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Article
Development of a Colorimetric Sensor Array for the Discrimination of Chinese Liquors Based on Selected Volatile Markers Determined by GC-MS Jun-jie Li, chun-xia Song, Junchang Hou, Danqun Huo, cai-
hong Shen, Xiaogang Luo, mei Yang, and huan-bao Fa
J. Agric. Food Chem., Just Accepted Manuscript ? DOI: 10.1021/jf503345z ? Publication Date (Web): 07 Oct 2014
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Page 1 of 33Journal of Agricultural and Food Chemistry
Development of a Colorimetric Sensor Array for the Discrimination 1
of Chinese Liquors Based on Selected Volatile Markers Determined 2
by GC-MS
3
Jun-Jie Li a, Chun-Xia Song a, Chang-Jun Hou a*,Dan-Qun Huo a, Cai-Hong Shen b, 4
Xiao-Gang Luo a, Mei Yang a, and Huan-Bao Fa c
5
a Key Laboratory of Biorheology Science and Technology, Ministry of Education, 6
College of Bioengineering, Chongqing University, Chongqing, 400044, PR China
7
b National Engineering Research Center of Solid-State Brewing, Luzhou Laojiao 8
Group Co.Ltd., Luzhou, Sichuan, 646000, PR China
9
c College of Chemistry an
d Chemical Engineering, Chongqing University,
10
Chongqing 400044, China
11
12
ABSTRACT
13
A brand-new colorimetric sensor array was developed to discrimination 12 high
14
alcoholic Chinese base liquor from Luzhou Co., Ltd. and 15 commercial Chinese 15
liquor of different brands as well as flavor types. 17 volatile compounds within four 16
chemical groups were determined as markers in the base liquor by GC-MS analysis 17
and Factor Analysis Method (FAM). A specialized colorimetric sensor array 18
composed of 20 sensitive dots were fabricated accordingly to obtain sensitive 19
interaction with different types of volatile markers. Discrimination of the liquor 20
samples was subsequently performed using chemometric and statistical methods, 21
including principal component analysis (PCA) and hierarchical clustering analysis 22 Page 2 of 33
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Page 3 of 33Journal of Agricultural and Food Chemistry
(HCA). The results suggested that facile identification of either base liquors with
23
high alcoholic volume or commercial liquors of same flavor types could be achieved
24
by analysis of the color change profiles. The response of the sensor improved
25
significantly in comparison with those that rely on non-specific interactions, and no
26
mis-classi?cation was observed for both liquor samples using two chemometric
27
methods. Besides, it was also found that the discrimination is closely related to the
28
characteristic flavor compounds (esters, aldehydes and acids) and alcoholic strength
29
in liquors, its performance was even comparable with GC-MS.
30
31
KEYWORDS: Chinese liquor; colorimetric sensor array; GC-MS; chemometrics
32
33
INTRODUCTION
34
Chinese liquor, Baijiu in Chinese, is a popular alcoholic beverage in China and
35
many other countries. As one of the oldest distillates in the world, it has a history
36
over thousands of years, enjoys a long lasting popularity in China, and possesses an
37
irreplaceable position in traditional Chinese culture (1, 2). According to statistics,
38
annual consumption of Chinese liquor has been estimated to exceed 10 billion liters,
39
with a production over 7,000,000 tons merely in 2010 (3). Typically, Chinese liquor
40
is distilled after a complex fermentation process using cereals, mostly sorghum,
41
wheat, rice and corn. The solid-state fermentation is catalyzed by a natural mixed
42
saccharifying and fermenting agent called “Daqu”, which is abundant with
43
microorganisms including bacteria, yeast and fungi, and usually made from wheat or
44
a mixture of wheat, pea, and barley, and so on (2, 4). The fermented cereals are then
45
taken out to perform distillation to obtain raw liquor (base liquor). Freshly distilled 46
base liquor as well as young liquor has undesirable characteristics not preferable for 47
drinking (5). It needs to go through a long aging process, ranging from months to 48
years partly in accordance with final quality of product, to develop a well-balanced 49
“matured” liquor, and is finally blended by specialist to obtain commercial liquors of 50
unique flavor and taste (6).
51
Different raw materials and specialized brewing techniques lead to a variety of 52
flavor types, which also determine the quality grade of resultant Chinese liquors.
