Jiang Huan-yu, Peng Yong-shi, Xie Li-juan, Ying Yi-bin
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2008 Aug;28(8):1763-6.
Near-infrared spectroscopy technique is non-destructive, simple, fast, highly efficient, cheap to implement, and very recurrent with no sample preparation, and has been a rapid and non-destructive modem qualitative and quantitative technique that has been widely used in many fields. As a powerful analytical tool in product quality determination, this technology is based on the measurement of vibration frequencies of chemical bonds in functional group such as C-C, C-H, O-H, C=O and N-H upon absorption of radiation. However, NIR spectra are affected by the status of spectrometer and the set of parameters when scanning, such as accuracy of wavelength, resolution of apparatus, noise, scan time and uniformity of sample size. To provide foundation with optimum test condition when modeling, the influence of scan number on NIR diffuse spectra of tomato leaf and chlorophyll prediction model precision was studied. 102 tomato leaf samples were used in this experiment. Partial least-squares (PLS) was used to develop models and evaluate and compare these models. The results show that scan number does have effect on NIR spectra and prediction models. Variance value of root mean square (RMS) noise of NIR spectra diminished gradually with the increment of scan number. The spectral quality with high scan number was high, however, the system error of instrument increased too. The spectral quality with low scan number was low, while the spectra were smooth and system error of instrument decreased too. The determination coefficient of chlorophyll calibration and prediction model was highest with 128 scan number, however, the model was not robust. But with 32 scan number, although the coefficient was low, the calibration and prediction model was robust and only a short test time was needed. At the same time, the difference of models to predict chlorophyll contents with different scan numbers was not distinct (alpha = 0.05). Different influence factors should be considered when modeling.
近红外光谱技术具有无损、简单、快速、高效、实施成本低且无需样品制备即可多次重复使用的特点，是一种快速无损的现代定性和定量技术，已在许多领域得到广泛应用。作为产品质量测定中的一种强大分析工具，该技术基于在吸收辐射时对诸如C-C、C-H、O-H、C=O和N-H等官能团中化学键振动频率的测量。然而，近红外光谱会受到光谱仪状态以及扫描时参数设置的影响，如波长精度、仪器分辨率、噪声、扫描时间和样品尺寸的均匀性等。为了在建模时提供最佳测试条件的依据，研究了扫描次数对番茄叶片近红外漫反射光谱及叶绿素预测模型精度的影响。本实验使用了102个番茄叶片样品。采用偏最小二乘法（PLS）建立模型并对这些模型进行评估和比较。结果表明，扫描次数确实会对近红外光谱和预测模型产生影响。近红外光谱的均方根（RMS）噪声方差值随着扫描次数的增加而逐渐减小。扫描次数多时光谱质量高，但仪器系统误差也增大。扫描次数少时光谱质量低，但光谱平滑且仪器系统误差也减小。叶绿素校准和预测模型的决定系数在扫描次数为128时最高，但该模型不稳定。而扫描次数为32时，虽然系数较低，但校准和预测模型稳定且测试时间短。同时，不同扫描次数下预测叶绿素含量的模型差异不显著（α = 0.05）。建模时应考虑不同的影响因素。
