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Effects of copper on the Raman spectra of E. coli MG1655: (A) Averages of seven Raman spectra obtained following exposure of the bacteria to different concentrations of copper. The highlighted bands are those that allow the spectra to be classified according to the different concentrations of toxicant. (B) Three-dimensional (3D) representation of the spectral distribution according to the three most significant components from the ICA. (C) Loadings of the most significant ICs from the analysis of Raman spectra of E. coli MG1655 exposed to copper. The spectra were decomposed by ICA, and the most significant ICs were then selected. (D) ANOVA analysis of the distribution of the spectra according to the most significant component (p-value < 0.05). (E) Classification results of the sFDA performed after the pre-processing steps of spectrum selection (size of sample: 24 spectra).
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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...
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... analysis of the spectral signature of E. coli MG1655 cells exposed to copper shows Raman bands impacted by this toxicant ( Figure 5). The most significant spectral changes are in the DNA and RNA bands at 785, 810 cm −1 , and bands located between 1050 and 1150 cm −1 associated with the DNA −PO 2 phosphate groups. ...
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... most significant spectral changes are in the DNA and RNA bands at 785, 810 cm −1 , and bands located between 1050 and 1150 cm −1 associated with the DNA −PO 2 phosphate groups. The intensity of all these bands decreases with increasing copper concentration ( Figure 5B). Nevertheless, two subgroups may be observed: the spectra for low copper concentrations (0 and 0.25 mg.L −1 ) have the same Raman profiles, in particular for the band at 810 cm −1 . ...
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... toxicity also impacted the bands located between 1200 and 1500 cm −1 associated with amides and proteins. Highlighted by the most discriminating component of the ICA results, these bands are the most significant for characterizing the effects of copper on E. coli MG1655 (Figure 5C,D). These ICs were also analyzed by ANOVA ( Figure 5E), which confirmed the existence of two sub-groups: one for low concentrations (0 and 0.25 mg.L −1 ) and another for higher concentrations (1 and 2.5 mg.L −1 ). ...
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... by the most discriminating component of the ICA results, these bands are the most significant for characterizing the effects of copper on E. coli MG1655 (Figure 5C,D). These ICs were also analyzed by ANOVA ( Figure 5E), which confirmed the existence of two sub-groups: one for low concentrations (0 and 0.25 mg.L −1 ) and another for higher concentrations (1 and 2.5 mg.L −1 ). The ANOVA done on the IC8 (specific to the DNA −PO 2 phosphate group band at 1100 cm −1 ) underlines this result. ...
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... addition, although less significant, other variations also appear in a similar way for these metals. An increased intensity can be observed in the bands located between 1200 and 1400 cm −1 , which are associated with amides III, which are characteristic of proteins and lipids ( Figure 5B). These changes can be attributed to the denaturation phenomena [53]. ...
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... and arsenic, which have similar toxicity mechanisms, thus seem to induce equivalent variations in the Raman spectra of E. coli. Stepwise factorial discriminant analysis (sFDA) shows good classification scores for the control and the different copper concentrations (0.25, 1 and 2.5 mg.L −1 ), correct at 97, 85, 93 and 81%, respectively ( Figure 5E). Most of the misclassified spectra can be attributed to neighbouring groups. ...
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.
