%
\n\t\t\t\t\t\t
0.41%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
SVM Linear
\n\t\t\t\t\t\t
0.48%
\n\t\t\t\t\t\t
0.34%
\n\t\t\t\t\t\t
0.38%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
NN (2-1-1)
\n\t\t\t\t\t\t
0.43%
\n\t\t\t\t\t\t
0.35%
\n\t\t\t\t\t\t
0.39%
\n\t\t\t\t\t
\n\t\t\t\t
Table 5.
Test results.
\n\t\t\t
Figure 21.
Single axe pdfs (left), model with 10G (centre), vector plots and SVM linear model.
\n\t\t
\n\t\t
\n\t\t\t
13. More realistic distributions
\n\t\t\t
Unfortunately, the distribution functions for real scores are not as clear-cut as those presented in Fig. 21. Scores for impostor subject samples, for example, are not grouped around 0, but rather approach 1. Similarly, genuine subject sample scores often tend to diverge from 1. Distributions similar to those in Fig. 22 are relatively common. To illustrate this, pdf.genuine = Beta (9,2) and pdf.impostor = Beta (6,5) have been chosen for the first score (s1).
\n\t\t\t
Figure 22.
Single axe score1 pdfs (left) and score2 pdfs (right)..
\n\t\t\t
These particular distributions don’t display any symmetrical property then the equilibrium point estimated loking for FAR = FRR and as a result the threshold value of th = 0.7 with an EER of 15.0% has been obtained. Eve more, the optimal threshold value does not coincide here with the ERR. Then in order to minimize the cost function threshold must adopt a value of 0.707 yielding the error rates of FAR = 16.01%, FRR = 13.89% and MER = 14.95%.
\n\t\t\t
In order to further simulate real conditions, score 2 has been supposed here to display a different behavior, to wit, pdf.genuine = Beta (8,4) and pdf.impostor = Beta (4,4), as it is displayed in Fig 22\n\t\t\t
\n\t\t\t
As can be seen, the equilibrium point is found here at a value of EER = 29.31% and th = 0.5896 and the minimum of the cost function at FAR =34.84%, FRR = 23.13%, MER = 28.99% and th = 0.5706%.
\n\t\t\t
If these two distributions are combined and a two-dimensional score space is established, the resulting pdfs can be represented as the one in Fig.23. It plots these two-dimensional density distributions where de genuine one is found near the point (1,1) while the impostor one is located farther from it.
\n\t\t\t
Figure 23.
Combined density distribution, 3D view (left), contour lines (right).
\n\t\t\t
Applying the GMM trainer with 10 Gaussian functions to these distributions, the images in Fig. 24 are obtained representing the set of the 10 Gaussians making up the genuine model; the set of 10 Gaussians making up the impostor model and representing the impostor and genuine models as the weighted sum of each of their Gaussian functions.
\n\t\t\t
Figure 24.
Contour lines for GMM 10G models. Individual genuine Gaussians (left), individual impostor Gaussians (centre), both models right (right).
\n\t\t\t
Equivalent representations can be obtained using a GMM with 3 Gaussians
\n\t\t\t
Figure 25.
Contour lines for GMM 3G models (left),and GMM 1G models (centre), vectors and SVM boundary (right).
\n\t\t\t
As in previous sections tests conducted with GMM, SVM and ANN classifiers yield the following results:
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Classifier
\n\t\t\t\t\t\t
FAR
\n\t\t\t\t\t\t
FRR
\n\t\t\t\t\t\t
MER
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 10G
\n\t\t\t\t\t\t
10.19%
\n\t\t\t\t\t\t
9.64%
\n\t\t\t\t\t\t
9.91%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 3G
\n\t\t\t\t\t\t
10.96%
\n\t\t\t\t\t\t
8.94%
\n\t\t\t\t\t\t
9.96%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 1G
\n\t\t\t\t\t\t
10.92%
\n\t\t\t\t\t\t
9.19 %
\n\t\t\t\t\t\t
10.06%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
SVM Linear
\n\t\t\t\t\t\t
10.44 %
\n\t\t\t\t\t\t
9.29 %
\n\t\t\t\t\t\t
9.86 %
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
ANN (2-1-1)
\n\t\t\t\t\t\t
10.49%
\n\t\t\t\t\t\t
9.285 %
\n\t\t\t\t\t\t
9.87 %
\n\t\t\t\t\t
\n\t\t\t\t
Table 6.
Results from classifiers test
\n\t\t
\n\t\t
\n\t\t\t
14. Match score normalization
\n\t\t\t
As described in earlier sections of the present chapter, the data sources in a system of match score fusion are the result of different monobiometric recognition subprocesses working in parallel. For this reason, the scores yielded are often not homogeneous.
\n\t\t\t
In the most trivial case, the source of this lack is different meaning of the scores, they may represent the degree of similarity between the sample and the model or the degree of disimilarity or directly represent the degree of subsystem confidence in the decision made.
\n\t\t\t
Other sources of non-homogeneous scores are include the different numeric scales or the different value ranges according to which results are delivered, as well as the various ways in which the non-linearity of biometric features is presented. Finally, the different statistical behavior of scores must also be taken into account when performing the fusion. For these reasons, the score normalization, transferring them to a common domain, is essential prior to their fusion. In this way, score normalization must be seen as a vital phase in the design of a combination schema for score level fusion.
\n\t\t\t
Score normalization may be understood as the change in scale, location and linearity of scores obtained by distinct monobiometric recognition subprocesses. In a good normalization schema, estimates of transformation parameters must not be overly sensitive to the presence of outliers (robustness) and must also obtain close to optimal results (efficiency) (Nandakumar et al. 2005)(Jain et al 2005)(Huber 1981)
\n\t\t\t
There are multiple techniques that can be used for score normalization. Techniques such as min-max, z-score, median and MAD, double sigmoid and double linear transformations have been evaluated in diverse publications (Snelick et al. 2003) (Puente et al. 2010).
\n\t\t
\n\t\t
\n\t\t\t
15. Min-max normalization
\n\t\t\t
Perhaps the simplest of currently existing score normalization techniques is min-max normalization. In min-max normalization, the goal is to reduce dynamic score ranges to a known one (tipically: 0-1) while, at the same time, retaining the form of the original distributions.
\n\t\t\t
For the use this technique, it is necessary that maximum and minimum values (max, min) were provided by the matcher prior to normalize. Alternatively, these may be evaluated as the maximum and minimum data of the values used during system training. In this way, a linear transformation is carried out where 0 is assigned to the minimum value 1 to the maximum value[1] -. This transformation function is shown in the following formula 16:
Applying this transformation to the observations generated in a previous section of the current chapter (see ’13. More realistic distributions’), the following results are obtained:
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Classifier
\n\t\t\t\t\t\t
FAR
\n\t\t\t\t\t\t
FRR
\n\t\t\t\t\t\t
MER
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 10G
\n\t\t\t\t\t\t
17.62%
\n\t\t\t\t\t\t
17.67 %
\n\t\t\t\t\t\t
17.64%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 3G
\n\t\t\t\t\t\t
17.27%
\n\t\t\t\t\t\t
18.47 %
\n\t\t\t\t\t\t
17.87%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 1G
\n\t\t\t\t\t\t
19.49%
\n\t\t\t\t\t\t
16.36%
\n\t\t\t\t\t\t
17.93%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
SVM Linear
\n\t\t\t\t\t\t
18.22%
\n\t\t\t\t\t\t
17.31 %
\n\t\t\t\t\t\t
17.77 %
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
ANN (2-1-1)
\n\t\t\t\t\t\t
16.70%
\n\t\t\t\t\t\t
18.80%
\n\t\t\t\t\t\t
17.75%
\n\t\t\t\t\t
\n\t\t\t\t
Table 7.
Result after min-max normalization
\n\t\t
\n\t\t
\n\t\t\t
16. Z-score normalization
\n\t\t\t
Due to its conceptual simplicity, one of the most frequently used transformations is z-score normalization. In z-score normalization, the statistical behavior of the match scores is homogenized through their transformation into other scores with a mean of 0 and a standard deviation of 1.
Clearly, it is necessary that the mean and standard deviation of the original match scores be known prior to normalization or, as in min-max normalization, they should be estimated from training data.
\n\t\t\t
Z-score distributions do not retain the forms of the input distributions, save in cases of scores with a Gaussian distribution, and this technique does not guarantee a common numerical range for the normalized scores.
\n\t\t\t
Test results from z-score normalization are shown below:
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Classifier
\n\t\t\t\t\t\t
FAR
\n\t\t\t\t\t\t
FRR
\n\t\t\t\t\t\t
MER
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 10G
\n\t\t\t\t\t\t
10.13%
\n\t\t\t\t\t\t
9.76 %
\n\t\t\t\t\t\t
9.94%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 3G
\n\t\t\t\t\t\t
10.96%
\n\t\t\t\t\t\t
8.96%
\n\t\t\t\t\t\t
9.96%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 1G
\n\t\t\t\t\t\t
10.92%
\n\t\t\t\t\t\t
9.19%
\n\t\t\t\t\t\t
10.06%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
SVM Linear
\n\t\t\t\t\t\t
10.38%
\n\t\t\t\t\t\t
9.33%
\n\t\t\t\t\t\t
9.86%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
ANN (2-1-1)
\n\t\t\t\t\t\t
9.43%
\n\t\t\t\t\t\t
10.36%
\n\t\t\t\t\t\t
9.90%
\n\t\t\t\t\t
\n\t\t\t\t
Table 8.
Result after z-score normalization
\n\t\t
\n\t\t
\n\t\t\t
17. Median and MAD
\n\t\t\t
The median and MAD (median absolute deviation) normalization technique uses the statistical robustness resulting from the median of a random distribution to make it less sensitive to the presence of outliers. Nevertheless, median and MAD is generally less effective than z-score normalization, does not preserve the original distribution and does not guarantee a common range of normalized match scores.
According to the double sigmoid normalization technique, match scores are converted to the interval [0,1]. While the conversion is not linear, scores located on the overlap are nevertheless mapped onto a linear distribution (Fahmy et al. 2008).
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Classifier
\n\t\t\t\t\t\t
FAR
\n\t\t\t\t\t\t
FRR
\n\t\t\t\t\t\t
MER
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 10G
\n\t\t\t\t\t\t
11.40%
\n\t\t\t\t\t\t
10.04%
\n\t\t\t\t\t\t
10.72%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 3G
\n\t\t\t\t\t\t
10.32%
\n\t\t\t\t\t\t
9.94%
\n\t\t\t\t\t\t
10.13%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GMM 1G
\n\t\t\t\t\t\t
11.19%
\n\t\t\t\t\t\t
10.20%
\n\t\t\t\t\t\t
10.70%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
SVM Linear
\n\t\t\t\t\t\t
11.10%
\n\t\t\t\t\t\t
9.66%
\n\t\t\t\t\t\t
10.38%
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
ANN (2-1-1)
\n\t\t\t\t\t\t
10.70%
\n\t\t\t\t\t\t
9.65%
\n\t\t\t\t\t\t
10.06%
\n\t\t\t\t\t
\n\t\t\t\t
Table 10.
Test results for double sigmoid normalization.
\n\t\t
\n\t\t
\n\t\t\t
19. Double linear normalization
\n\t\t\t
Scores yielded by monobiometric classifiers are interpreted as pair of a decision and confidence. The decision, thus, is made according to the location side of the score is located respect to the threshold while confidence is de distance between them. Thus, the greater the distance to the threshold, the greater will be the weight assigned to the score for the final decision.
