[Rapid Diagnosis of Sound, Yellow and Citrus Greening Leaves with Hyperspectral Imaging].

The feasibility was investigated for identifying sound, yellow and citrus greening leaves of navel orange trees based on hyperspectral imaging combined with correlation analysis and discriminant partial least square (DPLS) methods. The hyperspectral data of sound, yellow and citrus greening leaves were recorded in the wavelength range of 374.28～1 016.89 nm. Two regions of interest (ROI) were marked symmetrically on both sides along main veins with an ellipse of major axis of 60 pixels and minor axis of 30 pixels. The average reflectance spectrum was extracted from ROI regions. A pair wavelengths of 502.79 and 374.28 nm were chosen with correlation analysis method in the wavelength range of 374.28～1 016.89 nm. The classification model was developed with the peak ratio of the pair wavelengths. This model was effective to sound leaves with the recognition accuracy of 1.7% but yellow and citrus greening leaves. The DPLS model was employed with the preprocessing spectra of second derivative and Savitzky-Golay smoothing. The recognition accuracy of this model was 100% for citrus greening leaves and yellow ones. The number of latent variables (LVs) was optimized with the leave one out cross validation method. The optimal LVs, correlation coefficient and standard error of calibration of the DPLS model were 17, 0.96 and 0.13, respectively. The correction classification rate of the DPLS model was 100% for yellow leaves and citrus greening ones. Two-step method of the peak ratio models combination with the DPLS was proposed for identifying sound, yellow and citrus greening leaves. The new samples were applied to evaluation the classification ability of the two-step method, which included sound leaves of 10, citrus greening leaves of 10 and yellow leaves of 10. The correction classification rate reached 96.7%. Experimental results showed that it was feasible to identify sound, yellow and citrus greening leaves by hyperspectral imaging coupled with the peak ratio and DPLS models.

[Application of Raman Spectroscopy Technique to Agricultural Products Quality and Safety Determination].

The quality and safety of agricultural products and people health are inseparable. Using the conventional chemical methods which have so many defects, such as sample pretreatment, complicated operation process and destroying the samples. Raman spectroscopy as a powerful tool of analysing and testing molecular structure, can implement samples quickly without damage, qualitative and quantitative detection analysis. With the continuous improvement and the scope of the application of Raman spectroscopy technology gradually widen, Raman spectroscopy technique plays an important role in agricultural products quality and safety determination, and has wide application prospects. There have been a lot of related research reports based on Raman spectroscopy detection on agricultural product quality safety at present. For the understanding of the principle of detection and the current development situation of Raman spectroscopy, as well as tracking the latest research progress both at home and abroad, the basic principles and the development of Raman spectroscopy as well as the detection device were introduced briefly. The latest research progress of quality and safety determination in fruits and vegetables, livestock and grain by Raman spectroscopy technique were reviewed deeply. Its technical problems for agricultural products quality and safety determination were pointed out. In addition, the text also briefly introduces some information of Raman spectrometer and the application for patent of the portable Raman spectrometer, prospects the future research and application.

[Applications of near infrared reflectance spectroscopy technique (NIRS) to soil attributes research].

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.

[Estimation of the TN and SOM contents in soil from GAN NAN navel orange plant area by NIR diffuse spectroscopy].

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).

[Fast determination of heavy metal Cu in Ludwigia prostrata leaves using near infrared diffuse spectroscopy].

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.

[Analysis of chlorophyll in Gannan navel orange with algorithm of GA and SPA based on hyperspectral].

The hyperspectral effective information of Gannan navel orange was extracted by genetic algorithm (GA) and successive projections algorithm (SPA) for partial least squares (PLS) model. Average spectral was extracted from region of interest (ROD) of hyperspectral images after preprocessing. GA and SPA were used to select 27 and 8 characteristic wavelengths for chlorophyll model with PLS. The correlation coefficients and rms error of GA-PLS were 0.80 and 2.45, and the correlation coefficients and rms error of SPA-PLS were 0.83 and 2.30, respectively. Overall results sufficiently demonstrate that SPA-PLS model has a greater advantage which can be combined with hyperspectral technique to be a nondestructive and rapid analytical method.

