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1. Introduction
The development of world economy is closely related with energy consumption. According to the economics research by Department of Agriculture of the U.S., since 2000, the energy consumption quantity begins to rise sharply at an average annual growth rate of 2.5% which approximates with the global real GDP (gross domestic product) growth. In China, the second largest economy in the world, this index increases by 15.1% in 2004. Excessive energy consumption, together with the environmental pollution, has become a huge threat to the sustainable development of human beings. Thus, the continuous pursuing for higher energy utilization has drawn the attention of many researchers.
There are three categories of indices to evaluate energy utilization summarized by Ang[1] which are thermodynamic indicators, physical-based indicators, and monetary-based indicators. Different with the first two indicators, outputs in monetary-based indicators are measured in form of currency. This causes monetary-based indicators popularly used in measuring energy efficiency of various levels, not only the common production process at the micro-level but also the comparison between countries at the macro-level.
Ang [1] introduces some key indices belonging to the category of monetary-based indicators. Energy intensity (EI), which is defined as the quotient of total energy consumption divided by total output (GDP or GNP), is used to estimate one’s energy efficiency roughly[1]. Energy coefficient is another index referring to the quotient of growth rate of total energy consumption divided by growth rate of total output, which is usually applied in comparison among various countries or regions [2]. However, the stability of energy coefficient is very poor, especially when the growth rate of one country’s GDP approaches to 0. Benefit for its clear definition, simple calculation and easily improvement, EI becomes the most frequently-used index in energy efficiency evaluation from both points of practice and research.
Most of literatures studying energy efficiency adopt energy intensity to analyze energy utilization efficiency, for instance, Howarth et al. [3] and Greening et al. [4], both of which are quoted frequently by other researchers. However, for simplicity, total energy consumption used in EI calculation only considers the sum of all kinds of energy consumption. EI neglects the structure of energy consumption, that’s why the index may estimate the energy efficiency inaccurately. Different energy storage capacities and consumption habits make energy consumption structure to be an indispensable influence factor in evaluation. In order to deal with this problem, Xu and Liang [5] introduced a weighted energy intensity model based on data envelopment analysis to evaluate the energy efficiency considering energy consumption structure.
Data envelopment analysis (DEA), a popular approach to evaluate the relative efficiency of homogenous decision making units (DMU) with multiple inputs and multiple outputs[6], has been widely used in the energy efficiency analysis and gained a lot of research achievement [7]. For example, in recent literatures, Mohammadi et al. [8] used DEA approach to evaluate energy efficiency of kiwifruit production in Iran. Rao et al.[9] developed an improved DEA model to analyze energy efficiency and energy savings potential in China. Bian and Yang [10] summarized several DEA models for measuring the energy efficiency and proposed an extended Shannon-DEA method to define a comprehensive concept of energy efficiency.
However, EI index based on DEA concentrates on the transforming degree of energy consumption to GDP or other economic statistical data, and ignores the function of non resource inputs such as labor and capital stock which also play an essential role during the production process. Boyd and Pang [11] introduced the concept of total factor energy efficiency (TFEE) and proposed a model to estimate the linkage between energy efficiency and productivity of the glass industry. References [12] and [13, 14] developed a series of models in estimating total factor energy efficiencies of 29 regions of China and Japan.
Except for using DEA model to analyze the energy efficiency at a given time, this chapter intends to investigate the dynamic change of energy efficiency over periods by adopting Malmquist production index (MPI) technique. First applied to study on the consumers’ behavior, after improved for many years, MPI approach deserves high praise in input-output analysis for the reason as follows: (1) no need for the price of input or output; (2) no need for the assumption of behavior pattern; (3) to get more intensive result of dynamic change easily[15]. MPI divides the total production growth rate into two parts, catch-up effect and frontier-shift effect, from which the cause of the change in energy efficiency can be clarified[16].
The current chapter tries to compare the total factor energy efficiencies of 48 countries all over the world in 2003 and analyze the dynamic change in total factor energy efficiencies of provinces of China over the period of 2000-2003 by the proposed model. The rest of this chapter is organized as follows. In Section 2, we introduce several methods for measuring the total factor energy efficiency and the dynamic change based on DEA and MPI technique. Section 3 shows how to use the proposed approach in analyzing the energy efficiency of 48 countries in 2003 and section 4 presents a dynamic example of total factor energy efficiency estimation of 30 provinces in china. Section 5 concludes this chapter.
2. Methodology
2.1.Energy efficiency considering energy structure based on DEA model
Suppose that there are n homogenous decision making units (DMU) to be evaluated, denoted by DMUj (j = 1, 2, …, n). Each DMU consumes m type of energy inputs xij (i = 1, 2, …, m) to produce s types of outputs yrj (r = 1, 2, …, s).
Xu and Liangintroduced weighted energy intensity model (WEI) based on DEA to evaluate the energy efficiency considering energy structure. Energy efficiency of DMU0 is obtained by the following fractional programming:
In the empirical example, xij stand for all kinds of energy consumption like crude oil, natural gas, coal and so on while yrj are outputs. The vector of vi stands for the weights of the energy consumption xij which represents the energy structure. In addition, the vector of μr is the weight of the output yrj. According to DEA technique, DMU0 is efficient if there is a parameter bundle (vi, μr) making the target value equal to 1. The production frontier constituted by all of the efficient DMUs suggests an improvement direction to the non-efficient DMUs.
Halkos & Tzeremeshave noticed that the scale of countries has influence on the energy efficiency especially when estimating the various countries and regions[17]. Some small countries could be efficient under the condition of variable return-to-scale (VRS) as there is less restrictive[18]. Banker et al.[19] improved an extension based on the variable return-to-scale assumption by adding a convexity constraint.
Here we transform Programming (1) into an integral linear programming and add the VRS assumption. Then we obtain the following program:
2.2.Total. factor energy efficiency based on DEA model
The concept of total factor energy efficiency investigates deeply into the energy consumption and production procedure and takes the non-resource inputs into account. As some representative examples, capital stock and labor are usually included. Following program is used to evaluate the total factor energy efficiency:
2.3.Total. factor energy efficiency based on Malmquist production index
The above sections discuss the efficiency evaluation at a given time while this section presents the efficiency evaluating model during a period. Malmquist production index (MPI) is widely applied in measuring the dynamic variation trend of input-output efficiency by dividing the total efficiency into two parts, catch-up effect and frontier-shift effect [20]. Catch-up effect detects whether the efficiency of DMU makes progress during the period. If the numerical value of catch-up effect is more than 1, then we can make sure that the technical efficiency of DMU gets improvement and DMU is closer to the production frontier. Frontier-shift effect is used to assess the technique advancement which is measured by the transform degree of production frontier at different time-points. If the numerical value of frontier-shift effect is more than 1, it means the production technique of the latter is better than that of the former.
We assume that the production possibility set at time t, denoted by St, includes all of the feasible production bundles, input xt and output yt. For each time-point t, we have
WhereDit(xt+1,yt+1)means the efficiency of DMU(xt+1,yt+1)at time t andDit+1(xt,yt)means the efficiency of DMU(xt,yt)at timet+1.
The Malmquist production index can be measured as follows:
MPI=catch-up×frontier-shiftE9
We notice that there need four efficiencies to obtain the MPI and two of which can be obtained by the linear program (3). The other two efficiencies,Dit(xt+1,yt+1) andDit+1(xt,yt), can be measured by the following two models.
3. A comparative analysis of energy efficiency of 48 countries
In this chapter, energy efficiency analysis of 48 countries in 2003 is illustrated. The major countries and regions all over the world are included in our consideration such as the United States, China, Russia, Japan and so on. Primary energy consumption includes oil, natural gas, coal, nuclear energy and hydropower. We incorporate oil and natural gas consumption as the first part of energy input. Nuclear power and hydropower are incorporated as the second part of energy input. Coal is the third input. Labor and capital stock are adopted as the non-resource input. Gross Domestic Product (GDP) is the only output.
The data on energy input are collected from World Petroleum Yearbook (2004). GDP and labor are obtained from the World Development Indicators database (2003). Due to the unavailability on the data of capital stock of some countries, we use the index of adjust savings after consumption of fixed capital as a substitute. The data is available from the website of World Bank. All of the data collected are summarized in Table 1.
Category
Indicators
Max
Min
Mean
Energy inputs
Oil & natural gas
1481.1
5.3
105.62
Nuclear power & hydropower
799.7
0.1
51
Coal
242.8
0.2
22.27
Non-energy inputs
Labor
129483.9
362.1
10208.44
Capital stock
13012
8.1
933.06
Output
GDP
109486
99
6828.9
Table 1.
Summary of inputs and output
Table 2 shows the results of energy efficiency considering energy structure measured by model (2). Countries in column 2 are ranked by GDP. The third column represents energy efficiency considering energy structure. Results indicate that: (1) there are 10 efficient DMUs including US, Japan, Italy and so on; (2) 21 countries’ energy efficiency scores lie on the interval of 0.5-1 and the typical countries are Britain, Germany, Mexico, etc; (3) the energy efficiency scores of the rest 17 countries are at very low level, less than 0.5; (4) the return-to-scale situation of most developed countries is in decreasing stage while in contrast many developing countries behave increasing returns to scale.