53
According to Liquor Association of China, Chinese liquor now can be divided into 54
ten main flavor types, that is, Luozhou-flavor (strong-flavor), Fen-flavor 55
(light-flavor), Maotai-flavor (sauce-flavor), rice-flavor, and other flavor type 56
(sesame-flavor, chicken-flavor, Medicine-flavor, Soy-flavor, Special-flavor, mixed 57
flavor and so on) (7). Daqu exerts the most significant impact on the flavor 58
appearance of liquors because the microbial community it contains are closely 59
associated with the degradation of raw materials, the production of alcohol, and the 60
formation of aromatic compounds (8-10). With respect to flavor type, brand and 61
geographical origin, the average price of Chinese liquors might vary from several to 62
thousand dollars on the market. Therefore, adulteration and counterfeit of famous 63
liquors to make higher profits has long presented an irritating problem in the liquor 64
industry. Besides, there is also an ever-increasing concern about the protection of 65
geographical indications (GIs) as a part of agricultural policy in both China and 66 Page 4 of 33
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European countries (11, 12). Thus efficient and reliable discrimination of Chinese
67
liquors has important economic and cultural values.
68
Practically, oenophiles are employed to identify the aroma of liquors and
69
discriminate their differences in quality. But the results may be objective and
70
fluctuant due to a lot of psychological and/or physical reasons. Besides, it is difficult
71
for them to distinguish liquors belonging to similar flavor type, or forged liquors
72
produced by diluting industrial alcohol with water (13). In view of reliability,
73
accuracy and reproducibility, the most efficient analytical methods undoubtedly rest
74
on those depend on large equipment, including headspace solid-phase
75
micro-extraction gas chromatography (14), ambient glow discharge ionization mass
76
spectrometry (15), head space-solid phase micro-extraction-mass spectrometry (3,
77
16), gas chromatography-mass spectrometry (GC-MS) (1, 6), and infrared
78
spectroscopy (17-19). However, unevadable drawbacks such as complicated
79
pre-treatment, time-consuming procedures, requirement for professional operation
80
and also high cost, greatly hinder their application for in-situ real-time measurements
81
(7, 15, 20). Furthermore, due to complicated composition of ingredients in the liquor,
82
it is rather difficult to realize an accurate recognition of the overall characteristics of
83
hundreds of Chinese liquors through component-by-component analysis of
84
individual sample. Hence, it is of great significance to develop a rapid and reliable
85
method to realize convenient evaluation of the authenticity of Chinese liquor as well
86
as easy characterization of unique personality of each liquor sample.
87
Analysis methods based on sensory techniques, commonly referred to as
88
electronic nose or electronic tongue, have offered a powerful alternative to analyze 89
foodstuff in the recent years (20, 21). Treating the complicated mixed sample as a 90
single analyte, they are able to give a combined sensor response to the target thus 91
achieve fast yet efficient discrimination of those mixtures (13, 22). The challenge is 92
that those sensors required relatively complicate post pattern recognition methods to 93
collect feature information, and can hardly avoid signal overlap of closely similar 94
samples since they are usually lack of chemical discrimination (22-24). In view of 95
those shortcomings, the colorimetric sensor array outstands as a good candidate to 96
dress above-mentioned problems (25). Inspired by the mammalian gustatory and 97
olfactory systems, such sensor can give unique visible fingerprints to the complex 98
analytes in either the liquid phase or the head-gas (26, 27). Successful application of 99
colorimetric sensor arrays have been reported to distinguish drinks including beers 100
(28), soft drinks (26), coffees (29) and also Chinese liquors (12, 23). However, high 101
concentration of ethanol and water in high alcoholic Chinese liquors might not only 102
mask the response of other compounds, which contain more individual information 103
of the liquor rather than alcoholic strength, and also shorten the sensor life (30). 104
Besides, as most of the sensitive dots in the sensor array rely on non-specific 105
interactions such as Van der Waals interaction, Lewis acid-base interaction, π-π 106
interaction, and physical adsorption, efficient discrimination of liquor samples that 107
resemble closely in flavor and composition still remains a big challenge.
108
In the present study, we fabricated a new colorimetric sensor array based on 109
volatile markers determined by GC-MS to characterize Chinese liquor. Totally 17 110 Page 6 of 33
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volatile markers was selected applying factor analysis to fabricate a 4×5sensor 111
array sensitive to different groups of markers. Data analysis was performed by PCA 112
and HCA to distinguish 12 base liquor samples and 15 commercial liquors. The main 113
focus of this study is to develop a reliable sensor to discriminate high alcoholic 114
liquors and to identify different commercial Chinese liquors of different brands and 115
flavor types. This effort develops a simple method that may prove to be useful for 116
the quality control of Chinese liquors in the market and even in mass production.
