Applied Spectroscopy

Particle size, scatter, and multi-collinearity are long-standing problems encountered in diffuse reflectance spectrometry. Multiplicative combinations of these effects are the major factor inhibiting the interpretation of near-infrared diffuse reflectance spectra. Sample particle size accounts for the majority of the variance, while variance due to chemical composition is small. Procedures are presented whereby physical and chemical variance can be separated. Mathematical transformations—standard normal variate (SNV) and de-trending (DT)—applicable to individual NIR diffuse reflectance spectra are presented. The standard normal variate approach effectively removes the multiplicative interferences of scatter and particle size. De-trending accounts for the variation in baseline shift and curvilinearity, generally found in the reflectance spectra of powdered or densely packed samples, with the use of a second-degree polynomial regression. NIR diffuse reflectance spectra transposed by these methods are free from multi-collinearity and are not confused by the complexity of shape encountered with the use of derivative spectroscopy.

A two-dimensional (2D) correlation method generally applicable to various types of spectroscopy, including IR and Raman spectroscopy, is introduced. In the proposed 2D correlation scheme, an external perturbation is applied to a system while being monitored by an electromagnetic probe. With the application of a correlation analysis to spectral intensity fluctuations induced by the perturbation, new types of spectra defined by two independent spectral variable axes are obtained. Such two-dimensional correlation spectra emphasize spectral features not readily observable in conventional one-dimensional spectra. While a similar 2D correlation formalism has already been developed in the past for analysis of simple sinusoidally varying IR signals, the newly proposed formalism is designed to handle signals fluctuating as an arbitrary function of time, or any other physical variable. This development makes the 2D correlation approach a universal spectroscopic tool, generally applicable to a very wide range of applications. The basic property of 2D correlation spectra obtained by the new method is described first, and several spectral data sets are analyzed by the proposed scheme to demonstrate the utility of generalized 2D correlation spectra. Potential applications of this 2D correlation approach are then explored.

This paper is concerned with the quantitative analysis of multicomponent mixtures by diffuse reflectance spectroscopy. Near-infrared reflectance (NIRR) measurements are related to chemical composition but in a nonlinear way, and light scatter distorts the data. Various response linearizations of reflectance (R) are compared ( R with Saunderson correction for internal reflectance, log 1/ R, and Kubelka-Munk transformations and its inverse). A multi-wavelength concept for optical correction (Multiplicative Scatter Correction, MSC) is proposed for separating the chemical light absorption from the physical light scatter. Partial Least Squares (PLS) regression is used as the multivariate linear calibration method for predicting fat in meat from linearized and scatter-corrected NIRR data over a broad concentration range.

All the response linearization methods improved fat prediction when used with the MSC; corrected log 1/ R and inverse Kubelka-Munk transformations yielded the best results. The MSC provided simpler calibration models with good correspondence to the expected physical model of meat. The scatter coefficients obtained from the MSC correlated with fat content, indicating that fat affects the NIRR of meat with an additive absorption component and a multiplicative scatter component.

A new graphically oriented local modeling procedure called interval partial least-squares ( iPLS) is presented for use on spectral data. The iPLS method is compared to full-spectrum partial least-squares and the variable selection methods principal variables (PV), forward stepwise selection (FSS), and recursively weighted regression (RWR). The methods are tested on a near-infrared (NIR) spectral data set recorded on 60 beer samples correlated to original extract concentration. The error of the full-spectrum correlation model between NIR and original extract concentration was reduced by a factor of 4 with the use of iPLS ( r = 0.998, and root mean square error of prediction equal to 0.17% plato), and the graphic output contributed to the interpretation of the chemical system under observation. The other methods tested gave a comparable reduction in the prediction error but suffered from the interpretation advantage of the graphic interface. The intervals chosen by iPLS cover both the variables found by FSS and all possible combinations as well as the variables found by PV and RWR, and iPLS is still able to utilize the first-order advantage.

Fluorescence data of replicate samples obtained from different fluorescence spectrometers or by the same spectrometer but with different instrument settings can have great intensity differences. In order to compare such data an intensity calibration must be applied. Here we explain a simple calibration method for fluorescence intensity using only the integrated area of a water Raman peak. By applying this method to data from three different instruments, we show that it is possible to remove instrument-dependent intensity factors, and we present results on a unified scale of Raman units. The method presented is a rapid and simple approach suitable for routine measurements with no need for hazardous chemicals.

