可转让性
近红外光谱
校准
波长
材料科学
尺度不变特征变换
计算机科学
数学
人工智能
光电子学
光学
统计
机器学习
物理
特征提取
罗伊特
作者
Ruiwen Shu,Ju Lee,Lei Ni,Shengchao Wu,Liguo Zhang,Jiong Ge,Shu Ye,Shaorong Luan
标识
DOI:10.1016/j.microc.2023.109522
摘要
The rapid determination of total plant alkaloids (TPA) content in tobacco products by means of near infrared (NIR) spectroscopy is becoming conventional detection method in tobacco industry. An established NIR model of TPA is anticipated to be shared by other devices as long as possible not needing to update the model. Present study developed a three-step wavelength selection method SISCW (selecting important and stable characteristic wavelengths)to build a robust NIR calibration model of TPA to improve its transferability and to prolong its use period at many other devices. For this, 292 flue-cured tobacco leaf samples from more than 10 regions of China, which were harvested in 2011–2013 and tested on the primary NIR device, were used to build the model. 77 samples harvested from 2011 to 2013 were applied to verify the transferability of the model on six secondary NIR devices. 180 samples harvested from 2014 to 2020 that were divided into 7 groups according to their growing years, were used to examine the long-term application capacity of the model on the seven NIR devices. The SISCW method integrates an image processing method of scale invariant feature transformation (SIFT) with analyzing standard deviation of the sample spectra (SDSS) and water absorption coefficients to select important and stable characteristic wavelengths (SISCW). The wavelengths selected by the SISCW were recorded as Uisc. The results indicated that the partial least square (PLS) calibration model of TPA based on Uisc (SISCW-PLS) performed well on both primary and the six secondary devices when it was employed to predict TPA of the 77 samples of 2011–2013. The model has run 7 years on the primary and 3 of the secondary devices from 2014 to 2020. This emphasizes the SISCW method has great potential for improving transferability and service-life of TPA calibration model.
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