How to choose the right spectral sensing solution?

Introduction

‍In a previous article in this series, we have explained an overview of using NIR spectroscopy, how it works, and its usage in different applications. Several factors need to be considered when deciding which NIR solution is best for your analytical needs. Keep reading to find out how to choose the right solution for you 👇🏼

What is spectral sensing?

Spectral sensing is a term coined to describe the capability of performing material testing with sensors that analyze materials’ spectral response, i.e., spectral sensing can be used as a synonym to spectroscopy when the sensing element is miniature, handheld, or autonomous. Hence, the principles discussed in our previous blog, which utilized NIR spectroscopy as an advanced technique for capturing and analyzing how light interacts with objects, are equally applicable to spectral sensing. This method provides valuable insights by analyzing the spectral response properties of objects.

Reasons to consider spectral sensing

While traditional methods have long relied on high performance, reliable instruments, and intricate setups, spectral sensing presents a more streamlined and versatile approach. The following scenarios highlight typical situations that signify the need for spectral sensing solutions:

• Processes involving sending samples to a lab are inefficient and slow.
• Tests are required at various locations across the organization.
• Difficulties arise in testing at different production stages.
• Conventional testing processes are inefficient.
• Frequent testing is necessary to maintain Good Manufacturing Practices (GMPs) & compliance with regulations.
• Delays in obtaining results from the lab impede crucial operational decisions, such as whether to unload loads of input ingredients in a production facility.
• Challenges persist in guaranteeing the quality and consistency of all production lots.

Overcoming these challenges requires accurate in-house analysis solutions to boost productivity and reduce time and money losses. But how to select the right one for your business? Keep reading to find out 👇

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Factors to consider when selecting a spectral sensing solution 

Selecting the appropriate spectral sensing solution poses challenges, often involving a delicate balance of trade-offs between various factors. One primary challenge lies in the need to balance the solution's specificity and versatility. Additionally, considerations such as cost, size, and ease of implementation further complicate the decision-making process. Achieving the optimal solution requires a nuanced understanding of the specific demands of the application and a judicious evaluation of the trade-offs involved. The following is a quick description of each of the most important factors to consider:

Accuracy of results

The precision of spectral sensor results directly influences decision-making processes and operational efficiency. Additionally, accuracy is pivotal in compliance with standards, especially in sectors where adherence to product characteristics is mandated. Accuracy is indispensable for maintaining the integrity of various business operations and ensuring long-term success.

Several technical specifications significantly impact the accuracy of a spectral sensor. For instance, spectral range, signal-to-noise ratio (SNR), and spectral resolution are critical factors influencing the precision of measurements.

Variety of parameters

The ability to analyze multiple parameters concurrently enhances efficiency and reduces the need for multiple sensors or time-consuming sequential analyses. This is particularly significant in fields such as agriculture, industrial processes, and cannabis analysis, where understanding several factors simultaneously is essential to making informed decisions. The sensor's spectral range is the most important factor affecting this capability. A wider spectral range allows for detecting a broader range of parameters. Additionally, the sensor's ability to handle complex spectral signatures and differentiate between closely spaced peaks influences its effectiveness in simultaneous parameter analysis. Striking a balance between these factors is key in choosing a spectral sensing solution that optimally meets the specific demands of multifaceted applications.

Portability

The portability of a spectral sensor is integral to its utility in a range of applications, emphasizing the importance of factors such as size, weight, design, and reliability in diverse environmental conditions. A compact and lightweight spectral sensor allows easy field deployment, enabling on-site measurements without logistical constraints. The design should be robust to withstand varying environmental conditions, ensuring reliability when the sensor is exposed to temperature extremes, humidity, or other challenging factors. Additionally, the sensor's reliability in terms of storage and operation in diverse environments is critical for its effectiveness in applications such as field research, environmental monitoring, or mobile quality control. The ability of a spectral sensor to maintain accurate and consistent performance across different conditions not only enhances its practicality but also broadens its scope of applications, making it a valuable tool for industries requiring mobility and adaptability.

Ease of use

The ease of use of a spectral sensing device is a pivotal factor influencing its widespread adoption across organizations. Unlike traditional lab instruments that often necessitate skilled personnel for operation and analysis, user-friendly spectral sensors streamline the process, enabling individuals with varying levels of expertise to utilize them effectively. These devices' intuitive interfaces and simplified procedures minimize the learning curve, allowing users across different departments to integrate spectral sensing seamlessly into their workflows. This democratization of analytical capabilities enhances organizational efficiency, as personnel without specialized training can perform routine measurements and analyses. The contrast with conventional lab instruments, which often require skilled technicians, underscores the transformative impact of user-friendly spectral sensors, making them accessible tools for a broader spectrum of applications within an organization.

Long-term consistency of results

Businesses must depend on solutions with consistent performance over time. Hence, the chosen spectral sensing solution must guarantee consistency by incorporating mechanisms that minimize deviations in spectral measurements, ensuring their reliability. Factors influencing the long-term consistency of spectral sensing results include sensor drift, which denotes gradual changes in measurement accuracy over time. Furthermore, the design of spectral sensors must be proven to maintain stability in diverse environmental conditions, such as fluctuations in temperature and humidity. This underscores the necessity for robust sensor designs to preserve stability under varying circumstances.

