Shan Jia-jia, Wu Jian-hu, Chen Jing-jing, Peng Yan-kun, Wang Wei, Li Yong-yu
College of Engineering, China Agricultural University, Beijing 100083, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2010 Oct;30(10):2729-33.
The research discussed the prediction method of apple's internal quality such as firmness and soluble solids content with the combination of parameters getting from hyperspectral fitting scattering curve. The research compared different molding methods using the combination of the three Lorentzian fitting parameters with partial least squares (PLS), stepwise multiple linear regression (SMLR) and neural network (NN). The normalized combination parameters and original combination parameters were used to establish prediction models, respectively. The partial least squares (PLS) prediction models using the combination of three original parameters gave a better results with the correlation of calibration Rc = 0.93, the standard error of calibration SEC = 0.56, the correlation of validation R = 0.84, and the standard error of validation SEV = 0.94 for firmness of apples. The partial least squares (PLS) prediction models using combination of normalized parameters also gave a good results with Rc = 0.95, and the standard error of calibration SEC= 0. 29, the correlation of validation Rv = 0. 83, and the standard error of validation SEV = 0.63 for soluble solids content of apples. The research showed that using hyperspectral scattering curve can detect apple quality attributes at the same time.
该研究探讨了利用从高光谱拟合散射曲线获取的参数组合来预测苹果内部品质（如硬度和可溶性固形物含量）的方法。该研究比较了使用三个洛伦兹拟合参数与偏最小二乘法（PLS）、逐步多元线性回归（SMLR）和神经网络（NN）相结合的不同建模方法。分别使用归一化组合参数和原始组合参数建立预测模型。使用三个原始参数组合的偏最小二乘（PLS）预测模型对苹果硬度给出了较好的结果，校准相关系数Rc = 0.93，校准标准误差SEC = 0.56，验证相关系数R = 0.84，验证标准误差SEV = 0.94。使用归一化参数组合的偏最小二乘（PLS）预测模型对苹果可溶性固形物含量也给出了良好的结果，校准相关系数Rc = 0.95，校准标准误差SEC = 0.29，验证相关系数Rv = 0.83，验证标准误差SEV = 0.63。该研究表明，利用高光谱散射曲线可以同时检测苹果的品质属性。