A new procedure, based on the laser-induced plasma spectroscopy (LIPS) technique, is proposed for calibration-free quantitative elemental analysis of materials. The method here presented, based on an algorithm developed and patented by IFAM-CNR, allows the matrix effects to be overcome, yielding precise and accurate quantitative results on elemental composition of materials without use of calibration curves. Some applications of the method are illustrated, for quantitative analysis of the composition of metallic alloys and quantitative determination of the composition of the atmosphere.

The previously reported nonreproducibility of the intensity of the OH stretching band of liquid water has been explored. It was found that it can be eliminated in measurements with the Circle® multiple ATR cell by ensuring that the ATR rod is coaxial with the glass liquid holder. It was also found that normal laboratory temperature variations of a few degrees change the intensity by ⩽∼1% of the peak height. A new imaginary refractive index spectrum of water has been determined between 4000 and 700 cm¹ as the average of spectra calculated from ATR spectra recorded by four workers in our laboratory over the past seven years. It was obtained under experimental and computational conditions superior to those used previously, but is only marginally different from the spectra reported in 1989. In particular, the integrated intensities of the fundamentals are not changed significantly from those reported in 1989. The available imaginary refractive index, k, values between 15,000 and 1 cm⁻¹ have been compared. The values that are judged to be the most reliable have been combined into a recommended k spectrum of H₂O(l) at 25 °C between 15,000 and 1 cm⁻¹, from which the real refractive index spectrum has been calculated by Kramers–Kronig transformation. The recommended values of the real and imaginary refractive indices and molar absorption coefficients of liquid water at 25 ± 1 °C are presented in graphs and tables. The real and imaginary dielectric constants and the real and imaginary molar polarizabilities in this wavenumber range can be calculated from the tables. Conservatively estimated probable errors of the recommended k values are given. The precision with which the values can be measured in one laboratory and the relative errors between regions are, of course, far smaller than these probable errors. The recommended k values should be of considerable value as interim standard intensities of liquid water, which will facilitate the transfer of intensities between laboratories.

Coherent anti-Stokes Raman spectroscopy (CARS) is a relatively new kind of Raman spectroscopy which is based on a nonlinear conversion of two laser beams into a coherent, laser-like Raman beam of high intensity in the anti-Stokes region. The emission is often many orders of magnitude greater than normal Raman scattering and, because of the coherent and anti-Stokes character of radiation, the method is very useful for obtaining Raman spectra of fluorescing samples, gases in discharges, plasmas, combustion, atmospheric chemistry. In this paper we outline the basic theory behind CARS and describe its unusual effects and drawbacks. We review the research to date on various materials, and indicate the possible future direction, utility and applications of CARS such as surface studies, fluctuation phenomena, reaction dynamics, photochemistry, kinetics, relaxation, and energy transfer.

Until very recently, handheld spectrometers were the domain of major analytical and security instrument companies, with turnkey analyzers using spectroscopic techniques from X-ray fluorescence (XRF) for elemental analysis (metals), to Raman, mid-infrared, and near-infrared (NIR) for molecular analysis (mostly organics). However, the past few years have seen rapid changes in this landscape with the introduction of handheld laser-induced breakdown spectroscopy (LIBS), smartphone spectroscopy focusing on medical diagnostics for low-resource areas, commercial engines that a variety of companies can build up into products, hyphenated or dual technology instruments, low-cost visible-shortwave NIR instruments selling directly to the public, and, most recently, portable hyperspectral imaging instruments. Successful handheld instruments are designed to give answers to non-scientist operators; therefore, their developers have put extensive resources into reliable identification algorithms, spectroscopic libraries or databases, and qualitative and quantitative calibrations. As spectroscopic instruments become smaller and lower cost, “engines” have emerged, leading to the possibility of being incorporated in consumer devices and smart appliances, part of the Internet of Things (IOT). This review outlines the technologies used in portable spectroscopy, discusses their applications, both qualitative and quantitative, and how instrument developers and vendors have approached giving actionable answers to non-scientists. It outlines concerns on crowdsourced data, especially for heterogeneous samples, and finally looks towards the future in areas like IOT, emerging technologies for instruments, and portable hyphenated and hyperspectral instruments.