A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry

作者：Tan Chao^*; Chen Hui; Wang Chao; Zhu Wanping; Wu Tong; Diao Yuanbo

来源：Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy , 2013, 105: 1-7.

DOI：10.1016/j.saa.2012.12.023

• Near-infrared
• Mid-infrared
• Calibration
• Multi-model fusion
• Beer
• Wine

Near and mid-infrared (NIR/MIR) spectroscopy techniques have gained great acceptance in the industry due to their multiple applications and versatility. However, a success of application often depends heavily on the construction of accurate and stable calibration models. For this purpose, a simple multi-model fusion strategy is proposed. It is actually the combination of Kohonen self-organizing map (KSOM), mutual information (MI) and partial least squares (PLSs) and therefore named as KMICPLS. It works as follows: First, the original training set is fed into a KSOM for unsupervised clustering of samples, on which a series of training subsets are constructed. Thereafter, on each of the training subsets, a MI spectrum is calculated and only the variables with higher MI values than the mean value are retained, based on which a candidate PLS model is constructed. Finally, a fixed number of PLS models are selected to produce a consensus model. Two NIR/MIR spectral datasets from brewing industry are used for experiments. The results confirms its superior performance to two reference algorithms, i.e., the conventional PLS and genetic algorithm-PLS (GAPLS). It can build more accurate and stable calibration models without increasing the complexity, and can be generalized to other NIR/MIR applications.

• 出版日期2013-3-15
• 单位宜宾学院