Comparison between proton transfer reaction mass spectrometry andnear infrared spectroscopy for the authentication of Brazilian coffee: Apreliminary chemometric studyMonteiro, P. I., Santos, J. S., Alvarenga Brizola, V. R., Pasini Deolindo, C. T., Koot, A., Boerrigter-Eenling, R.,van Ruth, S., Georgouli, K., Koidis, A., & Granato, D. (2018). Comparison between proton transfer reaction massspectrometry and near infrared spectroscopy for the authentication of Brazilian coffee: A preliminarychemometric study. Food Control, 91, 276-283. https://doi.org/10.1016/j.foodcont.2018.04.009
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Manuscript Number: FOODCONT-D-18-00413
Title: COMPARISON BETWEEN PROTON TRANSFER REACTION MASS SPECTROMETRY AND
NEAR INFRARED SPECTROSCOPY FOR THE AUTHENTICATION OF BRAZILIAN COFFEE: A
PRELIMINARY CHEMOMETRIC STUDY
Article Type: Research Paper
Keywords: Specialty coffee; Organic; Chemometrics; Volatile organic
compounds; Spectroscopic methods; Authenticity; PTR-MS.
Corresponding Author: Professor Daniel Granato, Ph.D
Corresponding Author's Institution: State University of Ponta Grossa
First Author: pablo Monteiro
Order of Authors: pablo Monteiro; Janio Santos; Carolina Deolindo; saskia
van Ruth; alex koot; Rita Boerrigter-Eenling; vitor brizola; Anastasios
Koidis; Konstantia Georgouli; Daniel Granato, Ph.D
Abstract: In this study, proton-transfer reaction mass spectrometry (PTR-
MS) and near-infrared spectroscopy (NIRS) were compared for the
authentication of geographical and farming system origins of Brazilian
coffees. For this purpose, n=19 organic (ORG) and n=26 conventional
(CONV) coffees from distinct producing regions were analyzed. Overall,
differences (p<0.05) in 44 and 68 ion intensities were observed between
the main producing regions and farming systems, respectively. Principal
component analysis was not effective in illustrating differences between
the coffees according to the farming system or geographical origin using
neither PTR-MS nor NIRS data. However, when the PLS-DA was applied, which
produced the best performing models overall compared to several other
techniques, the farming system was adroitly differentiated. The fact that
the classification performance (> 80%) was independent of the data
acquisition method used gives NIRS an edge over PTR-MS in the
differentiation of the farming system because of its rapid analysis and
cost. Differentiating geographic location was more complex. The PTR-MS
calibration models showed slightly better PLS-DA classification rates
compared to the NIRS models (69% vs. 61%, respectively), which is even
more evident when the alternative classifier is used (LDA-kNN, 69% vs.
39%, respectively). Coffee samples from either Minas Gerais (MG) or Sao
Paulo (SP) were differentiated from the other regions.
Suggested Reviewers: Filomena Nazarro PhD
Istituto di Scienze dell'Alimentazione, CNR-IS
Expert in chemometrics, food technology and NIRS
Amin Mousavi Khaneghah PhD
University of Campinas
Expert in food analysis and statistical evaluation.
Seyed Mohammad Bagher Hashemi
Fasa University
Expert in chemometrics and food technology
February, 2018
Dear Dr. R. Greiner,
I am pleased to submit on behalf of my colleagues and myself the manuscript
“COMPARISON BETWEEN PROTON TRANSFER REACTION MASS
SPECTROMETRY AND NEAR INFRARED SPECTROSCOPY FOR THE
AUTHENTICATION OF BRAZILIAN COFFEE: A PRELIMINARY
CHEMOMETRIC STUDY” for your consideration for publication in Food
Control.
1. Corresponding Author: Daniel Granato ([email protected])
2. Significance of the manuscript: Among the analytical methods used to
evaluate the quality traits of coffee powders, proton-transfer reaction mass
spectrometry (PTR-MS) and near infrared spectroscopy (NIR) stand out. As
there is no single marker to assess the geographical and farming system origins
of high-value added foods, multiple rapid and low-cost untargeted fingerprinting
methods, such as PTR-MS and spectroscopic methods, are becoming more
popular to solve problems related to food authenticity. Therefore, the main
objectives of the present work were to characterize and classify Brazilian
coffees from ORG and conventional (CONV) farming systems, from distinct
producing regions, using PTR-MS and NIR. In addition, to date there is no
information about the discrimination of farming systems and producing region of
Brazilian coffees in the literature using PTR-MS and NIRS. Here we showed the
main differences between farming systems and producing regions. It is
important to note that the manuscript has not been submitted for consideration
in any other Journal.
Cover Letter
1
COMPARISON BETWEEN PROTON TRANSFER REACTION MASS SPECTROMETRY 1
AND NEAR INFRARED SPECTROSCOPY FOR THE AUTHENTICATION OF 2
BRAZILIAN COFFEE: A PRELIMINARY CHEMOMETRIC STUDY 3
4
Pablo Inocêncio Monteiro1, Jânio Sousa Santos1, Vitor Rafael Alvarenga Brizola2, Carolina 5
Turnes Pasini Deolindo2, Alex Koot3, Rita Boerrigter-Eenling3, Saskia van Ruth3,4, 6
Konstantia Georgouli5, Anastasios Koidis5, Daniel Granato1,2* 7
8
1Graduation Program in Food Science and Technology, 2Deparment of Food Engineering, 9
State University of Ponta Grossa. Av. Carlos Cavalcanti, 4748, 84030-900, Ponta Grossa, 10
Brazil. E-mail: [email protected] or [email protected] 11
3RIKILT, Wageningen University and Research, P.O. Box 230, 6700 AE, Wageningen, the 12
Netherlands. 13
4Food Quality and Design group, Wageningen University and Research, P.O. Box 17, 14
6700 AA, Wageningen, the Netherlands. 15
5Institute for Global Food Security, Queen’s University Belfast, Ireland, United Kingdom. 16
17
18
*ManuscriptClick here to view linked References
2
Abstract 19
In this study, proton-transfer reaction mass spectrometry (PTR-MS) and near-infrared 20
spectroscopy (NIRS) were compared for the authentication of geographical and farming 21
system origins of Brazilian coffees. For this purpose, n=19 organic (ORG) and n=26 22
