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Reflective fiber-optic sensor for on-line nondestructive monitoring of Aspergillus on the surface of cultural paper relics

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Abstract

A reflective fiber-optic sensor was created to realize on-line nondestructive monitoring of the growth process of Aspergillus on the surface of cultural paper relics. The sensor consisted of one tapered input and six output optical fibers. The operating principle of the device was established. The sensitivity of the sensor was checked. Sensors were used to monitor the growth of Aspergillus niger, Aspergillus flavus, and Aspergillus tamarrii on the papers. The morphology of Aspergillus was characterized. The sensor reveals a linear relationship between the output signal of the sensor and the thickness of Aspergillus biofilm with a detection limit of 10 µm.

© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

1. Introduction

Cultural paper relics are the most important medium preserving historic and cultural information of past civilizations. However, paper is rich in organic matter and provides nutrients for the growth and reproduction of mold microorganisms [1]. Therefore, paper artifacts are susceptible to mold erosion [2]. Among all molds, Aspergillus is the most common and destructive for cultural paper relics [3]. Hence, in-situ (on-line nondestructive) monitoring of Aspergillus growth processes is extremely important to effectively prevent and control corrosion and erosion damage to paper relics.

Currently, the detection of molds on the surface of paper relics includes ex-situ (off-line) and on-line detection technologies. Among them, off-line detection techniques mainly include confocal laser scanning microscopy [4], X-ray fluorescence [5], spectrophotometry [6], scanning electron microscopy (SEM) [7], nuclear magnetic resonance spectroscopy [8], headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS) [9], and denaturing gradient gel electrophoresis [10]. Targowski et al. [5] used X-ray fluorescence technology to construct element distribution maps and analyzed the changes in the color components of different pages of parchment manuscripts. Sawoszczuk et al. [9] applied HS-SPME-GC-MS to determine the gases produced by mold metabolism on parchment relics and conducted qualitative and quantitative analyses to establish an index of mold metabolic activity. Although off-line technologies can effectively measure the growth and metabolites of mold on paper, their measurement processes require the mold to be separated from the paper, which is time consuming and can easily damage cultural relics. However, it is difficult to realize on-line nondestructive testing and early warning of the mold disease process on real cultural relics.

On-line detection methods include electronic noses [11], hyperspectral imaging [1214], and fiber-optic sensor technologies [1519]. Among these, fiber-optic sensors have the advantages of rapid response, anti-electromagnetic interference, long-distance transmission, small size, and realized distributed measurement [20]. Fiber-optic sensors are therefore considered the most promising new detection devices for on-line dynamic real-time detection of microorganisms (such as bacteria, viruses, molds, and yeasts). However, the current fiber-optic biofilm sensors are contact measurement devices, which are easily damaged and difficult to install and fix on paper relics [20,23]. In other words, the reported sensors are not easily applied to real-time dynamic monitoring of mold growth on the surface of paper relics [2025]. Fiber-optic spectroscopy sensors do not need to be in contact with paper relics; they can realize on-line nondestructive measurement and have been used to detect the surface pigment and color of such artifacts [2632]. However, fiber-optic spectroscopy sensors mainly operate in the infrared spectral band, and there are no UV-Vis spectral reflective sensors used to measure the growth of microorganisms (mold or bacteria) on the surface of paper relics. Furthermore, existing reflective sensors have a flat-structured fiber end-face and low sensor sensitivity owing to the limited number of studies that have investigated the influence of sensor parameters (fiber diameter, fiber end-face structure, fiber spacing, and optical path) on device sensitivity. Therefore, it is necessary to further study a high-sensitivity UV-Vis fiber-optic reflective sensor for on-line nondestructive detection of mold growth on the surface of paper relics.

To achieve on-line nondestructive monitoring of the Aspergillus growth process on the surface of paper, a reflective fiber-optic spectral sensor was created. First, a theoretical model of sensor measurement was established, and the influence of the diameter and taper angle of the input optical fiber, the distance between the input and output optical fibers, and the distance between the fiber-optic sensor probe and mold on the paper surface on the sensitivity of the sensor was investigated. Second, the sensor was used to monitor the growth process of Aspergillus niger (A. niger), Aspergillus flavus (A. flavus), and Aspergillus tamarrii (A. tamarrii) on the surface of paper samples, and the structure and thickness of Aspergillus biofilms were characterized via a camera, optical microscope, and field emission scanning electron microscopy (FESEM).

2. Materials and devices

2.1 Reflective fiber-optic spectral sensor

Silica optical fibers were provided by Nanjing Chunhui Technology Industry Co., Ltd. The fiber core and cladding were composed of pure silica and silicone rubber, respectively. The fiber numerical aperture was 0.37 ± 0.02, the operating temperature range was -50 °C–250 °C, and the transmittable spectral range was 200–1100 nm. The diameter of the output fiber was 500 µm, the diameters of the input fibers were 500 µm, 1000 µm, 1500 µm, and 2000 µm. The tapered input fiber is obtained by the grinding method, and the taper angles are 0°, 6°, 12°, and 18°, respectively. To assemble the reflective fiber-optic spectral sensor, 0.8-m fibers were prepared, and the two end faces of the fibers were polished smooth using fiber grinding paper (particle size of 1 µm). The tapered input fiber and six output fibers were coupled to a plastic sheath, and the sensor was fixed and packaged with UV shadowless glue. The structure of the prepared sensor is shown in Fig. 1.

 figure: Fig. 1.

Fig. 1. Schematic diagram of the sensor structure (a), optical micrograph of a tapered optical fiber (b), and an image of the sensor (c). (IOF: input optical fiber; OOF: output optical fiber.)

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2.2 Culture of Aspergillus on the surface of paper

The paper samples used for Aspergillus culture were purchased from China Xuan Paper Co., Ltd and composed of sandalwood bark (60%) and Shatian straw (40%). The paper thickness was approximately 167 µm, and the paper size was 1 cm × 2 cm. Before inoculation of the paper, equipment such as the paper, petri dishes, scissors, and tweezers were sterilized at a temperature of 121 °C and a pressure of 100 MPa for 20 min. The inoculation process was carried out in a biological safety cabinet (BSC-1100-LIIA2, Beijing Donglian Har Instrument Manufacturing Co., Ltd., China). The steps for culturing the mold samples on the surface of the papers were as follows:

The first step was to revive mold strains. After thawing with a porcelain bead cryopreservation tube, a porcelain bead with mold bacteria was placed in clean Sabouraud agar (SDA) to revive mold microorganisms. The regeneration process was performed in a constant temperature and humidity aseptic box (ZRG-800E-L, Shanghai Binglin Electronic Technology Co., Ltd., China). The temperature inside the box was 28 °C with a humidity of 80% RH and a regeneration time of 72 h.

The second step was to construct a spore suspension. The revived mold was cultivated until sporulation. The spores were rinsed from the plate with ultrapure water using a pipette gun, and the rinsed solution was collected for use.

The third step involved infecting the paper with mold. The high-temperature sterilized paper samples were soaked with nutrient solution (Table 1) and then placed into a petri dish to dry naturally. Once the paper dried, the spore liquid was added dropwise to the paper samples.

Tables Icon

Table 1. Composition and proportion of nutrient solution

The fourth step was sample culture. The moldy paper samples were directly cultured in a constant temperature and humidity sterile box at a temperature of 28 °C and a humidity of 80% RH. No light was required. The camera (D5200, Nikon, Japan), optical microscope (DK6000, Aute Optics, China) and FESEM (ΣIGMA-HD, Carl Zeiss AG, Germany) images of the A. niger, A. flavus, and A. tamarrii samples after seven days culture is shown in Fig. 2. Particularly, the optical microscope system with a resolution of ±1 µm was used to calibrate the Aspergillus thickness. For more details on the Aspergillus thickness test, see Ref [21].

 figure: Fig. 2.

Fig. 2. Images of Aspergillus species. (a–c) A. niger, (d–f) A. flavus, (g–i) A. tamarrii. The first row contains camera photographs, the second row contains optical micrographs, and the third row contains FESEM images.

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Figures 2(a) and (b) show the dark brown color of A. niger with thick velvety hyphae. Figures 2(b) and (c) show that the conidial head of A. niger was spherical, the spore diameter was 3.1–3.8 µm, and the stalk of the conidia was erect. Figures 2(d) and (e) show the yellow-green color of A. flavus. The stalk of the conidia was erect, and the conidial strings were arranged radially at the top of the small stalk. Figures 2(e) and (f) show that the A. flavus single spores were spherical and 3.0–3.4 µm in diameter. Figures 2(g) and (h) show that the color of A. tamarrii was brown, and the conidial head was radial. Moreover, Figs. 2(h) and (i) show that the apical capsule of A. tamarrii was slightly spherically flocculent, and the conidia were spherical and 4.8–5.4 µm in diameter.

2.3 Detection device

The measurement system was composed of a light source (DH-2000, Ocean Optics, China), fiber optic spectrometer (QEPRO-FL, Ocean Optics, China), computer, fiber-optic sensor, fiber-optic coupler, three-dimensional (3D) manipulator, objective table, and mold sample on the surface of paper (Fig. 3). The fiber-optic sensor was fixed on the 3D manipulator. During light transmission, the ultraviolet-visible light was first transmitted from the input fiber to the end of the fiber and then transmitted to the surface of the paper. After being absorbed, scattered, and reflected by Aspergillus microorganisms, it was transmitted to the output fiber and then detected by the spectrometer. To reduce the influence of light on the test results under ambient conditions, the measurement system was covered with a light shield. By checking the characteristic absorption spectrum of the spectrometer, information on the different molds on the paper surfaces and the change in mold biofilm thickness during mold growth could be obtained.

 figure: Fig. 3.

Fig. 3. Schematic diagram of the detection system.

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3. Measurement principle

Light ray incident on an Aspergillus biofilm is absorbed, reflected, and scattered by the Aspergillus cells (Fig. 4). That is, when the light beam emitted from the end face of the input optical fiber is transmitted to the surface of a cultural paper relic coated with Aspergillus, a part of the light enters the mold biofilm and is absorbed, a part is reflected from the surface of the Aspergillus to the surface of the output fibers, and another part is scattered by the Aspergillus. In this paper, Iin, Iout, IA, IR, and IS represent the sensor input light intensity (in this study, it was assumed that the light beams coupled to the input fiber from the external light source were emitted at the surface of the tapered region), output light intensity, absorbed light intensity, reflected light intensity, and scattered light intensity, respectively. Therefore, the relationship between Iin, IA, IR, and IS can be expressed as

$${I_R} = {I_{in}} - {I_A} - {I_S}.$$

 figure: Fig. 4.

Fig. 4. Schematic diagram of the principle of mold detection on the surface of paper.

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Because the input fiber of the sensor has a tapered structure, interference between the core and cladding modes will occur when the light beam in the input optical fiber is transmitted to the tapered region. According to the principle of double-beam interference, the relationship between Iin, the light intensity of the core mode I1, the light intensity of the cladding mode I2, and the fiber taper angle $\delta$ can be expressed as [33]

$${I_{\textrm{in}}} = {I_1} + {I_2} + \sqrt {2{I_1}{I_2}} \cos \varDelta \varphi,$$
$$\varphi = 2\pi \varDelta n[{({r_1} - {r_0})/\tan \delta } ]/\lambda,$$
where $\varDelta n$ is the refractive index difference between the fiber core and cladding, r1 is the radius of the input fiber, r0 is the radius of the tapered tip of the input fiber, and $\lambda$ is the wavelength of the incident light.

Because the biofilm has a porous structure [34] and the mold size is 2–6 µm (Fig. 2), when the light beams emitted from the tapered region of the input fiber are transmitted to the surface of the Aspergillus biofilm, Mie scattering is produced. According to Mie scattering theory, the relationship between IS and Iin can be expressed as [35]

$${I_\textrm{S}} = {I_{in}}\exp ( - {\eta _s}{Q_{ext}}), $$
where ${\eta _s}$ is the occupancy rate of the effective evanescent field power, and ${Q_{ext}}$ is the extinction efficiency of the mold biofilm.

In addition, according to the Beer–Lambert law, when the light beam passes through the mold biofilm (a weakly absorbing medium), the input light intensity Iin_1 and the output light intensity Iout_1 can be expressed as [36]

$${I_{\textrm{out}\_1}} = {I_{in}}_{\_1}\exp ( - \varepsilon l)\textrm{ = }({I_{in}} - {I_S})\exp ( - \varepsilon l), $$
where ε is the attenuation coefficient of Aspergillus biofilm to light, which is determined by the absorption characteristics of Aspergillus intrinsic to light, and l is the thickness (height) of Aspergillus biofilm. From Eq. (4), the absorbed light intensity of the light beam on the surface of the Aspergillus biofilm IA can be expressed as
$${I_\textrm{A}} = {I_{in\_1}} - {I_{\textrm{out}\_1}} = ({I_{in}} - {I_\textrm{S}}) \cdot [1 - \exp ( - \varepsilon l)], $$
Substituting Eqs. (3) and (5) into Eq. (1), when the light beam emitted by the input optical fiber is absorbed and scattered by the Aspergillus cells, the light intensity reflected by the biofilm can be expressed as
$${I_R} = {I_{in}} - {I_A} - {I_S} = {I_{in}}\exp ( - \varepsilon l)[1 - \exp (\eta {}_s{Q_{ext}}). $$
Figure 4 shows that the part of the light beam reflected from the surface of the Aspergillus biofilm is approximately Gaussian and can be received by the output fiber. This received reflected light intensity was affected by the distribution characteristics of the output fibers. Therefore, Iout of the output fiber can be expressed as [37]
$${I_{\textrm{out}}} = 6\int\!\!\!\int\limits_{{S_R}} {{I_R}({x, d} )d{S_R}}, $$
where $d{S_R} = 2\beta xdx$, $\beta = \arccos \left( {\frac{{{p^2} + {x^2} - r_2^2}}{{2px}}} \right)$, p is the distance between the input and output fibers, and r2 is the radius of the output fiber. Iout can be further expressed as
$$\begin{array}{l} {I_{\textrm{out}}} = 6\int_0^{{r_1} + {r_2} + p} {2\beta \frac{{2({I_1} + {I_2} + \sqrt {2{I_1}{I_2}} \cos \{{2\pi \varDelta n[({r_1} - {r_0})/\tan \delta ]} \}/\lambda )}}{{\pi {w^2}(2d)}}} \\ \textrm{ } \times [1 - \exp ({\eta _s}{Q_{ext}})]\exp (\frac{{ - 2{x^2}}}{{{w^2}(2d)}} - \varepsilon l)xdx\\ \end{array}, $$
where d is the distance between the fiber end-face and the Aspergillus biofilm (optical path of 2d), $w(2d)$ is the spot area of the Gaussian beam 2d away from the fiber end-face, $w(2d) = {r_1} + 2d\tan (\arcsin NA)$, and NA is the numerical aperture of the fiber.

