A general strategy to quantify analytes through fluorescence chromaticity and luminosity

Article abstract of DOI:10.1016/j.cclet.2020.07.012

To realize a fast, easy-operation and precise way using fluorescence probes to quantify analytes is a goal to facilitate detection, especially in situ. Herein, we are reporting an approach which can be generally employed for the differentiation and quanti

Full text of DOI:10.1016/j.cclet.2020.07.012

Journal Pre-proof
A general strategy to quantify analytes through fluorescence chromaticity
and luminosity
Tianyu Liang, Peiwei Yang, Tianhong Wu, Menghan Shi, Xiayu Xu,
Taotao Qiang, Xiaolong Sun
PII:
S1001-8417(20)30403-4
https://doi.org/10.1016/j.cclet.2020.07.012
CCLET 5737
DOI:
Reference:
To appear in:
Chinese Chemical Letters
Received Date:
Revised Date:
Accepted Date:
28 April 2020
4 July 2020
6 July 2020
Please cite this article as: Liang T, Yang P, Wu T, Shi M, Xu X, Qiang T, Sun X, A general
strategy to quantify analytes through fluorescence chromaticity and luminosity, Chinese
Chemical Letters (2020), doi: https://doi.org/10.1016/j.cclet.2020.07.012
This is a PDF file of an article that has undergone enhancements after acceptance, such as
the addition of a cover page and metadata, and formatting for readability, but it is not yet the
definitive version of record. This version will undergo additional copyediting, typesetting and
review before it is published in its final form, but we are providing this version to give early
visibility of the article. Please note that, during the production process, errors may be
discovered which could affect the content, and all legal disclaimers that apply to the journal
pertain.
© 2020 Published by Elsevier.

Communication
A general strategy to quantify analytes through fluorescence chromaticity and
luminosity
Tianyu Lianga,b, Peiwei Yanga,c, Tianhong Wua, Menghan Shia, Xiayu Xua,c, Taotao Qiangb,*, Xiaolong
Suna,*
a The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an
710049, China
b College of Bioresources and Materials Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China
c Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, China
* Corresponding authors.
E-mail address: x.l.sun86@xjtu.edu.cn (X. Sun), qiangtt515@163.com (T. Qiang).
Graphical abstract
Input
Dark-box
Computer
Invert image
Output
Region segment
Eroded label
Calibration curve
Computational analysis
A general approach for the detection and quantitation of analytes and unknown samples through photography were reported in
this research article.

