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Soil is a much complicated substance, because animals, plants and microbes live together, organic and inorganic exist together. So soil contains a large amount of information. The traditional method in laboratory is a time-consuming effort. But the technology of near infrared reflectance spectroscopy (NIRS) has been widely used in many areas, owing to its rapidness, high efficiency, no pollution and low cost, NIRS has become the most important method to detect the composition of soil. This paper mainly introduce some traditional methods in laboratory, the basic processes of soil detection by NIRS, some algorithms for data preprocessing and modeling. Besides, the present paper illustrates the latest research progress and the development of portable near infrared instruments of the soil. According to this paper, the authors also hope to promote the application conditions of NIRS in the grassland ecology research in China, and accelerate the modernization of research measures in this area.

The soil sampled from GAN NAN navel orange plant area was selected as research object, and the feasibility of analyzing the total nitrogen (TN) and soil organic matter (SOM) of soil was investigated by near infrared spectroscopy (NIR) techniques in the wavelength range of 4 000 - 7 500 cm(-1). Different pretreatment methods including multiplicative scatter correction (MSC), first derivative(1st D), second derivative (2nd D), Savitzkv-Golay (SG), standard normalized variate (SNV) and baseline were used. The partial least square regress (PLS) was built for the calibration models. The best TN model using SG pretreatment features the prediction correlation coefficients (r(c)) of 0.802, the root mean square error of calibration (RMSEC) of 2.754, the calibration correlation coefficients (r(p)) of 0.715, and the root mean square error of prediction (RMSEP) of 3.077 in the wave-length range of 4 000 - 7 500 cm(-1). The best SOM model using SNV pretreatment has r(c) of 0.848, RMSEC of 0.128, r(p) of 0.790, and RMSEP of 0.152. The results showed that the NIR diffuse reflectance can be used for quick estimate of the TN and SOM contents in soil with the wavelength range of 4 000 - 7 500 cm(-1).

Heavy metal ions in plants can be determined by using the near-infrared spectral (NIRS) technique, because they combine with the organic molecular groups that have NIRS absorptions. The present article analyzed the fast detection of heavy metal Cu in Ludwigia prostrata leaves by near infrared diffuse spectral technology. Different preprocessing methods were compared, combined with partial least squares (PLS), and the fast detection models of heavy metal Cu in Ludwigia prostrata leaves were established. The results showed that the best model was obtained by PLS with the preprocessing method of average smoothing. The correlation coefficient (r) and root mean square error of calibration(RMSECV) was 0.950 and 5.99 respectively; External validation correlation coefficient (r) and root mean square error of prediction(RMSEP) was 0.923 and 7.38 respectively. The study shows that fast determination of heavy metal Cu in Ludwigia prostrata leaves using near infrared diffuse spectroscopy is feasible.

The objective of the present research was to optimize the model of sugar content in navel orange for improving the detection presicion by the online near infrared spectroscopy. The reference wavelength was chosen by coefficient of variation of the different wavelengths in the calibration set in the wavelength range of 700.28 - 933.79 nm. Then the spectra were transformed into ratio specra. The absorbance and ration spectra were pretreated by different preprocessing methods. The models of sugar content were developed by partial least squares (PLS) and least squares support vector regression (LSSVR). The 30 unknown navel orange samples were applied to evaluate the performance of the models. By comparison of the predictive performances, the LSSVR model was the best among the models with the first derivative preprocessing and ration spectra. The correlation coeffiecient (R(P)) of the best model was 0.85, the root mean square error of prediction (RMSEP) was 0.41 Brix. The results suggested that it was feasible to improve the precision of online near infrared spectroscopy detecting sugar content in navel orange by the optimization of reference wavelengths, the first derivative preprocessing and LSSVR.

In the present study, NIRS was applied to nondestructive and rapid measurement of firmness and surface color of pear. In order to improve the prediction precision and eliminate the influence of uninformative variables on model robustness, Monte Carlo uninformative variables elimination (MC-UVE) and Monte Carlo uninformative variables elimination based on wavelet transform (WT-MC-UVE) methods were proposed for variable selection in firmness and surface color NIR spectral modeling. Results show that WT-MC-UVE can reduce the modeling variables from 1451 to 210, and get similar prediction results for firmness. WT-MC-UVE improved the prediction precision for surface color, the root mean square error of prediction (RMSEP) and calibration variables were reduced from 1.06 and 1451 to 0.90 and 220 respectively, and the correlation coefficient (r) was improved from 0.975 to 0.981. The proposed method is able to select important wavelength from the NIR spectra, and makes the prediction more robust and accurate in quantitative analysis of firmness and surface color.

The detection principle of NIR technology for nondestructive measurement of fruit internal quality was briefly introtive analysis was given among several instruments. The latest progress was summarized at home and abroad. Finally, the development and trend of NIR instruments for detecting fruit quality was analyzed.

