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Nondestructive measuring soluble solid contents and weight of intact pears based on on-line near-infrared spectroscopy

机译:在线近红外光谱法无损测量完整梨的可溶性固形物含量和重量

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The objective of this study was to investigate the potential of MRS as an accurate, reliable and nondestructive method to measure SSC and weight parameters of intact pears. The effects of fruit moving speed on SSC and weight prediction were evaluated. Diffuse reflectance spectra of 200 samples were collected by a fiber spectrometer in the range of 840~950nm at 5 different speeds: 2, 3, 4, 5 and 6 fruits per second. One-way analysis of variance (ANOVA) method was employed to assess effect of speed with partial least squares regression (PLSR). At the speed of 4 fruits per second, five types of data pretreatment were adopted before calibration. Multiple linear regression (MLR), principal component regression (PCR) and PLSR models were developed in the calibration set (n=150). 50 unknown samples were applied to evaluate the performance of the models. SSC was measured with a root mean squares error of prediction (RMSEP) comprised between 0.60 and 0.88 °Brix and correlation coefficient (r) between 0.57 and 0.85. As far as weight was concerned, the best results were obtained by PLSR combined with smoothing spectra (r=0.93, RMSEP=14.29 g). The results demonstrated that on-line NIRS was feasible for measuring SSC and weight of intact pears nondestructively.
机译:这项研究的目的是研究MRS作为测量完整梨的SSC和重量参数的准确,可靠和无损方法的潜力。评估了水果移动速度对SSC和体重预测的影响。光纤光谱仪以5种不同的速度(每秒2、3、4、5和6个水果)在840〜950nm范围内收集了200个样品的漫反射光谱。采用单向方差分析(ANOVA)方法通过偏最小二乘回归(PLSR)评估速度的影响。以每秒4个水果的速度,在校准之前采用了五种类型的数据预处理。在校准集中建立了多元线性回归(MLR),主成分回归(PCR)和PLSR模型(n = 150)。应用了50个未知样品来评估模型的性能。 SSC的测量均方根误差均方根误差(RMSEP)在0.60和0.88°Brix之间,相关系数(r)在0.57和0.85之间。就重量而言,通过PLSR结合平滑光谱可获得最佳结果(r = 0.93,RMSEP = 14.29 g)。结果表明,在线NIRS可以无损地测量完整梨的SSC和重量。

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