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Predicting soluble solid content and firmness in apple fruit by means of laser light backscattering image analysis

机译:通过激光反向散射图像分析预测苹果果实中的可溶性固形物含量和硬度

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Laser-induced light backscattering imaging was studied regarding its potential for analyzing apple soluble solids content (SSC) and fruit flesh firmness. Images of the backscattering of light on the fruit surface were obtained from 'Elstar' and 'Pinova' apples using laser diodes emitting at five wavelength bands. Image processing algorithms were tested to correct for dissimilar equator and shape of fruit. Particularly the frequencies of gray scale intensities obtained for selected wavelengths were used for the first time to calibrate on the fruit firmness and SSC using partial least squares regression. Calibration with highest performance for predicting 'Elstar' SSC was based on the corrected intensity frequency of raw data set with correlation coefficient of r = 0.89 and standard error of cross validation %SECV = 4.14. For evaluating 'Elstar' flesh firmness, corrected frequency gave the highest r = 0.90, and %SECV = 5.49. An inter-cul-tivar test-set validation of the method resulted in SEP < 13% for SSC and firmness prediction.
机译:研究了激光诱导的光反向散射成像技术在分析苹果可溶性固形物含量和果肉硬度方面的潜力。水果表面上的光向后散射图像是使用在五个波段发射的激光二极管从'Elstar'和'Pinova'苹果获得的。测试了图像处理算法以校正不同的赤道和水果形状。特别是,第一次使用选定波长获得的灰度强度频率,使用偏最小二乘回归对水果的硬度和SSC进行校准。预测“ Elstar” SSC的最高性能校准基于校正后的原始数据强度频率,相关系数为r = 0.89,交叉验证的标准误差为%SECV = 4.14。为了评估“ Elstar”果肉的紧实度,校正的频率给出最高的r = 0.90,%SECV = 5.49。该方法的沟间测试集验证导致SSC和牢固性预测的SEP <13%。

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