[Construction and simplification of the calibration model for spectral analysis of fuel oil properties based on mutual information method].

Near infrared diffuse reflectance (NIRS) and ultraviolet (UV) spectral analysis were adopted for quantitative determination of octane number and monoaromatics in fuel oil. Partial least squares regression (PLSR) was used for construction of vibrational spectral calibration models. Variables selection strategy based on mutual information (MI) theory was introduced to optimize the models for improving the precision and reducing the complexity. The results indicate that MI-PLSR method can effectively improve the predictive ability of the models and simplify them. For octane number models, the root mean square error of prediction (RMSEP) and the number of calibration variables were reduced from 0.288 and 401 to 0.111 and 112, respectively, and correlation coefficient (R) was improved from 0.985 to 0.998. For monoaromatics models, RMSEP and the number of calibration variables were reduced from 0.753 and 572 to 0.478 and 37, respectively, and R was improved from 0.996 to 0.998. Vibrational spectral analysis combined with MI-PLSR method can be used for quantitative analysis of fuel oil properties, and improve the cost-effectiveness.

[Partial least squares regression variable screening studies on apple soluble solids NIR spectral detection].

Abstract To improve the predictive ability and robustness of the NIR correction model of the soluble solid content (SSC) of apple, the reverse interval partial least squares method, genetic algorithm and the continuous projection method were implemented to select variables of the NIR spectroscopy of the soluble solid content (SSC) of apple, and the partial least squares regression model was established. By genetic algorithm for screening of the 141 variables of the correction model, prediction has the best effect. And compared to the full spectrum correction model, the correlation coefficient increased to 0.96 from 0.93, forecast root mean square error decreased from 0.30 degrees Brix to 0.23 degrees Brix. This experimental results show that the genetic algorithm combined with partial least squares regression method improved the detection precision of the NIR model of the soluble solid content (SSC) of apple.

Detection of early bruises on loquat using hyperspectral imaging technology coupled with band ratio and improved Otsu method.

The bruising is one of the major factors affecting the quality of loquat and the bruised areas of loquat are also prone to harbor bacteria and molds. Therefore, it is critical to detect early bruises of loquat. In this study, a method based on hyperspectral imaging technology coupled with band ratio and improved Otsu method was proposed to detect early bruises of loquat. Firstly, the principal component cluster analysis was used to analyze the three regions of Vis-NIR (397.5-1014.0 nm), Vis (397.5-780.0 nm), and NIR (780.0-1014.0 nm), respectively. It was found that the Vis-NIR and NIR spectral regions along PC1 could be used to effectively distinguish bruised tissues. Then, the key wavelength images corresponding to the two regions were selected according to the load curve, respectively, and two sets of PC images and band ratio images of them were established. After comparison, it was found that the band ratio image Q651.3 / 904.3 was the most suitable for subsequent analysis of detecting early bruises of loquat. Finally, in order to evaluate the segmentation effect of the improved Otsu method, the segmentation results of the global threshold and the Otsu method were compared with it, respectively, and it was found that the performance of the improved Otsu method was best. However, since the stem-end area and the bruised area have similar intensity features causing mis-segmentation, the stem-end area was removed by curvature-assisted Hough transform circle detection (CACD) algorithm. And all test set samples were used to evaluate the performance of the proposed method, and the overall accuracy of it was 96.0 %. The results show that the detection method proposed in this study has the potential to detect early bruises of loquat in online practical applications, and it provides a theoretical basis for hyperspectral imaging in the bruise detection of fruit.

[Selection of visible-NIR variables based on extraction and successive projections algorithm].

The pixels were 2 048 or 3 648 for the most Si charge coupled device dector. The interval between the adjacent wavelengths was few. The pretreatment could not deal with the spectra well. Spectral matrix was reconstructed by equal interval extraction in the wavelength range of 600.09-980.47nm. The variables for developing partial least squares (PLS) models were chosen by genetic algorithm (GA) and successive projections algorithm (SPA) from the pretreatment spectra. The models' predictive ability was evaluated by leave-one-out cross validation. By comparison, the best results were obtained by the SPA-PLS models. The standard errors of cross validation (SECV) were 0.661 degrees Brix, 0.067% and 2.91 mg x (100 g)(-1) for soluble solids, total adicity and vitamin C, respectively. The results suggested that the predictive ability can be improved by equal interval extraction method and SPA for determinating the quality of Nanfeng mandarin fruits.