\n\t\t\t
Generally, this distance does not enjoy a homogeneous distribution for scores of genuine and impostor observations. As a result, in distributions such as that presented in Fig.23, scores of impostor samples tend to have a greater likelihood than those of genuine scores.
\n\t\t\t
In order to compensate that kind of heterogeneity, a transformation has been proposed in (Puente et al. 2010) to make distributions more uniform around the decision threshold:
The principal conclusion that can be drawn from the present chapter is undoubtedly the great advantage provided by score fusion relative to monobiometric systems. In combining data from diverse sources, error rates (EER, FAR and FRR) can be greatly reduced and system stability greatly increased through a higher AUC.
\n\t\t\t
This improvement has been observed with each of the classifiers discussed in the present chapter. Nevertheless and in consideration of comparative studies of normalization techniques and fusion algorithms, it can be noted that the specific improvement produced depends on the algorithms used and the specific case at hand. It is not possible, therefore, to state a priori which techniques will be optimal in any given case. Rather, it is necessary to first test different techniques in order to pinpoint the normalization and fusion methods to be used.
\n\t\t\t
One final conclusion that stands out is that improvements in error rates are directly linked to the number of biometric features being combined. From this, it may be deduced that the greater the number of features being fused, the larger the improvement will be in the error rates.
\n\t\t
\n\t\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/17742.pdf",chapterXML:"https://mts.intechopen.com/source/xml/17742.xml",downloadPdfUrl:"/chapter/pdf-download/17742",previewPdfUrl:"/chapter/pdf-preview/17742",totalDownloads:1584,totalViews:141,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"October 25th 2010",dateReviewed:"March 30th 2011",datePrePublished:null,datePublished:"August 9th 2011",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/17742",risUrl:"/chapter/ris/17742",book:{slug:"advanced-biometric-technologies"},signatures:"Luis Puente, María Jesús Poza, Belén Ruíz and Diego Carrero",authors:[{id:"30373",title:"Prof.",name:"Belen",middleName:null,surname:"Ruiz-Mezcua",fullName:"Belen Ruiz-Mezcua",slug:"belen-ruiz-mezcua",email:"bruiz@inf.uc3m.es",position:null,institution:null},{id:"53715",title:"Prof.",name:"Luis",middleName:null,surname:"Puente",fullName:"Luis Puente",slug:"luis-puente",email:"lpuente@it.uc3m.es",position:null,institution:null},{id:"53989",title:"Prof.",name:"María Jesús",middleName:null,surname:"Poza Lara",fullName:"María Jesús Poza Lara",slug:"maria-jesus-poza-lara",email:"mpoza@pa.uc3m.es",position:null,institution:{name:"Carlos III University of Madrid",institutionURL:null,country:{name:"Spain"}}},{id:"53990",title:"Mr",name:"Diego",middleName:null,surname:"Carrero-Figueroa",fullName:"Diego Carrero-Figueroa",slug:"diego-carrero-figueroa",email:"dcarrero@di.uc3m.es",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction ",level:"1"},{id:"sec_2",title:"2. Biometrics",level:"1"},{id:"sec_3",title:"3. Biometric fusion",level:"1"},{id:"sec_4",title:"4. Data sources",level:"1"},{id:"sec_5",title:"5. Fusion level",level:"1"},{id:"sec_6",title:"6. Biometric performances",level:"1"},{id:"sec_7",title:"7. Single scores distribution",level:"1"},{id:"sec_8",title:"8. Multiple score fusion",level:"1"},{id:"sec_9",title:"9. Using gaussian mixture model classifiers",level:"1"},{id:"sec_10",title:"10. Using support vector machine classifiers",level:"1"},{id:"sec_11",title:"11. Using neural network classifiers",level:"1"},{id:"sec_12",title:"12. Beta distributions",level:"1"},{id:"sec_13",title:"13. More realistic distributions",level:"1"},{id:"sec_14",title:"14. Match score normalization",level:"1"},{id:"sec_15",title:"15. Min-max normalization",level:"1"},{id:"sec_16",title:"16. Z-score normalization",level:"1"},{id:"sec_17",title:"17. Median and MAD",level:"1"},{id:"sec_18",title:"18. Double sigmoid normalization",level:"1"},{id:"sec_19",title:"19. Double linear normalization",level:"1"},{id:"sec_20",title:"20. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tHuber\n\t\t\t\t\t\t\tP. J.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1981\n\t\t\t\t\tRobust Statistics (John Wiley & Sons, 1981).\n\t\t\t'},{id:"B2",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMartin\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDoddington\n\t\t\t\t\t\t\tG. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKamm\n\t\t\t\t\t\t\tT. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tOrdowski\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPrzybocki\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1997\n\t\t\t\t\tThe DET Curve in Assessment of Detection Task Performance, Eurospeech, 1997, 1895\n\t\t\t\t\t1898 .\n\t\t\t'},{id:"B3",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tCappelli\n\t\t\t\t\t\t\tR. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMaio\n\t\t\t\t\t\t\tD.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMaltoni\n\t\t\t\t\t\t\tD.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2000\n\t\t\t\t\tCombining fingerprint classifiers, in: Proceedings of First International Workshop on Multiple Classifier Systems, 2000, 351\n\t\t\t\t\t361 .\n\t\t\t'},{id:"B4",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tGhosh\n\t\t\t\t\t\t\tJ. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMulticlassifier\n\t\t\t\t\t\t\tSystems.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tBack\n\t\t\t\t\t\t\tto.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tthe\n\t\t\t\t\t\t\tFuture.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tProceedings\n\t\t\t\t\t\t\tof.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tthe\n\t\t\t\t\t\t\tThird.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tInternational\n\t\t\t\t\t\t\tWorkshop.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\ton\n\t\t\t\t\t\t\tMultiple.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tClassifier\n\t\t\t\t\t\t\tSystems.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1\n\t\t\t\t\t15\n\t\t\t\t\t115 June 24-26, 2002.\n\t\t\t'},{id:"B5",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKumar\n\t\t\t\t\t\t\tH. G.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tImran\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2010\n\t\t\t\t\tResearch Avenues in Multimodal Biometrics. IJCA, Special Issue on RTIPPR(1):1-8, 2010. Published By Foundation of Computer Science.\n\t\t\t'},{id:"B6",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRoss\n\t\t\t\t\t\t\tS. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tNandakumar\n\t\t\t\t\t\t\tK.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tJain\n\t\t\t\t\t\t\tA. K.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2006\n\t\t\t\t\tHandbook of Multibiometrics, Springer Publishers, 1st edition, 2006. 0-38722-296-0\n\t\t\t\t\n\t\t\t'},{id:"B7",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRoss\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2007\n\t\t\t\t\tAn Introduction to Multibiometrics, Proc. of the 15th European Signal Processing Conference (EUSIPCO), (Poznan, Poland), September 2007.\n\t\t\t'},{id:"B8",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRoss\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPoh\n\t\t\t\t\t\t\tN.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2009\n\t\t\t\t\tMultibiometric Systems: Overview, Case Studies and Open Issues, in Handbook of Remote Biometrics for Surveillance and Security, M. Tistarelli, S. Z. Li and R. Chellappa (Eds.), Springer, 2009. 978-1-84882-384-6\n\t\t\t\t\n\t\t\t'},{id:"B9",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tTejas\n\t\t\t\t\t\t\tJ. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSomnath\n\t\t\t\t\t\t\tD.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDebasis\n\t\t\t\t\t\t\tS.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2009\n\t\t\t\t\tMultimodal biometrics: state of the art in fusion techniques, International Journal of Biometrics, 1 n.4, 393\n\t\t\t\t\t417 , July 2009\n\t\t\t'},{id:"B10",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tLi\n\t\t\t\t\t\t\t.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tS. Z.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2009\n\t\t\t\t\tEncyclopedia of Biometrics. Springer Science + Business Media, LLC. 2009. 978-0-38773-200-28.\n\t\t\t'},{id:"B11",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tVillegas\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tParedes\n\t\t\t\t\t\t\tR.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2009\n\t\t\t\t\tScore Fusion by Maximizing the Area under the ROC Curve. IbPRIA ‘09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis. Springer-Verlag Berlin, Heidelberg 2009. 978-3-64202-171-8\n\t\t\t\t\tDOI 10.1007/978-3-642-02172-5_61.\n\t\t\t'},{id:"B12",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMarzban\n\t\t\t\t\t\t\tC.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2004\n\t\t\t\t\tA comment on the roc curve and the area under it as performance measures. Technical report, The Applied Physics Laboratory and the Department of Statistics, University of Washington (2004)\n\t\t\t'},{id:"B13",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDoddington\n\t\t\t\t\t\t\tG. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tLiggett\n\t\t\t\t\t\t\tW. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMartin\n\t\t\t\t\t\t\tA. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPrzybocki\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tReynolds\n\t\t\t\t\t\t\tD.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1998\n\t\t\t\t\tSheeps, Goats, Lambs and Wolves: A Statistical Analysis of Speaker Performance in the NIST 1998 Speaker Recognition Evaluation, Proc. ICSLD 98, Nov. 1998.\n\t\t\t'},{id:"B14",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tNandakumar\n\t\t\t\t\t\t\tK.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tJain\n\t\t\t\t\t\t\tA. K.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRoss\n\t\t\t\t\t\t\tA. A.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2005\n\t\t\t\t\tScore normalization in multimodal biometric systems. Pattern Recognition 38 (1212), 2270 EOF\n\t\t\t\t\t2285 EOF (2005)\n\t\t\t'},{id:"B15",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tJain\n\t\t\t\t\t\t\tK. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tNandakumar\n\t\t\t\t\t\t\tK.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRoss\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2005\n\t\t\t\t\tScore Normalization in Multimodal Biometric Systems, Pattern Recognition, 38\n\t\t\t\t\t12\n\t\t\t\t\t2270\n\t\t\t\t\t2285 , December 2005. Winner of the Pattern Recognition Society Best Paper Award (2005).\n\t\t\t'},{id:"B16",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRoss\n\t\t\t\t\t\t\tA. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRattani\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tTistarelli\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2009\n\t\t\t\t\tExploiting the Doddington Zoo Effect in Biometric Fusion, Proc. of 3rd IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), (Washington DC, USA), September 2009.\n\t\t\t'},{id:"B17",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSnelick\n\t\t\t\t\t\t\tR. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tIndovina\n\t\t\t\t\t\t\tM. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tYen\n\t\t\t\t\t\t\tJ.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMink\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2003\n\t\t\t\t\tMultimodal Biometrics: Issues in Design and Testing, in Proceedings of Fifth International Conference on Multimodal Interfaces, (Vancouver, 2003), 68\n\t\t\t\t\t72 .\n\t\t\t'},{id:"B18",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPuente\n\t\t\t\t\t\t\tL. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPoza\n\t\t\t\t\t\t\tM. J. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRuíz\n\t\t\t\t\t\t\tB.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tGarcía\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2010\n\t\t\t\t\tScore normalization for Multimodal Recognition Systems. Journal of Information Assurance and Security, 5 2010, 409\n\t\t\t\t\t417 .\n\t\t\t'},{id:"B19",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tFahmy\n\t\t\t\t\t\t\tM. S.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAtyia\n\t\t\t\t\t\t\tA. F.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tElfouly\n\t\t\t\t\t\t\tR. S.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2008\n\t\t\t\t\tBiometric Fusion Using Enhanced SVM Classification, Intelligent Information Hiding and Multimedia Signal Processing, 2008. IIHMSP ‘08 International Conference on, vol., no., 1043\n\t\t\t\t\t1048 , 15-17 Aug. 2008\n\t\t\t'},{id:"B20",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPrzybocki\n\t\t\t\t\t\t\tM. A. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMartin\n\t\t\t\t\t\t\tA. F.