Because of the complexity of near infrared spectral data, effective strategies are necessary proposed for accurate quantitative analysis purpose. This work explores a new self-construction strategy for the arrangement of conventional near infrared two-dimensional spectra into new self-constructed three-dimensional spectra, and investigate the feasibility of N-way partial least squares combined with the new self-constructed three-dimensional near infrared spectra for obtaining accurate quantitative determination results. A proof-of-concept model system, the quantitative analysis of four components (moisture, oil, protein, and starch) in corn samples, was applied to evaluate the performance of the proposed strategy. The ability of the newly proposed approach to predict the target compounds was checked with test samples. The established models have good predictive power for the target compounds with acceptable values of Rp (range from 0.82 to 0.997) and RMSEP (range from 0.03 to 0.47). Compared with partial least squares method on pretreated near infrared spectra and N-way partial least squares method on the basis of near infrared self-constructed three-dimensional spectra, the proposed method is competitive.

中文翻译：

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N路偏最小二乘结合新的自构建策略——一种利用近红外光谱数据定量测定多种化合物的有前途的方法

由于近红外光谱数据的复杂性，需要提出有效的策略以实现准确的定量分析。这项工作探索了一种新的自建策略，将常规近红外二维光谱排列成新的自建三维光谱，并研究了 N 路偏最小二乘法结合新自建三向光谱的可行性。三维近红外光谱，以获得准确的定量测定结果。应用概念验证模型系统对玉米样品中的四种成分（水分、油、蛋白质和淀粉）进行定量分析，以评估所提出策略的性能。使用测试样品检查了新提出的方法预测目标化合物的能力。建立的模型对目标化合物具有良好的预测能力，其 Rp（范围从 0.82 到 0.997）和 RMSEP（范围从 0.03 到 0.47）的可接受值。与预处理近红外光谱的偏最小二乘法和基于近红外自建三维光谱的N路偏最小二乘法相比，该方法具有竞争力。