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首页> 外文期刊>International Journal of Food Properties >Non-destructive evaluation of maturity and quality parameters of pomegranate fruit by visibleear infrared spectroscopy
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Non-destructive evaluation of maturity and quality parameters of pomegranate fruit by visibleear infrared spectroscopy

机译:可见/近红外光谱技术无损评估石榴果实的成熟度和品质参数

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摘要

In this study, the potential of visible and near infrared spectroscopy was investigated to classify the maturity stage and to predict the quality attributes of pomegranate variety "Ashraf" such as total soluble solids content, pH, and titratable acidity during four distinct maturity stages between 88 and 143 days after full bloom. Principal component analysis was used to distinguish among different maturities. The prediction models of internal quality attributes of the pomegranate were developed by partial least squares regression. The transmission spectra of pomegranate were obtained in the wavelength range from 400 to 1100 nm. In this research several preprocessing methods were utilized including centering, smoothing (Savitzky-Golay algorithm, median filter), normalization (multiplicative scatter correction and standard normal variate) and differentiation (first derivative and second derivative). It concluded that different preprocessing techniques had effects on the classification performance of the model using the principal component analysis method. In general, standard normal variate and multiplicative scatter correction gave better results than the other pretreatments. The correlation coefficients (r), root mean square error of calibration and ratio performance deviation for the calibration models were calculated: r = 0.93, root mean square error of calibration = 0.22 degrees Brix and ratio performance deviation = 6.4 degrees Brix for total soluble solids; r = 0.84, root mean square error of calibration = 0.064 and ratio performance deviation = 4.95 for pH; r = 0.94, root mean square error of calibration = 0.25 and ratio performance deviation = 5.35 for titratable acidity.
机译:在这项研究中,研究了可见光和近红外光谱的潜力,以对成熟阶段进行分类并预测石榴品种“ Ashraf”的质量属性,例如在88个之间的四个不同成熟阶段中的总可溶性固形物含量,pH和可滴定酸度盛开后的143天。主成分分析用于区分不同的到期日。通过偏最小二乘回归建立了石榴内部品质属性的预测模型。石榴的透射光谱是在400至1100 nm的波长范围内获得的。在这项研究中,使用了几种预处理方法,包括居中,平滑(Savitzky-Golay算法,中值滤波器),归一化(乘法散射校正和标准正态变量)和微分(一阶导数和二阶导数)。结论是,使用主成分分析方法,不同的预处理技术会对模型的分类性能产生影响。通常,标准正态变量和乘法散点校正比其他预处理提供更好的结果。计算了相关系数(r),校正的均方根误差和校正模型的比性能偏差:r = 0.93,校正的均方根误差= 0.22度白利糖度和比率性能偏差= 6.4度白利糖度对于总可溶性固体; r = 0.84,pH的校准均方根误差= 0.064,比率性能偏差= 4.95; r = 0.94,可滴定酸度的校准均方根误差= 0.25,比率性能偏差= 5.35。

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