Development of a Colorimetric Sensor Array for the Discrimination of

Oct 7, 2014 - Analytical Chemistry 2015 87 (15), 7810-7816 ... A minimalist Chinese liquor identification system based on a colorimetric sensor array ...
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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,† Chang-Jun Hou,*,† Dan-Qun Huo,† Cai-Hong Shen,§ Xiao-Gang Luo,† Mei Yang,† and Huan-Bao Fa‡ †

Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, and ‡College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, People’s Republic of China § National Engineering Research Center of Solid-State Brewing, Luzhou Laojiao Group Company, Ltd., Luzhou, Sichuan 646000, People’s Republic of China S Supporting Information *

ABSTRACT: A new colorimetric sensor array was developed for the discrimination of 12 high-alcoholic Chinese base liquors from Luzhou Co., Ltd., and 15 commercial Chinese liquor of different brands as well as flavor types. Seventeen volatile compounds within four chemical groups were determined as markers in the base liquor by GC-MS analysis and factor analysis method (FAM). A specialized colorimetric sensor array composed of 20 sensitive dots was fabricated accordingly to obtain sensitive interaction with different types of volatile markers. Discrimination of the liquor samples was subsequently performed using chemometric and statistical methods, including principal component analysis (PCA) and hierarchical clustering analysis (HCA). The results suggested that facile identification of either base liquors with high-alcoholic volume or commercial liquors of the same flavor types could be achieved by analysis of the color change profiles. The response of the sensor improved significantly in comparison with those that rely on nonspecific interactions, and no misclassification was observed for both liquor samples using two chemometric methods. Besides, it was also found that the discrimination is closely related to the characteristic flavor compounds (esters, aldehydes, and acids) and alcoholic strength in liquors, and its performance was even comparable with that of GC-MS. KEYWORDS: Chinese liquor, colorimetric sensor array, GC-MS, chemometrics

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10 main flavor types, that is, Luozhou flavor (strong flavor), Fen flavor (light flavor), Maotai flavor (sauce-flavor), rice flavor, and other flavor types (sesame flavor, chicken flavor, medicine flavor, soy flavor, special flavor, mixed flavor, and so on).7 Daqu exerts the most significant impact on the flavor type of liquors because the microbial community it contains is closely associated with the degradation of raw materials, the production of alcohol, and the formation of aromatic compounds.8−10 With respect to flavor type, brand, and geographical origin, the average price of Chinese liquors might vary from several to thousands of dollars on the market. Therefore, adulteration and counterfeiting of famous liquors to make higher profits has long presented an irritating problem in the liquor industry. Besides, there is also an everincreasing concern about the protection of geographical indications (GIs) as a part of the agricultural policy in both China and European countries.11,12 Thus, efficient and reliable discrimination of Chinese liquors has important economic and cultural values. Practically, enophiles are employed to identify the aroma of liquors and discriminate their differences in quality, but the results may be objective and fluctuant due to many psychological and/or physical reasons. Besides, it is difficult for them

INTRODUCTION Chinese liquor, “Baijiu” in Chinese, is a popular alcoholic beverage in China and many other countries. As one of the oldest distillates in the world, it has a history over thousands of years, enjoys a long-lasting popularity in China, and possesses an irreplaceable position in traditional Chinese culture.1,2 According to statistics, annual consumption of Chinese liquor has been estimated to exceed 10 billion liters, with a production over 7,000,000 tons merely in 2010.3 Typically, Chinese liquor is distilled after a complex fermentation process using cereals, mostly sorghum, wheat, rice, and corn. The solid-state fermentation is catalyzed by a natural mixed saccharifying and fermenting agent called “Daqu”, which is abundant with microorganisms including bacteria, yeast, and fungi and usually made from wheat or a mixture of wheat, pea, barley, and so on.2,4 The fermented cereals are then taken out to perform distillation to obtain raw liquor (base liquor). Freshly distilled base liquor as well as young liquor has undesirable characteristics not preferable for drinking.5 It needs to go through a long aging process, ranging from months to years partly in accordance with final quality of product, to develop a well-balanced “matured” liquor and is finally blended by specialists to obtain commercial liquors of unique flavor and taste.6 Different raw materials and specialized brewing techniques lead to a variety of flavor types, which also determine the quality grade of the resultant Chinese liquors. According to the Liquor Association of China, Chinese liquor now can be divided into © XXXX American Chemical Society

Received: July 15, 2014 Revised: September 16, 2014 Accepted: October 7, 2014

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Figure 1. (A) Difference map of a selection-oriented sensor array with five parallel dots. (B) Actual map of the detection-oriented sensor array and arrangement of the sensing dots.

samples that are similar in flavor and composition remains a big challenge. In the present study, we fabricated a new colorimetric sensor array based on volatile markers determined by GC-MS to characterize Chinese liquor. In total 17 volatile markers were selected by applying factor analysis to fabricate a 4 × 5 sensor array sensitive to different groups of markers. Data analysis was performed by principal component analysis (PCA) and hierarchical clustering analysis (HCA) to distinguish 12 base liquor samples and 15 commercial liquors. The main focus of this study is to develop a reliable sensor to discriminate highalcoholic liquors and to identify different commercial Chinese liquors of different brands and flavor types. This effort develops a simple method that may prove to be useful for the quality control of Chinese liquors in the market and even in mass production.

