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Model performance of partial least squares in utilizing the visible spectroscopy data for estimation of algal biomass in a photobioreactor
Accepted Manuscript Rapid estimation of algae biomass in a photobioreactor by means of visible spectroscopy and the statistical method of Partial Least Squares Remedios Martínez-Guijarro, Maria Pachés, Jose Ferrer, Aurora Seco
PII: DOI: Reference:
S2352-1864(17)30036-6 https://doi.org/10.1016/j.eti.2018.01.005 ETI 193
To appear in:
Environmental Technology & Innovation
Received date : 7 February 2017 Revised date : 22 November 2017 Accepted date : 13 January 2018 Please cite this article as: Martínez-Guijarro R., Pachés M., Ferrer J., Seco A., Rapid estimation of algae biomass in a photobioreactor by means of visible spectroscopy and the statistical method of Partial Least Squares. Environmental Technology & Innovation (2018), https://doi.org/10.1016/j.eti.2018.01.005 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Rapid estimation of algae biomass in a photobioreactor by means of visible spectroscopy
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Abstract
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Keywords
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1. Introduction
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2. Materials and Methods
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2.1.Experimental start up and sample collection
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3. Results and discussion
187
3.1.Microalgae culture composition
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3.2.Statistical PLS modelling
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4. Conclusions
250
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Acknowledgements
261
&'10#1# 0!&.0-(#!2& 1 ##,13..-02#" 72&#. ,'1# 0!&-3," 2'-, .0-(#!21
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264
References
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41 &1;;-9 9;1A <4;1=)91);-)8897)+0;7-:;15);-+747<9897,<+16/
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Figure 1: " ! %$" #%"" #!$ !" " ! ! '# # #! ! '# !# Figure 2: !# # #"# Figure 3:# !' #" Figure 4: (a) $! ! # $! "#& Scenedesmus " "!% %!"$" !# !# #"#b) S# !'!"$" !#%!" Figure 5: # #"# $!! # $! "#& Scenedesmus" "!%%!"$" !# Figure 6: "#& Scenedesmus" "!% !# #%# " "
0.5445
1.2536
1.6127
1.6733
1.4564
1.5504
1.4593
1.4626
1.4711
1.3900
1.4792
1.7203
1.4191
1.4310
1.4182
1.4084
1.4060
1.4148
1.4033
1.3928
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T14
T15
T16
T17
T21
T22
T23
T24
T25
1.3946
1.4054
1.4165
1.4078
1.4097
1.4193
1.4322
1.4208
1.7149
1.4805
1.3916
1.4723
1.4637
1.4604
1.5491
1.4574
1.6688
1.6095
1.2593
0.5482
0.4114
0.2210
0.2215
0.4112
401
400
T1
Wavelength (400-750 nm) T0
Tables
SAMPLES
1.3967
1.4073
1.4180
1.4090
1.4109
1.4204
1.4335
1.4212
1.7080
1.4817
1.3932
1.4730
1.4644
1.4605
1.5481
1.4580
1.6633
1.6062
1.2653
0.5522
0.4118
0.2207
402 0.1780 0.3111 0.4142 1.0622 1.4136 1.5490 1.4570 1.5498 1.4742 1.4667 1.4781 1.4226 1.4841 1.6212 1.4327 1.4277 1.4100 1.3507 1.3279 1.3521 1.3166 1.2855
… … … … … … … … … … … … … … … … … … … … … … 1.2687
1.3013
1.3380
1.3129
1.3386
1.4018
1.4198
1.4260
1.6084
1.4764
1.4132
1.4712
1.4605
1.4690
1.5437
1.4481
1.5334
1.3932
1.0398
0.4048
0.3082
0.1764
501
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
0.9115
0.9536
0.9994
0.9635
1.0191
1.1241
1.1426
1.1489
1.2254
1.2169
1.0325
1.2196
1.2292
1.2616
1.2878
1.1502
1.0934
0.9533
0.6501
0.2604
0.2364
0.1451
600
Matrix of predictive data
1.2510
1.2851
1.3231
1.2967
1.3255
1.3924
1.4114
1.4182
1.5944
1.4675
1.4022
1.4634
1.4531
1.4632
1.5370
1.4380
1.5167
1.3713
1.0164
0.3955
0.3053
0.1748
502
0.9160
0.9581
1.0039
0.9680
1.0236
1.1286
1.1472
1.1537
1.2307
1.2218
1.0369
1.2246
1.2340
1.2660
1.2928
1.1549
1.0983
0.9578
0.6532
0.2615
0.2363
0.1453
601
0.9206
0.9627
1.0087
0.9727
1.0284
1.1334
1.1520
1.1584
1.2365
1.2267
1.0416
1.2298
1.2390
1.2708
1.2980
1.1599
1.1036
0.9626
0.6566
0.2628
0.2362
0.1454
602
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
0.0341
0.0676
0.0806
0.0671
0.0938
0.0856
0.1051
0.0837
0.1418
0.1447
0.0950
0.1315
0.1293
0.1534
0.1475
0.1358
0.1219
0.1112
0.0744
0.0570
0.1372
0.1022
747
Table 1. Matrix of raw data of Calibration data set. (X-variables and Y-variables).