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tLe \n\t\t\t\t\t\t\tA. N.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2006\n\t\t\t\t\tNIST speaker recognition evaluation chronicles- Part 2", Proc. Odyssey 2006: Speaker Lang. Recognition Workshop, 1 2006.\n\t\t\t'}],footnotes:[{id:"fn1",explanation:"The common structure of all biometric recognition systems is performed in two phases: (1) an initial training phase in which one or various biometric models are generated for each subject, and a later one called recognition phase, in which biometric samples are captured and matched against the models."},{id:"fn2",explanation:"The present chapter interprets scores as representing similarity. While, in practice, scores may also indicate difference, no generality is lost here by interpreting scores in this way since a linear transformation of the type (s’ = K-s) can always be established."},{id:"fn3",explanation:"Term derived from the Greek monos (one) + bios (life) + metron (measure) and preferred by the authors of the present chapter over the term “unibiometric”, also found in the literature but involving a mix of Greek and Latin morphological units. The same comment should be made about polybiometric and multibiometric terms."},{id:"fn4",explanation:"For the training and tests of the GMMs performed here a version of EM algorithm has been used. http://www.mathworks.com/matlabcentral/fileexchange/8636-emgm\n\t\t\t\t\t"},{id:"fn5",explanation:"v' indicates the transpose v vector."},{id:"fn6",explanation:"For the examples presented in this chapter, SVM-Light software has been used."},{id:"fn7",explanation:"For the examples with ANN, Neural Network Toolbox™ have been used. http://www.mathworks.com/products/neuralnet/\n\t\t\t\t\t"},{id:"fn8",explanation:"For the examples, the same number of genuine and imposter vectors were randomly generated as the previous sections"},{id:"fn9",explanation:"Where match scores indicate the difference between a sample and reference, 1 should be assigned to the minimum value and 0 to the maximum."}],contributors:[{corresp:"yes",contributorFullName:"Luis Puente",address:"",affiliation:'
'}],corrections:null},book:{id:"456",title:"Advanced Biometric Technologies",subtitle:null,fullTitle:"Advanced Biometric Technologies",slug:"advanced-biometric-technologies",publishedDate:"August 9th 2011",bookSignature:"Girija Chetty and Jucheng Yang",coverURL:"https://cdn.intechopen.com/books/images_new/456.jpg",licenceType:"CC BY-NC-SA 3.0",editedByType:"Edited by",editors:[{id:"30495",title:"Dr.",name:"Girija",middleName:null,surname:"Chetty",slug:"girija-chetty",fullName:"Girija Chetty"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},chapters:[{id:"17738",title:"Multimodal Fusion for Robust Identity Authentication: Role of Liveness Checks",slug:"multimodal-fusion-for-robust-identity-authentication-role-of-liveness-checks",totalDownloads:1792,totalCrossrefCites:0,signatures:"Girija Chetty and Emdad Hossain",authors:[{id:"30495",title:"Dr.",name:"Girija",middleName:null,surname:"Chetty",fullName:"Girija 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Streckfus and Cynthia Guajardo-Edwards",authors:[{id:"29033",title:"Prof.",name:"Charles",middleName:"F.",surname:"Streckfus",fullName:"Charles Streckfus",slug:"charles-streckfus"},{id:"128868",title:"Dr.",name:"Cynthia",middleName:null,surname:"Guajardo-Edwards",fullName:"Cynthia Guajardo-Edwards",slug:"cynthia-guajardo-edwards"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"67397",title:"Lignocellulosic Ethanol: Technology and Economics",doi:"10.5772/intechopen.86701",slug:"lignocellulosic-ethanol-technology-and-economics",body:'\n
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1. Introduction
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The need to slow down and eventually stop global warming has driven commercial production of the bioethanol in the past two decades because the use of renewable fuel is one of the few ways to mitigate climate change as it helps reduce GHG emissions. Multiple independently produced datasets confirm that between 1880 and 2012, the global average land and ocean surface temperature increased by 0.85 [0.65–1.06]°C [1]. Since 1979 the rate of warming has approximately doubled (0.13°C/decade, against 0.07°C/decade) [2, 3]. The scientific consensus as of 2013 stated in the intergovernmental panel on climate change (IPCC) Fifth Assessment Report is that it “is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century.” In 2018 the IPCC published a Special Report on Global Warming of 1.5°C which warned that, if the current rate of greenhouse gas (GHG) emissions is not mitigated, global warming is likely to reach 1.5°C between 2030 and 2052 causing major crises. The report said that preventing such crises will require a swift transformation of the global economy that has “no documented historic precedent” [4].
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A mandate required developed countries to take the lead in reducing their emissions and was sustained in the Kyoto Protocol to the United Nations Framework Convention on Climate Change (UNFCCC), which entered into legal effect in 2005. In ratifying the Kyoto Protocol, most developed countries accepted legally binding commitments to limit their emissions. Biofuel mandates are set in more than 60 nations and incentives are provided by the governments to boost bioethanol production [5].
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In the U.S., production, transportation and fermentation of the corn was adapted quickly by industry for fuel ethanol production, primarily because corn was the only crop that had the existing infrastructure to easily modify for this purpose, especially when initially incentivized with tax credits, subsidies and import tariffs. Figure 1 shows total U.S. corn use from 1986 to 2018. The amount of corn used for ethanol production increased substantially between 2001 and 2010, as nearly all gasoline was transitioned to 10% ethanol. From 2013, the trend remains consistent with production and usage remaining relatively constant.
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Figure 1.
The U.S. corn for fuel ethanol, feed, and other use. Source: the United States Department of Agriculture Economic Research Service Feed Grain Yearbook.
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There is still some debate on whether biofuel production from food feedstock can truly reduce GHG emissions. The United Nations Intergovernmental Panel on Climate Change released two of its Working Group reports state that “Biofuels have direct, fuel-cycle GHG emissions that are typically 30–90% lower than those for gasoline or diesel fuels. However, since for some biofuels indirect emissions—including from land use change—can lead to greater total emissions than when using petroleum products, policy support needs to be considered on a case by case basis” (IPCC 2014 Chapter 8). The report lists many potential negative risks of ethanol production from food feedstock, such as direct conflicts between land for fuels and land for food, other land-use changes, water scarcity, loss of biodiversity and nitrogen pollution through the excessive use of fertilizers.
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Also, the potential of using bioethanol from food feedstock to replace petroleum fuels is limited. The United States will use over 130 billion gallons of gasoline in 2014, and over 50 billion gallons of diesel. On average, one bushel of corn can be used to produce just 2.8 gallons of ethanol. If all of the production of corn in the U.S. were converted into ethanol, it would only displace 25% of that 130 billion.
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On the other hand, there is less controversy over GHG reduction from production of lignocellulosic ethanol production as cellulosic materials are mostly the wastes of the agriculture and forest industry. The shift from food crop feedstocks to waste residues and native grasses offers significant opportunities for a range of players, from farmers to biotechnology firms, and from project developers to investors [6]. However, the process to convert lignocellulosic materials to ethanol is much more complex than that used to covert starch and sugars into ethanol.
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Cellulosic ethanol industry is still in its infancy. In the U.S., as of 2013, the first commercial-scale plants to produce cellulosic biofuels have begun operating. In the following 5 years, cellulosic ethanol production grown from 0 to 10 million gallons [7], and most likely topping 15 million in 2018. However, that is far from the Renewable Fuel Standard’s original target of 7 billion gallons of cellulosic biofuel by 2018 and 16 billion by 2022. Of all five commercial cellulosic ethanol plants that were built/to be built in the U.S. from 2010 to 2016, only POET’s Emmetsburg, Iowa facility is still in operation in 2019 (Table 1). In 2017, the total cellulosic ethanol produced was less than half the nameplate capacity (25 million gallons year−1) of this single plant [13].
The status of the U.S. commercial lignocellulosic ethanol facilities.
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The future of bioethanol generation from lignocellulosic materials is not clear at this point of time. The sustainability of this renewable fuel business will depend on the success of development of cost-cutting technologies for every stage of lignocellulosic ethanol production.
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2. Ethanol generation from biomass
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2.1 First-generation bioethanol
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First-generation biofuel includes biodiesel produced from vegetable oils through transesterification and bioethanol generated from food feedstock, mainly starchy materials (e.g., corn, wheat, barley, cassava, potato) and sucrose-containing feedstock (e.g., sugarcane, sugar beet, sweet sorghum) [14]. First-generation bioethanol is produced from fermentation of these starchy and sucrose-containing materials in four basic steps: enzymatic saccharification or hydrolysis of starch into sugars, microbial (yeast) fermentation of sugars, distillation, and dehydration.
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\nFigure 2 shows global ethanol production by country or region, from 2007 to 2017. Together, the U.S. and Brazil produce 85% of the world’s ethanol. The vast majority of Brazil ethanol is produced from sugarcane.
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Figure 2.
Global ethanol production by country or region, from 2007 to 2017. Source: Renewable Fuels Association. Last updated October 2018.
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The United States is the world’s leading producer of ethanol, with nearly 16 billion gallons in 2017 alone, mainly produced from corn. The annual U.S. production of ethanol from 1981 to 2018 is shown in Figure 3.
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Figure 3.
The U.S. annual production of ethanol from 1981 to 2018 [15].
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2.2 Second generation bioethanol
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Second and subsequent generations of biofuels including bioethanol are produced from non-food raw materials [16]. Second-generation bioethanol is typically produced from sugars derived from lignocellulosic biomass. Various types of biomass have been studied for production of biofuels including agricultural wastes (e.g., corn stover, wheat straw, corn cob, rice husk, and sugar cane bagasse), energy crops which grow on low-quality soil (perennial grasses such as Miscanthus sinensis and M. giganteus and switchgrass), forest-based woody wastes (bark, sawdust, softwood trimmings and hardwood chips), waste from parks and gardens (leaves, grasses, and branches), municipal solid wastes such as food waste, kraft paper and paper sludge, the whey-a byproduct of the cheese industry, and crude glycerol from the biodiesel industry.
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The amount of available lignocellulosic biomass far exceeds the amount of food feedstock that can be used for biofuel production. However, the production of lignocellulosic bioethanol requires feedstock preparation prior to fermentation and finding/developing microbes that are able to hydrolyze polysaccharides and ferment sugars from cellulose and hemicellulose breakdown.
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2.3 Third generation bioethanol
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The term third generation biofuel refers to biofuel derived from algae and has only recently enter the mainstream. Previously, algae were grouped with other non-food biomass types as feedstock for second generation biofuels. However, the uniqueness in algae’s production methods and potential of much higher yields of biofuel production warrants its separation from other types of non-food biomass to form their own category.
