[A new wavelength selection algorithm based on the fusion of multiple models].

NIR spectroscopy makes a feature of a large number of wavelengths with a much smaller set of samples. However, some of the wavelengths contribute no information to the modeling. Even worse, they may contain the irrelevant information such as noise and background, which may result in a complex model and/or bad predictive ability of the model. So, it's important to do research in-depth to eliminate these wavelengths and improve the quality of the final model. The present paper firstly summarizes the variable selection methods based on a single PLS regression model and concludes that (1) the cross-validation can be used to select optimal model with good predictive ability, but the resulting model may be not suitable for selecting variables; (2) selecting variables based on a single regression model is inaccurate and instable because a single vector of regression coefficients may not measure the importance of the variables correctly and may vary with models of different complexity. On basis of this analysis, this paper proposed a new method for variable selection based on the fusion of multiple PLS models. This method fuses the multiple PLS regression coefficients to form a vector, then a threshold is determined to eliminate the variables whose corresponding element in the vector is lower than this threshold. Finally, this method is verified by 3 well-known NIR datasets and compared with the UVE-PLS and GA-PLS algorithms. The experiments show that this method may result in a model with less complexity and/or better predictive ability. Moreover, the proposed method is elegant and efficient and therefore can be put in practical use.

[A new algorithm for NIR modeling based on manifold learning].

Manifold learning is a new kind of algorithm originating from the field of machine learning to find the intrinsic dimensionality of numerous and complex data and to extract most important information from the raw data to develop a regression or classification model. The basic assumption of the manifold learning is that the high-dimensional data measured from the same object using some devices must reside on a manifold with much lower dimensions determined by a few properties of the object. While NIR spectra are characterized by their high dimensions and complicated band assignment, the authors may assume that the NIR spectra of the same kind of substances with different chemical concentrations should reside on a manifold with much lower dimensions determined by the concentrations, according to the above assumption. As one of the best known algorithms of manifold learning, locally linear embedding (LLE) further assumes that the underlying manifold is locally linear. So, every data point in the manifold should be a linear combination of its neighbors. Based on the above assumptions, the present paper proposes a new algorithm named least square locally weighted regression (LS-LWR), which is a kind of LWR with weights determined by the least squares instead of a predefined function. Then, the NIR spectra of glucose solutions with various concentrations are measured using a NIR spectrometer and LS-LWR is verified by predicting the concentrations of glucose solutions quantitatively. Compared with the existing algorithms such as principal component regression (PCR) and partial least squares regression (PLSR), the LS-LWR has better predictability measured by the standard error of prediction (SEP) and generates an elegant model with good stability and efficiency.

[Research on the near-infrared spectrometer system].

Near-infrared spectrometer is the integration of spectrum test technology, stoichiometry technology and computer technology. In the present paper, based on effective food ingredients and non-invasive quantitative detection, the development process of the micro-near-infrared spectrometer system was introduced. Spectrometer is the basis of the system. This paper focuses on the development of the micro-near-infrared spectrometer applicable to on-line real-time testing. A micro-near-infrared spectrometer prototype was developed successfully, its main technical parameter was tested, and the result shows: its operating wavelength is: 850-1 690 nm, optical resolution is: less than 10 nm, and its performance has achieved the level of the congener foreign products. Stoichiometric technology and computer technology is the core of the system. LS-LWR modeling methods were proposed. Finally, the quantitative test for glucose water solution using the micro-near-infrared spectrometer shows that the correlation coefficient of prediction model is 0.995, and the corresponding RMSEP is 0.06.