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Biogenic amines- and sulfides-responsive gold nanoparticles for real-time visual detection of raw meat, fish, crustaceans, and preserved meat
Journal Pre-proofs Biogenic amines- and sulfides-responsive gold nanoparticles for real-time visual detection of raw meat, fish, crustaceans, and preserved meat Cheuk-Fai CHOW PII: DOI: Reference:
S0308-8146(19)32046-1 https://doi.org/10.1016/j.foodchem.2019.125908 FOCH 125908
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
29 May 2019 13 November 2019 13 November 2019
Please cite this article as: CHOW, C-F., Biogenic amines- and sulfides-responsive gold nanoparticles for real-time visual detection of raw meat, fish, crustaceans, and preserved meat, Food Chemistry (2019), doi: https://doi.org/ 10.1016/j.foodchem.2019.125908
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1
Biogenic
amines-
and
sulfides-responsive
gold
2
nanoparticles for real-time visual detection of raw meat,
3
fish, crustaceans, and preserved meat
4 5
Cheuk-Fai CHOW*
6
Department of Science and Environmental Studies, The Education University of Hong Kong,
7
10 Lo Ping Road, Tai Po, Hong Kong SAR, China
8
*Email: [email protected]
9 10
Abstract
11
A colorimetric probe based on gold nanoparticles (AuNPs) which is sensitive to two important
12
volatile biogenic markers, i.e., dimethyl sulfide and histamine, is developed to monitor the
13
spoilage of raw meat, fish, crustaceans, and preserved meat. The colorimetric detection is
14
attributed to the transformation of the non-aggregated form of AuNPs to its aggregated form
15
upon binding of the biomarkers. The AuNPs enable the detection of dimethyl sulfide and
16
histamine at limits of 0.5 and 0.035 μg/mL, respectively. Furthermore, the probe exhibits
17
excellent selectivity for those markers in the presence of other volatiles commonly generated
18
by spoiled real meat and seafood. A sequential and positive causative relationship is exhibited
19
among the storage period, the total bacteria count, the DMS evolved, and the chemosensing
20
signal generated. Thus, this probe serves as a nondestructive and cost-effective detector for the
21
real-time monitoring of meat spoilage.
22 1
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Keywords: dimethyl sulfide, dimethyl disulfide, dimethyl trisulfide, histamine, chemosensing
2
24
1. Introduction
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The explosive growth of food safety problems pushes us to search for a simple and accurate
26
food monitoring system. Detection of volatile biogenic biomarkers released from food is a
27
common strategy to determine food freshness and avoid food poisoning (WHO, Food Safety).
28
Common approach to the quality control of meat and seafood is to monitor the levels of volatile
29
biogenic sulfides and amines, respectively, they release (Alexandrakis, Brunton, Downey, &
30
Scannell, 2012; Mikš-Krajnik, Yoon, & Yuk, 2015; Tománková, Borˇilová, Streinhauserová,
31
& Gallas, 2012; Al-Attabi, D’arcy, & Deeth, 2009; Romano, Perello, deRevel, & Lonvaud-
32
Funel, 2008). Research has demonstrated that dimethyl sulfide (DMS, CH3SCH3), dimethyl
33
disulfide (DMSS, CH3SSCH3), and dimethyl trisulfide (DMSSS, CH3SSSCH3) levels in raw
34
beef, pork, and poultry increase as the meat spoils (Lovestead & Bruno, 2010; Varlet &
35
Fernandez, 2010; Isogal et al., 2009). Furthermore, histamine is a well-known biomarker for
36
seafood monitoring (Scallan, Griffin, Angulo, Tauxe, & Hoekstra, 2011; Landete, de las Rivas,
37
Marcobal, & Munoz, 2007; Vinci & Antonelli, 2002; Onal, 2007; Liu, Petty, Sazama, &
38
Swager, 2015). For example, according to the U.S. Food and Drug Administration (FDA), the
39
histamine level in fresh mackerel should not exceed 50 μg/g (FDA, 2004). Machiels (2003)
40
and Jaffres (2011) used gas chromatography-mass spectrometry (GC-MS) to assess the degree
41
of spoilage of fresh beef and shrimp by analyzing biogenic sulfides and amines levels
42
respectively. Furthermore, the analysis of biogenic sulfides and amines recently described in
43
the literature make use of molecular imprinting methods (Greene & Shimizu 2005), indicator-
44
displacement assays (Chow, Lam & Wong, 2013; Chow, Ho, Sun, Lu, Wong, Tang, Gong,
45
2017), and enzymatic techniques (Mertz & Zimmerman, 2003). However, although those
3
46
analytical methods are precise and reasonably accurate, they are costly, require special
47
procedures and conditions, and need highly specialized equipment (UV-vis spectrometer,
48
spectrofluorometer, and GC-MS). Therefore, simple methods and technologies for monitoring
49
the freshness of food products are essential.
50
Nanotechnology has been starting to attract industrial and academic research attention as
51
a means to address food freshness and food poisoning problems for three decades. Gold
52
nanoparticles (AuNPs), silver nanoparticles (AgNPs), zinc oxide nanoparticles (ZnO-NPs), and
53
titanium dioxide nanoparticles (TiO2-NPs) are often used as the active packaging materials to
54
inhibit bacterial growth on food (Akbar & Anal, 2014; De Moura, Mattoso, & Zucolotto, 2012;
55
El-Wakil, Hassan, Abou-Zeid, & Dufresne, 2015; Bumbudsanpharoke & Ko, 2015; Matteo,
56
Alessandra, & Saverio, 2009; Liu, Lu, Huang, Li & Xu, 2018). Furthermore, nano-clay and
57
bio-nano-composites, such as chitosan, carboxymethyl cellulose, starch, and cellophane, have
58
been used as barriers to prevent the contact of oxygen and moisture with food (Youssef & El-
59
Sayed, 2018). However, while these nano-technological approaches are proven to prevent food
60
spoilage, there are only rare examples of nanotechnology being used to monitor food freshness
61
(Ali, 2012; Chen, Zhou & Zhao, 2018; Liu, Lu, Huang, Li & Xu, 2018).
62
Accordingly, in the current study, we applied AuNPs as a naked-eye colorimetric sensor
63
for the detection of DMS, allowing us to monitor the freshness of foods readily (raw chicken,
64
raw pork, raw beef, preserved raw chicken, preserved raw pork, preserved raw beef, raw
65
salmon, raw tuna, raw toothfish, raw lobster, raw shrimp, and raw prawn). The basic principle
66
of this technology is that free AuNPs, which initially present a red color, form grey-colored
67
aggregates in the presence of DMS produced during food spoilage. Although AuNPs have been
4
68
used in biology and biochemistry for the detection of sulfur-containing compounds such as
69
cysteine, esterase, and DNA (Jongjinakool et al., 2014; Li et al., 2012; Li et al., 2014), no
70
studies of AuNP are found (i) to determine DMS in real food samples and (ii) to correlate its
71
sensing signal produced with the number of bacteria generated and concentration of the volatile
72
biomarker generated.
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2. Experimental Section
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2.1 Materials and chemicals
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Potassium gold(III) chloride (KAuCl4), DMS (99%), and histamine were obtained from
77
Aldrich (Steinheim, Germany). DMSS (99%) was obtained from Acros (Geel, Belgium).
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DMSSS (98%) was purchased from TCI (Tokyo, Japan). Dimethyl sulfoxide (DMSO, 99%)
79
was purchased from Anaqua Chemicals Supply (Cleveland, USA). Potassium dihydrogen
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phosphate, trisodium citrate, and sodium borohydride (NaBH4) were purchased from
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Honeywell Riedel-de Haen (Seelze, Germany). Total plate count agar (tryptone glucose yeast
82
agar) was purchased from Oxoid Ltd., (Basingstoke, Hampshire, England). The water used in
83
all syntheses and spectroscopic titrations was deionized and sterilized.
