Figure 3 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Validation of the best drying time for a bacterial suspension after filtration for subsequent measurement on the semi-automatic system: (A) Average of 21 Raman spectra of E. coli MG1655 bacteria performed after the same drying time on three quartz fiber filters (seven spectra per filter). (B) Average correlation level of each Raman band per drying time with the corresponding Raman band of reference spectrum (E. coli deposited on gold surface). (C) Repeatability or homogeneity of the measurements represented by the autocorrelation level between seven spectra from each sample for every time interval. (D) Reproducibility of measurements represented by the correlation level between all three samples for every time period (21 spectra per time period).
Source publication
Water quality monitoring requires a rapid and sensitive method that can detect multiple hazardous pollutants at trace levels. This study aims to develop a new generation of biosensors using a low-cost fiber-optic Raman device. An automatic measurement system was thus conceived, built and successfully tested with toxic substances of three different...
Contexts in source publication
Context 1
... cultures were deposited on three quartz filters and measured over six different drying times (5, 45, 90, 135, 180, 220 min). Every spectrum in Figure 3A represents an average of 21 spectra (seven spectra from three samples of filtrated bacteria with identical drying times). Visual inspection of these spectra allows to identify 11 Raman bands impacted by drying time, which were labeled from A to K (Figure 3A,B). ...
Context 2
... spectrum in Figure 3A represents an average of 21 spectra (seven spectra from three samples of filtrated bacteria with identical drying times). Visual inspection of these spectra allows to identify 11 Raman bands impacted by drying time, which were labeled from A to K (Figure 3A,B). It can be seen that the RNA band (808 cm −1 , band C) decreases with drying time, which signifies a morphological change in the bacteria. ...
Context 3
... 220 min, the bacterial RNA band has almost disappeared, which indicates the death of the cell [50]. The best time to extract information from a microorganism about its viability and toxicity response is when a control sample (one not exposed to the toxic substance) has a DNA/RNA ratio of the order of 1 (780 cm −1 /808 cm −1 ), as shown in Figure 3A (bands at positions B and C). The correlations of the 11 Raman bands with the reference spectrum were calculated to determine at what moment the spectra from filtrated bacteria could be qualified for toxicity evaluation. ...
Context 4
... reference spectrum is the Raman signal issued from E. coli deposited on a gold surface. The average correlation values of each Raman band per drying time with the corresponding Raman band of the reference spectrum are presented in the table of Figure 3B. ...
Context 5
... the Raman bands presented were evaluated with >90% or >98% correlation confidence. As can be seen in Figure 3B, during the first hour of measurements, the spectrum of a wet bacterium on a quartz filter is quite noisy, which is reflected by the lower correlation values and, as a result, only two Raman bands have a correlation of >98% with the reference spectrum. The correlation values increase with time and, as can be seen for a drying time of 135 min, eight of the Raman bands have a correlation >98% and all 11 Raman bands have a good correlation, with a reliability >90%. ...
Context 6
... confirm these observations, the repeatability and reproducibility of measurements were calculated to show whether the Raman signal from a sample was stable over time and between samples. The repeatability of measurements is represented by the autocorrelation level between spectra from each sample for every time interval ( Figure 3C). The dispersion of values from three samples taken after 5 and 45 min of drying is about 5%, which is very significant in terms of accuracy requirements for toxicity measurements (it should be of the order of 1%). ...
Context 7
... from 90 min of drying time, the dispersion in autocorrelation values between spectra is at an acceptable level of 98%. The reproducibility of measurements illustrates the correlation level between all three samples for every time period (21 spectra per period), which was also calculated ( Figure 3D). This value is greater than 98% at 90 and 135 min of drying time. ...
Citations
... A spectrometer is a basic optical detection instrument that can obtain the spectral information of the measured object. At present, various spectrometers have been widely used in color measurement, gas composition analysis, agricultural medicine, food safety and other fields [1][2][3][4][5][6], and have become among the most important optical detection instruments. Among them, the Raman spectrometer [7][8][9], which is composed of a probe and a spectrometer, is a rapidly developed instrument in recent years. ...
Raman spectroscopy, measured by a Raman spectrometer, is usually disturbed by the instrument response function and noise, which leads to certain measurement error and further affects the accuracy of substance identification. In this paper, we propose a spectral reconstruction method which combines the existing maximum a posteriori (MAP) method and deep learning (DL) to recover the degraded Raman spectrum. The proposed method first employs the MAP method to reconstruct the measured Raman spectra, so as to obtain preliminary estimated Raman spectra. Then, a convolutional neural network (CNN) is trained by using the preliminary estimated Raman spectra and the real Raman spectra to learn the mapping from the preliminary estimated Raman spectra to the real Raman spectra, so as to achieve a better spectral reconstruction effect than merely using the MAP method or a CNN. To prove the effectiveness of the proposed spectral reconstruction method, we employed the proposed method and some traditional spectral reconstruction methods to reconstruct the simulated and measured Raman spectra, respectively. The experimental results show that compared with traditional methods, the estimated Raman spectra reconstructed by the proposed method are closer to the real Raman spectra.
... A transducer converts a biochemical signal, resulting from the interaction of a biological component, into a measurable signal. Thus, when the interaction between the analyte and the bioreceptor occurs, a quantifiable signal is generated, which can be optical, electrochemical, thermometric, piezoelectric, magnetic, or micromechanical [4][5][6]. ...
The coronavirus pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has accelerated the development of biosensors based on new materials and techniques. Here, we present our effort to develop a fast and affordable optical biosensor using photoluminescence spectroscopy for anti-SARS-CoV-2 antibody detection. The biosensor was fabricated with a thin layer of the semiconductor polymer Poly[(9,9-di-n-octylfluorenyl-2,7-diyl)-alt-2,2′-bithiophene-5,5′-diyl)] (F8T2) as a signal transducer material. We mounted the biosensors by depositing a layer of F8T2 and an engineered version of RBD from the SARS-CoV-2 spike protein with a tag to promote hydrophobic interaction between the protein and the polymeric surface. We validated the biosensor sensitivity with decreasing anti-RBD polyclonal IgG concentrations and challenged the biosensor specificity with human serum samples from both COVID-19 negative and positive individuals. The antibody binding to the immobilized antigen shifted the F8T2 photoluminescence spectrum even at the low concentration of 0.0125 µg/mL. A volume as small as one drop of serum (100 µL) was sufficient to distinguish a positive from a negative sample without requiring multiple washing steps and secondary antibody reactions.