conventional (CONV) coffees from distinct producing regions were analyzed. Overall, 23
differences (p<0.05) in 44 and 68 ion intensities were observed between the main 24
producing regions and farming systems, respectively. Principal component analysis was 25
not effective in illustrating differences between the coffees according to the farming system 26
or geographical origin using neither PTR-MS nor NIRS data. However, when the PLS-DA 27
was applied, which produced the best performing models overall compared to several 28
other techniques, the farming system was adroitly differentiated. The fact that the 29
classification performance (> 80%) was independent of the data acquisition method used 30
gives NIRS an edge over PTR-MS in the differentiation of the farming system because of 31
its rapid analysis and cost. Differentiating geographic location was more complex. The 32
PTR-MS calibration models showed slightly better PLS-DA classification rates compared 33
to the NIRS models (69% vs. 61%, respectively), which is even more evident when the 34
alternative classifier is used (LDA-kNN, 69% vs. 39%, respectively). Coffee samples from 35
either Minas Gerais (MG) or Sao Paulo (SP) were differentiated from the other regions. 36
37
Keywords: Specialty coffee; Organic; Chemometrics; Volatile organic compounds; 38
Spectroscopic methods; Authenticity; PTR-MS. 39
40
41
3
1. Introduction 42
Brazil was the largest producer (43.2%) and exporter of coffee in 2015, totaling 37.1 43
million 60 kg-sacks (US$ 6.16 billion) and is the second largest consumer of the beverage. 44
Brazilian coffees are recognized for their high organoleptic quality and the main importer 45
countries are the United States of America, Germany, Italy, Japan and Belgium (Brasil, 46
2016). Due to stresses in the global market, in the last 10 years, producers have started to 47
invest in organic farming because of the consumers’ demand for higher quality, and in 48
2016 there is an estimate of totaling 5,000 ha (~65,000 60 kg-sacks). The organic farming 49
is regulated both in European countries (i.e., EC 834/07 and EC 889/08) and in Brazil (law 50
number 10831 from 23rd December 2003) to ensure that consumers buy authentic 51
products. As coffee has a considerable high price in the international market, especially 52
organic coffee, this commodity needs to be constantly analyzed for authenticity tracing its 53
origin is an important task (Granato, Carrapeiro, Fogliano, & van Ruth, 2016). A method 54
for a rapid and/or accurate determination of authenticity of coffee would be of help for 55
producers and importers alike. For instance, the assessment of its odor components has 56
been successfully used for that purpose (Özdestan et al., 2013; Yener et al., 2015; Colzi et 57
al., 2017). 58
Among the analytical methods used to evaluate the quality traits of ground coffee, 59
proton-transfer reaction mass spectrometry (PTR-MS) and near infrared spectroscopy 60
(NIRS) emerge (Bertone et al., 2016; Danezis et al., 2016). The advantage of PTR-MS 61
and NIRS over chromatographic techniques (i.e., GC-MS or GC-FID) is that they are 62
nondestructive techniques that do not require extensive sample preparation, thus multiple 63
items may be assessed in a workday. Moreover, both techniques are highly sensitive and, 64
therefore, represent suitable alternatives to trace a product’s authenticity, typicality, and 65
geographical origin (de Toledo et al., 2017; Correia et al., 2018). 66
4
In the literature, the use of PTR-switching reagent ion-MS coupled with 67
chemometrics was reported to classify coffee brews and ground coffees from Brazil, 68
Ethiopia, Guatemala, Costa Rica, Colombia, and India (Yener et al., 2015). However, there 69
is no information available in the literature regarding the analysis of volatile organic 70
compounds (VOCs) of specialty Brazilian coffees produced in different regions with the 71
use of distinct farming systems. In addition, the use of multivariate statistical methods has 72
the ability to look at minute differences between samples, which is suited for such subtle 73
changes that are expected for ground coffee (Tavares et al., 2016). Therefore, the use of a 74
rapid and sensitive analytical method coupled with appropriate data analysis seems to be 75
a suitable approach for authentication purposes. 76
As there is no single marker to assess the geographical and farming system origins 77
of high-value added foods, multiple rapid and accurate untargeted fingerprinting methods, 78
such as PTR-MS and spectroscopic methods, are becoming more popular to solve 79
problems related to food authenticity (Santos, Lopo, Rangel, & Lopes, 2016; Botelho, 80
Oliveira, & Franca, 2017). According to Black, Chevallier, and Elliott (2016) and van Ruth, 81
Huisman, and Luning (2017), the adulteration of food products has grown considerably in 82
the last years, with all foods susceptible, especially specialty coffees because of their high 83
commercial value. Therefore, the main objectives of the present work were to characterize 84
and to authenticate Brazilian coffees from organic (ORG) and conventional (CONV) 85
farming systems and from distinct producing regions, using NIRS and PTR-MS. 86
87
2. Material and Methods 88
2.1 Coffee samples 89
A total of 45 authentic coffee samples were acquired in Curitiba/PR, Brazil, and 90
belonged to the following classes: n=19 ORG and n=26 CONV coffees. These samples 91
were produced in different Brazilian states: Minas Gerais (MG; n=13), São Paulo (SP; 92
5
n=11), Paraná (PR; n=8), Espírito Santo (ES; n=3), and Bahía (BA; n=2). Some 93
commercial blends of PR/MG/SP (n=1), MG/SP (n=6), PR/ES/Roraima (RO) (n=1) were 94
also included in the study. ORG samples were certified by third parties (IBD, TECPAR, 95
and/or Demeter) and all brands have the seal from the Brazilian Ministry of Agriculture, 96
Livestock, and Supply (MAPA) and from the Brazilian Association of Coffee Industries 97