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When it comes to the potential to produce fuel, algae is unique in several ways. First, algae produce an oil that can easily be refined into diesel or even certain components of gasoline [17]. Second, it can be genetically manipulated to produce a wide list of fuels including biodiesel, butanol, gasoline, methane, ethanol, vegetable oil, and jet fuel [18]. Third, it is also capable of producing outstanding yields. In fact, algae have been used to produce up to 9000 gallons of biofuel per acre, which is 10-fold what the best traditional feedstock have been able to generate. Yields as high as 20,000 gallons per acre are believed to be attainable. According to the US Department of Energy, yields of 10-fold high mean that only 0.42% of the U.S. land area would be needed to generate enough biofuel to meet all the U.S. needs.
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Algae do have a down side: they require large amounts of water, nitrogen and phosphorus to grow. So much that the production of fertilizer to meet the needs of algae used to produce biofuel would produce more greenhouse gas emissions than were saved by using algae-based biofuel. It also means the cost of algae-base biofuel is much higher than fuel from other sources. This single disadvantage means that the large-scale implementation of algae to produce biofuel will not occur for a long time, if at all. In fact, after investing more than $600 million USD into research and development of algae, Exxon Mobil came to the conclusion in 2013 that algae-based biofuels will not be viable for at least 25 years which was calculated on strictly economical term without considering the environmental impacts that have yet to be solved [19].
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3. Overview of bioethanol generation from lignocellulosic biomass
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3.1 Composition of lignocellulosic feedstock for bioethanol
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Dry plant materials are mainly comprised of three types of biopolymers: cellulose, hemicellulose, and lignin. Cellulose and hemicellulose account for more than half of the entire dry biomass (see Table 2) [28]. Ethanol yield and conversion efficiency depend on the type of biomass, and benefit from a high content of cellulose and hemicellulose and low lignin content [29]. The domains of the three polymers in plant cell walls are connected strongly through covalent and hydrogen bonds. These bonds make lignocellulosic material resistant to degradation [30] and different methods of pretreatment [31].
Cellulose is a β-glucan linear polymer of 500–14,000 d-glucose units d-glucose linked by β-1,4-glycosidic bonds. Around 36 hydrogen-bonded glucan chains form insoluble microfibrils in secondary cell wall [32]. The cellulose structure is highly crystalline and thus is difficult to break in enzymatic hydrolysis [33]. High temperature (320°C) and pressure (25 MPa) are needed to melt and dissolve this rigid crystalline structure in water, in sharp contrast with the liquefaction temperature 95–105°C of starch at pH = 6.0–6.5, and the saccharification temperatures of 60–65°C at pH = 4.0–4.5 [34, 35].
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Hemicellulose is a branched heteropolymer of different monosaccharides including pentoses (d-xylose and l-arabinose) and hexoses (d-mannose, d-galactose, d-glucose) and a small amount of sugar acids called uronic acids [36]. The d-pentose sugars are dominant with occasionally small amounts of l-sugars as well. Among pentoses, xylose is present in the largest amount, although in softwoods mannose can be the most abundant sugar. Typical sugar acids in the hemicellulose structure include d-glucuronic, 4-O-ethylglucuronic and d-galacturonic acids. Meaningful quantities of l-arabinose are contained in corn fiber and specific herbaceous crops [37].
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C5 sugars such as xylose and arabinose are mostly found in xyloglucan, xylan, arabinan and arabinogalactan (substructures of pectin), which are components of polysaccharides in the plant cell wall [38]. Xylan is the largest hemicellulose component, consisted of β-1,4-linked xylose residues with side branches of α-arabinofuranose and α-glucuronic acids and contribute to cross-linking of cellulose microfibrils and lignin through ferulic acid residues [39].
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Lignin is a natural three-dimensional polymer (600–15,000 kda) bio-synthesized from phenylpropanoid units via radical reactions [40]. Lignin accounts for 20–35 wt% in woody biomass (40–50 wt% in bark) and 10–20 wt% in agricultural stems [41]. In lignin, phenolic units are connected by more than eight different linkages, among them arylglycerol β-aryl ether (β-O-4) is the dominant linkage in both softwood and hardwood in most plants, consisting of ~50% of spruce linkages and 60% of birch and eucalyptus linkage [42]. It has long been recognized as the major renewable source of aromatic chemicals such as phenols and aromatic hydrocarbons.
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Due to the complex polymer structure and heterogeneity in the ways monomeric units are linked, lignin is particularly difficult to biodegrade, making it an undesirable component in plant cell walls for bioethanol production. In plant cell wall, lignin functions like a glue to hold all components together [43]. As such, its recalcitrant character makes this three-dimensional polymer molecule a physical barrier to the enzymes that act on cellulose and hemicellulose.
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In biorefinery, around 62 million tonnes of lignin is obtained in the commercial production of lignocellulosic ethanol. A large amount of lignin is also being generated in the pulp industry as lignin has also to be separated from cellulose for a different reason: the aromatic components in lignin can turn yellow as it is oxidized slowly in air. Despite that lignin has mainly been burned to supply heat and to generate electricity, it has long been recognized as the major renewable source of aromatic polymer and chemicals [44].
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Due to the lower oxygen content in lignin as compared to that in cellulose, the energy value of lignin could be as high as cellulose despite of its lower weight percentage in lignocellulosic biomass. This has generated a lot of interest in converting lignin into liquid fuels using thermochemical and biological methods including pyrolysis, hydrothermal liquefaction, and enzymatic decomposition [45]. Among these methods, hydrothermal liquefaction has been more investigated recently and appears to be a promising way to decompose lignin into bio oil which could be further processed into liquid transportation fuels.
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3.2 Biochemical conversion of biomass into ethanol
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Second-generation bioethanol is produced using a process involving the four primary steps of (i) pre-treatment, (ii) hydrolysis to sugars, (iii) fermentation, and (iv) product/coproduct recovery [46]. During pre-treatment, the feedstock is subjected to physical (heat, steam) or chemical (acid or base) conditions that disrupt the fibrous matrix of the material, resulting in the separation of the hemicelluloses from the cellulose chains and the lignin that binds them together. Hydrolysis follows pre-treatment, releasing individual glucose from cellulose and hexose and pentose from hemicellulose. These monomers can then be fermented to ethanol by yeasts that have been modified to ferment both hexose and pentose sugars and adapted to deal with the inhibitors that are produced during pre-treatment and unavoidably associated with the hexose and pentose sugars [34]. Distillation and dehydration of the aqueous ethanol solution produces ethanol of 99.9% purity. Coproduct recovery will depend upon the feedstock and pre-treatment process used and can include a range of products such as extractives, lignin, and unhydrolyzed cellulose [47].
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In the following three sections (Sections 4–7), each of the four primary steps will be reviewed. Current topics of research, which are concentrated on recombinant fermentative microbes development and a consolidated process of hydrolysis and co-fermentation of hexoses and pentoses, will be covered in Section 8. A review on cost analysis is given in Section 9 to present opportunities for cost reduction for second-generation bioethanol production.
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4. Pretreatment of lignocellulosic biomass
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4.1 Objectives of pretreatment and basic methods
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Without pretreatment before the enzymatic saccharification stage, the non-biodegradable lignin in lignocellulosic material presents as a major obstacle to the enzymatic hydrolysis of crystalline cellulose and hemicellulose which themselves already have low digestibility [48]. Pretreatment removes or decomposes the lignin (delignification) [49] and thus makes cellulose and hemicellulose more readily available to cellulases and hemicellulose’s.
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In principle, there are three methods for pretreatment: biological, chemical and physical processes. Some processes, where chemical and physical actions are inherently inseparable, are termed physiochemical. Two or all of these basic methods can be used in combination to gain benefits from each method. Various pretreatment methods have been described and compared critically in a recent review [50].
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Biological treatment uses microorganisms such as white, brown or soft rot fungi which break up the structure of lignin via the action of extracellular lignolytic enzymes released by the fungi [51]. Further research is needed to overcome the issues of selectivity, cost, retention time and effectiveness to make it a practical choice [50].
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Chemical treatments include treatment with bases, diluted acids, and oxygen as an oxidizer. These reagents react with lignin and cause the polymer to breakdown into smaller and more soluble fragments. Physical pretreatment is usually performed before chemical or biological treatment to reduces cell wall crystallinity and particle size by physical milling or grinding [50]. In some treatment methods, both physical action and chemical reaction play important roles in lignin removal. Such physicochemical pretreatment can involve steam explosion, liquid hot water, ammonia fiber explosion, ammonia recycle percolation or a supercritical carbon dioxide.
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Pretreatment contributes a vital role in the cost evaluation process of whole technology, because they contribute about 30–35% of overall production cost [52]. There are many issues that arise from this process [50] including loss of sugars (mainly pentose sugars derived from hemicellulose degradation), and generation of toxic substances that inhibits the downstream fermentation process. Both need to be minimized to make ethanol production more efficient.
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4.2 Steam explosion
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Steam explosion has become one of the most adopted pretreatment processes, where hydrolysis of hemicellulose also happens which improves cellulose digestibility. It is a physiochemical method that uses both physical changes caused by sudden pressure reduction and heat- and catalyst-induced chemical changes. An impregnation agent is sometimes used before the pretreatment step. Upon steam explosion after 1–5 min soaking in 160–270°C and 20–50 bar steam, fibers loose up and sugar polymers (mainly hemicellulose) partially degrade into sugars via hydrolysis of glycoside bonds in polysaccharides and lignin into soluble fragments including some inhibitors and phenolic products [50]. The process allows for subsequent solubilization of hemicellulose in water and lignin in organic or alkaline solvent. Cellulose undergoes some degree of polymerization but is still insoluble in water or organic solvents and remains in the solid phase. Acid (sulfuric acid and sulfur dioxide) impregnation before steam explosion reduce the time and temperature necessary for proper depolymerization of the feedstock, increases the efficiency of enzymatic hydrolysis of polysaccharides to glucose and xylose and reduce enzyme consumption [53]. Compared to other methods of biomass fractionation, steam explosion uses less dangerous chemicals, less demanding on investment and energy consumption [54]. Steam explosion is not recommended for agricultural and hardwood wastes with high contents of pentoses and low levels of lignin, due to the susceptibility of pentoses to thermal degradation. Steam explosion is recommended for processing straw and bagasse.
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4.3 Inhibitors generated in pretreatment
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One of the lasting issues in the second-generation bioethanol production is the formation of inhibitors during the pretreatment. The inhibitors create unfriendly environments for fermentative microbes, increases the length of lag phase, causes loss of cell density and lower growth rates of fermenting microbes, and consequently decreases ethanol yields [55]. The commonly observed inhibitors are aldehydes such as 5-hydroxymethyl-2-furaldehyde and 2-furaldehyde (furfural), weak organic acids (formic, acetic and levulinic acids) and phenolic compounds [56]. Acetic acid is the major organic acid found in hydrolysates coming from the hydrolysis of acetyl side-chain groups in hemicellulose [57]. Cell growth of fermentative microbes is inhibited by the intracellular process of anions of weak acids. Furan aldehydes are poisonous for microbes and phenolic compounds interfere with the function and integrity of cell membranes [58].
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There are several methods used for the removal of inhibitors [59]. The detoxification of lignocellulosic hydrolysates can be performed using inhibitor sorbents such as excess of lime, active carbon or lignite (brown coal).
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5. Enzymatic hydrolysis of polysaccharides
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After pretreatment to partially remove lignin and loose up polysaccharide structures, polysaccharides need to be hydrolyzed into sugar molecules which will be converted into ethanol by fermentation [38]. The hydrolysis can be accomplished chemically via acid-catalyzed cleavage of glycosidic bonds or by enzymes produced by microbes. Enzymatic method is more popular due to less impact on the environment and higher selectivity in the hydrolysis. Glucose and xylose are the main products in hydrolysates from the enzymatic breakdown of polysaccharides.