117
118
MATERIALS AND METHODS
119
Chemicals and Stock Solutions Preparation
120
All the 12 base Chinese liquors (listed in table S1) were kindly provided by 121
Luzhou laojiao Co. Ltd. (Luzhou, China) and 15 commercially available Chinese 122
liquors (listed in table S2) were purchased from local supermarket in Chongqing city, 123
China. 5,10,15,20-tetraphenyl-porphine, 5,10,15,20-tetraphenyl-porphine Indium, 124
5,10,15,20-tetrakis(penta-fluorophenyl)-porphyrin Iron(III) chloride, and 125
5,10,15,20-tetraphenyl-porphine Zinc were obtained from Frontier Scientific (Logan, 126
UT, USA). All the other indicator dyes(see table S3) were supplied by 127
Sigma-Aldrich (St. Louis, MO, USA). 2, 4-dinitrophenylhydrazine (DNPH) and 128
ammonium ceric nitrate were purchased from Aladin Reagent (Shanghai, China).
129
Hydrochloric acid (HCl), sulphuric acid (H2SO4), NaOH, potassium persulfate, nitric 130
acid, absolute ethanol and other reagents of analytical pure were all obtained from 131
Chuandong Reagent (Chengdu, China). Porous hydrophilic membrane used for dye 132
staining was bought from Shanghai Minglie Chemical Engineering Science Co. Ltd. 133
(Shanghai, China). Ultra-pure water was generated by a Millipore Direct-Q Water 134
system (Molsheim, France). A PHS-3C pH detector (Shanghai Jingke Equipment, 135
Shanghai, China), ultrasonic atomizer apparatus (Shanghai Yuyue Medical 136
Equipment Company, Shanghai, China) and a Media microwave oven were used in 137
the study.
138
DNPH solution was prepared according to our previous study (31). To be 139
specific, 0.4 g DNPH was added to a mixed solution of 3 mL water and 10 mL 140
ethanol, and then 2 mL concentrated sulfuric acid was added dropwise. The solution 141
was stirred for 10 min and then went through filter paper to obtain a yellow DNPH 142
store solution. The prophyrin solutions were prepared using DMF solution and 143
stored in dark place before use. Marker solutions used for sensitive dot selection 144
were freshly prepared using mixed solution of absolute ethanol and deionized water 145
(60:40) upon detection. All other solutions without specification were prepared using 146
deionized water.
147
Analysis of Volatile Components in Base Liquors Using GC-MS
148
Agilent 5975I GC-MS with a 7683B automatic sampler was coupled with a FID 149
detector to analysis the volatile components in the 12 base Chinese liquors. Using a 150
Cross-linked silica capillary column (30 mm in length, 0.25 mm in inside diameter 151
and 0.25 μm in coating thickness), the initial column temperature was set at 40℃152
held for 2 min and then heated to 80℃with a rate of 5℃/min. It was lasted for 2 153
min and then heated to 260℃with a heating rate of 10℃/min, held for 10 min. 154 Page 8 of 33
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High-purity nitrogen was applied as eluant gas using split sampling with a split ratio 155
of 30:1. The sample volume for GC was 1 μl and 2-5 μl for MS. Resultant data about 156
the volatile components were analyzed using a SPSS (version 18.0) software.
157
Fabrication of the Sensor Array
158
After determination of the volatile markers, sensitive dyes were subsequently 159
selected for each group of markers. A virtual library of possible combination for 160
different marker was first constructed according to former studies and our previous 161
study. Practically, taking the selection of sensitive dots for acidic markers as an 162
example, acetic acid solutions with a concentration of 0.4 g/L (average concentration 163
of 12 base liquor samples) were prepared using 60% ethanol (in deionized water) as 164
standard simulated samples. Since the pH of all the base liquors was detected 165
between 3.1 and 4.1, Congo red and bromophenol blue were selected as responsive 166
dyes. The mixture of dyes was kept in ultrasonic agitation for ten seconds and then a 167
quartz capillary was used to deliver approximately 0.1 μL solution onto the surface 168
of a porous hydrophilic membrane to fabricate a selection-oriented sensor array 169
(figure 1 A ). Once printed, the arrays were placed in a 500 ml beaker saturated in 170
nitrogen atmosphere for 30 min and subsequently dried in a 60℃oven for 24 h after 171
which the oven temperature was reduced to 35℃and the arrays left for another 24 h.