No.
Country
WEI-VRS
Rank
RTS
1
United States
1.0000
1
D
2
Japan
1.0000
1
D
3
Germany
0.9086
12
D
4
Britain
0.9969
11
D
5
France
1.0000
1
D
6
Italy
1.0000
1
D
7
China
0.3391
40
D
8
Canada
0.3471
38
D
9
Mexico
0.7755
18
D
10
Korea
0.3359
41
D
11
India
0.3526
36
D
12
Australia
0.8422
16
D
13
Netherlands
1.0000
1
D
14
Brazil
0.2938
42
D
15
Russian Federation
0.0707
48
D
16
Switzerland
1.0000
1
C
17
Sweden
0.8560
15
I
18
Austria
0.7669
19
D
19
Turkey
0.3469
39
D
20
Norway
0.8601
14
I
21
Poland
0.6479
22
D
22
Indonesia
0.2256
45
D
23
Greece
0.5793
26
D
24
Finland
0.7047
21
I
25
South Africa
0.4902
32
I
26
Ireland
1.0000
1
C
27
Portugal
0.5838
24
I
28
Thailand
0.1894
46
I
29
Iran
1.0000
1
C
30
Argentina
0.4893
33
I
31
Malaysia
0.2928
43
D
32
Czech Republic
0.5073
30
I
33
Hungary
0.5485
27
I
34
Egypt
0.5417
28
I
35
Pakistan
0.2818
44
I
36
Philippines
0.5805
25
I
37
New Zealand
0.7114
20
I
38
Columbia
0.5025
31
I
39
Chile
0.4777
34
I
40
Peru
0.8972
13
D
41
Romania
0.3488
37
I
42
Bangladesh
1.0000
1
C
43
Ukraine
0.1096
47
I
44
Slovakia
0.6364
23
I
45
Kazakhstan
0.5090
29
I
46
Bulgaria
0.8084
17
I
47
Lithuania
1.0000
1
D
48
Uzbekistan
0.3637
35
I
Table 2.
Energy efficienciesof 48 countries considering energy structure
It is particularly pointed out that the energy efficiency of China is only 0.3394 which is the worst among the top 10 countries ranked by GDP. The information of the input/output shown in Table 3 release that there are two reasons for that. First, the technical efficiency of energy consumption of china is lower, compared with Italy for example which has approximate output. Second, by comparison with 10 efficient countries, China has an improper construction of energy consumption that mainly relies on coal resource. Considering the heavy environmental pollution with coal’s burning, adjusting the structure of energy consumption is imperative.
No.
Country
GDP
Oil & Gas
Nuclear & hydropower
Coal
1
Japan
43009
317.6
112.2
75
2
Ireland
1537
12.1
1.6
0.2
3
Bangladesh
519
15.2
0.4
0.2
4
Netherlands
5115
79.9
9.2
0.9
5
France
17576
133.6
12.4
114.6
6
Italy
14683
156.6
15.3
10
7
Iran
1371
126.4
0.7
2
8
Switzerland
3201
14.7
0.1
14.5
9
Lithuania
182
5.3
0.1
3.7
10
United States
109486
1481.1
573.9
242.8
11
China
14170
304.7
799.7
73.8
Table 3.
Input/output of 10 efficient countries and China
Table 4 represents total factor energy efficiency calculated by model (3) & (4). Countries in column 2 are ranked by GDP. Column 3 and 5 show two kinds of results due to the different setting-ups of return-to-scale. Column 3 indicates the total factor energy efficiency based on constant return-to-scale which can be viewed as pure technical efficiency. Column 5 indicates the total factor energy efficiency based on variable return-to-scale. The last column shows the status of each one’s return-to-scale. It is noticed that only five countries are efficient both in CRS and VRS. Quantity of efficient country in column 5 is more than that in column 3. Notice that the return-to-scale effect of top 14 countries in the table is in decreasing stage while most of the last 18 countries are in increasing stage.
It is interesting to analyze the situation of china. It can be observed from table 4 that China is in stage of decreasing return-to-scale effect and TFEE is ranked 30, still lower than all of the developed countries and most of the developing countries. This is mainly caused by lower technical efficiency shown in column 3. Therefore, there are at least 3 ways to enhance the total factor energy efficiency of China, including (1) improving the output of GDP, (2) re-arranging the allocation of energy inputs and non-resource inputs and (3) improving the technical efficiency of production.
No.
Country
TFEE-CRS
Rank
TFEE-VRS
Rank
RTS
1
United States
0.8654
8
1.0000
1
D
2
Japan
0.9177
6
1.0000
1
D
3
Germany
0.8179
10
1.0000
1
D
4
Britain
0.8016
11
1.0000
1
D
5
France
0.6395
17
1.0000
1
D
6
Italy
0.8892
7
1.0000
1
D
7
China
0.3139
32
0.8663
30
D
8
Canada
0.5787
18
0.8417
31
D
9
Mexico
0.7008
14
1.0000
1
D
10
Korea
0.3154
31
0.7827
37
D
11
India
0.3086
33
0.8897
28
D
12
Australia
0.6449
16
0.9020
26
D
13
Netherlands
0.7462
12
1.0000
1
D
14
Brazil
0.2581
37
0.8341
33
D
15
Russian Federation
0.0696
47
1.0000
1
C
16
Switzerland
1.0000
1
1.0000
1
C
17
Sweden
0.8394
9
1.0000
1
C
18
Austria
0.7431
13
0.8304
34
D
19
Turkey
0.3375
27
1.0000
1
C
20
Norway
1.0000
1
1.0000
1
C
21
Poland
0.5168
22
0.8130
35
D
22
Indonesia
0.1895
40
0.8941
27
D
23
Greece
0.5743
19
0.7196
42
D
24
Finland
0.6992
15
0.7779
38
I
25
South Africa
0.4727
25
0.7215
41
C
26
Ireland
1.0000
1
1.0000
1
C
27
Portugal
0.5362
21
0.6063
46
I
28
Thailand
0.1784
42
0.6815
44
D
29
Iran
1.0000
1
1.0000
1
C
30
Argentina
0.4820
23
1.0000
1
C
31
Malaysia
0.3039
34
0.8014
36
I
32
Czech Republic
0.3341
28
0.5315
48
I
33
Hungary
0.3335
29
0.6999
43
I
34
Egypt
0.5630
20
1.0000
1
C
35
Pakistan
0.2114
38
0.8803
29
I
36
Philippines
0.3321
30
1.0000
1
I
37
New Zealand
0.4625
26
0.9426
25
I
38
Columbia
0.2926
35
0.7719
39
I
39
Chile
0.2814
36
0.7570
40
I
40
Peru
0.4730
24
1.0000
1
C
41
Romania
0.1478
44
0.8352
32
I
42
Bangladesh
1.0000
1
1.0000
1
C
43
Ukraine
0.0442
48
0.5624
47
I
44
Slovakia
0.1883
41
0.6789
45
I
45
Kazakhstan
0.1065
45
1.0000
1
I
46
Bulgaria
0.1558
43
1.0000
1
I
47
Lithuania
0.2046
39
1.0000
1
I
48
Uzbekistan
0.0710
46
1.0000
1
I
Table 4.
TFEE of 48 countries
4. A dynamic analysis of energy efficienciesof 30 Chinese provinces during 2000-2003
This section aims to investigate the total factor energy efficiency of main areas in china using the time-series data from 2000 to 2003. These areas shown in table 5 include 12 provinces in the east area, 10 provinces in the central area and 8 provinces in the west area. Consisting of fast-developing regions like Beijing, Shanghai, Guangdong etc., the east area owns GDP output around half of the country. The central area contains inland provinces such as Shanxi, Jilin, Henan etc. This area has a large population and tremendous potential. Compared with the other areas, the west area is the least developed region in China, containing provinces of Sichuan, Guizhou, Yunnan etc. In our study, Tibet, which is also a province in the west area, is missing due to the unavailability of data. Similar as the analysis in the above section, GDP is the only output and non-resource inputs are capital stock and labor while energy inputs are represented as crud oil, coal and electric power. The data on energy input are collected from China Energy Statistical Year Book (2004). GDP and labor data are collected from the Statistical Year Book of China published by National Bureau of Statistics during 2000-2003. The data on capital stock comes from Jun et al. [23].
Curves in Figure 1 show the difference among the average TFEE scores of the provinces in the east, central and west areas using model (4). Obviously the east area is the most efficient and the west area is worst in any year. Meanwhile, it is shown that energy efficiencies for all areas are gradually improving. The detailed results are listed in Table 6. It can be easily observed from the table that most of efficient provinces are in the east area. TFEE scores of Liaoning, Shanghai, Jiangsu, Guangdong, Guangxi, Hainan, Fujian are all at a high level. Provinces in the central area are not as good as the provinces in the east area except Anhui which is adjacent to the east area. Another province in the central area, Shanxi, for specially, has very low TFEE scores during the four years and makes little progress. The situation in the west area is even worse other than Sichuan, Yunnan, Qinghai and Ningxia.
