中文摘要：

通过对苹果的CT扫描灰度图像进行傅里叶变换，在变换后的频域图像中提取16个参数，并结合苹果的可溶性固形物质量分数、可滴定酸度、pH值和含水率，采用主成分回归和偏最小二乘回归的方法建立模型，预测苹果的内部品质。结果表明，采用主成分回归分析建立模型，主成分的累计贡献率选取99%的情况下，F检验的P均小于0.05，预测效果良好。采用偏最小二乘回归时潜变量的个数为12时，各模型的误差平方和最小，预测效果良好。对两个模型进行误差率验证表明主成分回归的模型略优于偏最小二乘回归模型。

英文摘要：

The apple CT gray scale images scanned by CT were transformed by Fourier transform, and 16 parameters were extracted from each frequency domain after transformation. Combined with the soluble solid, the titrable acidity, the pH value and the moisture content of apple, the principal components regression （PCR） and the partial least squares regression （PLSR） were employed to establish the prediction models of apples’ internal quality. In the PCR, the first ten principal components were chosen with contribution rate reaching 99%. The models show good prediction results by the F criterion with all the P values lower than 0.05. In the PLSR, each content model has the lowest sum of squared errors when the number of latent variables is 12, which indicated a good prediction result. The results show that the models built by PCR have higher predictive ability than that of PLS method in the matter of errors.