Abstract

Current implementations of fiber-optic Raman spectroscopy probes are frequently based on non-contact probes with a fixed focus and thus and have to precisely maintain the probe-to-sample distance to ensure a sufficient signal collection. We propose and experimentally demonstrate a novel hand-held fiber-optic Raman probe design, which is based on a liquid lens autofocusing unit, combined with a distance sensor and an in-house developed algorithm to precisely determine the probe-to-sample distance. The reported probe significantly improves the signal stability even for hand-held operation, while reducing distance-dependent artifacts for the acquisition of Raman spectra and can improve the acquisition of Raman spectra in a variety of applications.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

In recent years, there has been a significant effort to use Raman spectroscopy in a clinical and pharmaceutical environment, because it is label-free and non-invasive and can provide specific molecular fingerprint information from organic and inorganic materials [16]. Because of their small size fiber-optic Raman probes provide significant advantages in comparison to high-performance, but rigid microscopy setups, because they allow a direct access to measurement sites where needed. Hence, they are favored for complex environments, such as in vivo clinical applications, over confocal Raman microscopy platforms [79]. Typically, fiber-optic probes acquire Raman signal in contact or by focusing the excitation light using a fixed focal length objective lens, which requires the user to maintain the same probe-to-sample distance to effectively excite and collect the Raman signal. This restricts the usability and the advantages of the probe as a hand-held device [10]. To circumvent this challenge one approach is to use Raman probes in contact [11] or attach a removable distance regulator to the tip of the probe [12]. However, in certain instances, e.g. clinical and pharmaceutical applications, using a probe in contact is not desirable to avoid any disturbance or contamination of the sample and the probe head. Furthermore, the risk of deforming and damaging the sample increases when measuring in contact. As such, there is a demand for fiber-optical probes, which allow a stable non-contact measurement, but automatically maintaining the correct focal distance between the probe and the sample.

In recent years, there has been a continuous development of novel beam shaping approaches, which allow a rapid adjustment of the focal distance. One of the most commonly used is implemented with liquid lenses, which are based on electrowetting, i.e. the change of the wettability of a droplet on a substrate induced by changing the voltage between the droplet and the insulating substrate. This change leads to deformation of the droplet curvature, and as such, change in the optical focal length [13,14]. Liquid lenses have been readily used for various applications, such as autofocus cameras [15,16], microscopy imaging [17], optical coherence tomography angiography [18], optical sectioning tomography [19], lasers [20] and eyeglasses [21]. Also, a commercial Raman spectrometer based on a liquid lens has been recently made available [22,23].

In this work, we present a new fiber optical Raman probe design and implementation with an integrated liquid lens that allows a rapid focal distance adjustment and enables significant signal stabilization when compared to a fixed-focus implementations. We provide the design considerations and validate experimentally the in-house build fiber-optic Raman probe with the autofocus feature operated in hand-held mode. The probe design is based on coaxial, dual fibers that deliver the excitation light and acquire the Raman signal through a voltage-driven liquid lens. Thanks to the stable and fast focal distance adjustment, the liquid lens combined with the in-house build autofocus algorithm can minimize the influence from distance changes during hand-held operation. The approach has a high potential for pharmaceutical characterization and clinical in-vivo applications and can further be extended to any other optical spectroscopic approach, which requires focusing light onto a sample.

2. Materials and methods

The in-house build probe is designed in a coaxial, dual-channel configuration, where one channel guides the excitation laser to the probe and the second guides the Raman signal to the spectrometer (see Fig. 1). The laser light is delivered by a 105 µm fiber and passes through a 785 nm band-pass filter to remove the silica Raman background from the excitation fiber. A liquid lens unit (C-S-25H0-096-03; Corning Varioptic Lenses, Lyon, France [24]) with an f# of 3.7 was used in-reverse. The generated Raman signal is collected by the liquid lens and, after passing a dichroic and long-pass filter, coupled into a 200 µm multimode fiber that is attached to a spectrometer. The liquid lens has a variable dioptric power ranging from −15 diopters to +38 diopters and is connected to the power supply through a flexible printed circuit (FPC) cable. The liquid lens has an anti-reflective coating, which has been optimized in the near infrared and has a transmission above 90% between 800–1100 nm, covering the high- and low-wavenumber region for a 785 nm Raman excitation. The supply driver (USB-M Flexiboard, Corning Varioptic Lenses, Lyon, France) is software-controlled through a USB cable, delivering a 1 kHz alternating current (AC) voltage to drive the liquid lens and allowing the liquid lens a response time of about 15 ms. The Raman spectroscopy setup is equipped with a 785 nm laser, reaching a maximum output power of 300 mW (FERGIE-785 nm laser; Princeton Instruments, Trenton, New Jersey), a spectrometer (Acton LS785; Princeton Instruments, Trenton, New Jersey) and a back-illuminated deep-depletion charged coupled device (BI-DD-CCD, PIXIS-100-BReXcelon; Princeton Instruments, Trenton, New Jersey).

 figure: Fig. 1.

Fig. 1. Diagram of the in-house build Raman probe with an integrated liquid lens as the objective lens and a distance sensor, which is attached to the probe by a 3D-printed holder to maintain a defined relation for the realization of the autofocus function (a). Photograph of the hand-held Raman probe (b).

Download Full Size | PPT Slide | PDF

Generally, there are two ways to realize an autofocus function for the designed probe: open- and close-loop implementation. The open-loop method uses an external sensor to adjust the focus distance of the liquid lens; the closed-loop method is changing the focus distance by evaluating the Raman signal intensity. In principle, the closed-loop method is more favorable, as no additional hardware is required for the implementation of the autofocusing procedure, while providing a high accuracy [16]. The main disadvantage, especially for low-light implementations, is that multiple acquisitions (between 8 to 12) are required [25] to find the correct voltage for the liquid lens, making it less feasible for low-light applications. In the open-loop method the distance measurement is decoupled from the signal acquisition, since an additional sensor is used, which can be correlated to the correct focal position using a look-up table (LUT). This can be done, since the response of the liquid lens has a linear focusing power vs. voltage response. Thus, the whole procedure can be faster, but at the cost of increased complexity. Since Raman spectroscopy is a very low-light application, i.e. dependent on the sample less than one in a billion photons get scattered, only the open-loop implementation appears to be feasible. However, because the open-loop method alone can lead to some loss in accuracy a combined method for a fast and precise autofocusing procedure, i.e. open-loop for coarse search and closed-loop for fine adjustment can be desirable. For the coarse search, as indicated in Fig. 1(b), an ultrasonic distance sensor (UM12-1172271; SICK, Germany), which is voltage-driven and has a response time of 30 ms is read out by a multifunction I/O device (USB6001; National Instruments, Austin, Texas) and is fixed to the probe with a three-dimensional (3D) printed holder. Adding the 15 ms response time of the liquid lens, the total response time for the open-loop method is 45 ms. For the fine search, we apply a closed-loop method to find the best driven voltage for the liquid lens by iterative maximization of the Raman signal. The approach is similar to the sharpness-based optimization in Ref. [16]. The recorded signal, i.e. defined Raman band, is compared to an initial value and the voltage of the lens is adjusted accordingly and a new signal is recorded. If the signal is increasing the algorithm changes the voltage continuous in the same direction. If the signal is decreasing the algorithm goes to the opposite direction from the initial starting point until the signal decreases again, and the algorithm stops. We have been using bands with the highest intensity when performing the closed-loop implementation. One has to keep in mind that potential problems stemming from sample autofluorescence or heterogeneous molecular content distribution could arise when using only a single band. In such situations, a more comprehensive approach, for example based on spectral fitting should be applied. Nevertheless, for the proof-of-principle experiment the single-band approach has been quite suitable. Once the distance sensor detects a distance change beyond the certain threshold, the autofocus procedure will go back to the open-loop method followed by the closed-loop method. It is also important to point-out that the closed-loop approach can only be used when sufficient Raman signal is generated, or the change in the probe-to-sample distance does not change faster than the signal maximization procedure.

For the open-loop method, a calibration is performed by building a functional relationship between the distance sensor and the liquid lens. As shown in Fig. 2, there is always a trigonometric function between the focus length f and measured distance ds of the distance sensor as following:

$$f = cos\alpha \cdot {d_s} - {d_0}$$
α and d0 represents the angle and vertical distance between the lens with the distance sensor and are constant once the liquid lens and distance sensor are fixed with the probe. Additionally, the focal length f is equal to the reciprocal of the dioptric power D, which itself is linear to the driving voltage UL in the optical power [13,24], and ds is linear to the readout voltage US of the distance sensor; a1, b1, a2, b2 are respectively constants for each equations:
$$\left\{ {\begin{array}{l} {f = \frac{1}{D} = \frac{1}{{{a_1} \cdot {U_L} + {b_1}}}}\\ {{d_S} = {a_2} \cdot {U_S} + {b_2}} \end{array}} \right.$$
By combining Eq. (1) and (2), we can determine the relationship between UL and US as following:
$${U_L} = \frac{1}{{cos\alpha {a_1}{a_2} \cdot {U_S} + cos\alpha {a_1}{b_2} - {a_1}{d_0}}} - \frac{{{b_1}}}{{{a_1}}}$$
To simplify Eq. (3), the constants are combined, and new constant characters are assigned, resulting in a mathematic formula for the autofocusing of the probe as following:
$${U_L} = \frac{1}{{a \cdot {U_S} + b}} + c$$
Instead of performing complex measurements of α, d0, ds or establishing the relationship between f and UL, a calibration function for the relation between UL and US can be built by changing the probe-to-sample distance step by step, while measuring the voltages of the distance sensor and the best-driven voltages according to the Raman signal intensities. To find corresponding best-driver voltages for the highest Raman signals we additionally used the close-loop approach, which is based on the iterative feedback of the difference of the Raman signals. The closed-loop method is also applied for the fine search for the autofocus procedure afterwards. Based on the combined open-loop and closed-loop method, the probe allows fast, dynamic and accurate adjustment of the focus length, while the probe is moved over the sample, and a simultaneous Raman signal acquisition.

 figure: Fig. 2.