to distinguish liquors belonging to similar flavor types or forged liquors produced by diluting industrial alcohol with water.13 In view of reliability, accuracy, and reproducibility, the most efficient analytical methods undoubtedly depend on large equipment, including headspace solid-phase microextraction gas chromatography,14 ambient glow discharge ionization mass spectrometry,15 headspace solid phase microextraction−mass spectrometry,3,16 gas chromatography−mass spectrometry (GC-MS),1,6 and infrared spectroscopy.17−19 However, unavoidable drawbacks such as complicated pretreatment, timeconsuming procedures, requirement for professional operation, and also high cost,greatly hinder their application for in situ realtime measurements.7,15,20 Furthermore, due to the complicated composition of ingredients in the liquor, it is rather difficult to realize an accurate recognition of the overall characteristics of hundreds of Chinese liquors through component-by-component analysis of individual sample. Hence, it is of great significance to develop a rapid and reliable method to realize convenient evaluation of the authenticity of Chinese liquor as well as easy characterization of the unique personality of each liquor sample. Analysis methods based on sensory techniques, commonly referred to as electronic nose or electronic tongue, have offered a powerful alternative to analyze foodstuffs in the recent years.20,21 Treating the complicated mixed sample as a single analyte, they are able to give a combined sensor response to the target and thus achieve fast yet efficient discrimination of those mixtures.13,22 The challenge is that those sensors require relatively complicated post pattern recognition methods to collect feature information and can hardly avoid signal overlap of closely similar samples because they usually lack chemical discrimination.22−24 In view of those shortcomings, the colorimetric sensor array stands out as a good candidate to address the above-mentioned problems.25 Inspired by the mammalian gustatory and olfactory systems, such a sensor can give unique visible fingerprints to the complex analytes in either the liquid phase or the headgas.26,27 Successful application of colorimetric sensor arrays has been reported to distinguish drinks including beers,28 soft drinks,26 coffees,29 and also Chinese liquors.12,23 However, the high concentration of ethanol and water in high-alcoholic Chinese liquors might not only mask the response of other compounds, which contain more individual information on the liquor rather than alcoholic strength, but also shorten the sensor life.30 Besides, as most of the sensitive dots in the sensor array rely on nonspecific interactions such as van der Waals interaction, Lewis acid−base interaction, π−π interaction, and physical adsorption, efficient discrimination of liquor

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MATERIALS AND METHODS

Chemicals and Stock Solution Preparation. All 12 base Chinese liquors (listed in Table S1 of the Supporting Information (SI)) were kindly provided by Luzhou laojiao Co. Ltd. (Luzhou, China), and 15 commercially available Chinese liquors (listed in SI Table S2) were purchased from a local supermarket in Chongqing city, China. 5,10,15,20-Tetraphenylporphine, 5,10,15,20-tetraphenylporphine indium, 5,10,15,20-tetrakis(pentafluorophenyl)porphyrin iron(III) chloride, and 5,10,15,20-tetraphenylporphine zinc were obtained from Frontier Scientific (Logan, UT, USA). All other indicator dyes (see SI Table S3) were supplied by Sigma-Aldrich (St. Louis, MO, USA). 2,4-Dinitrophenylhydrazine (DNPH) and ammonium ceric nitrate were purchased from Aladin Reagent (Shanghai, China). Hydrochloric acid (HCl), sulfuric acid (H2SO4), NaOH, potassium persulfate, nitric acid, absolute ethanol, and other reagents of analytical purity were all obtained from Chuandong Reagent (Chengdu, China). Porous hydrophilic membrane used for dye staining was bought from Shanghai Minglie Chemical Engineering Science Co. Ltd. (Shanghai, China). Ultrapure water was generated by a Millipore Direct-Q Water system (Molsheim, France). A PHS-3C pH detector (Shanghai Jingke Equipment, Shanghai, China), ultrasonic atomizer apparatus (Shanghai Yuyue Medical Equipment Co., Shanghai, China), and Media microwave oven were used in the study. DNPH solution was prepared according to our previous study.31 To be specific, 0.4 g of DNPH was added to a mixed solution of 3 mL of water and 10 mL of ethanol, and then 2 mL of concentrated sulfuric acid was added dropwise. The solution was stirred for 10 min and then went through filter paper to obtain a yellow DNPH store solution. The prophyrin solutions were prepared using DMF solution and stored in a dark place before use. Marker solutions used for sensitive dot selection were freshly prepared using a mixed solution of absolute ethanol and deionized water (60:40) upon detection. All other solutions without specification were prepared using deionized water. B

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acetaldehyde furfural acetal methanol isoamylol sec-butyl alcohol n-propyl alcohol isobutanol 2-pentanol n-butanol 2-methylbutanol hexanol n-amyl alcohol acetic acid propanoic acid butyric acid isopropylacetic acid valeric acid caproic acid ethyl formate ethyl acetate propionic ether ethyl butyrate isoamyl acetate ethyl valerate ethyl hexanoate ethyl heptylate ethyl lactate ethyl palmitate ethyl oleate 3-hydroxy-2butanone

volatile compound 0.0002 0.0017 0.0771 0.0023 0.0021 0.0011 0.0014 0.0029 0.0022 0.0018 0.0014 0.0020 0.0010 0.0101 0.0011 0.0018 0.0030

0.0260 ± 0.0012 0.3768 ± 0.0089 0.3227 ± 0.0051 0.7104 ± 0.0083 0.0899 ± 0.0014 0.1848 ± 0.0020 0.0538 ± 0.0010 0.0402 ± 0.0025 1.203 ± 0.0410 0.0168 ± 0.0021 3.2648 ± 0.1260 0.0205 ± 0.0065 0.0144 ± 0.0076 0.1067 ± 0.0030

2

0.0729 0.0441 0.2028 0.0812 0.0105 0.0165 0.0887 0.0642 0.0142 0.0551 0.2782 0.0487 0.0178 0.4166 0.0145 0.2459 0.0327