84 85
2.2 Synthesis and characterization
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2.2.1 Synthesis and characterization of AuNPs
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KAuCl4 (3 × 10-5 mol) and trisodium citrate (2 × 10-4 mol) were added to a conical flask
88
containing 50 mL water. NaBH4 (9 × 10-5 mol) was then added, and the mixture was stirred
89
under ambient conditions for 1 h. During the reaction, the color of the mixture changed from
5
90
pale-yellow to deep-red (immediately after NaBH4 addition) and finally to red. The red solution
91
was mixed with DMSO (33.33 mL) and then diluted with water to 100.00 mL in a volumetric
92
flask. The mixture was allowed to stand overnight prior to titration experiments. The stability
93
of AuNPs (82.5 μg/mL) in H2O/DMSO (2:1, v/v) was assessed by monitoring the UV-Vis
94
absorption of the mixture at 520 nm over 400 h.
95 96
2.2.2 Titration of the AuNP solution by various analytes
97
Titrations of AuNP (82.5 μg/mL) against DMS, DMSS, DMSSS, histamine, acetic acid,
98
triethylamine, ethylphenol, and phenol (0–50 μg/mL) in H2O/DMSO (2:1, v/v) were conducted
99
under ambient conditions. The UV-Vis spectroscopic responses of the resulting solutions at
100
520 nm were monitored.
101 102
2.2.3 Response times of AuNP solutions towards DMS, DMSS, DMSSS, and histamine
103
The response times of AuNP solutions (82.5 μg/mL) in H2O/DMSO (2:1, v/v) towards
104
DMS (1.0 μg/mL), DMSS (0.8 μg/mL), DMSSS (0.8 μg/mL), and histamine (0.16 μg/mL)
105
were assessed under ambient conditions by monitoring the UV-Vis absorptions at 520 and 670
106
nm of the mixtures over time (0–25 h). The scanning frequency was 0.5 min per cycle.
107 108
2.3 Analysis of real meat and seafood samples by (i) AuNP; (ii) GC-MS; and (iii) bacteria
109
counts
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2.3.1 Sample processing
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Twelve different types of fresh meat in four categories, i.e., (i) raw chicken, pork, and beef,
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112
(ii) raw tuna, salmon, and toothfish, (iii) raw lobster, prawn, and shrimp, and (iv) preserved
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raw chicken, pork, and beef, were tested in this study. Each category was randomly purchased
114
from a supermarket. All the samples purchased were skinless and boneless. For the lobster,
115
prawn, and shrimp, their shells were aseptically removed in the laboratory. All the batches were
116
brought to the laboratory in an ice chest and analyzed within 4 h. Seven kinds of meat including
117
the raw chicken, pork, beef, preserved raw chicken, preserved raw pork, preserved raw beef,
118
and raw shrimp originated from China; while the raw lobster came from Canada; the raw prawn
119
came from Malaysia, raw tuna originated from Philippine, raw salmon and toothfish came from
120
Norway and France respectively. Each batch was cut into small portions (6(L)×4(W)×2(H) cm3)
121
for the experiments. 200-mL microwave boxes with lids, plastic containers, and thermoplastic
122
polyurethane films were sterilized in an autoclave and/or under UV irradiation.
123 124
2.3.2 Chemosensing by AuNP
125
6 × 100-g meat samples were used to conduct the sensing experiment, and the whole set
126
of experiments was repeated three times. In general, six 200-mL microwave boxes with each
127
containing the 100-g meat sample and a plastic container with 3.0 mL of AuNP solution (82.5
128
μg/mL) capped by a thermoplastic polyurethane film were sealed and kept in a refrigerator at
129
4 °C for 0–16 days. The headspace left in the box was around 1/3 of its volume. After storage
130
at a particular day, the color of AuNP solutions was analyzed by using the pentone color chart
131
(Red: Pantone 1979c, Purple: Pantone 242c, Grey: Pantone 5285c; and Pale Grey: Pantone
132
5305c) and UV-Vis spectroscopy. GC-MS was used to determine the amount of DMS
133
generated and diffused in the solutions. The meat samples were tested for total bacteria count.
7
134
Three independent tests were conducted for each type of meat.
135 136
2.3.3 GC-MS analysis
137
GC-MS experiments were carried out on a Hewlett-Packard 6890 GC system equipped
138
with a 5973 mass-selective detector. Injections were performed with an HP 7683 autosampler.
139
Helium was used as the carrier gas at a flow rate of 1 mL/min. The separation was performed
140
with an HP-5MS capillary column (30 m × 0.25 mm, 0.25 µm). The GC conditions were a 10:1
141
injection split with the column temperature starting at 40 °C (1 min) and then increasing at
142
20 °C/min up to 100 °C (1 min), 20 °C/min up to 180 °C (1 min), 10 °C/min up to 230 °C, and
143
40 °C/min up to 280 °C (held for 1 min). The injection temperature was 280 °C, and the transfer
144
line temperature was 280 °C. The ionizing voltage was 70 eV, and the source temperature was
145
250 °C. The mass-selective detector was operated in full scan mode with an m/z range of 50–
146
550. The solvent delay was 1 min.
147 148
2.3.4 Microbiological analysis
149
The total aerobic bacteria counts for the meat samples were obtained using the pour plate
150
method as defined by the National Standard of China (GB 4789.2-2016). The meat (25 g) was
151
blended with KH2PO4 buffer (225 mL) for 2 min to form a 1:10 mixture. From this solution,
152
serial dilutions from 1:102 to 1:107 were prepared. A 1-mL aliquot of the dilution was used as
153
the bacteria group, and 1 mL of the KH2PO4 buffer alone was used as the control. The sample
154
and control were added to separate Petri dishes (90 mm diameter). Total plate count agar (20
155
mL) was then poured into the dishes, which were rotated to mix the solutions and agar evenly.
8
156
The dishes having 30–300 colony-forming units (CFUs) were selected for conducting total
157
plate counts.
158 159
2.4 Statistical analysis
160
The linear regression model was used to study the relationship among the storage period,
161
the total bacteria count, the DMS evolved, and the sensing signal generated from AuNP for
162
raw beef in 4 °C by the package SPSS 25 (Starkey, Geesink, Oddy, & Hopkins, 2015; Starkey,
163
Geesink, van de Ven, & Hopkins, 2017). The regression coefficients, standard errors, and
164
probability level were evaluated from the models, and a sequential model was validated to test
165
the causative relationship between those factors.
166 167
3. Results and Discussion
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3.1 Characterization of AuNPs
169
3.1.1 Synthesis and Morphology of AuNPs
170
AuNPs were synthesized by reacting to a KAuIIICl4 complex with three molar equivalents
171
of NaBH4 as a reducing agent and 6.7 molar equivalents of trisodium citrate as the capping
172
agent. Aqueous DMSO, i.e., H2O/DMSO (2:1, v/v) was used as the medium as it stabilizes
173
AuNPs and does not freeze at low temperature (67% hydration of DMSO decreases its melting
174
point from 18 to -18 °C). Figure 1a shows a transmission electron microscopy (TEM) image
175
of disaggregated AuNPs with diameters of 12 ± 5 nm. Figure 1b shows a TEM image of the
176
DMS-induced aggregate form of the sodium-citrate-capped AuNPs.
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178
3.1.2 Chemosensing properties of AuNPs
179
Titrations of AuNP solutions (82.5 μg/mL) against biogenic sulfides and amines were
180
conducted under ambient conditions in aqueous DMSO. Figures 2a–b show the UV-Vis
181
responses of AuNP solutions towards DMS and histamine, respectively. As the more biogenic
182
compound is added, the absorption of the AuNP solution at 520 nm decreases while that at 670
183
nm increases. The color of test solution changes from red (Pantone 1979c) to purple (Pantone
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242c) and finally to pale gray (Pantone 5305c) (Figures 2a–b, insets). Figures S1–2 show the
185
UV-Vis spectroscopic changes of AuNP solutions in response to increasing amounts of two
186
other common biogenic sulfides, DMSS and DMSSS. The AuNP method detection limits for
187
DMS, DMSS, DMSSS, and histamine were found to be 0.5, 0.2, 0.25, and 0.035 μg/mL,
188
respectively. Thus, the results confirm that biogenic sulfides and amines induce the aggregation
189
of free AuNPs, resulting in UV-Vis spectroscopic changes.