500
…
Predictors (absorbances), X-variables
0.0326
0.0660
0.0791
0.0656
0.0922
0.0841
0.1037
0.0824
0.1401
0.1429
0.0936
0.1298
0.1276
0.1518
0.1460
0.1343
0.1205
0.1103
0.0738
0.0566
0.1365
0.1018
748
0.0312
0.0645
0.0777
0.0641
0.0908
0.0827
0.1024
0.0811
0.1386
0.1411
0.0920
0.1282
0.1259
0.1502
0.1444
0.1328
0.1190
0.1094
0.0731
0.0560
0.1358
0.1013
750
Responses
Matrix response data
9.99E+09
1.07E+10
1.09E+10
1.01E+10
1.15E+10
1.08E+10
1.09E+10
1.05E+10
1.04E+10
9.97E+09
9.01E+09
1.09E+10
1.04E+10
8.81E+09
8.39E+09
6.45E+09
6.45E+09
6.56E+09
1.82E+09
8.43E+08
2.04E+08
1.26E+08
Y-variables
Scenedesmus, Cell L-1
Comp
R2X
R2X
R2Y
Eigen.
R2Y
(cum)
Q2
Q2
limit
(cum)
PRESS
(cum)
1
0.786
0.786
17.3
0.625
0.625
0.526
0.05
0.526
4.69668E8
2
0.132
0.918
2.9
0.214
0.839
0.484
0.05
0.755
2.08529E8
3
0.0399
0.957
0.879
0.0713
0.91
0.292
0.05
0.827
1.64453E8
4
0.0283
0.986
0.623
0.0638
0.974
0.644
0.05
0.938
7.25648E7
5
0.00765
0.993
0.168
0.0115
0.985
0.31
0.05
0.957
5.4538E7
6
0.00252
0.996
0.0554
0.00494
0.99
0.143
0.05
0.964
5.32291E7
7
0.00317
0.999
0.0698
0.00177
0.992
0.105
0.05
0.967
2.78644E7
8
0.000264
0.999
0.00581
0.00171
0.994
-0.251
0.05
0.964
3.70488E7
9
0.000105
1
0.00232
0.00168
0.995
-0.492
0.05
0.961
4.95326E7
10
0.000124
1
0.00273
0.00094
0.996
-0.228
0.05
0.957
6.95879E7
Table 2. Partial Least Square (PLS) results.
Source
Sum of Squares
Df
Mean Square
F-ratio
P-Value
Model
1.9379E10
7
2.76842E9
249.351
0.0
Residual
1.55435E8
14
1.11025E7
Total (corr.)
1.95344E10
21
Table 3. Analysis of Variance for Y-data (PLS model).