Equation (8) shows that the output light intensity of the sensor is controlled by structural parameters, including the radius and taper angle of the input fiber r1 and $\delta$, respectively, the distance between the input and output fibers p, and the optical path d. Therefore, to obtain a high-sensitivity sensor, it is necessary to further study the influence of the sensor’s structural parameters on its output spectrum and sensitivity.

4. Results and discussion

4.1 Influence of input fiber diameter on the output spectrum and sensitivity

To construct a high-sensitivity sensor, various sensors were fabricated with input fiber diameters of 500 µm, 1000 µm, 1500 µm, and 2000 µm (Fig. 5). Paper samples coated with A. niger over different culture periods were observed using a camera, optical microscope, and FESEM (Fig. 6). The influence of input fiber diameter on the output spectrum and sensitivity of the sensor was studied (Figs. 7(a)–(b)).

 figure: Fig. 5.

Fig. 5. Images of the sensor with different input fiber diameters: (a) 500 µm, (b) 1000 µm, (c) 1500 µm, and (d) 2000 µm.

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 figure: Fig. 6.

Fig. 6. Images of paper samples coated with A. niger. (a–d) Day one, (e–h) day two, (i–l) day four, (m–p) day six, (q–t) day seven; The first row contains camera photographs, the second row contains optical micrographs, and the third and fourth rows are FESEM images.

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 figure: Fig. 7.

Fig. 7. Influence of input fiber diameter on the sensor output spectrum (a) and sensitivity (b). (The taper angle of the input fiber was 0°, the diameter of the output fiber was 500 µm, the distance between the input and output fibers was 100 µm, and the distance between the sensor end-face and paper sample was 20 mm.)

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Figures 6(a)–(t) show the vigorous growth of A. niger on the surface of the paper samples. With an increase in culture time, flat-lying mold spores grew upright hyphae. The density of the hyphae on the paper surface increased, and the height from the conidial head at the top of hypha to the surface of the paper increased to 978.2 µm (Fig. 6(r)). The FESEM images show how the spore head of A. niger changed from a concave ellipsoid in the middle to a spherical shape. The diameter of the conidium head increased from ∼33.9 µm (day one) to ∼136.2 µm (day seven), and the spore diameter increased from ∼2.7 µm (day one) to ∼3.8 µm (day seven).

Figure 7 shows the absorption spectra of A. niger in the range of 200–900 nm with a characteristic absorption peak at 418 nm (The base line signal measurement is the initial paper without Aspergillus, i.e., non-infected paper). The absorbance of A. niger first increased and then decreased with an increase in the diameter of the input fiber. That is, the sensitivity of the sensor first increased and then decreased. When the diameter of the input fiber was 1500 µm, the sensitivity of the sensor reached its maximum value (0.000249/µm). The reason for this is that when the diameter of the input fiber was small, the intensity of the external light source coupled to the input fiber was weak, resulting in a low light intensity from the input fiber. That is, the intensity of light transmitted to the surface of the paper was low; hence, the attenuation of the light by the A. niger biofilm was small (light absorption and scattering attenuation were small), and the output fiber’s light intensity change was small (low absorbance). As the diameter of the input optical fiber increased, the light intensity transmitted to the surface of the paper increased, the attenuation of light by A. niger on the surface of the paper increased, the change in the output light intensity from the output fiber increased (the absorbance increased), and the sensitivity of the sensor increases. However, when the diameter of the input fiber reached 1500 µm, further increases in the diameter resulted in a decrease in the sensitivity of the sensor. The reason behind this is that an excessively large input fiber diameter increases the spacing between the output fibers (Fig. 5), resulting in a weakening of the reflected light intensity received by the output fiber from the surface of the paper. This decreases the amount of light attenuation from absorption and scattering by A. niger cells. Therefore, the variation in the output fiber light intensity and the sensor’ absorbance decreases, along with the sensitivity of the sensor.

4.2 Influence of input fiber taper angle on the output spectrum and sensitivity

To further improve the sensitivity of the sensor, one end of the input fiber with a diameter of 1500 µm was modified into a tapered structure with taper angles of 0°, 6°, 12°, and 18°. Images of the sensors are shown in Fig. 8. The effects of the input fiber taper angle on the output spectrum and sensitivity of the sensors were studied (Fig. 9).

 figure: Fig. 8.

Fig. 8. Images of the sensors with different input fiber taper angles: (a) 0°, (b) 6°, (c) 12°, (d) 18°.

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 figure: Fig. 9.

Fig. 9. Influence of input fiber taper angle on the sensor output spectrum (a) and sensitivity (b). (The length of the tapered region was 1500 µm, the distance between the input and output fibers was 100 µm, the diameter of output fiber was 500 µm, and the distance between the sensor end-face and paper sample was 20 mm.)

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Figure 9 shows that the absorbance of A. niger first increased and then decreased with an increase in the input fiber taper angle. That is, the sensor sensitivity first increased and then decreased. When the input fiber taper angle was 12°, the sensitivity of the sensor reached a maximum value (0.000301/µm). The reason behind this behavior is that the light intensity emitted from the surface of the fiber increased with an increase in the input fiber taper angle. When the taper angle was less than 12°, the intensity of the light transmitted to the A. niger biofilm increased, resulting in greater light absorption and scattering by the A. niger biofilm; hence, the sensitivity of the sensor increased. However, when the taper angle was greater than 12°, the sensitivity of the sensor decreased. A significantly large taper angle will further increase the light radiation intensity on the surface of the input fiber taper region and reduce the radiation light intensity at the tip of this region. However, the light radiated from the surface of the tapered region was absorbed and decayed by the sensor’s jacket, resulting in a decrease in the light intensity at the A. niger biofilm as well as a decrease in the attenuation of light reflected back to the output fiber, absorbance, and sensitivity of the sensor.

4.3 Influence of fiber spacing and optical path on the sensor output spectrum and sensitivity

To further improve the sensitivity, sensors with various distances (100 µm, 500 µm, 1000 µm, and 2000 µm) between the input and output fibers were prepared using an input fiber with a diameter of 1500 µm and a taper angle of 12° and an output fiber with a diameter of 500 µm (Fig. 10). The influence of the distance between the input and output fibers on the output spectrum and sensitivity of the sensor was also studied (Figs. 11(a)–(b)). On this basis, the effect of the distance between the sensor probe and the surface of the paper sample (optical path) on the output spectrum and sensitivity of the sensor was further studied (Fig. 11(c)).

 figure: Fig. 10.

Fig. 10. Images of sensor probes with different distances between the input and output fibers: (a) 100 µm, (b) 500 µm, (c) 1000 µm, and (d) 2000µm.

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 figure: Fig. 11.

Fig. 11. Influence of different distances between the input and output fibers on the absorption spectrum (a) and sensitivity of the sensors (b). The influence of the optical path on the sensitivity of the sensors (c).

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Figures 11(a)–(b) show that the absorbance of the sensor decreased with an increase in distance between the input and output fibers. In other words, the sensor sensitivity decreased. The reason for this is that when the distance increased, the ability of the output fiber to receive the reflected light from the biofilm decreased (the light reflected back from the surface of the biofilm obeys a Gaussian distribution; therefore, the larger the distance, the lower the reflected light energy that can be received by the output fiber, see Fig. 4), resulting in a decrease in the optical attenuation of the output fiber, absorbance, and sensitivity of the sensor. When the distance between the input and output fibers was 100 µm, the sensor exhibited its highest sensitivity (0.000316/µm).

Figure 11(c) shows the variation in sensor sensitivity with optical path for a device with an input fiber diameter of 1500 µm, input fiber taper angle of 12°, output fiber diameter of 500 µm, and distance between the input fiber and the output fiber set to 100 µm. Here, the sensitivity first increased and then decreased with an increase in the optical path owing to the Gaussian distribution of the reflected light (Fig. 4). When the optical path was less than 30 mm, the radius of the equivalent reflection surface decreased with decreasing distance; the output optical fibers gradually moved away from their acceptable light range, the received reflected light intensity decreased, and thus the sensitivity decreased. When the optical path was greater than 30 mm, an increase in the optical path increased the diffuse reflection loss. There was also a decrease in the intensity of the reflected light received by the output fiber, attenuation of light by A. niger, and sensitivity.

Combining Figs. 7(b), 9(b), and 11(b)–(c), it is clear that when the diameter and taper angle of the input fiber was 1500 µm and 12°, respectively, the distance between the input fiber and output fiber was 100 µm, and the optical path was 30 mm, the sensitivity of the sensor reached its maximum. There was a linear relationship between sensor absorbance and A. niger biofilm thickness: yB = 0.000385 xB + 0.0017 (R2 = 0.991), where xB was in the range of 118.8–978.2 µm, and the sensitivity reached 0.000385/µm, as shown in Fig. 12.

 figure: Fig. 12.

Fig. 12. Relationship between A. niger at different heights and absorbance

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4.4 Response characteristics of the sensor to the growth process of A. flavus and A. tamarrii on the surface of paper samples

To characterize the growth of A. flavus and A. tamarrii on the surface of paper, samples under different culture periods were first characterized via a camera, optical microscope, and FESEM. The experimental results are shown in Fig. 13 and Fig. 14.

 figure: Fig. 13.

Fig. 13. Images of paper samples coated with A. flavus. (a–d) Day one, (e–h) day two, (i–l) day four, (m–p) day six, (q–t) day seven; The first row contains camera photographs, the second row contains optical micrographs, and the third and fourth rows are FESEM images.

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 figure: Fig. 14.

Fig. 14. Images of paper samples coated with A. tamarrii. (a–d) Day one, (e–h) day two, (i–l) day four, (m–p) day six, (q–t) day seven; The first row contains camera photographs, the second row contains optical micrographs, and the third and fourth rows are FESEM images.

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Figures 13(a)–(t) show the vigorous growth of A. flavus on the surface of the paper samples. With an increase in the incubation time, flat-lying mold spores grew upright hyphae, and the density of the hyphae increased. The height from the conidial head at the top of a hypha to the surface of the paper sample increased to 964.3 µm (Fig. 13(r)). The FESEM images show that the stalks of A. flavus had clusters of spherical conidia with a rough surface. The diameter of the conidial head increased from ∼40.4 µm (day one) to ∼155.2 µm (day seven). The surface of the A. flavus spores one day after inoculation was flat, and the diameter of the spores was 2.5 µm. As the incubation time increased, clear burrs appeared on the surface of the spores, and the spore diameter increased from ∼3.0 µm (day two) to ∼3.4 µm (day seven).

Figures 14(a)–(t) show the vigorous growth of A. tamarrii on the surface of the paper samples. With an increase in incubation time, upright hyphae grew from the flat-lying mold spores, and the density of the hyphae on the surface of the paper increased. The height of the conidial head at the top of the hyphae from the surface of the paper increased to 997.4 µm (Fig. 14(r)). The FESEM images show that the hyphae were velvety with a slightly spherically flocculent to loose radial conidial head. The diameter of the conidial head increased from ∼45.5 µm (day one) to ∼130.2 µm (day seven), and the diameter of the apical subglobose spores increased from ∼4.3 µm (day one) to ∼5.4 µm (day seven).

To verify whether the sensor can qualitatively and quantitatively monitor A. flavus and A. tamarrii on the surface of the paper samples, the sensor was used to monitor these molds over different culture periods. The diameter of the input fiber was 1500 µm, the taper angle of the input fiber was 12°, the distance between the input and output fibers was 100 µm, and the optical path was 30 mm (Fig. 15).

 figure: Fig. 15.

Fig. 15. Biofilm thickness detection on paper samples. (a, c) A. flavus, (b, d) A. tamarrii.

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Figures 15(a) and (b) show that A. flavus had a characteristic absorption peak at 282 nm and A. tamarrii had two peaks at 275 nm and 350 nm, respectively, in the 200–900 nm spectral range. Herein, the different Aspergillus strains show different absorption wavelengths, because the different Aspergillus species produce different metabolites and pigment chemicals. Furthermore, different substances have different molecular space structures, resulting in different absorption wavelengths [38]. Therefore, the Aspergillus strains showed their unique absorption wavelength (see Fig. 7(a) and Fig. 15(a-b)). These facts illustrate that the UV-Vis fiber-optic spectral sensor developed in this study can realize qualitative measurement of Aspergillus on the surface of papers.

Figures 15(c) and (d) show the quantitative response of the sensor to A. flavus and A. tamarrii under different periods. Note that the mold biofilm had different thicknesses. There was a linear relationship between sensor absorbance and A. flavus biofilm thickness: yY = 0.000408 xY – 0.026 (R2 = 0.990), where xY was in the range 181.3–964.3 µm, and the sensor sensitivity reached 0.000408/µm. The absorbance of the sensor (350 nm) was linearly related to the A. tamarrii thickness: yA = 0.000463 xA + 0.032 (R2 = 0.994), where xA was in the range 108.9–997.4 µm, and the detection sensitivity reached 0.000463/µm. Therefore, as shown in Fig. 12 and Figs. 15(c)–(d), the sensor can achieve quantitative detection of the growth of Aspergillus biofilm on the surface of paper samples.