(ABIC). Coffees belonged to Coffea arabica (n=41) or blends of Coffea arabica/C. 98
canephora var. robusta (concentration of each variety was not informed; n=4). Because of 99
the ‘not informed’ proportion of Coffea arabica/C. canephora var. robusta in those (n=4) 100
samples, the effect of the coffee species was not investigated in the current study. 101
All roasted beans were provided by the manufacturers that have certificate of 102
traceability (geographical origin or cultivation system) provided by the Brazilian Association 103
of Coffee Producers (ABIC) and beans were manufactured in each factory using the 104
‘medium roast’ technique (processing at 210-220 oC for 10-13 min). Coffee beans were 105
ground in an analytical mill (QUIMIS-6298A21) to obtain a sample size of 60 Tyler mesh. 106
107
2.2 Proton-transfer reaction mass spectrometry (PTR-MS) 108
VOCs in coffee samples were analyzed by PTR-MS in the range of m/z 20-200 109
(mass detection rate of 0.2 s/mass) using the same experimental conditions and 110
equipment (Ionicon Analytik, Innsbruck, Austria) described earlier (Granato, Koot, & van 111
Ruth, 2015). For the headspace analysis, 0.50 g of coffee was inserted in a 250 mL glass 112
flask and temperature was equilibrated at 40°C/30 min in a water bath, following the 113
procedures recommended by Özdestan et al. (2013). VOCs were analyzed via headspace 114
using five complete mass scans (the 1st and 5th scans were discarded), three times (one 115
analysis per day), in three independent samples. All of the measurements were carried out 116
under drift tube conditions (120–140 Td, Td = Townsend; 1 Td = 10−17 V cm2 mol−1) over 117
the mass range and a dwell time of 0.2 s/mass, giving a cycle time of about 40 s. For each 118
6
measurement of coffee samples, VOCs in the headspace of an empty glass flask served 119
as blank. Masses m/z 32 and m/z 37 were removed from the data set (oxygen and water 120
clusters, respectively) and mass ion intensities were converted to volume mixing ratio 121
(ppbv) values using the principles outlined by Hansel et al. (1995). 122
123
2.3 Near infrared spectroscopy (NIRS) 124
NIRS is a spectroscopic method that uses the near-infrared region of the 125
electromagnetic spectrum (from about 750 to 2500 nm) to study vibrational frequencies of 126
chemical bonds, enabling one to identify functional groups present in a matrix. In the 127
current study, NIRS spectra were recorded by means of a Buchi NIR-Flex system (BÜCHI 128
Labortechnik GmbH, Essen, Germany) equipped with a holder for a small Petri dish 129
(diameter of 35 mm) over the NIRS range of 800 – 2500 nm with a 2.5 nm sampling 130
interval in the reflectance mode. Measurements were taken on 0.5-1 cm-thick portion 131
(about 2.5 g), evenly distributed into Petri dishes. All the NIRS measurements were 132
performed in duplicate for each sample on two independent samples, totaling 4 133
results/sample. The final spectra that were used for chemometric modeling were the 134
average of those replicates. 135
136
2.4 Statistical analysis 137
2.4.1 Spectral data pre-treatment 138
The NIR spectral data initially contained 2051 variables resulting from spectral 139
acquisition from 819.67 to 2500 nm. The visible and shortwave part (400-1098 nm) of the 140
spectra were cut and then Standard Normal Variance (SNV) (Barnes, Dhanoa and Lister, 141
1989), detrending (Barnes, Dhanoa and Lister, 1989) and Savitzky-Golay smoothing 142
(polynomial order=2, frame size=9) (Savitzky and Golay, 1964) were applied to the signal 143
resulting in 1275 pre-processed variables. The PTR-MS mass spectrometry data were 144
7
used without preprocessing (in total 177 variables) after removing the values for m/z 32 145
and m/z 37. 146
147
2.4.2 Exploratory data analysis 148
Two discrimination factors were used to explore the data: a) the farming system of 149
the coffee where samples grouped into two classes (ORG and CONV) and b) the 150
producing regions. In the latter case, samples were distributed in five subclasses 151
corresponding to four distinct coffee producing areas of Brazil: Minas Gerais (n=13); São 152
Paulo (n=11); combined São Paulo and Minas Gerais (n=6), Paraná (n=8) and one 153
additional group (OTHER, n=7) that includes the rest of the regions in the dataset. 154
Especially for PTR-MS data, inferential analysis aiming to compare the VOCs (m/z) 155
ORG and CONV coffees as well as samples from the four main regions (Minas Gerais, 156
São Paulo, Paraná, and Espírito Santo) was performed according to the procedures 157
outlined by Granato, de Araújo Calado, and Jarvis (2014). Probability values below 5% 158
were used to highlight differences between farming systems or between producing 159
regions. For both PTR-MS and NIRS data, principal component analysis (PCA) was also 160
performed according to standard practices to explore the data structure and look for 161
obvious clustering (Nunes et al., 2015; Margraf et al., 2016). 162
163
2.4.3 Classification of coffees 164
Several supervised classification methods were employed in the dataset (partial 165
least squares – discriminant analysis, PLS-DA; soft independent modeling of class 166
analogy, SIMCA; k-nearest neighbors, kNN;, principal component analysis coupled with 167
kNN, PCA-kNN; linear discriminant analysis coupled with kNN, LDA-kNN; Support Vector 168
Machines, SVM, and LDA-SVM with multiple configuration variables). The best performing 169
methods presented here were PLS-DA and LDA-kNN. The latter technique first uses LDA 170
8
to reduce the dimensionality of the data and then classifies into the define groups non-171
parametrically using the best k factor (Georgouli, Martinez Del Rincon & Koidis, 2017). 172
Two validation procedures were performed to estimate the classification models: one 173
validation step was carried out with an arbitrary dataset (validation set) consisting of 30% 174