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.0004 0.0004 0.008 0.002 0.0005 0.009 0.0006 0.0004 0.0004 0.0006 0.0008 0.0003 0.0005 0.0009 0.0005 0.0044 0.0012

1

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.0175 ± 0.0006 0.2578 ± 0.0010 0.03921 ± 0.0005 0.9094 ± 0.0004 0.0826 ± 0.0044 0.2013 ± 0.0017 0.0607 ± 0.0084 0.0349 ± 0.0012 1.4568 ± 0.0017 0.0168 ± 0.0001 3.2531 ± 0.0264 0.0172 ± 0.0005 0.0138 ± 0.0010 0.0730 ± 0.0008

0.0921 0.0325 0.2500 0.0866 0.0128 0.0209 0.0719 0.0607 0.0153 0.0454 0.2616 0.0427 0.0176 0.4167 0.0185 0.1534 0.0221

3

0.0134 0.2361 0.2374 0.6508 0 0.1483 0.0449 0.0220 1.5631 0.0151 2.9841 0.0212 0.0147 0.0443

0.0007 0.0013 0.0019 0.0036 0.0018 0.0013 0.0009 0.0064 0.0006 0.0141 0.0007 0.0011 0.0037

± ± ± ± ± ± ± ± ± ± ± ± ±

0.0613 ± 0.0010 0.0397 ± 0.0014 0.1692 ± 0.0013 0.0688 ± 0.0022 0.0100 ± 0.0007 0.0067 ± 0.0005 0.0536 ± 0.0011 0.0510 ± 0.0015 0.0064 ± 0.0008 0.0272 ± 0.0012 0.2799 ± 0.0033 0.0379 ± 0.0016 0.0166 ± 0.0015 0.3307 ± 0.0026 0.0188 ± 0.0010 0.01202 ± 0.0033 0.0176 ± 0.0009 0.0180 0.2547 0.2592 1.4858 0.0621 0.6704 0.0203 0.0708 8.0944 0.0468 1.4161 0.0504 0.0119 0.0184

0.0345 0.0290 0.0870 0.0512 0.0430 0.0114 0.0268 0.0112 0.0102 0.0409 0.0430 0.0294 0.0160 0.4423 0.0133 0.1851 0.0154

4

± ± ± ± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.0012 0.0031 0.0021 0.0015 0.0005 0.0042 0.0015 0.0017 0.164 0.0007 0.0402 0.0014 0.0007 0.0010

0.0016 0.0006 0.0013 0.0009 0.0007 0.0011 0.0017 0.0008 0.0004 0.0014 0.0008 0.0013 0.0012 0.0020 0.0009 0.0024 0.0017 0.0139 0.1899 0.3453 2.2313 0.0689 0.8816 0.0219 0.0848 8.6231 0.0472 1.4302 0.0450 0.0122 0.0213

0.0238 0.0297 0.1272 0.0519 0.0475 0.0158 0.0284 0.0133 0.0116 0.0396 0.0475 0.0273 0.0160 0.4279 0.0127 0.1637 0.0132

5

± ± ± ± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.0004 0.0017 0.0036 0.0190 0.0018 0.0035 0.0014 0.0013 0.0274 0.0025 0.0260 0.0014 0.0007 0.0005

0.0013 0.0018 0.0011 0.0021 0.0030 0.0014 0.0017 0.0007 0.0009 0.0022 0.0015 0.0024 0.0008 0.0028 0.0011 0.0021 0.0006 0.0186 0.2504 0.1492 0.0502 0 0.0156 0.0180 0.0025 0.4086 0.0046 1.8266 0.0429 0.0108 0.0176

0.0098 0.0867 0.0246 0.0180 0.0027 0 0.0124 0 0 0 0.0027 0.0074 0.0139 0.4563 0.0169 0.2098 0.0195

6 0.0004 0.0030 0.0011 0.0016 0.0006

C

± ± ± ± ± ± ± ± ±

± ± ± ±

± ± ± ± ± ± ±

0.0017 0.0019 0.0004 0.0023 0.0003 0.0106 0.0017 0.0006 0.0011

0.0013 0.0028 0.0010 0.0014

0.0008 0.0005 0.0007 0.0027 0.0018 0.0014 0.0015

± 0.0013

± ± ± ± ±

7 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.0022 0.0011 0.0013 0.0011 0.0008 0.0009 0.0020 0.0007 0.0002 0.0011 0.0013 0.0018 0.0010 0.0015 0.0008 0.0021 0.0013

0.0116 ± 0.0009 0.1913 ± 0.0016 0.344 ± 0.0014 1.0874 ± 0.0102 0.0714 ± 0.0011 0.2444 ± 0.0014 0.0385 ± 0.0012 0.0290 ± 0.0015 1.8658 ± 0.0369 0.0160 ± 0.0014 2.7088 ± 0.0121 0.0182 ± 0.0006 0.0134 ± 0.0012 0.0477 ± 0.0021

0.0693 0.0335 0.1749 0.0618 0.0094 0.0378 0.0695 0.0615 0.0100 0.0494 0.2704 0.0365 0.0167 0.3417 0.0142 0.1307 0.0161

liquor

Table 1. Concentrations (Grams per Liter) of Principal Volatile Components in Different Raw Liquors

0.0218 0.2957 0.3100 1.2526 0.0953 0.2313 0.0883 0.0519 0.8848 0.0165 3.2366 0.0175 0.0148 0.1350

0.1928 0.0447 0.4617 0.1136 0.0182 0.0561 0.1016 0.0828 0.0216 0.1001 0.3083 0.0563 0.0203 0.3484 0.0194 0.1779 0.0285

8

± ± ± ± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.0008 0.0016 0.0013 0.0033 0.0009 0.0014 0.0014 0.0015 0.0023 0.0019 0.0471 0.0005 0.0013 0.0016

