College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
Chemosphere. 2021 Jun;272:129908. doi: 10.1016/j.chemosphere.2021.129908. Epub 2021 Feb 8.
Rapid detection tasks in soil environment are generally implemented by various spectrometers and chemometric models. To reduce costs for model construction, calibration transfer from laboratory spectral instruments to portable devices has recently received extensive attention. In different application cases of model transference, most conventional methods require extra time to tune hyperparameters and to select calibration transfer techniques. Based on the near-infrared (NIR) analytical technique, this work aimed at exploring a transfer learning strategy to detect plastic pollution levels in the soil by transferring the model from a high-throughput hyperspectral image (HSI) system to an ultra-portable NIR sensor. Transfer learning was explored to diagnose the proper calibration transfer algorithm and construct the transferable model. For transferable model construction, conventional calibration transfer algorithms (Direct Standardization (DS) or Repeatability file (Repfile)) served as a pre-processing step, and non-parametric transfer learning algorithm (Easy Transfer Learning (EasyTL)) was explored in the modeling step. Supporting vector machine (SVM) was carried out as a typical modeling algorithm for comparison. For transformation algorithms selection, a distance metric algorithm, maximum mean discrepancy (MMD), was performed on spectral feature matrices before and after DS or Repfile transformation. On three transfer tasks, the results indicated that the Repfile-EasyTL model was a promising solution with higher accuracy, much lower time costs, less parameters, and dependency on the increase of standard samples than other models (SVM, DS-SVM, Repfile-SVM, EasyTL, DS-EasyTL). Moreover, MMD distance presented the great potential to serve as an indicator to vote the optimal calibration transfer algorithm before the modeling step.
土壤环境中的快速检测任务通常通过各种光谱仪和化学计量学模型来实现。为了降低模型构建成本，最近人们广泛关注从实验室光谱仪器到便携式设备的校准转移。在模型转移的不同应用案例中，大多数传统方法需要额外的时间来调整超参数并选择校准转移技术。基于近红外（NIR）分析技术，本工作旨在探索一种迁移学习策略，通过将模型从高通量高光谱图像（HSI）系统转移到超便携 NIR 传感器，来检测土壤中的塑料污染水平。迁移学习用于探索诊断适当的校准转移算法和构建可转移模型。对于可转移模型的构建，传统的校准转移算法（直接标准化（DS）或重复文件（Repfile））作为预处理步骤，在建模步骤中探索了非参数迁移学习算法（Easy Transfer Learning（EasyTL））。支持向量机（SVM）作为一种典型的建模算法进行比较。对于转换算法的选择，在 DS 或 Repfile 转换前后，在光谱特征矩阵上执行距离度量算法，最大均值差异（MMD）。在三个转移任务中，结果表明，Repfile-EasyTL 模型是一种很有前途的解决方案，具有更高的准确性、更低的时间成本、更少的参数，并且对标准样本数量的增加的依赖性小于其他模型（SVM、DS-SVM、Repfile-SVM、EasyTL、DS-EasyTL）。此外，MMD 距离具有作为建模步骤之前投票最佳校准转移算法的指标的巨大潜力。