ABSTRACT
To realize a fast, easy-operation and precise way using fluorescence probes to quantify analytes is a goal to facilitate detection, especially in situ. Herein, we
are reporting an approach which can be generally employed for the differentiation and quantitation of analytes through fluorescence chromaticity and
luminosity. Seven representative fluorescent probes, targeting pH, cysteine, hydrogen sulfide, hydrogen peroxide, palladium and hydrazine, were synthesized
and tested. Without utilizing costly instrumentations, portable devices were employed to collect data of photographs from the fluorescence samples in responses
to different analytes. Subsequently, the photographic images were digitally processed to generate calibration curves between chromaticity/luminosity verse
concentrations after mapping to the CIE 1931 xyY standard color space. Good linear calibration curves and quantitative analysis of unknown samples with low
errors through the spectral technology demonstrated the reliability of this method. Thus, we showed the analytical method with a simple and on-site
constructible/portable device which is promising for applications in more fluorescence probes
Keywords: Fluorescence probes, Portable device, Calibration curve, Photography, Chromaticity and luminosity
Fluorescent chemosensors have become powerful tools for applications in various fields [1-6]. They could make detection and
identification of certain substrates, while a large number of review articles have been reported, such as detection of enzymes activity [7],
transition metal cations [8], pHs [9], chemical warfare [10], reactive oxygen species [11]. In terms of optical detection frequently used
nowadays, general spectroscopy measurements utilizing sophisticated instruments such as fluorescence spectroscopy, UV-vis absorption
spectroscopy, 96 well plate reader and circularly dichroism spectropolarimetry, are often required. However, the instrumentations are
usually costly, not readily field deployable, and operation could be time-consuming. Thus, technologies using simple, efficient, and
ideally integrated systems are in great chasing [12]. In 2002, Citterio and co-workers introduced digital color analysis [13]. They utilized
digital information of visual colorimetry, which was applied to a lithium ion sensor system with the color matching functions as response
to lithium ions. In addition, Bunz et al. utilized digital color analysis to interconvert of photographic emission spectroscopy through the
colorimetric changes of pyridine substituted cruciform fluorophores in the presence of carboxylic acids [14,15]. However, analytical
processes in these works were complex for both data extraction and output analysis.
Previously, we developed a new photography technology for differentiation and quantitation of G- and V‑ nerve agent mimics coupled
with fluoride and thiol self-propagating cascades [16]. While in this article, we are aiming to generalize this image analysis pipeline
through digital analysis of chromaticity and luminosity via a GitHub to perform fluorescence detection [16]. This approach can be used
on general fluorescent probes with fluorometric signal responses towards various targets for qualitative and quantitative analysis. Instead
of complex instruments for the signal detection, we monitored the fluorescence images under a self-made dark-box by a common cellular
phone camera. Photographic images of each reaction in a 96-well plate are digitally patterned to identify the individual reaction well,
and their pixel values are sampled. Chromaticity/luminosity of the pixel for each well are mapped onto the CIE 1931 xyY color space.
The photography of calibration wells with known concentrations of analyte formed linear calibration curves against which the unknown
samples’ chromaticities are then compared. The standard curves are informative for calibration to infer unknown analyte concentration
as reflection of the certain chemical reaction. Linear calibration curves and quantitative analysis of unknown samples with low errors
through the spectral technology of chromaticity and luminosity demonstrated the reliability of this method which is promising for use in
a wide scope of this field.
To testify this, we synthesized seven common fluorescent probes with different color distributions, for the evaluation of changes in
responses to pH, Cys, NH2NH2, H2S, H2O2 and Pd2+, respectively (Fig. 1) [17-23]. Their emission distribution of the typical fluorescent
probes covers the certain ranges of visible light, including fluorescein (λex = 450 nm, λem = 520 nm), naphthalimide (λex = 410 nm, λem
=
540 nm), rhodamine (λex = 420 nm, λem = 590 nm), coumarin (λex = 496 nm, λem = 616 nm) and benzopyran (λex = 520 nm, λem = 670 nm).
In these examples, probes 1, 2, 5 and 6 generates ratiometric signal changes, while probes 3, 4 and 7 generates singular turn-on signals.
In terms of tools and technological process for photo images (Fig. 2), we took an approach, one that would replace a conventional 96-
well plate reader, be inexpensive, and portable device could be constructed in the field [16].