4.5 Limit of detection (LOD) of the sensor

To evaluate the sensor’s LOD for mold on the surface of the paper samples, the sensor was used to monitor mold growth in its early stages (from inoculation to a mold height of 180 µm). The diameter of the input fiber was 1500 µm, the taper angle of the input fiber was 12°, the distance between the input and output fibers was 100 µm, and the optical path was 30 mm. The experimental results are shown in Fig. 16.

 figure: Fig. 16.

Fig. 16. Response characteristics of the sensor to the early stages of three different mold growths.

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Figure 16 shows that the sensor responds when the mold spore thickness on the paper surface reaches 10 µm, indicating that the sensor’s LOD for Aspergillus reached 10 µm. The absorbance of the sensor increased with an increase in the culture time of Aspergillus. Therefore, the sensor developed in this study can realize qualitative and quantitative detection of the entire Aspergillus growth process from mold infection to maturity on paper samples.

5. Conclusion

To realize on-line nondestructive monitoring of mold biofilm growth on the surface of cultural paper relics, a reflective fiber-optic spectral sensor was developed. A theoretical model of sensor measurement was established, and the effects of the sensor’s structural parameters on its sensitivity were studied. The results demonstrated that the sensitivity of the sensor reached its maximum when the diameter and taper angle of the input fiber was 1500 µm and 12°, respectively, the distance between the input and output fibers was 100 µm, and the optical path was 30 mm. The developed sensor was used for on-line monitoring of the growth process of Aspergillus and showed that quantitative and qualitative detection of different Aspergillus species on the surface of paper can be achieved. The characteristic absorption peaks of A. niger, A. flavus, and A. tamarrii were 418 nm, 282 nm, and 275 nm and 350 nm, respectively. A linear response to paper samples infected with mold (A. niger, A. flavus, and A. tamarrii) within seven days of culture was obtained. The detection sensitivity of the sensor to A. niger, A. flavus, and A. tamarrii reached 0.000385, 0.000408, and 0.000463/µm, respectively, and the LOD reached 10 µm. The sensor proposed in this paper may be widely applied to the qualitative and quantitative detection of mold and microorganisms on the surface of paper, which will help promote the scientific and technological development of cultural relic preservation and protection techniques.

Funding

Chongqing University of Technology Graduate Innovation Project (gzlcx20222023); Chongqing University of Technology Graduate Innovation Project (gzlcx20222021); National Key Research and Development Program of China (2020YFC1522500); Innovation Research Group of Universities in Chongqing (CXQT21035); National Natural Science Foundation of China (52176178, 51876018); Technology Innovation and Application Development Special of the Chongqing Science and Technology Bureau (cstc2020jscx-msxmX0097).

Acknowledgments

The authors thank the Chongqing Science and Technology Bureau and National Natural Science Foundation of China for their funding support. We also thank the Key Scientific Research Base of Pest and Mold Control of Heritage Collection (Chongqing China Three Gorges Museum), National Cultural Heritage Administration, for providing the mold strains.

Disclosures

The authors declare that there are no conflicts of interest related to this article.

Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

References

1. M. R. Zhang, Y. D. Hu, J. Liu, Y. Pei, K. Y. Tang, and Y. Lei, “Biodeterioration of collagen-based cultural relics: A review,” Fungal Biol. Rev. 39, 46–59 (2022). [CrossRef]  

2. K. Sterflinger and F. Pinzari, “The revenge of time: fungal deterioration of cultural heritage with particular reference to books, paper and parchment,” Environ. Microbiol. 14(3), 559–566 (2012). [CrossRef]  

3. Z. H. Jia, C. Yang, F. N. Zhao, X. L. Chao, Y. H. Li, and H. P. Xing, “One-step reinforcement and deacidification of paper documents: Application of Lewis base—Chitosan nanoparticle coatings and analytical characterization,” Coatings 10(12), 1226 (2020). [CrossRef]  

4. G. K. Villena, T. Fujikawa, S. Tsuyumu, and M. Gutiérrez-Correa, “Structural analysis of biofilms and pellets of Aspergillus niger by confocal laser scanning microscopy and cryo scanning electron microscopy,” Bioresour. Technol. 101(6), 1920–1926 (2010). [CrossRef]  

5. P. Targowski, M. Pronobis-Gajdzis, A. Surmak, M. Iwanicka, E. A. Kaszewska, and M. Sylwestrzak, “The application of macro-X-ray fluorescence and optical coherence tomography for examination of parchment manuscripts,” Stud Conserv 60(sup1), S167–S177 (2015). [CrossRef]  

6. M. H. Hadwan, “Simple spectrophotometric assay for measuring catalase activity in biological tissues,” BMC Biochem. 19(1), 7–8 (2018). [CrossRef]  

7. A. I. González-Ramírez, A. Ramírez-Granillo, M. G. Medina-Canales, A. V. Rodríguez-Tovar, and M. A. Martínez-Rivera, “Analysis and description of the stages of Aspergillus fumigatus biofilm formation using scanning electron microscopy,” BMC Microbiol. 16(1), 243 (2016). [CrossRef]  

8. X. Kang, A. Kirui, A. Muszyński, M. C. D. Widanage, A. Chen, P. Azadi, P. Wang, F. Mentink-Vigier, and T. Wang, “Molecular architecture of fungal cell walls revealed by solid-state NMR,” Nat. Commun. 9(1), 1–12 (2018). [CrossRef]  

9. T. Sawoszczuk, J. Syguła-Cholewińska, and J. M. del Hoyo-Meléndez, “Application of HS-SPME-GC-MS method for the detection of active molds on historical parchment,” Anal. Bioanal. Chem. 409(9), 2297–2307 (2017). [CrossRef]  

10. G. Muyzer and K. Smalla, “Application of denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE) in microbial ecology,” Antonie Van Leeuwenhoek 73(1), 127–141 (1998). [CrossRef]  

11. A. D. Wilson, “Applications of electronic-nose technologies for noninvasive early detection of plant, animal and human diseases,” Chemosensors 6(4), 45 (2018). [CrossRef]  

12. C. Cucci, J. K. Delaney, and M. Picollo, “Reflectance hyperspectral imaging for investigation of works of art: old master paintings and illuminated manuscripts,” Acc. Chem. Res. 49(10), 2070–2079 (2016). [CrossRef]  

13. T. Vitorino, A. Casini, C. Cucci, M. J. Melo, M. Picollo, and L. Stefani, “Non-invasive identification of traditional red lake pigments in fourteenth to sixteenth centuries paintings through the use of hyperspectral imaging technique,” Appl. Phys. A 121(3), 891–901 (2015). [CrossRef]  

14. F. Daniel, A. Mounier, J. P’erez-Arantegui, C. Pardos, N. Prieto-Taboada, S. Fdez-Ortiz de Vallejuelo, and K. Castro, “Hyperspectral imaging applied to the analysis of Goya paintings in the Museum of Zaragoza (Spain),” Microchem. J. 126, 113–120 (2016). [CrossRef]  

15. W. J. Hu, Y. Y. Huang, C. Y. Chen, Y. K. Liu, T. Guo, and B. O. Guan, “Highly sensitive detection of dopamine using a graphene functionalized plasmonic fiber-optic sensor with aptamer conformational amplification,” Sens. Actuators, B 264, 440–447 (2018). [CrossRef]  

16. J. M. Sonawane, C. I. Ezugwu, and P. C. Ghosh, “Microbial fuel cell-based biological oxygen demand sensors for monitoring wastewater: state-of-the-art and practical applications,” ACS Sens. 5(8), 2297–2316 (2020). [CrossRef]  

17. M. Ramakrishnan, G. Rajan, Y. Semenova, and G. Farrell, “Overview of fiber optic sensor technologies for strain/temperature sensing applications in composite materials,” Sensors 16(1), 99 (2016). [CrossRef]  

18. N. Cennamo, M. Pesavento, and L. Zeni, “A review on simple and highly sensitive plastic optical fiber probes for bio-chemical sensing,” Sens. Actuators, B 331, 129393 (2021). [CrossRef]  

19. P. Q. Zhu, J. J. Wang, F. Rao, C. Y. Yu, G. Y. Zhou, and X. G. Huang, “Differential Fresnel-reflection-based fiber biochemical sensor with temperature self-compensation for high-resolution measurement of Cd2 + concentration in solution,” Sens. Actuators, B 282, 644–649 (2019). [CrossRef]  

20. N. B. Zhong, Y. W. Wu, Z. K. Wang, H. X. Chang, D. J. Zhong, Y. L. Xu, X. Y. Hu, and L. W. Hang, “Monitoring microalgal biofilm growth and phenol degradation with fiber-optic sensors,” Anal. Chem. 91(23), 15155–15162 (2019). [CrossRef]  

21. N. B. Zhong, Q. Liao, X. Zhu, and R. Chen, “A fiber-optic sensor for accurately monitoring biofilm growth in a hydrogen production photobioreactor,” Anal. Chem. 86(8), 3994–4001 (2014). [CrossRef]  

22. Q. Liao, N. B. Zhong, X. Zhu, Y. Huang, and R. Chen, “Enhancement of hydrogen production by optimization of biofilm growth in a photobioreactor,” Int. J. Hydrog. Energy 40(14), 4741–4751 (2015). [CrossRef]  

23. N. B. Zhong, M. F. Zhao, and Y. S. Li, “U-shaped, double-tapered, fiber-optic sensor for effective biofilm growth monitoring,” Biomed. Opt. Express 7(2), 335–351 (2016). [CrossRef]  

24. M. Chen, X. Xin, H. M. Liu, Y. W. Wu, N. B. Zhong, and H. X. Chang, “Monitoring Biohydrogen Production and Metabolic Heat in Biofilms by Fiber Bragg Grating Sensors,” Anal. Chem. 91(12), 7842–7849 (2019). [CrossRef]  

25. L. Z. Jiao, N. B. Zhong, X. D. Zhao, S. X. Ma, X. L. Fu, and D. M. Dong, “Recent advances in fiber-optic evanescent wave sensors for monitoring organic and inorganic pollutants in water,” Trends Analyt Chem 127, 115892 (2020). [CrossRef]  

26. L. Q. Wang, G. C. Dang, L. P. Zheng, and X. Q. Wang, “Studies on the identification of pigments on relics and the analysis of color changes by fiber optics reflectance spectroscopy,” Anal. Lett. 34(13), 2403–2414 (2001). [CrossRef]  

27. C. Ricci, C. Miliani, B. G. Brunetti, and A. Sgamellotti, “Non-invasive identification of surface materials on marble artifacts with fiber optic mid-FTIR reflectance spectroscopy,” Talanta 69(5), 1221–1226 (2006). [CrossRef]  

28. M. Spring, C. Ricci, D. A. Peggie, and S. G. Kazarian, “ATR-FTIR imaging for the analysis of organic materials in paint cross sections: case studies on paint samples from the National Gallery, London,” Anal. Bioanal. Chem. 392(1-2), 37–45 (2008). [CrossRef]  

29. C. Miliani, F. Rosi, A. Daveri, and B. G. Brunetti, “Reflection infrared spectroscopy for the non-invasive in situ study of artists’ pigments,” Appl. Phys. A 106(2), 295–307 (2012). [CrossRef]  

30. D. Buti, F. Rosi, B. G. Brunetti, and C. Miliani, “In-situ identification of copper-based green pigments on paintings and manuscripts by reflection FTIR,” Anal. Bioanal. Chem. 405(8), 2699–2711 (2013). [CrossRef]  

31. B. Fonseca, C. S. Patterson, M. Ganio, D. MacLennan, and K. Trentelman, “Seeing red: towards an improved protocol for the identification of madder-and cochineal-based pigments by fiber optics reflectance spectroscopy (FORS),” Herit. Sci. 7(1), 92 (2019). [CrossRef]  

32. M. Corradini, L. de Ferri, and G. Pojana, “Fiber Optic Reflection Spectroscopy–Near-Infrared Characterization Study of Dry Pigments for Pictorial Retouching,” Appl Spectrosc 75(4), 445–461 (2021). [CrossRef]  

33. Y. Zhao, H. Zhao, R. Q. Lv, and J. Zhao, “Review of optical fiber Mach–Zehnder interferometers with micro-cavity fabricated by femtosecond laser and sensing applications,” Opt. Lasers Eng. 117, 7–20 (2019). [CrossRef]  

34. N. B. Zhong, Z. K. Wang, M. Chen, X. Xin, R. H. Wu, Y. Y. Cen, and Y. S. Li, “Three-layer-structure polymer optical fiber with a rough inter-layer surface as a highly sensitive evanescent wave sensor,” Sens. Actuators, B 254, 133–142 (2018). [CrossRef]  

35. F. Dai, K. W. Li, W. C. Zhou, W. Zhang, M. X. Yu, C. Q. Liu, and Y. H. Wu, “High-sensitivity nanofiber biochemical sensor based on nanomagnetic bead amplification,” Acta Optics 34(12), 33–39 (2014).

36. Q. Song, F. Luan, Z. Shi, T. R. Li, and M. Q. Wang, “Design of turbidity remote monitoring system based on fx-11a optical fiber sensor,” in Proceedings of IEEE Conference on 2020 Prognostics and Health Management Conference (IEEE, 2020), pp. 291–294.

37. S. Y. Zhu and H. M. Cao, “Theoretical Modeling and Simulation Implementation of Reflective Fiber Beam Probe,” Instrum Sci Technol 5, 275–278 (2013).

