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2014 Vol. 34, No. 10 Published: 2014-10-01

光谱学与光谱分析

	2593	Congratulate Academician Wanzhen LU 90th Birthday
		Xiao-li CHU
		This coming September will mark the 90th birthday of Academician Wanzhen LU, one of the best scientists in the field of analytical chemistry and petroleum che mistry both in China and around the world. Academician LU was born in Tianjin, Peoples’ Republic of China on September 2 9, 1924. Her father is a famous Chinese industrialist and technologist in textil es. Her mother is an educated and charming lady. Academician LU has one elder br ot her, two younger brothers and a younger sister. Since they all grew up in such a scholarly family, all of them received excellent educations.
		2014 Vol. 34 (10): 2593-2594 [Abstract] ( 590 ) PDF (1997 KB) ( 338 )

	2595	Research and Application Progress of Near Infrared Spectroscopy Analytical Technology in China in the Past Five Years
		CHU Xiao-li, LU Wan-zhen
		DOI: 10.3964/j.issn.1000-0593(2014)10-2595-11
		In the past decade, near infrared spectroscopy (NIR) has expanded rapidly and been applied widely in many fields in China. The recent progress of the research and application of NIR analytical technology in China especially in the past five years has been reviewed. It includes hardware and software R&D, Chemometric algorithms and experimental methods research, and quantitative and qualitative applications in the typical fields such as food, agriculture, pharmaceuticals, petrochemicals, forestry, and medical diagnosis. 209 references are cited, which are mainly published in national journals, professional magazines, and book chapters. The developing trend of near infrared spectroscopy and the strategies to further promote its innovation and development in China in the near future are put forward and discussed.
		2014 Vol. 34 (10): 2595-2605 [Abstract] ( 736 ) PDF (977 KB) ( 992 )

	2606	A Correction Method of Baseline Drift of Discrete Spectrum of NIR
		HU Ai-qin, YUAN Hong-fu^*, SONG Chun-feng, LI Xiao-yu
		DOI: 10.3964/j.issn.1000-0593(2014)10-2606-06
		In the present paper, a new correction method of baseline drift of discrete spectrum is proposed by combination of cubic spline interpolation and first order derivative. A fitting spectrum is constructed by cubic spline interpolation, using the datum in discrete spectrum as interpolation nodes. The fitting spectrum is differentiable. First order derivative is applied to the fitting spectrum to calculate derivative spectrum. The spectral wavelengths which are the same as the original discrete spectrum were taken out from the derivative spectrum to constitute the first derivative spectra of the discrete spectra, thereby to correct the baseline drift of the discrete spectra. The effects of the new method were demonstrated by comparison of the performances of multivariate models built using original spectra, direct differential spectra and the spectra pretreated by the new method. The results show that negative effects on the performance of multivariate model caused by baseline drift of discrete spectra can be effectively eliminated by the new method.
		2014 Vol. 34 (10): 2606-2611 [Abstract] ( 628 ) PDF (3872 KB) ( 677 )

	2612	Prediction of the Side-Cut Product Yield of Atmospheric/Vacuum Distillation Unit by NIR Crude Oil Rapid Assay
		WANG Yan-bin¹, HU Yu-zhong², LI Wen-le¹, ZHANG Wei-song², ZHOU Feng¹, LUO Zhi³
		DOI: 10.3964/j.issn.1000-0593(2014)10-2612-05
		In the present paper, based on the fast evaluation technique of near infrared, a method to predict the yield of atmospheric and vacuum line was developed, combined with H/CAMS software. Firstly, the near-infrared(NIR) spectroscopy method for rapidly determining the true boiling point of crude oil was developed. With commercially available crude oil spectroscopy database and experiments test from Guangxi Petrochemical Company, calibration model was established and a topological method was used as the calibration. The model can be employed to predict the true boiling point of crude oil. Secondly, the true boiling point based on NIR rapid assay was converted to the side-cut product yield of atmospheric/vacuum distillation unit by H/CAMS software. The predicted yield and the actual yield of distillation product for naphtha, diesel, wax and residual oil were compared in a 7-month period. The result showed that the NIR rapid crude assay can predict the side-cut product yield accurately. The near infrared analytic method for predicting yield has the advantages of fast analysis, reliable results, and being easy to online operate, and it can provide elementary data for refinery planning optimization and crude oil blending.
		2014 Vol. 34 (10): 2612-2616 [Abstract] ( 801 ) PDF (3694 KB) ( 640 )