Fig. 1. Structures of fluorescence probes used in this study.
Samples of concentrations
1
2
3
4
5
6
7
8
9
10 11 12
A
B
C
D
E
F
Probe 1
Probe 2
Probe 3
Probe 4
Input
Filing
G
H
96-well plates
Computer
Dark-box
CIE 1931 coordinates
Probe 1
Probe 3
Probe 2
Probe 4
Invert image
Region segment
Eroded label
Output
Calibration curve
Computational analysis
Fig. 2. Schematic illustration of the general strategy for preparation of samples, photos taken and digital analysis of chromaticity and luminosity through
photography.
In these approaches, ratiometric signal sensing has the potential to provide higher sensitivity and reliability due to the inherent self-
calibration provided through monitoring multiple emissions [24,25]. Previously, we also demonstrated that chromaticity is more
informative than luminosity in measuring ratiometric fluorescence changes [16]. Thus, in analysis of ratiometric signal changes herein,
we sought to use information of chromaticity and photographic technique to analyse probes 1 and 2 in response to pH and cysteine,
respectively. As can be revealed by Fig. 3A, emission of probe 1 in wells 1-10 shifted from green to red with decreasing of pH changes
(pH: 7.82, 6.16, 5.86, 5.58, 5.26, 4.96, 4.66, 4.36, 4.06, 3.39) which can be easily captured by the naked eye. The protonation of pyridine
in probe 1 triggered the intramolecular electron transfer (ICT) effect causing the large red-shift of the signals [17]. It should be noted that
the fluorometric photos were recorded instantly after pH changes for the samples. The representative chromaticities followed a linear
trend in xy space with decreasing pH values. While in photographical analysis of cysteine by probe 2 (Fig. 3B), color of fluorescence
images were changed from blue to yellow with increasing doses of Cys triggers (0, 13.3, 26.6, 40.0, 50.0, 63.3, 73.3, 80.0, 86.6, 100.0
µmol/L) in wells 1-10. The electron-withdrawing carbonyl group in probe 2 was cleaved by Cys, which triggered stronger electron-push
capacity of oxygen for the optical changes [18]. The images were recorded after 30 min until the reactions complete (Fig. S1 and S6 in
Supporting information). The chromaticities also followed a good linear trend in xy space with increasing concentrations of cysteine.
Thus, representative chromaticities from our calibration wells tended to be linearly distributed across all experiments and the linearity is
a consequence of ratiometric fluorescence which can potentially provide calibration curves for the detection of unknown samples.
In addition to ratiometric signal responses, singular turn-on sensing by fluorescence probes usually generates low detection limit, fast
response and high selectivity [26,27]. Also, turn-on chemosensors are the most common examples owing to the design easiness and
structural diversity. In this project, we found out that Y portion in CIE 1931 xyY color space symbols luminosity. Herein, in analysis of
turn-on fluorescence changes in probes 3 and 4, we sought to use our luminosity and photographic technique to quantitate NH2NH2 and
H2S, respectively. Reaction between probe 3 and hydrazine, transformed fluorophore structure into ring open form, causing the
fluorescence turn-on [22]. In Fig. 3C, images brightness from samples of probe 3 increased gradually in a dose-dependent manner with
NH2NH2 triggers (0, 4.0, 6.0, 8.0, 10.0, 12.0, 16.0, 20.0, 24.0, 28.0 µmol/L) in wells 1-10 while the color compositions keep constant
and representative pixels change only in luminosities. The fluorometric photos were taken after 15 min (Fig. S2 in Supporting
information). The luminosities from wells 1-10 followed a good linear trend in Y space with increasing concentrations of hydrazine, with
R2 = 0.98 (Fig. 3C inset). Next in analysis of H2S by probe 4 (Fig. 3D), azido moiety in 4 could be converted to amino-group by H2S to