Fig. 2. Repetitive positions of the sample at close distance (a), correct distance (b) and far distance (c). The triangle function between f and ds is always true once the liquid lens and the distance sensor are fixed.

Download Full Size | PPT Slide | PDF

3. Results and discussion

3.1 Calibration

For calibration, the probe is placed on a manual z-translational stage, which allows to accurately change the probe-to-sample distance step-by-step. For each step the voltage of the distance sensor VS as well as the related best-driving voltage of the liquid lens UL is measured. Figure 3(a) shows a repetitive result of finding the best driving voltage for the liquid lens for US = 0.24 V using the closed-loop method based on iterative feedback. Here, the output excitation power from the probe was about 80 mW and the detector was set to an acquisition time of 0.5 s, acquiring Raman spectra from a piece of plastic sample. Then, the measurement was repeated by changing the positions of the z-translational stage. The recorded data of the voltage readout of the distance sensor vs. the related best-driver voltage for the liquid lens (green dots) and the curve fit (blue dashed curve) using the derived mathematic relation, Eq. (4), are plotted in Fig. 3(b). The corresponding Raman spectra, after baseline removal [26], acquired during the calibration procedure are shown in Fig. 3(c). The Raman bands of each spectrum are clearly comparable, despite the different probe-to-sample distances. There is, however, a small difference between the recorded intensities, which are mainly due to the fact that various focal length lead to different numerical apertures (NA), and in consequence lead to different ability to accept the inelastically scattered light. The red and blue dot indicates the intensity of the Raman band around 830 cm−1 for US = 0.21 V and 0.43 V respectively, thus the longer focal length leads to the smaller NA, reduced ability to accept light, and vice versa. This effect certainly can be compensated by either calculating the NA-dependent change of the intensity or by mapping this change experimentally. However, the correction will be limited by different noise-levels of the signals.

 figure: Fig. 3.

Fig. 3. Calibration procedure to determine the best-driver voltage to the liquid lens for US = 0.24 V (a), of various voltages of readout of distance sensor vs. best driven voltages for the liquid lens (b), and of the recorded Raman spectra during the whole procedure (c).

Download Full Size | PPT Slide | PDF

3.2 Evaluation of autofocus function

To evaluate the autofocus function established in the calibration procedure, Fig. 3(b), we have placed the sample on a manual z-translational stage and changed the probe-to-sample distance over a range of 4 mm, i.e. ± 1.8 mm around the central focal position. During the gradual and periodical change Raman spectra were recorded simultaneously. The corresponding signal, here the Raman band intensity at 830 cm−1, is plotted in blue at the top of Fig. 4(a) and the distance change vs. time is plotted in the green at the bottom of Fig. 4(a). Representative spectra from three measurement points (MPs), corresponding to different probe-to-sample distances and indicated by the dots in Fig. 4(a), are plotted in Fig. 4(b). As was already seen in Fig. 4(a), the signal intensity has not significantly changed over the distance range of 3.6 mm, e.g. the ratio of the distance range −1.8 mm and +1.8 mm (MP3 to MP1) is 0.77 (1259/1632). As stated before, the observed intensity difference comes from the changing in the NA, thus shorter focal length can accept more inelastically scattered Raman signal, resulting in higher Raman intensities. Although the intensity at each MP does vary, the corresponding Raman spectra are clearly recognizable.

 figure: Fig. 4.

Fig. 4. Comparison and characterization of Raman signal acquired with fixed and autofocus adjustment. Raman intensity of the band at 830 cm−1 and distances recorded from continuously changing the objective-to-sample distance with autofocus focal length (a) and with fixed focal length (c). Raman spectra of three represent measurement points (MPs) respectively for both cases (b) and (d). Intensity of measurements from (a) and (c) plotted vs. sample-to-probe distance for autofocus probe (e) and with fixed focal length probe (f) additionally with a 5th-oder polynomial fit. Intensity normalized fits, I_auto and I_fixed, and ratios of those values provide the gain-factor for the autofocusing approach in comparison to fixed-focal lens (g).

Download Full Size | PPT Slide | PDF

For comparison, we also performed the same experiment, but without the developed autofocus features, i.e. with a fixed focal length, Fig. 4(c, d). From these plots we can deduce that the focal length is around 5 mm, where the highest Raman signal intensity is collected. Changing the distances of the sample to the probe in the same range as in the previous experiment, +1.8 mm to −1.8 mm, leads to a significant signal intensity loss, to a point where almost no signal can be recovered. This can be seen in the Raman spectra measured at MP4-6, Fig. 4(d). Among these three points, point 6 is located in the best focus and has the highest intensity spectrum (blue). The MPs 4 (red) and 5 (green) are out of focus measurements, resulting in significantly reduced signal intensity, with a ratio between lowest MP4 to highest MP6, of 0.12 (177/1433). In other words, the collected signal is reduced ∼8-fold, while for the autofocusing approach only ∼1.3-fold, resulting in a 6-fold improvement for this case. It is evident that the improvement must be distance-dependent, because for an identical focal length and probe-to-sample distance, the signals must be equal, i.e. the gain factor is one, while for out-of-focus measurements the autofocus probe would provide a measurable improvement. To determine the distance-depended gain-factor we have plotted the distance dependent changes for each distance-intensity pair in a scatter plots for both measurement conditions, Fig. 4(e and f). A 5th order polynomial fitting was applied to the data points, providing an approximation for the probe-to-sample intensity variation, blue lines. The curves for both focusing approaches are strikingly different. In the autofocusing approach the signal intensity is increasing when the focus is located closer to the probe; for the fixed-focal length probe the signal follows a Gaussian shape. The two distance-intensity curves were normalized to the max. intensity and plotted as I_auto and I_fixed in common distance range, Fig. 4(g). It can be seen that at the same focal length and sample-to-probe distance the signals are identical but begin to deviate for out-of-focus measurements for the fixed focus. To determine the achievable and distance-dependent gain factor we also plot in red the ratio between the normalized curves, Fig. 4(g). From this data- it can be seen that the gain will differ for the probe-to-sample distance, and can be as high as 10-fold for the current implementation. The experimental characterization demonstrates that the reported Raman probe with the liquid lens has a z-axis working range of ∼2.8 mm to 6.4 mm instead of a fixed working distance, and effectively compensates for distance-dependent signal reductions. The current working range is limited by the maximum tunability of the liquid lens but can be improved with an optimized and custom-designed liquid lens tailored for the applications. With the help of the autofocus feature the probe dynamically adjusts the focal length to fit with the objective-to-sample distance to get clear and distinguished Raman spectra.

3.3 Performance and further discussion

To show the improved functionality using the autofocusing hand-held probe, we have first performed a measurement with a fixed focal length and an acquisition time of 0.5 s. Because the hand movement is quite rapid, only the open loop method was used to correct for the distance changes. The intensity of the strongest band at 823 cm−1 (blue trace) and the distance (green trace) are plotted versus time, Fig. 5(a). As expected, it is not feasible to maintain a stable sample-to-probe distance, resulting in a reduction of the signal intensity, which can be seen on the high standard deviation as indicated in the graph. The corresponding mean spectrum with standard deviation is plotted in Fig. 5(b). To show that the newly developed probe can improve the signal collection during a real hand-held measurement 100 Raman spectra were continuously acquired from the same sample, while holding the probe by hand. The intensity band at 823 cm−1 (blue trace) and the distance (green trace) are plotted versus time, Fig. 5( c) and the corresponding mean spectrum with standard deviation is plotted in Fig. 5(d). During the hand-held acquisitions the probe-to-sample distance (green trace) changes significantly, up to 3 mm, the intensity (blue trace), however, remains very stable when compared to the variations for the fixed focal distance probe, Fig. 5(a). The improvement can be seen when comparing the mean spectra and the standard deviation Fig. 5(b) and (c). Even if there are some sudden hand movements, the autofocus function can rapidly adjust the focal length to achieve improved results. To better compare those results, the intensity change and distance change were plotted in a box plot, Fig. 5(e). It can be clearly seen that while the variations of the distances are comparable, the standard deviation of the intensity is significantly reduced.

 figure: Fig. 5.

Fig. 5. Raman signal recorded vs. the distances in handheld operation for both cases. Raman intensity of the band at 823 cm−1 and the sample-to-probe are plotted vs. time for the fixed-focal length (a); corresponding mean Raman spectrum (blue) with standard deviation (red) (b). Raman intensity of the band at 823 cm−1 and the sample-to-probe are plotted vs. time for the autofocus method (c); corresponding mean Raman spectrum (blue) with standard deviation (red) (d).