The microalgae biomass needs to be controlled in a batch culture It has been applied a spectroscopy technique combined with Chemometrics methods A good lineal adjusted between cell density observed vs. predicted is obtained A PLS model to assess microalgae biomass is feasible, quick and reliable tool
PII: DOI: Reference:
S2352-1864(17)30036-6 https://doi.org/10.1016/j.eti.2018.01.005 ETI 193
To appear in:
Environmental Technology & Innovation
Received date : 7 February 2017 Revised date : 22 November 2017 Accepted date : 13 January 2018 Please cite this article as: Martínez-Guijarro R., Pachés M., Ferrer J., Seco A., Rapid estimation of algae biomass in a photobioreactor by means of visible spectroscopy and the statistical method of Partial Least Squares. Environmental Technology & Innovation (2018), https://doi.org/10.1016/j.eti.2018.01.005 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
1
Rapid estimation of algae biomass in a photobioreactor by means of visible spectroscopy
2
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Abstract
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Keywords
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("0- *& $!(-+ 11!1-0.2(-,1.$"20 02( *$ 12 /3 0$1.'-2-!(-0$ "2-0 Scenedesmus1.
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28
1. Introduction
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2. Materials and Methods
91
2.1.Experimental start up and sample collection
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2.2.Analytical techniques
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3. Results and discussion
187
3.1.Microalgae culture composition
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248
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249
4. Conclusions
250
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251
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252
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253
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254
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256
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257
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258
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259
" '*72- 1130#2& 22&#.0-!#11-$5 12#5 2#020# 2+#,2'1! 00'#"-32!-00#!2*7
260
Acknowledgements
261
&'10#1# 0!&.0-(#!2& 1 ##,13..-02#" 72&#. ,'1# 0!&-3," 2'-, .0-(#!21
262
," 5&-1#13..-02'1%0 2#$3**7
263
!),-5*#"%#"
264
References
265
41 &1;;-9 9;1A <4;1=)91);-)8897)+0;7-:;15);-+747<9897,<+16/
266
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267
,71
268
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269
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270
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271
,71 2>);9-:
272
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273
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274
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297
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299
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301
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310
311
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313
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316
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318
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Figure 1: " ! %$" #%"" #!$ !" " ! ! '# # #! ! '# !# Figure 2: !# # #"# Figure 3:# !' #" Figure 4: (a) $! ! # $! "#& Scenedesmus " "!% %!"$" !# !# #"#b) S# !'!"$" !#%!" Figure 5: # #"# $!! # $! "#& Scenedesmus" "!%%!"$" !# Figure 6: "#& Scenedesmus" "!% !# #%# " "
0.5445
1.2536
1.6127
1.6733
1.4564
1.5504
1.4593
1.4626
1.4711
1.3900
1.4792
1.7203
1.4191
1.4310
1.4182
1.4084
1.4060
1.4148
1.4033
1.3928
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T14
T15
T16
T17
T21
T22
T23
T24
T25
1.3946
1.4054
1.4165
1.4078
1.4097
1.4193
1.4322
1.4208
1.7149
1.4805
1.3916
1.4723
1.4637
1.4604
1.5491
1.4574
1.6688
1.6095
1.2593
0.5482
0.4114
0.2210
0.2215
0.4112
401
400
T1
Wavelength (400-750 nm) T0
Tables
SAMPLES
1.3967
1.4073
1.4180
1.4090
1.4109
1.4204
1.4335
1.4212
1.7080
1.4817
1.3932
1.4730
1.4644
1.4605
1.5481
1.4580
1.6633
1.6062
1.2653
0.5522
0.4118
0.2207
402 0.1780 0.3111 0.4142 1.0622 1.4136 1.5490 1.4570 1.5498 1.4742 1.4667 1.4781 1.4226 1.4841 1.6212 1.4327 1.4277 1.4100 1.3507 1.3279 1.3521 1.3166 1.2855
… … … … … … … … … … … … … … … … … … … … … … 1.2687
1.3013
1.3380
1.3129
1.3386
1.4018
1.4198
1.4260
1.6084
1.4764
1.4132
1.4712
1.4605
1.4690
1.5437
1.4481
1.5334
1.3932
1.0398
0.4048
0.3082
0.1764
501
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
0.9115
0.9536
0.9994
0.9635
1.0191
1.1241
1.1426
1.1489
1.2254
1.2169
1.0325
1.2196
1.2292
1.2616
1.2878
1.1502
1.0934
0.9533
0.6501
0.2604
0.2364
0.1451
600
Matrix of predictive data
1.2510
1.2851
1.3231
1.2967
1.3255
1.3924
1.4114
1.4182
1.5944
1.4675
1.4022
1.4634
1.4531
1.4632
1.5370
1.4380
1.5167
1.3713
1.0164
0.3955
0.3053
0.1748
502
0.9160
0.9581
1.0039
0.9680
1.0236
1.1286
1.1472
1.1537
1.2307
1.2218
1.0369
1.2246
1.2340
1.2660
1.2928
1.1549
1.0983
0.9578
0.6532
0.2615
0.2363
0.1453
601
0.9206
0.9627
1.0087
0.9727
1.0284
1.1334
1.1520
1.1584
1.2365
1.2267
1.0416
1.2298
1.2390
1.2708
1.2980
1.1599
1.1036
0.9626
0.6566
0.2628
0.2362
0.1454
602
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
0.0341
0.0676
0.0806
0.0671
0.0938
0.0856
0.1051
0.0837
0.1418
0.1447
0.0950
0.1315
0.1293
0.1534
0.1475
0.1358
0.1219
0.1112
0.0744
0.0570
0.1372
0.1022
747
Table 1. Matrix of raw data of Calibration data set. (X-variables and Y-variables).
