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Correlation spectroscopy can be used to describe the linear correlationship between the absorbance and concentration data in the whole spectra range and clearly figure out the characteristic peak position of the sample under test. Meantime, this chart plays an extremely important role in offering the precise information for choosing the optimal wavelength set during the calibration process. Multiple scatter correct (MSC) spectroscopy is a kind of multiple variable scatter correction technique, and can effectively remove the base shift and tilt phenomenon caused by MSC. As a result, the ratio of signal to noise is improved greatly. Based on this feature, the new idea of the MSC technique was introduced into the preceding data treatment for the creation of correlation chart, and through careful experiment this idea was proved to be correct and effective.

In the present paper the saponin in Chinese ginseng was analysed quantitatively by using near infrared spectroscopy. The spectral characteristics of the primary ingredients in Chinese ginseng were obtained by applying second derivative, MSC (Multiple Scatter Correction), and correlation chart to the original absorbance spectra of ginseng. Meantime, in combination with the PLS algorithm the calibration process was performed for the quantitative analysis of saponin in Chinese ginseng. The result obtained shows a fine precision of the method, with RMSEC of 0.154% and correlation coefficient of 0.9828.

Multiple scattering correction(MSC) algorithm can be used effectively to remove the effect of scattering due to the physical factors such as the density and humidity of sample granule, and as a result the ratio of signal to noise is improved greatly. Meantime correlation spectrum plays a important role in the choice of optimum wavelength set because it describes the linear correlationship between the absorbance and concentration of the sample's ingredient under analysis. However, the correlation spectrum obtained by unitary linear regression(ULR) at single wavelength channel can be easily affected by the scattering so as to cover up the characteristic linear information of the sample. In order to solve the problem in the present paper MSC was applied to obtain useful signal and suppress noise of correlation spectrum. Through the careful calibration experiment of ginseng sample this idea has proved to be correct, and satisfactory result was obtained.

In the present paper the analysis of the total content of sugar in Chinese ginseng was carried out by using near infrared spectroscopy, and in combination with the PLS algorithm the calibration analysis was performed at the same time. The result shows that the precision obtained through common chemical methods is almost the same as the one through NIR spectroscopy. The SEC (standard error of calibration) is 1.9% and the correlation coefficient is 0.951 7, which proves that this technique is well suitable for rapid quantitative determination of the total content of sugar in Chinese ginseng, and is simple, cheap, and without any chemical pollution, and can present real time result.

For spectrometers which don't operate in consistently scanning mode such as fixed-filter spectrometer, diode arrays spectrometer and so on, how to find an optimal wavelengths combination rapidly and accurately has always been a key problem in NIR so far. The conventional methods to choose the optimal wavelengths for calibration are Forward Stepwise multiple linear Regression (FSR) and Backward Stepwise Regression (BSR), which choose the optimal wavelengths based on the t test result of all the calibration wavelengths. However, maybe the wavelength eliminated is very useful in application and the wavelength chosen may not be so useful as expected, as a result it is compulsory to make more tests to verify the truly applicable wavelength combination for calibration. In this paper the combination of the combination-making algorithm in combinatorics and computer languages which operate facing matrix is used to choose the optimal combination of wavelengths for calibration automatically and efficiently by computer. The most robust calibration equation can be obtained by making analysis of calibration regression that operates to find the minimum root mean standard error of calibration.

During the manufacture and debugging of the NIR instruments it is compulsory to make final calibration analysis to verify whether the instruments operate correctly. However, the conventional method of NIR calibration needs to make calibration analysis for the instruments with all the samples at hand. In fact in order to cover the samples that will be encountered in the future the amount of the samples set and the labor that the personnel need to offer are enormous. In this paper a new algorithm is presented which can be used to effectively eliminate the similar samples in the original sample set. By using this algorithm we have chosen 94 optimal samples in the original 178 sample set successfully. After performing calibration experiment we found that the sample set chosen by this algorithm are equally representative to the original sample set and obtained almost the same precision compared to the original sample set when the two sample sets were individually calibrated. This algorithm brings great relief to the labor of the workers, presents the possibility of performing more experiments and greatly improves the efficiency of performing calibration experiment. As a result, the amount of calibration sets and the labor of the personnel are reduced remarkably.