Tu Zhen-Hua, Ji Bao-Ping, Meng Chao-Ying, Zhu Da-Zhou, Shi Bo-Lin, Qing Zhao-Shen
College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 Oct;29(10):2760-4.
In the present study, the fruit flesh firmness of apple was analyzed by near infrared (NIR) spectroscopy using an FT-NIR spectrometer. The sensitive spectral regions that provide the lowest prediction error were analyzed by different well-known variable selection methods, including dynamic backward interval partial least-squares (dynamic biPLS), sequential application of backward interval partial least-squares and genetic algorithm(dynamic biPLS & GA-PLS), and iterative genetic algorithm partial least-squares (iterative GA-PLS). Iterative GA-PLS, dynamic biPLS & GA-PLS led to a distinct reduction in the number of spectral data points with better predictive quality. Furthermore, the majority of selected wavelengths were content with the characteristic of the sorption bands of fruit flesh firmness. Pectin constituents, complex non-starch polysaccharides, which are related to texture change in apple, play an important role in their harvest maturity, ripening and storage. Comparing NIR characteristic wavelengths of apple flesh firmness and typical absorption bands for pectin, it was found that characteristic wavelengths of apple flesh firmness were consistent with the pectins relevant spectral regions. Therefore, the NIR characteristic wavelengths of apple firmness based on GA and iPLS reflected the chemical component of apple and the results were reasonable.
在本研究中，使用傅里叶变换近红外光谱仪通过近红外（NIR）光谱分析苹果的果肉硬度。通过不同的知名变量选择方法分析提供最低预测误差的敏感光谱区域，包括动态反向区间偏最小二乘法（动态biPLS）、反向区间偏最小二乘法与遗传算法的顺序应用（动态biPLS & GA-PLS）以及迭代遗传算法偏最小二乘法（迭代GA-PLS）。迭代GA-PLS、动态biPLS & GA-PLS导致光谱数据点数量明显减少，且预测质量更好。此外，大多数选定波长符合果肉硬度吸附带的特征。果胶成分是与苹果质地变化相关的复杂非淀粉多糖，在其收获成熟度、成熟和储存过程中起重要作用。比较苹果果肉硬度的近红外特征波长和果胶的典型吸收带，发现苹果果肉硬度的特征波长与果胶相关光谱区域一致。因此，基于遗传算法和间隔偏最小二乘法的苹果硬度近红外特征波长反映了苹果的化学成分，结果是合理的。
