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    刘燕德, 胡宣, 朱明旺, 等. 近红外在线检测装置参数对苹果糖度模型适用性的影响[J]. 华南农业大学学报, 2022, 43(5): 108-114. doi: 10.7671/j.issn.1001-411X.202112027
    引用本文: 刘燕德, 胡宣, 朱明旺, 等. 近红外在线检测装置参数对苹果糖度模型适用性的影响[J]. 华南农业大学学报, 2022, 43(5): 108-114. doi: 10.7671/j.issn.1001-411X.202112027
    shu
    LIU Yande, HU Xuan, ZHU Mingwang, et al. Influence of near-infrared on-line detection device parameters on the applicability of apple soluble solid content model[J]. Journal of South China Agricultural University, 2022, 43(5): 108-114. doi: 10.7671/j.issn.1001-411X.202112027
    Citation: LIU Yande, HU Xuan, ZHU Mingwang, et al. Influence of near-infrared on-line detection device parameters on the applicability of apple soluble solid content model[J]. Journal of South China Agricultural University, 2022, 43(5): 108-114. doi: 10.7671/j.issn.1001-411X.202112027
    shu

    近红外在线检测装置参数对苹果糖度模型适用性的影响

    Influence of near-infrared on-line detection device parameters on the applicability of apple soluble solid content model

      摘要
    • 摘要:
      目的  近红外(Near-infrared)光谱在线检测装置的检测速度和积分时间等因素会影响所建立苹果糖度模型的性能，本文旨在分析检测速度和积分时间对模型适用性的影响，以提高在线检测的精度。
      方法  近红外光谱在线检测装置检测速度和积分时间分别设置为0.3 m/s+100 ms、0.5 m/s+70 ms、0.5 m/s+100 ms、0.5 m/s+120 ms、0.5 m/s+150 ms，共5个实验组，试验所用苹果样本共180个，在350~1 150 nm波长下采集5个试验组苹果的近红外光谱，应用偏最小二乘法(Partial least squares，PLS)建立苹果可溶性固形物含量(Soluble solid content，SSC)的预测模型。
      结果  近红外在线检测装置积分时间对苹果糖度的检测存在阈值，当积分时间低于70 ms时，模型预测性能较差。预测模型中建模集与预测集的检测速度与积分时间相同时的预测效果优于二者不同时的。检测速度和积分时间会影响在线检测的精度，不同检测速度和积分时间下，光线在苹果内部的传输路线不同，会导致光纤探头获得的内部信息有所差异，使预测性能变差。在0.3 m/s+100 ms、0.5 m/s+100 ms、0.5 m/s+120 ms和0.5 m/s+150 ms 4个试验组中使用Kennard-Stone算法挑选出135个具有代表性的样本光谱，建立了混合检测速度和积分时间的预测模型，其预测集相关系数(RP)均在0.85以上，预测集均方根误差(RMSEP)均低于0.65。
      结论  本研究建立的混合检测速度和积分时间的预测模型可对苹果的糖度达到更好的预测，满足不同检测装置参数下苹果糖度在线检测的要求。

       

      Abstract:
      Objective  The performance of the apple soluble solid content model was influenced by the detection speed and integration time of the near-infrared (NIR) spectroscopy on-line detection device. The aim of this study was to analyze the influence of detection speed and integration time on the applicability of the model, and improve the accuracy of on-line detection.
      Method  The detection speed and integration time of the on-line detection device of NIR spectroscopy were setted as 0.3 m/s and 100 ms, 0.5 m/s and 70 ms, 0.5 m/s and 100 ms, 0.5 m/s and 120 ms, 0.5 m/s and 150 ms respectively. A total of 180 apple samples were used for the experiment, the NIR spectra of five experimental groups of apples were collected at 350~1 150 nm, and the partial least squares (PLS) method was applied to establish the prediction model of apple soluble solid content (SSC).
      Result  There was a threshold for the integration time. When the integration time was shorter than 70 ms, the prediction performance of the model was poor. The prediction performance of the prediction model with the same detection speed and integration time for modeling set and prediction set was superior to that of the prediction model with different dection speed and integration time. Detection speed and integration time would affect the accuracy of on-line detection. The different transmission routes of light inside the apple at different detection speeds and integration time could lead to differences in the internal information obtained by the fiber optic probe, making the prediction performance worse. The 135 representative sample spectra were selected from four groups of 0.3 m/s and 100 ms, 0.5 m/s and 100 ms, 0.5 m/s and 120 ms, 0.5 m/s and 150 ms using the Kennard-Stone algorithm, the prediction models of the mixed detection speed and integration time were established, with the correlation coefficients (RP) of the prediction set all above 0.85, the root mean square errors (RMSEP) all below 0.65.
      Conclusion  The established prediction model of mixed detection speed and integration time can better predict apple sugar content and meet the requirements of apple sugar content on-line detection under different parameters of detection devices.

       

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