38. L. L. Qu, Q. Jia, C. Y. Liu, W. Wang, L. F. Duan, G. H. Yang, C. Q. Han, and H. T. Li, “Thin layer chromatography combined with surface-enhanced raman spectroscopy for rapid sensing aflatoxins,” J. Chromatogr. A 1579, 115–120 (2018). [CrossRef]  

References

  • View by:

  1. M. R. Zhang, Y. D. Hu, J. Liu, Y. Pei, K. Y. Tang, and Y. Lei, “Biodeterioration of collagen-based cultural relics: A review,” Fungal Biol. Rev. 39, 46–59 (2022).
    [Crossref]
  2. K. Sterflinger and F. Pinzari, “The revenge of time: fungal deterioration of cultural heritage with particular reference to books, paper and parchment,” Environ. Microbiol. 14(3), 559–566 (2012).
    [Crossref]
  3. Z. H. Jia, C. Yang, F. N. Zhao, X. L. Chao, Y. H. Li, and H. P. Xing, “One-step reinforcement and deacidification of paper documents: Application of Lewis base—Chitosan nanoparticle coatings and analytical characterization,” Coatings 10(12), 1226 (2020).
    [Crossref]
  4. G. K. Villena, T. Fujikawa, S. Tsuyumu, and M. Gutiérrez-Correa, “Structural analysis of biofilms and pellets of Aspergillus niger by confocal laser scanning microscopy and cryo scanning electron microscopy,” Bioresour. Technol. 101(6), 1920–1926 (2010).
    [Crossref]
  5. P. Targowski, M. Pronobis-Gajdzis, A. Surmak, M. Iwanicka, E. A. Kaszewska, and M. Sylwestrzak, “The application of macro-X-ray fluorescence and optical coherence tomography for examination of parchment manuscripts,” Stud Conserv 60(sup1), S167–S177 (2015).
    [Crossref]
  6. M. H. Hadwan, “Simple spectrophotometric assay for measuring catalase activity in biological tissues,” BMC Biochem. 19(1), 7–8 (2018).
    [Crossref]
  7. A. I. González-Ramírez, A. Ramírez-Granillo, M. G. Medina-Canales, A. V. Rodríguez-Tovar, and M. A. Martínez-Rivera, “Analysis and description of the stages of Aspergillus fumigatus biofilm formation using scanning electron microscopy,” BMC Microbiol. 16(1), 243 (2016).
    [Crossref]
  8. X. Kang, A. Kirui, A. Muszyński, M. C. D. Widanage, A. Chen, P. Azadi, P. Wang, F. Mentink-Vigier, and T. Wang, “Molecular architecture of fungal cell walls revealed by solid-state NMR,” Nat. Commun. 9(1), 1–12 (2018).
    [Crossref]
  9. T. Sawoszczuk, J. Syguła-Cholewińska, and J. M. del Hoyo-Meléndez, “Application of HS-SPME-GC-MS method for the detection of active molds on historical parchment,” Anal. Bioanal. Chem. 409(9), 2297–2307 (2017).
    [Crossref]
  10. G. Muyzer and K. Smalla, “Application of denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE) in microbial ecology,” Antonie Van Leeuwenhoek 73(1), 127–141 (1998).
    [Crossref]
  11. A. D. Wilson, “Applications of electronic-nose technologies for noninvasive early detection of plant, animal and human diseases,” Chemosensors 6(4), 45 (2018).
    [Crossref]
  12. C. Cucci, J. K. Delaney, and M. Picollo, “Reflectance hyperspectral imaging for investigation of works of art: old master paintings and illuminated manuscripts,” Acc. Chem. Res. 49(10), 2070–2079 (2016).
    [Crossref]
  13. T. Vitorino, A. Casini, C. Cucci, M. J. Melo, M. Picollo, and L. Stefani, “Non-invasive identification of traditional red lake pigments in fourteenth to sixteenth centuries paintings through the use of hyperspectral imaging technique,” Appl. Phys. A 121(3), 891–901 (2015).
    [Crossref]
  14. F. Daniel, A. Mounier, J. P’erez-Arantegui, C. Pardos, N. Prieto-Taboada, S. Fdez-Ortiz de Vallejuelo, and K. Castro, “Hyperspectral imaging applied to the analysis of Goya paintings in the Museum of Zaragoza (Spain),” Microchem. J. 126, 113–120 (2016).
    [Crossref]
  15. W. J. Hu, Y. Y. Huang, C. Y. Chen, Y. K. Liu, T. Guo, and B. O. Guan, “Highly sensitive detection of dopamine using a graphene functionalized plasmonic fiber-optic sensor with aptamer conformational amplification,” Sens. Actuators, B 264, 440–447 (2018).
    [Crossref]
  16. J. M. Sonawane, C. I. Ezugwu, and P. C. Ghosh, “Microbial fuel cell-based biological oxygen demand sensors for monitoring wastewater: state-of-the-art and practical applications,” ACS Sens. 5(8), 2297–2316 (2020).
    [Crossref]
  17. M. Ramakrishnan, G. Rajan, Y. Semenova, and G. Farrell, “Overview of fiber optic sensor technologies for strain/temperature sensing applications in composite materials,” Sensors 16(1), 99 (2016).
    [Crossref]
  18. N. Cennamo, M. Pesavento, and L. Zeni, “A review on simple and highly sensitive plastic optical fiber probes for bio-chemical sensing,” Sens. Actuators, B 331, 129393 (2021).
    [Crossref]
  19. P. Q. Zhu, J. J. Wang, F. Rao, C. Y. Yu, G. Y. Zhou, and X. G. Huang, “Differential Fresnel-reflection-based fiber biochemical sensor with temperature self-compensation for high-resolution measurement of Cd2 + concentration in solution,” Sens. Actuators, B 282, 644–649 (2019).
    [Crossref]
  20. N. B. Zhong, Y. W. Wu, Z. K. Wang, H. X. Chang, D. J. Zhong, Y. L. Xu, X. Y. Hu, and L. W. Hang, “Monitoring microalgal biofilm growth and phenol degradation with fiber-optic sensors,” Anal. Chem. 91(23), 15155–15162 (2019).
    [Crossref]
  21. N. B. Zhong, Q. Liao, X. Zhu, and R. Chen, “A fiber-optic sensor for accurately monitoring biofilm growth in a hydrogen production photobioreactor,” Anal. Chem. 86(8), 3994–4001 (2014).
    [Crossref]
  22. Q. Liao, N. B. Zhong, X. Zhu, Y. Huang, and R. Chen, “Enhancement of hydrogen production by optimization of biofilm growth in a photobioreactor,” Int. J. Hydrog. Energy 40(14), 4741–4751 (2015).
    [Crossref]
  23. N. B. Zhong, M. F. Zhao, and Y. S. Li, “U-shaped, double-tapered, fiber-optic sensor for effective biofilm growth monitoring,” Biomed. Opt. Express 7(2), 335–351 (2016).
    [Crossref]
  24. M. Chen, X. Xin, H. M. Liu, Y. W. Wu, N. B. Zhong, and H. X. Chang, “Monitoring Biohydrogen Production and Metabolic Heat in Biofilms by Fiber Bragg Grating Sensors,” Anal. Chem. 91(12), 7842–7849 (2019).
    [Crossref]
  25. L. Z. Jiao, N. B. Zhong, X. D. Zhao, S. X. Ma, X. L. Fu, and D. M. Dong, “Recent advances in fiber-optic evanescent wave sensors for monitoring organic and inorganic pollutants in water,” Trends Analyt Chem 127, 115892 (2020).
    [Crossref]
  26. L. Q. Wang, G. C. Dang, L. P. Zheng, and X. Q. Wang, “Studies on the identification of pigments on relics and the analysis of color changes by fiber optics reflectance spectroscopy,” Anal. Lett. 34(13), 2403–2414 (2001).
    [Crossref]
  27. C. Ricci, C. Miliani, B. G. Brunetti, and A. Sgamellotti, “Non-invasive identification of surface materials on marble artifacts with fiber optic mid-FTIR reflectance spectroscopy,” Talanta 69(5), 1221–1226 (2006).
    [Crossref]
  28. M. Spring, C. Ricci, D. A. Peggie, and S. G. Kazarian, “ATR-FTIR imaging for the analysis of organic materials in paint cross sections: case studies on paint samples from the National Gallery, London,” Anal. Bioanal. Chem. 392(1-2), 37–45 (2008).
    [Crossref]
  29. C. Miliani, F. Rosi, A. Daveri, and B. G. Brunetti, “Reflection infrared spectroscopy for the non-invasive in situ study of artists’ pigments,” Appl. Phys. A 106(2), 295–307 (2012).
    [Crossref]
  30. D. Buti, F. Rosi, B. G. Brunetti, and C. Miliani, “In-situ identification of copper-based green pigments on paintings and manuscripts by reflection FTIR,” Anal. Bioanal. Chem. 405(8), 2699–2711 (2013).
    [Crossref]
  31. B. Fonseca, C. S. Patterson, M. Ganio, D. MacLennan, and K. Trentelman, “Seeing red: towards an improved protocol for the identification of madder-and cochineal-based pigments by fiber optics reflectance spectroscopy (FORS),” Herit. Sci. 7(1), 92 (2019).
    [Crossref]
  32. M. Corradini, L. de Ferri, and G. Pojana, “Fiber Optic Reflection Spectroscopy–Near-Infrared Characterization Study of Dry Pigments for Pictorial Retouching,” Appl Spectrosc 75(4), 445–461 (2021).
    [Crossref]
  33. Y. Zhao, H. Zhao, R. Q. Lv, and J. Zhao, “Review of optical fiber Mach–Zehnder interferometers with micro-cavity fabricated by femtosecond laser and sensing applications,” Opt. Lasers Eng. 117, 7–20 (2019).
    [Crossref]
  34. N. B. Zhong, Z. K. Wang, M. Chen, X. Xin, R. H. Wu, Y. Y. Cen, and Y. S. Li, “Three-layer-structure polymer optical fiber with a rough inter-layer surface as a highly sensitive evanescent wave sensor,” Sens. Actuators, B 254, 133–142 (2018).
    [Crossref]
  35. F. Dai, K. W. Li, W. C. Zhou, W. Zhang, M. X. Yu, C. Q. Liu, and Y. H. Wu, “High-sensitivity nanofiber biochemical sensor based on nanomagnetic bead amplification,” Acta Optics 34(12), 33–39 (2014).
  36. Q. Song, F. Luan, Z. Shi, T. R. Li, and M. Q. Wang, “Design of turbidity remote monitoring system based on fx-11a optical fiber sensor,” in Proceedings of IEEE Conference on 2020 Prognostics and Health Management Conference (IEEE, 2020), pp. 291–294.
  37. S. Y. Zhu and H. M. Cao, “Theoretical Modeling and Simulation Implementation of Reflective Fiber Beam Probe,” Instrum Sci Technol 5, 275–278 (2013).
  38. L. L. Qu, Q. Jia, C. Y. Liu, W. Wang, L. F. Duan, G. H. Yang, C. Q. Han, and H. T. Li, “Thin layer chromatography combined with surface-enhanced raman spectroscopy for rapid sensing aflatoxins,” J. Chromatogr. A 1579, 115–120 (2018).
    [Crossref]

2022 (1)

M. R. Zhang, Y. D. Hu, J. Liu, Y. Pei, K. Y. Tang, and Y. Lei, “Biodeterioration of collagen-based cultural relics: A review,” Fungal Biol. Rev. 39, 46–59 (2022).
[Crossref]

2021 (2)

N. Cennamo, M. Pesavento, and L. Zeni, “A review on simple and highly sensitive plastic optical fiber probes for bio-chemical sensing,” Sens. Actuators, B 331, 129393 (2021).
[Crossref]

M. Corradini, L. de Ferri, and G. Pojana, “Fiber Optic Reflection Spectroscopy–Near-Infrared Characterization Study of Dry Pigments for Pictorial Retouching,” Appl Spectrosc 75(4), 445–461 (2021).
[Crossref]

2020 (3)

J. M. Sonawane, C. I. Ezugwu, and P. C. Ghosh, “Microbial fuel cell-based biological oxygen demand sensors for monitoring wastewater: state-of-the-art and practical applications,” ACS Sens. 5(8), 2297–2316 (2020).
[Crossref]

L. Z. Jiao, N. B. Zhong, X. D. Zhao, S. X. Ma, X. L. Fu, and D. M. Dong, “Recent advances in fiber-optic evanescent wave sensors for monitoring organic and inorganic pollutants in water,” Trends Analyt Chem 127, 115892 (2020).
[Crossref]

Z. H. Jia, C. Yang, F. N. Zhao, X. L. Chao, Y. H. Li, and H. P. Xing, “One-step reinforcement and deacidification of paper documents: Application of Lewis base—Chitosan nanoparticle coatings and analytical characterization,” Coatings 10(12), 1226 (2020).
[Crossref]

2019 (5)

P. Q. Zhu, J. J. Wang, F. Rao, C. Y. Yu, G. Y. Zhou, and X. G. Huang, “Differential Fresnel-reflection-based fiber biochemical sensor with temperature self-compensation for high-resolution measurement of Cd2 + concentration in solution,” Sens. Actuators, B 282, 644–649 (2019).
[Crossref]

N. B. Zhong, Y. W. Wu, Z. K. Wang, H. X. Chang, D. J. Zhong, Y. L. Xu, X. Y. Hu, and L. W. Hang, “Monitoring microalgal biofilm growth and phenol degradation with fiber-optic sensors,” Anal. Chem. 91(23), 15155–15162 (2019).
[Crossref]

M. Chen, X. Xin, H. M. Liu, Y. W. Wu, N. B. Zhong, and H. X. Chang, “Monitoring Biohydrogen Production and Metabolic Heat in Biofilms by Fiber Bragg Grating Sensors,” Anal. Chem. 91(12), 7842–7849 (2019).
[Crossref]