0.0014 0.0009 0.0020 0.0015 0.0007 0.0013 0.0006 0.0017 0.0011 0.0008 0.0014 0.0007 0.0013 0.0020 0.0007 0.0010 0.0013 0.1809 0.0107 0.3087 0.8323 0.0381 0.2195 0.0366 0.0244 2.8279 0.0150 2.6423 0.0279 0.0125 0.0442

0.0872 0.0327 0.1586 0.0628 0.0102 0.0135 0.0613 0.0525 0.0076 0.0355 0.3598 0.0356 0.0162 0.2296 0.0164 0.1041 0.0122

9

± ± ± ± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.0027 0.0007 0.0019 0.0030 0.00011 0.0007 0.0014 0.0009 0.0023 0.0005 0.0277 0.0018 0.0008 0.0011

0.0065 0.0020 0.0013 0.0011 0.0008 0.0005 0.0012 0.0011 0.0004 0.0007 0.0016 0.0013 0.0006 0.0041 0.0014 0.0036 0.0009 0.0304 0.4618 0.2362 2.1080 0.0886 0.8740 0.0236 0.0949 9.2601 0.0599 1.2717 0.0162 0.0125 0.0194

0.0479 0.0353 0.0965 0.0542 0.0429 0.0357 0.0364 0.0117 0.0151 0.0622 0.0429 0.0320 0.0163 0.2936 0.0095 0.2997 0.0335 ± ± ± ± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

10

0.0013 0.0030 0.0017 0.0405 0.0032 0.0024 0.0011 0.0026 0.0713 0.0011 0.0121 0.0014 0.0006 0.0007

0.0034 0.0016 0.0012 0.0021 0.0017 0.0015 0.0014 0.0008 0.0013 0.0018 0.0012 0.0010 0.0009 0.0065 0.0007 0.0022 0.0015 0.0265 0.3868 0.4744 1.1516 0.0937 0.4356 0.0274 0.0637 4.3751 0.0373 1.3765 0.0158 0.0093 0.0274

0.0486 0.0444 0.1264 0.0569 0.0474 0.0592 0.0470 0.0163 0.0216 0.0689 0.0474 0.0344 0.0174 0.4109 0.0104 0.2588 0.0307 ± ± ± ± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

11

0.0011 0.0024 0.0018 0.0031 0.0011 0.0020 0.0006 0.0013 0.0241 0.0015 0.0180 0.0009 0.0003 0.0014

0.0020 0.0016 0.0019 0.0017 0.0008 0.0014 0.0007 0.0005 0.0009 0.0016 0.0010 0.0014 0.0006 0.0037 0.0005 0.0030 0.0013

0.0329 0.6080 0.1782 0.0656 0 0.0231 0.0188 0.0038 0.5728 0.0066 2.3821 0.0401 0.0125 0.0201

0.0098 0.0632 0.0226 0.0250 0.0062 0.0021 0.0120 0 0 0.0073 0.0062 0.0148 0.0143 0.5795 0.0161 0.3484 0.0333

± ± ± ± ± ± ± ± ±

0.0010 0.0006 0.0004 0.0015 0.0004 0.0035 0.0019 0.0008 0.0007

0.0021 0.0037 0.0020 0.0011

0.0004 0.0006 0.0013 0.0009 0.0024 0.0014 0.0015 0.0012

± ± ± ± ± ± ± ± ± ± ± ±

0.0007 0.0016 0.0013 0.0012 0.0007 0.0003 0.0009

± ± ± ± ± ± ±

12

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Analysis of Volatile Components in Base Liquors Using GCMS. An Agilent 5975I GC-MS with a 7683B automatic sampler was coupled with a FID detector to analyze the volatile components in the 12 base Chinese liquors. Using a cross-linked silica capillary column (30 mm length, 0.25 mm inside diameter, and 0.25 μm coating thickness), the initial column temperature was set at 40 °C, held for 2 min, and then heated to 80 °C at a rate of 5 °C/min; this temperature was held for 2 min and then raised to 260 °C at a heating rate of 10 °C/min and held for 10 min. High-purity nitrogen was applied as eluant gas using split sampling with a split ratio of 30:1. The sample volume for GC was 1 μL and 2−5 μL for MS. Resultant data about the volatile components were analyzed using SPSS (version 18.0) software. Fabrication of the Sensor Array. After determination of the volatile markers, sensitive dyes were subsequently selected for each group of markers. A virtual library of possible combinations for different markers was first constructed according to former studies and our previous study. Practically, taking the selection of sensitive dots for acidic markers as an example, acetic acid solutions with a concentration of 0.4 g/L (average concentration of 12 base liquor samples) were prepared using 60% ethanol (in deionized water) as standard simulated samples. Because the pH of all the base liquors was detected between 3.1 and 4.1, Congo red and bromophenol blue were selected as responsive dyes. The mixture of dyes was kept in ultrasonic agitation for 10 s, and then a quartz capillary was used to deliver approximately 0.1 μL of solution onto the surface of a porous hydrophilic membrane to fabricate a selection-oriented sensor array (Figure 1A). Once printed, the arrays were placed in a 500 mL beaker saturated in nitrogen atmosphere for 30 min and subsequently dried in a 60 °C oven for 24 h, after which the oven temperature was reduced to 35 °C and the arrays were left for another 24 h. The image of the sensor array was captured before detection; 5 mL of standard simulated sample atomized with an ultrasonic atomizer apparatus was then sprayed upon the array sealed with preservative film in Petri plate (8 cm in diameter) and held for at least 5 min before its image was taken finally. Comparing the images of the array before and after exposure to the sample, color change profiles were automatically obtained. The detailed working principle of the sensory system and data process can be found in our former study.32 By analyzing resultant color change (RGB) and the relative standard error of parallel dots, the combination and concentration of the dyes in the sensitive dots were subsequently optimized. After optimization of all the sensitive dots, the 4 × 5 sensor array (see Figure 1B) was fabricated finally. The sensor arrays were subjected to the above-mentioned preparing procedure, and the arrays were stored in a nitrogen-flushed dark environment before use. Discrimination of the Liquor Samples. After fabrication of the sensor array, 12 base liquor samples and 15 commercial Chinese liquors were analyzed subsequently. Briefly, 5 mL of liquor sample was atomized with an ultrasonic atomizer apparatus and then sprayed upon the array sealed with preservative film in a Petri plate (8 cm in diameter) and then held for 1 min. Then, it was placed in the microwave oven and treated for 2 min (400 W, 60 °C). Data acquisition was performed with a routine procedure mentioned in our previous experiments and finally analyzed using the SPSS software. It should be noted that the detection of each sample, both liquor samples and simulated ones, was performed repeatedly at least five times, and the average images were used for follow-up data analysis.