\n
Enzymes produced by the filamentous fungi such as Aspergillus nidulans, Aspergillus niger, Penicillium spp. and Trichoderma reesei are dominant in commercial biorefinery [38]. Among different types of cellulases, endoglucanases attack the internal glycosidic bonds in the amorphous cellulose regions, causing fragmentation of the cellulose structure, and exoglucanase works of the termini of β-glucan molecules to release glucose molecules one at a time, while β-glucosidase attacks catalyzes the hydrolysis of the glycosidic bonds to terminal non-reducing residues in beta-d-glucosides and oligosaccharides to release one or two glucose units at a time [60]. The costs of cellulases are high, spurring the development of methods to recycle hydrolysis enzymes [61]. Inclusion of hemicellulose’s, such as endoxylanases, xylosidases, exoxylanases and other accessory enzymes, such as esterase’s and arabinosidase’s, in the hydrolysis step improves the efficiency of enzymatic hydrolysis of lignocellulosic biomass and helps reduce enzyme loading and costs [62].
\n
Various strains of yeasts and bacteria are being investigated with the goal of developing a consolidated process of hydrolysis and co-fermentation of glucose and xylose, without the need for adding exogenous cellulases [63].
\n
\n
\n
6. Fermentation of lignocellulosic hydrolysates
\n
Sugars in the hydrolysate are converted into ethanol by fermentation using microorganisms such as yeasts. Ethanol-producing ability of yeasts depends on lignocellulosic hydrolysate, their strain and fermentation conditions (temperature, pH, aeration and nutrient supplementation). For use in industrial bioethanol production, microorganisms (mainly yeasts) must show thermotolerance and high fermentative activity for simple carbohydrates such as glucose and xylose. They should also be resistant to environmental stressors, including inhibitors mentioned in Section 4.3, acidic pH, high sugar level at the beginning of fermentation (causing hyperosmotic stress), and higher temperatures which prevents microbiological contamination, and are able to grow on various lignocellulosic substrates at a fast growth rate [58, 64].
\n
Saccharomyces cerevisiae JRC6 and Candida tropicalis JRC1 are recommended for hydrolysates after alkali pretreatment and acid pretreatment, respectively [41]. Saccharomyces sp. yeasts are used in biorefineries to ferment glucose released during starch hydrolysis. Apart from glucose, they are capable of fermenting galactose and mannose.
\n
Zymomonas mobilis is a Gram negative, facultative anaerobic, non-sporulating, polarly-flagellated, rod-shaped bacterium. It has notable bioethanol-producing capabilities, which surpass yeast in some respects. However, it only ferments glucose, fructose and sucrose [65]. This prevents them from being used in industrial production of bioethanol. The Z. mobilis strains are tolerant to ethanol concentration up to 120 g/L, and have low nutritional requirements for growth [58]. However, its tolerance to acetic acid is low: as little as 2.5 g/L of HOAc. Its recombinant strain AX101 also has low tolerance to acetic acid.
\n
\n
\n
7. Distillation and dehydration (drying) of bioethanol
\n
After fermentation, the mash is heated so that the ethanol evaporates. This process, known as distillation, separates the ethanol, but its purity is limited to 95–96% due to the formation of a water-ethanol azeotrope with maximum 96.5% v/v) ethanol. This hydrous ethanol can be used as a fuel alone, but is not miscible in all ratios with gasoline, so the water fraction is typically removed before ethanol is added to gasoline.
\n
Water can be removed by passing hydrous ethanol vapor through a bed of molecular sieve beads. The bead’s pores are sized to allow adsorption of water while excluding ethanol. Two beds are often used so that one is available to adsorb water while the other is being regenerated. This dehydration technology can save 3000 BTUs/gallon over the azeotropic distillation and has been adopted by most modern ethanol plants.
\n
Recent research has demonstrated that complete dehydration prior to blending with gasoline is unnecessary. When the azeotropic mixture is blended directly with gasoline, water separates from the gasoline/ethanol phase and can be removed in a two-stage counter-current setup of mixer-settler tanks with minimal energy consumption [66].
\n
\n
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8. LCA on GHG emissions and techno-economic evaluation of lignocellulosic ethanol production
\n
Numerous life cycle analyses (LCAs) of lignocellulosic ethanol have been published over the last 15 years and several reviews of these LCA studies have been completed and are cited in a more recent review [67]. These studies show a clear reduction in GHG emissions for lignocellulosic ethanol compared to gasoline. However, accurate quantification of GHG emission reduction is hard to obtain as gaps remain in understanding life cycle performance due to insufficient data, and model and methodological issues. Critical unresolved issues that are expected to impact its energy/GHG emissions performance include feedstock-related emissions, consequential versus attributional life cycle aspects, choice of system boundaries, and allocation methods.
\n
Decisions regarding feedstock, process technology and co-products can significantly impact GHG emissions calculations. Predicted life cycle GHG emissions vary widely depending on how the following key parameters are considered: nitrogen-related emissions due to supplemental fertilizer requirements and the N content of feedstock, cellulase requirements, farming energy, ethanol yield, and how the value of co-products such as lignin are realized, among others.
\n
Government support (i.e., Ethanol mandate, tax credit, etc.) is not expected to last forever. To be sustainable, lignocellulosic biofuels production must meet or exceed the economic performance of their first-generation counterparts. The growth in the capacity of commercial lignocellulosic ethanol production has been slow in the past decade, despite significantly better predicted performance on various environmental and energy security criteria than corn-based ethanol in the various techno-economic evaluations published before 2010 [68]. The slow growth has been due to both large technological risk, large capital cost, and the poor predicted economic performance of biorefineries in the short term.
\n
An LCA of US softwood cellulosic ethanol was reported in 2012 by Stephen et al. [68]. In the paper, the base case (capacity: 50 mL ethanol year−1) softwood ethanol production cost was compared with costs of ethanol produced from corn and sugarcane found in the literature. Softwood lignocellulosic ethanol was predicted to have a production cost of $0.90 L−1, 250–300% higher than US corn and Brazilian sugarcane ethanol production costs, which were in the range of $0.30–$0.40 L−1. The lignocellulosic base case scale of 50 mL year−1, compared to 150 mL year−1 of US corn and 365 mL year−1 of and Brazilian sugarcane, is much smaller as it was chosen based both on the projects funded under the US Department of Energy’s commercial biorefinery program and those operating in other places such as Denmark. Production costs of sugar- or starch-based ethanol are expected to continue to decline to $0.22–$0.25 L−1 by 2020. Thus, second-generation ethanol is not going to catch up with first-generation ethanol on production cost soon.
\n
Another very recent techno-economic evaluation was performed on production cost of ethanol produced from corn stover using either biochemical or thermochemical methods. For heat integrated biochemical route, the predicted bioethanol product costs at $2.00 for a production capacity of 43,300,000 gallon year−1 [69]. This result was clearly an underestimation of lignocellulosic ethanol as a major cost item, capital investment cost, was not included. Furthermore, the corn stover price of 46.8 $/ton was an underestimation, and feedstock transportation cost was not included in LCA. Feedstock cost can impact total cost by 40 percent according to a Lux Research report of 2016 [70]. The Brazilian birefinery company Raizen has the lowest projected minimum ethanol selling price of $2.17 per gallon while Abengoa’s capital-intensive $500 million Hugoton facility has the highest price of $4.55 with feedstock cost emerging as the most critical variable. The low cost of Raizen’s cellulosic ethanol is largely attributed to its access to low cost sugarcane straw and sugarcane bagasse ($40 and $38 per dry metric ton), respectively, compared with corn stover ($90) used by Abengoa and POET-DSM and wheat straw ($75) used by Beta Renewables [71].
\n
\n
\n
9. Opportunities for cost reduction
\n
It is apparent that second-generation ethanol is currently much more costly to produce than first-generation ethanol. It is hard to predict when the cost of lignocellulosic ethanol will be reduced to the level of corn/sugar cane ethanol. Dramatic reductions in the capital and operational costs must occur before the potential superior environmental benefits from cellulosic ethanol relative to corn ethanol can be realized. Pretreatment, enzymatic hydrolysis and distillation are responsible for much of the cost of producing bioethanol. Currently, intensive research is being conducted to improve each of the processes to make them more economical.
\n
\n
9.1 Pretreatment
\n
An effective pretreatment increases specific surface area of biomass, making cellulose better available for the action of hydrolytic enzymes obtained from fungi and bacteria, minimizing reductions in enzyme activity, and thus improving the rate of biomass hydrolysis and providing the highest possible concentration of fermentable sugars. Effective pretreatment also reduces the degradation of monosugars [72]. In selecting pretreatment methods, factors such as their environmental impact and recycling of chemical compounds (for example ammonia in the ammonia fiber explosion process [73, 74]) must be considered. Different pretreatment methods and their combinations are being explored for different types of biomass [50].
\n
Better results, e.g., improved ethanol yield, have been obtained from combination of two or more pretreatment methods, but have resulted often at the cost of more energy consumption compared to single method of pretreatment. Among single treatment methods, dilute acid pretreatment is more suitable for various types of biomass as it solubilizes most of hemicellulose and partially remove lignin [50].
\n
It is vital to analyze the pros and cons of each pretreatment technology before scaling up for industrial application. However, technoeconomic assessment will only give a rough estimate on capital cost and the final fuel cost in commercial scale production when many research findings are still in pilot scale level and demonstration plant level [52].
\n
\n
\n
9.2 Pentose fermentation
\n
Efficient fermentation of pentoses helps reduce ethanol production cost since pentoses can be 25.8 wt% as in sugarcane bagasse [75, 76] 22.3–74.9 wt% in corn stover (Table 3). Wild microorganisms are incapable of producing ethanol in high yields, as they are unable to utilize both pentoses and hexoses. Pentose-specific transporter proteins and enzymatic reactions determining the metabolism of pentoses such as l-arabinose and d-xylose have not been found in naturally occurring baker’s yeast.
Hexose, pentose and lignin contents in different types of biomass.
S. bagasse = sugarcane bagasse.
\n
Owing to large microbial biodiversity, fermentation of pentoses can be achieved either by finding a potent naturally occurring pentose utilizing microorganism or by a genetically engineered C5 utilizing strain [78, 79]. One effective strategy is to create recombinant strain with genes for xylose metabolism [80]. Genetic engineering has been conducted mainly on Saccharomyces cerevisiae yeast, [81] the Gram-positive bacteria Clostridium cellulolyticum and Lactobacillus casei and the Gram-negative bacteria Zymomonas mobilis, Escherichia coli and Klebsiella oxytoca [43]. Recombinant yeasts consume xylose much slower than glucose, thus requiring prolonged fermentation time due to a lack of reaction intermediates and efficient pentose transporters [82].
\n
A common problem of xylose-fermenting strains is the production of xylitol or the reabsorption of ethanol, which lead to low ethanol yield. One grand challenge is glucose repression, which results in di-auxic fermentation of a mixture of glucose and pentoses since glucose prevents the catabolism and/or utilization of other non-glucose sugars, leading reduced volumetric ethanol yield [83]. Approaches and conditions sought to improve glucose and xylose fermentation to ethanol are reviewed in a recent paper with emphasis on microbial systems used to maximize biomass resource efficiency, ethanol yield, and productivity [64].
