Discover the power of predicted IR spectra with Wiley:
Accelerate Your Analytical Workflow with Expanded Spectral Data Coverage
As a knowledge company and leader in spectral databases, Wiley combines over 60 years of expertise in infrared (IR) spectroscopy and spectral data excellence with the most current machine-learning (ML) techniques to expand the chemical space available to you with the new Wiley Database of Predicted IR Spectra.
Wiley Science Solutions created an AI-powered spectrum prediction engine using its high-quality Fourier-transform infrared spectroscopy (FTIR) empirical spectral database—the largest commercially available —to generate this high-quality database of predicted IR spectra.
This predicted database can be used along with empirical IR spectral reference database in unknown identification to expand your chemical compound coverage and improve your analytical workflow. And because the Wiley Database of Predicted IR Spectra is built using Wiley’s trusted databases and informatics tools, you can be confident the predicted spectra are based on sound scientific principles and methods.
This ground-breaking predicted IR library:
- Provides access to an additional 250,00 predicted IR spectra to increase the already large catalog of available Wiley ATR-IR and FT-IR spectra
- Aids in the analysis of unknowns, especially for rarer compounds and materials, when a match cannot be found in the empirical databases
- Is useful for functional group classification and is particularly strong with rigid structures
- Is derived from Wiley’s high-quality, comprehensive empirical reference datasets, including Sadtler spectra
- Covers of a wide range of compound classes for use across various application areas
- Includes useful metadata, such as physical properties and chemical structures, along with spectra to narrow your spectral search results
- Is validated by external and internal SMEs
As you can see below, the predicted data (blue) fills in the chemical space of the empirical training set (red) as it works within the boundaries of what can be predicted from the underlying empirical data.
When it comes to spectral analysis, the more data you have the better. This predicted library contains the following valuable metadata to help you narrow and refine your search results:
- Chemical Structure
- Chemical Name
- Exact Mass
- Molecular Weight
- Nominal mass
- Spectrum/Structure Validation Score
External & Internal Validation and Usage Recommendations
Wiley conducted both external and internal studies by SMEs to validate results from the predicted library. From these two studies, the SMEs concluded that the library characterizes unknown spectral functional groups and performs well when searched against with sample spectra. SMEs suggest optimal workflow for the predicted library is to use it when the empirical library results in either low hit quality index (HQI) scores, poor matching, or no matches to help users classify and determine the structural characteristics of unknown compounds.
While Wiley has the largest commercially available library of extensive and high-quality empirical datasets, the total coverage is significantly smaller than the overall chemical space in use by chemists, life scientists, and materials scientists – i.e., the total number of possible molecules and compounds within a set of elements and rules. Augmenting our empirical coverage within the bounds of a predictive model (the chemical space of the underlying training set) is a strategy to help improve overall coverage for unknown identification, especially for rarer compounds and materials.
Predictive libraries, particularly when constructed from extensive and high-quality empirical reference datasets such as Wiley's extensive spectral collections (including premium Sadtler spectra), demonstrate performance levels closely approaching that of empirical datasets. Consequently, they can be effectively integrated into the analytical qualification workflow.
You can use this collection along with empirical databases to identify, classify, and verify unknown compounds by IR spectroscopy in various applications, such as: environmental, forensics/toxicology, pharmaceutical, biotech, automotive/aerospace, food/cosmetics, and many more.
Trusted Data from a Trusted Source
Wiley is an authoritative source for spectral data. Their renowned Sadtler databases were processed according to rigorous protocols to ensure they are of the highest quality. These qualification procedures start at data acquisition and continue throughout the database development process. Any data acquired from trusted partners is thoroughly vetted before inclusion in our collections.
KnowItAll is a trademark of John Wiley & Sons, Inc. in certain jurisdictions.
Subscription content is subject to change as data may be added or deleted from collections periodically.
Covers a range of chemical compound classes, including:
- Flavors and Fragrances
- Industrial compounds
- Estrogens and steroids
- Volatiles in Food
- Natural Products
Wiley Database of Predicted IR Spectra (Annual Subscription)
Wiley Database of Predicted IR Spectra (Annual Subscription Renewal)
- Recommended as a supplement to the KnowItAll IR Spectral Library collection of curated empirical data.
- For use with KnowItAll 2024 software.
- KnowItAll software is compatible with most major instrument formats. For full KnowItAll file format compatibility information, please visit sciencesolutions.wiley.com/compatibility.