	2617	Update of Near-Infrared Models for Testing Ceftazidime, Water and Arginine in Ceftazidime for Injection
		ZOU Wen-bo, FENG Yan-chun, HU Chang-qin^*
		DOI: 10.3964/j.issn.1000-0593(2014)10-2617-06
		To find a more reasonable index to decide whether the universal quantitative NIR model needs to be updated and to develop a general method to update universal quantitative NIR models, the quantitative models for testing ceftazidime, water and arginine contents in ceftazidime for injection were taken as example. The study was performed by analyzing the similarity between new sample spectra and the training set spectra of the original models. At first, new samples of ceftazidime for injection were divided into five groups by cluster analysis. Then representative samples of each group were selected by sample selection strategy. Spectra of those samples were used to update the original quantitative models. The prediction deviation of the new ceftazidime powder injection samples by the models before and after updating was calculated. Decreasing the prediction deviation was regarded as the standard to decide if the updating was effective. At the same time, the correlation coefficient of new sample spectra and reference sample spectra was defined as the index to study the general method for model updating. (Reference sample refers to training set sample) Finally, the proposed method was validated by updating universal models for testing ceftazidime, water and arginine contents in ceftazidime powder injections. Results show that the correlation coefficient of new sample spectra and training set sample spectra of the original model was calculated within modeling wavelength range. It was proved that when correlation coefficient r_T<96.5%, the model needs to be updated. Accordingly, r_T=96.5% was set as the threshold. The quantitative models were updated by the method mentioned above. As a result, when testing ceftazidime for injection containing sodium carbonate using newly updated models, the average predicting deviation of ceftazidime contents decreased from 8.1% to 2.3%. And the average predicting deviation of water contents decreased from 2.2% to 0.3%. Meanwhile, with regard to samples containing arginine using the updated models, the average predicting deviation of ceftazidime contents decreased from 7.0% to 1.9%. The average predicting deviation of water contents decreased from 0.6% to 0.3%. And that of arginine contents decreased from 2.3% to 0.4%. Conclusion: The newly updated models can be used for testing ceftazidime, water and arginine contens in ceftazidime for injection samples of domestic market. It is reasonable to set r_T as the index to decide whether the model needs updating. Moreover, it is necessary to take PCA scores graph of new sample spectra and training set spectra of the original model into account. The proposed method for updating models can be used as a usual approach. And r_T=96.5% can be set as the threshold to determine whether the model needs to be updated.
		2014 Vol. 34 (10): 2617-2622 [Abstract] ( 346 ) PDF (2255 KB) ( 366 )

	2623	Using 2-DCOS to Identify the Molecular Spectrum Peaks for the Isomer in the Multi-Component Mixture Gases Fourier Transform Infrared Analysis
		ZHAO An-xin^{1, 2}, TANG Xiao-jun^1*, ZHANG Zhong-hua^{1, 3}, LIU Jun-hua¹
		DOI: 10.3964/j.issn.1000-0593(2014)10-2623-04
		The generalized two-dimensional correlation spectroscopy and Fourier transform infrared were used to identify hydrocarbon isomers in the mixed gases for absorption spectra resolution enhancement. The Fourier transform infrared spectrum of n-butane and iso-butane and the two-dimensional correlation infrared spectrum of concentration perturbation were used for analysis as an example. The all band and the main absorption peak wavelengths of Fourier transform infrared spectrum for single component gas showed that the spectra are similar, and if they were mixed together, absorption peaks overlap and peak is difficult to identify. The synchronous and asynchronous spectrum of two-dimensional correlation spectrum can clearly identify the iso-butane and normal butane and their respective characteristic absorption peak intensity. Iso-butane has strong absorption characteristics spectrum lines at 2 893, 2 954 and 2 893 cm^-1, and n-butane at 2 895 and 2 965 cm^-1. The analysis result in this paper preliminary verified that the two-dimensional infrared correlation spectroscopy can be used for resolution enhancement in Fourier transform infrared spectrum quantitative analysis.
		2014 Vol. 34 (10): 2623-2626 [Abstract] ( 701 ) PDF (2432 KB) ( 654 )

	2627	Recent Progress in Diagnosis of Malignant Tumors by Fourier Transform Infrared Spectroscopy
		TIAN Pei-rong, ZHANG Wei-tao, XU Zhi^*
		DOI: 10.3964/j.issn.1000-0593(2014)10-2627-05
		Malignant tumors pose a serious threat to mankind health and life. As a result, early diagnosis is very important. In recent years, Fourier transform infrared spectroscopy has shown enormous development potential of cancer diagnosis. Compared with traditional methods, this technology has apparent advantages in the aspects of accuracy, rapidity, noninvasion, in situ, cheapness, automation, replication, without pretreatment and early diagnosis at the molecular level. This paper summarized study progress that FTIR technology applied in diagnosis of respiratory system tumor, digestive system tumor, urinary genital system tumor, brain tumor, skin tumor and blood system tumor, and combined with the international present state of clinical medicine, spectroscopy and chemometrics, five prospects were put forward: expand the sample size and undertake multi-center study; combined with endoscopy and puncture biopsy to guide real-time in situ diagnosis and biopsy during surgery; further automated; find more efficient chemometric methods; the identification of individual parameters has yet to be confirmed by further studies. With the further development and improvement of FTIR technology, it will become an important method for the diagnosis of malignant tumors, and may even as a routine screening tool applied to stage and grade the tumors.
		2014 Vol. 34 (10): 2627-2631 [Abstract] ( 651 ) PDF (928 KB) ( 489 )