172
The image of the sensor array was captured before detection, 5 mL standard 173
simulated sample atomized with an ultrasonic atomizer apparatus was then sprayed 174
upon the array sealed with preservative film in petri plate (8 cm in diameter) and 175
lasted for at least 5 min before its image was taken finally. Comparing the images of 176
the array before and after exposure to the sample, color change profiles were 177
automatically obtained. Detailed working principle of the sensory system and data 178
process can be found in our former study (32). By analyzing resultant color change 179
(RGB) and the relative standard error of parallel dots, the combination and 180
concentration of the dyes in the sensitive dots were subsequently optimized. After 181
optimization of all the sensitive dots, the 4×5sensor array (see figure 1B) was 182
fabricated finally. The sensor arrays were subjected to above-mentioned preparing 183
procedure, and the arrays were stored in a nitrogen-flushed dark environment before 184
use.
185
Discrimination of the Liquor Samples
186
After fabrication of the sensor array, 12 base liquor samples and 15 commercial 187
Chinese liquors were analyzed subsequently. Briefly, 5 mL liquor sample was 188
atomized with an ultrasonic atomizer apparatus and then sprayed upon the array 189
sealed with preservative film in a petri plate (8 cm in diameter) and lasted for 1min. 190
Then, it was placed in the micro-wave oven and treated for 2 min (400 W, 60℃). 191
Data acquisition was performed with a routine procedure mentioned in previous 192
experiments and finally analyzed using the SPSS software. It should be noted that all 193
the detection of each sample, both liquor samples and simulated ones, was 194
performed repeatedly for at least five time, and the average images were used for 195
follow-up data analysis.
196
197
RESULTS AND DISCUSSION
198 Page 10 of 33
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Selection of Markers
199 The composition of the volatile components analyzed by GC-MS of 12 base
200
liquors was listed in table 1. There were more than 30 volatile compounds existed in
201
those liquors and their concentration varied significantly in different samples. In
202
order to simplify data analysis, Factor Analysis Method was first applied to find the
203
principal components (PCs). Table 2 summarized the eigen values of the correlation
204
matrixes. As can be seen from the table, the first 10 components covered more than
205
99.8% of accumulative contribution rate dominated by the first three PCs. Then
206
maximum orthogonal rotation of the factor loading matrices was performed using
207
varimax method to find the relationship between the volatile compounds and the first
208
3 principal components. It was found that the first PC had closer positive
209
relationship with acetaldehyde, furfural, acetal, isoamylol, 2-butyl alcohol, n-propyl
210
alcohol, isobutanol, n-butanol, and isoamyl acetate. Those compounds were
211
normally regarded as the origin of taste (or mouthfeel) of with Chinese liquors. Since
212
ester compounds (ethyl acetate, ethyl butyrate, ethyl valerate, ethyl hexanoate and
213
ethyl lactate), which are important substance constitute the flavor of Chinese liquors
214
(33, 34), exhibited higher contribution rate to the second PC than other compounds,
215
we can assume that the second component mainly depends on the flavor composition.
216
For the third principal component, organic acid including acetic acid, butyric acid,
217
isopropylacetic acid and caproic acid dominated its cumulative load value. As a
218
result, totally 17 kinds of compounds were selected to serve as marker in the base
219
liquors, that is, acetaldehyde, furfural, acetal, isoamylol, 2-butyl alcohol, n-propyl
220
Page 11 of 33Journal of Agricultural and Food Chemistry
alcohol, isobutanol, n-butanol, acetic acid, butyric acid, isopropylacetic acid, caproic
221
acid, ethyl acetate, ethyl butyrate, ethyl valerate, ethyl hexanoate and ethyl lactate.
222
We subsequently re-analyzed the 12 base liquors (each with five control samples)
223
using only the concentration information of the selected markers to testify whether it
224
can discriminate the 60 base liquor sample. As expected, PCA and HCA all showed
225
correct discrimination of the liquors directly use concentration information of
226
selected 17 markers (figure 2B and C). Most importantly, the factor analysis result
227
resemble closely with that obtained from all the volatile compounds, which again
228
demonstrated the feasibility of selected 17 volatile compounds to distinguish the
229
base liquor samples.
