Spectral Transformation for Concentration Prediction Accuracy
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Solution Overview
Problem
Existing methods for predicting the concentration of substances in solutions using optical analysis spectra face challenges such as low accuracy due to non-linear relationships between concentration and spectra, and inefficiencies in spectral preprocessing.
Innovation Solution
A method involving machine learning with a transformed spectrum of a substance to generate a concentration prediction model, where the spectrum is transformed using optimal transformation conditions derived from various transformation methods to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multivariate linear combinations or simple machine learning algorithms are used for concentration prediction, then the analysis can be performed faster, but the accuracy is low when the relationship between concentration and spectrum is non-linear
Solution Approach 1:
The patent transforms the spectral data using various transformation methods (first, second, and third transformation methods) to change the parameters of the input data. This allows the model to better capture non-linear relationships between concentration and spectrum while maintaining computational efficiency. The transformation converts the raw spectral parameters into transformed spectral parameters that are more suitable for accurate concentration prediction.
Solution Approach 2:
The patent employs a dynamic approach by selecting and combining multiple transformation methods adaptively. The system dynamically adjusts the transformation strategy based on the characteristics of the spectral data, allowing it to handle both linear and non-linear relationships effectively. This dynamic transformation approach improves accuracy without sacrificing analysis speed.
2Measurement precision
If decision tree, random forest, or deep-learning methods are used for nonlinear regression, then the accuracy improves, but the model only adjusts learning parameters which may lower accuracy due to various substance information in the spectrum
Solution Approach 1:
The patent extracts and separates the transformation step from the model learning process. By applying spectral transformation before feeding data to the machine learning model, it extracts the non-linear relationship patterns from the raw spectral data. This extraction allows simpler models to achieve better accuracy without requiring complex model architectures or extensive parameter adjustments.
Solution Approach 2:
The patent performs spectral transformation as a preliminary action before the main concentration prediction task. By pre-processing the spectral data through various transformation methods and selecting optimal transformed spectra, the system prepares the data in advance to be more suitable for modeling. This preliminary transformation simplifies the subsequent learning process and improves overall accuracy.
3Measurement precision
If spectrum transformation is applied to improve prediction accuracy, then the concentration prediction accuracy improves, but the process requires selecting optimal transformation conditions from multiple transformation methods
Solution Approach 1:
The patent implements a feedback mechanism where the system evaluates the performance of different transformation methods and selects the optimal transformation based on prediction accuracy. The machine learning model provides feedback on which transformed spectra yield the best results, allowing the system to automatically select and refine the optimal transformation conditions without manual intervention.
Solution Approach 2:
The system performs self-service by automatically selecting optimal transformation conditions from multiple transformation methods. The machine learning model evaluates different transformed spectra and autonomously determines which transformation yields the best concentration prediction accuracy, eliminating the need for manual spectral preprocessing optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The proposed method enhances the accuracy of concentration prediction models by optimizing spectral transformation, leading to improved performance in predicting substance concentrations.
Implementation Method 1
spectra may be acquired through optical analysis methods using spectrometers such as IR, Raman, and UV-vis
Data Source
AI summary
There is disclosed a method for generating a concentration prediction model for predicting a concentration of a target substance through machine learning. The method includes a training data generation step of generating an optimal transformed spectrum obtained by transforming a basic spectrum of a substance of a known concentration according to a predetermined transformation condition as training data, and a concentration prediction model generation step of generating a concentration prediction model by machine learning the optimal transformed spectrum generated in the training data generation step and transformed according to the predetermined transformation condition and an actually measured concentration of a substance corresponding to the optimal transformed spectrum, and the method may improve accuracy in substance concentration prediction by suppressing a spectral change caused by compounds other than an analyte to be predicted and maximizing the spectral change with a concentration of the analyte.