Abstract Near-infrared spectroscopy (NIRs) has been widely applied in the field of traditional Chinese medicine (TCM). However, the quality of TCM inevitably changes with time and origin, which will result in a decline in the prediction accuracy of constructed NIR models. In this study, models of White Paeony Root were maintained to ensure the accuracy of the predictive values of new samples. The applicability of the original models to the new samples were validated by principal component analysis and model evaluation indexes. Chemometric methods including spectral subtraction correction (SSC) and hierarchical cluster analysis-recalibration (HCA-RC) were introduced to maintain the constructed models. The results demonstrated that, according to the performances of the models, the model maintenance ability of HCA-RC was stronger than that of SSC. With the application of HCA-RC, relative standard error of prediction of moisture, albiflorin and paeoniflorin content were reduced from 5.650%, 29.83% and 39.89% to 2.350%, 14.23% and 14.57%, respectively. This result revealed that HCA-RC could better extract common features from historical and new batches of samples. In addition, the updated HCA-RC models can still be applied for the rapid detection of White Paeony Root. Model maintenance ameliorated the problems of the original models that occurred after a period of application. The updated models contained the characteristics of new batches of samples, which had the potential for long-term continuous application and improved detection efficiency.

中文翻译：

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保持近红外光谱模型对白芍多参数测定的预测能力

摘要 近红外光谱（NIRs）已广泛应用于中医药领域。然而，中医的质量不可避免地随着时间和起源而变化，这将导致构建的近红外模型的预测精度下降。在本研究中，保留了白芍模型，以确保新样本预测值的准确性。通过主成分分析和模型评价指标验证了原始模型对新样本的适用性。引入了包括光谱减法校正 (SSC) 和分层聚类分析-重新校准 (HCA-RC) 在内的化学计量学方法来维护构建的模型。结果表明，从模型的性能来看，HCA-RC的模型维护能力强于SSC。随着HCA-RC的应用，水分、白花素和芍药苷含量预测的相对标准误差分别从5.650%、29.83%和39.89%降低到2.350%、14.23%和14.57%。该结果表明，HCA-RC 可以更好地从历史和新批次的样本中提取共同特征。此外，更新后的HCA-RC模型仍可用于白芍的快速检测。模型维护改善了原有模型在使用一段时间后出现的问题。更新后的模型包含了新批次样品的特征，具有长期连续应用和提高检测效率的潜力。83% 和 39.89% 分别为 2.350%、14.23% 和 14.57%。该结果表明，HCA-RC 可以更好地从历史和新批次的样本中提取共同特征。此外，更新后的HCA-RC模型仍可用于白芍的快速检测。模型维护改善了原有模型在使用一段时间后出现的问题。更新后的模型包含了新批次样品的特征，具有长期连续应用和提高检测效率的潜力。83% 和 39.89% 分别为 2.350%、14.23% 和 14.57%。该结果表明，HCA-RC 可以更好地从历史和新批次的样本中提取共同特征。此外，更新后的HCA-RC模型仍可用于白芍的快速检测。模型维护改善了原有模型在使用一段时间后出现的问题。更新后的模型包含了新批次样品的特征，具有长期连续应用和提高检测效率的潜力。模型维护改善了原有模型在使用一段时间后出现的问题。更新后的模型包含了新批次样品的特征，具有长期连续应用和提高检测效率的潜力。模型维护改善了原有模型在使用一段时间后出现的问题。更新后的模型包含了新批次样品的特征，具有长期连续应用和提高检测效率的潜力。