Abstract To obtain robust near-infrared (NIR) spectroscopy data calibration models, variable selection and model updating with recalibration approaches were used for predicting quality parameters in pear fruit. For variables selection, interval partial least-squares regression and covariate selection approaches were used and compared. Model updating with recalibration was performed by incorporating a few new samples in the calibration set of existing batch data. The interaction of variable selection and model updating was also explored. The results showed that with variable selection, the model performance when tested on a new independent batch of fruit was greatly improved. Further, the model updating with only a few new samples resulted in a reduction of the bias when tested on the new batch. In the case of MC prediction, the variable selection reduced the bias from 1.31 % to 0.19 % and the RMSEP from 1.44 % to 0.58 %, compared to the standard partial least-squares regression (PLS2R). In the case of SSC prediction, the variable selection reduced the bias from -0.62 % to 0.07 % and the RMSEP from 0.90 % to 0.63 %, compared to the standard PLS2R. With a combination of variable selection and model updating the bias and RMSEP were further reduced. The interval-based method performed better compared to the filter-based method. As few as only 10 samples from the new batch already lead to a significant improvement in model performance. In the case of MC, spectral regions of 749-759 nm and 879-939 nm were identified as the most important region. In the case of the SSC, 709-759 nm and 789-999 nm were found to be important spectral regions. Robust models made on selected variables combined with model updating strategy can support to make NIR spectroscopy a preferred choice for non-destructive assessment of quality features of fresh fruit.

中文翻译：

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使用变量选择和模型更新方法的近红外光谱改进梨果实中水分和可溶性固形物含量的预测

摘要 为了获得稳健的近红外 (NIR) 光谱数据校准模型，使用变量选择和模型更新与重新校准方法来预测梨果实的质量参数。对于变量选择，使用并比较了区间偏最小二乘回归和协变量选择方法。通过在现有批次数据的校准集中加入一些新样本，进行重新校准的模型更新。还探讨了变量选择和模型更新的相互作用。结果表明，通过变量选择，在新的独立批次水果上测试时的模型性能得到了极大的提高。此外，当在新批次上进行测试时，仅使用几个新样本进行模型更新会减少偏差。在 MC 预测的情况下，与标准偏最小二乘回归 (PLS2R) 相比，变量选择将偏差从 1.31% 减少到 0.19%，将 RMSEP 从 1.44% 减少到 0.58%。在 SSC 预测的情况下，与标准 PLS2R 相比，变量选择将偏差从 -0.62% 减少到 0.07%，将 RMSEP 从 0.90% 减少到 0.63%。通过结合变量选择和模型更新，进一步降低了偏差和 RMSEP。与基于过滤器的方法相比，基于区间的方法表现更好。新批次中只有 10 个样本已经导致模型性能的显着提高。在 MC 的情况下，749-759 nm 和 879-939 nm 的光谱区域被确定为最重要的区域。在 SSC 的情况下，发现 709-759 nm 和 789-999 nm 是重要的光谱区域。