Figure 1.
TFEE of 30 provinces during 2000-2003
Table 7 is used to clarify which part makes the energy efficiency get improvement. During 2000 to 2001, the average value of Malmquist production index (MPI) for all provinces is 1.13 which means the efficiency in 2001 is better than 2000. Catch-up effect (CE) and frontier-shift effect (FE) are two parameters to distinguish which part is functioned. The data on the last row show that the average value of CE is 1.00 and FE is 1.13. That is to say, the technical efficiency in 2001 is almost the same as that in 2000, while the production frontier gets improvement during the two years. Improvement is also achieved in next two years, but there is something different that both CE and FE are working.
In order to compare the trends of 3 areas, we make a summary in table 8 using the average data. It is clear that the central area has the greatest achievement and the west area is following. FE is always doing better than CE.
No.
Province
Area
2000
2001
2002
2003
1
Beijing
E
0.8644
0.9557
0.9634
1.0000
2
Tianjin
E
0.9911
1.0000
1.0000
1.0000
3
Hebei
E
0.7702
0.8028
0.7848
0.7663
4
Liaoning
E
1.0000
1.0000
1.0000
1.0000
5
Shanghai
E
1.0000
1.0000
1.0000
1.0000
6
Jiangsu
E
1.0000
1.0000
1.0000
1.0000
7
Zhejiang
E
0.9949
1.0000
1.0000
1.0000
8
Shandong
E
1.0000
0.8836
0.9994
1.0000
9
Guangdong
E
1.0000
1.0000
1.0000
1.0000
10
Guangxi
E
1.0000
1.0000
1.0000
0.9406
11
Hainan
E
1.0000
1.0000
1.0000
1.0000
12
Fujian
E
1.0000
1.0000
1.0000
1.0000
13
Shanxi
C
0.4510
0.4469
0.5150
0.5847
14
Inner M
C
0.6664
0.6929
0.7194
0.8298
15
Jilin
C
0.6657
0.6826
0.6890
0.7548
16
Heilongjiang
C
0.8323
0.7791
0.8795
0.9683
17
Anhui
C
1.0000
1.0000
1.0000
1.0000
18
Jiangxi
C
0.9508
0.9800
1.0000
1.0000
19
Henan
C
0.9075
0.9044
0.8967
1.0000
20
Hubei
C
1.0000
1.0000
0.9077
0.9541
21
Hunan
C
0.9284
0.9305
0.9138
0.9342
22
Chongqing
C
1.0000
1.0000
1.0000
1.0000
23
Sichuan
W
1.0000
1.0000
1.0000
1.0000
24
Guizhou
W
0.5258
0.5232
0.5203
0.6556
25
Yunnan
W
1.0000
1.0000
1.0000
1.0000
26
Shaanxi
W
0.5191
0.5307
0.5701
0.6067
27
Gansu
W
0.4206
0.4229
0.4153
0.4126
28
Qinghai
W
1.0000
1.0000
1.0000
1.0000
29
Ningxia
W
1.0000
1.0000
1.0000
1.0000
30
Xinjiang
W
0.7356
0.7431
0.7497
0.8371
Table 6.
TFEE of 30 provinces during 2000-2003
No.
Province
2000-2001
2001-2002
2002-2003
CE
FE
MPI
CE
FE
MPI
CE
FE
MPI
1
Beijing
1.11
1.05
1.17
1.01
1.05
1.06
1.03
1.07
1.11
2
Tianjin
1.01
1.03
1.04
1.00
1.02
1.02
1.00
1.07
1.07
3
Hebei
1.04
1.16
1.20
0.98
1.13
1.11
0.98
1.11
1.09
4
Liaoning
1.00
1.04
1.04
1.00
1.00
1.00
1.00
1.09
1.09
5
Shanghai
1.00
1.16
1.16
1.00
1.14
1.14
1.00
1.19
1.19
6
Jiangsu
1.00
1.20
1.20
1.00
1.16
1.16
1.00
1.10
1.10
7
Zhejiang
1.00
1.08
1.08
1.00
1.09
1.09
1.00
1.13
1.13
8
Shandong
0.88
1.10
0.97
1.13
1.10
1.24
1.00
1.15
1.15
9
Guangdong
1.00
1.18
1.18
1.00
1.03
1.03
1.00
1.06
1.06
10
Guangxi
1.00
1.08
1.08
1.00
1.00
1.00
0.94
1.07
1.01
11
Hainan
1.00
0.23
0.23
1.00
1.01
1.01
1.00
0.72
0.72
12
Fujian
1.00
1.08
1.08
1.00
1.10
1.10
1.00
1.07
1.07
13
Shanxi
0.99
1.04
1.03
1.16
1.06
1.23
1.14
1.05
1.19
14
Inner M
1.04
1.04
1.08
1.04
1.12
1.16
1.15
1.04
1.20
15
Jilin
1.03
1.01
1.04
1.01
1.02
1.03
1.10
0.98
1.08
16
Heilongjiang
0.94
1.01
0.95
1.13
0.98
1.11
1.10
0.96
1.05
17
Anhui
1.00
1.06
1.06
1.00
1.03
1.03
1.00
1.07
1.07
18
Jiangxi
1.03
1.00
1.03
1.02
1.00
1.02
1.00
0.96
0.96
19
Henan
1.00
1.16
1.16
0.99
1.14
1.13
1.11
4.19
4.65
20
Hubei
1.00
1.02
1.02
0.91
1.03
0.94
1.05
0.99
1.04
21
Hunan
1.00
1.00
1.00
0.98
0.98
0.96
1.02
0.96
0.98
22
Chongqing
1.00
3.58
3.58
1.00
3.82
3.82
1.00
4.27
4.27
23
Sichuan
1.00
1.53
1.53
1.00
1.68
1.68
1.00
1.59
1.59
24
Guizhou
1.00
0.95
0.95
0.99
1.02
1.01
1.26
0.82
1.04
25
Yunnan
1.00
1.09
1.09
1.00
1.12
1.12
1.00
1.16
1.16
26
Shaanxi
1.02
1.00
1.02
1.07
0.99
1.06
1.06
0.96
1.02
27
Gansu
1.00
1.02
1.02
0.98
1.03
1.01
0.99
1.04
1.03
28
Qinghai
1.00
0.94
0.94
1.00
0.94
0.94
1.00
0.94
0.94
29
Ningxia
1.00
0.91
0.91
1.00
0.91
0.91
1.00
0.90
0.90
30
Xinjiang
1.01
1.01
1.02
1.01
1.01
1.02
1.12
0.97
1.09
Average
1.00
1.13
1.13
1.01
1.16
1.17
1.04
1.26
1.30
Table 7.
Changes of 30 provinces during 2000-2003
Areas
2000-2001
2001-2002
2002-2003
CE
FE
MPI
CE
FE
MPI
CE
FE
MPI
East area
1.00
1.03
1.04
1.02
1.07
1.09
1.01
1.07
1.08
Central area
1.00
1.32
1.32
1.01
1.35
1.36
1.06
1.71
1.81
West area
1.00
1.06
1.06
1.01
1.09
1.09
1.05
1.05
1.10
Table 8.
Average data of areas during 2000-2003
It is interesting to investigate the individual province. Here are some examples. First, we make a comparison between Shanghai and Hainan both of which are efficient during the periods. However, detailed data indicate that Shanghai keeps making frontier forward gradually while Hainan are opposite except year 2002. This could be explained by that the location of Hainan on the frontier is on the edge. Second, take Beijing for example. Beijing is non-efficient from 2000 to 2002 and finally becomes efficient at 2003 by making efforts on improving technical efficiency and putting frontier forward. Third, energy efficiency of Shandong province suffers a decline and is back to the normal level later. MPI during first period is decreasing mainly caused by declining CE. In the next two years, some parameters get recovery which makes MPI increasing.
5. Conclusion
This chapter reviews the development process of the evaluation technique of energy efficiency and focuses on introducing the concept of energy intensity. However, missing the structure of energy consumption causes the energy efficiency estimated inaccurately. Thus, the current chapter introduces a weighed energy efficiency model based on DEA to fix it. Energy cannot produce production without non-energy inputs such as labor and capital. That’s why we extend the method to the total factor energy efficiency model. Energy efficiency of China and other 47 countries in 2003 are employed to illustrate the models. Results show that unbalance of energy efficiency exists. For china, specially, it needs to adjust energy consumption structure as its poor energy efficiency and improve GDP since its total factor energy efficiency is at a lower level than some developed countries.