Download Full Size | PPT Slide | PDF

Furthermore, since the response-time effects is more noticeable with reduced acquisition time, measurements were also performed for 0.1 s and 1 s, Fig. 6. The intensity and distance vs. time traces are plotted in Fig. 6(a) and (c); and the corresponding mean Raman spectra with the standard deviation are plotted in Fig. 6(b) and (d). A comparison between the different acquisition times shows that it can influence the intensity compensation, i.e. for shorter acquisition time it is more challenging to adjust the distance change, when compared to longer acquisition time. This is due to speed limitation resulting from the open-loop method, which is approx. 45 ms and as such, close to the acquisition time of 100 ms. Once the hand movement occurs during the acquisition the liquid lens may delay the adjustment of the focal length, leading to higher intensity changes. Nevertheless, the stability is still higher than for the fixed focal length, which can be seen by comparing Fig. 5(a) and Fig. 6(a). For longer acquisition time, the delay of liquid lens response is not obvious and does not play a significant role for the intensity, thus the intensity is more stable.

 figure: Fig. 6.

Fig. 6. Raman spectra acquired with the autofocusing probe at different acquisition times. Raman intensity of the band at 823 cm−1 and the sample-to-probe distance plotted vs. time for an acquisition time of 0.1s (a); corresponding mean Raman spectrum (blue) with standard deviation (red) (c). Raman intensity of the band at 823 cm−1and the sample-to-probe plotted vs. time for an acquisition time of 1.0 s (a); corresponding mean Raman spectrum (blue) with standard deviation (red) (d).

Download Full Size | PPT Slide | PDF

In addition, we have used the developed probe for Raman imaging, which provides visual information on the distribution of macromolecules in the sample. In this experiment the probe was fixed vertically on a holder and a bio-sample (a piece of pork meat) was placed tilted by 15° on a X-Y motor scanning stage (MLS203-1, Thorlabs, Newton, New Jersey), Fig. 7(a). An image with 14×14 points and a step size of 500 µm was acquired using fixed focal length configuration with an acquisition time of 0.5 s. The net z-directionally displacement from one side to the other was approx. 1.8 mm. The Raman image based on the intensity mapping of the Raman band at 1459 cm−1 and corresponding mean spectrum with standard deviation are shown in Fig. 7(b) and (c), respectively. As expected, due to the tilt of the sample and the resulting change in the probe-to-sample distances a significant intensity change can be observed in the x-dimension in the image. Because the tilt was in the x-axis only, the probe-to-sample distances is the same in the y-axis. The experiment was repeated in the same location, but with the use of the autofocus probe. Because the probe was fixed in place, the close loop approach was also used. The resulting Raman image and corresponding mean Raman spectrum with the standard deviation are shown as Fig. 7(d) and (e), respectively. For comparison, the intensity variation for the 1459 cm−1 band for the fixed focal length and for the autofocus mode were plotted in a box plot, Fig. 7(f). Comparing the results, it is obvious that using the liquid lens with the autofocus approach almost completely offsets the influence of tilt, and as such, the mismatch between the probe-to-sample distance and the focal length. The method can, therefore, also be used for performing Raman imaging on uneven surface, which was realized using translational piezo stages. It is, however, important to point out that the limitation of the implemented ultrasonic distance sensor is that a flat surface of at least 3 mm2, i.e. ca. 1 mm in diameter is, required to reflect the ultrasound back to the receiver, thus, the probe can work with the sample which satisfies this condition, including liquid or powdery samples. The current combined autofocus algorithm is restricted to the sample with highly uneven surface; on the other hand, the closed-loop algorithm has no restriction on this kind of applications, however, if a very fast sampling is required it could limit the autofocus procedure. As such, the designed probe with only the open-loop approach, may be restricted to low-resolution. However, modifications on the ultrasound sensor, which allow improved focusing ability could significantly improve the resolution and the range of applications.

 figure: Fig. 7.

Fig. 7. Photograph of the setup doing Raman imaging using our present probe on a tilted placed bio-sample (a); Raman image result with fixed focal length (b) and corresponding mean Raman spectra (blue) with the standard deviation (red) (c); Comparison Raman image result with the autofocus method (d) and corresponding mean Raman spectra (blue) with the standard deviation (red) (e). The intensity variation for the band at 1459 cm−1 for the fixed focal length and for the autofocus mode are plotted in (f).

Download Full Size | PPT Slide | PDF

The comparison between the performance of the fixed focal length probe and variable focal length probe shows a significant improved performance for the signal acquisition for hand-held Raman probes, allowing the acquisition of Raman spectra without reducing the signal intensity. Furthermore, the proposed approach can be extend to many other optical modalities, which need to focus light to the sample, such as fluorescence lifetime imaging microscopy (FLIM), second harmonic generation (SHG) imaging, coherent anti-Stokes Raman scattering (CARS) and others. It is, however, important to point out that for non-linear modalities the change in the NA could result in a significant signal generation reduction, which is why a thorough characterization of the change of the NA is essential.

4. Conclusion

In the reported work, we present a hand-held autofocus fiber-optic Raman probe, which allows to overcome the probe-to-sample distance problems by dynamically adjusting the focal length of the probe. The reported approach is realized based on a liquid lens implementation combined with an open and closed-loop algorithm, where the open-loop method uses an ultrasonic distance sensor for coarse search and adjusts the focal length and the closed-loop method uses feedback of Raman intensity for fine search. The performance of the proposed probe was experimentally evaluated on a sample by acquiring spectra, while continuously changing the probe-to-sample distance during a hand-held operation. Through comparison to control experiments using a fixed focal length it could be shown that the autofocus allows to acquire Raman spectra without reducing the distance-related signal intensity, whereas the experiment with a fixed focal-distance probe, resulted in significant signal loss. Under current experimental conditions, the present probe has a dynamic distance range of about from 2.8 mm to 6.4 mm, and a response time of 45 ms, which can easily tolerate the instability of hand-held operation. The close-loop implementation can in future developments significantly benefit from spectral fitting approaches, which could help to deal with potential problems from sample autofluorescence or heterogeneous molecular content distribution, when compared to using a single Raman band. This reported work can significantly improve the performance of hand-held Raman probes and enables the acquisition of spectra without reduced performance. Furthermore, this approach can be extended to other spectroscopy and microscopy modalities, such as FLIM, SHG, CARS and others.

Funding

China Scholarship Council; European Research Council; Bundesministerium für Bildung und Forschung (01KL1904); Horizon 2020 Framework Programme (No 667933).

Acknowledgements

W.Y. received a scholarship support from the China Scholarship Council.

Disclosures

The authors declare no conflicts of interest.

References

1. E. Cordero, I. Latka, C. Matthäus, I. W. Schie, and J. Popp, “In-vivo Raman spectroscopy: from basics to applications,” J. Biomed. Opt. 23(07), 1 (2018). [CrossRef]  

2. M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018). [CrossRef]  

3. E. Upchurch, M. Isabelle, G. R. Lloyd, C. Kendall, and H. Barr, “An update on the use of Raman spectroscopy in molecular cancer diagnostics: current challenges and further prospects,” Expert Rev. Mol. Diagn. 18(3), 245–258 (2018). [CrossRef]  

4. F. Nardella, M. Beck, P. Collart-Dutilleul, G. Becker, C. Boulanger, L. Perello, A. Gairard-Dory, B. Gourieux, and G. Ubeaud-Séquier, “A UV-Raman spectrometry method for quality control of anticancer preparations: Results after 18 months of implementation in hospital pharmacy,” Int. J. Pharm. 499(1-2), 343–350 (2016). [CrossRef]  

5. H. Chen, Z. M. Zhang, L. Miao, D. J. Zhan, Y. B. Zheng, Y. Liu, F. Lu, and Y. Z. Liang, “Automatic standardization method for raman spectrometers with applications to pharmaceuticals,” J. Raman Spectrosc. 46(1), 147–154 (2015). [CrossRef]  

6. L. Lê, M. Berge, A. Tfayli, A. Baillet Guffroy, P. Prognon, A. Dowek, and E. Caudron, “Quantification of gemcitabine intravenous drugs by direct measurement in chemotherapy plastic bags using a handheld Raman spectrometer,” Talanta 196(November 2018), 376–380 (2019). [CrossRef]  

7. I. Latka, S. Dochow, C. Krafft, B. Dietzek, and J. Popp, “Fiber optic probes for linear and nonlinear Raman applications - Current trends and future development,” Laser Photonics Rev. 7(5), 698–731 (2013). [CrossRef]  

8. C. Krafft, S. Dochow, I. Latka, B. Dietzek, and J. Popp, “Diagnosis and screening of cancer tissues by fiber-optic probe Raman spectroscopy,” Biomed. Spectrosc. Imaging 1(1), 39–55 (2012). [CrossRef]  

9. O. Stevens, I. E. Iping Petterson, J. C. C. Day, and N. Stone, “Developing fibre optic Raman probes for applications in clinical spectroscopy,” Chem. Soc. Rev. 45(7), 1919–1934 (2016). [CrossRef]  

10. Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for Raman spectrometry and its application,” J. Opt. 46(1), 62–67 (2017). [CrossRef]  

11. J. Wang, M. S. Bergholt, W. Zheng, and Z. Huang, “Development of a beveled fiber-optic confocal Raman probe for enhancing in vivo epithelial tissue Raman measurements at endoscopy,” Opt. Lett. 38(13), 2321 (2013). [CrossRef]  

12. . “Integrated Raman Probe,” https://www.ipslasers.com/integrated-Raman-probe.html.