of the all spectral data and containing samples from all groups in proportion (while 70% of 175
the data is left for calibration of the model) and another validation with venetian blinds 176
cross validation, where 1/7th of all data in each iteration (7 splits) predicted with the 177
remaining 6/7th of the samples. The results were evaluated using the correct classification 178
rate also known as accuracy (%). Specifically, in cross validation procedure, the mean 179
correct classification rate and the standard deviation over these iterations are the main 180
evaluation metrics. All chemometric data preprocessing was performed by means of in–181
house Matlab routines (The MathWorks Inc., USA). 182
183
3. Results and Discussion 184
3.1 Exploratory data analysis 185
PTR-MS mean data are shown in Figure 1. In a general view, ORG samples had 186
higher ion intensities. Using inferential analysis (one-way ANOVA/unpaired t-test), a total 187
of 68 ion intensities were found to be different (p<0.05) between farming systems in the 188
m/z 20-200 (Supplementary material). In a study with organic and conventional coffees 189
marketed in the Netherlands, Özdestan et al. (2013) found seventeen ions out of the range 190
m/z 26–200 showed significant differences between organic and regular coffees from 191
various locations (Americas, Asia, and Africa). These differences may be due to the 192
various biosynthesis pathways regulators in response to biotic interactions and 193
environmental/agronomical factors employed in the ORG system, such as low sun 194
exposure, differences between soil type, chemical composition, and nutritional status 195
compared to CONV and ORG produce (Dudareva et al., 2013). Two other hypotheses 196
9
arise: i) The samples come from commercial companies and, as such, it is known that 197
different roasting history of coffee may result in distinct volatile composition and that may 198
have interfered with the results; ii) During roasting, many compounds may be decomposed 199
because of the high temperature, so the ideal analytical approach to know the exact 200
compounds in our samples would be the use of GC-FID or GC-MS or even PTR–TOF–MS 201
(Colzi et al., 2017). PTR-MS data provide relative abundance values for selected ions (m/z 202
20-200) but PTR-MS cannot distinguish isobaric compounds, therefore PTR-MS should be 203
used as an untargeted method to characterize complex and different food samples. 204
Coffees from Minas Gerais and Espírito Santo had the highest ion intensities as 205
compared to the other regions (Figure 1). Differences (p<0.05) in 44 ion intensities were 206
observed between the main Brazilian producing regions (Table 1 - Supplementary 207
material) and most differences were located in m/z<121. These results are mainly related 208
to the terroir effect, that is, differences in geomorphology, soil type and its 209
chemical/biological conditions, and climate conditions, especially temperature, sunlight 210
exposure, humidity, and irrigation between regions (Muilwijk et al., 2015). These results 211
also suggest that the sunlight exposure, water stress, and temperature play an important 212
role in the biosynthesis of specific compounds as the samples from Paraná (the southern 213
state) had the lowest mean ion intensities. Unlike PTR-MS data, analyzing the NIRS 214
spectrum of coffees from different geographical locations (Figure 2B) it is not possible to 215
establish a pattern, indicating that a complex statistical data treatment is required for these 216
results (see below). 217
Similarly to what was observed for PTR-MS, NIRS data showed it is not so easy to 218
differentiate the farming systems (Figure 2A). Indeed, CONV coffee has a higher intensity 219
in the range 800 – 1100 nm, which may be related to the degree of roasting (Maillard and 220
caramelization reactions) at high temperatures. Similarly, ORG coffee presented a higher 221
absorbance in the 1400-1600 nm range, which might be related to the presence of 222
10
carbohydrates. The region between 1200 and 1450 nm was already shown to be related to 223
differences between C. arabica and C canephora coffees (Correia et al., 2018), but in 224
Figures 2A and 2B, the differences in absorbance between coffees from distinct locations 225
and farming systems are negligible. The band in the 1400–1500 nm region refers to the 226
first overtone of the OH functional group of chlorogenic acid isomers present in coffees, 227
which are the main phenolic compounds in coffee. For instance, ORG samples (Figure 2A) 228
and coffee produced in Paraná state (Figure 2B) have a higher absorbance (Workman & 229
Weyer, 2008). 230
Principal component analysis (PCA) as an unsupervised pattern recognition 231
technique was also applied for exploratory purposes to the PTR-MS and to the NIRS 232
datasets for both classification criteria separately (Figure 3). The first three principal 233
components (PCs) that compose the PCA scatter plot explain the majority of the variation 234
observed in the data (>95% in all cases). The mapping of the samples indicated that there 235
is no clear differentiation of the groups using either the producing systems (Figure 3A, B) 236
or the producing regions (Figure 3C, D). In other words, there is no evident pattern in the 237
spectral data from the two techniques (PTR-MS and NIRS) both in terms of farming 238
systems or producing regions. Similarly, another hypothesis is plausible: the first 239
dimensions may be explained by the roasting process (the exact temperature and time 240
each coffee lot was subjected to) or brand, while the provenance and farming systems 241
may be explained in higher dimensions (projection of the samples in a more complex 242
structure). Our results support the findings obtained by Colzi et al. (2017) who used PTR-243
TOF-MS (m/z 20-210) coupled with PCA aiming to distinguish Arabica and Robusta 244
coffees: two principal components were able to explain more than 94% of data variability 245