As a key part, the chapter adopts Malmquist production index technique to analyze the dynamic change in energy efficiency of Chinese provinces which can further explore the reason for the variation of energy efficiency deeply. The chapter uses the proposed models to investigate the changes in energy efficiency of provinces in china during the period of 2000 to 2003. We find that the east area has better energy efficiency than the central and west area but lower improving rapid. In addition, it is interesting to find that energy efficiency of most provinces improves due to the extending frontier. Although our work mainly focuses on estimating energy efficiency at the macro-level, it can provide guidance to managers and manufacturers at the micro level.
Acknowledgement
We would like to thank the financial support by Program for New Century Excellent Talents in University, Ministry of Education of China and National Natural Science Foundation of China (Grant No. 70801056, 71121061, 71090401/71090400 and 71110107024).
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Introduction",level:"1"},{id:"sec_2",title:"2. Methodology",level:"1"},{id:"sec_2_2",title:"2.1.Energy efficiency considering energy structure based on DEA model",level:"2"},{id:"sec_3_2",title:"2.2.Total. factor energy efficiency based on DEA model",level:"2"},{id:"sec_4_2",title:"2.3.Total. factor energy efficiency based on Malmquist production index",level:"2"},{id:"sec_6",title:"3. A comparative analysis of energy efficiency of 48 countries",level:"1"},{id:"sec_7",title:"4. A dynamic analysis of energy efficienciesof 30 Chinese provinces during 2000-2003",level:"1"},{id:"sec_8",title:"5. Conclusion",level:"1"},{id:"sec_9",title:"Acknowledgement",level:"1"}],chapterReferences:[{id:"B1",body:'Ang, B., Monitoring changes in economy-wide energy efficiency: From energy-GDP ratio to composite efficiency index.Energy Policy2006574582'},{id:"B2",body:'AngB.statisticalA.analysisof.energycoefficients.Energy economics199193110'},{id:"B3",body:'Howarth, R.B., et al., Manufacturing energy use in eight OECD countries: Decomposing the impacts of changes in output, industry structure and energy intensity.Energy economics, 1991135142'},{id:"B4",body:'GreeningA.L.GreeneD. L.DifiglioC.Energyefficiency.consumption-therebound.effect-asurvey.Energy efficiency and consumption-the rebound effect-a survey.Energy Policy2000389401'},{id:"B5",body:'XuXiping.liangLiang.Energy Efficiency and Productivity of China: Compared with other Countries. ICCS 2007Part III, LNCS 4489, 988991Beijing, China.'},{id:"B6",body:'CharnesA.CooperW. 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1. Introduction
The animal milk is already a highly consumed and appreciated by the human being for many centuries. Its consumption may exist from 8000 BC to 10,000 BC through the aurochs, ancestors of the actual cows, were domesticated and its milk used for consumption [1].
Milk is a highly valued and important food for the human diet. Since birth, this food can provide nutrients capable of transmitting not only energy but also bioactive components and immune cells that are responsible for anti-inflammatory, anti-infectious, and probiotic actions [2].
Since milk is a food that contains almost all the nutrients necessary for the maintenance of our body, it is still widely consumed, even in adulthood. Nowadays, use the term “milk” for the secreted fluid of the cow, and the secreted fluid of other animals is called “animal name + milk”, for example, sheep’s milk, goat milk and buffalo milk [3].
Milk consumption is very important, especially in underdeveloped countries, as it is an important source of energy, protein and fat, being one of the most important foods for consumption in malnourished children [4].
For several reasons, more consumers choose alternatives to plant-based milk. The most common reasons are allergies to milk protein, lactose intolerance or lifestyle choices, such as vegetarianism [5].
Because of this, currently an increase in research and development of plant-based milks and their derivatives, such as yogurts, ice cream and fermented beverages, has been carried out in order to bring more consumption options for individuals who cannot or do not wish to consume animal milk. Thus, the objective of this chapter is providing some information on types of milk substitutes and their functionality in food formulations and in human health.
2. Animal milk: processing and composition
As a natural food, milk has a rapid multiplication of different microbial groups. Therefore, both raw milk and dairy products, with the exception of some cheeses, must go through a thermal process, such as pasteurization, thermization or ultra-high temperature (UHT) so that pathogenic microorganisms are eliminated and so the milk is safe for consumption [6].
The UHT process for milk is a technique that uses a heat treatment with temperature between 135 and 150°C with pause times between 1 and 10 s. There are two types of heating for this processing: direct and indirect heating. In direct heating, the milk itself encounters a saturated steam. In indirect heating, an external heating medium will indirectly heat milk by conduction and convection through a barrier that acts as a heat exchanger. The indirect heating is the most used by industries [7].
As for milk pasteurization, the milk is heated to a specific temperature, keeping the milk at this temperature for a specific length and successively a rapid cooling step with a temperature below 7°C. The temperature and length of the pasteurization process will depend on which microbial or chemical effects are desirable. When submitting milk to the pasteurization process, one must think that the higher the temperature and the longer the process, the drastic decrease in the number of microorganisms will be possible, but there will also be a damage to the nutritional constituents of milk [8].
As for the thermization technique, a temperature of 63°C is used for 15 s, being milder than pasteurization; this process is used only to improve the quality of the milk, since there is no effective elimination of pathogenic microorganisms that may be present in the milk [8].
From whole milk, several by-products, such as cream, buttermilk, skimmed milk, and derived by-products, such as caseins and whey protein can be produced by the dairy industry. The processing of both whole milk and these by-products can be seen in Figure 1 [9].
Figure 1.
Processing of whole milk and its by-products [10].
Milk is a complex biological fluid that contains fat, proteins, vitamins, minerals, enzymes and sugar in its composition. Mammalian animals show similarities regarding the nutritional composition of their milks however, the influence of genetic factors, nutritional factors and environmental conditions can alter the composition of milk, even in animals of the same species. Table 1 presents a general composition of the milk of different mammals [11].
Species
Water
Proteins
Fat
Lactose
Ash
Cow
87.2
3.5
3.7
4.9
0.72
Sheep
82.7
5.5
6.4
4.7
0.92
Goat
86.5
3.6
4.0
5.1
0.82
Camel
87.7
3.5
3.4
4.7
0.71
Table 1.
The composition of milk from different mammals in g/100 g milk [12].
Milk plays a fundamental role in human nutrition and health, which is why the intake of this food is so important to be consumed by both children and adults. The milk consumed by humans comes mainly from the dairy cattle, but in some parts of the world, there is the consumption of milk from other animal species, for example, buffalo, goats, sheep, and camels. Cow’s milk protein consists of approximately 80% casein (w/w) and 20% of whey protein (w/w) [13].
In animal milk, exist four main caseins that are naturally present: αs1, αs2, B e k, with casein k being the most relevant due to its importance in the stability of the micelle and in the processing of various dairy derivatives [12].
As for whey protein, the main proteins are β-lactoglobulin, α-lactalbumin, serum albumin, immunoglobulins (IgG1, IgG2, IgA and IgM) and lactoferrin. This class of proteins is classified as high biological value since it presents essential amino acids and branched chain amino acids (not synthesized by our organism). With this has received a lot of attention for the food development industry, mainly in sports nutrition, since that these amino acids are important for tissue growth and repair [14].
Bovine milk fat has more than 400 fatty acids with different chemical compositions. Its composition consists of triacylglycerol, diacylglycerol, free fatty acids, and cholesterol [15].
The composition of milk fat will in fact depend on external factors to which dairy cattle are subjected, such as factors related to food (feed offered to cattle) or rumen microbial activity. In addition, other factors such as stage of lactation and mastitis can also influence the final composition of milk fat [16].
Milk fat is the natural source that most presents short-chain fatty acids (C4:0 – C8:0) and also contains a high amount of medium chain fatty acids (C10:0 – C14:0) which causes that this food is almost exclusive to certain bioactive fatty acids considered beneficial to human health. An example of this is butyric acid (C4:0) and conjugated linoleic acid that are not found in significant amounts in other foods in our diet [17, 18].
Regarding carbohydrate, lactose is the main present in the composition of milk. This compound if formed by the union of a D-galactose molecule with a D-glucose molecule. Thus, lactose can be hydrolyzed through an enzymatic action by β-galactosidase that will transform it into its constituent monosaccharides, i.e., galactose and glucose. This action is of great importance for the food industry, since lactose, despite being a sugar, does not have a sweet taste, but its constituents have such a sweet taste, in addition to being more soluble than lactose [12, 19].
In addition, when lactic acid bacteria meet with lactose, they hydrolyze it into lactic acid, thus making milk a favorable medium for fermentation, since the pH of the milk falls and coagulates, and it may then be possible to produce dairy products, like cheese and yogurts. However, this requires controlled fermentation, as unwanted fermentation obviously results in the deterioration of milk [20].
Although lactose has many advantages for the food industry, this component of milk is also responsible for making it impossible for many people around the world to consume milk, since they have a lactose intolerance. The pathophysiological aspects or lifestyles that remove the consumption of milk by the population will be found in more detail in the next topic.