[Detection of firmness and surface color of pear by near infrared spectroscopy based on Monte Carlo uninformative variables elimination method].

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.

[Research on optimization of model for detecting sugar content of navel orange by online near infrared spectroscopy].

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.

[Review of portable NIR instruments for detecting fruit interior quality].

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.

[Application of Raman spectroscopy technique to food quality and safety determination].

With the merits of doing without sample pretreatment, easy operation, short response time and high sensitivity, Raman spectroscopy technique can acquire samples' physical chemistry and the deep structure information. It has been widely applied in petrol chemical, biomedicine, geoarchaeology, criminal justice, gem identification, etc. Raman spectroscopy has good application prospect in food quality and safety determination, for its spectra are not extremely sensitive to polar materials such as water. The detection principle, classification and the system composition of Raman spectroscopy technique were introduced briefly. The latest research progress in food constituent analysis and pesticide residue determination by Raman spectroscopy was reviewed. Finally, its key technologies for food quality and safety determination were pointed out and the future research was prospected.

[Nondestructive measurement of vitamin C in Nanfeng tangerine by visible/near-infrared diffuse reflectance spectroscopy].

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.

[Research on the soluble solids content of pear internal quality index by near-infrared diffuse reflectance spectroscopy].

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.

[Visible transmittance techniques for rapid analysis of sugar content of fresh navel orange juices].

The feasibility of visible spectroscopic technology for rapid quantifying sugar content (SC) of navel orange fresh juices was investigated by means of spectral transmittance technique. A total of 55 juice samples were used to develop the calibration and prediction models. Calibration models based on different spectral ranges and different spectral pretreatment were compared in the precent research. Performance of different models was investigated in terms of root mean square errors of prediction (RMSEP) and correlation coefficient (r) of validation set of samples. The correlation coefficient of calibration model for SC was 0.965, the correlation coefficient of prediction model for SC was 0.857, and the corresponding RMSEP was 0.562. The results show that visible transmittance technique is a feasible method for non-invasive estimation of fruit juice SC.

[Near infrared diffuse reflectance spectroscopy for rapid analysis of soluble solids content in navel orange].

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.

[Estimation of soluble solids content of apple fresh juice by FTNIR spectroscopy].

The feasibility of Fourier transform near infrared (FTNIR) spectroscopic technology for rapid quantifying the soluble solids content (SSC) of apple fresh juice was investigated by means of spectral transmittance technique. A total of 60 juice samples were used to develop the calibration models. The relationship between laboratory SSC and FTNIR spectra of apple fresh juices was analyzed via principal component regression (PCR) and partial least squares (PLS) regression method using TQ 6.2.1 quantitative software. Models based on different spectral ranges were compared. Performance of different models was assessed in terms of root mean square errors of prediction (RMSEP) and correlation coefficients (r2) of validation set of samples. The best prediction models had a RMSEP of 0.603 degree Brix and correlation coefficient (r2) of 0.997 for SSC. The results show that FTNIR transmittance technique is a feasible, accurate and fast method for non-invasive estimation of fruit fresh juice soluble solids content.

[The pear acidity quantified analysis using PLS methods and Fourier transform near-infrared spectroscopy].

Fourier transform near infrared(FT-NIR) spectrum of intact Xueqing pear was obtained by diffusion reflectance. Partial least squares (PLS) regression was carried out, describing the relationships between the data sets of laboratory data and the FT-NIR spectra. Different wave number ranges were chosen for regression and spectral information abstraction. The 3D-curves were shown for different factors, root mean square errors of cross validation (RMSECV), and prediction residual sum of squares (PRESS). Analysis results show that the best calibration model gave the relative high correlation coefficient of 0. 79 and the low standard errors of prediction of 0. 019 when the best wave number range was 5 452-12 285 cm(-1) and the best factor was 7. The method of selecting advantageous wavelength ranges is feasible to obtain high prediction precision.