Y. Zhao, H. Zhao, R. Q. Lv, and J. Zhao, “Review of optical fiber Mach–Zehnder interferometers with micro-cavity fabricated by femtosecond laser and sensing applications,” Opt. Lasers Eng. 117, 7–20 (2019).
[Crossref]

B. Fonseca, C. S. Patterson, M. Ganio, D. MacLennan, and K. Trentelman, “Seeing red: towards an improved protocol for the identification of madder-and cochineal-based pigments by fiber optics reflectance spectroscopy (FORS),” Herit. Sci. 7(1), 92 (2019).
[Crossref]

2018 (6)

L. L. Qu, Q. Jia, C. Y. Liu, W. Wang, L. F. Duan, G. H. Yang, C. Q. Han, and H. T. Li, “Thin layer chromatography combined with surface-enhanced raman spectroscopy for rapid sensing aflatoxins,” J. Chromatogr. A 1579, 115–120 (2018).
[Crossref]

N. B. Zhong, Z. K. Wang, M. Chen, X. Xin, R. H. Wu, Y. Y. Cen, and Y. S. Li, “Three-layer-structure polymer optical fiber with a rough inter-layer surface as a highly sensitive evanescent wave sensor,” Sens. Actuators, B 254, 133–142 (2018).
[Crossref]

W. J. Hu, Y. Y. Huang, C. Y. Chen, Y. K. Liu, T. Guo, and B. O. Guan, “Highly sensitive detection of dopamine using a graphene functionalized plasmonic fiber-optic sensor with aptamer conformational amplification,” Sens. Actuators, B 264, 440–447 (2018).
[Crossref]

A. D. Wilson, “Applications of electronic-nose technologies for noninvasive early detection of plant, animal and human diseases,” Chemosensors 6(4), 45 (2018).
[Crossref]

M. H. Hadwan, “Simple spectrophotometric assay for measuring catalase activity in biological tissues,” BMC Biochem. 19(1), 7–8 (2018).
[Crossref]

X. Kang, A. Kirui, A. Muszyński, M. C. D. Widanage, A. Chen, P. Azadi, P. Wang, F. Mentink-Vigier, and T. Wang, “Molecular architecture of fungal cell walls revealed by solid-state NMR,” Nat. Commun. 9(1), 1–12 (2018).
[Crossref]

2017 (1)

T. Sawoszczuk, J. Syguła-Cholewińska, and J. M. del Hoyo-Meléndez, “Application of HS-SPME-GC-MS method for the detection of active molds on historical parchment,” Anal. Bioanal. Chem. 409(9), 2297–2307 (2017).
[Crossref]

2016 (5)

A. I. González-Ramírez, A. Ramírez-Granillo, M. G. Medina-Canales, A. V. Rodríguez-Tovar, and M. A. Martínez-Rivera, “Analysis and description of the stages of Aspergillus fumigatus biofilm formation using scanning electron microscopy,” BMC Microbiol. 16(1), 243 (2016).
[Crossref]

C. Cucci, J. K. Delaney, and M. Picollo, “Reflectance hyperspectral imaging for investigation of works of art: old master paintings and illuminated manuscripts,” Acc. Chem. Res. 49(10), 2070–2079 (2016).
[Crossref]

F. Daniel, A. Mounier, J. P’erez-Arantegui, C. Pardos, N. Prieto-Taboada, S. Fdez-Ortiz de Vallejuelo, and K. Castro, “Hyperspectral imaging applied to the analysis of Goya paintings in the Museum of Zaragoza (Spain),” Microchem. J. 126, 113–120 (2016).
[Crossref]

M. Ramakrishnan, G. Rajan, Y. Semenova, and G. Farrell, “Overview of fiber optic sensor technologies for strain/temperature sensing applications in composite materials,” Sensors 16(1), 99 (2016).
[Crossref]

N. B. Zhong, M. F. Zhao, and Y. S. Li, “U-shaped, double-tapered, fiber-optic sensor for effective biofilm growth monitoring,” Biomed. Opt. Express 7(2), 335–351 (2016).
[Crossref]

2015 (3)

Q. Liao, N. B. Zhong, X. Zhu, Y. Huang, and R. Chen, “Enhancement of hydrogen production by optimization of biofilm growth in a photobioreactor,” Int. J. Hydrog. Energy 40(14), 4741–4751 (2015).
[Crossref]

P. Targowski, M. Pronobis-Gajdzis, A. Surmak, M. Iwanicka, E. A. Kaszewska, and M. Sylwestrzak, “The application of macro-X-ray fluorescence and optical coherence tomography for examination of parchment manuscripts,” Stud Conserv 60(sup1), S167–S177 (2015).
[Crossref]

T. Vitorino, A. Casini, C. Cucci, M. J. Melo, M. Picollo, and L. Stefani, “Non-invasive identification of traditional red lake pigments in fourteenth to sixteenth centuries paintings through the use of hyperspectral imaging technique,” Appl. Phys. A 121(3), 891–901 (2015).
[Crossref]

2014 (2)

N. B. Zhong, Q. Liao, X. Zhu, and R. Chen, “A fiber-optic sensor for accurately monitoring biofilm growth in a hydrogen production photobioreactor,” Anal. Chem. 86(8), 3994–4001 (2014).
[Crossref]

F. Dai, K. W. Li, W. C. Zhou, W. Zhang, M. X. Yu, C. Q. Liu, and Y. H. Wu, “High-sensitivity nanofiber biochemical sensor based on nanomagnetic bead amplification,” Acta Optics 34(12), 33–39 (2014).

2013 (2)

S. Y. Zhu and H. M. Cao, “Theoretical Modeling and Simulation Implementation of Reflective Fiber Beam Probe,” Instrum Sci Technol 5, 275–278 (2013).

D. Buti, F. Rosi, B. G. Brunetti, and C. Miliani, “In-situ identification of copper-based green pigments on paintings and manuscripts by reflection FTIR,” Anal. Bioanal. Chem. 405(8), 2699–2711 (2013).
[Crossref]

2012 (2)

C. Miliani, F. Rosi, A. Daveri, and B. G. Brunetti, “Reflection infrared spectroscopy for the non-invasive in situ study of artists’ pigments,” Appl. Phys. A 106(2), 295–307 (2012).
[Crossref]

K. Sterflinger and F. Pinzari, “The revenge of time: fungal deterioration of cultural heritage with particular reference to books, paper and parchment,” Environ. Microbiol. 14(3), 559–566 (2012).
[Crossref]

2010 (1)

G. K. Villena, T. Fujikawa, S. Tsuyumu, and M. Gutiérrez-Correa, “Structural analysis of biofilms and pellets of Aspergillus niger by confocal laser scanning microscopy and cryo scanning electron microscopy,” Bioresour. Technol. 101(6), 1920–1926 (2010).
[Crossref]

2008 (1)

M. Spring, C. Ricci, D. A. Peggie, and S. G. Kazarian, “ATR-FTIR imaging for the analysis of organic materials in paint cross sections: case studies on paint samples from the National Gallery, London,” Anal. Bioanal. Chem. 392(1-2), 37–45 (2008).
[Crossref]

2006 (1)

C. Ricci, C. Miliani, B. G. Brunetti, and A. Sgamellotti, “Non-invasive identification of surface materials on marble artifacts with fiber optic mid-FTIR reflectance spectroscopy,” Talanta 69(5), 1221–1226 (2006).
[Crossref]

2001 (1)

L. Q. Wang, G. C. Dang, L. P. Zheng, and X. Q. Wang, “Studies on the identification of pigments on relics and the analysis of color changes by fiber optics reflectance spectroscopy,” Anal. Lett. 34(13), 2403–2414 (2001).
[Crossref]

1998 (1)

G. Muyzer and K. Smalla, “Application of denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE) in microbial ecology,” Antonie Van Leeuwenhoek 73(1), 127–141 (1998).
[Crossref]

Azadi, P.

X. Kang, A. Kirui, A. Muszyński, M. C. D. Widanage, A. Chen, P. Azadi, P. Wang, F. Mentink-Vigier, and T. Wang, “Molecular architecture of fungal cell walls revealed by solid-state NMR,” Nat. Commun. 9(1), 1–12 (2018).
[Crossref]

Brunetti, B. G.

D. Buti, F. Rosi, B. G. Brunetti, and C. Miliani, “In-situ identification of copper-based green pigments on paintings and manuscripts by reflection FTIR,” Anal. Bioanal. Chem. 405(8), 2699–2711 (2013).
[Crossref]

C. Miliani, F. Rosi, A. Daveri, and B. G. Brunetti, “Reflection infrared spectroscopy for the non-invasive in situ study of artists’ pigments,” Appl. Phys. A 106(2), 295–307 (2012).
[Crossref]

C. Ricci, C. Miliani, B. G. Brunetti, and A. Sgamellotti, “Non-invasive identification of surface materials on marble artifacts with fiber optic mid-FTIR reflectance spectroscopy,” Talanta 69(5), 1221–1226 (2006).
[Crossref]

Buti, D.

D. Buti, F. Rosi, B. G. Brunetti, and C. Miliani, “In-situ identification of copper-based green pigments on paintings and manuscripts by reflection FTIR,” Anal. Bioanal. Chem. 405(8), 2699–2711 (2013).
[Crossref]

Cao, H. M.

S. Y. Zhu and H. M. Cao, “Theoretical Modeling and Simulation Implementation of Reflective Fiber Beam Probe,” Instrum Sci Technol 5, 275–278 (2013).

Casini, A.

T. Vitorino, A. Casini, C. Cucci, M. J. Melo, M. Picollo, and L. Stefani, “Non-invasive identification of traditional red lake pigments in fourteenth to sixteenth centuries paintings through the use of hyperspectral imaging technique,” Appl. Phys. A 121(3), 891–901 (2015).
[Crossref]

Castro, K.

F. Daniel, A. Mounier, J. P’erez-Arantegui, C. Pardos, N. Prieto-Taboada, S. Fdez-Ortiz de Vallejuelo, and K. Castro, “Hyperspectral imaging applied to the analysis of Goya paintings in the Museum of Zaragoza (Spain),” Microchem. J. 126, 113–120 (2016).
[Crossref]

Cen, Y. Y.

N. B. Zhong, Z. K. Wang, M. Chen, X. Xin, R. H. Wu, Y. Y. Cen, and Y. S. Li, “Three-layer-structure polymer optical fiber with a rough inter-layer surface as a highly sensitive evanescent wave sensor,” Sens. Actuators, B 254, 133–142 (2018).
[Crossref]

Cennamo, N.

N. Cennamo, M. Pesavento, and L. Zeni, “A review on simple and highly sensitive plastic optical fiber probes for bio-chemical sensing,” Sens. Actuators, B 331, 129393 (2021).
[Crossref]

Chang, H. X.

N. B. Zhong, Y. W. Wu, Z. K. Wang, H. X. Chang, D. J. Zhong, Y. L. Xu, X. Y. Hu, and L. W. Hang, “Monitoring microalgal biofilm growth and phenol degradation with fiber-optic sensors,” Anal. Chem. 91(23), 15155–15162 (2019).
[Crossref]

M. Chen, X. Xin, H. M. Liu, Y. W. Wu, N. B. Zhong, and H. X. Chang, “Monitoring Biohydrogen Production and Metabolic Heat in Biofilms by Fiber Bragg Grating Sensors,” Anal. Chem. 91(12), 7842–7849 (2019).
[Crossref]

Chao, X. L.

Z. H. Jia, C. Yang, F. N. Zhao, X. L. Chao, Y. H. Li, and H. P. Xing, “One-step reinforcement and deacidification of paper documents: Application of Lewis base—Chitosan nanoparticle coatings and analytical characterization,” Coatings 10(12), 1226 (2020).
[Crossref]

Chen, A.

X. Kang, A. Kirui, A. Muszyński, M. C. D. Widanage, A. Chen, P. Azadi, P. Wang, F. Mentink-Vigier, and T. Wang, “Molecular architecture of fungal cell walls revealed by solid-state NMR,” Nat. Commun. 9(1), 1–12 (2018).
[Crossref]

Chen, C. Y.

W. J. Hu, Y. Y. Huang, C. Y. Chen, Y. K. Liu, T. Guo, and B. O. Guan, “Highly sensitive detection of dopamine using a graphene functionalized plasmonic fiber-optic sensor with aptamer conformational amplification,” Sens. Actuators, B 264, 440–447 (2018).
[Crossref]

Chen, M.

M. Chen, X. Xin, H. M. Liu, Y. W. Wu, N. B. Zhong, and H. X. Chang, “Monitoring Biohydrogen Production and Metabolic Heat in Biofilms by Fiber Bragg Grating Sensors,” Anal. Chem. 91(12), 7842–7849 (2019).
[Crossref]

N. B. Zhong, Z. K. Wang, M. Chen, X. Xin, R. H. Wu, Y. Y. Cen, and Y. S. Li, “Three-layer-structure polymer optical fiber with a rough inter-layer surface as a highly sensitive evanescent wave sensor,” Sens. Actuators, B 254, 133–142 (2018).
[Crossref]

Chen, R.

Q. Liao, N. B. Zhong, X. Zhu, Y. Huang, and R. Chen, “Enhancement of hydrogen production by optimization of biofilm growth in a photobioreactor,” Int. J. Hydrog. Energy 40(14), 4741–4751 (2015).
[Crossref]

N. B. Zhong, Q. Liao, X. Zhu, and R. Chen, “A fiber-optic sensor for accurately monitoring biofilm growth in a hydrogen production photobioreactor,” Anal. Chem. 86(8), 3994–4001 (2014).
[Crossref]

Corradini, M.

