Liu Yuan, Chen Wei-Hua, Hou Qiao-Juan, Wang Xi-Chang, Dong Ruo-Yan, Wu Hao
Guang Pu Xue Yu Guang Pu Fen Xi. 2014 Apr;34(4):937-41.
Near infrared spectroscopy (NIR) was used in this experiment to evaluate the freshness of ice-stored large yellow croaker (Pseudosciaena crocea) during different storage periods. And the TVB-N was used as an index to evaluate the freshness. Through comparing the correlation coefficent and standard deviations of calibration set and validation set of models established by singly and combined using of different pretreatment methods, different modeling methods and different wavelength region, the best TVB-N models of ice-stored large yellow croaker sold in the market were established to predict the freshness quickly. According to the research, the model shows that the best performance could be established by using the normalization by closure (Ncl) with 1st derivative (Dbl) and normalization to unit length (Nle) with 1st derivative as the pretreated method and partial least square (PLS) as the modeling method combined with choosing the wavelength region of 5 000-7 144, and 7 404-10 000 cm(-1). The calibration model gave the correlation coefficient of 0.992, with a standard error of calibration of 1.045 and the validation model gave the correlation coefficient of 0.999, with a standard error of prediction of 0.990. This experiment attempted to combine several pretreatment methods and choose the best wavelength region, which has got a good result. It could have a good prospective application of freshness detection and quality evaluation of large yellow croaker in the market.
本实验采用近红外光谱(NIR)技术评估冰藏大黄鱼(Pseudosciaena crocea)在不同贮藏期的新鲜度,并以挥发性盐基氮(TVB-N)作为评价新鲜度的指标。通过比较不同预处理方法、不同建模方法以及不同波长区域单独使用和组合使用所建立模型的校正集和验证集的相关系数及标准差,建立了市场上冰藏大黄鱼TVB-N的最佳模型,以快速预测其新鲜度。研究表明,以归一化闭包(Ncl)一阶导数(Dbl)和单位长度归一化(Nle)一阶导数为预处理方法,偏最小二乘法(PLS)为建模方法,并选择5000 - 7144和7404 - 10000 cm(-1)波长区域,可建立性能最佳的模型。校正模型的相关系数为0.992,校正标准误差为1.045;验证模型的相关系数为0.999,预测标准误差为0.990。本实验尝试将多种预处理方法相结合并选择最佳波长区域,取得了良好效果,在大黄鱼市场新鲜度检测和质量评价方面具有良好的应用前景。