13. B. Berge and J. Peseux, “Variable focal lens controlled by an external voltage: An application of electrowetting,” Eur. Phys. J. E 3(2), 159–163 (2000). [CrossRef]  

14. B. Berge, “Liquid lens technology: principle of electrowetting based lenses and applications to imaging,” in 18th IEEE International Conference on Micro Electro Mechanical Systems, 2005. MEMS 2005. (IEEE, n.d.), pp. 227–230.

15. J. Fuentes-Fernández, S. Cuevas, L. C. Álvarez-Nuñez, and A. Watson, “Tests and evaluation of a variable focus liquid lens for curvature wavefront sensors in astronomy,” Appl. Opt. 52(30), 7256–7264 (2013). [CrossRef]  

16. S. Pasinetti, I. Bodini, M. Lancini, F. Docchio, and G. Sansoni, “Experimental characterization of an autofocus algorithm based on liquid lens objective for in-focus imaging in the macro range,” in Proceedings - 2017 7th International Workshop on Advances in Sensors and Interfaces, IWASI 2017 (IEEE, 2017), pp. 195–200.

17. M. Bathe-Peters, P. Annibale, and M. J. Lohse, “All-optical microscope autofocus based on an electrically tunable lens and a totally internally reflected IR laser,” Opt. Express 26(3), 2359 (2018). [CrossRef]  

18. J. Yang, L. Liu, J. P. Campbell, D. Huang, and G. Liu, “Handheld optical coherence tomography angiography,” Biomed. Opt. Express 8(4), 2287 (2017). [CrossRef]  

19. C. Xu, W. Zhao, J. Hu, B. Zhang, and S. Wang, “Liquid lens-based optical sectioning tomography for three-dimensional flame temperature measurement,” Fuel 196, 550–563 (2017). [CrossRef]  

20. F. Zhang, Y. Yao, X. Qu, T. Zhang, and B. Pei, “Dual-beam laser autofocusing system based on liquid lens,” Opt. Laser Technol. 88, 198–204 (2017). [CrossRef]  

21. N. Hasan, A. Banerjee, H. Kim, and C. H. Mastrangelo, “Tunable-focus lens for adaptive eyeglasses,” Opt. Express 25(2), 1221 (2017). [CrossRef]  

22. . “The First in the Industry to use an Autofocus Solution in a Raman Spectrometer,” https://serstech.com/industry-first-autofocus-an-interview-with-katja-szybek/.

23. K. Szybek and J. Sonnvik, “Raman spectroscopy system,” U.S. patent US10295408B2 (2019).

24. . “C-S-25H0-096 Auto Focus Lens Module,” https://www.corning.com/worldwide/en/innovation/corning-emerging-innovations/corning-varioptic-lenses/auto-focus-lens-modules-c-s-series/varioptic-C-S-25H0-096.html.

25. . “The product brochure of CorningVariopticLenses,” https://www.corning.com/media/worldwide/Innovation/documents/FINAL_CorningVariopticLenses_productbrochure_5.4.18_lowresWEB.pdf.

26. S.-J. Baek, A. Park, Y.-J. Ahn, and J. Choo, “Baseline correction using asymmetrically reweighted penalized least squares smoothing,” Analyst 140(1), 250–257 (2015). [CrossRef]  

References

  • View by:
  • |
  • |
  • |

  1. E. Cordero, I. Latka, C. Matthäus, I. W. Schie, and J. Popp, “In-vivo Raman spectroscopy: from basics to applications,” J. Biomed. Opt. 23(07), 1 (2018).
    [Crossref]
  2. M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018).
    [Crossref]
  3. E. Upchurch, M. Isabelle, G. R. Lloyd, C. Kendall, and H. Barr, “An update on the use of Raman spectroscopy in molecular cancer diagnostics: current challenges and further prospects,” Expert Rev. Mol. Diagn. 18(3), 245–258 (2018).
    [Crossref]
  4. F. Nardella, M. Beck, P. Collart-Dutilleul, G. Becker, C. Boulanger, L. Perello, A. Gairard-Dory, B. Gourieux, and G. Ubeaud-Séquier, “A UV-Raman spectrometry method for quality control of anticancer preparations: Results after 18 months of implementation in hospital pharmacy,” Int. J. Pharm. 499(1-2), 343–350 (2016).
    [Crossref]
  5. H. Chen, Z. M. Zhang, L. Miao, D. J. Zhan, Y. B. Zheng, Y. Liu, F. Lu, and Y. Z. Liang, “Automatic standardization method for raman spectrometers with applications to pharmaceuticals,” J. Raman Spectrosc. 46(1), 147–154 (2015).
    [Crossref]
  6. L. Lê, M. Berge, A. Tfayli, A. Baillet Guffroy, P. Prognon, A. Dowek, and E. Caudron, “Quantification of gemcitabine intravenous drugs by direct measurement in chemotherapy plastic bags using a handheld Raman spectrometer,” Talanta 196(November 2018), 376–380 (2019).
    [Crossref]
  7. I. Latka, S. Dochow, C. Krafft, B. Dietzek, and J. Popp, “Fiber optic probes for linear and nonlinear Raman applications - Current trends and future development,” Laser Photonics Rev. 7(5), 698–731 (2013).
    [Crossref]
  8. C. Krafft, S. Dochow, I. Latka, B. Dietzek, and J. Popp, “Diagnosis and screening of cancer tissues by fiber-optic probe Raman spectroscopy,” Biomed. Spectrosc. Imaging 1(1), 39–55 (2012).
    [Crossref]
  9. O. Stevens, I. E. Iping Petterson, J. C. C. Day, and N. Stone, “Developing fibre optic Raman probes for applications in clinical spectroscopy,” Chem. Soc. Rev. 45(7), 1919–1934 (2016).
    [Crossref]
  10. Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for Raman spectrometry and its application,” J. Opt. 46(1), 62–67 (2017).
    [Crossref]
  11. J. Wang, M. S. Bergholt, W. Zheng, and Z. Huang, “Development of a beveled fiber-optic confocal Raman probe for enhancing in vivo epithelial tissue Raman measurements at endoscopy,” Opt. Lett. 38(13), 2321 (2013).
    [Crossref]
  12. . “Integrated Raman Probe,” https://www.ipslasers.com/integrated-Raman-probe.html .
  13. B. Berge and J. Peseux, “Variable focal lens controlled by an external voltage: An application of electrowetting,” Eur. Phys. J. E 3(2), 159–163 (2000).
    [Crossref]
  14. B. Berge, “Liquid lens technology: principle of electrowetting based lenses and applications to imaging,” in 18th IEEE International Conference on Micro Electro Mechanical Systems, 2005. MEMS 2005. (IEEE, n.d.), pp. 227–230.
  15. J. Fuentes-Fernández, S. Cuevas, L. C. Álvarez-Nuñez, and A. Watson, “Tests and evaluation of a variable focus liquid lens for curvature wavefront sensors in astronomy,” Appl. Opt. 52(30), 7256–7264 (2013).
    [Crossref]
  16. S. Pasinetti, I. Bodini, M. Lancini, F. Docchio, and G. Sansoni, “Experimental characterization of an autofocus algorithm based on liquid lens objective for in-focus imaging in the macro range,” in Proceedings - 2017 7th International Workshop on Advances in Sensors and Interfaces, IWASI 2017 (IEEE, 2017), pp. 195–200.
  17. M. Bathe-Peters, P. Annibale, and M. J. Lohse, “All-optical microscope autofocus based on an electrically tunable lens and a totally internally reflected IR laser,” Opt. Express 26(3), 2359 (2018).
    [Crossref]
  18. J. Yang, L. Liu, J. P. Campbell, D. Huang, and G. Liu, “Handheld optical coherence tomography angiography,” Biomed. Opt. Express 8(4), 2287 (2017).
    [Crossref]
  19. C. Xu, W. Zhao, J. Hu, B. Zhang, and S. Wang, “Liquid lens-based optical sectioning tomography for three-dimensional flame temperature measurement,” Fuel 196, 550–563 (2017).
    [Crossref]
  20. F. Zhang, Y. Yao, X. Qu, T. Zhang, and B. Pei, “Dual-beam laser autofocusing system based on liquid lens,” Opt. Laser Technol. 88, 198–204 (2017).
    [Crossref]
  21. N. Hasan, A. Banerjee, H. Kim, and C. H. Mastrangelo, “Tunable-focus lens for adaptive eyeglasses,” Opt. Express 25(2), 1221 (2017).
    [Crossref]
  22. . “The First in the Industry to use an Autofocus Solution in a Raman Spectrometer,” https://serstech.com/industry-first-autofocus-an-interview-with-katja-szybek/ .
  23. K. Szybek and J. Sonnvik, “Raman spectroscopy system,” U.S. patent US10295408B2 (2019).
  24. . “C-S-25H0-096 Auto Focus Lens Module,” https://www.corning.com/worldwide/en/innovation/corning-emerging-innovations/corning-varioptic-lenses/auto-focus-lens-modules-c-s-series/varioptic-C-S-25H0-096.html .
  25. . “The product brochure of CorningVariopticLenses,” https://www.corning.com/media/worldwide/Innovation/documents/FINAL_CorningVariopticLenses_productbrochure_5.4.18_lowresWEB.pdf .
  26. S.-J. Baek, A. Park, Y.-J. Ahn, and J. Choo, “Baseline correction using asymmetrically reweighted penalized least squares smoothing,” Analyst 140(1), 250–257 (2015).
    [Crossref]