3. Adverse reactions to milk composition
Although milk is a good food source of compounds because it is associated with the supply of several essential nutrients to our diet, there are several concerns that must be taken into account for the consumption of this food, both in terms of different health problems and lifestyles that a person can manifest.
One of the adverse effects on human health is allergy to cow’s milk protein, which occurs due to an adverse immune response to the cow’s milk dietary antigen. The allergic process starts with casein, which makes up about 80% of milk proteins. When these proteins are digested, they are converted into opioid compounds called β-casomorphines that binds to the A1 allele of β-casein, thus causing allergy to the human body, especially in children and newborns, since your body does not yet recognize some proteins of cow’s milk [21].
Another adverse cause that can be affected by the consumption of animal milk is lactose intolerance. This condition occurs when a person is unable to digest and absorb lactose from the diet. This is because there is a decline in lactase expression after weaning, commonly called “lactase non-persistence” [22].
Among the symptoms that identify lactose intolerance are gastrointestinal symptoms that can be presented with mild to moderate signs of indigestion, flatulence, nausea, diarrhea, and abdominal cramps after consumption of milk and dairy products [23].
Another factor that excludes animal milk from the diet, but that is not associated with any disease but with lifestyle is the case of vegans. The basic principle of vegetarianism is not consuming any type of red meat, chicken, or fish, but it may or may not include products of animal origin, such as eggs, milk and their derivates. Although it is often not very clear, veganism not only supports the exclusion of animal foods from its diet due to the animal’s suffering, but also as a way of supporting the inclusion of plant foods produced by local producers that will present less environmental impact [5, 24].
Therefore, such conditions point to the need for not only animal milk but also its derivatives to obtain an alternative that is as healthy, so that people who cannot consume this food are also able to digest nutrients necessary for a good diet, in addition to presenting other food alternatives that allow you to diversify eating routines.
4. Plant-based milk alternatives
Currently, there are several researches for replacing milk using plant-based milk. This type of milk-like is a water-soluble extract based from vegetables, legumes, cereals, pseudocereals and nuts [25].
Vegetable milk processing can have several types of vegetable milk-like processing that will depend on the raw material from which this vegetable milk-like will be extracted. However, there are processing steps that are common to most vegetable milks, such as pre-treatment of raw material, extraction of milk, incorporation of additives, suspension, stabilization and adequate storage to improve shelf life of the product, as can be seen in Figure 2 [26].
Figure 2.
Basic flow diagram to produce probiotic vegetable milk [26].
As it is still a studied product, there is still no concrete definition and classification in the literature of these plant-based milks. However, a general classification of these milk alternatives is divided into 5 subcategories, which are: cereal based, legume based, nut based, seed based and pseudocereal based [27].
In addition to providing lactose substitution, plant-based milks are also capable of providing health benefits to the general population and not only to those who should restrict lactose from the diet. This occurs because of the raw materials used in the production of these plant-based milks, since they have compounds that bring health benefits due to nutrients and micronutrients present in the composition of these foods that allow such beneficial actions to the human body.
Legumes are an important category in the divisions of vegetable milk, and chickpeas (Cicer arietinum) are rich in carbohydrates, proteins, vitamins, minerals, and fiber. Chickpeas have a high content of unsaturated fatty acids, such as linoleic acid (18: 2) and oleic acid (18: 1), and also have an excellent source of phosphorus, potassium, calcium, magnesium, sodium, iron, copper, manganese, and zinc [28].
In chickpeas, polysaccharide (starch) is the largest component followed by fibers, contains monosaccharides (ribose, glucose, galactose, and fructose), disaccharides (sucrose, maltose), oligosaccharides (stachyose, ciceritol, raffinose, and verbascose) [29].
The fiber content of chickpeas in chickpeas is 18–22 g for every 100 g of raw material, with 4–8 g of soluble fibers and 10–18 g of insoluble fibers that are major players in cholesterol control total and LDL [30].
When chickpeas are cooked, they undergo ultrastructural changes that influence their nutritional, physical, and functional properties with significant decreases in antinutritional components and significant increases in dietary fibers and protein digestibility [31].
Chickpeas are a source of carbohydrates and proteins and can be used to develop products with greater added nutritional value, also, phytosterols present in the lipid fraction of chickpeas have antioxidant properties even at high temperatures [30, 32].
Soy (Glycine max) is rich in carbohydrates, proteins, and lipids. Soy milk is a good source of monounsaturated and polyunsaturated essential fatty acids, with significant amounts of polyunsaturated fatty acids, such as linoleic (18: 2) and linolenic acids (18: 3), has no cholesterol, and has a considerable content of vitamins and minerals. Soy milk is a good source of monounsaturated and polyunsaturated essential fatty acids. Soy protein is composed of all essential amino acids, many of which are present in quantities that correspond to those necessary for humans [5, 27].
Soy contains up to 35–45% protein and 20% fat and acts as an important source of protein, especially in people who follow a vegetarian diet. Due to its nutritional content, soy milk is used as a cow substitute for more than four decades. It also appears that the total number of calories available is comparable to a balanced nutritional profile [33].
Isoflavones appear to be the functionally active component responsible for the beneficial effects of soy. Isoflavones are well known for their protective effect against cancer, cardiovascular disease, and osteoporosis. Genistein is the most abundant isoflavone in soy and is considered the most biologically active [27].
Soy germination is known to be beneficial in reducing antinutrients as a trypsin inhibitor, phytic acid, flatulent, etc. Soy milk prepared by the traditional method presents some problems, many works and researches are trying to improve the quality by eliminating the strange taste and beans, inhibiting anti-nutritional factors, reducing the phytic acid content, improving the production of soy milk. All efforts generally address one or two of the associated problems and use heat-intensive methods to reduce antinutrients, thereby reducing the overall nutritional quality of soy milk. Germination, which is a natural non-thermal and non-chemical process, results in better quality processed products and soy germination would be an alternative to facilitate the development of products such as soy milk, which are usually prepared by heat treatment and moist protein-causing denaturation [34].
The pea (Pisum sativum) contains proteins that are easily digestible and has a high-quality amino acid profile, as well as a high content of lysine and arginine. Pea proteins have a very particular amino acid profile, different from other plant proteins. The amino acid profile of pea proteins is arginine, lysine, and branched-chain amino acids (isoleucine, leucine, and valine), glutamine, and glutamic acid [35].
Pea protein is a rich source of starch, fiber, vitamins, and minerals and easy to digest. Therefore, it can be used successfully as a substitute for animal protein in dairy products. Total pea proteins are divided into two main groups of albumin and globulins. However, pea’s main storage proteins are often called legumin, vicilin, and convicilin, which make up the globulin fraction, which does not denature at different temperatures [36].
Lupine (Lupinus termis) is rich in alkaloids, amino acids, carbohydrates, and proteins with moderate gelatin properties compared to soy proteins. Lupine is a good source of nutrients, not just proteins, but also lipids, fibers, minerals, and vitamins. Lupines contain phytochemicals with antioxidant capacities, such as polyphenols, mainly tannins, and flavonoids [37].
With a high protein content of 25–40% can be used in milk-like products and substitutes, there is a growing interest in the production of lupine, due to its potential as a protein source, or for pharmaceutical purposes due to the high alkaline content, such as a natural component. Lupine milk-like plays a key role in meeting demand as an alternative to cow or human milk [37, 38].
Plant-based milks elaborated from seeds contain proteins that, like bovine milk proteins, tend to clot when they are acidified, heated, or enzymatically treated. Herewith, this type of vegetable milk-like is also capable of forming many products called “dairy products”, such as creams, yogurts, ice cream and cheeses [39].
The most common types of vegetable milk-like produced from the seed is sesame milk. Sesame (Sesamum indicum) is a grain that contains high levels of phenolic compounds in their composition, which are considered with high antioxidant activity [40].
However, in sesame only a small amount of these components is present in their free form, the remainder being linked to glucose in the form of mono/di/tri-glucoside lignan, thus not exercising its antioxidant activity. However, when fermenting sesame milk-like with a lactic acid β-glucosidase, the glycoside bonds are broken resulting in a compound called aglycone, which increases the product’s antioxidant activity [41].
Because of this, initially, only sesame milk-like may not be considered an advantageous vegetable milk-like when compared to other plant-based milks, but in application of yogurts and fermented beverages, this food can have great nutritional advantages.
Another type of seed that can be used to elaborated vegetable milk is pumpkin seed (Cucurbita maxima Linn). This seed is widely consumed in the form of snacks in different regions of the word, but due to its nutritional advantages, it has been thought about its use in the development of other types of products [42].
This seed is an important nutritional source, as it contains lipids (30.66%), protein (33.48%) and carbohydrate (28.68%). Still, its extract proved to be effective against diabetes and hypercholesterolemia due to the components present in its composition [43].
Flaxseed is a food rich in all essential fatty acids, especially omega-3 that is responsible for increased immunity and brain function. In addition, it has amino acids that are responsible for maintaining proper cellular function through the synthesis of protein. As a result, flaxseed has gained presence in research for the development of vegetable milks-like since its use improves the nutritional quality of the final product [44].