M. Corradini, L. de Ferri, and G. Pojana, “Fiber Optic Reflection Spectroscopy–Near-Infrared Characterization Study of Dry Pigments for Pictorial Retouching,” Appl Spectrosc 75(4), 445–461 (2021).
[Crossref]

Cucci, C.

C. Cucci, J. K. Delaney, and M. Picollo, “Reflectance hyperspectral imaging for investigation of works of art: old master paintings and illuminated manuscripts,” Acc. Chem. Res. 49(10), 2070–2079 (2016).
[Crossref]

T. Vitorino, A. Casini, C. Cucci, M. J. Melo, M. Picollo, and L. Stefani, “Non-invasive identification of traditional red lake pigments in fourteenth to sixteenth centuries paintings through the use of hyperspectral imaging technique,” Appl. Phys. A 121(3), 891–901 (2015).
[Crossref]

Dai, F.

F. Dai, K. W. Li, W. C. Zhou, W. Zhang, M. X. Yu, C. Q. Liu, and Y. H. Wu, “High-sensitivity nanofiber biochemical sensor based on nanomagnetic bead amplification,” Acta Optics 34(12), 33–39 (2014).

Dang, G. C.

L. Q. Wang, G. C. Dang, L. P. Zheng, and X. Q. Wang, “Studies on the identification of pigments on relics and the analysis of color changes by fiber optics reflectance spectroscopy,” Anal. Lett. 34(13), 2403–2414 (2001).
[Crossref]

Daniel, F.

F. Daniel, A. Mounier, J. P’erez-Arantegui, C. Pardos, N. Prieto-Taboada, S. Fdez-Ortiz de Vallejuelo, and K. Castro, “Hyperspectral imaging applied to the analysis of Goya paintings in the Museum of Zaragoza (Spain),” Microchem. J. 126, 113–120 (2016).
[Crossref]

Daveri, A.

C. Miliani, F. Rosi, A. Daveri, and B. G. Brunetti, “Reflection infrared spectroscopy for the non-invasive in situ study of artists’ pigments,” Appl. Phys. A 106(2), 295–307 (2012).
[Crossref]

de Ferri, L.

M. Corradini, L. de Ferri, and G. Pojana, “Fiber Optic Reflection Spectroscopy–Near-Infrared Characterization Study of Dry Pigments for Pictorial Retouching,” Appl Spectrosc 75(4), 445–461 (2021).
[Crossref]

del Hoyo-Meléndez, J. M.

T. Sawoszczuk, J. Syguła-Cholewińska, and J. M. del Hoyo-Meléndez, “Application of HS-SPME-GC-MS method for the detection of active molds on historical parchment,” Anal. Bioanal. Chem. 409(9), 2297–2307 (2017).
[Crossref]

Delaney, J. K.

C. Cucci, J. K. Delaney, and M. Picollo, “Reflectance hyperspectral imaging for investigation of works of art: old master paintings and illuminated manuscripts,” Acc. Chem. Res. 49(10), 2070–2079 (2016).
[Crossref]

Dong, D. M.

L. Z. Jiao, N. B. Zhong, X. D. Zhao, S. X. Ma, X. L. Fu, and D. M. Dong, “Recent advances in fiber-optic evanescent wave sensors for monitoring organic and inorganic pollutants in water,” Trends Analyt Chem 127, 115892 (2020).
[Crossref]

Duan, L. F.

L. L. Qu, Q. Jia, C. Y. Liu, W. Wang, L. F. Duan, G. H. Yang, C. Q. Han, and H. T. Li, “Thin layer chromatography combined with surface-enhanced raman spectroscopy for rapid sensing aflatoxins,” J. Chromatogr. A 1579, 115–120 (2018).
[Crossref]

Ezugwu, C. I.

J. M. Sonawane, C. I. Ezugwu, and P. C. Ghosh, “Microbial fuel cell-based biological oxygen demand sensors for monitoring wastewater: state-of-the-art and practical applications,” ACS Sens. 5(8), 2297–2316 (2020).
[Crossref]

Farrell, G.

M. Ramakrishnan, G. Rajan, Y. Semenova, and G. Farrell, “Overview of fiber optic sensor technologies for strain/temperature sensing applications in composite materials,” Sensors 16(1), 99 (2016).
[Crossref]

Fdez-Ortiz de Vallejuelo, S.

F. Daniel, A. Mounier, J. P’erez-Arantegui, C. Pardos, N. Prieto-Taboada, S. Fdez-Ortiz de Vallejuelo, and K. Castro, “Hyperspectral imaging applied to the analysis of Goya paintings in the Museum of Zaragoza (Spain),” Microchem. J. 126, 113–120 (2016).
[Crossref]

Fonseca, B.

B. Fonseca, C. S. Patterson, M. Ganio, D. MacLennan, and K. Trentelman, “Seeing red: towards an improved protocol for the identification of madder-and cochineal-based pigments by fiber optics reflectance spectroscopy (FORS),” Herit. Sci. 7(1), 92 (2019).
[Crossref]

Fu, X. L.

L. Z. Jiao, N. B. Zhong, X. D. Zhao, S. X. Ma, X. L. Fu, and D. M. Dong, “Recent advances in fiber-optic evanescent wave sensors for monitoring organic and inorganic pollutants in water,” Trends Analyt Chem 127, 115892 (2020).
[Crossref]

Fujikawa, T.

G. K. Villena, T. Fujikawa, S. Tsuyumu, and M. Gutiérrez-Correa, “Structural analysis of biofilms and pellets of Aspergillus niger by confocal laser scanning microscopy and cryo scanning electron microscopy,” Bioresour. Technol. 101(6), 1920–1926 (2010).
[Crossref]

Ganio, M.

B. Fonseca, C. S. Patterson, M. Ganio, D. MacLennan, and K. Trentelman, “Seeing red: towards an improved protocol for the identification of madder-and cochineal-based pigments by fiber optics reflectance spectroscopy (FORS),” Herit. Sci. 7(1), 92 (2019).
[Crossref]

Ghosh, P. C.

J. M. Sonawane, C. I. Ezugwu, and P. C. Ghosh, “Microbial fuel cell-based biological oxygen demand sensors for monitoring wastewater: state-of-the-art and practical applications,” ACS Sens. 5(8), 2297–2316 (2020).
[Crossref]

González-Ramírez, A. I.

A. I. González-Ramírez, A. Ramírez-Granillo, M. G. Medina-Canales, A. V. Rodríguez-Tovar, and M. A. Martínez-Rivera, “Analysis and description of the stages of Aspergillus fumigatus biofilm formation using scanning electron microscopy,” BMC Microbiol. 16(1), 243 (2016).
[Crossref]

Guan, B. O.

W. J. Hu, Y. Y. Huang, C. Y. Chen, Y. K. Liu, T. Guo, and B. O. Guan, “Highly sensitive detection of dopamine using a graphene functionalized plasmonic fiber-optic sensor with aptamer conformational amplification,” Sens. Actuators, B 264, 440–447 (2018).
[Crossref]

Guo, T.

W. J. Hu, Y. Y. Huang, C. Y. Chen, Y. K. Liu, T. Guo, and B. O. Guan, “Highly sensitive detection of dopamine using a graphene functionalized plasmonic fiber-optic sensor with aptamer conformational amplification,” Sens. Actuators, B 264, 440–447 (2018).
[Crossref]

Gutiérrez-Correa, M.

G. K. Villena, T. Fujikawa, S. Tsuyumu, and M. Gutiérrez-Correa, “Structural analysis of biofilms and pellets of Aspergillus niger by confocal laser scanning microscopy and cryo scanning electron microscopy,” Bioresour. Technol. 101(6), 1920–1926 (2010).
[Crossref]

Hadwan, M. H.

M. H. Hadwan, “Simple spectrophotometric assay for measuring catalase activity in biological tissues,” BMC Biochem. 19(1), 7–8 (2018).
[Crossref]

Han, C. Q.

L. L. Qu, Q. Jia, C. Y. Liu, W. Wang, L. F. Duan, G. H. Yang, C. Q. Han, and H. T. Li, “Thin layer chromatography combined with surface-enhanced raman spectroscopy for rapid sensing aflatoxins,” J. Chromatogr. A 1579, 115–120 (2018).
[Crossref]

Hang, L. W.

N. B. Zhong, Y. W. Wu, Z. K. Wang, H. X. Chang, D. J. Zhong, Y. L. Xu, X. Y. Hu, and L. W. Hang, “Monitoring microalgal biofilm growth and phenol degradation with fiber-optic sensors,” Anal. Chem. 91(23), 15155–15162 (2019).
[Crossref]

Hu, W. J.

W. J. Hu, Y. Y. Huang, C. Y. Chen, Y. K. Liu, T. Guo, and B. O. Guan, “Highly sensitive detection of dopamine using a graphene functionalized plasmonic fiber-optic sensor with aptamer conformational amplification,” Sens. Actuators, B 264, 440–447 (2018).
[Crossref]

Hu, X. Y.

N. B. Zhong, Y. W. Wu, Z. K. Wang, H. X. Chang, D. J. Zhong, Y. L. Xu, X. Y. Hu, and L. W. Hang, “Monitoring microalgal biofilm growth and phenol degradation with fiber-optic sensors,” Anal. Chem. 91(23), 15155–15162 (2019).
[Crossref]

Hu, Y. D.

M. R. Zhang, Y. D. Hu, J. Liu, Y. Pei, K. Y. Tang, and Y. Lei, “Biodeterioration of collagen-based cultural relics: A review,” Fungal Biol. Rev. 39, 46–59 (2022).
[Crossref]

Huang, X. G.

P. Q. Zhu, J. J. Wang, F. Rao, C. Y. Yu, G. Y. Zhou, and X. G. Huang, “Differential Fresnel-reflection-based fiber biochemical sensor with temperature self-compensation for high-resolution measurement of Cd2 + concentration in solution,” Sens. Actuators, B 282, 644–649 (2019).
[Crossref]

Huang, Y.

Q. Liao, N. B. Zhong, X. Zhu, Y. Huang, and R. Chen, “Enhancement of hydrogen production by optimization of biofilm growth in a photobioreactor,” Int. J. Hydrog. Energy 40(14), 4741–4751 (2015).
[Crossref]

Huang, Y. Y.

W. J. Hu, Y. Y. Huang, C. Y. Chen, Y. K. Liu, T. Guo, and B. O. Guan, “Highly sensitive detection of dopamine using a graphene functionalized plasmonic fiber-optic sensor with aptamer conformational amplification,” Sens. Actuators, B 264, 440–447 (2018).
[Crossref]

Iwanicka, M.

P. Targowski, M. Pronobis-Gajdzis, A. Surmak, M. Iwanicka, E. A. Kaszewska, and M. Sylwestrzak, “The application of macro-X-ray fluorescence and optical coherence tomography for examination of parchment manuscripts,” Stud Conserv 60(sup1), S167–S177 (2015).
[Crossref]

Jia, Q.

L. L. Qu, Q. Jia, C. Y. Liu, W. Wang, L. F. Duan, G. H. Yang, C. Q. Han, and H. T. Li, “Thin layer chromatography combined with surface-enhanced raman spectroscopy for rapid sensing aflatoxins,” J. Chromatogr. A 1579, 115–120 (2018).
[Crossref]

Jia, Z. H.

Z. H. Jia, C. Yang, F. N. Zhao, X. L. Chao, Y. H. Li, and H. P. Xing, “One-step reinforcement and deacidification of paper documents: Application of Lewis base—Chitosan nanoparticle coatings and analytical characterization,” Coatings 10(12), 1226 (2020).
[Crossref]

Jiao, L. Z.

L. Z. Jiao, N. B. Zhong, X. D. Zhao, S. X. Ma, X. L. Fu, and D. M. Dong, “Recent advances in fiber-optic evanescent wave sensors for monitoring organic and inorganic pollutants in water,” Trends Analyt Chem 127, 115892 (2020).
[Crossref]

Kang, X.

X. Kang, A. Kirui, A. Muszyński, M. C. D. Widanage, A. Chen, P. Azadi, P. Wang, F. Mentink-Vigier, and T. Wang, “Molecular architecture of fungal cell walls revealed by solid-state NMR,” Nat. Commun. 9(1), 1–12 (2018).
[Crossref]

Kaszewska, E. A.

P. Targowski, M. Pronobis-Gajdzis, A. Surmak, M. Iwanicka, E. A. Kaszewska, and M. Sylwestrzak, “The application of macro-X-ray fluorescence and optical coherence tomography for examination of parchment manuscripts,” Stud Conserv 60(sup1), S167–S177 (2015).
[Crossref]

Kazarian, S. G.

M. Spring, C. Ricci, D. A. Peggie, and S. G. Kazarian, “ATR-FTIR imaging for the analysis of organic materials in paint cross sections: case studies on paint samples from the National Gallery, London,” Anal. Bioanal. Chem. 392(1-2), 37–45 (2008).
[Crossref]

Kirui, A.

X. Kang, A. Kirui, A. Muszyński, M. C. D. Widanage, A. Chen, P. Azadi, P. Wang, F. Mentink-Vigier, and T. Wang, “Molecular architecture of fungal cell walls revealed by solid-state NMR,” Nat. Commun. 9(1), 1–12 (2018).
[Crossref]

Lei, Y.

M. R. Zhang, Y. D. Hu, J. Liu, Y. Pei, K. Y. Tang, and Y. Lei, “Biodeterioration of collagen-based cultural relics: A review,” Fungal Biol. Rev. 39, 46–59 (2022).
[Crossref]

Li, H. T.

L. L. Qu, Q. Jia, C. Y. Liu, W. Wang, L. F. Duan, G. H. Yang, C. Q. Han, and H. T. Li, “Thin layer chromatography combined with surface-enhanced raman spectroscopy for rapid sensing aflatoxins,” J. Chromatogr. A 1579, 115–120 (2018).
[Crossref]

Li, K. W.