190
Figure 2c shows the results obtained from the spectroscopic titrations of AuNP solutions
191
(82.5 μg/mL) with the analytes DMS, DMSS, DMSSS, histamine, acetic acid, triethylamine,
192
ethylphenol, and phenol (0–50 μg/mL). Of these analytes, only DMS, DMSS, DMSSS, and
193
histamine induce UV-Vis responses at 520 nm in the AuNP solutions. The naked-eye detection
194
limits for DMS, DMSS, DMSSS, and histamine were found to be 1.5, 0.325, 0.40, and 0.075
195
μg/mL, respectively (Table S1). Figure S3 shows the response time for the above detections.
196
In the presence of DMSS (0.8 μg/mL), the absorption of the AuNP solution (82.5 μg/mL) at
197
520 nm decreases with increasing reaction time while that at 670 nm increases in the first 5
198
min and then starts to drop until becoming steady at 1,400 min.
199
10
200
3.2. Analysis of real meat and seafood samples by (i) AuNP; (ii) GC-MS; and (iii) bacteria
201
counts
202
Chemosensing by AuNP solutions for real samples was studied using four different classes
203
of samples (raw meat, fish, crustaceans, and preserved raw meat) during storage at 4 °C for 16
204
days. The AuNP solutions were assessed in terms of their color changes and UV-Vis
205
spectroscopic signals (A0-A/A0 at 520 nm). The real samples were assessed in terms of their
206
total bacteria counts and the amount of DMS generated.
207
Figures 3a–b show that, for raw beef stored at 4 °C, the total bacteria count, the DMS
208
generated, and the UV-Vis spectroscopic signal (A0-A/A0 at 520 nm) of the AuNP solution
209
increase with the increasing number of storage days. Figure 3c shows the color changes
210
observed for the AuNP solution with increasing storage time for raw beef. The color of AuNP
211
solution gradually changes from its original red to purple then to grey-purple from day 1 to 15.
212
Figures S4–S14 show the results of the same experimental setup for raw chicken, pork,
213
preserved beef, preserved chicken, preserved pork, salmon, tuna, toothfish, lobster, shrimp, and
214
prawn, respectively, stored at 4 °C in terms of total bacteria count and UV-Vis signal (A0-A/A0
215
at 520 nm). All show a similar sequential and positive causative relationship.
216
The microbiological criteria for food are commonly used as indicators of food quality. In
217
the current study, the foods were divided into satisfactory, acceptable, and unsatisfactory
218
classes according to their bacterial colony counts. The satisfactory class is microbiologically
219
safe, the acceptable level exhibits the potential to cause public health problems, and the
220
unsatisfactory level necessitates medical attention for the consumer (Centre for Food Safety,
221
2007 and 2014). Table S2 shows the hygiene quality (total plate counts) for different categories
11
222
of food. Figures 4a–b show the relationship between the UV-Vis spectroscopic signals (A0-
223
A/A0 at 520 nm) generated by the AuNP detector solutions with respect to the total bacteria
224
counts for the raw and preserved raw meat, respectively.
225 226
3.3. Statistic relationship among the sensing signal, storage period, bacteria growth, and DMS
227
generated.
228
The statistical relationships among those variables were analyzed by the linear regression
229
model fitted using the package SPSS 25 (Starkey, Geesink, Oddy, & Hopkins, 2015; Starkey,
230
Geesink, van de Ven, & Hopkins, 2017). We postulated that the relationship is either in a
231
multivariate or sequential way. Two different models were used to identify their relationship.
232
The models are as follows:
233 234 235 236 237 238 239 240
Model 1. Sensing Signal = Storage period + Bacteria growth + DMS generated Model 2. Storage day → Bacteria growth → DMS generated → Sensing signal Model 2.1 Bacteria growth = Storage period Model 2.2 DMS generated = Bacteria growth Model 2.3 Sensing Signal = DMS generated
241
Among all the models proposed, Models 2.1–2.3 were statistical significant with P = 0.000
242
for their corresponding variables (Table 1). All the positive coefficients in Models 2.1–2.3
243
revealed the positive causative relationships between the factors. R2 values of Models 2.1–2.3
244
were found as 0.970–0.986. While the Model 1 was statistically insignificance (P > 0.05). It is
245
believed that the factors tested in the Model 2 are in a sequential and positive causative
246
relationship (the equations of Models 2.1–2.3 are as follows, respectively, Bacteria growth =
247
0.253 × Storage period + 4.886; DMS generated = 2.571 × Bacteria growth - 13.432; and
12
248
Sensing Signal = 0.042 × DMS generated + 0.022).
249
Although beef, chicken, and pork are different kinds of meat, they show a similar
250
relationship between their total bacteria counts and AuNP chemosensing responses. With
251
increasing total bacteria count found in the meat (raw and preserved raw), the sensing signal
252
generated by the AuNP increases. The signal responses for the raw meat are 0.075, 0.15, and
253
0.26 when the total bacteria counts are 106, 107, and 108, respectively. The signal responses
254
from the preserved raw meat are 0, 0.1, and 0.25 when the total bacteria counts are 106, 107,
255
and 108, respectively. According to food safety standards, raw meat and meat mixed with
256
dressings, dips, and pastes are classified as unsatisfactory when the CFU is higher than 107.
257
Thus, response signals (A0-A/A0 at 520 nm) of >0.15 and >0.1 can be used as alarm values for
258
the spoilage of raw and preserved raw meat, respectively.
259
Figures 4c–d demonstrate the correlations between the total bacteria counts and AuNP
260
detector UV-Vis signals (A0-A/A0 at 520 nm) for raw fish and crustaceans, respectively.
261
Salmon, tuna, and toothfish are representative examples of sea fish, while lobster, shrimp, and
262
prawn are crustaceans. All results present a positive relationship between total bacteria count
263
and AuNP chemosensing response. The signals from the raw fish are 0.15, 0.45, and 0.95 when
264
their total bacteria counts are 106, 107, and 108, respectively. The signals from the crustaceans
265
are 0.2, 0.35, and 0.7 when their total bacteria counts are 106, 107, and 108, respectively. The
266
higher levels of biogenic amines were found as the reason for the larger sensing signal response
267
for the fish and crustaceans compared to those for the meat samples (Cohen et al., 2015;
268
Odeyemi et al., 2018). According to food safety standards, raw seafood, including sushi,
269
sashimi, raw ready-to-eat fish, and crustaceans, are classified as unsatisfactory when the CFU
13
270
is higher than 107. Thus, UV-Vis signals of >0.45 and >0.35 can be used as alarms for the
271
spoilage of raw fish and crustaceans, respectively.
272 273
4. Conclusions
274
AuNPs were successfully synthesized, and their sensing properties toward various volatile
275
biogenic compounds (VBCs) were evaluated. The results indicated that AuNPs are highly
276
stable in a 2:1 H2O/DMSO mixture and that their colorimetric response is selective for DMS,
277
DMSS, DMSSS, and histamine but not acetic acid, trimethylamine, ethylphenol, and phenol.
278
The limits of detection for DMS, DMSS, DMSSS, and histamine are 0.5, 0.2, 0.25, and 0.035
279
μg/mL, respectively. Furthermore, naked-eye detection limits for these four VBCs using the
280
developed probe are 1.5, 0.325, 0.4, and 0.075 μg/mL.
281
The AuNP solution was applied to the detection of VBCs from 12 different raw meat
282
samples stored at 4 °C, and a strong correlation between their bacteria counts and AuNP
283
sensing signal was observed. AuNP solution UV-Vis signals (A0-A/A0 at 520 nm) of 0.15, 0.1,
284
0.45, and 0.35 were found to represent alarm values for dangerous spoilage levels (i.e., CFUs
285
of 107) in raw meat, preserved raw meat, raw fish and raw crustaceans, respectively.