Robustness of calibration models

Calibration models are the mathematical models that decode the spectral data generated by the spectral sensor to helpful information that humans can understand, i.e., analysis results. The reliability of calibration models is paramount when selecting a spectral sensing solution, as it directly influences the accuracy and applicability of the results. A robust calibration model ensures precise and consistent measurements. Characteristics of a robust model include its suitability across various sample variations, reflecting the model's versatility and ability to adapt to different materials or substances. A high-quality calibration model also can detect outliers effectively, enhancing the solution's resilience in real-world scenarios. The experience of the model developer is a key factor, as a seasoned developer brings a wealth of knowledge in constructing and maintaining models, addressing potential challenges, and continuously refining the calibration process. Therefore, businesses should prioritize spectral sensing solutions with well-validated calibration models to ensure the accuracy, adaptability, and reliability required for diverse applications.

Data management

The importance of a robust data management solution within a spectral sensing platform cannot be overstated, as it significantly impacts the efficiency and usability of the technology. The following are some key characteristics to look for:

• Effective data management ensures seamless organization, storage, and retrieval of a wide range of data, including spectral data, devices, calibration models, users, locations, …etc. This level of data integration is crucial for accommodating diverse datasets generated in real-world scenarios.
• Scalability to accommodate growing datasets
• User-friendly interfaces for easy navigation
• Compatibility with various file formats.
• Integration capabilities with existing data systems within an organization enhance interoperability.
• The inclusion of advanced analytics capabilities enhances the platform's utility by allowing users to analyze trends and patterns within the collected spectral data. This feature empowers organizations to make more informed business decisions based on comprehensive insights. 
• Data safety and security are critical for maintaining data integrity and safeguarding against potential loss. 

Total cost of ownership

When considering the adoption of a spectral sensing solution, it is imperative to move beyond a narrow focus on instrument prices and instead evaluate the total cost of ownership (TCO). TCO encompasses various components such as the initial device price, accessories and consumables costs, device maintenance plan, model development and/or subscription fees, ongoing model maintenance, personnel qualification and/or training costs, and software subscriptions. Evaluating TCO provides a comprehensive understanding of the investment required throughout the device's lifecycle. Additionally, businesses should carefully assess the suitability of payment terms offered by providers, ensuring alignment with their budgetary and operational needs. Adopting a holistic perspective on costs facilitates accurate budgeting and ensures that the chosen spectral sensing solution remains financially viable and continues to deliver value over time.

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Key technical specifications

Examining the technical specifications of a spectral sensor is important when selecting an effective solution, as these specifications directly influence critical considerations like accuracy and versatility. Here are the most important technical specifications.

Spectral range

The spectral range of a spectral sensing device refers to the span of wavelengths it can capture and analyze. The working principle of the spectral sensor usually limits it. This parameter is critical in determining the device's applicability across different materials and substances. The two most important factors are

• Width of the spectral range: A broad spectral range allows the sensor to gather information from a wide spectrum of wavelengths, providing versatility in analyzing various samples and parameters. For example, in animal nutrition, a spectral sensor with a broad range can assess feed mix ratios for various feed types. In addition, for each feed type, different parameters can be analyzed, such as dry matter, protein, starch, amino acids, fiber, fat, etc. On the other hand, a more specific spectral range might be suitable for targeted applications, such as identifying specific chemical compounds in pharmaceuticals or analyzing particular features in geological samples.
• Position within the spectral range: Spectra at higher wavelengths usually offer distinct advantages. Certain information can only be captured at higher wavelengths due to reduced overlaps between the spectral responses of different molecular structures. This characteristic enhances the specificity of analysis, allowing for in-depth exploration of internal compositions and chemical structures. Conversely, lower wavelengths in NIR contribute to surface-level insights.

These two factors are illustrated in the graphic below, where absorption bands corresponding to common sample parameters are shown across the NIR spectral range. 

Spectral resolution

Spectral resolution defines the ability of a sensor to distinguish between adjacent wavelengths. A higher spectral resolution allows for finer discrimination of spectral features, enabling the detection of subtle differences in the composition or properties of materials. In the near-infrared (NIR) spectrum, absorption bands are typically broad. Hence, spectral resolutions within the 12 nm to 20 nm range have been proven to offer sufficiently robust performance. This range strikes a balance, providing the necessary detail to discern features in NIR absorption spectra while accommodating the inherent broadness of the absorption bands.

SNR: Spectral signal-to-noise ratio

Signal-to-noise ratio (SNR) is a critical metric in spectral sensing that measures the strength of the signal of interest against different noise sources. A high SNR indicates a strong and clear signal relative to the noise level, enhancing the precision and reliability of spectral measurements. The minimum acceptable SNR varies depending on the application. A well-optimized SNR is fundamental to ensuring the quality and integrity of spectral data, making it a key consideration when evaluating and selecting spectral sensing solutions for diverse applications.

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