F. Dai, K. W. Li, W. C. Zhou, W. Zhang, M. X. Yu, C. Q. Liu, and Y. H. Wu, “High-sensitivity nanofiber biochemical sensor based on nanomagnetic bead amplification,” Acta Optics 34(12), 33–39 (2014).

Li, T. R.

Q. Song, F. Luan, Z. Shi, T. R. Li, and M. Q. Wang, “Design of turbidity remote monitoring system based on fx-11a optical fiber sensor,” in Proceedings of IEEE Conference on 2020 Prognostics and Health Management Conference (IEEE, 2020), pp. 291–294.

Li, Y. H.

Z. H. Jia, C. Yang, F. N. Zhao, X. L. Chao, Y. H. Li, and H. P. Xing, “One-step reinforcement and deacidification of paper documents: Application of Lewis base—Chitosan nanoparticle coatings and analytical characterization,” Coatings 10(12), 1226 (2020).
[Crossref]

Li, Y. S.

N. B. Zhong, Z. K. Wang, M. Chen, X. Xin, R. H. Wu, Y. Y. Cen, and Y. S. Li, “Three-layer-structure polymer optical fiber with a rough inter-layer surface as a highly sensitive evanescent wave sensor,” Sens. Actuators, B 254, 133–142 (2018).
[Crossref]

N. B. Zhong, M. F. Zhao, and Y. S. Li, “U-shaped, double-tapered, fiber-optic sensor for effective biofilm growth monitoring,” Biomed. Opt. Express 7(2), 335–351 (2016).
[Crossref]

Liao, Q.

Q. Liao, N. B. Zhong, X. Zhu, Y. Huang, and R. Chen, “Enhancement of hydrogen production by optimization of biofilm growth in a photobioreactor,” Int. J. Hydrog. Energy 40(14), 4741–4751 (2015).
[Crossref]

N. B. Zhong, Q. Liao, X. Zhu, and R. Chen, “A fiber-optic sensor for accurately monitoring biofilm growth in a hydrogen production photobioreactor,” Anal. Chem. 86(8), 3994–4001 (2014).
[Crossref]

Liu, C. Q.

F. Dai, K. W. Li, W. C. Zhou, W. Zhang, M. X. Yu, C. Q. Liu, and Y. H. Wu, “High-sensitivity nanofiber biochemical sensor based on nanomagnetic bead amplification,” Acta Optics 34(12), 33–39 (2014).

Liu, C. Y.

L. L. Qu, Q. Jia, C. Y. Liu, W. Wang, L. F. Duan, G. H. Yang, C. Q. Han, and H. T. Li, “Thin layer chromatography combined with surface-enhanced raman spectroscopy for rapid sensing aflatoxins,” J. Chromatogr. A 1579, 115–120 (2018).
[Crossref]

Liu, H. M.

M. Chen, X. Xin, H. M. Liu, Y. W. Wu, N. B. Zhong, and H. X. Chang, “Monitoring Biohydrogen Production and Metabolic Heat in Biofilms by Fiber Bragg Grating Sensors,” Anal. Chem. 91(12), 7842–7849 (2019).
[Crossref]

Liu, J.

M. R. Zhang, Y. D. Hu, J. Liu, Y. Pei, K. Y. Tang, and Y. Lei, “Biodeterioration of collagen-based cultural relics: A review,” Fungal Biol. Rev. 39, 46–59 (2022).
[Crossref]

Liu, Y. K.

W. J. Hu, Y. Y. Huang, C. Y. Chen, Y. K. Liu, T. Guo, and B. O. Guan, “Highly sensitive detection of dopamine using a graphene functionalized plasmonic fiber-optic sensor with aptamer conformational amplification,” Sens. Actuators, B 264, 440–447 (2018).
[Crossref]

Luan, F.

Q. Song, F. Luan, Z. Shi, T. R. Li, and M. Q. Wang, “Design of turbidity remote monitoring system based on fx-11a optical fiber sensor,” in Proceedings of IEEE Conference on 2020 Prognostics and Health Management Conference (IEEE, 2020), pp. 291–294.

Lv, R. Q.

Y. Zhao, H. Zhao, R. Q. Lv, and J. Zhao, “Review of optical fiber Mach–Zehnder interferometers with micro-cavity fabricated by femtosecond laser and sensing applications,” Opt. Lasers Eng. 117, 7–20 (2019).
[Crossref]

Ma, S. X.

L. Z. Jiao, N. B. Zhong, X. D. Zhao, S. X. Ma, X. L. Fu, and D. M. Dong, “Recent advances in fiber-optic evanescent wave sensors for monitoring organic and inorganic pollutants in water,” Trends Analyt Chem 127, 115892 (2020).
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T. Vitorino, A. Casini, C. Cucci, M. J. Melo, M. Picollo, and L. Stefani, “Non-invasive identification of traditional red lake pigments in fourteenth to sixteenth centuries paintings through the use of hyperspectral imaging technique,” Appl. Phys. A 121(3), 891–901 (2015).
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C. Miliani, F. Rosi, A. Daveri, and B. G. Brunetti, “Reflection infrared spectroscopy for the non-invasive in situ study of artists’ pigments,” Appl. Phys. A 106(2), 295–307 (2012).
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C. Ricci, C. Miliani, B. G. Brunetti, and A. Sgamellotti, “Non-invasive identification of surface materials on marble artifacts with fiber optic mid-FTIR reflectance spectroscopy,” Talanta 69(5), 1221–1226 (2006).
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X. Kang, A. Kirui, A. Muszyński, M. C. D. Widanage, A. Chen, P. Azadi, P. Wang, F. Mentink-Vigier, and T. Wang, “Molecular architecture of fungal cell walls revealed by solid-state NMR,” Nat. Commun. 9(1), 1–12 (2018).
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G. Muyzer and K. Smalla, “Application of denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE) in microbial ecology,” Antonie Van Leeuwenhoek 73(1), 127–141 (1998).
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F. Daniel, A. Mounier, J. P’erez-Arantegui, C. Pardos, N. Prieto-Taboada, S. Fdez-Ortiz de Vallejuelo, and K. Castro, “Hyperspectral imaging applied to the analysis of Goya paintings in the Museum of Zaragoza (Spain),” Microchem. J. 126, 113–120 (2016).
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F. Daniel, A. Mounier, J. P’erez-Arantegui, C. Pardos, N. Prieto-Taboada, S. Fdez-Ortiz de Vallejuelo, and K. Castro, “Hyperspectral imaging applied to the analysis of Goya paintings in the Museum of Zaragoza (Spain),” Microchem. J. 126, 113–120 (2016).
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B. Fonseca, C. S. Patterson, M. Ganio, D. MacLennan, and K. Trentelman, “Seeing red: towards an improved protocol for the identification of madder-and cochineal-based pigments by fiber optics reflectance spectroscopy (FORS),” Herit. Sci. 7(1), 92 (2019).
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M. Spring, C. Ricci, D. A. Peggie, and S. G. Kazarian, “ATR-FTIR imaging for the analysis of organic materials in paint cross sections: case studies on paint samples from the National Gallery, London,” Anal. Bioanal. Chem. 392(1-2), 37–45 (2008).
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M. R. Zhang, Y. D. Hu, J. Liu, Y. Pei, K. Y. Tang, and Y. Lei, “Biodeterioration of collagen-based cultural relics: A review,” Fungal Biol. Rev. 39, 46–59 (2022).
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N. Cennamo, M. Pesavento, and L. Zeni, “A review on simple and highly sensitive plastic optical fiber probes for bio-chemical sensing,” Sens. Actuators, B 331, 129393 (2021).
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K. Sterflinger and F. Pinzari, “The revenge of time: fungal deterioration of cultural heritage with particular reference to books, paper and parchment,” Environ. Microbiol. 14(3), 559–566 (2012).
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L. L. Qu, Q. Jia, C. Y. Liu, W. Wang, L. F. Duan, G. H. Yang, C. Q. Han, and H. T. Li, “Thin layer chromatography combined with surface-enhanced raman spectroscopy for rapid sensing aflatoxins,” J. Chromatogr. A 1579, 115–120 (2018).
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M. Ramakrishnan, G. Rajan, Y. Semenova, and G. Farrell, “Overview of fiber optic sensor technologies for strain/temperature sensing applications in composite materials,” Sensors 16(1), 99 (2016).
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M. Ramakrishnan, G. Rajan, Y. Semenova, and G. Farrell, “Overview of fiber optic sensor technologies for strain/temperature sensing applications in composite materials,” Sensors 16(1), 99 (2016).
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A. I. González-Ramírez, A. Ramírez-Granillo, M. G. Medina-Canales, A. V. Rodríguez-Tovar, and M. A. Martínez-Rivera, “Analysis and description of the stages of Aspergillus fumigatus biofilm formation using scanning electron microscopy,” BMC Microbiol. 16(1), 243 (2016).
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Rao, F.

P. Q. Zhu, J. J. Wang, F. Rao, C. Y. Yu, G. Y. Zhou, and X. G. Huang, “Differential Fresnel-reflection-based fiber biochemical sensor with temperature self-compensation for high-resolution measurement of Cd2 + concentration in solution,” Sens. Actuators, B 282, 644–649 (2019).
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M. Spring, C. Ricci, D. A. Peggie, and S. G. Kazarian, “ATR-FTIR imaging for the analysis of organic materials in paint cross sections: case studies on paint samples from the National Gallery, London,” Anal. Bioanal. Chem. 392(1-2), 37–45 (2008).
[Crossref]

C. Ricci, C. Miliani, B. G. Brunetti, and A. Sgamellotti, “Non-invasive identification of surface materials on marble artifacts with fiber optic mid-FTIR reflectance spectroscopy,” Talanta 69(5), 1221–1226 (2006).
[Crossref]

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A. I. González-Ramírez, A. Ramírez-Granillo, M. G. Medina-Canales, A. V. Rodríguez-Tovar, and M. A. Martínez-Rivera, “Analysis and description of the stages of Aspergillus fumigatus biofilm formation using scanning electron microscopy,” BMC Microbiol. 16(1), 243 (2016).
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D. Buti, F. Rosi, B. G. Brunetti, and C. Miliani, “In-situ identification of copper-based green pigments on paintings and manuscripts by reflection FTIR,” Anal. Bioanal. Chem. 405(8), 2699–2711 (2013).
[Crossref]

C. Miliani, F. Rosi, A. Daveri, and B. G. Brunetti, “Reflection infrared spectroscopy for the non-invasive in situ study of artists’ pigments,” Appl. Phys. A 106(2), 295–307 (2012).
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M. Ramakrishnan, G. Rajan, Y. Semenova, and G. Farrell, “Overview of fiber optic sensor technologies for strain/temperature sensing applications in composite materials,” Sensors 16(1), 99 (2016).
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C. Ricci, C. Miliani, B. G. Brunetti, and A. Sgamellotti, “Non-invasive identification of surface materials on marble artifacts with fiber optic mid-FTIR reflectance spectroscopy,” Talanta 69(5), 1221–1226 (2006).
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Shi, Z.

Q. Song, F. Luan, Z. Shi, T. R. Li, and M. Q. Wang, “Design of turbidity remote monitoring system based on fx-11a optical fiber sensor,” in Proceedings of IEEE Conference on 2020 Prognostics and Health Management Conference (IEEE, 2020), pp. 291–294.

Smalla, K.

G. Muyzer and K. Smalla, “Application of denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE) in microbial ecology,” Antonie Van Leeuwenhoek 73(1), 127–141 (1998).
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Spring, M.

M. Spring, C. Ricci, D. A. Peggie, and S. G. Kazarian, “ATR-FTIR imaging for the analysis of organic materials in paint cross sections: case studies on paint samples from the National Gallery, London,” Anal. Bioanal. Chem. 392(1-2), 37–45 (2008).
[Crossref]

Stefani, L.

T. Vitorino, A. Casini, C. Cucci, M. J. Melo, M. Picollo, and L. Stefani, “Non-invasive identification of traditional red lake pigments in fourteenth to sixteenth centuries paintings through the use of hyperspectral imaging technique,” Appl. Phys. A 121(3), 891–901 (2015).
[Crossref]

Sterflinger, K.

K. Sterflinger and F. Pinzari, “The revenge of time: fungal deterioration of cultural heritage with particular reference to books, paper and parchment,” Environ. Microbiol. 14(3), 559–566 (2012).
[Crossref]

Surmak, A.

P. Targowski, M. Pronobis-Gajdzis, A. Surmak, M. Iwanicka, E. A. Kaszewska, and M. Sylwestrzak, “The application of macro-X-ray fluorescence and optical coherence tomography for examination of parchment manuscripts,” Stud Conserv 60(sup1), S167–S177 (2015).
[Crossref]

Sygula-Cholewinska, J.

T. Sawoszczuk, J. Syguła-Cholewińska, and J. M. del Hoyo-Meléndez, “Application of HS-SPME-GC-MS method for the detection of active molds on historical parchment,” Anal. Bioanal. Chem. 409(9), 2297–2307 (2017).
[Crossref]

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P. Targowski, M. Pronobis-Gajdzis, A. Surmak, M. Iwanicka, E. A. Kaszewska, and M. Sylwestrzak, “The application of macro-X-ray fluorescence and optical coherence tomography for examination of parchment manuscripts,” Stud Conserv 60(sup1), S167–S177 (2015).
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M. R. Zhang, Y. D. Hu, J. Liu, Y. Pei, K. Y. Tang, and Y. Lei, “Biodeterioration of collagen-based cultural relics: A review,” Fungal Biol. Rev. 39, 46–59 (2022).
[Crossref]

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P. Targowski, M. Pronobis-Gajdzis, A. Surmak, M. Iwanicka, E. A. Kaszewska, and M. Sylwestrzak, “The application of macro-X-ray fluorescence and optical coherence tomography for examination of parchment manuscripts,” Stud Conserv 60(sup1), S167–S177 (2015).
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B. Fonseca, C. S. Patterson, M. Ganio, D. MacLennan, and K. Trentelman, “Seeing red: towards an improved protocol for the identification of madder-and cochineal-based pigments by fiber optics reflectance spectroscopy (FORS),” Herit. Sci. 7(1), 92 (2019).
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T. Vitorino, A. Casini, C. Cucci, M. J. Melo, M. Picollo, and L. Stefani, “Non-invasive identification of traditional red lake pigments in fourteenth to sixteenth centuries paintings through the use of hyperspectral imaging technique,” Appl. Phys. A 121(3), 891–901 (2015).
[Crossref]

Wang, J. J.