286 287
Acknowledgments
288
This work was funded by Grants from the Innovation Technology Commission of Hong Kong
289
SAR (ITS/251/16FX).
290 291
Conflicts of interest
14
292
The authors declare no conflicts of interest.
293 294
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403
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404
20
405
Table 1. The statistical relationships among the storage period, the bacteria growth, the amount
406
of DMS evolved, and the color changes of the AuNP sensor for the freshness of raw beef
407
(regression coefficients, standard errors, and probability level).
408
409
Model 1a Intercept Storage period Bacteria DMS
Coefficient 0.414 0.013 -0.078 0.053
Std. error 0.22 0.008 0.043 0.013
P-value 0.2 0.267 0.211 0.06
Model 2.1b Intercept Storage period
Coefficient 4.886 0.253
Std. error 0.198 0.022
P-value 0.000 0.000
Model 2.2c Intercept Bacteria growth
Coefficient -13.432 2.571
Std. error 1.408 0.208
P-value 0.001 0.000
Model 2.3d Coefficient Std. error P-value Intercept 0.022 0.013 0.167 DMS generated 0.042 0.003 0.000 aR2 = 0.995; bR2 = 0.970; cR2 = 0.975; dR2 = 0.986
21
410 411
Figure 1. TEM images of (a) the non-aggregated form of sodium-citrate-capped AuNPs, and
412
(b) the DMS-induced aggregated form of the sodium-citrate-capped AuNPs.
22
413 414
Figure 2. UV-Vis spectroscopic titration results and (insets) naked-eyed detectable responses
415
of AuNP solutions (82.5 μg/mL) to (a) DMS (0–2.5 μg/mL) and (b) histamine (0–0.12 μg/mL).
416
(c) Responses of AuNP solutions (82.5 μg/mL) to DMS, DMSS, DMSSS, histamine, acetic
417
acid, triethylamine, ethylphenol and phenol (0–50 μg/mL). All titrations were conducted under
418
ambient conditions in H2O/DMSO (2:1, v/v). 23
419 420
Figure 3. Total bacteria count (—●—) and (a) UV-Vis signals (A0-A/A0 at 520 nm) of AuNP
421
solutions (—▲—), (b) DMS generated (——) by raw beef vs. storage time (4 °C), and (c)
422
naked-eyed-detectable responses of AuNP solutions (82.5 μg/mL) with storage time for raw
423
beef (4°C). The error bars are the standard errors of the means from three independent
424
experiments. 24
425 426 427
Figure 4. UV-Vis signals (A0-A /A0 at 520 nm) for AuNP solutions generated with respect to
428
total bacteria count in (a) raw chicken, pork, and beef, and (b) preserved raw chicken, pork,
429
and beef (in BBQ sauce), (c) raw salmon, tuna, and toothfish, and (d) raw lobster, shrimp, and
430
prawn. The error bars are the standard errors of the means from three independent experiments.
431 432 433
25
Supporting information
434 435 436
Biogenic
amines-
and
sulfides-responsive
gold
437
nanoparticles for real-time visual detection of raw meat,
438
fish, crustaceans, and preserved meat
439 440
Cheuk-Fai CHOW*
441 442
Department of Science and Environmental Studies, The Education University of Hong Kong,
443
10 Lo Ping Road, Tai Po, Hong Kong SAR, China
444
*Email: [email protected]
445 446
26
447
Table S1. The naked-eye detection limits of the AuNP solution to DMS, DMSS, DMSSS, and
448
histamine in various H2O:DMSO mixture. Naked
eye DMS
DMSS
DMSSS
Histamine
detection limit H2O: DMSO ratio 7:3
0.5 μg/mL
0.25 μg/mL
0.25 μg/mL
----
6.67:3.33
1.5 μg/mL
0.325 μg/mL
0.4 μg/mL
0.075 μg/mL
6:4
2.5 μg/mL
0.5 μg/mL
1 μg/mL
----
449 450
27
451
Table S2. Classification of Microbiological Quality Food category
Examples
Results (CFU/g) Satisfactory
Extended
shelf Modified
life food products packaging
atmosphere <106 (MAP)
requiring
vacuum-packed
refrigeration*
products,
e.g.,
Acceptable
Unsatisfactory
106-<108
≥108
106-<107
≥107
or
beef,
pork, chicken, fish, fruit, and vegetables Raw ready-to-eat Sushi, sashimi, smoked <106 meat
and
fish, salmon, etc.
cold smoked fish* 452
* Centre for Food Safety, 2014
28
453 454 455
Figure S1. (a) UV-Vis spectroscopic titrations and (b) naked eyed responses of AuNP solution
456
(82.5 μg/mL) by DMSS (0–1.25 μg/mL). The studies were conducted at the ambient conditions
457
in H2O:DMSO mixture (vol:vol 2:1).
29
458 459 460
Figure S2. (a) UV-Vis spectroscopic titrations and (b) naked eyed responses of AuNP solution
461
(82.5 μg/mL) by DMSSS (0–1.25 μg/mL). The studies were conducted at the ambient
462
conditions in H2O:DMSO mixture (vol:vol 2:1).
30
463 464
Figure S3. The UV-Vis spectroscopic responses, A/A0 520 nm () and 670 nm (◄), of the
465
AuNP solution in the presence of DMSS (0.8 μg/mL) with respect to time. The reaction was
466
conducted at the ambient conditions in H2O:DMSO mixture (vol:vol 2:1).
31
467 468 469
Figure S4. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
470
520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw chicken with respect
471
to the storage day (4 ºC) (——). The error bars are the standard errors of the means from
472
three independent experiments.
473
474 475 476
Figure S5. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
477
520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw pork with respect
478
to the storage day (4 ºC) (——). The error bars are the standard errors of the means from
479
three independent experiments.
32
480 481 482
Figure S6. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
483
520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw preserved beef
484
(BBQ sauce) with respect to the storage day (4ºC) (——). The error bars are the standard
485
errors of the means from three independent experiments.
486
487 488 489
Figure S7. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
490
520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw preserved chicken
491
(BBQ sauce) with respect to the storage day (4ºC) (——). The error bars are the standard
492
errors of the means from three independent experiments.
33
493 494
Figure S8. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
495
520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw preserved pork
496
(BBQ sauce) with respect to the storage day (4ºC) (——). The error bars are the standard
497
errors of the means from three independent experiments.
498
499 500 501
Figure S9. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
502
520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw salmon fish with
503
respect to the storage day (4ºC) (——). The error bars are the standard errors of the means
504
from three independent experiments.
34
505 506 507
Figure S10. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
508
520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw tuna fish with
509
respect to the storage day (4ºC) (——). The error bars are the standard errors of the means
510
from three independent experiments.
511
512 513 514
Figure S11. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
515
520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw toothfish with
516
respect to the storage day (4ºC) (——). The error bars are the standard errors of the means
517
from three independent experiments.
35
518 519 520
Figure S12. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
521
520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw lobster with respect
522
to the storage day (4ºC) (——). The error bars are the standard errors of the means from
523
three independent experiments.
524
525 526 527
Figure S13. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
528
520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw shrimp with respect
529
to the storage day (4ºC) (——). The error bars are the standard errors of the means from
530
three independent experiments.
36
531 532
Figure S14. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
533
520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw prawn with respect
534
to the storage day (4ºC) (——). The error bars are the standard errors of the means from
535
three independent experiments.
536 537 538 539 540 541 542
Highlights
A novel AuNP-based sensor for meat/fish spoilage is developed The sensor detects dimethyl sulfide at 0.5 ppm and histamine at 0.035 ppm The sensor exhibits excellent selectivity for the markers of interest A sequential and positive causative statistic model of the detection is established
543 544 545
Declaration of interests
546 547 548
☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
549 550 551
☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
37
552 553 554 555 556 557
Author Contributions Section
558 559
Biogenic amines- and sulfides-responsive gold nanoparticles for real-time visual detection of
560
raw meat, fish, crustaceans, and preserved meat
561 562
Cheuk-Fai CHOW*
563
Department of Science and Environmental Studies, The Education University of Hong Kong,
564
10 Lo Ping Road, Tai Po, Hong Kong SAR, China
565
*Email: [email protected]
566
567
Chow C.F. is the sole author of this manuscript. He designed the study, wrote the article, and
568
made the final approval of the version to be submitted.
