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Nondestructive measurement soluble solids content of apple byportable and online near infrared spectroscopy

机译:非破坏性测量可溶性固体含量的苹果和在线近红外光谱

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Two different near infrared spectrometric systems were used to determine soluble solids content (SSC) of intact apple, including a portable near infrared (NIR) spectrometer and an online NIR system. The pretreatment methods were applied to improve the predictive results. The moving average smoothing was significant. The effective wavelength regions were chosen by interval partial least squares (iPLS) and backward iPLS (Bipls). Then the models were developed by partial least square regression (PLSR) and least square support machine (LS-SVM). Performance comparisons were made in the context of 30 unknown samples prediction. The LS-SVM models were better than others with correlation coefficient (R) and root mean square error of prediction (RMSEP) of (0.88, 0.80° Brix) and (0.82, 1.01° Brix) for portable and online measurement mode, respectively. The results demonstrated that the online measurement mode was not as well as the portable.
机译:两种不同的近红外光谱系统用于确定完整苹果的可溶性固体含量(SSC),包括便携式近红外(NIR)光谱仪和在线NIR系统。采用预处理方法来改善预测结果。移动的平均平滑是显着的。通过间隔部分最小二乘(IPLS)和向后IPLS(BIPLS)选择有效波长区域。然后,模型由部分最小二乘回归(PLSR)和最小二乘支持机(LS-SVM)开发。在30个未知样本预测的上下文中进行了性能比较。 LS-SVM型号比具有相关系数(R)的相关系数(R)和用于(0.88,0.80°Brix)的预测(Rmsep)和(0.82,1.01°Brix)的均方根误差,分别用于便携式和在线测量模式。结果表明,在线测量模式并不像便携式。

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