P. Q. Zhu, J. J. Wang, F. Rao, C. Y. Yu, G. Y. Zhou, and X. G. Huang, “Differential Fresnel-reflection-based fiber biochemical sensor with temperature self-compensation for high-resolution measurement of Cd2 + concentration in solution,” Sens. Actuators, B 282, 644–649 (2019).
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L. Q. Wang, G. C. Dang, L. P. Zheng, and X. Q. Wang, “Studies on the identification of pigments on relics and the analysis of color changes by fiber optics reflectance spectroscopy,” Anal. Lett. 34(13), 2403–2414 (2001).
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Q. Song, F. Luan, Z. Shi, T. R. Li, and M. Q. Wang, “Design of turbidity remote monitoring system based on fx-11a optical fiber sensor,” in Proceedings of IEEE Conference on 2020 Prognostics and Health Management Conference (IEEE, 2020), pp. 291–294.

Wang, P.

X. Kang, A. Kirui, A. Muszyński, M. C. D. Widanage, A. Chen, P. Azadi, P. Wang, F. Mentink-Vigier, and T. Wang, “Molecular architecture of fungal cell walls revealed by solid-state NMR,” Nat. Commun. 9(1), 1–12 (2018).
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X. Kang, A. Kirui, A. Muszyński, M. C. D. Widanage, A. Chen, P. Azadi, P. Wang, F. Mentink-Vigier, and T. Wang, “Molecular architecture of fungal cell walls revealed by solid-state NMR,” Nat. Commun. 9(1), 1–12 (2018).
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L. L. Qu, Q. Jia, C. Y. Liu, W. Wang, L. F. Duan, G. H. Yang, C. Q. Han, and H. T. Li, “Thin layer chromatography combined with surface-enhanced raman spectroscopy for rapid sensing aflatoxins,” J. Chromatogr. A 1579, 115–120 (2018).
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L. Q. Wang, G. C. Dang, L. P. Zheng, and X. Q. Wang, “Studies on the identification of pigments on relics and the analysis of color changes by fiber optics reflectance spectroscopy,” Anal. Lett. 34(13), 2403–2414 (2001).
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N. B. Zhong, Y. W. Wu, Z. K. Wang, H. X. Chang, D. J. Zhong, Y. L. Xu, X. Y. Hu, and L. W. Hang, “Monitoring microalgal biofilm growth and phenol degradation with fiber-optic sensors,” Anal. Chem. 91(23), 15155–15162 (2019).
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N. B. Zhong, Z. K. Wang, M. Chen, X. Xin, R. H. Wu, Y. Y. Cen, and Y. S. Li, “Three-layer-structure polymer optical fiber with a rough inter-layer surface as a highly sensitive evanescent wave sensor,” Sens. Actuators, B 254, 133–142 (2018).
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X. Kang, A. Kirui, A. Muszyński, M. C. D. Widanage, A. Chen, P. Azadi, P. Wang, F. Mentink-Vigier, and T. Wang, “Molecular architecture of fungal cell walls revealed by solid-state NMR,” Nat. Commun. 9(1), 1–12 (2018).
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Wu, Y. W.

N. B. Zhong, Y. W. Wu, Z. K. Wang, H. X. Chang, D. J. Zhong, Y. L. Xu, X. Y. Hu, and L. W. Hang, “Monitoring microalgal biofilm growth and phenol degradation with fiber-optic sensors,” Anal. Chem. 91(23), 15155–15162 (2019).
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M. Chen, X. Xin, H. M. Liu, Y. W. Wu, N. B. Zhong, and H. X. Chang, “Monitoring Biohydrogen Production and Metabolic Heat in Biofilms by Fiber Bragg Grating Sensors,” Anal. Chem. 91(12), 7842–7849 (2019).
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M. Chen, X. Xin, H. M. Liu, Y. W. Wu, N. B. Zhong, and H. X. Chang, “Monitoring Biohydrogen Production and Metabolic Heat in Biofilms by Fiber Bragg Grating Sensors,” Anal. Chem. 91(12), 7842–7849 (2019).
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L. L. Qu, Q. Jia, C. Y. Liu, W. Wang, L. F. Duan, G. H. Yang, C. Q. Han, and H. T. Li, “Thin layer chromatography combined with surface-enhanced raman spectroscopy for rapid sensing aflatoxins,” J. Chromatogr. A 1579, 115–120 (2018).
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Yu, C. Y.

P. Q. Zhu, J. J. Wang, F. Rao, C. Y. Yu, G. Y. Zhou, and X. G. Huang, “Differential Fresnel-reflection-based fiber biochemical sensor with temperature self-compensation for high-resolution measurement of Cd2 + concentration in solution,” Sens. Actuators, B 282, 644–649 (2019).
[Crossref]

Yu, M. X.

F. Dai, K. W. Li, W. C. Zhou, W. Zhang, M. X. Yu, C. Q. Liu, and Y. H. Wu, “High-sensitivity nanofiber biochemical sensor based on nanomagnetic bead amplification,” Acta Optics 34(12), 33–39 (2014).

Zeni, L.

N. Cennamo, M. Pesavento, and L. Zeni, “A review on simple and highly sensitive plastic optical fiber probes for bio-chemical sensing,” Sens. Actuators, B 331, 129393 (2021).
[Crossref]

Zhang, M. R.

M. R. Zhang, Y. D. Hu, J. Liu, Y. Pei, K. Y. Tang, and Y. Lei, “Biodeterioration of collagen-based cultural relics: A review,” Fungal Biol. Rev. 39, 46–59 (2022).
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Zhang, W.

F. Dai, K. W. Li, W. C. Zhou, W. Zhang, M. X. Yu, C. Q. Liu, and Y. H. Wu, “High-sensitivity nanofiber biochemical sensor based on nanomagnetic bead amplification,” Acta Optics 34(12), 33–39 (2014).

Zhao, F. N.

Z. H. Jia, C. Yang, F. N. Zhao, X. L. Chao, Y. H. Li, and H. P. Xing, “One-step reinforcement and deacidification of paper documents: Application of Lewis base—Chitosan nanoparticle coatings and analytical characterization,” Coatings 10(12), 1226 (2020).
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Y. Zhao, H. Zhao, R. Q. Lv, and J. Zhao, “Review of optical fiber Mach–Zehnder interferometers with micro-cavity fabricated by femtosecond laser and sensing applications,” Opt. Lasers Eng. 117, 7–20 (2019).
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Zhao, J.

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Zhao, M. F.

Zhao, X. D.

L. Z. Jiao, N. B. Zhong, X. D. Zhao, S. X. Ma, X. L. Fu, and D. M. Dong, “Recent advances in fiber-optic evanescent wave sensors for monitoring organic and inorganic pollutants in water,” Trends Analyt Chem 127, 115892 (2020).
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Zhao, Y.

Y. Zhao, H. Zhao, R. Q. Lv, and J. Zhao, “Review of optical fiber Mach–Zehnder interferometers with micro-cavity fabricated by femtosecond laser and sensing applications,” Opt. Lasers Eng. 117, 7–20 (2019).
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Zheng, L. P.

L. Q. Wang, G. C. Dang, L. P. Zheng, and X. Q. Wang, “Studies on the identification of pigments on relics and the analysis of color changes by fiber optics reflectance spectroscopy,” Anal. Lett. 34(13), 2403–2414 (2001).
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N. B. Zhong, Y. W. Wu, Z. K. Wang, H. X. Chang, D. J. Zhong, Y. L. Xu, X. Y. Hu, and L. W. Hang, “Monitoring microalgal biofilm growth and phenol degradation with fiber-optic sensors,” Anal. Chem. 91(23), 15155–15162 (2019).
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L. Z. Jiao, N. B. Zhong, X. D. Zhao, S. X. Ma, X. L. Fu, and D. M. Dong, “Recent advances in fiber-optic evanescent wave sensors for monitoring organic and inorganic pollutants in water,” Trends Analyt Chem 127, 115892 (2020).
[Crossref]

M. Chen, X. Xin, H. M. Liu, Y. W. Wu, N. B. Zhong, and H. X. Chang, “Monitoring Biohydrogen Production and Metabolic Heat in Biofilms by Fiber Bragg Grating Sensors,” Anal. Chem. 91(12), 7842–7849 (2019).
[Crossref]

N. B. Zhong, Y. W. Wu, Z. K. Wang, H. X. Chang, D. J. Zhong, Y. L. Xu, X. Y. Hu, and L. W. Hang, “Monitoring microalgal biofilm growth and phenol degradation with fiber-optic sensors,” Anal. Chem. 91(23), 15155–15162 (2019).
[Crossref]

N. B. Zhong, Z. K. Wang, M. Chen, X. Xin, R. H. Wu, Y. Y. Cen, and Y. S. Li, “Three-layer-structure polymer optical fiber with a rough inter-layer surface as a highly sensitive evanescent wave sensor,” Sens. Actuators, B 254, 133–142 (2018).
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Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Figures (16)

Fig. 1.
Fig. 1. Schematic diagram of the sensor structure (a), optical micrograph of a tapered optical fiber (b), and an image of the sensor (c). (IOF: input optical fiber; OOF: output optical fiber.)
Fig. 2.
Fig. 2. Images of Aspergillus species. (a–c) A. niger, (d–f) A. flavus, (g–i) A. tamarrii. The first row contains camera photographs, the second row contains optical micrographs, and the third row contains FESEM images.
Fig. 3.
Fig. 3. Schematic diagram of the detection system.
Fig. 4.
Fig. 4. Schematic diagram of the principle of mold detection on the surface of paper.
Fig. 5.
Fig. 5. Images of the sensor with different input fiber diameters: (a) 500 µm, (b) 1000 µm, (c) 1500 µm, and (d) 2000 µm.
Fig. 6.
Fig. 6. Images of paper samples coated with A. niger. (a–d) Day one, (e–h) day two, (i–l) day four, (m–p) day six, (q–t) day seven; The first row contains camera photographs, the second row contains optical micrographs, and the third and fourth rows are FESEM images.
Fig. 7.
Fig. 7. Influence of input fiber diameter on the sensor output spectrum (a) and sensitivity (b). (The taper angle of the input fiber was 0°, the diameter of the output fiber was 500 µm, the distance between the input and output fibers was 100 µm, and the distance between the sensor end-face and paper sample was 20 mm.)
Fig. 8.
Fig. 8. Images of the sensors with different input fiber taper angles: (a) 0°, (b) 6°, (c) 12°, (d) 18°.
Fig. 9.
Fig. 9. Influence of input fiber taper angle on the sensor output spectrum (a) and sensitivity (b). (The length of the tapered region was 1500 µm, the distance between the input and output fibers was 100 µm, the diameter of output fiber was 500 µm, and the distance between the sensor end-face and paper sample was 20 mm.)
Fig. 10.
Fig. 10. Images of sensor probes with different distances between the input and output fibers: (a) 100 µm, (b) 500 µm, (c) 1000 µm, and (d) 2000µm.
Fig. 11.
Fig. 11. Influence of different distances between the input and output fibers on the absorption spectrum (a) and sensitivity of the sensors (b). The influence of the optical path on the sensitivity of the sensors (c).
Fig. 12.
Fig. 12. Relationship between A. niger at different heights and absorbance
Fig. 13.
Fig. 13. Images of paper samples coated with A. flavus. (a–d) Day one, (e–h) day two, (i–l) day four, (m–p) day six, (q–t) day seven; The first row contains camera photographs, the second row contains optical micrographs, and the third and fourth rows are FESEM images.
Fig. 14.
Fig. 14. Images of paper samples coated with A. tamarrii. (a–d) Day one, (e–h) day two, (i–l) day four, (m–p) day six, (q–t) day seven; The first row contains camera photographs, the second row contains optical micrographs, and the third and fourth rows are FESEM images.
Fig. 15.
Fig. 15. Biofilm thickness detection on paper samples. (a, c) A. flavus, (b, d) A. tamarrii.
Fig. 16.
Fig. 16. Response characteristics of the sensor to the early stages of three different mold growths.

Tables (1)

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Table 1. Composition and proportion of nutrient solution

Equations (9)

Equations on this page are rendered with MathJax. Learn more.

I R = I i n I A I S .
I in = I 1 + I 2 + 2 I 1 I 2 cos Δ φ ,
φ = 2 π Δ n [ ( r 1 r 0 ) / tan δ ] / λ ,
I S = I i n exp ( η s Q e x t ) ,
I out _ 1 = I i n _ 1 exp ( ε l )  =  ( I i n I S ) exp ( ε l ) ,
I A = I i n _ 1 I out _ 1 = ( I i n I S ) [ 1 exp ( ε l ) ] ,
I R = I i n I A I S = I i n exp ( ε l ) [ 1 exp ( η s Q e x t ) .
I out = 6 S R I R ( x , d ) d S R ,
I out = 6 0 r 1 + r 2 + p 2 β 2 ( I 1 + I 2 + 2 I 1 I 2 cos { 2 π Δ n [ ( r 1 r 0 ) / tan δ ] } / λ ) π w 2 ( 2 d )   × [ 1 exp ( η s Q e x t ) ] exp ( 2 x 2 w 2 ( 2 d ) ε l ) x d x ,
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