569 570 571
38
S0308-8146(19)32046-1 https://doi.org/10.1016/j.foodchem.2019.125908 FOCH 125908
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
29 May 2019 13 November 2019 13 November 2019
Please cite this article as: CHOW, C-F., Biogenic amines- and sulfides-responsive gold nanoparticles for real-time visual detection of raw meat, fish, crustaceans, and preserved meat, Food Chemistry (2019), doi: https://doi.org/ 10.1016/j.foodchem.2019.125908
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© 2019 Published by Elsevier Ltd.
1
Biogenic
amines-
and
sulfides-responsive
gold
2
nanoparticles for real-time visual detection of raw meat,
3
fish, crustaceans, and preserved meat
4 5
Cheuk-Fai CHOW*
6
Department of Science and Environmental Studies, The Education University of Hong Kong,
7
10 Lo Ping Road, Tai Po, Hong Kong SAR, China
8
*Email: [email protected]
9 10
Abstract
11
A colorimetric probe based on gold nanoparticles (AuNPs) which is sensitive to two important
12
volatile biogenic markers, i.e., dimethyl sulfide and histamine, is developed to monitor the
13
spoilage of raw meat, fish, crustaceans, and preserved meat. The colorimetric detection is
14
attributed to the transformation of the non-aggregated form of AuNPs to its aggregated form
15
upon binding of the biomarkers. The AuNPs enable the detection of dimethyl sulfide and
16
histamine at limits of 0.5 and 0.035 μg/mL, respectively. Furthermore, the probe exhibits
17
excellent selectivity for those markers in the presence of other volatiles commonly generated
18
by spoiled real meat and seafood. A sequential and positive causative relationship is exhibited
19
among the storage period, the total bacteria count, the DMS evolved, and the chemosensing
20
signal generated. Thus, this probe serves as a nondestructive and cost-effective detector for the
21
real-time monitoring of meat spoilage.
22 1
23
Keywords: dimethyl sulfide, dimethyl disulfide, dimethyl trisulfide, histamine, chemosensing
2
24
1. Introduction
25
The explosive growth of food safety problems pushes us to search for a simple and accurate
26
food monitoring system. Detection of volatile biogenic biomarkers released from food is a
27
common strategy to determine food freshness and avoid food poisoning (WHO, Food Safety).
28
Common approach to the quality control of meat and seafood is to monitor the levels of volatile
29
biogenic sulfides and amines, respectively, they release (Alexandrakis, Brunton, Downey, &
30
Scannell, 2012; Mikš-Krajnik, Yoon, & Yuk, 2015; Tománková, Borˇilová, Streinhauserová,
31
& Gallas, 2012; Al-Attabi, D’arcy, & Deeth, 2009; Romano, Perello, deRevel, & Lonvaud-
32
Funel, 2008). Research has demonstrated that dimethyl sulfide (DMS, CH3SCH3), dimethyl
33
disulfide (DMSS, CH3SSCH3), and dimethyl trisulfide (DMSSS, CH3SSSCH3) levels in raw
34
beef, pork, and poultry increase as the meat spoils (Lovestead & Bruno, 2010; Varlet &
35
Fernandez, 2010; Isogal et al., 2009). Furthermore, histamine is a well-known biomarker for
36
seafood monitoring (Scallan, Griffin, Angulo, Tauxe, & Hoekstra, 2011; Landete, de las Rivas,
37
Marcobal, & Munoz, 2007; Vinci & Antonelli, 2002; Onal, 2007; Liu, Petty, Sazama, &
38
Swager, 2015). For example, according to the U.S. Food and Drug Administration (FDA), the
39
histamine level in fresh mackerel should not exceed 50 μg/g (FDA, 2004). Machiels (2003)
40
and Jaffres (2011) used gas chromatography-mass spectrometry (GC-MS) to assess the degree
41
of spoilage of fresh beef and shrimp by analyzing biogenic sulfides and amines levels
42
respectively. Furthermore, the analysis of biogenic sulfides and amines recently described in
43
the literature make use of molecular imprinting methods (Greene & Shimizu 2005), indicator-
44
displacement assays (Chow, Lam & Wong, 2013; Chow, Ho, Sun, Lu, Wong, Tang, Gong,
45
2017), and enzymatic techniques (Mertz & Zimmerman, 2003). However, although those
3
46
analytical methods are precise and reasonably accurate, they are costly, require special
47
procedures and conditions, and need highly specialized equipment (UV-vis spectrometer,
48
spectrofluorometer, and GC-MS). Therefore, simple methods and technologies for monitoring
49
the freshness of food products are essential.
50
Nanotechnology has been starting to attract industrial and academic research attention as
51
a means to address food freshness and food poisoning problems for three decades. Gold
52
nanoparticles (AuNPs), silver nanoparticles (AgNPs), zinc oxide nanoparticles (ZnO-NPs), and
53
titanium dioxide nanoparticles (TiO2-NPs) are often used as the active packaging materials to
54
inhibit bacterial growth on food (Akbar & Anal, 2014; De Moura, Mattoso, & Zucolotto, 2012;
55
El-Wakil, Hassan, Abou-Zeid, & Dufresne, 2015; Bumbudsanpharoke & Ko, 2015; Matteo,
56
Alessandra, & Saverio, 2009; Liu, Lu, Huang, Li & Xu, 2018). Furthermore, nano-clay and
57
bio-nano-composites, such as chitosan, carboxymethyl cellulose, starch, and cellophane, have
58
been used as barriers to prevent the contact of oxygen and moisture with food (Youssef & El-
59
Sayed, 2018). However, while these nano-technological approaches are proven to prevent food
60
spoilage, there are only rare examples of nanotechnology being used to monitor food freshness
61
(Ali, 2012; Chen, Zhou & Zhao, 2018; Liu, Lu, Huang, Li & Xu, 2018).
62
Accordingly, in the current study, we applied AuNPs as a naked-eye colorimetric sensor
63
for the detection of DMS, allowing us to monitor the freshness of foods readily (raw chicken,
64
raw pork, raw beef, preserved raw chicken, preserved raw pork, preserved raw beef, raw
65
salmon, raw tuna, raw toothfish, raw lobster, raw shrimp, and raw prawn). The basic principle
66
of this technology is that free AuNPs, which initially present a red color, form grey-colored
67
aggregates in the presence of DMS produced during food spoilage. Although AuNPs have been
4
68
used in biology and biochemistry for the detection of sulfur-containing compounds such as
69
cysteine, esterase, and DNA (Jongjinakool et al., 2014; Li et al., 2012; Li et al., 2014), no
70
studies of AuNP are found (i) to determine DMS in real food samples and (ii) to correlate its
71
sensing signal produced with the number of bacteria generated and concentration of the volatile
72
biomarker generated.
73 74
2. Experimental Section
75
2.1 Materials and chemicals
76
Potassium gold(III) chloride (KAuCl4), DMS (99%), and histamine were obtained from
77
Aldrich (Steinheim, Germany). DMSS (99%) was obtained from Acros (Geel, Belgium).
78
DMSSS (98%) was purchased from TCI (Tokyo, Japan). Dimethyl sulfoxide (DMSO, 99%)
79
was purchased from Anaqua Chemicals Supply (Cleveland, USA). Potassium dihydrogen
80
phosphate, trisodium citrate, and sodium borohydride (NaBH4) were purchased from
81
Honeywell Riedel-de Haen (Seelze, Germany). Total plate count agar (tryptone glucose yeast
82
agar) was purchased from Oxoid Ltd., (Basingstoke, Hampshire, England). The water used in
83
all syntheses and spectroscopic titrations was deionized and sterilized.
