Fish Gelatin-based Films for Gas Sensing

Inês Pimentel Moreira

, Laura Sato, Cláudia Alves, Susana Palma

and Ana Cecília Roque

UCIBIO, Chemistry Department, School of Science and Technology, NOVA University of Lisbon,

2829-516 Caparica, Portugal

Keywords: Gas-sensing, Electronic Nose, Fish Gelatin, Liquid Crystal, Ionic Liquid, Volatile Organic Compounds.

Abstract: Electronic noses (e-noses) mimic the complex biological olfactory system, usually including an array of gas

sensors to act as the olfactory receptors and a trained computer with signal-processing and pattern recognition

tools as the brain. In this work, a new stimuli-responsive material is shown, consisting of self-assembled

droplets of liquid crystal and ionic liquid stabilised within a fish gelatin matrix. These materials change their

opto/electrical properties upon contact with volatile organic compounds (VOCs). By using an in-house

developed e-nose, these new gas-sensing films yield characteristic optical signals for VOCs from different

chemical classes. A support vector machine classifier was implemented based on 12 features of the signals.

The results show that the films are excellent identifying hydrocarbon VOCs (toluene, heptane and hexane)

(95% accuracy) but lower performance was found to other VOCs, resulting in an overall 60.4% accuracy.

Even though they are not reusable, these sustainable gas-sensing films are stable throughout time and

reproducible, opening several opportunities for future optoelectronic devices and artificial olfaction systems.

1 INTRODUCTION

Artificial olfaction mimics the sense of smell in

humans, which relies on complex systems that start

with a binding event of odours to an array of olfactory

receptors and finish with signal processing and

pattern recognition by the brain (Gutiérrez &

Horrillo, 2014). Electronic noses (e-noses) have

arisen as an emerging tool for the detection of odours

– sets of volatile organic compounds (VOCs) – in

several areas such as medicine, food quality or

environment (Barbosa et al., 2018). Since the

traditionally used gas sensors in e-noses are metal

oxide semiconductors or synthetic conducting

polymers and both present several drawbacks

(Baldwin et al., 2011), there is a continuous search for

alternative gas-sensing materials.

Liquid crystals (LC) are unique responsive

materials due to their ability to change molecular

order as a response to chemical and physical stimuli,

with a long history in a variety of technologies. The

design of LC materials that respond to targeted

biological or chemical species has more recently

https://orcid.org/0000-0002-5502-091X

https://orcid.org/0000-0002-1851-8110

https://orcid.org/0000-0002-4586-3024

arisen for gas sensing technologies (Carlton et al.,

2013). This is made possible due to the high

sensitivity of the LC ordering to molecular-level

events, endowing the amplification of small changes

into optical responses (Shibaev et al., 2015).

Ionic liquids (IL) have also been explored as gas

sensing materials, due to their negligible vapour

pressure and high ionic conductivity (IC) (Rehman &

Zeng, 2015). They are known as designer solvents, as

the choice of the cation and anion endows some

tunability from a large structural and functional

diversity (Meng et al., 2012). Different ionic liquids

have been combined with gelatin to make ionogels as

chemiresistive gas sensors (Carvalho et al., 2014).

More recently, a new type of gas sensors composed

of self-assembled droplets of LC and IL stabilised

within a polymeric matrix has been reported (Hussain

et al., 2017). These materials change their

opto/electrical properties in the presence of VOCs

and can be used as sensing elements in an e-nose. A

study on LC/IL droplets embedded in bovine gelatin

showed that such materials can accurately classify 11

distinct VOCs (Esteves et al., 2019). Gelatin is

achieved from the partial hydrolysis of the fibrous

Moreira, I., Sato, L., Alves, C., Palma, S. and Roque, A.

Fish Gelatin-based Films for Gas Sensing.