	2632	Applications and Prospects of On-Line Near Infrared Spectroscopy Technology in Manufacturing of Chinese Materia Medica
		LI Yang, WU Zhi-sheng^, PAN Xiao-ning, SHI Xin-yuan, GUO Ming-ye, XU Bing, QIAO Yan-jiang^
		DOI: 10.3964/j.issn.1000-0593(2014)10-2632-07
		The quality of Chinese materia medica (CMM) is affected by every process in CMM manufacturing. According to multi-unit complex features in the production of CMM, on-line near infrared spectroscopy (NIR) is used as an evaluating technology with its rapid, non-destructive and non-pollution etc. advantages. With the research in institutions, the on-line NIR applied in process analysis and control of CMM was described systematically, and the on-line NIR platform building was used as an example to clarify the feasibility of on-line NIR technology in CMM manufacturing process. Then, from the point of application by pharmaceutical companies, the current on-line NIR research on CMM and its production in pharmaceutical companies was relatively comprehensively summarized. Meanwhile, the types of CMM productions were classified in accordance with two formulations (liquid and solid dosage formulations). The different production processes (extraction, concentration and alcohol precipitation, etc.) were used as liquid formulation diacritical points; the different types (tablets, capsules and plasters, etc.) were used as solid dosage formulation diacritical points, and the reliability of on-line NIR used in the whole process in CMM production was proved in according to the summary of literatures in recent 10 years, which could support the modernization of CMM production.
		2014 Vol. 34 (10): 2632-2638 [Abstract] ( 629 ) PDF (1179 KB) ( 408 )

	2639	Applications of Near Infrared Reflectance Spectroscopy Technique (NIRS) to Soil Attributes Research
		LIU Yan-de¹, XIONG Song-sheng¹, LIU De-li²
		DOI: 10.3964/j.issn.1000-0593(2014)10-2639-06
		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.
		2014 Vol. 34 (10): 2639-2644 [Abstract] ( 619 ) PDF (1214 KB) ( 401 )

	2645	*Study on the Detection of Active Ingredient Contents of Paecilomyces hepiali* Mycelium via Near Infrared Spectroscopy**
		TENG Wei-zhuo¹, SONG Jia¹, MENG Fan-xin², MENG Qing-fan¹, LU Jia-hui¹, HU Shuang¹, TENG Li-rong^1,2, WANG Di^1, XIE Jing^1
		DOI: 10.3964/j.issn.1000-0593(2014)10-2645-07
		Partial least squares (PLS) and radial basis function neural network (RBFNN) combined with near infrared spectroscopy (NIR) were applied to develop models for cordycepic acid, polysaccharide and adenosine analysis in Paecilomyces hepialid fermentation mycelium. The developed models possess well generalization and predictive ability which can be applied for crude drugs and related productions determination. During the experiment, 214 Paecilomyces hepialid mycelium samples were obtained via chemical mutagenesis combined with submerged fermentation. The contents of cordycepic acid, polysaccharide and adenosine were determined via traditional methods and the near infrared spectroscopy data were collected. The outliers were removed and the numbers of calibration set were confirmed via Monte Carlo partial least square (MCPLS) method. Based on the values of degree of approach (Da), both moving window partial least squares (MWPLS) and moving window radial basis function neural network (MWRBFNN) were applied to optimize characteristic wavelength variables, optimum preprocessing methods and other important variables in the models. After comparison, the RBFNN, RBFNN and PLS models were developed successfully for cordycepic acid, polysaccharide and adenosine detection, and the correlation between reference values and predictive values in both calibration set (R²_c) and validation set (R²_p) of optimum models was 0.941 7 and 0.966 3, 0.980 3 and 0.985 0, and 0.976 1 and 0.972 8, respectively. All the data suggest that these models possess well fitness and predictive ability.
		2014 Vol. 34 (10): 2645-2651 [Abstract] ( 734 ) PDF (2017 KB) ( 288 )