84 85
2.2 Synthesis and characterization
86
2.2.1 Synthesis and characterization of AuNPs
87
KAuCl4 (3 × 10-5 mol) and trisodium citrate (2 × 10-4 mol) were added to a conical flask
88
containing 50 mL water. NaBH4 (9 × 10-5 mol) was then added, and the mixture was stirred
89
under ambient conditions for 1 h. During the reaction, the color of the mixture changed from
5
90
pale-yellow to deep-red (immediately after NaBH4 addition) and finally to red. The red solution
91
was mixed with DMSO (33.33 mL) and then diluted with water to 100.00 mL in a volumetric
92
flask. The mixture was allowed to stand overnight prior to titration experiments. The stability
93
of AuNPs (82.5 μg/mL) in H2O/DMSO (2:1, v/v) was assessed by monitoring the UV-Vis
94
absorption of the mixture at 520 nm over 400 h.
95 96
2.2.2 Titration of the AuNP solution by various analytes
97
Titrations of AuNP (82.5 μg/mL) against DMS, DMSS, DMSSS, histamine, acetic acid,
98
triethylamine, ethylphenol, and phenol (0–50 μg/mL) in H2O/DMSO (2:1, v/v) were conducted
99
under ambient conditions. The UV-Vis spectroscopic responses of the resulting solutions at
100
520 nm were monitored.
101 102
2.2.3 Response times of AuNP solutions towards DMS, DMSS, DMSSS, and histamine
103
The response times of AuNP solutions (82.5 μg/mL) in H2O/DMSO (2:1, v/v) towards
104
DMS (1.0 μg/mL), DMSS (0.8 μg/mL), DMSSS (0.8 μg/mL), and histamine (0.16 μg/mL)
105
were assessed under ambient conditions by monitoring the UV-Vis absorptions at 520 and 670
106
nm of the mixtures over time (0–25 h). The scanning frequency was 0.5 min per cycle.
107 108
2.3 Analysis of real meat and seafood samples by (i) AuNP; (ii) GC-MS; and (iii) bacteria
109
counts
110
2.3.1 Sample processing
111
Twelve different types of fresh meat in four categories, i.e., (i) raw chicken, pork, and beef,
6
112
(ii) raw tuna, salmon, and toothfish, (iii) raw lobster, prawn, and shrimp, and (iv) preserved
113
raw chicken, pork, and beef, were tested in this study. Each category was randomly purchased
114
from a supermarket. All the samples purchased were skinless and boneless. For the lobster,
115
prawn, and shrimp, their shells were aseptically removed in the laboratory. All the batches were
116
brought to the laboratory in an ice chest and analyzed within 4 h. Seven kinds of meat including
117
the raw chicken, pork, beef, preserved raw chicken, preserved raw pork, preserved raw beef,
118
and raw shrimp originated from China; while the raw lobster came from Canada; the raw prawn
119
came from Malaysia, raw tuna originated from Philippine, raw salmon and toothfish came from
120
Norway and France respectively. Each batch was cut into small portions (6(L)×4(W)×2(H) cm3)
121
for the experiments. 200-mL microwave boxes with lids, plastic containers, and thermoplastic
122
polyurethane films were sterilized in an autoclave and/or under UV irradiation.
123 124
2.3.2 Chemosensing by AuNP
125
6 × 100-g meat samples were used to conduct the sensing experiment, and the whole set
126
of experiments was repeated three times. In general, six 200-mL microwave boxes with each
127
containing the 100-g meat sample and a plastic container with 3.0 mL of AuNP solution (82.5
128
μg/mL) capped by a thermoplastic polyurethane film were sealed and kept in a refrigerator at
129
4 °C for 0–16 days. The headspace left in the box was around 1/3 of its volume. After storage
130
at a particular day, the color of AuNP solutions was analyzed by using the pentone color chart
131
(Red: Pantone 1979c, Purple: Pantone 242c, Grey: Pantone 5285c; and Pale Grey: Pantone
132
5305c) and UV-Vis spectroscopy. GC-MS was used to determine the amount of DMS
133
generated and diffused in the solutions. The meat samples were tested for total bacteria count.
7
134
Three independent tests were conducted for each type of meat.
135 136
2.3.3 GC-MS analysis
137
GC-MS experiments were carried out on a Hewlett-Packard 6890 GC system equipped
138
with a 5973 mass-selective detector. Injections were performed with an HP 7683 autosampler.
139
Helium was used as the carrier gas at a flow rate of 1 mL/min. The separation was performed
140
with an HP-5MS capillary column (30 m × 0.25 mm, 0.25 µm). The GC conditions were a 10:1
141
injection split with the column temperature starting at 40 °C (1 min) and then increasing at
142
20 °C/min up to 100 °C (1 min), 20 °C/min up to 180 °C (1 min), 10 °C/min up to 230 °C, and
143
40 °C/min up to 280 °C (held for 1 min). The injection temperature was 280 °C, and the transfer
144
line temperature was 280 °C. The ionizing voltage was 70 eV, and the source temperature was
145
250 °C. The mass-selective detector was operated in full scan mode with an m/z range of 50–
146
550. The solvent delay was 1 min.
147 148
2.3.4 Microbiological analysis
149
The total aerobic bacteria counts for the meat samples were obtained using the pour plate
150
method as defined by the National Standard of China (GB 4789.2-2016). The meat (25 g) was
151
blended with KH2PO4 buffer (225 mL) for 2 min to form a 1:10 mixture. From this solution,
152
serial dilutions from 1:102 to 1:107 were prepared. A 1-mL aliquot of the dilution was used as
153
the bacteria group, and 1 mL of the KH2PO4 buffer alone was used as the control. The sample
154
and control were added to separate Petri dishes (90 mm diameter). Total plate count agar (20
155
mL) was then poured into the dishes, which were rotated to mix the solutions and agar evenly.
8
156
The dishes having 30–300 colony-forming units (CFUs) were selected for conducting total
157
plate counts.
158 159
2.4 Statistical analysis
160
The linear regression model was used to study the relationship among the storage period,
161
the total bacteria count, the DMS evolved, and the sensing signal generated from AuNP for
162
raw beef in 4 °C by the package SPSS 25 (Starkey, Geesink, Oddy, & Hopkins, 2015; Starkey,
163
Geesink, van de Ven, & Hopkins, 2017). The regression coefficients, standard errors, and
164
probability level were evaluated from the models, and a sequential model was validated to test
165
the causative relationship between those factors.
166 167
3. Results and Discussion
168
3.1 Characterization of AuNPs
169
3.1.1 Synthesis and Morphology of AuNPs
170
AuNPs were synthesized by reacting to a KAuIIICl4 complex with three molar equivalents
171
of NaBH4 as a reducing agent and 6.7 molar equivalents of trisodium citrate as the capping
172
agent. Aqueous DMSO, i.e., H2O/DMSO (2:1, v/v) was used as the medium as it stabilizes
173
AuNPs and does not freeze at low temperature (67% hydration of DMSO decreases its melting
174
point from 18 to -18 °C). Figure 1a shows a transmission electron microscopy (TEM) image
175
of disaggregated AuNPs with diameters of 12 ± 5 nm. Figure 1b shows a TEM image of the
176
DMS-induced aggregate form of the sodium-citrate-capped AuNPs.
177
9
178
3.1.2 Chemosensing properties of AuNPs
179
Titrations of AuNP solutions (82.5 μg/mL) against biogenic sulfides and amines were
180
conducted under ambient conditions in aqueous DMSO. Figures 2a–b show the UV-Vis
181
responses of AuNP solutions towards DMS and histamine, respectively. As the more biogenic
182
compound is added, the absorption of the AuNP solution at 520 nm decreases while that at 670
183
nm increases. The color of test solution changes from red (Pantone 1979c) to purple (Pantone
184
242c) and finally to pale gray (Pantone 5305c) (Figures 2a–b, insets). Figures S1–2 show the
185
UV-Vis spectroscopic changes of AuNP solutions in response to increasing amounts of two
186
other common biogenic sulfides, DMSS and DMSSS. The AuNP method detection limits for
187
DMS, DMSS, DMSSS, and histamine were found to be 0.5, 0.2, 0.25, and 0.035 μg/mL,
188
respectively. Thus, the results confirm that biogenic sulfides and amines induce the aggregation
189
of free AuNPs, resulting in UV-Vis spectroscopic changes.