Table 2. Results of Factor Analysis Using the Concentrations of Principal Volatile Components component

eigen value

contribution rate (%)

accumulative contribution rate (%)

1 2 3 4 5 6 7 8 9 10

12.918 9.217 6.213 1.262 0.876 0.758 0.283 0.195 0.158 0.064

40.369 28.804 19.415 3.945 2.737 2.368 0.883 0.608 0.495 0.199

40.369 69.173 88.589 92.534 95.271 97.639 98.522 99.131 99.626 99.852

PCs. Then maximum orthogonal rotation of the factor loading matrices was performed using a varimax method to find the relationship between the volatile compounds and the first three principal components. It was found that the first PC had a closer positive relationship with acetaldehyde, furfural, acetal, isoamylol, 2-butyl alcohol, n-propyl alcohol, isobutanol, n-butanol, and isoamyl acetate. Those compounds were normally regarded as the origin of taste (or mouthfeel) of Chinese liquors. Because ester compounds (ethyl acetate, ethyl butyrate, ethyl valerate, ethyl hexanoate, and ethyl lactate), which are important substances constituting the flavor of Chinese liquors,33,34 exhibited a higher contribution rate to the second PC than other compounds, we can assume that the second component mainly depends on the flavor composition. For the third principal component, organic acids including acetic acid, butyric acid, isopropylacetic acid, and caproic acid dominated its cumulative load value (Table 3). As a result, in total 17 kinds of compounds were selected to serve as markers in the base liquors, those being acetaldehyde, furfural, acetal, isoamylol, 2-butyl alcohol, n-propyl alcohol, isobutanol, n-butanol, acetic acid, butyric acid, isopropylacetic acid, caproic acid, ethyl acetate, ethyl butyrate, ethyl valerate, ethyl hexanoate, and ethyl lactate. We subsequently reanalyzed the 12 base liquors (each with 5 control samples) using only the concentration information on the selected markers to confirm whether it can discriminate the 60 base liquor samples. As expected, PCA and HCA all showed correct discrimination of the liquors directly using the concentration information on the selected 17 markers (Figure 2B,C). Most importantly, the factor analysis result resembles closely that obtained from all of the volatile compounds, which again demonstrated the feasibility of the selected 17 volatile compounds to distinguish the base liquor samples. Fabrication of the Sensor Array. Successful discrimination of Chinese liquors using sensory techniques has been reported by our group and other groups previously.7,12,20 Because those studies rely on either complex post data analysis or nonspecific interaction between sensing materials and liquor samples, it remains a big challenge to realize in-depth recognition of each kind of Chinese liquor with respect to raw materials in production, brewing techniques, and even storage methods. In view of these problems, we endeavored to develop an easy yet efficient sensor to discriminate Chinese liquor using volatile makers selected for the base liquors. Despite the complexity of the selected 17 markers, they can be classified into 4 groups, including alcohol, acid, ester, and aldehyde, so it is not unreasonable to discriminate those liquors on the basis of

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RESULTS AND DISCUSSION Selection of Markers. The composition of the volatile components analyzed by GC-MS of 12 base liquors is listed in Table 1. There were more than 30 volatile compounds in those liquors, and their concentrations varied significantly in different samples. To simplify data analysis, factor analysis method was first applied to find the principal components (PCs). Table 2 summarizes the eigen values of the correlation matrices. As can be seen from the table, the first 10 components covered >99.8% of accumulative contribution rate dominated by the first three D

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Table 3. Cumulative Load Values of the Compounds for the First Three Principal Components (PC) variance

PC 1

PC 2

PC 3

variance

PC 1

PC 2

PC 3

acetaldehyde furfural acetal methanol isoamylol 2-butyl alcohol n-propyl alcohol isobutanol 2-pentanol n-butanol 2-methylbutanol hexanol n-amyl alcohol acetic acid butyric acid

0.928 0.810 0.916 0.030 0.914 0.945 0.931 0.844 0.638 0.881 0.669 0.738 0.771 −0.397 −0.277

−0.134 −0.584 −0.058 0.008 0.905 0.275 −0.183 −0.296 0.428 0.380 −0.345 0.092 0.107 −0.220 0.106

−0.107 0.427 −0.126 −0.223 0.047 0.246 −0.097 −0.261 0.127 0.144 −0.382 −0.074 −0.029 0.893 0.913

isopropylacetic acid valeric acid caproic acid ethyl formate ethyl acetate propionic ether ethyl butyrate isoamyl acetate ethyl valerate ethyl hexanoate ethyl heptylate ethyl lactate ethyl palmitate ethyl oleate 3-hydroxy-2-butanone

0.231 −0.080 −0.124 0.639 0.253 0.730 −0.035 0.892 0.248 −0.198 −0.023 0.562 −0.732 0.508 −0.376

−0.050 −0.087 0.022 0.232 0.937 0.523 0.991 −0.294 0.952 0.975 −0.780 0.929 0.115 −0.191 0.535

0.957 −0.904 0.915 −0.174 −0.128 0.127 −0.044 −0.082 0.069 −0.059 0.084 −0.120 −0.307 −0.117 0.212

Figure 2. (A) Cumulative percentage of the first three principal components. (B) Plot of the first two principal components by PCA with data obtained from GC-MS. (C) HCA dendrogram of 12 base liquors with 5 controls.