\n
\n
\n
9.3 Simultaneous saccharification and fermentation (SSF)
\n
Separate processes have been established for enzymatic hydrolysis of cellulose and hemicellulose and fermentation (SHF) of sugars in hydrolysate. In the SHF processes, saccharification and fermentation take place in separate vessels, so the two processes can be optimized separately. One drawback of SHF is that accumulation of simple carbohydrates (such as cellobiose) causes end-product inhibition of hydrolytic enzymes, for example cellulases or cellobioses. To prevent end-product inhibition, extra doses of β-glucosidase are needed together with the commercial cellulase preparations [84].
\n
There is a strong incentive to develop a process to perform simultaneous saccharification and fermentation (SSF) as it reduces investment costs by reducing the number of vessels and has the potential to become the preferred approach. In SSF, the problem of end-product feedback inhibition is largely eliminated because glucose molecules are fermented immediately by the fermentative microbes as it is produced from hydrolysis of cellulose [85]. However, the benefits come with a major downside which is an inherent mismatch between the optimal temperatures for the enzymes (fungal cellulases and hemicellulose’s) on the one hand, and yeast biocatalysts on the other. The temperature optima for saccharifying enzymes (50–55°C for cellulase) are higher than those for fermenting mesophilic culture. The optimal temperature for yeasts is below 35°C. Mesophilic yeasts (that thrive best in a moderate temperature) exhibit slower growth rates at higher temperatures. Currently, SSF must run at temperatures between the optimum temperature for cellulase and the optimum temperature for fermentative organisms. The compromise results in higher cellulase loading and an increase in enzyme costs. Efficient bioethanol production by SSF requires the use of thermotolerant ethanologenic yeast. It is a hot topic for research to genetically modify microorganisms with the ability to ferment at higher temperatures [43]. Some isolated yeasts, including Pichia, Candida, Saccharomyces and Wickerhamomyces, are found to grow at temperatures of 40°C and ferment sugars at higher temperatures [41]. To make SSF process highly efficient in ethanol production, the pentose metabolic pathway is been engineered into microorganisms to enables the use of C5 sugars by microbes that do not ferment them earlier [86].
\n
Reduction in enzyme cost is been sought by searching for new organisms with cellulolytic and hemicellulytic activities [87], lowering the enzyme dosage through protein engineering [86, 88], and improving cellulase thermostability for performing hydrolysis at elevated temperatures to increase the efficiency of cellulose hydrolysis [89]. Cellulase enzyme cost reductions are challenging as cellulase costs need to be significantly lower than those of amylase enzymes on a unit-of-protein basis. The high price of the enzymes encouraged research into solutions to the problem of glucose inhibition and to the deactivation caused by lignin by-products [90].
\n
Further integration of enzyme production with SSF leads to a new technology of consolidated bioprocessing (CBP). One area of research is aimed at engineering all three capabilities (saccharification, hexose fermentation and pentose fermentation) into a single strain for the CBP process [91, 92]. Cellulase-encoding genes may be introduced into specific species during recombination [63] to eliminate the need for exogenous cellulases in the process of SSF and decrease the capital costs of processing. CBP technology promises to eliminate costs associated with enzyme production and additional infrastructure/vessels [93].
\n
\n
\n
9.4 Other opportunities for cost reduction
\n
Working with a high dry matter (DM) concentration is also potentially an effective way to reduce the hydrolytic enzyme costs. However, high DM content causes an increase in viscosity, inadequate mass and heat transfer within the bioreactor, and, consequently, a strong reduction in the conversion of cellulose/hemicellulose to fermentable sugars. This problem could be overcome by adopting various fed-batch strategies or coprocessing substrates with different degrees of porosity [94].
\n
A variation of SSF, simultaneous saccharification and co-fermentation (SSCF), in which a starch material is co-fermented, has been adopted to address low ethanol concentration issue in lignocellulosic ethanol production. SSCF can reduce ethanol production cost by increasing ethanol concentration and thus reducing distillation cost [95].
\n
Recycling yeasts and enzymes is also an effective way to reduce the cost of ethanol production. The remaining unhydrolyzed solids with some enzymes adsorbed are collected by filtration or centrifuge and are recycled to the next cycle for further hydrolysis. In one study, the enzyme loading was reduced from 36 to 22.3 and 25.8 mg protein per gram glucan, respectively, for separate hydrolysis and fermentation (SHF) and for SSCF on AFEX™ pretreated corn stover [96]. Enzyme adsorption to the residual solids is probably inhibited at high sugar concentrations in the fast SHF process [97] and hence affected enzyme recycling. The fast SSCF process removed most of the sugars by fermentation but produced ethanol whose effect on enzyme adsorption is unclear.
\n
\n
\n
\n
10. Conclusion
\n
Cost effect renewable fuel generation from lignocellulosic materials is one of the few options the human beings have to slow down/eliminate global warming and achieve energy independence from fossil fuels. Second generation bioethanol is a promising path in the roadmap to the future world of renewable energy. The cellulosic ethanol industry is still in its infancy and its survival is relying on heavy policy support. Major technological advances at every stage of the cellulosic ethanol production are critically needed to lower the ethanol production cost to a level comparable to the corn ethanol. The key problems that remain to be solved include: (1) Effective and low-cost biomass pretreatment method that exposes polysaccharides to enzymes for efficient saccharification, (2) efficient fermentation of all sugars (pentoses and hexoses) released during the pretreatment and hydrolysis steps into ethanol, (3) development of enzymes that tolerate various inhibitors including monosaccharides (mainly glucose), and ethanol accumulation, and (4) heat-tolerant fermentation microbes and enzymes for efficient simultaneous saccharification and fermentation.
\n
\n
Acknowledgments
\n
The support of the South Dakota NSF EPSCoR Program (Grant No. IIA-1330842) is greatly appreciated.
\n
Conflict of interest
There is no conflict of interest involved in this work.
\n',keywords:"global warming, lignocellulosic biomass, second-generation bioethanol, saccharification, fermentation, life cycle analysis, techno-economic evaluation",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/67397.pdf",chapterXML:"https://mts.intechopen.com/source/xml/67397.xml",downloadPdfUrl:"/chapter/pdf-download/67397",previewPdfUrl:"/chapter/pdf-preview/67397",totalDownloads:1065,totalViews:0,totalCrossrefCites:5,dateSubmitted:"January 29th 2019",dateReviewed:"May 6th 2019",datePrePublished:"May 29th 2019",datePublished:null,dateFinished:null,readingETA:"0",abstract:"The accelerated global warming calls for fast development of solutions to curb excessive Greenhouse gas emission. Like most of other forms of renewable energy, lignocellulosic ethanol can help the human beings mitigate the climate deterioration and gain independence from fossil fuels. This chapter gives a survey of bioethanol production in the U.S. and world, describes classifications of three generations of bioethanol, provides an overview of all the stages of currently adopted process for the second-generation bioethanol production, briefs on new development on enzymes for hydrolysis and fermentation and new processes for ethanol generation, summarizes on recent life-cycle assessments of greenhouse gas emission and techno-economic evaluation of ethanol production. To sustain the infant cellulosic ethanol industry, substantial improvement in the following areas need to happen in a timely manner: (1) Effective and low-cost biomass pretreatment method, (2) efficient fermentation of all sugars released during the pretreatment and hydrolysis steps, (3) development of enzymes that tolerate various inhibitors including monosaccharides (mainly glucose) and ethanol, and (4) heat-tolerant fermentation microbes and enzymes for efficient simultaneous saccharification and fermentation. Genetic engineering is expected to play a key role in addressing most of the issues in these areas.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/67397",risUrl:"/chapter/ris/67397",signatures:"Cheng Zhang",book:{id:"7828",title:"Alcohol Fuels",subtitle:"Current Technologies and Future Prospect",fullTitle:"Alcohol Fuels - Current Technologies and Future Prospect",slug:"alcohol-fuels-current-technologies-and-future-prospect",publishedDate:"March 11th 2020",bookSignature:"Yongseung Yun",coverURL:"https://cdn.intechopen.com/books/images_new/7828.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"144925",title:"Dr.",name:"Yongseung",middleName:null,surname:"Yun",slug:"yongseung-yun",fullName:"Yongseung Yun"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Ethanol generation from biomass",level:"1"},{id:"sec_2_2",title:"2.1 First-generation bioethanol",level:"2"},{id:"sec_3_2",title:"2.2 Second generation bioethanol",level:"2"},{id:"sec_4_2",title:"2.3 Third generation bioethanol",level:"2"},{id:"sec_6",title:"3. Overview of bioethanol generation from lignocellulosic biomass",level:"1"},{id:"sec_6_2",title:"3.1 Composition of lignocellulosic feedstock for bioethanol",level:"2"},{id:"sec_7_2",title:"3.2 Biochemical conversion of biomass into ethanol",level:"2"},{id:"sec_9",title:"4. Pretreatment of lignocellulosic biomass",level:"1"},{id:"sec_9_2",title:"4.1 Objectives of pretreatment and basic methods",level:"2"},{id:"sec_10_2",title:"4.2 Steam explosion",level:"2"},{id:"sec_11_2",title:"4.3 Inhibitors generated in pretreatment",level:"2"},{id:"sec_13",title:"5. Enzymatic hydrolysis of polysaccharides",level:"1"},{id:"sec_14",title:"6. Fermentation of lignocellulosic hydrolysates",level:"1"},{id:"sec_15",title:"7. Distillation and dehydration (drying) of bioethanol",level:"1"},{id:"sec_16",title:"8. LCA on GHG emissions and techno-economic evaluation of lignocellulosic ethanol production",level:"1"},{id:"sec_17",title:"9. Opportunities for cost reduction",level:"1"},{id:"sec_17_2",title:"9.1 Pretreatment",level:"2"},{id:"sec_18_2",title:"9.2 Pentose fermentation",level:"2"},{id:"sec_19_2",title:"9.3 Simultaneous saccharification and fermentation (SSF)",level:"2"},{id:"sec_20_2",title:"9.4 Other opportunities for cost reduction",level:"2"},{id:"sec_22",title:"10. Conclusion",level:"1"},{id:"sec_23",title:"Acknowledgments",level:"1"},{id:"sec_26",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'\nClimate Change 2013: The Physical Science Basis, IPCC Fifth Assessment Report (WGI AR5). 