DOI: 10.5220/0010206200320039

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 1: BIODEVICES, pages 32-39

ISBN: 978-989-758-490-9

protein collagen, the principal constituent of animal

skin, bone and connective tissue (Karim & Bhat,

2009). Gelatin from marine sources has gained

importance as it appeared as an alternative to bovine

gelatin, associated with the Bovine Spongiform

Encephalopathy crisis. Additionally, the demand for

non-bovine and non-porcine gelatin has increased due

to religious and social reasons (Sarbon et al., 2013).

This work shows that fish gelatin is a valuable

alternative to bovine gelatin for the immobilization of

LC/IL droplets, yielding stable gas-sensing materials

with VOC-classification ability. Such information

will be beneficial for the future assembly of an array

of materials for sensing complex mixtures of VOCs.

2 MATERIALS & METHODS

2.1 Materials

Gelatin from cold water fish skin was purchased from

Sigma-Aldrich. The ionic liquid 1-Butyl-3-

methylimidazolium dicyanamide [BMIM][DCA]

(>98%) was purchased from IoLiTec (Germany) and

the liquid crystal 4-Cyano-4'-pentylbiphenyl (5CB)

(> 98%) from TCI Chemicals (Belgium). The

solvents dichloromethane and hexane were purchased

from VWR, ethanol (purity ≥ 99.8%) was purchased

from Sigma-Aldrich. Acetonitrile (purity ≥ 99.9%),

chloroform, diethyl ether (HPLC grade), ethyl

acetate, heptane, methanol (HPLC grade) and toluene

were supplied by Fisher Scientific. Acetone (purity ≥

99.5%) was purchased from Honeywell and

isopropanol (purity ≥ 99.5%) from ROTH. All

solvents were used as purchased.

2.2 Film Preparation

[BMIM][DCA], 5CB, fish gelatin and milliQ water

were mixed as previously reported (Hussain et al.,

2017). Gelatin from bovine skin (gel strength ≈ 225

g; Bloom Type B) normally presents a larger

concentration of proline and hydroxyproline when

compared to gelatin from cold water fish (Karim &

Bhat, 2009), which is key for the stabilisation

mechanism (Joly-Duhamel et al., 2002). The amount

added of each reagent was adjusted to ensure proper

gelification.

The gel was then spreaded into films on top of

microscope glass slides with an automatic film

applicator (TQC, The Netherlands) and a quadruplex

VF2168-043 at a defined 15 μm thickness. Three

negative control gels (C0, C1 and C2) were prepared

by following the same procedure described above, but

without including in the composition the ionic liquid

and the liquid crystal (C0), the liquid crystal (C1), or

the ionic liquid (C2). In the absence of ionic liquid,

its volume was replaced by milliQ water. All films

were left to stand at room temperature for at least 24

hours before used.

2.3 Film Characterisation

The films were observed using an optical microscope

(Axio Observer.Z1/7) (Zeiss, Germany) coupled with

an Axiocam 503 color camera. For the morphological

characterisation of LC droplets, images were taken

under crossed (90°) polarizers, giving polarised

optical microscopy (POM) images. ZEN 2.3 software

(ZEN Pro) was used for microscope control, image

acquisition and processing. The magnification used

was 100x.

A black mask with a 5 mm circular hole was

applied on the bottom of each glass slide where the

film was spreaded, in order to delimit the analysis

area to VOC exposure. The panoramic polarised

optical microscopy (POM) image of this circular area

was taken using the Tiles module within ZEN

software and a 100x magnification. The mean grey

value of the circular area was then measured using the

tools of FIJI distribution (Schindelin et al., 2012) of

ImageJ open software (Rueden et al., 2017) by

calculating the grey pixels over the total pixels in the

grey-scale image.