2019 (1)

L. Lê, M. Berge, A. Tfayli, A. Baillet Guffroy, P. Prognon, A. Dowek, and E. Caudron, “Quantification of gemcitabine intravenous drugs by direct measurement in chemotherapy plastic bags using a handheld Raman spectrometer,” Talanta 196(November 2018), 376–380 (2019).
[Crossref]

2018 (4)

E. Cordero, I. Latka, C. Matthäus, I. W. Schie, and J. Popp, “In-vivo Raman spectroscopy: from basics to applications,” J. Biomed. Opt. 23(07), 1 (2018).
[Crossref]

M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018).
[Crossref]

E. Upchurch, M. Isabelle, G. R. Lloyd, C. Kendall, and H. Barr, “An update on the use of Raman spectroscopy in molecular cancer diagnostics: current challenges and further prospects,” Expert Rev. Mol. Diagn. 18(3), 245–258 (2018).
[Crossref]

M. Bathe-Peters, P. Annibale, and M. J. Lohse, “All-optical microscope autofocus based on an electrically tunable lens and a totally internally reflected IR laser,” Opt. Express 26(3), 2359 (2018).
[Crossref]

2017 (5)

J. Yang, L. Liu, J. P. Campbell, D. Huang, and G. Liu, “Handheld optical coherence tomography angiography,” Biomed. Opt. Express 8(4), 2287 (2017).
[Crossref]

C. Xu, W. Zhao, J. Hu, B. Zhang, and S. Wang, “Liquid lens-based optical sectioning tomography for three-dimensional flame temperature measurement,” Fuel 196, 550–563 (2017).
[Crossref]

F. Zhang, Y. Yao, X. Qu, T. Zhang, and B. Pei, “Dual-beam laser autofocusing system based on liquid lens,” Opt. Laser Technol. 88, 198–204 (2017).
[Crossref]

N. Hasan, A. Banerjee, H. Kim, and C. H. Mastrangelo, “Tunable-focus lens for adaptive eyeglasses,” Opt. Express 25(2), 1221 (2017).
[Crossref]

Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for Raman spectrometry and its application,” J. Opt. 46(1), 62–67 (2017).
[Crossref]

2016 (2)

F. Nardella, M. Beck, P. Collart-Dutilleul, G. Becker, C. Boulanger, L. Perello, A. Gairard-Dory, B. Gourieux, and G. Ubeaud-Séquier, “A UV-Raman spectrometry method for quality control of anticancer preparations: Results after 18 months of implementation in hospital pharmacy,” Int. J. Pharm. 499(1-2), 343–350 (2016).
[Crossref]

O. Stevens, I. E. Iping Petterson, J. C. C. Day, and N. Stone, “Developing fibre optic Raman probes for applications in clinical spectroscopy,” Chem. Soc. Rev. 45(7), 1919–1934 (2016).
[Crossref]

2015 (2)

S.-J. Baek, A. Park, Y.-J. Ahn, and J. Choo, “Baseline correction using asymmetrically reweighted penalized least squares smoothing,” Analyst 140(1), 250–257 (2015).
[Crossref]

H. Chen, Z. M. Zhang, L. Miao, D. J. Zhan, Y. B. Zheng, Y. Liu, F. Lu, and Y. Z. Liang, “Automatic standardization method for raman spectrometers with applications to pharmaceuticals,” J. Raman Spectrosc. 46(1), 147–154 (2015).
[Crossref]

2013 (3)

2012 (1)

C. Krafft, S. Dochow, I. Latka, B. Dietzek, and J. Popp, “Diagnosis and screening of cancer tissues by fiber-optic probe Raman spectroscopy,” Biomed. Spectrosc. Imaging 1(1), 39–55 (2012).
[Crossref]

2000 (1)

B. Berge and J. Peseux, “Variable focal lens controlled by an external voltage: An application of electrowetting,” Eur. Phys. J. E 3(2), 159–163 (2000).
[Crossref]

Ahn, Y.-J.

S.-J. Baek, A. Park, Y.-J. Ahn, and J. Choo, “Baseline correction using asymmetrically reweighted penalized least squares smoothing,” Analyst 140(1), 250–257 (2015).
[Crossref]

Álvarez-Nuñez, L. C.

Annibale, P.

Baek, S.-J.

S.-J. Baek, A. Park, Y.-J. Ahn, and J. Choo, “Baseline correction using asymmetrically reweighted penalized least squares smoothing,” Analyst 140(1), 250–257 (2015).
[Crossref]

Baillet Guffroy, A.

L. Lê, M. Berge, A. Tfayli, A. Baillet Guffroy, P. Prognon, A. Dowek, and E. Caudron, “Quantification of gemcitabine intravenous drugs by direct measurement in chemotherapy plastic bags using a handheld Raman spectrometer,” Talanta 196(November 2018), 376–380 (2019).
[Crossref]

Baker, M. J.

M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018).
[Crossref]

Banerjee, A.

Barr, H.

E. Upchurch, M. Isabelle, G. R. Lloyd, C. Kendall, and H. Barr, “An update on the use of Raman spectroscopy in molecular cancer diagnostics: current challenges and further prospects,” Expert Rev. Mol. Diagn. 18(3), 245–258 (2018).
[Crossref]

Bathe-Peters, M.

Beck, M.

F. Nardella, M. Beck, P. Collart-Dutilleul, G. Becker, C. Boulanger, L. Perello, A. Gairard-Dory, B. Gourieux, and G. Ubeaud-Séquier, “A UV-Raman spectrometry method for quality control of anticancer preparations: Results after 18 months of implementation in hospital pharmacy,” Int. J. Pharm. 499(1-2), 343–350 (2016).
[Crossref]

Becker, G.

F. Nardella, M. Beck, P. Collart-Dutilleul, G. Becker, C. Boulanger, L. Perello, A. Gairard-Dory, B. Gourieux, and G. Ubeaud-Séquier, “A UV-Raman spectrometry method for quality control of anticancer preparations: Results after 18 months of implementation in hospital pharmacy,” Int. J. Pharm. 499(1-2), 343–350 (2016).
[Crossref]

Berge, B.

B. Berge and J. Peseux, “Variable focal lens controlled by an external voltage: An application of electrowetting,” Eur. Phys. J. E 3(2), 159–163 (2000).
[Crossref]

B. Berge, “Liquid lens technology: principle of electrowetting based lenses and applications to imaging,” in 18th IEEE International Conference on Micro Electro Mechanical Systems, 2005. MEMS 2005. (IEEE, n.d.), pp. 227–230.

Berge, M.

L. Lê, M. Berge, A. Tfayli, A. Baillet Guffroy, P. Prognon, A. Dowek, and E. Caudron, “Quantification of gemcitabine intravenous drugs by direct measurement in chemotherapy plastic bags using a handheld Raman spectrometer,” Talanta 196(November 2018), 376–380 (2019).
[Crossref]

Bergholt, M. S.

Bodini, I.

S. Pasinetti, I. Bodini, M. Lancini, F. Docchio, and G. Sansoni, “Experimental characterization of an autofocus algorithm based on liquid lens objective for in-focus imaging in the macro range,” in Proceedings - 2017 7th International Workshop on Advances in Sensors and Interfaces, IWASI 2017 (IEEE, 2017), pp. 195–200.

Boulanger, C.

F. Nardella, M. Beck, P. Collart-Dutilleul, G. Becker, C. Boulanger, L. Perello, A. Gairard-Dory, B. Gourieux, and G. Ubeaud-Séquier, “A UV-Raman spectrometry method for quality control of anticancer preparations: Results after 18 months of implementation in hospital pharmacy,” Int. J. Pharm. 499(1-2), 343–350 (2016).
[Crossref]

Byrne, H. J.

M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018).
[Crossref]

Campbell, J. P.

Caudron, E.

L. Lê, M. Berge, A. Tfayli, A. Baillet Guffroy, P. Prognon, A. Dowek, and E. Caudron, “Quantification of gemcitabine intravenous drugs by direct measurement in chemotherapy plastic bags using a handheld Raman spectrometer,” Talanta 196(November 2018), 376–380 (2019).
[Crossref]

Chalmers, J.

M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018).
[Crossref]

Chen, H.

H. Chen, Z. M. Zhang, L. Miao, D. J. Zhan, Y. B. Zheng, Y. Liu, F. Lu, and Y. Z. Liang, “Automatic standardization method for raman spectrometers with applications to pharmaceuticals,” J. Raman Spectrosc. 46(1), 147–154 (2015).
[Crossref]

Choo, J.