We finally chose 3:1 as the optimized ratio to obtain high stability of the resultant sensitive dot because it showed the lowest RSD (SI Figure S1B). Finally, in total 20 combinations were selected as sensor dots (see Table S3 (SI) for detailed information). Specifically, D11−D15 were specific to organic alcohols, D21−D23 were specific to esters (catalyzed by microwave), D24, D25, and D31 were specific to acid via acid−base reactions (see Figure S2 (SI)), and the rest were specific to aldehydes, the detection mechanism of which was explained in detail in our former study.31 Discrimination of the Base Liquors with High Alcoholic Strength. Sixty base wine samples (5 controls for each kind of

responsive materials sensitive to organic alcohol, acid, ester, and aldehyde. Considering that all of the sensitive dots are selected and fabricated in a similar way, we here choose dot, D12 (specific to alcohol), to illustrate the procedure to fabricate individual sensitive dots. Typically, when certain combinations of the dyes were selected, it was used to make a selectionoriented sensor array, which was then used to test simulated standard samples. We use Euclidean distance change (root of the sum of ΔR2, ΔG2, and ΔB2 of each sensor dot) to measure the response. As can be seen from Figure S1A (SI), different combinations of the dyes showed varied responses, and higher responses were observed when the ratios were set at 4:1 and 3:1. E

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low molecular weight (mostly ethanol), but completely different responses in other sensitive dots. This suggested that a difference in alcoholic strength did not conceal the colorimetric response of the sensor array. Besides, the responses of D11 and D12 in BL-4 and BL-5 were clearly different from other samples (even BL-10 with an alcoholic strength of 71.4%), indicating that the sensor had fine discrimination of alcoholic strength. Statistical analysis of the data was applied to get a further understanding of the interaction between the sensor and the base liquors. The cumulative load values of individual dots for the first three principal components, which cover >70% of the cumulative contribution rate, are listed in Table S4 in the SI. The results suggested that the first one mainly relied on the discrimination of esters, the second relied on aldehydes, and the third relied greatly on organic acids, which is partly consistent with analysis by GC-MS. Results of the first two principal components are plotted in Figure 4B; the successful classification of different base liquor samples is indicative of the good discrimination ability of the sensor array. Moreover, correct clustering of the 60 samples by HCA further proved the sensor to have excellent performance in screening among different base liquors (Figure 4C), and it also realized almost the same grouping of the samples as that by GC-MS analysis (Figure 2C), demonstrating its discriminating ability comparable with that of GC-MS to some extent. Therefore, we can conclude that the colorimetric sensor developed in the present study has good performance in distinguishing different base Chinese liquors and that the recognition is based on the combination of specific affinity of individual sensitive dots to volatile markers, which constitute the specificity of different liquors, including alcohols, esters, acids, and aldehydes. Discrimination of 15 Commercial Liquors. Lastly, the colorimetric sensor was applied to common Chinese liquors

base Chinese liquor) were then selected to test the recognition ability of the newly developed colorimetric sensor array. We first studied the time-dependent response of the sensor after its interaction with the liquor samples. Figure 3 shows that the

Figure 3. Time-dependent responses of the sensor array upon interaction with 12 base liquor samples.

Euclidean distance of all the samples increased until they reached equilibrium in 4 min. This indicates that 4 min is long enough for the interaction to establish an equilibrium. The colorful profiles of the base liquors in Figure 4A demonstrated that different base liquors showed significant difference, which can even be differentiated by the naked eye. When analyzing individual sensor dots, we found that samples BL-4 (72% in alcoholic strength) and BL-5 (72.1% in alcoholic strength) exhibited similar colorimetric responses in D11 and D12, specific to alcohols with

Figure 4. (A) Colorimetric maps of 12 base liquors. (B) Plot of the first two principal components by PCA with data obtained from the colorimetric sensor. (C) HCA dendrogram of 12 base liquors with 5 controls. F

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Figure 5. (A) Colorimetric maps of 15 Chinese liquors. (B) Plot of the first two principal components by PCA with data obtained from the colorimetric sensor. (C) HCA dendrogram of 15 Chinese liquors with 5 controls.

