Abstract
For conventional near-infrared spectroscopy (NIR) technology, even within the same sample, the NIR spectral signal can vary significantly with variation of spectrometers and the spectral collection environment. In order to improve the applicability and application of NIR prediction models, effective calibration transfer is essential. In this study, a stability-analysis-based feature selection algorithm (SAFS) for NIR calibration transfer is proposed, which is used to extract effective spectral band information with high stability between the master and slave instruments during the calibration transfer process. The stability of the spectrum bands shared between the master and slave instruments is used as the evaluation index, and the genetic algorithm was used to select suitable thresholds to filter out the spectral feature information suitable for calibration transfer. The proposed SAFS algorithm was applied to two near-infrared datasets of corn oil content and larch wood density. Simultaneously, its calibration transfer performances were compared with two classical feature selection methods. The effects of different preprocessing algorithms and calibration transfer algorithms were also assessed. The model with the feature variables selected by the SAFS obtained the best prediction. The SAFS algorithm can simplify the spectral data to be transferred and improve the transfer efficiency, and the universality of the SAFS allows it to be used to optimize calibration transfer in various situations. By combining different preprocessing and classic feature selection methods with this, the sensitivity of the correlation between spectral data and component information are improved significantly, as well as the effect of calibration transfer, which will be deeply developed.
Highlights
As one of the green and non-destructive testing methods, near-infrared spectroscopy has been used to analyze qualitative and quantitative data by constructing multivariate correction models
The Monte Carlo sampling method is used to calculate the absolute value of the correlation stability of the spectral data between wavelengths of the master and slave instruments before constructing the transfer model, reduce the data dimension, and improve the prediction and efficiency of the traditional calibration transfer algorithm
The Monte Carlo sampling method is used to calculate the absolute value of the correlation stability of the spectral data between master and slave instruments, and the genetic algorithm is used to find the optimal threshold of transfer effect to eliminate invalid information variables whose stability is lower
Summary
As one of the green and non-destructive testing methods, near-infrared spectroscopy has been used to analyze qualitative and quantitative data by constructing multivariate correction models. At present, it has been widely used in petrochemical, pharmaceutical, agriculture, forestry, and other fields [1,2,3,4]. The basic characteristics of the tested sample, the differences in the structure and principles between spectrometers, and the variations in the detected environment all have influenced on the reliability of the model, and can cause signal drift and absorption peak shape changes in the results of near-infrared detection, resulting in the disturbed accuracy of the model [5]. Calibration transfer is one of the effective methods to solve the above technical problem at present [8], which eliminates the time-consuming nature and workload of model reconstruction and has far-reaching implications for the application and development of NIR techniques
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