Another subcategory of plant-based milks is those obtained from pseudocereals, such as Quinoa (Chenopodium quinoa Willd.), a plant that belongs to the Chenopodiaceae family. Because it has a high amount of protein and an adequate balance of amino acids, this plant has been considered of great importance, especially for individuals who do not, or make little intake of foods rich in proteins and amino acids such as meat, eggs and milk [45].
Despite its great nutritional advantages, quinoa is still not a widely used food for developing new types of food products, as it has a high cost when compared to other types of food available, especially in the case of the development of plant-based milks-like [46].
Cereals are another subcategory that can develop a vegetable milk-like. Although there are countless cereals present around the word, the most common types of vegetable milk-like obtained from cereals are rice and oats.
Oat (Avena sativa L.) is a specie of cereal grain rich in biological substances, such as soluble dietary fiber, β-glucan, vitamin E and polyunsaturated fatty acids that make the consumption of this food of great importance for human health in the long term [47].
Among the components present in oats, β-glucan is the most important since this component has a prebiotic function in the gastrointestinal tract supporting the growth of microorganisms beneficial to our body; In addition, β-glucan moderates the glycemic response of the oat starch portion. In addition, this cereal contains several bioactive phytochemical, such as, for example, phenolic acids, flavonoids, carotenoids and phytosterols, in addition to the avenanthramides and steroidal saponins, which are found exclusively in this food [48, 49].
Although rice is low in protein, it is still a food to be taken into account for the production of plant-based milks, due to its underutilization and high nutrient profile. A natural fermentation with lactic acid bacteria there is a break in the antinutritional factors that cause an increase in the content of calcium, iron and magnesium, causing the beneficial bacteria in our gastrointestinal tract to collaborate in digestion and in the immunity of other internal organs [50].
Therefore, the use of isolated rice milk-like may not appear to be of any benefit, but when subjected to a fermentation process, which occurs in the production of many dairy products, this vegetable milk-like can have positive advantages for the final product.
Another subcategory that can be found to produce vegetable milk are nuts. Almond (Prunus dulcis) has as main components proteins, lipids, soluble sugars, minerals, and fibers. Most almonds are fatty, between 35 and 52%, followed by protein, 22 to 25%, with lipids mainly as unsaturated fatty acids and proteins as essential amino acids. Also they are rich in nutrients such as calcium, magnesium, selenium, potassium, zinc, phosphorus, and copper and, due to the presence of arabinose, they have prebiotic properties [5].
Almonds are rich in monounsaturated fatty acids (MUFA), which are considered useful for weight loss and control. There is also much convincing evidence that MUFA helps to reduce the content of low-density lipoproteins (LDL) in the blood. Almonds are also an essential source of various nutrients, including protein, fiber, vitamin E, manganese, and antioxidants, and are therefore reflected in almond milk-like. Its consumption has beneficial effects on human health, and it is especially related to the blood lipid profile and the risk of cardiovascular diseases [33].
Coconut (Cocos nucifera) milk-like can increase HDL (high-density lipoprotein) levels, which helps to reduce harmful LDL (low-density lipoprotein). Coconut fats have lauric fatty acid, which mainly contributes to increasing HDL cholesterol levels, which helps to lower LDL cholesterol levels in the bloodstream [33].
Coconut milk-like is used as a milk substitute (cow’s milk) in dairy products such as cheese, yoghurt, chocolate, and frozen dessert. In this way, coconut milk is considered one of the most suitable substitutes for milk. Therefore, milk and coconut milk can serve as substitutes for each other, depending on the purpose of the substitution [51].
Young coconut milk contains carbohydrates (mainly sucrose and some starch), lipids, and minerals, such as phosphorus, calcium, and potassium. Coconut protein is rich in lysine, methionine, and tryptophan. Coconut water extracted from young coconuts has a pleasant taste and balance of sodium, potassium, calcium, and magnesium since many plant extracts are mixed with the water of the coconut itself for yield and stability [52].
A summary of health benefits provided by the different raw materials used in the production of vegetable milk-like is presented in Table 2.
Milk alternative
Subcategory
Health benefits
Author, year
Oat Milk
Cereal
It has an anti-carcinogenic component and reduces LDL cholesterol levels; Rich in fibers, antioxidants and polyphenols.
Source of essential fatty acids, considered good for cardiovascular health; Therapeutic properties and protective roles against several age-related diseases.
Contains all essential amino acids and high quality fatty acids; Contains minerals and amino acids that help in memory and reducing anxiety in stressful situations.
Examples of raw materials in vegetable milks-like and their health benefits.
5. Use of milk substitutes in dairy products
Currently, there are not many researches focused on the use of plant-based milks in the production of dairy products such as fermented beverages, ice cream, yoghurt and cheese, for example. Despite this, the work that has been carried out in this area produces results that help not only to improve previous research but also to conduct new research and new products at the market.
Recently, a fermented beverage produced from lentil grains fermented with Lactobacillus strain was evaluated in order to analyze its biochemical and nutritional composition, in addition to the viable cell count during its storage time. The work concluded through the obtained results that the protocol used showed to have adequate potential for applications in other types of fermented beverages and with the possibility of using other types of legumes [56].
Adding the nutritional value of the legumes, and their generated vegetable extract, it can lead to the production of ice cream, with soy extract and soybean protein, with functional properties, that also impose specific structural characteristics of product. With the obtained results it is possible to conclude that the use of soy for the preparation of gelato shows differentiating characteristics in terms of protein content, solubility and viscosity of the final product, which is also well accepted by consumers [57].
An alternative to soy milk-like was made by the production of fresh and fermented chickpeas, producing plant-based beverages. The fresh chickpeas presents a good result in the nutritional and organoleptic quality of the product, being a potential substitute for soy in plant-based beverages, although further research is necessary to minimize the syneresis of the elaborated product [58].
Texture properties are related to sensory acceptance by consumers. Vegetable yogurts based on oats is possible, nevertheless the perception of the plant-based yoghurt in the mouth has greater variation compared to traditional yoghurt, due to its textural properties, such as thickness and creaminess inferior to the product elaborated with animal milk. Therefore, to be acceptable by consumers, it is recommended that its final texture properties be considered during the product development phase [59].
Another oat base to produce a fermented product like the traditional yoghurt can be used, leading to acceptable appearance and taste. However, is necessary to evaluate the physical and nutritional quality of products [60].
Ice creams fermented with Lactobacillus acidophilus (Bb-12) and Bifidobacterium bifidum (La-05) can be prepared from cow’s milk, soy, or coconut, as well as the combination of cow’s milk or coconut milk (1 = 25%, 2 = 50% and 3 = 75%) with soy milk (75%, 50%, and 25% respectively). The substitution of cow’s milk for soy and coconut milk increase the probiotic growth of (Bb-12) and (La-05), thus showing that soy and coconut vegetable milk-like ice creams provide a richer growth in amino acids and sugars for Bb12- and La-05 than cow’s milk. Thus, ice creams produced with plant extracts can be a good vehicle to deliver probiotic content [61].
Cow’s milk can be replaced by soy and coconut milk-like, and various combinations with cow’s milk for producing ice creams. The addition of vegetable milk-like increases the pH and decreased the melting rate, varying the viscosity and particle size [62].
In the development of technological products based on vegetable milk-like, the production of desserts with chocolate based on yams and rice, a difference on textural properties can be found. In comparison with products produced with animal milk such as brigadeiro (typical Brazilian desert), a difference in flavor is observed, due to the lack of some components of the plant extract, such as low-fat content. Among the chocolate desserts, the rice based presents a mild starchy flavor, which can end up providing less acceptance by consumers. The coconut-based dessert, with rice milk-like and yam, can stood out for its flavor and characteristics [63].
Curd can be developed by substituting cow’s milk for different plant extracts such as oats, rice, and almonds, adding Lactobacillus sp. Milk-like and curd from almond can be highly acceptable, with adequate pH and nutritious values. Milk-like and curd developed from plant sources may represent safe food as part of diet for people with lactose intolerance [64].
Yoghurt is an excellent probiotic source and because of that, when using a plant-based milk substitute for yoghurt production, it is interesting to consider whether such beverages are capable of maintain the minimum required of the population that are responsible for maintaining the probiotic characteristics of this type of product, as it was analyzed in a developed work, in which yogurts were produced using a vegetable beverages made from soy, rice and coconut. With the results achieved, it was possible to conclude that not only fermented beverages, but also non-fermented ones are able to supply and transport lactic acid bacteria and also other microorganisms, even without the fermentation process and subjecting the products to storage under refrigeration. Thus indicating that a yoghurt produced from a plant-based beverage is also capable of promoting desirable characteristics for consumers for this type of product, thus promoting a wider range of plant-based dairy derivatives also for consumers who are unfit to consume dairy derivatives from animal sources [65].