and partial-least squares-discriminant analysis classified 100% of coffee samples based 246
on their taxonomic category. From this perspective, it is evident that the differentiation of 247
11
coffee from taxonomic categories is a better strategy than the differentiation between 248
farming systems and producing regions. 249
250
3.3 Classification of coffees 251
Table 1 and 2 show the classification results obtained using the two discrimination 252
factors, namely farming system (two systems: organic and conventional) and the 253
producing region of coffee samples (5 geographic locations of Brazil, see material and 254
methods) for PTR-MS and NIRS data, respectively. The tables provide both the average 255
accuracy and the sensitivity per group for all the groups of the chemometric models. 256
Firstly, in terms of overall accuracy, the model performance with PTR-MS data is almost 257
identical to the performance with NIRS data which is rather unusual taking into account 258
that NIRS spectroscopy is a rapid method, more accessible, and less chemical 259
‘information-heavy’ compared to the PTR-MS analysis (Colzi et al., 2017). In particular, 260
when the PLS-DA is applied, which produces the best performing models overall, the 261
classification rate is exactly the same for prediction of the farming systems with results 262
only varying slightly according to the validation method (arbitrary chosen dataset vs cross 263
validation) used. Nonlinear classification methods, such as SVM, did not produce good 264
classification results (>50%, detailed data not shown) which indicates that the both the 265
mass and the spectral data are rather linear and linear classifiers (LDA, PLS-DA, LDA-266
kNN) work very well. This is due to the nature of the chemometric data (Osorio, Haughey, 267
Elliott, & Koidis, 2015). Results showed that prediction of the conventional and organic as 268
farming systems of coffees is relatively better due to the very good chemometric models 269
produced independently of the data acquisition method used (PTR-MS and NIRS) 270
compared to the prediction of the geographic origin within Brazil. 271
Using NIRS and PLS-DA (Table 2), all ORG coffee samples were discriminated, but 272
the classification results were fair different when LDA-kNN was applied to NIRS data. It 273
12
has to be highlighted, both PLS-DA and LDA-kNN cannot perform very well in case of 274
large imbalances in the sample size of the classes which cause a biased estimation of the 275
class membership towards the class with the most members (Berrueta, Alonso-Salces & 276
Héberger, 2007). Although the classes are mostly balanced in this problem (19 ORG vs 26 277
CONV) it appears that there is less variance in the organic class (the chemical 278
composition of these samples proved to be more standardized) and more 279
variance/variability can be found in the conventional coffee class where multiple types of 280
processed coffees are included from a wide variety of sources/locations. 281
The prediction of the geographic origin is obviously a more difficult classification 282
problem due the relatively higher number of classes is involved (5 vs. 2) and therefore a 283
drop in model’s performance is expected compared to the first problem. Apart from the 284
lower overall performance, the PTR-MS calibration models have slightly better PLS-DA 285
classification rates compared to that of NIRS models (69% vs. 61%, respectively), which is 286
even more evident when the alternative classifier is used (LDA-kNN, 69% vs. 39%, 287
respectively). The cross-validated rates are significantly lower than their non-cross 288
validated counterparts and consistent with the trends mentioned above which is again 289
expected. However, they provide a better picture of the models’ performance especially if 290
the standard deviation is taken into account. 291
Coffee samples from either Minas Gerais (MG) or São Paulo (SP) were more 292
discriminative, and thus better predicted, than samples from other Brazilian regions using 293
PTR-MS data. In these cases, PTR-MS had a slight advantage in prediction accuracy over 294
NIRS. These results reiterate that NIRS accuracy is equally good as PTS-MS, indicating a 295
clear advantage of NIRS as the preferred screening technique. Using PTR-MS data, all 296
samples from Minas Gerais were adroitly classified using both the PLS-DA and LDA-kNN 297
classifiers. Coffee from Paraná state seems to be better distinguished from the other 298
regions using NIRS and PLS-DA (100% accuracy), while coffee samples from Minas 299
13
Gerais were again well distinguished from the other regions using NIRS coupled with LDA-300
kNN (100% accuracy). The blends of coffees from São Paulo and Minas Gerais were not 301
differentiated neither using PTR-MS nor NIRS measurements (accuracy below 70%). It is 302
important to note that each Brazilian state has similar dimensions to those of other 303
countries: Minas Gerais (586,522 km2; Spain), Bahia (564,733 km2; France), São Paulo 304
(248,222 km2; United Kingdom), Paraná (199,307 km2; Senegal), and Espírito Santo 305
(46,095 km2; Switzerland). Therefore, any classification methods proposed for Brazilian 306
coffees may be compared to other classification methods for intraregional areas in Europe. 307
Additionally, this work is the first report in the literature comparing the VOCs of coffees 308
coming from the main producing regions in Brazil. This characterization may help the 309
sector to propose flavor compounds unique to certain regions and establish the typicality 310
of Brazilian coffee. 311
According to Botelho, Oliveira, and Franca (2017), the coffee origin has become 312
increasingly relevant for the producers, since it allows the consumers to relate the singular 313
characteristics of their preferred product to its respective provenance. These authors 314
