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From Light to Insight: Grasping the Principles of NIR Spectroscopy

By 
Ahmed Korayem
8 Min Read
March 27, 2024

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Introduction

In our previous blog about NIR spectroscopy, we tackled the definition of NIR spectroscopy. We encourage you to read this article if you are unfamiliar with NIR Spectroscopy. But here’s a quick recap of key points. NIR is a non-destructive analytical method operating in the near-infrared region of the electromagnetic spectrum. This region spans in the wavelength ranges from approximately 900 nanometers to 2500 nanometers (nm); by interacting with the material, the light holds valuable information about the molecular composition and properties of materials. Unlike other spectroscopic techniques, NIR doesn't require extensive sample preparation, making it suitable for rapid and routine analysis. Its fundamental principles are rooted in the interaction between light and matter, enabling us to understand complex material compositions in real-time.

In this blog, we further explore some of the key fundamentals of NIR spectroscopy. Keep reading for details 👇🏼

What is the working principle of NIR Spectroscopy?

“The NIR identification principle is based on the reason that a material will absorb NIR energy and transmit, transreflect, or reflect it in a unique pattern according to the physical and chemical characteristics of the sample. Hence, we can use NIR for both qualitative and quantitative analysis. In the electromagnetic spectrum NIR region is located at the wavelength range between 780 and 2565 nm and wave number range 12820-3959 cm1 hence, it covers the wavelength range adjacent to the mid-infrared, which may extend to the visible region.” (source)

The working principle of NIR spectroscopy revolves around the interaction between near-infrared light and molecular vibrations within a sample. This interaction leads to the formation of characteristic absorption bands, enabling qualitative and quantitative analysis across various applications.

As near-infrared light interacts with a substance, it induces vibrational transitions in the molecular bonds, specifically involving overtones and combinations of fundamental vibrations. Fundamental vibrations occur at higher energy levels, typically in the mid-infrared region. However, NIR spectroscopy leverages the overtones and combinations of these fundamental vibrations that fall into the near-infrared range.

These overtones and combinations provide a unique fingerprint for different molecular structures. The absorption of near-infrared light by the sample leads to the formation of absorption bands. These bands are characteristic of the molecular constituents present in the sample. The amount of light absorbed at specific wavelengths corresponds to the concentration of the respective molecular species.

NIR spectroscopy excels in quantitative analysis due to its ability to correlate the intensity of absorption bands with the concentration of specific components within a sample. Calibration models are developed using known reference samples, enabling accurate predictions of the composition of unknown samples.

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What are the different ways samples can interact with light for a successful NIR analysis?

To capture the absorption spectrum of the sample, spectrometers commonly use one of these three sampling methods: 

1. Transmission:

Transmission sampling consists of directing light through a NIR transparent sample holder. It’s typically used in NIR transparent and translucent materials like liquids, oils, fuels, polymers/plastics, etc. Typically involves a light source on one side of the sample and a detector on the opposite side. The sample is often placed in a NIR transparent container or cuvette.

According to Beer-Lambert law, the amount of light absorbed by the sample correlates with the concentration levels of its different compositions and the thickness of the sample. Therefore, the design of the sample holder needs to guarantee that the thickness of the sample interacting with the light entering the NIR spectrometer is always the same for every measurement.

2. Diffuse reflectance: 

In diffuse reflectance sampling, the incident light is scattered in various directions upon interacting with the sample's surface.

This technique is advantageous for heterogeneous and opaque samples and is often used in soil analysis and food quality assessment applications. It’s also used for surface analysis of polymers and coatings.

Sample heterogeneity refers to the uneven distribution of components or properties within a sample. Dealing with heterogeneous samples in analytical chemistry and spectroscopy poses challenges, given that the composition, structure, or characteristics can vary across different regions. Analyzing such samples requires techniques capable of providing representative measurements, considering these variations.

In NIR Spectroscopy, it is preferable to use instruments that can capture a large sample area to minimize the undesired effects of sample heterogeneity and ensure the representativeness of measured samples. In some cases where sample heterogeneity is high (such as forages), techniques involving the movement of either the sample or the instrument during measurement can be employed to improve representativeness. Alternatively, samples may need to be ground. However, this is usually the least desirable option as it involves destroying the sample.

3.  Transflectance:

Transflectance is a combination of "transmission" and "reflectance". In transflectance measurements, incident light penetrates the sample, and both transmitted and reflected light are simultaneously collected and analyzed.

This technique is particularly valuable when dealing with semi-transparent or partially reflective samples, offering advantages in situations where traditional transmission or reflectance methods alone may not provide sufficient information. Transflectance spectroscopy finds applications in various fields, including pharmaceuticals, biology, and materials science, providing a versatile approach to gathering comprehensive data from samples exhibiting a combination of transparency and reflectivity. Similar to transmission measurements, the thickness of the samples affects the reading of the NIR spectrometer. To make accurate transflectance measurements, attention should be given to the design of optical components and the setup's geometry to ensure consistency and repeatability of how the sample is presented to the light.

How to select the suitable sampling technique for my NIR analysis?