recover the electron donating ability [23]. This reactive behaviour results in the fluorescence enhancement after addition of H2S triggers
(0, 16.6, 33.2, 49.8, 66.4, 83.0, 99.6, 116.2, 132.8, 149.4 µmol/L) in wells 1-10 (Fig. 3D). The fluorometric changes were recorded after
60 min for fluorescence photo collection (Fig. S3 in Supporting information). As well, luminosities of samples followed a good linear
trend in Y space with increasing concentrations of hydrogen sulfide (R2 = 0.99) (Fig. 3D inset). Thus, luminosities from our calibration
wells demonstrated a good linear trend across these experiments which can potentially provide standard curves.
Probe 1 – pH
Probe 2 - Cys
A
B
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
6
9
10
10
9
8
7
5
4
1
3
3
5
6
7
8
9
10
2
4
2
1
Probe 4 – H2
S
Probe 3 – NH2NH2
Probe 4 – H2S
C
D
1
2
3
4
5
6
7
8
9
10
1
1
2
2
3
3
4
4
5
6
6
7
7
8
8
9
9
10
10
5
0.12
0.08
0.04
0.00
y = 0.0047x - 0.0088
R2 = 0.9815
0.016
0.012
0.008
0.004
0.000
y = 0.0001x + 0.0006
R2 = 0.9948
0
5
10 15 20 25 30
NH2-NH2 (mol/L)
0
20 40 60 80 100 120
H2S (mol/L)
Fig. 3. Fluorescence photo images and chromaticities for probe 1 (A) and probe 2 (B); Fluorescence photo images and luminosity for probe 3 (C) and probe 4 (D);
Linear relationship between fluorescence luminosities and NH2NH2 in probe 3 (inset in C) and H2S doses in probe 4 (inset in D). (A) pH titrations (1-10 wells):
7.82, 6.16, 5.86, 5.58, 5.26, 4.96, 4.66, 4.36, 4.06, 3.39); (B) Cys titrations (1-10 wells): 0, 13.3, 26.6, 40.0, 50.0, 63.3, 73.3, 80.0, 86.6, 100.0 µmol/L; (C) NH2NH2
titrations (1-10 wells):0, 4.0, 6.0, 8.0, 10.0, 12.0, 16.0, 20.0, 24.0, 28.0 µmol/L; (D) H2S titrations (1-10 wells): 0, 16.6, 33.2, 49.8, 66.4, 83.0, 99.6, 116.2, 132.8,
149.4 µmol/L.
We have thus demonstrated that photographic diagnostic methods coupled with ratiometric and turn-on fluorescence probes. The
digitally processed chromaticity and luminosity presented good relationships in xy or Y space through photography. On the basis of these
results, our methodology for analyzing substances through photography can be used to differentiate and quantitate various analytes.
Fluorescence chromaticity and luminosity as a powerful tool for the detection of certain analytes have been testified above. Rapid and
precise detection of unknown samples is a critical goal for the in situ detection in real environment.
In the next study, we applied our methodology to quantitate unknown samples through calibration curves with standard samples (Fig.
4). Probe 5 and probe 6 were employed in the chromaticity investigation in response to H2O2 and Pd2+, which all output ratiometric
fluorescence signals [19,20]. In Figs. 4A and 4B, probes 5 and 6 in samples (wells 1-10) were incubated with standards of H2O2 and Pd2+,
respectively (Figs. S4 and S5 in Supporting information), across a range of concentrations near the expected concentration of the
unknown analyte to generate calibration curves. Unknown samples were juxtaposed in the same 96-well plates for imaging consistency.
Chromaticities from the calibration wells (1-10) were used to construct calibration curves: each curve is a piecewise linear interpolation
between each successive calibration well’s representative chromaticities. Analyte concentrations for each unknown sample (a-j) was
inferred and calculated by interpolation of the standard analytes [16]. Next, pH-triggered fluorescence turn-on in probe 7 was to analyse
unknown samples through luminosity [21]. In Fig. 4C, pH in samples (1-9) were incubated with standards of probe 7. Good linear
relationship formed between luminosity and standard samples to generate a calibration curve with R2 = 0.99. On the basis, all other wells
containing unknown analytes were calculated to generate the theoretical doses of sample to compare with input of the actual doses.

