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Accurate and nondestructive detection of apple brix and acidity based on visible and near-infrared spectroscopy

机译：基于可见和近红外光谱的Apple Brix和酸度的准确和无损检测

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摘要

Rapid, nondestructive and accurate detection of internal qualities of the apple is an important research interest. In this study, the brix, acidity and brix/acidity ratio of the apple were rapidly detected by visible and near-infrared spectroscopy (VIS-NIRS). By scanning spectra and measuring the reference values of brix and acidity of apple samples, the relationship models between the spectra and brix, acidity, brix/acidity ratiowere, respectively, established. Sample division, characteristic wavelength optimization, and modeling methods were compared systematically, and the optimal prediction model of each quality index was determined. The experimental results show that the competitive adaptive reweighted sampling method can effectively select characteristic wavelengths, which not only improves the prediction speed, but also greatly enhances the prediction accuracy. The established partial least squares models based on these selected characteristic wavelengths all have high accuracy and robustness for the three quality indices. The determination coefficients of the models are 0.9899, 0.9615, 0.9535, and the relative percent deviation are 9.9269, 5.0987, 4.6374, respectively. All this work proves that VIS-NIRS can be used for rapid and nondestructive detection of the internal qualities of an apple. (C) 2021 Optical Society of America
机译：快速、无损、准确地检测苹果内部品质是一个重要的研究方向。在本研究中，通过可见光和近红外光谱（VIS-NIRS）快速检测苹果的锤度、酸度和锤度/酸度比。通过扫描光谱和测定苹果样品的锤度和酸度参考值，分别建立了光谱与锤度、酸度、锤度/酸度比的关系模型。系统比较了样品划分、特征波长优化和建模方法，确定了各质量指标的最佳预测模型。实验结果表明，竞争自适应加权采样方法能够有效地选择特征波长，不仅提高了预测速度，而且大大提高了预测精度。基于这些特征波长建立的偏最小二乘模型对三个质量指标都具有较高的精度和鲁棒性。模型的确定系数分别为0.9899、0.9615、0.9535，相对偏差分别为9.9269、5.0987、4.6374。所有这些工作证明，VIS-NIRS可以用于苹果内部品质的快速无损检测。（2021）美国光学学会

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  • 来源
    《Applied optics》 |2021年第13期|共8页
  • 作者

    Zhang Yunqi; Chen Yong; Wu Yun; Cui Chaoyuan;

  • 作者单位

    Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Hefei 230031 Peoples R China;

    Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Hefei 230031 Peoples R China;

    Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Hefei 230031 Peoples R China;

    Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Hefei 230031 Peoples R China;

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