2.4 Acquisition of Optical Signals upon

Exposure to VOCs

3 selected films were placed in an in-house built e-

nose, to study their optical responses to VOC

exposure. The 3 different controls were also added,

filling the 6-sensor slot chamber. Within these films,

the LC molecular rearrangement upon exposure of

sensing films to VOCs is what gives an optical signal

(Figure 1a), which can thus be analysed. As

previously described in (Hussain et al., 2017; Santos

et al., 2019), each slot in the detection system is

composed of a light emitting diode (LED), a sensing

film sandwiched between two crossed polarizers

(90°) and a light dependent resistor (LDR) (which is

represented in Figure 1b). The LC is arranged in

radial configuration when the films are exposed to air

(Figure 1c), being able to rotate the plane of the

incident polarized light, which allows it to cross the

second polarizer and reach the LDR (Figure 1b).

However, the presence of a VOC analyte triggers the

LC configuration to switch from radial to isotropic

Fish Gelatin-based Films for Gas Sensing

Figure 1: (a) Characteristic optical signal given by the e-nose (upon exposure to hexane, in the example); (b) Schematic

representation of the e-nose working mode, with the film sandwiched between two crossed polarizers: the signal is thus given

by the inability of light to cross the second crossed polarizer when radial-to-isotropic configuration change happens. (c)

Schematic representation of liquid crystal/ionic liquid droplets with LC in radial and isotropic configuration, together with

polarised optical microscopy images of the hybrid gels recorded in real time upon exposure to hexane and recovery with air.

(Figure 1c), losing the ability to rotate the plane of

polarized light and thus hindering light to pass

through the second polarizer (Figure 1b).

The sensing films were exposed to a sequence set

of 11 VOCs, with increased polarity (see partition

coefficients in Table 1). Before starting any

experiment, pure solvents were heated up to 37°C for

15 min in a sample vial to ensure headspace

saturation. The resulting gas in the headspace was

then pumped through the sensors, using cycles of 5

seconds exposure to VOC and 15 seconds recovery

with air, for a total of 15 minutes (45 sequential

cycles). Optical signals were acquired at a sampling

rate of 90 Hz. Different batches of films were

produced so that triplicates were analysed and

reproducibility was assured. Each film was

characterised before and after the exposure to the set

of 11 VOCs.

2.4.1 Signal Processing and Automatic

VOC Classification

Twelve features were extracted from each cycle of the

optical signals and used as input variables to build an

automatic VOC classifier algorithm based on support

Vector Machine (SVM). The chosen features were

the ones that gave the best performances, as reported

in (Santos et al., 2019). Data from three film batches

were used to train, so that the SVM classifier could

learn a VOC classification model. Testing was

performed using data from a fourth film batch. The

normalised classification results were presented in a

confusion matrix (in percentage).

Table 1: Set of 11 VOCs, divided by chemical class,

chemical structure and partition coefficient (logP)

properties.

Chemical class VOC Structure logP

Hydrocarbons

Heptane

3.42

Hexane

Aromatic

hydrocarbons

Toluene

2.52

Chlorinated

Chloroform

1.67

Dichloromethane

1.01

BIODEVICES 2021 - 14th International Conference on Biomedical Electronics and Devices

Table 1: Set of 11 VOCs, divided by chemical class,

chemical structure and partition coefficient (logP)

properties (cont.).

Ethers Diethyl ether 0.76

Esters Ethyl acetate

0.29

Ketones Acetone

0.2

Nitrogenated Acetonitrile

0.17

Alcohols

Ethanol

0.07

Methanol

-0.27

3 RESULTS AND DISCUSSION

3.1 Characterisation of Gas-sensing

Films

Before using the films made of 5CB/[BMIM][DCA]

droplets immobilised in a fish gelatin matrix as

sensors for pure VOCs, they were characterised and

studied.

3.1.1 Stability over Time

Polarised optical microscopy (POM) was used to

characterise the morphology of the sensing films. The

images show that it was possible to produce

homogeneous films filled with LC/IL droplets

stabilised by the fish gelatin matrix (Figure 2a). The

film stability over time was studied by following the

same region of interest (ROI) throughout 1 month of

storage at room temperature and humidity, taking

images each 1 week (Figure 2a-e).

