Integrated near-infrared spectral sensing

Spectral sensing is increasingly used in applications ranging from industrial process monitoring to agriculture. Sensing is usually performed by measuring reflected or transmitted light with a spectrometer and processing the resulting spectra. However, realizing compact and mass-manufacturable spectrometers is a major challenge, particularly in the infrared spectral region where chemical information is most prominent. Here we propose a different approach to spectral sensing which dramatically simplifies the requirements on the hardware and allows the monolithic integration of the sensors. We use an array of resonant-cavity-enhanced photodetectors, each featuring a distinct spectral response in the 850-1700 nm wavelength range. We show that prediction models can be built directly using the responses of the photodetectors, despite the presence of multiple broad peaks, releasing the need for spectral reconstruction. The large etendue and responsivity allow us to demonstrate the application of an integrated near-infrared spectral sensor in relevant problems, namely milk and plastic sensing. Our results open the way to spectral sensors with minimal size, cost and complexity for industrial and consumer applications.

A comparison of spectral reconstruction methods applied to non-zero temperature NRQCD meson correlation functions

We present results from the fastsum collaboration’s programme to determine the spectrum of the bottomonium system as a function of temperature. Three different methods of extracting spectral information are discussed: a Maximum Likelihood approach using a Gaussian spectral function for the ground state, the Backus Gilbert method, and the Kernel Ridge Regression machine learning procedure. We employ the fastsum anisotropic lattices with 2+1 dynamical quark flavours, with temperatures ranging from 47 to 375 MeV.

A Method for Correcting the Interference of Overlapping Absorption Lines Using Second Harmonic Spectral Reconstruction

The measurement accuracy of trace gas detection based on infrared absorption spectroscopy is influenced by the overlap of absorption lines. A method for correcting the interference of overlapping absorption lines using second harmonic spectral reconstruction (2f-SR) is proposed to improve the measurement accuracy. 2f-SR includes three parts: measurement of gas temperature and use of the differences in temperature characteristics of absorption lines to correct the temperature error, 2f signal restoration based on laser characteristics to eliminate the influence of waveform change on overlapping absorption lines, and fast multi-peak fitting for the separation of interference from overlapping absorption lines. The CH4 measurement accuracy based on overlapping absorption lines is better than 0.8% using 2f-SR. 2f-SR has a lower minimum detection limit (MDL) and a higher detection accuracy than the separation of overlapping absorption lines based on the direct absorption method. The MDL is reduced by two to three orders of magnitude and reaches the part per million by volume level. 2f-SR has clear advantages for correcting the interference of overlapping absorption lines in terms of both MDL and measurement accuracy.

Spectral Reconstruction Using an Iteratively Reweighted Regulated Model from Two Illumination Camera Responses

An improved spectral reflectance estimation method was developed to transform captured RGB images to spectral reflectance. The novelty of our method is an iteratively reweighted regulated model that combines polynomial expansion signals, which was developed for spectral reflectance estimation, and a cross-polarized imaging system, which is used to eliminate glare and specular highlights. Two RGB images are captured under two illumination conditions. The method was tested using ColorChecker charts. The results demonstrate that the proposed method could make a significant improvement of the accuracy in both spectral and colorimetric: it can achieve 23.8% improved accuracy in mean CIEDE2000 color difference, while it achieves 24.6% improved accuracy in RMS error compared with classic regularized least squares (RLS) method. The proposed method is sufficiently accurate in predicting the spectral properties and their performance within an acceptable range, i.e., typical customer tolerance of less than 3 DE units in the graphic arts industry.

NUScon: a community-driven platform for quantitative evaluation of nonuniform sampling in NMR

Although the concepts of nonuniform sampling (NUS​​​​​​​) and non-Fourier spectral reconstruction in multidimensional NMR began to emerge 4 decades ago (Bodenhausen and Ernst, 1981; Barna and Laue, 1987), it is only relatively recently that NUS has become more commonplace. Advantages of NUS include the ability to tailor experiments to reduce data collection time and to improve spectral quality, whether through detection of closely spaced peaks (i.e., “resolution”) or peaks of weak intensity (i.e., “sensitivity”). Wider adoption of these methods is the result of improvements in computational performance, a growing abundance and flexibility of software, support from NMR spectrometer vendors, and the increased data sampling demands imposed by higher magnetic fields. However, the identification of best practices still remains a significant and unmet challenge. Unlike the discrete Fourier transform, non-Fourier methods used to reconstruct spectra from NUS data are nonlinear, depend on the complexity and nature of the signals, and lack quantitative or formal theory describing their performance. Seemingly subtle algorithmic differences may lead to significant variabilities in spectral qualities and artifacts. A community-based critical assessment of NUS challenge problems has been initiated, called the “Nonuniform Sampling Contest” (NUScon), with the objective of determining best practices for processing and analyzing NUS experiments. We address this objective by constructing challenges from NMR experiments that we inject with synthetic signals, and we process these challenges using workflows submitted by the community. In the initial rounds of NUScon our aim is to establish objective criteria for evaluating the quality of spectral reconstructions. We present here a software package for performing the quantitative analyses, and we present the results from the first two rounds of NUScon. We discuss the challenges that remain and present a roadmap for continued community-driven development with the ultimate aim of providing best practices in this rapidly evolving field. The NUScon software package and all data from evaluating the challenge problems are hosted on the NMRbox platform.

Investigating the Upper-Bound Performance of Sparse-Coding-Based Spectral Reconstruction from RGB Images

In Spectral Reconstruction (SR), we recover hyperspectral images from their RGB counterparts. Most of the recent approaches are based on Deep Neural Networks (DNN), where millions of parameters are trained mainly to extract and utilize the contextual features in large image patches as part of the SR process. On the other hand, the leading Sparse Coding method ‘A+’—which is among the strongest point-based baselines against the DNNs—seeks to divide the RGB space into neighborhoods, where locally a simple linear regression (comprised by roughly 102 parameters) suffices for SR. In this paper, we explore how the performance of Sparse Coding can be further advanced. We point out that in the original A+, the sparse dictionary used for neighborhood separations are optimized for the spectral data but used in the projected RGB space. In turn, we demonstrate that if the local linear mapping is trained for each spectral neighborhood instead of RGB neighborhood (and theoretically if we could recover each spectrum based on where it locates in the spectral space), the Sparse Coding algorithm can actually perform much better than the leading DNN method. In effect, our result defines one potential (and very appealing) upper-bound performance of point-based SR.