Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China.
Molecules. 2022 Jul 13;27(14):4485. doi: 10.3390/molecules27144485.
Inspired by aquaphotomics, the optical path length of measurement was regarded as a perturbation factor. Near-infrared (NIR) spectroscopy with multi-measurement modals was applied to the discriminant analysis of three categories of drinking water. Moving window- nearest neighbor (MW-kNN) and Norris derivative filter were used for modeling and optimization. Drawing on the idea of game theory, the strategy for two-category priority compensation and three-model voting with multi-modal fusion was proposed. Moving window correlation coefficient (MWCC), inter-category and intra-category MWCC spectra, and -shortest distances plotting with MW-kNN were proposed to evaluate weak differences between two spectral populations. For three measurement modals (1 mm, 4 mm, and 10 mm), the optimal MW-kNN models, and two-category priority compensation models were determined. The joint models for three compensation models' voting were established. Comprehensive discrimination effects of joint models were better than their sub-models; multi-modal fusion was better than single-modal fusion. The best joint model was the dual-modal fusion of compensation models of one- and two-category priority (1 mm), one- and three-category priority (10 mm), and two- and three-category priority (1 mm), validation's total recognition accuracy rate reached 95.5%. It fused long-wave models (1 mm, containing 1450 nm) and short-wave models (10 mm, containing 974 nm). The results showed that compensation models' voting and multi-modal fusion can effectively improve the performance of NIR spectral pattern recognition.
受水色光学的启发，将光程测量视为一个扰动因素。近红外（NIR）光谱采用多测量模态，用于三种饮用水的判别分析。采用移动窗口-最近邻（MW-kNN）和 Norris 导数滤波器进行建模和优化。借鉴博弈论的思想，提出了具有多模态融合的两类优先级补偿和三模型投票策略。提出了移动窗口相关系数（MWCC）、跨类和同类 MWCC 谱以及 MW-kNN 的最短距离绘图，用于评估两个光谱群体之间的微弱差异。对于三种测量模态（1mm、4mm 和 10mm），确定了最佳的 MW-kNN 模型和两类优先级补偿模型。建立了三个补偿模型投票的联合模型。联合模型的综合判别效果优于其子模型；多模态融合优于单模态融合。最佳联合模型是两类优先级补偿模型（1mm）、两类和三类优先级补偿模型（10mm）以及两类和三类优先级补偿模型（1mm）的双模态融合，验证的总识别准确率达到 95.5%。它融合了长波模型（1mm，包含 1450nm）和短波模型（10mm，包含 974nm）。结果表明，补偿模型投票和多模态融合可以有效提高近红外光谱模式识别的性能。