500
…
Predictors (absorbances), X-variables
0.0326
0.0660
0.0791
0.0656
0.0922
0.0841
0.1037
0.0824
0.1401
0.1429
0.0936
0.1298
0.1276
0.1518
0.1460
0.1343
0.1205
0.1103
0.0738
0.0566
0.1365
0.1018
748
0.0312
0.0645
0.0777
0.0641
0.0908
0.0827
0.1024
0.0811
0.1386
0.1411
0.0920
0.1282
0.1259
0.1502
0.1444
0.1328
0.1190
0.1094
0.0731
0.0560
0.1358
0.1013
750
Responses
Matrix response data
9.99E+09
1.07E+10
1.09E+10
1.01E+10
1.15E+10
1.08E+10
1.09E+10
1.05E+10
1.04E+10
9.97E+09
9.01E+09
1.09E+10
1.04E+10
8.81E+09
8.39E+09
6.45E+09
6.45E+09
6.56E+09
1.82E+09
8.43E+08
2.04E+08
1.26E+08
Y-variables
Scenedesmus, Cell L-1
Comp
R2X
R2X
R2Y
Eigen.
R2Y
(cum)
Q2
Q2
limit
(cum)
PRESS
(cum)
1
0.786
0.786
17.3
0.625
0.625
0.526
0.05
0.526
4.69668E8
2
0.132
0.918
2.9
0.214
0.839
0.484
0.05
0.755
2.08529E8
3
0.0399
0.957
0.879
0.0713
0.91
0.292
0.05
0.827
1.64453E8
4
0.0283
0.986
0.623
0.0638
0.974
0.644
0.05
0.938
7.25648E7
5
0.00765
0.993
0.168
0.0115
0.985
0.31
0.05
0.957
5.4538E7
6
0.00252
0.996
0.0554
0.00494
0.99
0.143
0.05
0.964
5.32291E7
7
0.00317
0.999
0.0698
0.00177
0.992
0.105
0.05
0.967
2.78644E7
8
0.000264
0.999
0.00581
0.00171
0.994
-0.251
0.05
0.964
3.70488E7
9
0.000105
1
0.00232
0.00168
0.995
-0.492
0.05
0.961
4.95326E7
10
0.000124
1
0.00273
0.00094
0.996
-0.228
0.05
0.957
6.95879E7
Table 2. Partial Least Square (PLS) results.
Source
Sum of Squares
Df
Mean Square
F-ratio
P-Value
Model
1.9379E10
7
2.76842E9
249.351
0.0
Residual
1.55435E8
14
1.11025E7
Total (corr.)
1.95344E10
21
Table 3. Analysis of Variance for Y-data (PLS model).
The microalgae biomass needs to be controlled in a batch culture It has been applied a spectroscopy technique combined with Chemometrics methods A good lineal adjusted between cell density observed vs. predicted is obtained A PLS model to assess microalgae biomass is feasible, quick and reliable tool