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The technologies behind :
  • the UV-VIS-NIR spectrometry

  • the AI enabled chemometrics

Introduction to NIR spectroscopy

When you hold your hand out to a burning fire you “feel” the heat being emitted by the fire but what is happening? The fire gives out light and infrared (IR) radiation; from a fire most of this is near infrared (NIR) radiation. Some of the NIR radiation is absorbed by water molecules in your skin. This raises the temperature of the water and results in an increase in temperature in the surrounding tissue which is detected by nerves in your skin. This radiation was discovered in 1800 by William Herschel, a musician and very successful amateur astronomer (he discovered the planet Uranus) because he wanted to know if any particular colour was associated with heat from sunlight. He found that the heat maximum was beyond the red end of the spectrum. Herschel could not believe that light and his “radiant heat” were related but he was wrong.

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By 1835 Ampere had demonstrated that the only difference between light and what he named “infrared radiation” was their wavelength. Then in 1864 James Maxwell wrote “This velocity [of electromagnetic force] is so nearly that of light that it seems we have strong reason to conclude that light itself (including radiant heat and other radiations) is an electromagnetic disturbance in the form of waves propagated through the electromagnetic field according to electromagnetic laws”. What we now call the electromagnetic spectrum is shown here above

Near-Infrared spectroscopy

Near-infrared spectroscopy (NIRS) is a spectroscopic method that uses the near-infrared region of the electromagnetic spectrum (from 780 nm to 2500 nm). Typical applications include medical and physiological diagnostics and research including blood sugar, pulse oximetry, functional neuroimaging, sports medicine, elite sports training, ergonomics, rehabilitation, neonatal research, brain computer interface, urology (bladder contraction), and neurology (neurovascular coupling). There are also applications in other areas as well such as pharmaceutical, food and agrochemical quality control, atmospheric chemistry, combustion research and astronomy.

Theory

In order to access the origin of a NIR spectra, to be able. to interpret it and have an important tool to guide in analytical method development, one should be familiar. with the fundamentals of vibrational spectroscopy. The NIR spectrum originates from radiation energy transferred to mechanical energy associated with the motion of atoms held together by chemical bonds in a molecule. Although. many would approach method development in a purely. empirical way, knowledge of the theory can help to look. at the important wavelengths and quicker optimization of the modelling stage.

Near-infrared spectroscopy is based on molecular overtone and combination vibrations. Such transitions are forbidden by the selection rules of quantum mechanics. As a result, the molar absorptivity in the near-IR region is typically quite small. One advantage is that NIR can typically penetrate much further into a sample than mid infrared radiation. Near-infrared spectroscopy is, therefore, not a particularly sensitive technique, but it can be very useful in probing bulk material with little or no sample preparation. The molecular overtone and combination bands seen in the near-IR are typically very broad, leading to complex spectra; it can be difficult to assign specific features to specific chemical components. Multivariate (multiple variables) calibration techniques (e.g., principal components analysis, partial least squares, or artificial neural networks) are often employed to extract the desired chemical information. Careful development of a set of calibration samples and application of multivariate calibration techniques is essential for near-infrared analytical methods

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UV-VIS spectroscopy

Ultraviolet–visible spectroscopy or ultraviolet–visible spectrophotometry (UV–Vis or UV/Vis) refers to absorption spectroscopy or reflectance spectroscopy in part of the ultraviolet and the full, adjacent visible spectral regions. This means it uses light in the visible and adjacent ranges. The absorption or reflectance in the visible range directly affects the perceived color of the chemicals involved. In this region of the electromagnetic spectrum, atoms and molecules undergo electronic transitions

Instrumentation

Instrumentation for near-IR (NIR) spectroscopy is similar to instruments for the UV-visible and mid-IR ranges. There is a source, a detector, and a dispersive element (such as a prism, or, more commonly, a diffraction grating) to allow the intensity at different wavelengths to be recorded. Fourier transform NIR instruments using an interferometer are also common, especially for wavelengths above ~1000 nm. Depending on the sample, the spectrum can be measured in either reflection or transmission.

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Applications

Typical applications of NIR spectroscopy include the analysis of food products, pharmaceuticals, combustion products, and a major branch of astronomical spectroscopy. Some examples

Near-infrared spectroscopy is widely applied in agriculture for determining the quality of forages, grains, and grain products, oilseeds, coffee, tea, spices, fruits, vegetables, sugarcane, beverages, fats, and oils, dairy products, eggs, meat, and other agricultural products. It is widely used to quantify the composition of agricultural products because it meets the criteria of being accurate, reliable, rapid, non-destructive, and inexpensive

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See the excellent website NIR for Food

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AI-enabled Chemometrics

Currently, spectroscopy is the analytical technique which most applies chemometrics. Chemometrics is the use of mathematical and statistical techniques for extracting relevant information from analytical data, in the present case, the spectral data. Both Chemometrics and spectrum technology have evolved in a symbiosis where spectroscopy achieves more robust identification and quantitation models and extends its applicability, while posing new challenges to chemometrics that motivate the improvement of many of its techniques. An extensive review of the subject is out of the scope here. The reader is referred to reviews and textbooks which have treated this subject, some of them in direct connection with spectroscopy. The most employed technique for qualitative analysis using spectroscopy, supplied by many software packages, is based on Principal Component Analysis and is known as SIMCA (Software Independent Modelling Class Analogy). There is an arsenal of chemometric tools dedicated to make use of spectroscopic information. The most common are Multiple Linear Regression (MLR), Principal Component Regression (PCR) and Partial Least Square Regression (PLS). All presuppose a linear relationship between the spectral data and the concentration or other property value to be determined.

 

For quantitative treatment, it is worth mentioning Artificial Neural Networks (ANN) as an emerging alternative for calibration. This technique may present some advantages when non-linearity (not easily accommodated by PCR and PLS) between the spectral data and the quantitative information of interest exists.