Probe 5 – H2O2
Probe 6 – Pd2+
Known concentrations
Known concentrations
A
B
1
2
3
4
5
6
7
8
h
9
i
10
j
1
2
3
4
5
6
7
8
h
9
i
10
j
Unknown samples
Unknown samples
a
b
c
d
e
f
g
a
b
c
d
e
f
g
10
9
8
d
7
b
6
1
2
3
j
4
5
6
5
7
4
g
8
9
a
10
c
3
j
e
e
b
a
2
g
d
c
i
1
f
h
h
f
i
Known concentrations
C
Probe 7 – pH
D
1
2
3
4
5
6
7
f
8
9
Unknown samples
a
b
c
d
e
g
h
i
Probe 5
1
f
0.08
0.06
0.04
0.02
0.00
y = -0.0336x + 0.3371
2 = 0.9887
R
2
3
i
b
g
Probe 6
4
5
e
c
7.5 8.0 8.5 9.0 9.5 10.0
pH
6
d
7
8
9
h
a
Probe 7
Fig. 4. Fluorescence photo images; chromaticity for standard and unknown samples in probes 5 (A) and 6 (B). Luminosity for standard and unknown samples
from 7 (C). (A, top) Wells 1-10 are standard samples of known H2O2 concentrations (0, 28.57, 57.14, 85.71, 114.29, 142.29, 171.43, 200.0, 228.57, 257.14 µmol/L);
Wells a-j are unknown samples. (B, top) Wells 1-10 are standard samples of known Pd2+ concentrations (0, 6.6, 8.3, 10.0, 11.6, 13.3, 15, 16.6, 18.3, 25 µmol/L);
Wells a-j are unknown samples. (A, B, bottom) Chromaticities of randomly sampled pixels from calibration wells 1-10 are represented as color-coded open circles
and unknown samples are overlaid as points. (C, top) Wells 1-9 are standard samples of known pH (7.5, 8.5, 8.7, 8.9, 9.1, 9.3, 9.5, 9.7, 9.9); Wells a-i are unknown
samples. (C, bottom) Luminosities of randomly sampled pixels from calibration wells 1-9 are represented as color-coded open circles and the unknown samples
are overlaid as points. (C, inset) Linear relationship between fluorescence luminosities and pH in probe 7. (D) The structures of probes 5, 6, 7 and react with H2O2,
Pd2+ and H+ respectively.
As was revealed in Table 1, almost all the errors are below 20% and sample of well (a) in detection of Pd2+ showed zero error. Overall,
the calculation of standard deviation for all errors was 4.67%. To further test the reliability of this photographic technology, we monitored
and analysed the fluorescence images of probe 2 resulting from different cell phones, including Huawei, Xiaomi and Apple brands,
respectively (Fig. S7). The standard deviations for errors were 11.71%, 11.16% and 3.06% respectively (Tables S1-S3 in Supporting
information). In addition, we then carried out experiments for the investigation of selectivity for probes 3 and 5 in the presence of other
biological substrates (Figs. S8 and S9 in Supporting information). The photographical analysis showed the similar pixels and thus testified
the barely interferences to the luminosity and chromaticity from other substances. We have thus demonstrated that our approach of image
analysis is reliable for fluorescence signal amplification which has potential for further applications.
Table 1
Prediction of unknown samples through probes 5, 6 and 7.
Detection of probe 5- H2O2 (μmol/L)
Detection of probe 6- Pd2+ (μmol/L)
Detection of probe 7- pH
Wells
Cal.
Act.
Err. (%)
-0.07
-1.84
3.80
Wells
Cal.
Act.
Err. (%)
0.00
Wells
Cal.
9.83
8.72
9.19
9.41
9.10
7.93
8.73
9.76
8.69
Act.
9.78
8.66
8.95
9.35
9.00
8.00
8.53
9.63
8.35
Err. (%)
0.51
0.69
2.68
0.64
1.11
-0.88
2.34
1.35
4.07
a
b
c
d
e
f
g
h
i
138.40
210.26
74.12
224.70
132.70
42.70
155.40
67.06
18.70
136.40
138.50
214.20
71.40
221.40
135.70
42.80
157.10
78.50
21.40
128.50
a
b
c
d
e
f
g
h
i
12.50
10.70
4.09
15.25
5.86
17.54
14.47
17.69
17.08
10.26
12.50
10.80
5.00
15.80
7.50
19.00
14.20
20.00
17.50
9.20
a
b
c
d
e
f
g
h
i
-0.93
-18.20
-3.48
-21.87
-7.68
1.90
-11.55
-2.40
11.52
1.49
-2.21
-0.23
-1.08
14.57
12.62
6.15
j
j
Standard deviation for error = 4.67%
Cal.: calculated; Act.:actual; Err.:error.

In summary, we generalized an approach which can be employed for the differentiation and quantitation of analytes through
fluorescence chromaticity and luminosity. This is to realize a fast, easy-operation and precise way using fluorescence probes to quantify
analytes to facilitate detection. Seven representative fluorescent probes were synthesized and tested in the corresponding experiments. A
self-made dark-box, a cell phone, and 96-well plates were utilized as portable devices. Photographic images were digitally processed to
generate calibration curves between chromaticity/luminosity verse concentrations after mapping to the CIE 1931 xyY standard color
space. Good linear calibration curves and quantitative analysis of unknown samples with low errors through the spectral technology
demonstrated the reliability of this method. The analytical method of photography coupled with a simple and on-site
constructible/portable device can be employed for further applications. The limitations of this method include not real portable device
was constructed thus data analysis has to be operated through computer. Additionally, for the detection of unknown samples, standard
samples have to be used for calibration each time. The range of the concentrations of standard samples was not easily determined.
Furthermore, it is a challenge to apply this photography technology into bio-imaging detection in a complex environment.
Statement of interest
The authors declare no competing financial interest.
Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 21907080), Natural Science Foundation of Shaanxi
(No. 2020JM-069), Key Scientific Research Group of Shaanxi Province (No. 2020TD-009) and the Youth Innovation Team of Shaanxi
Universities.