Selecting the right sampling technique is critical in ensuring the success and accuracy of analytical measurements. The choice of technique depends on various factors, including:

  • The nature of the sample, its physical state, and the specific information sought. Understanding the sample's characteristics, such as transparency, reflectivity, and homogeneity, is essential. For instance, transmission techniques are suitable for transparent samples, while diffuse reflectance is advantageous for heterogeneous and opaque materials.
  • The instrument's capabilities, with factors like sensitivity, resolution, and speed, influence the decision.
  • The practicality of the sampling method in terms of ease of use, cost-effectiveness, and sample preparation requirements.

Ultimately, thoughtful consideration of these factors ensures the selection of a sampling technique that aligns with the specific analytical goals and characteristics of the sample under investigation.

Other factors must be considered when choosing the spectral sensing solution that best fits your needs. Our article about considerations when selecting the right spectral sensing solution can give you some insights.

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How to interpret NIR spectra?

Interpreting NIR spectra requires a synergistic application of chemometrics to develop calibration models that extract valuable information and facilitate qualitative and quantitative analysis. 

What are Calibration models?

Calibration models are mathematical models that are trained for making predictions. They learn from past data, helping tools or systems recognize patterns and connections between different factors. Once trained, these models can make informed predictions in new situations. In the context of NIR Spectroscopy, calibration models are the tools that help us make predictions about the properties of a sample based on its NIR spectrum. It's like teaching a smart system to understand the relationship between the spectrum of the parameters to be analyzed.

What are chemometrics?

Chemometrics is a multidisciplinary field at the heart of interpreting NIR spectra. Combining principles from chemistry and advanced statistical methods, chemometrics helps analysts extract meaningful patterns and trends from complex spectral datasets.

The most commonly used chemometrics methods include:

  • Principal Component Analysis (PCA): PCA is applied in NIR spectroscopy when dealing with many spectral data. It helps identify the most critical features in the spectra to a smaller number of factors (principle components), simplifying the interpretation of complex spectra in applications like material identification
  • Partial Least Squares Regression (PLSR): PLSR establishes a linear relationship between the spectral data and the reference values of the properties of interest. It is commonly used in NIR spectroscopy for quantitative analysis. It's employed when predicting concentrations of specific components in a sample, like the moisture content in agricultural products.
  • Cluster Analysis: Cluster analysis involves grouping samples based on similarities in their NIR spectral patterns. This technique is valuable in identifying inherent patterns or subclasses within a set of samples. It is used in categorization applications such as ingredient sorting.

How are calibration models developed?

The key steps to build a calibration model are:

  1. We start by collecting several samples and measuring their properties using traditional methods to generate reference data.
  2. We capture the NIR spectra of the sample set to generate spectral data.
  3. Chemometrics are applied to the paired reference and spectral data to determine how specific features in the spectra correspond to the properties we're interested in.
  4. To analyze the performance of the model, the model is tested with another sample set with known reference values

The calibration model development process is illustrated in the following diagram.

Once the calibration model is trained, it becomes a reliable predictor. When we have a new sample with an unknown property, we can shine NIR light on it, capture its spectrum, and use the calibration model to estimate or predict its properties.

How to select the sample set used to develop calibration models?

When preparing the sample set for developing a calibration model, careful considerations are crucial to ensure the model's accuracy and reliability, including:

  • Representativeness: Ensure the sample set covers the full range of variations in the properties of interest, capturing diverse conditions and potential outliers.
  • Sample Size: Strike a balance; an adequate number of samples are needed for robustness, but an excessively large sample size may introduce unnecessary complexity.
  • Homogeneity: Samples should be consistent in terms of their properties and characteristics to avoid introducing confusion into the model.
  • Real-World Representation: Ensure the sample set accurately reflects conditions and variations encountered in practical applications to enhance the model's practical applicability.
  • Interfering Factors: Screen for interfering or confounding variables in the sample set that could introduce noise and impact model performance.

Creating a calibration model is a time-intensive process that demands careful consideration and requires substantial expertise. Building on existing robust models proves advantageous as these models have undergone rigorous validation and refinement, offering a reliable foundation for diverse applications. Expertise plays a crucial role in navigating the intricacies of model development, ensuring that the constructed model is accurate and applicable to a wide array of scenarios. In essence, leveraging existing expertise and robust models accelerates the calibration model's construction, promoting efficiency and accuracy in predicting the properties of new samples.

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Accelerating NIR Adoption with NeoSpectra: A Seamless Journey to Spectroscopic Success

In our exploration of the core principles of NIR Spectroscopy, we've revealed its capability to provide insightful information about material composition and properties. Yet, when deciding to adopt NIR Spectroscopy to address analytical needs, the initial decisions involved, such as choosing the right sampling method or developing a robust calibration model, may initially seem daunting.

The NeoSpectra platform ensures that these decisions can be easily made, ensuring that the adoption process is a breeze for organizations, regardless of their level of expertise in NIR Spectroscopy.

NeoSpectra devices can be effortlessly paired with an array of sampling accessories to cater to diverse sample types. These accessories are thoughtfully crafted to offer flexibility, user-friendly operation, and utmost accuracy, providing organizations with versatile solutions.

Regarding calibration models, the NeoSpectra LabStore takes the complexity out of the equation. It provides a wide array of ready-to-use calibration models meticulously crafted and maintained by reputable labs and expert model developers. This guarantees unparalleled accuracy and reliability, making the NeoSpectra platform a comprehensive solution for organizations starting their journey with NIR Spectroscopy.

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