2015. Available from: https://www.ipcc.ch/report/ar5/wg1 [Accessed: 15 April 2019]\n'},{id:"B2",body:'\nUAH v6.0 TLT Data, National Space Science and Technology Center. 2019. Available from: http://vortex.nsstc.uah.edu/data/msu/v6.0 [Accessed: 15 April 2019]\n'},{id:"B3",body:'\nUpper Air Temperature Data from Remote Sensing Systems. 2019. Available from: http://www.remss.com/measurements/upper-air-temperature [Accessed: 15 April 2019]\n'},{id:"B4",body:'\nDavenport C. Major Climate Report Describes a Strong Risk of Crisis as Early as 2040. The New York Times. 2018. Available from: http://www.nytimes.com/2018/10/07/climate/ipcc-climate-report-2040.html [Accessed: 15 April 2019]\n'},{id:"B5",body:'\nBiofuels Digest Article: Biofuels Mandates Around the World. 2018. Available from: https://www.biofuelsdigest.com/bdigest/2018/01/01/biofuels-mandatesaround-the-world-2018 [Accessed: 15 April 2019]\n'},{id:"B6",body:'\nPernick R, Wilder C. The Clean Tech Revolution: The Next Big Growth and Investment Opportunity. HarperCollins; 2007\n'},{id:"B7",body:'\nSchill S. Zero to 10 Million in 5 years. 2018. Available from: http://www.ethanolproducer.com/articles/15344/zero-to-10-million-in-5-years [Accessed: 15 April 2019]\n'},{id:"B8",body:'\nVoorhis D. Hugoton cellulosic ethanol plant sold out of bankruptcy. 2016. Available from: https://www.kansas.com/news/business/article119902263.html [Accessed: 15 April 2019]\n'},{id:"B9",body:'\nVoegele E. Bluefire announces EPC contract for mississippi project. 2014. Available from: http://ethanolproducer.com/articles/11521/bluefire-announces-epc-contract-for-mississippi-project [Accessed: 15 April 2019]\n'},{id:"B10",body:'\nVoegele E. Verbio to buy DuPont cellulosic ethanol plant, convert it to RNG. 2018. Available from: http://biomassmagazine.com/articles/15743/verbio-to-buy-dupontcellulosic-ethanol-plant-convert-it-to-rng [Accessed: 15 April 2019]\n'},{id:"B11",body:'\nMcGlashen A. As key partner departs, future dims for Michigan cellulosic biofuel plant. 2013. Available from: https://energynews.us/2013/08/06/midwest/as-key-partner-departs-future-dims-for-michigan-cellulosic-biofuel-plant/ [Accessed: 15 April 2019]\n'},{id:"B12",body:'\nPOET-DSM: Project Liberty. 2018. Available from: https://www.energy.gov/eere/bioenergy/poet-dsm-project-liberty\n\n'},{id:"B13",body:'\n\nhttps://www.forbes.com/sites/rrapier/2018/02/11/cellulosicethanol-falling-far-short-of-thehype/#484c7df4505f [Accessed: 15 April 2019]\n'},{id:"B14",body:'\nHo DP, Ngo HH, Guo W. A mini review on renewable sources for biofuel. Bioresource Technology. 2014;169:742-749\n'},{id:"B15",body:'\n\nhttps://ethanolrfa.org/resources/industry/statistics/#1549569747568-e7db9c54-efe4\n\n'},{id:"B16",body:'\nThompson W, Meyer S. Second generation biofuels and food crops: Co-products or competitors? Global Food Security. 2013;2(2):89-96\n'},{id:"B17",body:'\nYazdani SS, Gonzalez R. Anaerobic fermentation of glycerol: A path to economic viability for the biofuels industry. Current Opinion in Biotechnology. 2007;18(3):213-219\n'},{id:"B18",body:'\nPragya N, Pandey KK, Sahoo PK. A review on harvesting, oil extraction and biofuels production technologies from microalgae. Renewable and Sustainable Energy Reviews. 2013;24:159-171\n'},{id:"B19",body:'\n\nhttps://www.bloomberg.com/news/articles/2013-05-21/exxon-refocusing-algae-biofuels-program-after-100-million-spend\n\n'},{id:"B20",body:'\nLi X, Kim TH, Nghiem NP. Bioethanol production from corn stover using aqueous ammonia pretreatment and two-phase simultaneous saccharification and fermentation (TPSSF). Bioresource Technology. 2010;101:5910-5916\n'},{id:"B21",body:'\nNigam PS, Gupta N, Anthwal A. Pre-treatment of agro-industrial residues. In: Nigam PSP, Pandey A, editors. Biotechnology for Agro-Industrial Residues Utilization. The Netherlands: Springer; 2009. pp. 13-33\n'},{id:"B22",body:'\nStanmore BR. Generation of energy from sugarcane bagasse by thermal treatment. Waste and Biomass Valorization. 2010;1:77-79\n'},{id:"B23",body:'\nChandra R, Takeuchi H, Hasegawa T. Methane production from lignocellulosic agricultural crop wastes: A review in context to second generation of biofuel production. Renewable and Sustainable Energy Reviews. 2012;16:1462-1476\n'},{id:"B24",body:'\nHamelinck CN, van Hooijdonk G, Faaij APC. Ethanol from lignocellulosic biomass: Techno-economic performance in short-, middle- and long-term. Biomass and Bioenergy. 2005;28:384-410\n'},{id:"B25",body:'\nKaparaju P, Serrano M, Thomsen AB, Kongjan P, Angelidaki I. Bioethanol, biohydrogen and biogas production from wheat straw in a biorefinery concept. Bioresource Technology. 2009;100:2562-2568\n'},{id:"B26",body:'\nChen X, Yu J, Zhang Z, Lu C. Study on structure and thermal stability properties of cellulose fibres from rice straw. Carbohydrate Polymers. 2011;85:245-250\n'},{id:"B27",body:'\nMahvi AH, Maleki A, Eslami A. Potential of rice husk and rice husk ask for phenol removal in aqueous systems. American Journal of Applied Sciences. 2004;1(4):421-426\n'},{id:"B28",body:'\nZabed H, Sahu JN, Boyce AN, Faruq G. Fuel ethanol production from lignocellulosic biomass: An overview on feedstocks and technological approaches. Renewable and Sustainable Energy Reviews. 2016;66:751-774\n'},{id:"B29",body:'\nTye YY, Lee KT, Abdullah WNW, Leh CP. The world availability of non-wood lignocellulosic biomass for the production of cellulosic ethanol and potential pretreatments for the enhancement of enzymatic saccharification. Renewable and Sustainable Energy Reviews. 2016;60:155-172\n'},{id:"B30",body:'\nMosier N, Wyman C, Dale B, Elander R, Lee YY, Holtzapple M, et al. Features of promising technologies for pretreatment of lignocellulosic biomass. Bioresource Technology. 2005;96(6):673-686\n'},{id:"B31",body:'\nLimayem A, Ricke SC. Lignocellulosic biomass for bioethanol production: Current perspectives, potential issues and future prospects. Progress in Energy and Combustion Science. 2012;38(4):449-467\n'},{id:"B32",body:'\nZhao X, Zhang L, Liu D. Biomass recalcitrance. Part I: The chemical compositions and physical structures affecting the enzymatic hydrolysis of lignocellulose. Biofuels, Bioproducts and Biorefining. 2012;6(4):465-482\n'},{id:"B33",body:'\nRuel K, Nishiyama Y, Joseleau JP. Crystalline and amorphous cellulose in the secondary walls of Arabidopsis. Plant Science. 2012;193-194:48-61\n'},{id:"B34",body:'\nAchinas S, Euverink GJW. Consolidated briefing of biochemical ethanol production from lignocellulosic biomass. Electronic Journal of Biotechnology. 2016;23:44-53\n'},{id:"B35",body:'\nXu Q-S, Yan Y-S, Feng J-X. Efficient hydrolysis of raw starch and ethanol fermentation: A novel raw starch-digesting glucoamylase from Penicillium oxalicum. Biotechnology for Biofuels. 2016;9(1):216\n'},{id:"B36",body:'\nPeng F, Peng P, Xu F, Sun RC. Fractional purification and bioconversion of hemicelluloses. Biotechnology Advances. 2012;30(4):879-903\n'},{id:"B37",body:'\nHespell RB. Extraction and characterization of hemicellulose from the corn fiber produced by corn wet-milling processes. Journal of Agricultural and Food Chemistry. 1998;46(7):2615-2619\n'},{id:"B38",body:'\nBattaglia E, Hansen SF, Leendertse A, Madrid S, Mulder H, Nikolaev I, et al. Regulation of pentose utilisation by AraR, but not XlnR, differs in Aspergillus nidulans and Aspergillus niger. Applied Microbiology and Biotechnology. 2011;91(2):387-397\n'},{id:"B39",body:'\nBalakshin M, Capanema E, Gracz H, Chang H-m, Jameel H. Quantification of lignin–carbohydrate linkages with high-resolution NMR spectroscopy. Planta. 2011;233(6):1097-1110\n'},{id:"B40",body:'\nBoerjan W, Ralph J, Baucher M. Lignin biosynthesis. Annual Review of Plant Biology. 2003;54:519-546\n'},{id:"B41",body:'\nChoudhary J, Singh S, Nain L. Bioprospecting thermotolerant ethanologenic yeasts for simultaneous saccharification and fermentation from diverse environments. Journal of Bioscience and Bioengineering. 2017;123(3):342-346\n'},{id:"B42",body:'\nZakzeski J, Bruijnincx PCA, Jongerius AL, Weckhuysen BM. The catalytic valorization of lignin for the production of renewable chemicals. Chemical Reviews. 2010;110:3552-3599\n'},{id:"B43",body:'\nSenthilkimar V, Gunasekaran P. Bioethanol production from cellulosic substrates: Engineered bacteria and process integration challenges. Journal of Scientific and Industrial Research. 2005;64:845-853\n'},{id:"B44",body:'\nRagauskas AEA. Lignin valorization: Improving lignin processing in the biorefinery. Science. 2014;334(6185):1246843\n'},{id:"B45",body:'\nShen R, Tao L, Yang B. Biofuels, Bioproducts and Biorefining. 2018\n'},{id:"B46",body:'\nMabee WE. Updates on softwood-to-ethanol process development. Applied Biochemistry and Biotechnology. 2006;129:55-70\n'},{id:"B47",body:'\nSims R, Taylor M, Saddler J, Mabee W. From 1st- to 2nd-generation Biofuel Technologies. Paris; 2008. Available from: http://environmentportal.in/files/2nd_Biofuel_Gen.pdf [Accessed: 15 April 2019]\n'},{id:"B48",body:'\nMaurya DP, Singla A, Negi S. An overview of key pretreatment processes for biological conversion of lignocellulosic biomass to bioethanology. Biotechnology Advances. 2015;5(5):597-609\n'},{id:"B49",body:'\nCardona CA, Sánchez ÓJ. Fuel ethanol production: Process design trends and integration opportunities. Bioresource Technology. 2007;98(12):2415-2457\n'},{id:"B50",body:'\nKumari D, Singh R. Pretreatment of lignocellulosic wastes for biofuel production: A critical review. Renewable and Sustainable Energy Reviews. 2018;90:877-891\n'},{id:"B51",body:'\nSindhu R, Binod P, Pandey A. Biological pretreatment of lignocellulosic biomass: An overview. Bioresource Technology. 2016;199:76-82\n'},{id:"B52",body:'\nKumar MN, Ravikumar R, Thenmozhi S, Kumar MR, Shankar MK. Choice of pretreatment technology for sustainable production of bioethanol from lignocellulosic biomass: Bottle necks and recommendations. Waste and Biomass Valorization. 2019;10(6):1693-1709\n'},{id:"B53",body:'\nBallesteros I, Negro MJ, Oliva JM, Cabañas A, Manzanares P, Ballesteros M. Ethanol production from steam-explosion pretreated wheat straw. Applied Biochemistry and Biotechnology. 