evaluated the feasibility of fluorescence spectroscopy to differentiate the geographical 315
origin of Brazilian coffees cultivated in micro regions of Minas Gerais state (Cerrado 316
Mineiro, Matas de Minas, North, and South) and observed that Unfolded Partial Least 317
Squares with Discriminant Analysis (UPLS-DA) was able to discriminate coffees from 318
Cerrado Mineiro and those from the South. N-way partial least squares - discriminant 319
analysis (NPLS-DA) and Parallel Factor Analysis (PARAFAC) were not able to classify the 320
analyzed coffees using fluorescence spectroscopy. 321
In comparison to the results obtained here, De Luca et al. (2016) evaluated the 322
effects of coffee variety (C. arabica or C. robusta) and roasting time on NIRS and high-323
performance liquid chromatography (HPLC) profiles of coffee beans. Authors used PLS-324
DA to build classification models aiming to authenticate the samples and verified that PLS-325
14
DA resulted in 100% of correct classification of Arabica and 95% correct classification of 326
Robusta coffees, concluding that chemometrics may be used to authenticate the varietal 327
origin of coffee. Özdestan et al. (2013) tried to n = 110 differentiate specialty market 328
coffees (CONV, ORG, espresso, and Kopi Luwak coffee) based on headspace PTR-MS 329
data and PLS-DA and verified that 98% ORG (n = 42 out of 43) and 95% CONV (n = 63 330
out of 67) coffees were correctly classified. Additionally, PTR-TOF-MS was used to identify 331
the main VOCs of those coffees. Authors concluded that PTR-MS data coupled with 332
chemometrics is a promising strategy to authenticate the farming system used to cultivate 333
coffees. 334
335
Conclusions 336
Having screened several multivariate classification techniques, the most efficient 337
classification model for the prediction of the farming system of the coffee samples could be 338
developed with either PTR-MS or NIRS data using the typical band of the spectra and the 339
PLS-DA classifier. Our results indicate that the NIRS classification model - which is much 340
simpler to develop and deploy - can provide equally good results (up to 89% prediction 341
success) with less instrumentation complexity and at a reduced cost. For the prediction of 342
the geographic location, a PTR-MS model can provide some confidence (approx. 70% 343
correct classification) due to the complexity of the analytical problem with NIRS following 344
closely. More coffee samples should be included in the study in order to build a robust 345
authentication analytical method based on NIRS and PTR-MS. In order to give more 346
resolution to the chemometric model and represent emerging coffee farming systems, the 347
biodynamic coffee class can be introduced and characterized with enough authentic 348
samples and fresh approaches to model design such as data augmentation (Georgouli et 349
al. 2018). Moreover, our approach may be used to assess the farming system and 350
15
geographical origins of other high-value added foods especially if it is further validated 351
with additional samples. 352
353
Acknowledgements 354
Authors thank Coordination for the Improvement of Higher Education Personnel, 355
CAPES, for one M.Sc scholarship (P.M. I.), and Fundação Araucária for one PhD 356
scholarship (J. S. S.) and The Brazilian National Council for Scientific and Technological 357
Development, CNPq, for a productivity grant (process number 303188/2016-2) and a B.Sc 358
grant (C. T. P. Deolindo). 359
360
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442
FIGURE CAPTIONS 443
444
Figure 1: PTR-MS fingerprinting of Brazilian coffees from different farming systems (A and 445
B) and distinct producing regions (C). 446
447
Figure 2: Near infrared spectra (NIRS) of Brazilian coffee powders according to the 448
farming systems (A) and main producing regions (B). 449
450
19
Figure 3. Principal Components Analysis (PCA) scatter plots of all coffee samples (n=45) 451
with the PTR-MS (3A, 3C) and NIRS (3B, 3D) variables. Different colors represent different 452
discrimination factors: producing system (in 3A, 3B) and producing locations (3C, 3D). 453
454
HIGHLIGHTS
Brazilian coffee from different origins and farming systems were
assessed by PTR-MS and NIRS
68 ion intensities were found to be different between organic and
conventional coffees
Differences in 44 ion intensities were observed between the main
producing regions in Brazil
Coffee samples from either Minas Gerais or São Paulo were more
discriminative
GRAPHICAL ABSTRACT
*Highlights (for review)
A1
Figure
A2
// // //
A3
A4
A5 // // //
A6
A
Figure
B
(A) (B)
(C) (D)
Figure
Table 1: Classification rate for the discrimination of farming systems and
producing regions of Brazilian coffee using PTR-MS data.
Discrimination factor
PLS-DA PLS-DA (c.v.) LDA-KNN LDA-KNN (c.v.)
ORG 80 58 80 63
CONV 87 77 87 85
Total 85 69 ± 18 85 76 ± 16
MG 100 62 100 69
SP+MG 50 33 50 67
SP 67 54 67 46
PR 50 38 50 25
Other 50 43 50 52
Total 69 49 ± 30 69 53 ± 22 1 Takes into account both average sensitivity and average precision. All other values
present the sensitivity (%) of the particular class or group (i.e. number of class member
correctly predicted / total number of class members).
Table
Table 2: Classification rate for the discrimination of farming systems and
producing regions of Brazilian coffee using NIRS data.
Discriminating factor
PLS-DA PLS-DA (c.v.) LDA-KNN LDA-KNN (c.v.)
ORG 100 84 60 37
CONV 75 92 87 77
Total 85 89 ± 12 77 60 ± 11
MG 50 62 100 39
SP+MG 50 33 0 0
SP 67 36 0 82
PR 100 75 50 13
Other 50 29 0 0
Total 62 49 ± 29 39 33 ± 12 1 Takes into account both average sensitivity and average precision. All other values
present the sensitivity (%) of the particular class or group (i.e. number of class member
correctly predicted / total number of class members).
Table
Table 1: Ion intensities (given in ppbv) measured by proton-transfer reaction mass spectrometry of Brazilian coffees from different
