Calibration regression models based on visible and near-infrared (Vis/NIR) spectroscopy are now widely used in the rapid non-destructive prediction of agricultural products' quality parameters. However, the distributions of products' quality parameters and spectral responses are different in various batches of products, so a calibration regression model built on one dataset might be ineffective when tested in another. How to improve the generalization ability of models is a crucial problem and is formulated as ‘Calibration Transfer’. Calibration transfer was firstly proposed to eliminate the difference of spectral responses between spectrometers, but now it has been applied in the transfer between different domains of spectral samples or different components. In this paper, we proposed two robust models with great generalization ability in the calibration transfer task, respectively using dimension reduction and transfer learning, namely SPRS (Standard normal variate, Partial least squares dimension reduction, Ridge regression, Slope/bias) and SNV-based Aug-TrAdaBoost.R2. We tested the two models in spectral datasets of tea leaves to predict the moisture content of samples, it was found that SPRS and SNV-based Aug-TrAdaBoost.R2 can reach great performance over both source and target domains across different batches, different varieties, and different classes of tea leaf samples. SPRS and SNV-based Aug-TrAdaBoost.R2 achieved R² values of 0.9314 and 0.9895 in cross-tea-class prediction whereas traditional calibration method PLSR + S/B only achieved 0.4874. SPRS had low computation complexity and was more robust while SNV-based Aug-TrAdaBoost.R2 had higher accuracy in target domain prediction but was computation-consuming. The two proposed models showed the potentials of online automatic quality parameters prediction and high-accuracy prediction across domains of various samples.

基于可见和近红外（Vis / NIR）光谱的校准回归模型现已广泛用于农产品质量参数的快速无损预测中。但是，产品的质量参数和光谱响应的分布在不同批次的产品中是不同的，因此在另一个数据集上进行测试时，建立在一个数据集上的校准回归模型可能无效。如何提高模型的泛化能力是一个关键问题，被表述为“校准转移”。最初提出校正转移是为了消除光谱仪之间的光谱响应差异，但现在已将其应用于光谱样品的不同域或不同组分之间的转移。在本文中，我们提出了两个在校准转移任务中具有强大泛化能力的鲁棒模型，分别使用降维和转移学习，即SPRS（标准正态变量，偏最小二乘降维，Ridge回归，斜率/偏差）和基于SNV的Aug-TrAdaBoost .R2。我们在茶叶的光谱数据集中测试了这两个模型，以预测样品的水分含量，发现基于SPRS和SNV的Aug-TrAdaBoost.R2在不同批次，不同品种，以及不同类别的茶叶样品。基于SPRS和SNV的Aug-TrAdaBoost.R2获得R 坡度/偏差）和基于SNV的Aug-TrAdaBoost.R2。我们在茶叶的光谱数据集中测试了这两个模型，以预测样品的水分含量，发现基于SPRS和SNV的Aug-TrAdaBoost.R2在不同批次，不同品种，以及不同类别的茶叶样品。基于SPRS和SNV的Aug-TrAdaBoost.R2获得R 坡度/偏差）和基于SNV的Aug-TrAdaBoost.R2。我们在茶叶的光谱数据集中测试了这两个模型，以预测样品的水分含量，发现基于SPRS和SNV的Aug-TrAdaBoost.R2在不同批次，不同品种，以及不同类别的茶叶样品。基于SPRS和SNV的Aug-TrAdaBoost.R2获得R跨茶类预测中的^2个值分别为0.9314和0.9895，而传统的校准方法PLSR + S / B仅达到0.4874。SPRS具有较低的计算复杂度，并且更健壮，而基于SNV的Aug-TrAdaBoost.R2在目标域预测中具有较高的精度，但计算量很大。提出的两个模型显示了跨各种样本域的在线自动质量参数预测和高精度预测的潜力。