190
Figure 2c shows the results obtained from the spectroscopic titrations of AuNP solutions
191
(82.5 μg/mL) with the analytes DMS, DMSS, DMSSS, histamine, acetic acid, triethylamine,
192
ethylphenol, and phenol (0–50 μg/mL). Of these analytes, only DMS, DMSS, DMSSS, and
193
histamine induce UV-Vis responses at 520 nm in the AuNP solutions. The naked-eye detection
194
limits for DMS, DMSS, DMSSS, and histamine were found to be 1.5, 0.325, 0.40, and 0.075
195
μg/mL, respectively (Table S1). Figure S3 shows the response time for the above detections.
196
In the presence of DMSS (0.8 μg/mL), the absorption of the AuNP solution (82.5 μg/mL) at
197
520 nm decreases with increasing reaction time while that at 670 nm increases in the first 5
198
min and then starts to drop until becoming steady at 1,400 min.
199
10
200
3.2. Analysis of real meat and seafood samples by (i) AuNP; (ii) GC-MS; and (iii) bacteria
201
counts
202
Chemosensing by AuNP solutions for real samples was studied using four different classes
203
of samples (raw meat, fish, crustaceans, and preserved raw meat) during storage at 4 °C for 16
204
days. The AuNP solutions were assessed in terms of their color changes and UV-Vis
205
spectroscopic signals (A0-A/A0 at 520 nm). The real samples were assessed in terms of their
206
total bacteria counts and the amount of DMS generated.
207
Figures 3a–b show that, for raw beef stored at 4 °C, the total bacteria count, the DMS
208
generated, and the UV-Vis spectroscopic signal (A0-A/A0 at 520 nm) of the AuNP solution
209
increase with the increasing number of storage days. Figure 3c shows the color changes
210
observed for the AuNP solution with increasing storage time for raw beef. The color of AuNP
211
solution gradually changes from its original red to purple then to grey-purple from day 1 to 15.
212
Figures S4–S14 show the results of the same experimental setup for raw chicken, pork,
213
preserved beef, preserved chicken, preserved pork, salmon, tuna, toothfish, lobster, shrimp, and
214
prawn, respectively, stored at 4 °C in terms of total bacteria count and UV-Vis signal (A0-A/A0
215
at 520 nm). All show a similar sequential and positive causative relationship.
216
The microbiological criteria for food are commonly used as indicators of food quality. In
217
the current study, the foods were divided into satisfactory, acceptable, and unsatisfactory
218
classes according to their bacterial colony counts. The satisfactory class is microbiologically
219
safe, the acceptable level exhibits the potential to cause public health problems, and the
220
unsatisfactory level necessitates medical attention for the consumer (Centre for Food Safety,
221
2007 and 2014). Table S2 shows the hygiene quality (total plate counts) for different categories
11
222
of food. Figures 4a–b show the relationship between the UV-Vis spectroscopic signals (A0-
223
A/A0 at 520 nm) generated by the AuNP detector solutions with respect to the total bacteria
224
counts for the raw and preserved raw meat, respectively.
225 226
3.3. Statistic relationship among the sensing signal, storage period, bacteria growth, and DMS
227
generated.
228
The statistical relationships among those variables were analyzed by the linear regression
229
model fitted using the package SPSS 25 (Starkey, Geesink, Oddy, & Hopkins, 2015; Starkey,
230
Geesink, van de Ven, & Hopkins, 2017). We postulated that the relationship is either in a
231
multivariate or sequential way. Two different models were used to identify their relationship.
232
The models are as follows:
233 234 235 236 237 238 239 240
Model 1. Sensing Signal = Storage period + Bacteria growth + DMS generated Model 2. Storage day → Bacteria growth → DMS generated → Sensing signal Model 2.1 Bacteria growth = Storage period Model 2.2 DMS generated = Bacteria growth Model 2.3 Sensing Signal = DMS generated
241
Among all the models proposed, Models 2.1–2.3 were statistical significant with P = 0.000
242
for their corresponding variables (Table 1). All the positive coefficients in Models 2.1–2.3
243
revealed the positive causative relationships between the factors. R2 values of Models 2.1–2.3
244
were found as 0.970–0.986. While the Model 1 was statistically insignificance (P > 0.05). It is
245
believed that the factors tested in the Model 2 are in a sequential and positive causative
246
relationship (the equations of Models 2.1–2.3 are as follows, respectively, Bacteria growth =
247
0.253 × Storage period + 4.886; DMS generated = 2.571 × Bacteria growth - 13.432; and
12
248
Sensing Signal = 0.042 × DMS generated + 0.022).
249
Although beef, chicken, and pork are different kinds of meat, they show a similar
250
relationship between their total bacteria counts and AuNP chemosensing responses. With
251
increasing total bacteria count found in the meat (raw and preserved raw), the sensing signal
252
generated by the AuNP increases. The signal responses for the raw meat are 0.075, 0.15, and
253
0.26 when the total bacteria counts are 106, 107, and 108, respectively. The signal responses
254
from the preserved raw meat are 0, 0.1, and 0.25 when the total bacteria counts are 106, 107,
255
and 108, respectively. According to food safety standards, raw meat and meat mixed with
256
dressings, dips, and pastes are classified as unsatisfactory when the CFU is higher than 107.
257
Thus, response signals (A0-A/A0 at 520 nm) of >0.15 and >0.1 can be used as alarm values for
258
the spoilage of raw and preserved raw meat, respectively.
259
Figures 4c–d demonstrate the correlations between the total bacteria counts and AuNP
260
detector UV-Vis signals (A0-A/A0 at 520 nm) for raw fish and crustaceans, respectively.
261
Salmon, tuna, and toothfish are representative examples of sea fish, while lobster, shrimp, and
262
prawn are crustaceans. All results present a positive relationship between total bacteria count
263
and AuNP chemosensing response. The signals from the raw fish are 0.15, 0.45, and 0.95 when
264
their total bacteria counts are 106, 107, and 108, respectively. The signals from the crustaceans
265
are 0.2, 0.35, and 0.7 when their total bacteria counts are 106, 107, and 108, respectively. The
266
higher levels of biogenic amines were found as the reason for the larger sensing signal response
267
for the fish and crustaceans compared to those for the meat samples (Cohen et al., 2015;
268
Odeyemi et al., 2018). According to food safety standards, raw seafood, including sushi,
269
sashimi, raw ready-to-eat fish, and crustaceans, are classified as unsatisfactory when the CFU
13
270
is higher than 107. Thus, UV-Vis signals of >0.45 and >0.35 can be used as alarms for the
271
spoilage of raw fish and crustaceans, respectively.
272 273
4. Conclusions
274
AuNPs were successfully synthesized, and their sensing properties toward various volatile
275
biogenic compounds (VBCs) were evaluated. The results indicated that AuNPs are highly
276
stable in a 2:1 H2O/DMSO mixture and that their colorimetric response is selective for DMS,
277
DMSS, DMSSS, and histamine but not acetic acid, trimethylamine, ethylphenol, and phenol.
278
The limits of detection for DMS, DMSS, DMSSS, and histamine are 0.5, 0.2, 0.25, and 0.035
279
μg/mL, respectively. Furthermore, naked-eye detection limits for these four VBCs using the
280
developed probe are 1.5, 0.325, 0.4, and 0.075 μg/mL.
281
The AuNP solution was applied to the detection of VBCs from 12 different raw meat
282
samples stored at 4 °C, and a strong correlation between their bacteria counts and AuNP
283
sensing signal was observed. AuNP solution UV-Vis signals (A0-A/A0 at 520 nm) of 0.15, 0.1,
284
0.45, and 0.35 were found to represent alarm values for dangerous spoilage levels (i.e., CFUs
285
of 107) in raw meat, preserved raw meat, raw fish and raw crustaceans, respectively.
