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Nondestructive determination of pear internal quality indices by near-infrared spectrometry

机译:近红外光谱法无损测定梨的内部质量指标

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摘要

The objectives of the study were to establish relationships between the nondestructive near-infrared (NIR) spectral measurements and the major internal quality indices of pear ('Fengshui', Jiangxi) fruit, and to evaluate the use of NIR spectrometry in measuring the internal quality indices of pear fruit. Intact pear fruit were measured by reflectance NIR in 350-1800 nm range. In this study, Calibration models relating NIR spectra to soluble solids content (SSC), and firmness were developed based on multi-linear regression (MLR), Principal component analysis (PCA) and partial least square (PLS) regression with respect to the logarithms of the reflectance reciprocal log (1/R), its first derivative D_1log (1/R)and second derivative D_2log (1/R). The best combination, based on the prediction results, was MLR models with respect to D_1log (1/R) at equatorial position of pear fruit. Prediction with MLR models resulted correlation coefficients (R_p) of 0.9151 and 0.8125, and root mean standard error of prediction (RMSEP) of 0.6834 and 1.3778 for SSC and firmness, respectively. The preliminary results of the built models indicated that NIR spectroscopy could provide an accurate, reliable and nondestructive method for assessing the internal quality indices of pear fruit.
机译:该研究的目的是建立无损近红外(NIR)光谱测量结果与梨(“凤水”,江西)水果的主要内部质量指标之间的关系,并评估近红外光谱法在测量内部质量中的应用梨果实指数。通过在350-1800 nm范围内的反射NIR测量完整的梨果实。在这项研究中,基于多线性回归(MLR),主成分分析(PCA)和偏最小二乘(PLS)回归,开发了将NIR光谱与可溶性固体含量(SSC)和硬度相关的校准模型反射率倒数对数(1 / R),其一阶导数D_1log(1 / R)和二阶导数D_2log(1 / R)。根据预测结果,最佳组合是相对于梨果实赤道位置的D_1log(1 / R)的MLR模型。使用MLR模型进行预测时,SSC和牢固度的相关系数(R_p)为0.9151和0.8125,预测的均方根标准误差(RMSEP)为0.6834和1.3778。建立的模型的初步结果表明,近红外光谱技术可以为评估梨果实的内部质量指标提供一种准确,可靠和无损的方法。

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