The effect of syneresis on yoghurt is very important for evaluating the final product. Because of this, it is very important to evaluate this property of a yoghurt elaborated with plant extract. Therefore, a work evaluated the effect of storing a yoghurt elaborated from a plant extract on the syneresis of yoghurt and concluded that this property increased as the storage time passed and that the syneresis is inversely related to the pH of the product, thus being in agreement with other related works [66].
As noted, soy is the most used food and cited in research to replace milk and the use of plant extracts. A work conducted a functional and physical analysis of a product fermented using soy extract, fermented with a probiotic culture of kefir, and with added soy fiber and the results concluded that the product elaborated with the addition of fiber was firmer and with a lower syneresis value when compared to the product elaborated without the added fiber. With this, the product can be considered as an alternative to the existing products on the market, since, in addition to presenting such advantages even at the end of the storage analysis period, it presented an adequate probiotic culture count to be considered as a functional product [67].
Currently, a greater demand for symbiotic products, that is, those in which there is a combination of probiotic and prebiotic means has been offered to consumers in order to benefit human health. Therefore, several studies have developed products with symbiotic characteristics also for consumers who are unable to consume animal milk, and therefore, they use plant extracts to produce such analogs.
A work developed a symbiotic oat-based beverage, produced with a probiotic culture (L.plantarum) and prebiotic product (inulin) in order to evaluate its physical–chemical properties and its probiotic survival. Therefore, the work concluded that the probiotic culture of the product remained with adequate values for food during its storage period and its physical–chemical analysis showed a product with a low fat content and a high content of dietary fiber, being then a healthy alternative to be presented to consumers [68].
6. Conclusions
Although animal milk is a very important food for the human diet due to its supply of essential nutrients, which in some cases are not found in other foods, the use of vegetable milk-like is a viable alternative to offer consumers who cannot or do not choose to consume animal milk.
Therefore, plant-based milk substitutes have gained market share, as they are beneficial to health due to the raw materials that are used in their production. However, in the development field, much research must still be carried out for the formulation its products, since there is still not much works mainly in the elaboration of vegetable milk based dairy derivatives, improving the offer and the consumption options for the population.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"functional food, lactose intolerance, cow protein milk allergy, veganism, bioactive compounds",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/73915.pdf",chapterXML:"https://mts.intechopen.com/source/xml/73915.xml",downloadPdfUrl:"/chapter/pdf-download/73915",previewPdfUrl:"/chapter/pdf-preview/73915",totalDownloads:641,totalViews:0,totalCrossrefCites:1,dateSubmitted:"August 4th 2020",dateReviewed:"October 14th 2020",datePrePublished:"November 4th 2020",datePublished:"March 24th 2021",dateFinished:"November 4th 2020",readingETA:"0",abstract:"The consumption of vegetable milk has grown in recent years. Medical reasons are some reasons for the increase in the number of consumers of this type of drink. Lactose intolerance and allergy to cow’s milk protein are the major factors that lead to this consumption in addition to the option for a healthier lifestyle, there are also consumers concerned with animal health and welfare who are adept at restrictive diets like vegetarianism and veganism. Vegetable extracts are water-soluble extracts from legumes, oilseeds, cereals, or pseudocereals that resemble bovine milk in appearance, are considered substitutes for cow’s milk due to the similar chemical composition and can also be used as substitutes for direct use or in some animal milk-based preparations. In contrast, these substitutes have different sensory characteristics, stability, and nutritional composition of cow’s milk. Plant extracts have health-beneficial compounds, phenolic compounds, unsaturated fatty acids, antioxidant activity and bioactive compounds such as phytosterols and isoflavones making plant-based milk substitutes an interesting choice.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/73915",risUrl:"/chapter/ris/73915",signatures:"Laís Zandona, Caroline Lima and Suzana Lannes",book:{id:"9722",type:"book",title:"Milk Substitutes",subtitle:"Selected Aspects",fullTitle:"Milk Substitutes - Selected Aspects",slug:"milk-substitutes-selected-aspects",publishedDate:"March 24th 2021",bookSignature:"Małgorzata Ziarno",coverURL:"https://cdn.intechopen.com/books/images_new/9722.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83880-768-9",printIsbn:"978-1-78985-637-8",pdfIsbn:"978-1-83880-769-6",isAvailableForWebshopOrdering:!0,editors:[{id:"303881",title:"Prof.",name:"Małgorzata",middleName:null,surname:"Ziarno",slug:"malgorzata-ziarno",fullName:"Małgorzata Ziarno"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"160554",title:"Dr.",name:"Suzana",middleName:"Caetano Da Silva",surname:"Lannes",fullName:"Suzana Lannes",slug:"suzana-lannes",email:"scslan@usp.br",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Sao Paulo",institutionURL:null,country:{name:"Brazil"}}},{id:"334091",title:"Ms.",name:"Laís",middleName:null,surname:"Zandona",fullName:"Laís Zandona",slug:"lais-zandona",email:"lais.zandona@usp.br",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Sao Paulo",institutionURL:null,country:{name:"Brazil"}}},{id:"334092",title:"Ms.",name:"Caroline",middleName:null,surname:"Lima",fullName:"Caroline Lima",slug:"caroline-lima",email:"carolinelima@usp.br",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Sao Paulo",institutionURL:null,country:{name:"Brazil"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Animal milk: processing and composition",level:"1"},{id:"sec_3",title:"3. Adverse reactions to milk composition",level:"1"},{id:"sec_4",title:"4. Plant-based milk alternatives",level:"1"},{id:"sec_5",title:"5. Use of milk substitutes in dairy products",level:"1"},{id:"sec_6",title:"6. Conclusions",level:"1"},{id:"sec_10",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'LU A. Dairy, Science, Society, and the Environment. Oxford Research Encyclopedia of Environmental Science. 2017; 1-64. DOI: 10.1093/acrefore/9780199389414.013.316'},{id:"B2",body:'Zimmermann P, Curtis, N. Breast milk microbiota: a review of the factors that influence composition. Journal of Infection. 2020; 81:17-47. DOI: 10.1016/j.jinf.2020.01.023'},{id:"B3",body:'Belitz H D, Grosch P, Schieberle P. Milk and Dairy Products. In: Belitz H D, Grosch P, Schieberle, P, editors. Food Chemistry. 4th ed. Heidelberg: Springer; 2008. p. 498-521. 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Nutritional composition of chickpea (Cicerarietinum-L) and value-added products. Indian Journal of Community Health. 2014; 26: 102-106'},{id:"B33",body:'Vanga S K, Raghavan V. How well do plant-based alternatives fare nutritionally compared to cow’s milk? Journal of Food Science and Technology. 2018; 55: 10-20. DOI: 10.1007/s13197'},{id:"B34",body:'Murugkar D A. Effect of sprouting of soybean on the chemical composition and quality of soymilk and tofu. Journal of Food Science and Technology. 2014; 51: 915-921. DOI: 10.1007/s13197-011'},{id:"B35",body:'Yousseef M, et. al. Fermentation of cow milk and/or pea milk mixtures by different starter cultures: Physico-chemical and sensorial properties. LWT – Food Science and Technology. 2016; 69: 430-437'},{id:"B36",body:'Khiabanian N O, et. al. Chemical, textural, rheological, and sensorial properties of wheyless feta cheese as influenced by replacement of milk protein concentrate with pea protein isolate. 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Potentially symbiotic fermented beverage with aqueous extracts of quinoa (Cheopodium quinoa Willd) and soy. Food Science and Technology International. 2014; 21: 403-415. DOI: 10.1177/1082013214540672'},{id:"B56",body:'Verni M, et. al. Design and Characterization of a Novel Fermented Beverage from Lentil Grains. Foods. 2020; 9: 1-13. DOI: 10.3390/foods9070893'},{id:"B57",body:'Savio J, et. al. Development and Structural Behavior of Soybean Gelato. Food Technology and Biotechnology. 2018; 56: 1-19. DOI: 10.17113/ftb.56.04.18.5710'},{id:"B58",body:'Wang S, Chelikani V, Serventi L. Evaluation of chickpea as alternative to soy in plant-based beverages, fresh and fermented. LWT – Food Science and Technology. 2018; 97: 570-572. DOI: 10.1016/j.lwt.2018.07.067'},{id:"B59",body:'Greis M, et. al. Dynamic texture perception in plant-based yogurt alternatives: Identifying temporal drivers of liking by TDS. Food Quality and Preference. 2020; 86: 1-10. 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DOI: 10.22533/at.ed.42420220115'},{id:"B64",body:'Rai S R, Pachisia J, Singh S. A Study on the Acceptability of Plant-Based Milk and Curd among the Lactose Intolerant People Residing in Kolkata. 2018; 8: 38-43'},{id:"B65",body:'Zareba D, Malgorzata Z. The viability of yoghurt bacteria in selected plant beverages. Zesyty Problemowe Postepów Nauk Rolniczych. 2017; 591: 87-96. DOI: 10.22630/ZPPNR.2017.591.46'},{id:"B66",body:'Panesar P S, Shind C. Effect of storage on Syneresis, pH, Lactobacillus acidophilus Count, Bifidobacterium bifidum count of Aloe vera fortified probiotic yoghurt. Current Research in Dairy Sciences. 2012; 4: 17-23. DOI: 10.3923/crds.2012.17.23'},{id:"B67",body:'Baú T R, Garcia S, Ida E I. Evaluation of a functional soy product with addition of soy fiber and fermented with probiotic kefir culture. Brazilian archives of biology and technology: an international journal. 2014; 57: 402-409. DOI: 10.1590/S1516-89132014005000005'},{id:"B68",body:'Wang C, et al. Physiochemical properties and probiotic survivability of symbiotic oat-based beverage. Food science and Biotechnology. 2017; 27: 735-743. DOI: 10.1007/s10068-017-0290-0'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Laís Zandona",address:null,affiliation:'
Pharmaceutical-Biochemical Technology Department, Pharmaceutical Sciences School, University of Sao Paulo, Sao Paulo, Brazil
Pharmaceutical-Biochemical Technology Department, Pharmaceutical Sciences School, University of Sao Paulo, Sao Paulo, Brazil