The first and last images were aligned with

ImageJ, overlapped and then processed by a Python

script to compare them (Figure 2f). It was possible to

conclude that the films were stable after 1 month of

storage, since they remained in the same positions,

with just some fluctuations in size due to humidity

changes in the laboratory. However, in the following

works, the relative humidity must be controlled to

~50% until all film characterisation and smelling

experiments are complete.

3.1.2 Optically Active Area and Signal

Baseline

The delimited circular area of all the gas-sensing

films was analysed in the polarised optical

microscope before using them as sensors for the set

of 11 VOCs. There can be some variability in the

spreading of the gel, producing slightly different

films within the same gel batch. The presence of less

droplets within the specific 5 mm circular area (as for

the film in Figure 3a compared with the one in Figure

3c) lowers the brightness of the film (as calculated via

the mean grey value). A lower mean grey value

results in higher baselines of the optical signals

(Figure 3d), which will consequently affect the signal

amplitude.

Figure 2: Polarised optical microscopy images of the same area 1 day (a), 1 week (b), 2 weeks (c), 3 weeks (d), 4 weeks (e)

after preparation; Changes on the morphology between images a and e, after superimposed and analysed (f).

Fish Gelatin-based Films for Gas Sensing

Figure 3: Panoramic polarised optical microscopy image of

the whole optically-active area from 3 exposed films of the

same batch (a-c); Correlation between the optical signal

baseline observed from the e-nose and the mean grey value

taken from the tiles above (d).

3.2 Optical Response to VOCs

Figure 4: Optical signals (average of 3 films from the same

batch of fish-gelatin based gas-sensing films) upon

exposure to heptane, chloroform, acetone and ethanol.

The standard deviation of the average optical signal

(Figure 4) achieved from the 3 films of the same batch

is due to the slight variability in their optically active

area, as previously mentioned (Figure 3). The optical

signals differ for different VOCs (Figure 4), which is

explained by the affinity each of them presents to the

liquid crystal, ionic liquid or even biopolymeric

matrix that compose the films. When using these fish

gelatin-based sensors, the characteristic signal of

heptane is rectangular-shaped, which probably occurs

due to a quick effect on the LC configuration change

upon exposure and consecutive recovery with air. On

the other hand, ethanol follows more of a triangular-

type shape, which can be related to the tendency of its

hydroxyl group (Table 1) to interact with the fish

gelatin by hydrogen bonds. This competitive action

of ethanol into droplets or into the biopolymeric

matrix can possibly explain the slower radial-to-

isotropic and isotropic-to-radial configuration

change, or the destabilisation of the droplets within

the matrix.

3.2.1 VOC Signature and Discrimination

Ability

The characteristic signals yielded by the films allow

for a distinction ability between VOC chemical

classes. The performance of the classifier (accuracy

% of VOC prediction) is presented in a confusion

matrix (Figure 5), whereby the blue squares in the

diagonal represent the correct VOC prediction

accuracies. The best performance is achieved when

predicting the most hydrophobic volatiles

(hydrocarbons), distinguishing satisfactorily the

heptane, hexane and toluene (95% accuracy, in

average). In what concerns the alcohols, the accuracy

is not as high but it confuses only between ethanol

and methanol, which suggests that it is a good

prediction tool for VOC chemical classes.

Figure 5: Confusion matrix for implemented SVM-based

classifier, representing the prediction results for 11 VOCs.

The blue squares represent the frequency of correct

predictions and grey squares the frequency of failed

predictions (in percentage). Overall accuracy represents the

average frequency of correct predictions, calculated as the

average of the blue squares: 60.36%.

