Implementation of a novel low-noise InGaAs detector enabling rapid near-infrared multichannel Raman spectroscopy of pigmented biological samples

نویسندگان

  • Inês P. Santos
  • Peter J. Caspers
  • Tom Bakker Schut
  • Remco van Doorn
  • Senada Koljenović
  • Gerwin J. Puppels
چکیده

Pigmented tissues are inaccessible to Raman spectroscopy using visible laser light because of the high level of laser-induced tissue fluorescence. The fluorescence contribution to the acquired Raman signal can be reduced by using an excitationwavelength in the near infrared range around 1000nm. This will shift the Raman spectrum above 1100nm, which is the principal upper detection limit for silicon-based CCD detectors. For wavelengths above 1100nm indium gallium arsenide detectors can be used. However, InGaAs detectors have not yet demonstrated satisfactory noise level characteristics for demanding Raman applications. We have tested and implemented for the first time a novel sensitive InGaAs imaging camera with extremely low readout noise for multichannel Raman spectroscopy in the short-wave infrared (SWIR) region. The effective readout noise of two electrons is comparable to that of high quality CCDs and two orders of magnitude lower than that of other commercially available InGaAs detector arrays. With an in-house built Raman systemwe demonstrate detection of shot-noise limited high quality Raman spectra of pigmented samples in the highwavenumber region, whereas amore traditional excitation laser wavelength (671nm) could not generate a useful Raman signal because of high fluorescence. Our Raman instrument makes it possible to substantially decrease fluorescence background and to obtain high quality Raman spectra from pigmented biological samples in integration times well below 20s. Copyright © 2015 John Wiley & Sons, Ltd.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

ART-2a) against other classifiers for rapid sorting of post consumer plastics by remote near-infrared spectroscopic sensing using an InGaAs diode array

An Adaptive Resonance Theory Based Artificial Neural Network (ART-2a) has been compared with Multilayer Feedforward Backpropagation of Error Neural Networks (MLF-BP) and with the SIMCA classifier. All three classifiers were applied to achieve rapid sorting of post-consumer plastics by remote near-infrared (NIR) spectroscopy. A new semiconductor diode array detector based on InGaAs technology ha...

متن کامل

1064 nm Deep near-infrared (NIR) excited raman microspectroscopy for studying photolabile organisms.

We have constructed a 1064 nm deep near-infrared (NIR) excited multichannel Raman microspectrometer using an InP/InGaAsP multichannel detector. This microspectrometer achieves high sensitivity suitable for in vivo measurements of single living cells with lateral resolution of 0.7 μm and depth resolution of 3.1 μm. It has been applied to the structural analysis of living cyanobacterial cells, we...

متن کامل

Ultrasensitive chemical analysis by Raman spectroscopy.

In the Raman effect, incident light is inelastically scattered from a sample and shifted in frequency by the energy of its characteristic molecular vibrations. Since its discovery in 1927, the effect has attracted attention from a basic research point of view as well as a powerful spectroscopic technique with many practical applications. The advent of laser light sources with monochromatic phot...

متن کامل

Multichannel Fourier Transform Raman Spectroscopy: Combining the Advantages of CCDs with Interferometry

A common-path (Sagnac) interferometer combined with a chargecoupled device (CCD) was evaluated for Raman spectroscopy in the near-infrared region. A spatial interferogram of the scattered light was projected onto the face of the CCD, and a Fourier transform of the intensity vs. pixel data yielded a Raman spectrum. This mult ichannel Fourier transform (MCFT) technique retains several advantages ...

متن کامل

Comparison of an adaptive resonance theory based neural network ( ART-2a) against other classifiers for rapid sorting of post consumer plastics by remote near-infrared spectroscopic sensing using an InGaAs diode array

An Adaptive Resonance Theory Based Artificial Neural Network (ART-2a) has been compared with Multilayer Feedforward Backpropagation of Error Neural Networks (MLF-BP) and with the SIMCA classifier. All three classifiers were applied to achieve rapid sorting of post-consumer plastics by remote near-infrared (NIR) spectroscopy. A new semiconductor diode array detector based on InGaAs technology ha...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2015