2006;130(1-3):496-508\n'},{id:"B54",body:'\nLi J, Henriksson G, Gellerstedt G. Lignin depolymerization/repolymerization and its critical role for delignification of aspen wood by steam explosion. Bioresource Technology. 2007;98(16):3061-3068\n'},{id:"B55",body:'\nAlbers E, Larsson C. A comparison of stress tolerance in YPD and industrial lignocellulose-based medium among industrial and laboratory yeast strains. Journal of Industrial Microbiology & Biotechnology. 2009;36(8):1085-1091\n'},{id:"B56",body:'\nPalmqvist E, Hahn-Hägerdal B. Fermentation of lignocellulosic hydrolysates. II: Inhibitors and mechanisms of inhibition. Bioresource Technology. 2000;74(1):25-33\n'},{id:"B57",body:'\nWeil JR, Dien B, Bothast R, Hendrickson R, Mosier NS, Ladisch MR. Removal of fermentation inhibitors formed during pretreatment of biomass by polymeric adsorbents. Industrial and Engineering Chemistry Research. 2002;41(24):6132-6138\n'},{id:"B58",body:'\nDien BS, Cotta MA, Jeffries TW. Bacteria engineered for fuel ethanol production: Current status. Applied Microbiology and Biotechnology. 2003;63(3):258-266\n'},{id:"B59",body:'\nJönsson LJ, Alriksson B, Nilvebrant N-O. Bioconversion of lignocellulose: Inhibitors and detoxification. Biotechnology for Biofuels. 2013;6:1-10\n'},{id:"B60",body:'\nEnari TM, Markkanen P. Production of cellulolytic enzymes by fungi. Advances in Biochemical Engineering. 1977;5:1-24\n'},{id:"B61",body:'\nSims REH, Mabee W, Saddler JN, Taylor M. An overview of second generation biofuel technologies. Bioresource Technology. 2010;101(6):1570-1580\n'},{id:"B62",body:'\nAlvira P, Negro MJ, Ballesteros M. Effect of endoxylanase and α-l-arabinofuranosidase supplementation on the enzymatic hydrolysis of steam exploded wheat straw. Bioresource Technology. 2011;102(6):4552-4558\n'},{id:"B63",body:'\nKricka W, James TC, Fitzpatrick J, Bond U. Engineering Saccharomyces pastorianus for the co-utilisation of xylose and cellulose from biomass. Microbial Cell Factories. 2015;14(61)\n'},{id:"B64",body:'\nNosrati-Ghods N, Harrison STL, Isafiade AJ, Tai SL. Ethanol from biomass hydrolysates by efficient fermentation of glucose and xylose: A review. ChemBioEng Reviews. 2018;5(5):294-311\n'},{id:"B65",body:'\nAditiya HB, Mahlia TMI, Chong WT, Nur H, Sebayang AH. Second generation bioethanol production: A critical review. Renewable and Sustainable Energy Reviews. 2016;66:631-653\n'},{id:"B66",body:'\nStacey NT, Hadjitheodorou A, Glasser D. Gasoline preblending for energy-efficient bioethanol recovery. Energy & Fuels. 2016;30(10):8286-8291\n'},{id:"B67",body:'\nGerbrandt K, Chu PL, Simmonds A, Mullins KA, MacLean HL, Griffin WM, et al. Life cycle assessment of lignocellulosic ethanol: A review of key factors and methods affecting calculated GHG emissions and energy use. Current Opinion in Biotechnology. 2016;38:63-70\n'},{id:"B68",body:'\nStephen JD, Mabee WE, Saddler JN. Will second-generation ethanol be able to compete with first-generation ethanol? Opportunities for cost reduction. Biofuels, Bioproducts and Biorefining. 2012;6:159-176\n'},{id:"B69",body:'\nHossain MS, Theodoropoulos C, Yousuf A. Techno-economic evaluation of heat integrated second generation bioethanol and furfural coproduction. Biochemical Engineering Journal. 2019;144:89-103\n'},{id:"B70",body:'\n\nhttp://biomassmagazine.com/articles/12958/lux-cellulosic-ethanol-price-hinges-on-feedstock-cost\n\n'},{id:"B71",body:'\n\nhttps://www.globenewswire.com/news-release/2016/02/24/920865/0/en/Raizen-Has-Lowest-Price-as-Cellulosic-Ethanol-Hinges-on-Feedstock-Cost.html\n\n'},{id:"B72",body:'\nCao NJ, Krishnan MS, Du JX, Gong CS, Ho NWY, Chen ZD, et al. Ethanol production from corn cob pretreated by the ammonia steeping process using genetically engineered yeast. Biotechnology Letters. 1996;18(9):1013-1018\n'},{id:"B73",body:'\nLee WC, Kuan WC. Miscanthus as cellulosic biomass for bioethanol production. Biotechnology Journal. 2015;10(6):840-854\n'},{id:"B74",body:'\nAlvira P, Tomás-Pejó E, Ballesteros M, Negro MJ. Pretreatment technologies for an efficient bioethanol production process based on enzymatic hydrolysis: A review. Bioresource Technology. 2010;101(13):4851-4861\n'},{id:"B75",body:'\nMariano AP, Dias MOS, Junqueira TL, Cunha MP, Bonomi A, Filho RM. Utilization of pentoses from sugarcane biomass: Techno-economics of biogas vs. butanol production. Bioresource Technology. 2013;142:390-399\n'},{id:"B76",body:'\nZhu JY, Pan XJ. Woody biomass pretreatment for cellulosic ethanol production: Technology and energy consumption evaluation. Bioresource Technology. 2010;101:4992-5002\n'},{id:"B77",body:'\nArora R, Behera S, Kumar S. Bioprospecting thermophilic/thermotolerant microbes for production of lignocellulosic ethanol: A future perspective. Renewable and Sustainable Energy Reviews. 2015;51:699-717\n'},{id:"B78",body:'\nKim JH, Block DE, Mills DA. Simultaneous consumption of pentose and hexose sugars: An optimal microbial phenotype for efficient fermentation of lignocellulosic biomass. Applied Microbiology and Biotechnology. 2010;88(8):1077-1085\n'},{id:"B79",body:'\nOlsson L, Hahn-Hägerdal B. Fermentation of lignocellulosic hydrolysates for ethanol production. Enzyme and Microbial Technology. 1996;18(5):312-331\n'},{id:"B80",body:'\nSarris D, Papanikolaou S. Biotechnological production of ethanol: Biochemistry processes and technologies. Engineering in Life Sciences. 2016;16(4):307-329\n'},{id:"B81",body:'\nKo JK, Um Y, Woo HM, Kim KH, Lee SM. Ethanol production from lignocellulosic hydrolysates using engineered Saccharomyces cerevisiae harboring xylose isomerase-based pathway. Bioresource Technology. 2016;209:290-296\n'},{id:"B82",body:'\nOreb M, Dietz H, Farwick A, Boles E. Novel strategies to improve co-fermentation of pentoses with d-glucose by recombinant yeast strains in lignocellulosic hydrolysates. Bioengineered. 2012;3(6):347-351\n'},{id:"B83",body:'\nAvanthi A, Kumar S, Sherpa KC, Banerjee R. Bioconversion of hemicelluloses of lignocellulosic biomass to ethanol: An attempt to utilize pentose sugars. Biofuels. 2017;8(4):431-444\n'},{id:"B84",body:'\nOlofsson K, Bertilsson M, Lidén G. A short review on SSF: An interesting process option for ethanol production from lignocellulosic feedstocks. Biotechnology for Biofuels. 2008;1(7)\n'},{id:"B85",body:'\nWyman CE, Spindler DD, Grohmann K. Simultaneous saccharification and fermentation of several lignocellulosic feedstocks to fuel ethanol. Biomass and Bioenergy. 1992;3(5):301-307\n'},{id:"B86",body:'\nKricka W, Fitzpatrick J, Bond U. Challenges for the production of bioethanol from biomass using recombinant yeasts. Advances in Applied Microbiology. 2015;92:89-125\n'},{id:"B87",body:'\nDemain AL, Newcomb M, Wu JHD. Cellulase, clostridia, and ethanol. Microbiology and Molecular Biology Reviews. 2005;69(1):124-154\n'},{id:"B88",body:'\nBhat MK, Bhat S. Cellulose degrading enzymes and their potential industrial applications. Biotechnology Advances. 1997;15(3-4):583-620\n'},{id:"B89",body:'\nPatel AK, Singhania RR, Sim SJ, Pandey A. Thermostable cellulases: Current status and perspectives. Bioresource Technology. 2019;279:385-392\n'},{id:"B90",body:'\nLewandowska M, Szymanka K, Kordala N, Dabrowska A, Bednarski W, Juszczuk A. Evaluation of Mucor indicus and Saccharomyces cerevisiae capability to ferment hydrolysates of rape straw and Miscanthus giganteus as affected by the pretreatment method. Bioresource Technology. 2016;212:262-270\n'},{id:"B91",body:'\nBrodeur G, Yau E, Bada K, Collier J, Ramachandran KB, Ramakrishnan S. Chemical and physicochemical pretreatment of lignocellulosic biomass: A review. Enzyme Research. 2011;2011:787532\n'},{id:"B92",body:'\nParisutham V, Kim TH, Lee SK. Feasibilities of consolidated bioprocessing microbes: From pretreatment to biofuel production. Bioresource Technology. 2014;161:431-440\n'},{id:"B93",body:'\nChandel AK, Gonçalves BCM, Strap JL, da Silva S. Biodelignification of lignocellulose substrates: An intrinsic and sustainable pretreatment strategy for clean energy production. Critical Reviews in Biotechnology. 2013;35(3):281-293\n'},{id:"B94",body:'\nBattista F, Bolzonella D. Some critical aspects of the enzymatic hydrolysis at high dry-matter content: A review. 2018;12:711-723\n'},{id:"B95",body:'\nQin L, Zhao X, Li WC, Zhu JQ , Liu L, Li BZ, et al. Process analysis and optimization of simultaneous sacchari-cation and co-fermentation of ethylenediamine-pretreated corn stover for ethanol production. Biotechnology for Biofuels. 2018;11(1):118\n'},{id:"B96",body:'\nJin M, Gunawan C, Uppugundla N, Balan V, Dale BE. A novel integrated biological process for cellulosic ethanol production featuring high ethanol productivity, enzyme recycling and yeast cells reuse. Energy & Environmental Science. 2012;5:7168-7175\n'},{id:"B97",body:'\nFelby C, Jørgensen H, Kristensen JB. Yield-determining factors in high-solids enzymatic hydrolysis of lignocellulose. Biotechnology for Biofuels. 2009;2(1):11\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Cheng Zhang",address:"zhang@sdstate.edu",affiliation:'
Department of Chemistry and Biochemistry, South Dakota State University, Brookings, USA
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He was previously Assistant Professor (1974–1982)\nand Associate Professor (1982–2001) at the School of Medicine and Surgery, University of Bari, Italy. He graduated in Medicine and Surgery(1970) and completed postgraduate training in General Surgery (1975)\nand Emergency Surgery (1979) at University of Bari, Italy. He received\na diploma of 'Maitrise Universitaire en Pedagogie des Sciences de la\nSantè” from the University Paris-Nord Bobigny (1995). His main research\ninterest is hepatobiliarypancreatic surgery, specifically the management\nof acute pancreatitis and treatment of pancreatic and liver tumors. He\nhas published research papers, reviews, congress proceedings, and book\nchapters. He attended, in the period 1991–2016, for short periods every\nyear, the Hepatobiliarypancreatic Surgery Service of Beaujon Hospital,\nUniversitè de Paris, Clichy. He developed a seminar on 'Cystic Tumours\nof the Pancreas” for the Erasmus Program at Ghent University, Belgium, in2010–2011. 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If your research is financed through any of the below-mentioned funders, please consult their Open Access policies or grant ‘terms and conditions’ to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
Wellcome Trust (Funding available only to Wellcome-funded researchers/grantees)
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