S.-J. Baek, A. Park, Y.-J. Ahn, and J. Choo, “Baseline correction using asymmetrically reweighted penalized least squares smoothing,” Analyst 140(1), 250–257 (2015).
[Crossref]

Collart-Dutilleul, P.

F. Nardella, M. Beck, P. Collart-Dutilleul, G. Becker, C. Boulanger, L. Perello, A. Gairard-Dory, B. Gourieux, and G. Ubeaud-Séquier, “A UV-Raman spectrometry method for quality control of anticancer preparations: Results after 18 months of implementation in hospital pharmacy,” Int. J. Pharm. 499(1-2), 343–350 (2016).
[Crossref]

Cordero, E.

E. Cordero, I. Latka, C. Matthäus, I. W. Schie, and J. Popp, “In-vivo Raman spectroscopy: from basics to applications,” J. Biomed. Opt. 23(07), 1 (2018).
[Crossref]

Cuevas, S.

Day, J. C. C.

O. Stevens, I. E. Iping Petterson, J. C. C. Day, and N. Stone, “Developing fibre optic Raman probes for applications in clinical spectroscopy,” Chem. Soc. Rev. 45(7), 1919–1934 (2016).
[Crossref]

Dietzek, B.

I. Latka, S. Dochow, C. Krafft, B. Dietzek, and J. Popp, “Fiber optic probes for linear and nonlinear Raman applications - Current trends and future development,” Laser Photonics Rev. 7(5), 698–731 (2013).
[Crossref]

C. Krafft, S. Dochow, I. Latka, B. Dietzek, and J. Popp, “Diagnosis and screening of cancer tissues by fiber-optic probe Raman spectroscopy,” Biomed. Spectrosc. Imaging 1(1), 39–55 (2012).
[Crossref]

Docchio, F.

S. Pasinetti, I. Bodini, M. Lancini, F. Docchio, and G. Sansoni, “Experimental characterization of an autofocus algorithm based on liquid lens objective for in-focus imaging in the macro range,” in Proceedings - 2017 7th International Workshop on Advances in Sensors and Interfaces, IWASI 2017 (IEEE, 2017), pp. 195–200.

Dochow, S.

I. Latka, S. Dochow, C. Krafft, B. Dietzek, and J. Popp, “Fiber optic probes for linear and nonlinear Raman applications - Current trends and future development,” Laser Photonics Rev. 7(5), 698–731 (2013).
[Crossref]

C. Krafft, S. Dochow, I. Latka, B. Dietzek, and J. Popp, “Diagnosis and screening of cancer tissues by fiber-optic probe Raman spectroscopy,” Biomed. Spectrosc. Imaging 1(1), 39–55 (2012).
[Crossref]

Dowek, A.

L. Lê, M. Berge, A. Tfayli, A. Baillet Guffroy, P. Prognon, A. Dowek, and E. Caudron, “Quantification of gemcitabine intravenous drugs by direct measurement in chemotherapy plastic bags using a handheld Raman spectrometer,” Talanta 196(November 2018), 376–380 (2019).
[Crossref]

Fuentes-Fernández, J.

Gairard-Dory, A.

F. Nardella, M. Beck, P. Collart-Dutilleul, G. Becker, C. Boulanger, L. Perello, A. Gairard-Dory, B. Gourieux, and G. Ubeaud-Séquier, “A UV-Raman spectrometry method for quality control of anticancer preparations: Results after 18 months of implementation in hospital pharmacy,” Int. J. Pharm. 499(1-2), 343–350 (2016).
[Crossref]

Gardner, P.

M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018).
[Crossref]

Goodacre, R.

M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018).
[Crossref]

Gourieux, B.

F. Nardella, M. Beck, P. Collart-Dutilleul, G. Becker, C. Boulanger, L. Perello, A. Gairard-Dory, B. Gourieux, and G. Ubeaud-Séquier, “A UV-Raman spectrometry method for quality control of anticancer preparations: Results after 18 months of implementation in hospital pharmacy,” Int. J. Pharm. 499(1-2), 343–350 (2016).
[Crossref]

Hasan, N.

Henderson, A.

M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018).
[Crossref]

Hu, J.

C. Xu, W. Zhao, J. Hu, B. Zhang, and S. Wang, “Liquid lens-based optical sectioning tomography for three-dimensional flame temperature measurement,” Fuel 196, 550–563 (2017).
[Crossref]

Huang, D.

Huang, M.

Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for Raman spectrometry and its application,” J. Opt. 46(1), 62–67 (2017).
[Crossref]

Huang, Z.

Iping Petterson, I. E.

O. Stevens, I. E. Iping Petterson, J. C. C. Day, and N. Stone, “Developing fibre optic Raman probes for applications in clinical spectroscopy,” Chem. Soc. Rev. 45(7), 1919–1934 (2016).
[Crossref]

Isabelle, M.

E. Upchurch, M. Isabelle, G. R. Lloyd, C. Kendall, and H. Barr, “An update on the use of Raman spectroscopy in molecular cancer diagnostics: current challenges and further prospects,” Expert Rev. Mol. Diagn. 18(3), 245–258 (2018).
[Crossref]

Kazarian, S. G.

M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018).
[Crossref]

Kendall, C.

E. Upchurch, M. Isabelle, G. R. Lloyd, C. Kendall, and H. Barr, “An update on the use of Raman spectroscopy in molecular cancer diagnostics: current challenges and further prospects,” Expert Rev. Mol. Diagn. 18(3), 245–258 (2018).
[Crossref]

Kim, H.

Krafft, C.

I. Latka, S. Dochow, C. Krafft, B. Dietzek, and J. Popp, “Fiber optic probes for linear and nonlinear Raman applications - Current trends and future development,” Laser Photonics Rev. 7(5), 698–731 (2013).
[Crossref]

C. Krafft, S. Dochow, I. Latka, B. Dietzek, and J. Popp, “Diagnosis and screening of cancer tissues by fiber-optic probe Raman spectroscopy,” Biomed. Spectrosc. Imaging 1(1), 39–55 (2012).
[Crossref]

Lancini, M.

S. Pasinetti, I. Bodini, M. Lancini, F. Docchio, and G. Sansoni, “Experimental characterization of an autofocus algorithm based on liquid lens objective for in-focus imaging in the macro range,” in Proceedings - 2017 7th International Workshop on Advances in Sensors and Interfaces, IWASI 2017 (IEEE, 2017), pp. 195–200.

Latka, I.

E. Cordero, I. Latka, C. Matthäus, I. W. Schie, and J. Popp, “In-vivo Raman spectroscopy: from basics to applications,” J. Biomed. Opt. 23(07), 1 (2018).
[Crossref]

I. Latka, S. Dochow, C. Krafft, B. Dietzek, and J. Popp, “Fiber optic probes for linear and nonlinear Raman applications - Current trends and future development,” Laser Photonics Rev. 7(5), 698–731 (2013).
[Crossref]

C. Krafft, S. Dochow, I. Latka, B. Dietzek, and J. Popp, “Diagnosis and screening of cancer tissues by fiber-optic probe Raman spectroscopy,” Biomed. Spectrosc. Imaging 1(1), 39–55 (2012).
[Crossref]

Lê, L.

L. Lê, M. Berge, A. Tfayli, A. Baillet Guffroy, P. Prognon, A. Dowek, and E. Caudron, “Quantification of gemcitabine intravenous drugs by direct measurement in chemotherapy plastic bags using a handheld Raman spectrometer,” Talanta 196(November 2018), 376–380 (2019).
[Crossref]

Li, X.

Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for Raman spectrometry and its application,” J. Opt. 46(1), 62–67 (2017).
[Crossref]

Liang, Y. Z.

H. Chen, Z. M. Zhang, L. Miao, D. J. Zhan, Y. B. Zheng, Y. Liu, F. Lu, and Y. Z. Liang, “Automatic standardization method for raman spectrometers with applications to pharmaceuticals,” J. Raman Spectrosc. 46(1), 147–154 (2015).
[Crossref]

Liu, G.

Liu, L.

Liu, Y.

H. Chen, Z. M. Zhang, L. Miao, D. J. Zhan, Y. B. Zheng, Y. Liu, F. Lu, and Y. Z. Liang, “Automatic standardization method for raman spectrometers with applications to pharmaceuticals,” J. Raman Spectrosc. 46(1), 147–154 (2015).
[Crossref]

Lloyd, G. R.

E. Upchurch, M. Isabelle, G. R. Lloyd, C. Kendall, and H. Barr, “An update on the use of Raman spectroscopy in molecular cancer diagnostics: current challenges and further prospects,” Expert Rev. Mol. Diagn. 18(3), 245–258 (2018).
[Crossref]

Lohse, M. J.

Lu, F.

H. Chen, Z. M. Zhang, L. Miao, D. J. Zhan, Y. B. Zheng, Y. Liu, F. Lu, and Y. Z. Liang, “Automatic standardization method for raman spectrometers with applications to pharmaceuticals,” J. Raman Spectrosc. 46(1), 147–154 (2015).
[Crossref]

Martin, F. L.

M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018).
[Crossref]

Mastrangelo, C. H.

Matthäus, C.

E. Cordero, I. Latka, C. Matthäus, I. W. Schie, and J. Popp, “In-vivo Raman spectroscopy: from basics to applications,” J. Biomed. Opt. 23(07), 1 (2018).
[Crossref]

Miao, L.