230
Fabrication of the Sensor Array
231 Successful discrimination of Chinese liquors using sensory techniques have
232
been reported by our group and other groups previously (7, 12, 20). Since those
233
studies either rely on complex post data analysis or non-specific interaction between
234
sensing materials and liquors sample, it still remains a big challenge to realize
235
in-depth recognition of each kind of Chinese liquors with respect to raw materials in
236
production, brewing techniques and even storage methods. In view of these
237
problems, we endeavored to develop an easy yet efficient sensor to discriminate
238
Chinese liquor using volatile makers selected for the base liquors. Despite the
239
complexity of selected 17 markers, they can be classified into four groups, including
240
alcohol, acid, ester and aldehyde. So it is not unreasonable to discriminate those
241
liquors based on responsive materials sensitive to organic alcohol, acid, ester and
242
Page 12 of 33
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Page 13 of 33Journal of Agricultural and Food Chemistry
243
aldehyde. Considering that all the sensitive dots are selected and fabricated in a 244
similar way, we here choose dot D12 (specific to alcohol) to illustrate the procedure 245
to fabricate individual sensitive dots. Typically, when certain combination of the 246
dyes were selected, it was used to make a selection-oriented sensor array which was 247
then used to test simulated standard samples. We use Euclidean Distance change 248
(root of the sum of ?R2, ?G2 and ?B2 of each sensor dot) to measure the response.
249
As can be seen form figure S1A, different combination of the dyes showed varied 250
responses and higher responses were observed when the ratio were set at 4:1 and 3:1.
251
And we finally chose 3:1 as optimized ratio to obtain high stability of resultant 252
sensitive dot because it showed the lowest RSD (figure S1B). Finally, totally 20 253
combinations were selected as sensor dots (see table S3 for detailed information).
254
Specifically, D11 to D15 were specific to organic alcohols, D21 to D23 were specific 255
to esters (catalyzed by micro-wave), D24, D25 and D31 were specific to acid via 256
acid-base reactions (see figure S2), and the rest were specific to aldehydes, the 257
detection mechanism of which was explained in details in our former study (31).
Discrimination of the Base Liquors with High Alcoholic Strength
258
60 base wine samples (5 controls for each kind of base Chinese liquor) were
259
then selected to test the recognition ability of the newly developed colorimetric 260
sensor array. We first studied the time-dependent response of the sensor after its 261
interaction with the liquors samples. Figure 3 shows that Euclidean distance of all 262
the sample increased until they reached to equilibrium in 4 min. It indicates that 4 263
min is longer enough for the interaction to establish an equilibrium. The colorful 264
profiles of the based liquors in figure 4A demonstrated that different base liquors 265
showed significant difference which can even be differentiated by naked eyes. When 266
analyzing individual sensor dots, we found that sample BL-4 (72% in alcoholic 267
strength) and BL-5 (72.1% in alcoholic strength) exhibited similar colorimetric 268
responses in D11 and D12, specific to alcohols with low molecular weight (mostly 269
ethanol), but completely different in other sensitive dots. It suggested that difference 270
in alcoholic strength did not conceal the colorimetric response of the sensor array. 271
Besides, the responses of D11 and D12 in BL-4 and BL-5 were clearly different 272
from other samples (even BL-10 with an alcoholic strength of 71.4%), indicating 273
that the sensor had fine discrimination of alcoholic strength.
274
Statistical analysis of the data was applied to get further understanding of the 275
interaction between the sensor and the base liquors. The cumulative load values of 276
individual dot for the first 3 principal components, which cover over 70% of the 277
cumulative contribution rate, were list in table S4. The results suggested that the first 278
one mainly relied on discrimination of esters, the second relied on aldehydes and the 279
third greatly on organic acids, which is partly consistent with that analyzed by the 280
GC-MS. Results of the first two principal components were plotted in figure 4B, the 281
successful classification of different base liquor samples is indicative the good 282
discrimination ability of the sensor array. Moreover, correct clustering of the 60 283
samples by HCA further proved the sensor of excellent performance to screen among 284
different base liquors (figure 4C), and it also realized almost the same grouping of 285
the samples with that by GC-MS analysis (figure 2C), demonstrating its 286 Page 14 of 33
Journal of Agricultural and Food Chemistry
Page 15 of 33Journal of Agricultural and Food Chemistry
discriminating ability comparable with that of GC-MS to some extent. Therefore, we 287
can conclude that the colorimetric sensor developed in the present study have good 288
performance to distinguish different base Chinese liquors and the recognition is 289
based on the combination of specific affinity of individual sensitive dots to volatile 290
markers, which constitute the specificity of different liquors, including alcohols, 291
esters, acids and aldehydes.