cultivation systems and producing regions.
m/z Cultivation systems Producing regions
Organic (n=19) Conventional (n=26) p-Value Minas Gerais (n=13) Espírito Santo (n=3) São Paulo (n=11) Paraná (n=8) p-Value
26 0.2±0.1 0.2±0.1 0.857 0.3±0.1 0.3±0.0 0.2±0.1 0.2±0.1 0.240
27 6.6±1.5 6.8±1.0 0.635 7.4±1.0 6.1±0.9 6.4±1.0 6.0±1.2 0.001
28 0.7±0.2 0.6±0.2 0.099 0.6±0.2 0.7±0.1 0.6±0.1 0.6±0.1 0.029
29 57.3±12.4 56.7±8.0 0.852 60.4±8.3 54.5±2.0 54.1±5.8 52.4±11.3 0.004
30 31.3±11.0 30.5±7.6 0.791 32.1±9.1 36.7±1.1 30.1±5.2 24.7±8.5 0.012
31 20.4±4.3 18.2±2.5 0.031 18.4±2.3 17.0±2.0 18.3±3.1 19.0±4.0 0.038
33 4456.9±1152.8 3734.0±665.6 0.011 3713.4±512.3 3307.8±271.1 3921.9±925.6 3945.3±1093.0 0.043
34 53.7±14.5 44.3±8.2 0.009 43.7±6.3 39.9±3.4 46.8±11.9 47.6±13.1 0.038
35 9.5±2.7 7.9±1.6 0.016 8.0±1.1 6.9±0.5 8.2±2.1 8.3±2.4 0.034
36 0.1±0.1 0.1±0.1 0.278 0.1±0.1 0.1±0.0 0.2±0.1 0.1±0.1 0.050
38 0.5±0.2 0.7±0.3 0.016 0.7±0.3 0.3±0.2 0.8±0.2 0.4±0.2 0.004
39 54.3±9.9 49.7±6.4 0.062 52.3±7.1 49.1±8.9 48.6±6.0 49.8±9.9 0.030
40 2.5±0.5 2.4±0.3 0.210 2.5±0.4 2.4±0.2 2.2±0.4 2.5±0.6 0.201
41 180.3±31.6 163.9±19.9 0.038 170.4±22.1 162.8±30.0 161.8±21.0 166.0±30.6 0.044
42 33.4±20.5 29.8±15.3 0.498 35.3±20.0 27.9±3.0 27.4±13.4 25.8±5.3 0.525
43 5999.5±1764.2 6248.4±1268.1 0.584 6731.5±1159.9 6837.7±360.9 6027.2±1011.5 4838.2±1376.9 0.013
44 143.5±43.5 149.0±30.7 0.623 161.4±29.1 163.5±8.1 143.2±24.4 114.7±33.0 0.013
Table
45 314.0±70.7 280.0±42.3 0.051 293.1±44.0 260.6±45.2 281.2±52.4 283.6±74.3 0.093
46 9.7±2.1 8.8±1.1 0.060 9.2±1.3 8.6±1.1 8.5±1.5 8.5±2.0 0.037
47 320.1±214.5 431.7±206.2 0.085 509.4±269.0 424.4±76.5 377.9±108.3 225.9±158.7 0.059
48 4.2±2.8 5.4±2.5 0.147 6.4±3.3 5.4±1.0 4.8±1.3 2.8±1.9 0.063
49 1.9±1.0 2.4±1.0 0.116 2.8±1.3 2.3±0.3 2.1±0.6 1.5±0.7 0.084
50 0.1±0.1 0.1±0.1 0.940 0.1±0.1 0.1±0.1 0.1±0.1 0.1±0.1 0.261
51 12.2±3.3 9.8±2.2 0.005 9.7±1.8 9.1±1.2 10.5±2.8 10.3±3.1 0.021
52 0.4±0.1 0.4±0.1 0.019 0.4±0.1 0.4±0.0 0.4±0.1 0.4±0.1 0.034
53 30.9±5.7 28.7±3.8 0.141 30.5±4.5 29.4±5.1 27.7±3.7 28.1±4.6 0.022
54 1.6±0.4 1.4±0.3 0.058 1.5±0.3 1.4±0.3 1.5±0.3 1.3±0.4 0.371
55 38.8±7.9 37.0±7.6 0.457 39.7±9.0 33.8±4.8 37.1±6.3 33.7±7.5 0.187
56 2.9±0.7 2.7±0.5 0.107 2.9±0.6 2.5±0.4 2.7±0.6 2.6±0.6 0.236
57 577.7±115.2 553.2±69.3 0.380 584.0±71.8 545.9±28.1 540.2±57.4 512.6±105.4 0.031
58 21.2±4.3 20.5±2.4 0.472 21.5±2.4 20.3±2.0 19.9±2.0 19.1±3.8 0.024
59 120.7±33.7 109.8±26.2 0.228 117.4±28.5 99.6±26.2 111.8±31.4 101.3±30.2 0.146
60 14.1±3.0 13.0±1.9 0.161 13.5±2.3 13.0±1.4 12.7±2.0 12.4±2.9 0.032
61 6949.9±2272.1 7475.7±1680.3 0.377 8188.7±1455.4 8376.4±201.3 7096.6±1298.3 5520.2±1895.9 0.007
62 175.7±61.3 190.7±44.9 0.349 209.7±39.6 213.9±5.1 179.9±35.2 137.5±49.0 0.005
63 33.2±11.9 35.9±8.2 0.369 39.3±7.3 40.3±1.4 34.2±6.6 25.8±8.7 0.005
64 0.7±0.3 0.8±0.2 0.310 0.9±0.2 0.9±0.1 0.7±0.1 0.6±0.3 0.031
65 1.4±0.6 1.6±0.6 0.282 1.8±0.8 1.7±0.2 1.5±0.4 1.1±0.4 0.223
66 0.1±0.1 0.1±0.0 0.057 0.1±0.0 0.1±0.0 0.1±0.0 0.1±0.1 0.332
67 9.2±2.1 7.9±1.3 0.015 8.4±1.4 7.7±1.9 7.8±1.6 8.3±2.1 0.075
68 81.9±19.6 69.7±10.2 0.009 72.3±11.8 65.0±10.1 69.2±11.2 73.8±16.5 0.014
69 459.4±82.6 428.4±59.7 0.150 454.3±70.7 440.8±83.9 412.8±57.9 418.6±72.3 0.078
70 22.9±4.1 21.3±3.0 0.141 22.4±3.7 21.9±3.9 20.6±2.9 20.9±3.6 0.108
71 98.9±20.2 87.8±11.8 0.025 92.3±13.2 86.5±14.8 86.6±13.5 87.8±19.3 0.036
72 4.8±1.0 4.2±0.6 0.020 4.5±0.7 4.0±0.7 4.1±1.0 4.2±0.9 0.014
73 171.4±56.5 198.1±87.1 0.250 227.5±104.4 142.0±41.7 191.7±50.3 140.5±62.3 0.145
74 16.3±4.1 16.0±4.8 0.806 17.5±6.2 13.7±1.1 15.2±2.4 14.7±4.7 0.314
75 976.8±216.1 953.6±135.6 0.661 1007.4±133.9 939.7±26.7 927.3±103.2 858.4±200.2 0.054
76 34.7±7.8 33.7±4.8 0.584 35.7±4.7 33.1±1.0 32.9±3.7 30.3±7.2 0.053
77 4.8±1.1 4.6±0.7 0.399 4.9±0.7 4.6±0.2 4.6±0.6 4.2±1.0 0.015
78 0.2±0.1 0.2±0.1 0.264 0.2±0.1 0.2±0.1 0.2±0.1 0.2±0.1 0.670
79 12.2±2.9 10.7±1.4 0.021 11.1±1.6 11.1±1.7 10.8±2.1 10.6±2.5 0.059
80 444.1±178.5 315.0±82.1 0.002 327.3±84.8 268.2±78.4 334.9±134.6 394.6±192.2 0.089
81 2224.7±401.6 2082.3±284.1 0.170 2172.8±328.1 2143.5±394.2 2017.8±269.6 2032.1±341.8 0.058
82 137.5±25.3 128.6±17.8 0.175 134.0±20.2 133.0±26.1 124.0±16.4 125.9±21.5 0.042
83 54.6±10.0 51.7±6.3 0.251 52.6±6.9 56.5±6.3 50.3±7.4 50.3±8.8 0.036
84 4.3±0.9 3.9±0.5 0.057 4.0±0.7 4.0±0.8 3.9±0.6 3.8±0.9 0.179
85 88.9±17.8 87.8±9.1 0.784 90.6±9.6 94.4±6.4 84.1±10.5 80.0±13.4 0.006
86 7.1±1.5 6.4±0.7 0.068 6.7±0.8 6.6±0.7 6.4±1.0 6.2±1.3 0.036
87 584.3±137.4 479.0±98.5 0.004 492.2±97.3 466.6±74.7 474.9±97.8 567.1±185.7 0.136
88 29.6±7.3 24.1±5.9 0.007 24.8±5.7 23.5±4.0 23.7±5.7 29.0±10.2 0.183
89 109.6±23.2 97.2±12.8 0.027 103.4±14.2 97.7±11.6 94.6±14.9 96.4±21.4 0.022
90 5.1±1.1 4.5±0.6 0.018 4.8±0.6 4.5±0.4 4.3±0.7 4.4±1.0 0.023
91 3.1±0.6 2.7±0.5 0.020 3.0±0.6 3.0±0.2 3.0±0.4 2.7±0.3 0.073
92 0.3±0.1 0.2±0.1 0.003 0.2±0.1 0.2±0.1 0.2±0.1 0.3±0.1 0.092
93 2.1±0.5 1.9±0.3 0.019 2.0±0.3 2.0±0.5 2.0±0.3 2.0±0.5 0.258
94 6.9±3.0 4.7±1.7 0.003 4.9±1.6 3.9±1.3 4.9±2.2 6.5±3.6 0.103
95 165.3±45.8 137.4±25.7 0.013 145.0±28.8 121.4±35.4 140.0±34.6 149.0±46.7 0.099
96 28.8±5.9 27.4±4.3 0.354 29.0±4.8 29.0±7.2 26.5±3.8 25.4±5.1 0.046
97 344.1±70.0 312.1±44.3 0.068 326.5±49.5 303.5±53.3 309.4±45.8 310.3±68.2 0.048
98 28.5±5.7 25.7±3.4 0.046 26.8±3.6 24.9±4.2 25.5±4.0 25.9±5.7 0.067
99 159.5±27.6 149.3±19.3 0.153 157.2±22.8 155.0±26.1 144.5±20.3 144.8±25.0 0.114
100 12.6±2.4 11.4±1.6 0.044 12.1±2.0 11.6±2.2 11.0±2.0 11.5±2.3 0.068
101 166.2±36.2 148.2±21.4 0.043 156.1±25.2 144.9±23.9 147.7±28.5 145.3±32.6 0.062
102 10.4±2.3 9.1±1.5 0.023 10.0±2.0 9.0±2.0 9.1±2.0 9.2±2.1 0.131
103 149.5±25.1 141.2±19.9 0.221 146.0±21.0 162.4±19.6 139.6±19.6 129.8±16.4 0.023
104 8.5±1.4 8.1±1.3 0.311 8.4±1.4 9.3±1.0 8.0±1.0 7.4±1.0 0.014
105 3.9±0.7 3.5±0.5 0.023 3.6±0.6 3.4±0.8 3.4±0.5 3.5±0.5 0.058
106 0.9±0.3 0.7±0.2 0.007 0.7±0.2 0.7±0.2 0.7±0.2 0.8±0.2 0.031
107 6.9±1.4 6.2±0.9 0.038 6.5±1.0 5.9±1.5 6.1±0.7 6.7±1.5 0.165
108 7.8±3.3 5.6±2.6 0.018 5.6±1.8 4.8±1.7 5.7±2.1 8.0±5.1 0.216