H. Chen, Z. M. Zhang, L. Miao, D. J. Zhan, Y. B. Zheng, Y. Liu, F. Lu, and Y. Z. Liang, “Automatic standardization method for raman spectrometers with applications to pharmaceuticals,” J. Raman Spectrosc. 46(1), 147–154 (2015).
[Crossref]

Moger, J.

M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018).
[Crossref]

Nardella, F.

F. Nardella, M. Beck, P. Collart-Dutilleul, G. Becker, C. Boulanger, L. Perello, A. Gairard-Dory, B. Gourieux, and G. Ubeaud-Séquier, “A UV-Raman spectrometry method for quality control of anticancer preparations: Results after 18 months of implementation in hospital pharmacy,” Int. J. Pharm. 499(1-2), 343–350 (2016).
[Crossref]

Park, A.

S.-J. Baek, A. Park, Y.-J. Ahn, and J. Choo, “Baseline correction using asymmetrically reweighted penalized least squares smoothing,” Analyst 140(1), 250–257 (2015).
[Crossref]

Pasinetti, S.

S. Pasinetti, I. Bodini, M. Lancini, F. Docchio, and G. Sansoni, “Experimental characterization of an autofocus algorithm based on liquid lens objective for in-focus imaging in the macro range,” in Proceedings - 2017 7th International Workshop on Advances in Sensors and Interfaces, IWASI 2017 (IEEE, 2017), pp. 195–200.

Pei, B.

F. Zhang, Y. Yao, X. Qu, T. Zhang, and B. Pei, “Dual-beam laser autofocusing system based on liquid lens,” Opt. Laser Technol. 88, 198–204 (2017).
[Crossref]

Perello, L.

F. Nardella, M. Beck, P. Collart-Dutilleul, G. Becker, C. Boulanger, L. Perello, A. Gairard-Dory, B. Gourieux, and G. Ubeaud-Séquier, “A UV-Raman spectrometry method for quality control of anticancer preparations: Results after 18 months of implementation in hospital pharmacy,” Int. J. Pharm. 499(1-2), 343–350 (2016).
[Crossref]

Peseux, J.

B. Berge and J. Peseux, “Variable focal lens controlled by an external voltage: An application of electrowetting,” Eur. Phys. J. E 3(2), 159–163 (2000).
[Crossref]

Popp, J.

E. Cordero, I. Latka, C. Matthäus, I. W. Schie, and J. Popp, “In-vivo Raman spectroscopy: from basics to applications,” J. Biomed. Opt. 23(07), 1 (2018).
[Crossref]

I. Latka, S. Dochow, C. Krafft, B. Dietzek, and J. Popp, “Fiber optic probes for linear and nonlinear Raman applications - Current trends and future development,” Laser Photonics Rev. 7(5), 698–731 (2013).
[Crossref]

C. Krafft, S. Dochow, I. Latka, B. Dietzek, and J. Popp, “Diagnosis and screening of cancer tissues by fiber-optic probe Raman spectroscopy,” Biomed. Spectrosc. Imaging 1(1), 39–55 (2012).
[Crossref]

Prognon, P.

L. Lê, M. Berge, A. Tfayli, A. Baillet Guffroy, P. Prognon, A. Dowek, and E. Caudron, “Quantification of gemcitabine intravenous drugs by direct measurement in chemotherapy plastic bags using a handheld Raman spectrometer,” Talanta 196(November 2018), 376–380 (2019).
[Crossref]

Qu, X.

F. Zhang, Y. Yao, X. Qu, T. Zhang, and B. Pei, “Dual-beam laser autofocusing system based on liquid lens,” Opt. Laser Technol. 88, 198–204 (2017).
[Crossref]

Sansoni, G.

S. Pasinetti, I. Bodini, M. Lancini, F. Docchio, and G. Sansoni, “Experimental characterization of an autofocus algorithm based on liquid lens objective for in-focus imaging in the macro range,” in Proceedings - 2017 7th International Workshop on Advances in Sensors and Interfaces, IWASI 2017 (IEEE, 2017), pp. 195–200.

Schie, I. W.

E. Cordero, I. Latka, C. Matthäus, I. W. Schie, and J. Popp, “In-vivo Raman spectroscopy: from basics to applications,” J. Biomed. Opt. 23(07), 1 (2018).
[Crossref]

Song, B.

Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for Raman spectrometry and its application,” J. Opt. 46(1), 62–67 (2017).
[Crossref]

Sonnvik, J.

K. Szybek and J. Sonnvik, “Raman spectroscopy system,” U.S. patent US10295408B2 (2019).

Stevens, O.

O. Stevens, I. E. Iping Petterson, J. C. C. Day, and N. Stone, “Developing fibre optic Raman probes for applications in clinical spectroscopy,” Chem. Soc. Rev. 45(7), 1919–1934 (2016).
[Crossref]

Stone, N.

M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018).
[Crossref]

O. Stevens, I. E. Iping Petterson, J. C. C. Day, and N. Stone, “Developing fibre optic Raman probes for applications in clinical spectroscopy,” Chem. Soc. Rev. 45(7), 1919–1934 (2016).
[Crossref]

Sulé-Suso, J.

M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018).
[Crossref]

Sun, Z.

Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for Raman spectrometry and its application,” J. Opt. 46(1), 62–67 (2017).
[Crossref]

Szybek, K.

K. Szybek and J. Sonnvik, “Raman spectroscopy system,” U.S. patent US10295408B2 (2019).

Tfayli, A.

L. Lê, M. Berge, A. Tfayli, A. Baillet Guffroy, P. Prognon, A. Dowek, and E. Caudron, “Quantification of gemcitabine intravenous drugs by direct measurement in chemotherapy plastic bags using a handheld Raman spectrometer,” Talanta 196(November 2018), 376–380 (2019).
[Crossref]

Ubeaud-Séquier, G.

F. Nardella, M. Beck, P. Collart-Dutilleul, G. Becker, C. Boulanger, L. Perello, A. Gairard-Dory, B. Gourieux, and G. Ubeaud-Séquier, “A UV-Raman spectrometry method for quality control of anticancer preparations: Results after 18 months of implementation in hospital pharmacy,” Int. J. Pharm. 499(1-2), 343–350 (2016).
[Crossref]

Upchurch, E.

E. Upchurch, M. Isabelle, G. R. Lloyd, C. Kendall, and H. Barr, “An update on the use of Raman spectroscopy in molecular cancer diagnostics: current challenges and further prospects,” Expert Rev. Mol. Diagn. 18(3), 245–258 (2018).
[Crossref]

Wang, J.

Wang, S.

C. Xu, W. Zhao, J. Hu, B. Zhang, and S. Wang, “Liquid lens-based optical sectioning tomography for three-dimensional flame temperature measurement,” Fuel 196, 550–563 (2017).
[Crossref]

Wang, Y.

Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for Raman spectrometry and its application,” J. Opt. 46(1), 62–67 (2017).
[Crossref]

Watson, A.

Xu, C.

C. Xu, W. Zhao, J. Hu, B. Zhang, and S. Wang, “Liquid lens-based optical sectioning tomography for three-dimensional flame temperature measurement,” Fuel 196, 550–563 (2017).
[Crossref]

Yang, J.

Yao, Y.

F. Zhang, Y. Yao, X. Qu, T. Zhang, and B. Pei, “Dual-beam laser autofocusing system based on liquid lens,” Opt. Laser Technol. 88, 198–204 (2017).
[Crossref]

Yu, Z.

Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for Raman spectrometry and its application,” J. Opt. 46(1), 62–67 (2017).
[Crossref]

Zhan, D. J.

H. Chen, Z. M. Zhang, L. Miao, D. J. Zhan, Y. B. Zheng, Y. Liu, F. Lu, and Y. Z. Liang, “Automatic standardization method for raman spectrometers with applications to pharmaceuticals,” J. Raman Spectrosc. 46(1), 147–154 (2015).
[Crossref]

Zhang, B.

C. Xu, W. Zhao, J. Hu, B. Zhang, and S. Wang, “Liquid lens-based optical sectioning tomography for three-dimensional flame temperature measurement,” Fuel 196, 550–563 (2017).
[Crossref]

Zhang, F.

F. Zhang, Y. Yao, X. Qu, T. Zhang, and B. Pei, “Dual-beam laser autofocusing system based on liquid lens,” Opt. Laser Technol. 88, 198–204 (2017).
[Crossref]

Zhang, T.

F. Zhang, Y. Yao, X. Qu, T. Zhang, and B. Pei, “Dual-beam laser autofocusing system based on liquid lens,” Opt. Laser Technol. 88, 198–204 (2017).
[Crossref]

Zhang, Z. M.

H. Chen, Z. M. Zhang, L. Miao, D. J. Zhan, Y. B. Zheng, Y. Liu, F. Lu, and Y. Z. Liang, “Automatic standardization method for raman spectrometers with applications to pharmaceuticals,” J. Raman Spectrosc. 46(1), 147–154 (2015).
[Crossref]

Zhao, W.

C. Xu, W. Zhao, J. Hu, B. Zhang, and S. Wang, “Liquid lens-based optical sectioning tomography for three-dimensional flame temperature measurement,” Fuel 196, 550–563 (2017).
[Crossref]

Zheng, W.

Zheng, Y. B.