BIODEVICES 2021 - 14th International Conference on Biomedical Electronics and Devices

Figure 6: Polarised optical microscopy images of the same region of interest before (a) and after (b) each e-nose experiment

performed with the same gas-sensing films 1 week after preparation, 2 weeks and 4 weeks. (c) Changes on the morphology

between images a and b and for each e-nose experiment, after superimposed and analysed. (d) Response to heptane for each

of the e-nose experiments.

3.3 Sensor Reusability Capability

The same films were then exposed to the sequence of

11 VOCs a second and a third time, in order to study

their reusability capability. The sensing experiments

on the same films were performed 1, 2 and 4 weeks

after their production (Figure 6). When analysing the

morphological images of the same ROI before

(Figure 6a) and after (Figure 6b) each e-nose

experiment, it is obvious that the largest change

happened after the first experiment in the first week

(Figure 6c). Even though the response to heptane is

quite similar in all the repeated e-nose experiments

(Figure 6d), the films change after exposed for the

first time and do not show the exact same optical

signals. In fact, the overall prediction accuracy

decreased from 60.36 to 30.3% on the second

exposure, but it increased again to 45.8% on the third

exposure. Since the prediction accuracies upon a

second and third exposure vary, they cannot be

reused, unless the environmental conditions are

tightly controlled to avoid droplet swelling and

shrinking with relative humidity fluctuations. Even

though the sensing films, in general, decreased their

ability to discriminate VOCs upon sequential

exposures, chloroform, e.g., showed an increasing

accuracy prediction (Figure 7). In fact, some VOCs

as chloroform or ethanol overtook the 60% accuracy

threshold after being exposed more than once.

Figure 7: Comparison of prediction accuracies, for each

VOC, between the first e-nose experiment (as in the

presented confusion matrix) and the 2 next e-nose

experiments. Overall accuracies are 60.36, 30.3% and

45.8% for the first, second and third exposures.

3.4 Control Films

The response to VOCs given when using control

films as sensors was significantly different from the

gas-sensing films shown before (Figure 4). Controls

C0 and C1 do not possess liquid crystal in their

composition and thus do not respond to any VOCs,

presenting a flat line signal (only one example shown

Fish Gelatin-based Films for Gas Sensing

for heptane in Figure 8a). The POM images using

crossed polarizers show nothing (Figure 8b-c),

exactly due to the absence of birefringence. The

control C2, which has liquid crystal but no ionic

liquid, is able to detect the presence of VOCs, even

though its response is weaker than the ones given

from the gas-sensing films (Figure 4). The former

presents a lower signal amplitude than the latter, since

the C2 is overall darker (Figure 8d) than the sample

with ionic liquid (Figure 2a). This lower mean grey

value results in a higher baseline, as explored in

Figure 3d. The absence of ionic liquid results in

droplets that are not based on radial LC configuration,

which gives this control some variability and non-

consistency. It is possible to conclude from the

confusion matrix (not shown) that this control has

lower prediction accuracy than the samples with ionic

liquid. The importance of the ionic liquid, as well as

all the other components in the gas-sensing films, is

reiterated for the optical mechanism optimisation of

the e-nose.

Figure 8: Optical response when using the controls as

sensing films (heptane as an example) (a); Representative

POM images of the three controls: (b) C0 fish gelatin +

water; (c) C1 fish gelatin + IL + water; (d) C2 fish gelatin

+ LC + water.

