食品科学 ›› 2025, Vol. 46 ›› Issue (8): 34-40.doi: 10.7506/spkx1002-6630-20240425-236

• 基础研究 • 上一篇    下一篇

基于DS-PDS联用的苹果可溶性固形物近红外模型传递方法

成晔,黄浩冉,汪莹,熊智新   

  1. (南京林业大学轻工与食品学院,江苏?南京 210037)
  • 出版日期:2025-04-25 发布日期:2025-04-09
  • 基金资助:
    “十三五”国家重点研发计划重点专项(2019YFD1002300)

Calibration Transfer of Near-Infrared Spectroscopic Model for Soluble Solid Content Predication of Apples by the Combined Use of Direct Standardization and Piecewise Direct Standardization

CHENG Ye, HUANG Haoran, WANG Ying, XIONG Zhixin   

  1. (College of Light Industry and Food Science, Nanjing Forestry University, Nanjing 210037, China)
  • Online:2025-04-25 Published:2025-04-09

摘要: 为了实现苹果可溶性固形物含量(soluble solid content,SSC)近红外分析模型的传递,以2 台便携式近红外光谱仪测得的89 个苹果样品的SSC及光谱数据为研究对象。首先利用经异常样本剔除和预处理后的校正集建立主机的SSC偏最小二乘回归近红外光谱分析模型,然后采用直接标准化(direct standardization,DS)与分段直接标准化(piecewise direct standardization,PDS)联用的DS-PDS方法实现模型与从机共享。结果表明:与传统的DS和PDS方法相比,所建立的模型经过DS-PDS算法传递后,不仅明显提升了对从机样本的预测效果,并且还减弱了PDS算法模型传递光谱中出现的奇异锐锋(artifacts)现象。经传递后模型的验证集标准偏差与预测标准偏差比值从模型传递前的1.585 3上升到3.264 5,预测标准偏差则从1.093 2降低至0.530 9。因此采用DS-PDS算法能充分发挥DS与PDS算法各自的优点,较好地实现了2 台便携式光谱仪间的模型传递。

关键词: 近红外光谱;模型传递;可溶性固形物含量;直接标准化-分段直接标准化

Abstract: To achieve the transfer of a near-infrared (NIR) calibration model for determining the soluble solid content (SSC) in apples, this study employed the spectral data and SSC values of 89 apple samples measured with two portable NIR spectrometers. After outlier removal and preprocessing, the calibration set from the master instrument was used to establish the NIR model by partial least-squares regression (PLSR). Subsequently, the transfer of the NIR model between the master and slave instruments was accomplished by the combined use of direct standardization (DS) and piecewise direct standardization (PDS). The results indicated that compared with DS and PDS, the combined algorithm not only significantly improved the prediction performance of the model for the slave instrument, but also mitigated the artifacts caused by PDS. After calibration transfer, the ratio of standard deviation of the validation set to standard error of prediction (RPD) increased from 1.585 3 to 3.264 5, while the root mean square error of prediction (RMSEP) decreased from 1.093 2 to 0.530 9. Therefore, the proposed DS-PDS algorithm leverages the advantages of DS and PDS, allowing successful transfer of the calibration model between the two portable spectrometers.

Key words: near-infrared spectroscopy; calibration transfer; soluble solid content; direct standardization-piecewise direct standardization

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