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Simultaneous measurement of soluble solid content, pH, firmness and density of ‘Dangshan’ pear using FT-NIR spectrometry

机译:使用FT-NIR光谱仪同时测量“ D山”梨的可溶性固形物含量,pH,硬度和密度

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摘要

This research aims to investigate the feasibility to rapidly and simultaneously determine the quality characteristics of Chinese ‘Dangshan’ pear by using near infrared (NIR) spectroscopy. A total of 151 ‘Dangshan’ pears with various ripen stages were used in this study. The NIR spectrum from each fresh intact sample was calibrated against four major physiological properties: soluble solid content (SSC), pH, firmness and density. The calibration models for these parameters were developed using the partial least square (PLS) based on the original and the four preprocessed spectra, respectively. The optimum spectral regions, the best pretreatment method and the fewest number of regression factors in the PLS-models were decided according to the minimum standard error in cross-validation (SECV). Finally, the proposed calibration models were validated using an independent sample set to evaluate their predicting ability. The best models for SSC, pH and firmness showed good predictability with the lowest standard error of prediction (SEP) of 0.3490Brix, 0.081 and 0.310N, and corresponding correlation coefficients of 0.985, 0.910 and 0.905, respectively, indicating that it is possible to simultaneously measure SSC, pH and firmness of Chinese ‘Dangshan’ pear by using NIR spectroscopy with the satisfactory accuracy. However, validation results also showed that no satisfactory NIR calibration equation for the density could be obtained.
机译:这项研究旨在研究通过近红外(NIR)光谱法快速,同时确定中国“ D山”梨品质特征的可行性。这项研究共使用了151个成熟阶段各异的“’山”梨。针对四个主要的生理特性,对每个新鲜完整样品的NIR光谱进行了校准:可溶固体含量(SSC),pH,硬度和密度。这些参数的校准模型分别使用偏最小二乘(PLS)基于原始光谱和四个预处理光谱来开发。根据交叉验证(SECV)中的最小标准误差,确定PLS模型中的最佳光谱区域,最佳预处理方法和最少数量的回归因子。最后,使用独立样本集对提出的校准模型进行了验证,以评估其预测能力。最佳的SSC,pH和硬度模型显示出良好的可预测性,最低预测标准误差(SEP)为0.3490Brix,0.081和0.310N,相应的相关系数分别为0.985、0.910和0.905,表明有可能同时使用近红外光谱法同时测量Chinese山梨的SSC,pH和硬度,具有令人满意的精度。但是,验证结果还表明,无法获得令人满意的密度NIR校准方程式。

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