Figure 6. (A) Difference map of a selection-oriented sensor array with five parallel dots. (B) Actual map of the detection-oriented sensor array and arrangement of the sensing dot.

that are commercially available in the supermarket. The colorimetric maps of 15 selected kinds of Chinese liquors are shown in Figure 5A. It can be seen from the figure that different brands of Chinese liquors exhibited different responses in the sensor array and the differences can also be distinguished by the naked eye. Similar to the base liquors, liquor samples of the same alcoholic strength showed analogous colorimetric responses in D11 and D12 and were hardly alike in other dots, which is again verified that it is immune to high ethanol interfering. A plot of the first two principal components is shown in Figure 5A, demonstrating again the good recognition of the commercial Chinese liquors. PCA study also suggested that the first PC mostly relied on discrimination of esters and aldehydes. The second one mainly relied on organic acids and the third greatly on alcohols, which is slight different from base liquors. Moreover, the cumulative contribution rate (65.6%) of the first three principal components for the commercial liquor was lower than that of the base liquors. This might result from the change of marker compounds in liquor aging process of the commercial Chinese liquor by base liquor as well as its storage.35

A HCA study was also used to analyze the data (R, G, and B valued of each dot and Euclidean distance) obtained from colorimetric reaction. It was found that control samples of the same brand clustered together without any misclassification, and different liquors of the same flavor type grouped together first, then with others. However, samples of the same alcoholic strength did not cluster close with each other in the dendrogram. Those results suggested that discrimination of the Chinese liquors by the sensor array was dominated by the flavor types, relying on esters and aldehydes, instead of alcoholic strength. Moreover, we also compared the colorimetric response of the present sensor with that depending on cross-responsive mechanism by nonspecific interactions in former studies (Figure 6). This showed that the present one exhibited significantly better response to selected Chinese liquor samples, and the Euclidean distances were much higher than in the former, despite the former having more sensitive dots. In summary, facile discrimination of 12 high-alcoholic Chinese base liquors from Luzhou Co., Ltd., and 15 commercial Chinese liquors of different brands and flavor types was achieved with a freshly developed colorimetric sensor array. On the basis of 17 G

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spirits quality control and flavour assessment. Food Control 2012, 26 (2), 564−570. (8) Wang, C.; Shi, D.; Gong, G. Microorganisms in Daqu: a starter culture of Chinese Maotai-flavor liquor. World J. Microbiol. Biotechnol. 2008, 24 (10), 2183−2190. (9) Wu, X.; Zheng, X.; Han, B.; Vervoort, J.; Nout, M. J. R. Characterization of Chinese liquor starter, “Daqu”, by flavor type with 1 H NMR-based nontargeted analysis. J. Agric. Food Chem. 2009, 57 (23), 11354−11359. (10) Du, H.; Xu, Y. Determination of the microbial origin of geosmin in Chinese liquor. J. Agric. Food Chem. 2012, 60 (9), 2288−2292. (11) Kireeva, I. How to register geographical indications in the European Community. World Patent Inf. 2011, 33 (1), 72−77. (12) Qin, H.; Huo, D.; Zhang, L.; Yang, L.; Zhang, S.; Yang, M.; Shen, C.; Hou, C. Colorimetric artificial nose for identification of Chinese liquor with different geographic origins. Food Res. Int. 2012, 45 (1), 45−51. (13) Zhang, Q.; Xie, C.; Zhang, S.; Wang, A.; Zhu, B.; Wang, L.; Yang, Z. Identification and pattern recognition analysis of Chinese liquors by doped nano ZnO gas sensor array. Sens. Actuators, B 2005, 110 (2), 370−376. (14) Zhang, J.; Li, L.; Gao, N.; Wang, D.; Gao, Q.; Jiang, S. Feature extraction and selection from volatile compounds for analytical classification of Chinese red wines from different varieties. Anal. Chim. Acta 2010, 662 (2), 137−142. (15) Zhen, C.; Zhou, Y.; Zhang, N.; Wang, J.; Xiong, C.; Chen, S.; Nie, Z. Differentiation of Chinese liquors by using ambient glow discharge ionization mass spectrometry. Analyst 2013, 138, 3830− 3835. (16) Cheng, P.; Fan, W.; Xu, Y. Quality grade discrimination of Chinese strong aroma type liquors using mass spectrometry and multivariate analysis. Food Res. Int. 2013, 54 (2), 1753−1760. (17) Dong, D.; Zheng, W.; Wang, W.; Zhao, X.; Jiao, L.; Zhao, C. A new volatiles-based differentiation method of Chinese spirits using longpath gas-phase infrared spectroscopy. Food Chem. 2014, 155, 45− 49. (18) Chen, H.; Tan, C.; Wu, T.; Wang, L.; Zhu, W. Discrimination between authentic and adulterated liquors by near-infrared spectroscopy and ensemble classification. Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. 2014, 130 (0), 245−249. (19) Li, Z.; Wang, P.; Huang, C.; Shang, H.; Pan, S.; Li, X. Application of Vis/NIR spectroscopy for Chinese liquor discrimination. Food Anal. Methods 2014, 7 (6), 1337−1344. (20) Jing, Y.; Meng, Q.; Qi, P.; Zeng, M.; Li, W.; Ma, S. Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification. Rev. Sci. Instrum. 2014, 85 (5), 1−11. (21) Li, Z.; Wang, N.; Raghavan, G. S. V.; Vigneault, C. Volatiles evaluation and dielectric properties measurements of Chinese spirits for quality assessment. Food Bioprocess Technol. 2011, 4 (2), 247−253. (22) Zhou, Q.; Zhang, S.; Li, Y.; Xie, C.; Li, H.; Ding, X. A Chinese liquor classification method based on liquid evaporation with one unmodulated metal oxide gas sensor. Sens. Actuators, B 2011, 160 (1), 483−489. (23) Ya, Z.; He, K.; Lu, Z.; Yi, B.; Hou, C.; Shan, S.; Huo, D.; Luo, X. Colorimetric artificial nose for baijiu identification. Flavour Fragrance J. 2012, 27 (2), 165−170. (24) Janzen, M. C.; Ponder, J. B.; Bailey, D. P.; Ingison, C. K.; Suslick, K. S. Colorimetric sensor arrays for volatile organic compounds. Anal. Chem. 2006, 78 (11), 3591−3600. (25) Rakow, N. A.; Suslick, K. S. A colorimetric sensor array for odour visualization. Nature 2000, 406 (6797), 710−713. (26) Zhang, C.; Suslick, K. S. Colorimetric sensor array for soft drink analysis. J. Agric. Food Chem. 2007, 55 (2), 237−242. (27) Feng, L.; Musto, C. J.; Suslick, K. S. A simple and highly sensitive colorimetric detection method for gaseous formaldehyde. J. Am. Chem. Soc. 2010, 132 (12), 4046−4047. (28) Zhang, C.; Bailey, D. P.; Suslick, K. S. Colorimetric sensor arrays for the analysis of beers: a feasibility study. J. Agric. Food Chem. 2006, 54 (14), 4925−4931.

