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

Yande Liu, Xing-miao Chen, Aiguo Ouyang, Y. Ying
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Abstract

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 D1log (1/R)and second derivative D2log (1/R). The best combination, based on the prediction results, was MLR models with respect to D1log (1/R) at equatorial position of pear fruit. Prediction with MLR models resulted correlation coefficients (Rp) 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.
近红外光谱法无损测定梨内部品质指标
本研究的目的是建立无损近红外光谱测量与江西“风水”梨果实主要内部品质指标的关系,并评价近红外光谱测量在梨果实内部品质指标测量中的应用。采用近红外光谱(NIR)在350 ~ 1800 nm范围内对梨果实进行了反射率测量。本研究基于多元线性回归(MLR)、主成分分析(PCA)和偏最小二乘(PLS)回归,对反射率倒数对数(1/R)、一阶导数D1log (1/R)和二阶导数D2log (1/R)的对数建立了近红外光谱与可溶性固形物含量(SSC)和硬度的校正模型。根据预测结果,梨果实赤道位置D1log (1/R)的最佳组合为MLR模型。MLR模型预测SSC和firmness的相关系数(Rp)分别为0.9151和0.8125,预测均方根标准误差(RMSEP)分别为0.6834和1.3778。初步结果表明,近红外光谱技术可以为梨果实内部品质指标的评价提供一种准确、可靠、无损的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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