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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内部质量指标

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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:该研究的目标是建立非破坏性近红外(NIR)光谱测量与梨('风水',江西)果实的主要内部质量指标之间的关系,并评估NIR光谱测量测量内部质量的使用梨果索引。通过在350-1800nm范围内通过反射率测量完整的梨果。在该研究中,基于多线性回归(MLR),主成分分析(PCA)和局部最小二乘(PCA)和偏离逻辑的校准模型与可溶性固体含量(SSC)相关的校准模型和坚固性反距离数(1 / R),其第一衍生D_1Log(1 / R)和第二衍生物D_2Log(1 / R)。基于预测结果,最佳组合是梨果赤道位置的D_1LOG(1 / R)的MLR模型。对MLR模型的预测结果为0.9151和0.8125的相关系数(R_P),以及SSC和坚固性分别为0.6834和1.3778的预测(RMSEP)的根平均标准误差。建筑模型的初步结果表明,NIR光谱可以提供准确,可靠和无损的方法,用于评估梨果的内部质量索引。

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