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Nahhas received his Master’s degree in Computer Engineering from Illinois Institute of Technology, Chicago, Illinois, USA in 1996 and a Ph.D. in Electrical Engineering (Electronics) from the University of Pittsburgh, Pittsburgh, Pennsylvania, USA in 2001. Professor Nahhas’s research has been centered on developing new photonic and electronic devices at micro and nano-scales involving various functional materials such as rare-earth-doped oxides, wideband gap semiconductors, and nanostructured materials. Prof. Nahhas’s research investigates epitaxial growth and fabrication of ZnO and GaN optical devices. Fabrication of MSM photo-detectors has been developed on a macro-scale area of wafer surface using a directed self-organization method and has been investigated as an interaction medium in optical, electrical, chemical, and biological domains. Surface-Plasmon phenomena occurring in structures are of particular interest since many novel properties can be derived from those and can be incorporated into an on-chip configuration for interaction. Professor Nahhas has participated in reviewing several academic articles and dissertations in the area of electrical, electronics, communications, control engineering, and e-learning. He is an official reviewer of journals such as the American Journal of Nanomaterials—USA, many International conferences, and holds the Associate Professor position at several Saudi Universities. Professor Nahhas served in many prestigious leading positions including Dean of the College of Engineering at Al-Lith, head of the Department, Vice Dean at Umm Al Qura University, Makkah. Currently, he works at the College of Engineering and Islamic Architecture, Umm Al Qura University, Makkah, Kingdom of\nSaudi Arabia.",institutionString:"Umm al-Qura University",institution:{name:"Umm al-Qura University",institutionURL:null,country:{name:"Saudi Arabia"}}}]},generic:{page:{slug:"OA-publishing-fees",title:"Open Access Publishing Fees",intro:"
The Open Access model is applied to all of our publications and is designed to eliminate subscriptions and pay-per-view fees. This approach ensures free, immediate access to full text versions of your research.
As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 140,000 international scientists and researchers.
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1,400 GBP Chapter - Edited Volume
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4,000 GBP Compacts Monograph - Short Form
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850 GBP Journal Article (Across Portfolio)
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Services included are:
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XML Typesetting and pagination - web (PDF, HTML) and print files preparation
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Discoverability - electronic citation and linking via DOI
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Permanent and unrestricted online access to your work
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For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
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Added Value of Publishing with IntechOpen
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Choosing to publish with IntechOpen ensures the following benefits:
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Indexing and listing across major repositories, see details ...
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Long-term archiving
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Visibility on the world's strongest OA platform
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Live Performance Metrics to track readership and the impact of your chapter
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Dissemination and Promotion
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Proven world leader in Open Access book publishing with over 10 years experience
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+5,700 OA books published
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Most competitive prices in the market
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Fully compliant with OA funding requirements
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Optimized processes that assure your research is made available to the scientific community without delay
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Personal support during every step of the publication process
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+184,650 citations in Web of Science databases
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Currently strongest OA platform with over 175 million downloads
As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 140,000 international scientists and researchers.
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The Open Access Publishing Fee (OAPF) is payable only after your book chapter, monograph or journal article is accepted for publication.
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OAPF Publishing Options
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1,400 GBP Chapter - Edited Volume
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850 GBP Chapter - Book Series Topic (Annual Volume)
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10,000 GBP Monograph - Long Form
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4,000 GBP Compacts Monograph - Short Form
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850 GBP Journal Article (Across Portfolio)
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During the launching phase journals do not charge an APC, rather they will be funded by IntechOpen.
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*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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Services included are:
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An online manuscript tracking system to facilitate your work
\n\t
Personal contact and support throughout the publishing process from your dedicated Author Service Manager
\n\t
Assurance that your manuscript meets the highest publishing standards
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English language copyediting and proofreading, including the correction of grammatical, spelling, and other common errors
\n\t
XML Typesetting and pagination - web (PDF, HTML) and print files preparation
\n\t
Discoverability - electronic citation and linking via DOI
\n\t
Permanent and unrestricted online access to your work
\n
\n\n
What isn't covered by the Open Access Publishing Fee?
\n\n
If your manuscript:
\n\n
\n\t
Exceeds the number of pages defined by the publishing guidelines, an additional fee per page may be required
\n\t
If a manuscript requires Heavy Editing or Language Polishing, this will incur additional fees.
\n
\n\n
Your Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
\n\n
Open Access Funding
\n\n
To explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at funders@intechopen.com for further details or assistance.
\n\n
For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
\n\n
Added Value of Publishing with IntechOpen
\n\n
Choosing to publish with IntechOpen ensures the following benefits:
\n\n
\n\t
Indexing and listing across major repositories, see details ...
\n\t
Long-term archiving
\n\t
Visibility on the world's strongest OA platform
\n\t
Live Performance Metrics to track readership and the impact of your chapter
\n\t
Dissemination and Promotion
\n
\n\n
Benefits of Publishing with IntechOpen
\n\n
\n\t
Proven world leader in Open Access book publishing with over 10 years experience
\n\t
+5,700 OA books published
\n\t
Most competitive prices in the market
\n\t
Fully compliant with OA funding requirements
\n\t
Optimized processes that assure your research is made available to the scientific community without delay
\n\t
Personal support during every step of the publication process
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+184,650 citations in Web of Science databases
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Currently strongest OA platform with over 175 million downloads
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The diverse and heterogeneous nature of such collaborations requires a common shared semantic understanding of the complex and dense information that may be generated by transportation systems in buildings. The Standard Elevator Information Schema (SEIS) provides this in a format which is both machine and human readable. The role of the schema is to provide the ‘vocabulary’ for these collaborations. At the same time the schema specifies the properties, relationships and validation rules that define the information model, which could form the foundation upon which all elements of building transportation control and monitoring functions are constructed. SEIS is published under the Collective Commons licence and is free to download and incorporate into any product with the objective of reaching the broadest audience. This chapter discusses the origins and features of SEIS and provides a varied set of example applications. 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He completed a one-year Post-Doctoral Fellowship awarded by the DFAIT (Foreign Affairs and International Trade Canada) at the Institute of Biomedical Engineering of the University of New Brunswick (Canada) in 2010. Currently, he is Professor in the Faculty of Electrical Engineering (UFU). He has authored and co-authored more than 200 peer-reviewed publications in Biomedical Engineering. He has been a researcher of The National Council for Scientific and Technological Development (CNPq-Brazil) since 2009. He has served as an ad-hoc consultant for CNPq, CAPES (Coordination for the Improvement of Higher Education Personnel), FINEP (Brazilian Innovation Agency), and other funding bodies on several occasions. He was the Secretary of the Brazilian Society of Biomedical Engineering (SBEB) from 2015 to 2016, President of SBEB (2017-2018) and Vice-President of SBEB (2019-2020). He was the head of the undergraduate program in Biomedical Engineering of the Federal University of Uberlândia (2015 - June/2019) and the head of the Centre for Innovation and Technology Assessment in Health (NIATS/UFU) since 2010. He is the head of the Postgraduate Program in Biomedical Engineering (UFU, July/2019 - to date). He was the secretary of the Parkinson's Disease Association of Uberlândia (2018-2019). Dr. Andrade's primary area of research is focused towards getting information from the neuromuscular system to understand its strategies of organization, adaptation and controlling in the context of motor neuron diseases. 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Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. 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He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"24",type:"subseries",title:"Computer Vision",keywords:"Image Analysis, Scene Understanding, Biometrics, Deep Learning, Software Implementation, Hardware Implementation, Natural Images, Medical Images, Robotics, VR/AR",scope:"The scope of this topic is to disseminate the recent advances in the rapidly growing field of computer vision from both the theoretical and practical points of view. Novel computational algorithms for image analysis, scene understanding, biometrics, deep learning and their software or hardware implementations for natural and medical images, robotics, VR/AR, applications are some research directions relevant to this topic.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11420,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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