286 287
Acknowledgments
288
This work was funded by Grants from the Innovation Technology Commission of Hong Kong
289
SAR (ITS/251/16FX).
290 291
Conflicts of interest
14
292
The authors declare no conflicts of interest.
293 294
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Table 1. The statistical relationships among the storage period, the bacteria growth, the amount
406
of DMS evolved, and the color changes of the AuNP sensor for the freshness of raw beef
407
(regression coefficients, standard errors, and probability level).
408
409
Model 1a Intercept Storage period Bacteria DMS
Coefficient 0.414 0.013 -0.078 0.053
Std. error 0.22 0.008 0.043 0.013
P-value 0.2 0.267 0.211 0.06
Model 2.1b Intercept Storage period
Coefficient 4.886 0.253
Std. error 0.198 0.022
P-value 0.000 0.000
Model 2.2c Intercept Bacteria growth
Coefficient -13.432 2.571
Std. error 1.408 0.208
P-value 0.001 0.000
Model 2.3d Coefficient Std. error P-value Intercept 0.022 0.013 0.167 DMS generated 0.042 0.003 0.000 aR2 = 0.995; bR2 = 0.970; cR2 = 0.975; dR2 = 0.986
21
410 411
Figure 1. TEM images of (a) the non-aggregated form of sodium-citrate-capped AuNPs, and
412
(b) the DMS-induced aggregated form of the sodium-citrate-capped AuNPs.
22
413 414
Figure 2. UV-Vis spectroscopic titration results and (insets) naked-eyed detectable responses
415
of AuNP solutions (82.5 μg/mL) to (a) DMS (0–2.5 μg/mL) and (b) histamine (0–0.12 μg/mL).
416
(c) Responses of AuNP solutions (82.5 μg/mL) to DMS, DMSS, DMSSS, histamine, acetic
417
acid, triethylamine, ethylphenol and phenol (0–50 μg/mL). All titrations were conducted under
418
ambient conditions in H2O/DMSO (2:1, v/v). 23
419 420
Figure 3. Total bacteria count (—●—) and (a) UV-Vis signals (A0-A/A0 at 520 nm) of AuNP
421
solutions (—▲—), (b) DMS generated (——) by raw beef vs. storage time (4 °C), and (c)
422
naked-eyed-detectable responses of AuNP solutions (82.5 μg/mL) with storage time for raw
423
beef (4°C). The error bars are the standard errors of the means from three independent
424
experiments. 24
425 426 427
Figure 4. UV-Vis signals (A0-A /A0 at 520 nm) for AuNP solutions generated with respect to
428
total bacteria count in (a) raw chicken, pork, and beef, and (b) preserved raw chicken, pork,
429
and beef (in BBQ sauce), (c) raw salmon, tuna, and toothfish, and (d) raw lobster, shrimp, and
430
prawn. The error bars are the standard errors of the means from three independent experiments.
431 432 433
25
Supporting information
434 435 436
Biogenic
amines-
and
sulfides-responsive
gold
437
nanoparticles for real-time visual detection of raw meat,
438
fish, crustaceans, and preserved meat
439 440
Cheuk-Fai CHOW*
441 442
Department of Science and Environmental Studies, The Education University of Hong Kong,
443
10 Lo Ping Road, Tai Po, Hong Kong SAR, China
444
*Email: [email protected]
445 446
26
447
Table S1. The naked-eye detection limits of the AuNP solution to DMS, DMSS, DMSSS, and
448
histamine in various H2O:DMSO mixture. Naked
eye DMS
DMSS
DMSSS
Histamine
detection limit H2O: DMSO ratio 7:3
0.5 μg/mL
0.25 μg/mL
0.25 μg/mL
----
6.67:3.33
1.5 μg/mL
0.325 μg/mL
0.4 μg/mL
0.075 μg/mL
6:4
2.5 μg/mL
0.5 μg/mL
1 μg/mL
----
449 450
27
451
Table S2. Classification of Microbiological Quality Food category
Examples
Results (CFU/g) Satisfactory
Extended
shelf Modified
life food products packaging
atmosphere <106 (MAP)
requiring
vacuum-packed
refrigeration*
products,
e.g.,
Acceptable
Unsatisfactory
106-<108
≥108
106-<107
≥107
or
beef,
pork, chicken, fish, fruit, and vegetables Raw ready-to-eat Sushi, sashimi, smoked <106 meat
and
fish, salmon, etc.
cold smoked fish* 452
* Centre for Food Safety, 2014
28
453 454 455
Figure S1. (a) UV-Vis spectroscopic titrations and (b) naked eyed responses of AuNP solution
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(82.5 μg/mL) by DMSS (0–1.25 μg/mL). The studies were conducted at the ambient conditions
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in H2O:DMSO mixture (vol:vol 2:1).
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Figure S2. (a) UV-Vis spectroscopic titrations and (b) naked eyed responses of AuNP solution
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(82.5 μg/mL) by DMSSS (0–1.25 μg/mL). The studies were conducted at the ambient
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conditions in H2O:DMSO mixture (vol:vol 2:1).
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Figure S3. The UV-Vis spectroscopic responses, A/A0 520 nm () and 670 nm (◄), of the
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AuNP solution in the presence of DMSS (0.8 μg/mL) with respect to time. The reaction was
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conducted at the ambient conditions in H2O:DMSO mixture (vol:vol 2:1).
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Figure S4. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
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520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw chicken with respect
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to the storage day (4 ºC) (——). The error bars are the standard errors of the means from
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three independent experiments.
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Figure S5. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
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520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw pork with respect
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to the storage day (4 ºC) (——). The error bars are the standard errors of the means from
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three independent experiments.
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Figure S6. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
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520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw preserved beef
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(BBQ sauce) with respect to the storage day (4ºC) (——). The error bars are the standard
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errors of the means from three independent experiments.
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Figure S7. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
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520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw preserved chicken
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(BBQ sauce) with respect to the storage day (4ºC) (——). The error bars are the standard
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errors of the means from three independent experiments.
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Figure S8. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
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520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw preserved pork
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(BBQ sauce) with respect to the storage day (4ºC) (——). The error bars are the standard
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errors of the means from three independent experiments.
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Figure S9. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
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520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw salmon fish with
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respect to the storage day (4ºC) (——). The error bars are the standard errors of the means
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from three independent experiments.
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Figure S10. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
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520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw tuna fish with
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respect to the storage day (4ºC) (——). The error bars are the standard errors of the means
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from three independent experiments.
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Figure S11. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
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520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw toothfish with
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respect to the storage day (4ºC) (——). The error bars are the standard errors of the means
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from three independent experiments.
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Figure S12. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
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520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw lobster with respect
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to the storage day (4ºC) (——). The error bars are the standard errors of the means from
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three independent experiments.
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Figure S13. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
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520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw shrimp with respect
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to the storage day (4ºC) (——). The error bars are the standard errors of the means from
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three independent experiments.
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Figure S14. Total bacteria count (—●—) and (a) UV-Vis spectroscopic signals (A0-A /A0 at
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520 nm) of the AuNP solutions (—▲—) and (b) DMS generated from raw prawn with respect
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to the storage day (4ºC) (——). The error bars are the standard errors of the means from
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three independent experiments.
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Highlights
A novel AuNP-based sensor for meat/fish spoilage is developed The sensor detects dimethyl sulfide at 0.5 ppm and histamine at 0.035 ppm The sensor exhibits excellent selectivity for the markers of interest A sequential and positive causative statistic model of the detection is established
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Declaration of interests
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☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
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Author Contributions Section
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Biogenic amines- and sulfides-responsive gold nanoparticles for real-time visual detection of
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raw meat, fish, crustaceans, and preserved meat
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Cheuk-Fai CHOW*
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Department of Science and Environmental Studies, The Education University of Hong Kong,
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10 Lo Ping Road, Tai Po, Hong Kong SAR, China
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*Email: [email protected]
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Chow C.F. is the sole author of this manuscript. He designed the study, wrote the article, and
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made the final approval of the version to be submitted.
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