References
[1] X. Sun, T.D. James, Chem. Rev. 115 (2015) 8001-8037.
[2] H. Duan, Y. Ding, C. Huang, et al., Chin. Chem. Lett. 30 (2019) 55-57.
[3] X. Sun, Q. Xu, G. Kim, et al., Chem. Sci. 5 (2014) 3368-3373.
[4] X. Sun, B. M. Chapin, P. Metola, et al., Nat. Chem. 11 (2019) 768-778.
[5] L. Ding, Z. Tian, J. Hou, et al., Chin. Chem. Lett. 30 (2019) 558-562.
[6] S. H. Park, N. Kwon, J. H. Lee, J. Yoon, I. Shin, Chem. Soc. Rev. 49 (2020) 143-179.
[7] D. Chen, W. Qin, H. Fang, et al., Chin. Chem. Lett. 30 (2019) 1738-1744.
[8] L. Yu, Y. Qiao, L. Miao, Y. He, Y. Zhou, Chin. Chem. Lett. 29 (2018) 1545-1559.
[9] J.T. Hou, W.X. Ren, K. Li, et al., Chem. Soc. Rev. 46 (2017) 2076-2090.
[10] Y.J. Jang, K. Kim, O.G. Tsay, D.A. Atwood, D.G. Churchill, Chem. Rev. 115 (2015) PR1-PR76.
[11] L. Wu, A.C. Sedgwick, X. Sun, et al., Acc. Chem. Res. 52 (2019) 2582-2597.
[12] J. Krüger, K. Singh, A. O'Neill, et al., J. Micromech. Microeng. 12 (2002) 486-494.
[13] K. Suzuki, E. Hirayama, T. Sugiyama, et al., Anal. Chem. 74 (2002) 5766-5773.
[14] T. Schwaebel, S. Menning, U.H.F. Bunz, Chem. Sci. 5 (2012) 1422-1428.
[15] T. Schwaebel, O. Trapp, U.H.F. Bunz, Chem. Sci. 4 (2013) 273-281.
[16] X. Sun, A.A. Boulgakov, L.N. Smith, et al., ACS Cent. Sci. 4 (2018) 854-861.
[17] L. Long, X. Li, D. Zhang, et al., RSC Adv. 3 (2013) 12204-12209.
[18] B. Zhu, B. Guo, Y. Zhao, B. Zhang, B. Du, Biosens. Bioelectron. 55 (2014) 72-75.
[19] D. Srikun, E.W. Miller, D.W. Dornaille, C.J. Chang, J. Am. Chem. Soc. 130 (2008) 4596-4597.
[20] S. Sun, B. Qiao, N. Jiang, et al., Org. Lett. 16 (2014) 1132-1135.
[21] J. Xie, Y. Chen, W. Yang, D. Xu, K. Zhang, J. Photochem. Photobiol. A: Chem. 223 (2011) 111-118.
[22] S. Goswami, K. Aich, S. Das, et al., RSC Adv. 4 (2014) 14210-14214.
[23] W. Sun, J. Fan, C. Hu, et al., Chem. Commun. 49 (2013) 3890-3892.
[24] M.H. Lee, J.S. Kim, J.L. Sessler, Chem. Soc. Rev. 44 (2015) 4185-4191.
[25] H. Xiong, L. He, Y. Zhang, et al., Chin. Chem. Lett. 30 (2019) 1075-1077.
[26] Y. Xia, X. Liu, D. Wang, et al., Chin. Chem. Lett. 29 (2018) 1517-1520.
[27] X. Sun, T.D. James, E.V. Anslyn, J. Am. Chem. Soc. 140 (2018) 2348-2354.