H. Chen, Z. M. Zhang, L. Miao, D. J. Zhan, Y. B. Zheng, Y. Liu, F. Lu, and Y. Z. Liang, “Automatic standardization method for raman spectrometers with applications to pharmaceuticals,” J. Raman Spectrosc. 46(1), 147–154 (2015).
[Crossref]

Zou, Y.

Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for Raman spectrometry and its application,” J. Opt. 46(1), 62–67 (2017).
[Crossref]

Analyst (2)

M. J. Baker, H. J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S. G. Kazarian, F. L. Martin, J. Moger, N. Stone, and J. Sulé-Suso, “Clinical applications of infrared and Raman spectroscopy: state of play and future challenges,” Analyst 143(8), 1735–1757 (2018).
[Crossref]

S.-J. Baek, A. Park, Y.-J. Ahn, and J. Choo, “Baseline correction using asymmetrically reweighted penalized least squares smoothing,” Analyst 140(1), 250–257 (2015).
[Crossref]

Appl. Opt. (1)

Biomed. Opt. Express (1)

Biomed. Spectrosc. Imaging (1)

C. Krafft, S. Dochow, I. Latka, B. Dietzek, and J. Popp, “Diagnosis and screening of cancer tissues by fiber-optic probe Raman spectroscopy,” Biomed. Spectrosc. Imaging 1(1), 39–55 (2012).
[Crossref]

Chem. Soc. Rev. (1)

O. Stevens, I. E. Iping Petterson, J. C. C. Day, and N. Stone, “Developing fibre optic Raman probes for applications in clinical spectroscopy,” Chem. Soc. Rev. 45(7), 1919–1934 (2016).
[Crossref]

Eur. Phys. J. E (1)

B. Berge and J. Peseux, “Variable focal lens controlled by an external voltage: An application of electrowetting,” Eur. Phys. J. E 3(2), 159–163 (2000).
[Crossref]

Expert Rev. Mol. Diagn. (1)

E. Upchurch, M. Isabelle, G. R. Lloyd, C. Kendall, and H. Barr, “An update on the use of Raman spectroscopy in molecular cancer diagnostics: current challenges and further prospects,” Expert Rev. Mol. Diagn. 18(3), 245–258 (2018).
[Crossref]

Fuel (1)

C. Xu, W. Zhao, J. Hu, B. Zhang, and S. Wang, “Liquid lens-based optical sectioning tomography for three-dimensional flame temperature measurement,” Fuel 196, 550–563 (2017).
[Crossref]

Int. J. Pharm. (1)

F. Nardella, M. Beck, P. Collart-Dutilleul, G. Becker, C. Boulanger, L. Perello, A. Gairard-Dory, B. Gourieux, and G. Ubeaud-Séquier, “A UV-Raman spectrometry method for quality control of anticancer preparations: Results after 18 months of implementation in hospital pharmacy,” Int. J. Pharm. 499(1-2), 343–350 (2016).
[Crossref]

J. Biomed. Opt. (1)

E. Cordero, I. Latka, C. Matthäus, I. W. Schie, and J. Popp, “In-vivo Raman spectroscopy: from basics to applications,” J. Biomed. Opt. 23(07), 1 (2018).
[Crossref]

J. Opt. (1)

Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for Raman spectrometry and its application,” J. Opt. 46(1), 62–67 (2017).
[Crossref]

J. Raman Spectrosc. (1)

H. Chen, Z. M. Zhang, L. Miao, D. J. Zhan, Y. B. Zheng, Y. Liu, F. Lu, and Y. Z. Liang, “Automatic standardization method for raman spectrometers with applications to pharmaceuticals,” J. Raman Spectrosc. 46(1), 147–154 (2015).
[Crossref]

Laser Photonics Rev. (1)

I. Latka, S. Dochow, C. Krafft, B. Dietzek, and J. Popp, “Fiber optic probes for linear and nonlinear Raman applications - Current trends and future development,” Laser Photonics Rev. 7(5), 698–731 (2013).
[Crossref]

Opt. Express (2)

Opt. Laser Technol. (1)

F. Zhang, Y. Yao, X. Qu, T. Zhang, and B. Pei, “Dual-beam laser autofocusing system based on liquid lens,” Opt. Laser Technol. 88, 198–204 (2017).
[Crossref]

Opt. Lett. (1)

Talanta (1)

L. Lê, M. Berge, A. Tfayli, A. Baillet Guffroy, P. Prognon, A. Dowek, and E. Caudron, “Quantification of gemcitabine intravenous drugs by direct measurement in chemotherapy plastic bags using a handheld Raman spectrometer,” Talanta 196(November 2018), 376–380 (2019).
[Crossref]

Other (7)

. “Integrated Raman Probe,” https://www.ipslasers.com/integrated-Raman-probe.html .

S. Pasinetti, I. Bodini, M. Lancini, F. Docchio, and G. Sansoni, “Experimental characterization of an autofocus algorithm based on liquid lens objective for in-focus imaging in the macro range,” in Proceedings - 2017 7th International Workshop on Advances in Sensors and Interfaces, IWASI 2017 (IEEE, 2017), pp. 195–200.

B. Berge, “Liquid lens technology: principle of electrowetting based lenses and applications to imaging,” in 18th IEEE International Conference on Micro Electro Mechanical Systems, 2005. MEMS 2005. (IEEE, n.d.), pp. 227–230.

. “The First in the Industry to use an Autofocus Solution in a Raman Spectrometer,” https://serstech.com/industry-first-autofocus-an-interview-with-katja-szybek/ .

K. Szybek and J. Sonnvik, “Raman spectroscopy system,” U.S. patent US10295408B2 (2019).

. “C-S-25H0-096 Auto Focus Lens Module,” https://www.corning.com/worldwide/en/innovation/corning-emerging-innovations/corning-varioptic-lenses/auto-focus-lens-modules-c-s-series/varioptic-C-S-25H0-096.html .

. “The product brochure of CorningVariopticLenses,” https://www.corning.com/media/worldwide/Innovation/documents/FINAL_CorningVariopticLenses_productbrochure_5.4.18_lowresWEB.pdf .

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (7)

Fig. 1.
Fig. 1. Diagram of the in-house build Raman probe with an integrated liquid lens as the objective lens and a distance sensor, which is attached to the probe by a 3D-printed holder to maintain a defined relation for the realization of the autofocus function (a). Photograph of the hand-held Raman probe (b).
Fig. 2.
Fig. 2. Repetitive positions of the sample at close distance (a), correct distance (b) and far distance (c). The triangle function between f and ds is always true once the liquid lens and the distance sensor are fixed.
Fig. 3.
Fig. 3. Calibration procedure to determine the best-driver voltage to the liquid lens for US = 0.24 V (a), of various voltages of readout of distance sensor vs. best driven voltages for the liquid lens (b), and of the recorded Raman spectra during the whole procedure (c).
Fig. 4.
Fig. 4. Comparison and characterization of Raman signal acquired with fixed and autofocus adjustment. Raman intensity of the band at 830 cm−1 and distances recorded from continuously changing the objective-to-sample distance with autofocus focal length (a) and with fixed focal length (c). Raman spectra of three represent measurement points (MPs) respectively for both cases (b) and (d). Intensity of measurements from (a) and (c) plotted vs. sample-to-probe distance for autofocus probe (e) and with fixed focal length probe (f) additionally with a 5th-oder polynomial fit. Intensity normalized fits, I_auto and I_fixed, and ratios of those values provide the gain-factor for the autofocusing approach in comparison to fixed-focal lens (g).
Fig. 5.
Fig. 5. Raman signal recorded vs. the distances in handheld operation for both cases. Raman intensity of the band at 823 cm−1 and the sample-to-probe are plotted vs. time for the fixed-focal length (a); corresponding mean Raman spectrum (blue) with standard deviation (red) (b). Raman intensity of the band at 823 cm−1 and the sample-to-probe are plotted vs. time for the autofocus method (c); corresponding mean Raman spectrum (blue) with standard deviation (red) (d).
Fig. 6.
Fig. 6. Raman spectra acquired with the autofocusing probe at different acquisition times. Raman intensity of the band at 823 cm−1 and the sample-to-probe distance plotted vs. time for an acquisition time of 0.1s (a); corresponding mean Raman spectrum (blue) with standard deviation (red) (c). Raman intensity of the band at 823 cm−1and the sample-to-probe plotted vs. time for an acquisition time of 1.0 s (a); corresponding mean Raman spectrum (blue) with standard deviation (red) (d).
Fig. 7.
Fig. 7. Photograph of the setup doing Raman imaging using our present probe on a tilted placed bio-sample (a); Raman image result with fixed focal length (b) and corresponding mean Raman spectra (blue) with the standard deviation (red) (c); Comparison Raman image result with the autofocus method (d) and corresponding mean Raman spectra (blue) with the standard deviation (red) (e). The intensity variation for the band at 1459 cm−1 for the fixed focal length and for the autofocus mode are plotted in (f).

Equations (4)

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

f = c o s α d s d 0
{ f = 1 D = 1 a 1 U L + b 1 d S = a 2 U S + b 2
U L = 1 c o s α a 1 a 2 U S + c o s α a 1 b 2 a 1 d 0 b 1 a 1
U L = 1 a U S + b + c

Metrics