volatile markers determined by GC-MS analysis and subsequent factor analysis including acetaldehyde, furfural, acetal, isoamylol, 2-butyl alcohol, n-propyl alcohol, isobutanol, n-butanol, acetic acid, butyric acid, isopropylacetic acid, caproic acid, ethyl acetate, ethyl butyrate, ethyl valerate, ethyl hexanoate, and ethyl lactate, the sensor realized correct identification of both base liquors with high alcoholic volume and commercial liquors within the same flavor type just by comparison of the color change profiles. Statistical analysis including PCA and HCA suggested that no misclassification was observed for both liquor samples, and the discrimination had a close relationship with the characteristic flavor compounds (esters, aldehydes, and acids) and alcoholic strength in the liquors. Because the response of the sensor array improved significantly in comparison with those that rely on nonspecific interactions and its discrimination ability was partly comparable with GC-MS, we can envisage its potential application for quality surveillance of Chinese liquors in the market and even in mass production.

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ASSOCIATED CONTENT

S Supporting Information *

Detailed information about 12 base Chinese liquors and 15 commercial Chinese liquors, composition of each sensor dot, and other information. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*(D.H.) Phone: +86 23 65112673. Fax: +86 23 65102507. E-mail: [email protected]. Funding

We acknowledge 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. Notes

The authors declare no competing financial interest.

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REFERENCES

(1) Xiao, Z.; Yu, D.; Niu, Y.; Chen, F.; Song, S.; Zhu, J.; Zhu, G. Characterization of aroma compounds of Chinese famous liquors by gas chromatography−mass spectrometry and flash GC electronic-nose. J. Chromatogr., B 2014, 945−946, 92−100. (2) Du, H.; Fan, W.; Xu, Y. Characterization of geosmin as source of earthy odor in different aroma type Chinese liquors. J. Agric. Food Chem. 2011, 59 (15), 8331−8337. (3) Cheng, P.; Fan, W.; Xu, Y. Determination of Chinese liquors from different geographic origins by combination of mass spectrometry and chemometric technique. Food Control 2014, 35 (1), 153−158. (4) Du, H.; Lu, H.; Xu, Y.; Du, X. Community of environmental streptomyces related to geosmin development in Chinese liquors. J. Agric. Food Chem. 2013, 61 (6), 1343−1348. (5) Fan, W.; Qian, M. C. Headspace solid phase microextraction and gas chromatography−olfactometry dilution analysis of young and aged Chinese “Yanghe Daqu” liquors. J. Agric. Food Chem. 2005, 53 (20), 7931−7938. (6) Zheng, J.; Liang, R.; Wu, C.; Zhou, R.; Liao, X. Discrimination of different kinds of Luzhou-flavor raw liquors based on their volatile features. Food Res. Int. 2014, 56, 77−84. (7) Liu, M.; Han, X.; Tu, K.; Pan, L.; Tu, J.; Tang, L.; Liu, P.; Zhan, G.; Zhong, Q.; Xiong, Z. Application of electronic nose in Chinese H

dx.doi.org/10.1021/jf503345z | J. Agric. Food Chem. XXXX, XXX, XXX−XXX

Journal of Agricultural and Food Chemistry

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(29) Suslick, B. A.; Feng, L.; Suslick, K. S. Discrimination of complex mixtures by a colorimetric sensor array: coffee aromas. Anal. Chem. 2010, 82 (5), 2067−2073. (30) Garcı ́a, M.; Fernández, M. J.; Fontecha, J. L.; Lozano, J.; Santos, J. P.; Aleixandre, M.; Sayago, I.; Gutiérrez, J.; Horrillo, M. C. Differentiation of red wines using an electronic nose based on surface acoustic wave devices. Talanta 2006, 68 (4), 1162−1165. (31) Li, J.; Hou, C.; Huo, D.; Yang, M.; Fa, H.; Yang, P. Development of a colorimetric sensor array for the discrimination of aldehydes. Sens. Actuators, B 2014, 196, 10−17. (32) Hou, C.; Dong, J.; Zhang, G.; Lei, Y.; Yang, M.; Zhang, Y.; Liu, Z.; Zhang, S.; Huo, D. Colorimetric artificial tongue for protein identification. Biosens. Bioelectron. 2011, 26 (10), 3981−3986. (33) Zhu, S.; Lu, X.; Ji, K.; Guo, K.; Li, Y.; Wu, C.; Xu, G. Characterization of flavor compounds in Chinese liquor Moutai by comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry. Anal. Chim. Acta 2007, 597 (2), 340−348. (34) Fan, W.; Qian, M. C. Identification of aroma compounds in Chinese ‘Yanghe Daqu’ liquor by normal phase chromatography fractionation followed by gas chromatography−olfactometry. Flavour Fragrance J. 2006, 21 (2), 333−342. (35) Mo, X.; Fan, W.; Xu, Y. Changes in volatile compounds of Chinese rice wine wheat Qu during fermentation and storage. J. Inst. Brew. 2009, 115 (4), 300−307.

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