292
Discrimination of 15 Commercial Liquors
293
Lastly, the colorimetric sensor was applied to common Chinese liquors that are 294
commercially available in the supermarket. The colorimetric maps of selected 15 295
kinds of Chinese liquors are shown in figure 5A. It can be seen from the figure that 296
different brands of Chinese liquors exhibited different response in the sensor array 297
and the difference can also be distinguished by naked eyes. Similar to that of the 298
base liquors, liquor samples of the same alcoholic strength showed analogous 299
colorimetric responses in D11 and D12 and were hardly alike in other dots, which is 300
again verified it immune to high ethanol interfering. Plot of the first two principal 301
components was shown in figure 5A demonstrated again the good recognition of the 302
commercial Chinese liquors. PCA study also suggested that the first PC mostly 303
relied on discrimination of esters and aldehydes. The second one mainly relied on 304
organic acids and the third greatly on alcohols, which is slight different from base 305
liquors. Moreover, the cumulative contribution rate (65.6%) of the first three 306
principal components for the commercial liquor was lower than that of the base 307
liquors. It might resulted from of the change of marker compounds in liquor aging 308
process of the commercial Chinese liquor by base liquor as well as its storage (35). 309
HCA study was also used to analyzed the data (R, G and B value of each dot 310
and Euclidean distance) obtained from colorimetric reaction. It was found that 311
control samples of the same brand clustered together without any mis-classification, 312
and different liquors of the same flavor type grouped together firstly then with other 313
ones. However, samples of the same alcoholic strength did not cluster close with 314
each other in the dendrogram. Those results suggested that discrimination of the 315
Chinese liquors but the sensor array was dominated by the favor types, relying on 316
esters and aldehydes, instead of alcoholic strength. Moreover, we also compared the 317
colorimetric response of the present sensor with that depend on cross-responsive 318
mechanism by non-specific interactions in former studies (figure 6). It showed that 319
the present one exhibited significant better response to selected Chinese liquor 320
samples, and the Euclidean distance were much higher than the former, despite that 321
the former one had more sensitive dots.
322
In summary, facile discrimination of 12 high alcoholic Chinese base liquor from 323
Luzhou Co., Ltd. and 15 commercial Chinese liquor of different brands and flavor 324
types was achieved with a freshly developed colorimetric sensor array. Based on 17 325
volatile markers determined by GC-MS analysis and subsequent Factor Analysis 326
including acetaldehyde, furfural, acetal, isoamylol, 2-butyl alcohol, n-propyl alcohol, 327
isobutanol, n-butanol, acetic acid, butyric acid, isopropylacetic acid, caproic acid, 328
ethyl acetate, ethyl butyrate, ethyl valerate, ethyl hexanoate and ethyl lactate, the 329
sensor realized correct identification of both base liquors with high alcoholic volume 330 Page 16 of 33
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Page 17 of 33Journal of Agricultural and Food Chemistry
and commercial liquors within the same flavor type just by comparison of the color 331
change profiles. Statistical analysis including PCA and HCA suggested that no 332
mis-classi?cation was observed for both liquor samples, and the discrimination had a 333
close relationship with the characteristic flavor compounds (esters, aldehydes and 334
acids) and alcoholic strength in the liquors. Since the response of the sensor array 335
improved significantly in comparison with those that rely on non-specific 336
interactions and its discrimination ability was partly comparable with GC-MS, we 337
can envisage its potential application for quality surveillance of Chinese liquors in 338
the market and even in mass production.
339
AUTHOR INFORMATION
Corresponding Author
*Phone: +862365112673; fax: +862365102507.
E-mail address: houcj@https://www.doczj.com/doc/0b6113927.html, (D. Hou).
Notes
The authors declare no competing financial interest. ACKNOWLEDGEMENT
The authors would like to acknowledge the financial support from the National Natural Science Foundation (No.81171414, 81271930 and 31171684), Key Technologies R&D Program of Sichuan Province of China (2013FZ0043 and 2010NZ0093), Key Technologies R&D Program of China (2012BAI19B03) and Sharing fund of Chongqing University’s large equipment.
Supporting Information Available:
Detailed information about 12 base Chinese liquors and 15 commercial Chinese liquors, composition of each sensor dots, and other information can be found in the Supporting Materials. This material is available free of charge via the Internet at https://www.doczj.com/doc/0b6113927.html,
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