3.5 Comparison to Bovine

Gelatin-based Sensing Films

Fish gelatin-based sensing films appear as a

promising alternative to bovine gelatin-based sensing

films as they showed more homogeneous results and

lower variability to VOC prediction when comparing

different batches. Since independent validation was

used in this work, it would not be realistic to compare

the presented confusion matrix (Figure 5) with the

previously reported ones for bovine gelatin-based

films in (Esteves et al., 2019; Santos et al., 2019), that

used 10-fold cross validation. Thus, the accuracies of

the predictions for each VOC were taken for the 4

possible permutations of training and validation sets,

whereby 3 different batches were used to train and 1

batch to validate (Figure 9). Looking at the overall

tendency, fish gelatin-based films provided more

accurate predictions when compared to bovine gelatin

ones. In particular, the frequency of correct

predictions of hydrocarbons is dramatically larger

when fish gelatin-based films are used for gas sensing

(83.42% versus 60.92% obtained for bovine gelatin

sensors). In turn, bovine gelatin-based films could

accurately predict acetone and ethanol in a higher

frequency than fish gelatin-based sensors. Even

though firm conclusions cannot be taken because of

some variability between batches and analysis

methods, these variations might be related to the

structural differences between bovine and fish

gelatin. The larger content of proline and

hydroxyproline in bovine gelatin could eventually

increase its affinity to polar VOCs as ethanol.

Figure 9: Comparison of average accuracies, for each VOC,

when using fish gelatin and bovine gelatin-based sensors.

The correct predictions of 4 different analyses were

averaged. In average, the accuracy of fish gelatin sensors

was 60.36% while the one of bovine gelatin sensors was

50.23%.

4 CONCLUSIONS

In summary, we present a new class of gas-sensing

films based on self-assembled liquid crystal/ionic

liquid droplets within a fish gelatin matrix. The

produced homogeneous films have shown to be stable

throughout 1 month, despite some droplet slight

changes in the first week due to relative humidity

fluctuations. We here show that, when using these

gas-sensing films, some volatile organic compounds

have characteristic optical signals, leading to good

BIODEVICES 2021 - 14th International Conference on Biomedical Electronics and Devices

accuracies upon prediction of the sensed volatile,

especially for the discrimination of hydrocarbons or

for the distinction of the alcohols chemical class.

The gel production and sensing experiment was

reproducible, even though it was showed that the

films cannot be reused. Since the main morphological

changes happened after the first exposure to the set of

11 VOCs, the prediction accuracy increased for some

VOCs in the third exposure, e.g. chloroform.

The lack of birefringence and optical response

when using the controls without LC as sensor was

expected and reassures the key role of LC as the

optical probe. In turn, the importance of ionic liquids

with surfactant-like properties is also proved by the

control without IL, which detects VOCs but not in a

very consistent way due to the absence of droplets’

radial configuration.

Fish gelatin appeared as an alternative to bovine

gelatin to encapsulate LC/IL droplets and form

stimuli-responsive biosensors. Fish gelatin-based

films showed slightly higher capability to correctly

predict VOCs. Other biopolymeric matrices are being

investigated to create an array of sensors that

enhances selectivity for optoelectronic devices.

ACKNOWLEDGEMENTS

This project has received funding from the European

Research Council (ERC) under the EU Horizon 2020

research and innovation programme (grant agreement No.

SCENT-ERC-2014-STG-639123). This work was

supported by the Applied Molecular Biosciences Unit –

UCIBIO, which is financed by national funds from

FCT/MCTES (UID/Multi/04378/2020).

REFERENCES

Baldwin, E. A., Bai, J., Plotto, A., & Dea, S. (2011, May 2).

Electronic noses and tongues: Applications for the food

and pharmaceutical industries. Sensors, Vol. 11, pp.

4744–4766. https://doi.org/10.3390/s110504744

Barbosa, A. J. M., Oliveira, A. R., & Roque, A. C. A. (2018,

December 1). Protein- and Peptide-Based Biosensors in

Artificial Olfaction. Trends in Biotechnology, Vol. 36,

pp. 1244–1258.

https://doi.org/10.1016/j.tibtech.2018.07.004

Carlton, R. J., Hunter, J. T., Miller, D. S., Abbasi, R.,

Mushenheim, P. C., Tan, L. N., & Abbott, N. L. (2013).

Chemical and biological sensing using liquid crystals.