109 150.9±40.8 126.1±24.6 0.015 131.0±25.9 113.3±36.1 127.1±26.2 138.8±45.5 0.089
110 59.5±10.4 54.0±8.0 0.050 55.3±9.3 53.8±12.1 53.6±7.3 55.8±10.4 0.203
111 282.0±50.7 260.6±40.6 0.124 269.7±41.2 255.5±68.4 255.9±30.9 261.5±52.8 0.104
112 23.1±4.4 21.2±3.2 0.101 21.6±3.3 20.5±4.8 20.8±2.5 21.5±4.3 0.062
113 75.9±14.3 70.9±9.4 0.157 74.1±10.9 70.9±14.3 69.3±11.2 69.7±12.2 0.134
114 8.4±1.9 7.2±1.2 0.019 7.4±1.4 73.3±1.5 7.1±1.3 7.7±1.8 0.066
115 33.8±6.9 30.2±4.4 0.039 31.5±5.0 28.6±5.2 29.8±5.2 31.2±6.8 0.098
116 3.1±0.7 3.0±0.4 0.065 3.0±0.5 2.6±0.3 2.8±0.5 2.9±0.7 0.044
117 52.1±10.4 46.8±7.4 0.055 49.4±8.4 46.0±9.6 45.3±7.6 47.1±9.2 0.069
118 3.6±0.6 3.3±0.4 0.042 3.4±0.5 3.4±0.7 3.1±0.4 3.3±0.6 0.041
119 1.9±0.4 1.7±0.3 0.211 1.8±0.3 1.6±0.3 1.7±0.2 1.7±0.3 0.393
120 1.6±0.6 1.3±0.4 0.034 1.3±0.4 1.1±0.4 1.3±0.2 1.7±0.6 0.039
121 12.4±2.7 11.1±1.9 0.052 11.2±1.9 10.6±3.4 10.7±1.5 12.1±2.8 0.095
122 7.0±1.6 6.0±1.1 0.021 6.1±1.4 5.8±1.7 6.0±0.7 6.8±2.0 0.214
123 79.1±20.3 66.3±13.2 0.014 68.0±14.4 62.1±21.3 66.0±11.3 74.7±22.3 0.081
124 43.3±7.3 38.6±5.0 0.014 39.8±5.9 37.7±8.7 38.5±4.1 40.7±7.3 0.115
125 49.0±9.0 42.6±6.5 0.009 42.8±6.9 41.5±10.8 43.0±6.0 48.6±10.0 0.196
126 10.6±2.2 9.5±1.4 0.042 9.5±1.7 8.9±2.4 9.4±1.3 10.1±2.0 0.140
127 85.9±18.5 78.7±13.3 0.132 80.6±16.5 83.6±22.0 77.3±13.6 81.7±16.1 0.388
128 10.3±2.2 9.1±1.7 0.035 9.2±1.8 9.6±2.7 9.0±1.7 9.9±2.3 0.233
129 17.7±3.6 18.4±2.2 0.407 19.0±2.7 19.0±1.5 17.8±2.6 16.7±3.4 0.140
130 1.9±0.4 1.8±0.3 0.176 1.8±0.4 1.9±0.3 1.7±0.3 1.9±0.4 0.256
131 10.6±2.1 9.6±1.4 0.077 10.0±1.5 9.8±1.9 9.4±1.6 9.6±2.0 0.147
132 1.0±0.2 0.9±0.2 0.037 0.9±0.1 0.9±0.2 0.8±0.2 0.9±0.2 0.167
133 2.0±0.4 1.8±0.4 0.128 1.8±0.4 1.6±0.4 1.8±0.3 2.0±0.6 0.431
134 1.6±0.4 1.4±0.4 0.153 1.4±0.3 1.5±0.5 1.4±0.2 1.6±0.5 0.591
135 9.2±2.2 7.8±1.5 0.015 7.8±1.7 7.5±2.2 7.9±1.2 8.7±2.2 0.156
136 5.6±1.2 5.0±0.9 0.041 5.0±0.9 5.0±1.5 4.9±0.7 5.6±1.4 0.318
137 40.7±9.5 35.0±6.6 0.022 35.4±7.7 34.1±10.3 35.0±5.2 37.7±9.2 0.143
138 17.9±3.4 16.0±2.5 0.031 16.2±2.8 16.2±3.6 15.9±2.4 17.3±3.4 0.309
139 24.2±4.4 21.0±3.2 0.007 21.5±3.7 20.8±5.6 21.5±3.4 22.9±4.5 0.355
140 5.4±1.1 4.6±0.8 0.005 4.7±0.9 4.5±1.4 4.7±0.8 5.0±1.1 0.153
141 35.0±6.2 30.6±5.0 0.011 31.4±5.4 30.4±9.6 30.7±4.6 33.4±6.9 0.370
142 6.0±1.1 5.1±0.8 0.002 5.3±1.0 5.0±1.3 5.2±0.8 5.6±1.1 0.168
143 7.3±1.6 6.5±1.0 0.039 6.7±1.2 6.963±1.7 6.4±1.3 6.8±1.5 0.502
144 1.2±0.3 1.1±0.2 0.177 1.1±0.3 1.2±0.2 1.2±0.2 1.2±0.3 0.454
145 2.9±0.6 3.0±0.4 0.710 3.0±0.4 3.0±0.4 2.9±0.5 2.8±0.6 0.301
146 0.4±0.1 0.4±0.1 0.582 0.4±0.1 0.4±0.1 0.4±0.1 0.4±0.1 0.678
147 2.2±0.4 2.0±0.4 0.044 1.9±0.5 2.1±0.6 2.0±0.3 2.0±0.4 0.304
148 5.4±0.9 4.8±0.8 0.030 4.9±1.1 4.7±1.0 4.9±0.5 5.3±1.0 0.597
149 4.9±0.9 4.4±0.8 0.107 4.4±0.8 4.3±0.9 4.4±0.4 4.8±1.2 0.318
150 1.9±0.5 1.8±0.4 0.187 1.7±0.4 1.8±0.5 1.7±0.3 1.9±0.6 0.401
151 16.1±3.0 14.5±2.3 0.048 14.7±2.7 14.2±2.9 14.4±2.4 14.4±2.7 0.173
152 6.2±1.2 5.5±1.0 0.029 5.5±1.2 5.5±1.3 5.6±0.9 5.7±1.2 0.472
153 19.4±3.9 16.5±3.2 0.009 16.6±3.4 16.7±5.0 16.8±3.0 18.6±4.8 0.382
154 3.1±0.7 2.6±0.6 0.021 2.6±0.7 2.5±0.7 2.6±0.5 3.0±0.9 0.287
155 6.4±1.6 5.3±1.3 0.021 5.3±1.3 5.3±1.1 5.5±1.1 6.4±2.3 0.505
156 1.6±0.4 1.4±0.3 0.010 1.4±0.4 1.2±0.3 1.4±0.2 1.5±0.4 0.137
157 2.9±0.6 2.7±0.6 0.251 2.7±0.7 2.7±0.9 2.7±0.4 2.8±0.8 0.885
158 0.7±0.1 0.6±0.2 0.188 0.6±0.2 0.6±0.1 0.6±0.1 0.7±0.2 0.288
159 1.2±0.3 1.3±0.3 0.191 1.3±0.3 1.6±0.6 1.2±0.2 1.1±0.2 0.159
160 0.6±0.2 0.4±0.1 0.008 0.5±0.2 0.4±0.1 0.4±0.1 0.6±0.3 0.116
161 1.2±0.3 1.1±0.2 0.090 1.1±0.3 1.1±0.3 1.2±0.2 1.2±0.3 0.243
162 1.3±0.3 1.2±0.3 0.037 1.1±0.3 1.2±0.4 1.1±0.2 1.4±0.4 0.261
163 1.8±0.4 1.5±0.3 0.004 1.4±0.3 1.6±0.4 1.5±0.3 1.8±0.5 0.096
164 1.0±0.3 0.9±0.2 0.078 1.0±0.3 1.0±0.4 0.9±0.2 1.0±0.2 0.644
165 2.1±0.6 1.7±0.5 0.019 1.7±0.5 1.8±0.3 1.7±0.4 2.1±0.7 0.613
166 1.6±0.4 1.5±0.3 0.051 1.4±0.3 1.4±0.4 1.6±0.2 1.5±0.4 0.552
167 3.2±0.8 2.9±0.6 0.157 2.9±0.8 3.3±0.9 2.9±0.7 2.9±0.8 0.775
168 0.7±0.2 0.6±0.2 0.038 0.6±0.2 0.7±0.1 0.6±0.2 0.8±0.3 0.316
169 1.9±0.6 1.6±0.5 0.049 1.6±0.5 1.5±0.3 1.7±0.5 1.9±0.8 0.777
170 0.5±0.1 0.4±0.1 0.042 0.4±0.1 0.4±0.1 0.5±0.2 0.5±0.2 0.806
171 1.3±0.4 1.5±0.3 0.025 1.0±0.3 1.0±0.3 1.0±0.3 1.3±0.5 0.312
172 0.2±0.1 0.2±0.1 0.578 0.2±0.1 0.3±0.1 0.2±0.1 0.2±0.1 0.191
173 0.4±0.1 0.4±0.1 0.235 0.4±0.1 0.4±0.1 0.4±0.1 0.4±0.1 0.642
174 0.1±0.1 0.1±0.1 0.648 0.1±0.0 0.1±0.1 0.1±0.1 0.1±0.1 0.794
175 0.5±0.1 0.4±0.1 0.157 0.5±0.1 0.4±0.2 0.5±0.1 0.5±0.1 0.895
176 0.6±0.2 0.6±0.2 0.594 0.6±0.2 0.7±0.4 0.5±0.1 0.5±0.1 0.011
177 0.4±0.2 0.4±0.1 0.280 0.3±0.1 0.5±0.1 0.4±0.1 0.4±0.1 0.068
178 0.2±0.1 0.2±0.1 0.041 0.2±0.1 0.1±0.1 0.2±0.1 0.2±0.1 0.480
179 0.7±0.2 0.6±0.3 0.134 0.6±0.2 0.6±0.1 0.6±0.2 0.7±0.4 0.982
180 0.5±0.2 0.6±0.1 0.287 0.6±0.1 0.7±0.2 0.5±0.2 0.6±0.1 0.681
181 0.5±0.2 0.5±0.1 0.564 0.5±0.1 0.5±0.3 0.5±0.1 0.5±0.2 0.308
182 0.2±0.1 0.2±0.1 0.337 0.1±0.1 0.1±0.0 0.2±0.1 0.2±0.1 0.490
183 0.5±0.1 0.4±0.2 0.213 0.4±0.2 0.5±0.2 0.5±0.2 0.4±0.2 0.995
184 0.1±0.1 0.1±0.0 0.018 0.1±0.1 0.1±0.0 0.1±0.0 0.1±0.0 0.486
185 0.2±0.1 0.2±0.1 0.251 0.2±0.1 0.2±0.1 0.2±0.1 0.2±0.1 0.904
187 0.1±0.1 0.1±0.1 0.315 0.1±0.1 0.1±0.1 0.1±0.1 0.1±0.0 0.543
189 0.1±0.1 0.2±0.1 0.978 0.2±0.1 0.1±0.1 0.1±0.0 0.2±0.1 0.085
190 0.1±0.1 0.1±0.1 0.088 0.1±0.0 0.1±0.1 0.1±0.0 0.1±0.1 0.150
191 0.3±0.1 0.3±0.1 0.688 0.2±0.1 0.3±0.2 0.2±0.1 0.3±0.1 0.314
192 0.1±0.0 0.1±0.1 0.665 0.1±0.1 0.1±0.1 0.1±0.0 0.1±0.1 0.707
193 0.2±0.1 0.2±0.1 0.181 0.2±0.1 0.2±0.1 0.2±0.1 0.2±0.1 0.691
194 0.1±0.1 0.1±0.0 0.973 0.1±0.1 0.1±0.0 0.1±0.1 0.1±0.1 0.783
195 0.1±0.1 0.1±0.1 0.553 0.1±0.0 0.1±0.1 0.1±0.1 0.1±0.1 0.498
196 0 0.1±0.0 0.382 0.1±0.1 0 0.1±0.0 0.1±0.0 0.673
197 0.1±0.1 0.1±0.1 0.520 0.1±0.1 0.1±0.1 0.1±0.0 0.1±0.0 0.839
199 0.1±0.1 0.1±0.1 0.474 0.1±0.1 0.1±0.0 0.1±0.1 0.1±0.0 0.816
Note: Probability values obtained by Stydent-t test for independent samples (n=2 groups) or one-way ANOVA (n≥3 groups)
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