The present paper reports the study on visible/near-infrared (Vis/NIR) diffuse reflectance spectroscopy for nondestructive measurement of vitamin C in tangerine (Nanfeng, Jiangxi). In the wavelength range of 350-1800 nm, principal component analysis (PCA) and partial least squares (PLS) were used to build mathematic models with respect to pre-processing spectra. The principal components (PCs) and wavelength selection for calibration model were determined based on full cross validation and regression coefficients. The results, based on prediction for 10 unknown samples, are 0.813, 2.112 mg (100 g)(-1) and -0.810 mg (100 g)(-1) for prediction correlation coefficient, root mean square error of prediction, and prediction deviation, respectively. The analysis methods and conclusions of this experiment will provide some reference values for non-destructive measurement of vitamin C and other quality indices in Nanfeng tangerine using Vis/NIR diffuse reflectance spectrometric technique.

The objectives of the present study were to establish the relationships between the near-infrared diffuse reflectance (NIR) spectra and the soluble solids content (SSC) of internal quality index of pear fruit, and to evaluate the value of NIR spectrometry in measuring the SSC of internal quality index of pear fruit. NIR spectroscopy in the 350-1 800 nm range was used to analyze the SSC of internal quality index of pear fruit with multi-linear regression (MLR), principal component regression (PCR) and partial least square (PLS) regression. Meanwhile, the best combinations of different positions at pear fruit, the logarithms of the reflectance reciprocal log(1/R), its first derivative D1 log(1/R) and second derivative D2 log(1/R) were investigated. The best prediction results, based on the comparative analysis, were obtained with the PLS model and D1 log(1/R) at equatorial position of pear fruit. The results show that the predictions with PLS models, based on D1 log(1/R) at equatorial position of pear fruit, are correlation coefficients (R(p)) of 0.851 7 and root mean standard error of prediction (RMSEP) of 0.879 3 for SSC. The preliminary research on the built models indicates that NIR spectroscopy could provide an accurate, reliable and nondestructive method for assessing the SSC of internal quality index of pear fruit.

The potential of using visible and near infrared diffuse reflectance spectroscopy to assess soluble solids content (SSC) of intact navel orange was examined. A total 40 samples were used to develop the calibration and prediction models. NIR spectral data were collected in the spectral region between 350 and 2 500 nm and their second derivative spectra were used for the present study. Different scattering correction algorithms (no preprocessing and multiplicative scattering correction (MSC)) were compared. Camibration models based on different spectral ranges, different derivatives and different kinds of statistical models including partial least square (PLS) and principal component regression (PCR) were also compared. The best results of PLS models with the second derivative spectra are r = 0.929, RMSEC = 0.517 and RMSEP = 0.592 in the wavelength range of 361-2 488 nm. The results show that this method is feasible for non destructive assessing of SSC of the navel orange.

The objective of the present research was to study the potential of NIR diffuse reflectance spectroscopy as a way for nondestructive measurement of the firmness of "Xueqing" pear. NIR spectral data were collected in the spectral region between 800 nm and 2,630 nm, but only the data between 800 nm and 2,500 nm were used for the analysis. Spectral data smoothing of different gaps using Savitsky-Goaly method was discussed both qualitatively and quantitatively. The results showed that the smoothing of NIR original spectra had little influence on spectral data analysis. Statistical models were developed using partial least square method. Models for three kinds of mathematical treatments of spectra (original, first derivative and second derivative) were established in three spectral regions: 800-2,500 nm, 800-1,890 nm and 832-872 nm. The original spectra showed better results than the first derivative spectra and second derivative in all the three spectral regions. And for the three spectral regions, the full wavelength range of 800-2,500 nm offered the best result. The PLS model of original spectra in the range of 800-2,500 nm gave best predictions of pear firmness, with correlation coefficient (r) of 0.869 and root mean square errors of calibration (RMSEC) of 3.88 N for the calibration sample set and with correlation coefficient (r) of 0.840 and root mean square error of prediction (RMSEP) of 4.26 N for the validation sample set. The results of the study show that NIR diffuse reflectance specroscopy can be used to predict the firmness of "Xueqing" pear.

Based on wavelet transform (WT) by using the difference in wavelet modulus maxima evolution behaviors between singular signals and random noises in multi-scale space, the near infrared spectroscopic signals of 90 fruit samples were denoised by wavelet transform. The sugar content in intact apple was calculated by stepwise regression method. The result of calibration model after noise filtering was satisfactory. The relative standard error of prediction is reduced to 6.0% from 6.1% of original spectra. It is concluded that wavelet transform is an useful method to eliminate noise of NIR signals, as it makes the final calibration model more representative and stable and robust.

The nondestructive method for quantifying sugar content (SC) and available acid (VA) of intact apples using diffuse near infrared reflectance and optical fiber sensing techniques were explored in the present research. The standard sample sets and prediction models were established by partial least squares analysis (PLS). A total of 120 Shandong Fuji apples were tested in the wave number of 12,500 - 4000 cm(-1) using Fourier transform near infrared spectroscopy. The results of the research indicated that the nondestructive quantification of SC and VA, gave a high correlation coefficient 0.970 and 0.906, a low root mean square error of prediction (RMSEP) 0.272 and 0.056 2, a low root mean square error of calibration (RMSEC) 0.261 and 0.0677, and a small difference between RMSEP and RMSEC 0.011 a nd 0.0115. It was suggested that the diffuse nearinfrared reflectance technique be feasible for nondestructive determination of apple sugar content in the wave number range of 10,341 - 5461 cm(-1) and for available acid in the wave number range of 10,341 - 3818 cm(-1).