Liquid Crystals Reviews, 1(1), 29–51.

https://doi.org/10.1080/21680396.2013.769310

Carvalho, T., Vidinha, P., Vieira, B. R., Li, R. W. C., &

Gruber, J. (2014). Ion Jelly: A novel sensing material for

gas sensors and electronic noses. Journal of Materials

Chemistry C, 2(4), 696–700.

https://doi.org/10.1039/c3tc31496k

Esteves, C., Santos, G. M. C., Alves, C., Palma, S. I. C. J.,

Porteira, A. R., Filho, J., … Roque, A. C. A. (2019).

Effect of film thickness in gelatin hybrid gels for artificial

olfaction. Materials Today Bio, 1, 100002.

https://doi.org/10.1016/j.mtbio.2019.100002

Gutiérrez, J., & Horrillo, M. C. (2014, June 15). Advances in

artificial olfaction: Sensors and applications. Talanta,

Vol. 124, pp. 95–105.

https://doi.org/10.1016/j.talanta.2014.02.016

Hussain, A., Semeano, A. T. S., Palma, S. I. C. J., Pina, A. S.,

Almeida, J., Medrado, B. F., … Roque, A. C. A. (2017).

Tunable Gas Sensing Gels by Cooperative Assembly.

Advanced Functional Materials, 27(27), 1700803.

https://doi.org/10.1002/adfm.201700803

Joly-Duhamel, C., Hellio, D., & Djabourov, M. (2002). All

gelatin networks: 1. Biodiversity and physical chemistry.

Langmuir, 18(19), 7208–7217.

https://doi.org/10.1021/la020189n

Karim, A. A., & Bhat, R. (2009, May 1). Fish gelatin:

properties, challenges, and prospects as an alternative to

mammalian gelatins. Food Hydrocolloids, Vol. 23, pp.

563–576. https://doi.org/10.1016/j.foodhyd.2008.07.002

Meng, Z., Zheng, X., Tang, K., Liu, J., & Qin, S. (2012).

Dissolution of natural polymers in ionic liquids : A

review. (028), 1–29.

Mhd Sarbon, N., Badii, F., & Howell, N. K. (2013).

Preparation and characterisation of chicken skin gelatin

as an alternative to mammalian gelatin. Food

Hydrocolloids, 30(1), 143–151.

https://doi.org/10.1016/j.foodhyd.2012.05.009

Rehman, A., & Zeng, X. (2015, July 3). Methods and

approaches of utilizing ionic liquids as gas sensing

materials. RSC Advances, Vol. 5, pp. 58371–58392.

https://doi.org/10.1039/c5ra06754e

Rueden, C. T., Schindelin, J., Hiner, M. C., DeZonia, B. E.,

Walter, A. E., Arena, E. T., & Eliceiri, K. W. (2017).

ImageJ2: ImageJ for the next generation of scientific

image data. BMC Bioinformatics, 18(1), 529.

https://doi.org/10.1186/s12859-017-1934-z

Santos, G., Alves, C., Pádua, A. C., Palma, S., Gamboa, H.,

& Roque, A. C. (2019). An optimized e-nose for efficient

volatile sensing and discrimination. BIODEVICES 2019

- 12th International Conference on Biomedical

Electronics and Devices, Proceedings; Part of 12th

International Joint Conference on Biomedical

Engineering Systems and Technologies, BIOSTEC 2019,

36–46. https://doi.org/10.5220/0007390700360046

Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V.,

Longair, M., Pietzsch, T., … Cardona, A. (2012, July 28).

Fiji: An open-source platform for biological-image

analysis. Nature Methods, Vol. 9, pp. 676–682.

https://doi.org/10.1038/nmeth.2019

Shibaev, P. V., Wenzlick, M., Murray, J., Tantillo, A., &

Howard-Jennings, J. (2015). Rebirth of liquid crystals for

sensoric applications: Environmental and gas sensors.

Advances in Condensed Matter Physics, 2015.

https://doi.org/10.1155/2015/729186.

Fish Gelatin-based Films for Gas Sensing