	2652	Rapid Determination of the Multi-Marker Ingredients in Heterosmilacis Japonicae Rhizoma and Sophorae Flavescentis Radix with Near-Infrared Diffused Reflection Spectroscopy
		ZHAO Feng-chun, WAN Kai-yang, LIU Xiao-qian, ZHANG Qi-wei, GAO Hui-min^, WANG Zhi-min^
		DOI: 10.3964/j.issn.1000-0593(2014)10-2652-05
		A rapid NIRS method for determination of macrozamin in Heterosmilacis japonicae rhizoma (HJR), and the total content of oxymatrine and matrine (OMT+MT) as well as the total content of oxysophocarpine and sophocarpine (OSC+SC) in sophorae flavescens radix (SFR) was developed to explore the application feasibility of NIRS for the quality assurance system of Chinese patent drugs. The contents of macrozamin in HJR samples, and OMT+MT and OSC+SC in SFR samples were determined by HPLC as reference values. The NIR spectra of the samples were measured in a diffused reflection mode. The different characteristic wavebands and pretreatment methods were optimized. The quantitative calibration models between the NIR spectra and the content reference values of marker components in HJR and SFR samples, were established with partial least square method, and further optimized through the cross validation and external validation. The contents of macrozamin in 88 batches of HJR samples were over the range of 0.36~12.88 mg·g^-1. The total contents of OMT+MT and OSC+SC in 75 batches of SFR samples were over the range of 8.87~66.31 and 2.30~15.11 mg·g^-1, respectively. The performance of the final models for macrozamin, OMT+MT and OSC+SC was evaluated well according to correlation coefficients (r), root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP). The R² values of the cross-validation for macrozamin, OMT+MT and OSC+SC were 0.902 5, 0.949 1 and 0.913 7, and those of RMSECV were 0.961, 2.45 and 0.724 mg·g^-1 respectively. The R² values of external validation for the three models were 0.981 7, 0.982 6 and 0.960 9, and those of RMSEP were 0.693, 2.27 and 0.658 mg·g^-1, respectively. This is the first report on rapid determination of macrozamin in Heterosmilacis japonicae rhizoma, and oxymatrine, matrine, oxysophocarpine and sophocarpine in sophorae flavescens radix by NIRS method. The presented method can fulfill the requirement of rapid acquirement of chemical information of raw medicinal materials prior the manufacturing of compound Kushen injection.
		2014 Vol. 34 (10): 2652-2656 [Abstract] ( 605 ) PDF (3489 KB) ( 469 )

	2657	Application of Uncertainty Assessment in NIR Quantitative Analysis of Traditional Chinese Medicine
		XUE Zhong¹, XU Bing^1, LIU Qian¹, SHI Xin-yuan^{1, 2}, LI Jian-yu¹, WU Zhi-sheng¹, QIAO Yan-jiang^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2014)10-2657-05
		The near infrared (NIR) spectra of Liuyi San samples were collected during the mixing process and the quantitative models by PLS (partial least squares) method were generated for the quantification of the concentration of glycyrrhizin. The PLS quantitative model had good calibration and prediction performances (r_cal=0.998 5，RMSEC=0.044 mg·g^-1; r_val=0.947 4，RMSEP=0.124 mg·g^-1), indicating that NIR spectroscopy can be used as a rapid determination method of the concentration of glycyrrhizin in Liuyi San powder. After the validation tests were designed, the Liao-Lin-Iyer approach based on Monte Carlo simulation was used to estimate β-content-γ-confidence tolerance intervals. Then the uncertainty was calculated, and the uncertainty profile was drawn. The NIR analytical method was considered valid when the concentration of glycyrrhizin is above 1.56 mg·g^-1 since the uncertainty fell within the acceptable limits (λ=±20%). The results showed that uncertainty assessment can be used in NIR quantitative models of glycyrrhizin for different concentrations and provided references for other traditional Chinese medicine to finish the uncertainty assessment using NIR quantitative analysis.
		2014 Vol. 34 (10): 2657-2661 [Abstract] ( 724 ) PDF (1607 KB) ( 395 )

	2662	Determination of Wine Original Regions Using Information Fusion of NIR and MIR Spectroscopy
		XIANG Ling-li¹, LI Meng-hua¹, LI Jing-ming^2, LI Jun-hui¹, ZHANG Lu-da¹, ZHAO Long-lian^1
		DOI: 10.3964/j.issn.1000-0593(2014)10-2662-05
		Geographical origins of wine grapes are significant factors affecting wine quality and wine prices. Tasters’ evaluation is a good method but has some limitations. It is important to discriminate different wine original regions quickly and accurately. The present paper proposed a method to determine wine original regions based on Bayesian information fusion that fused near-infrared (NIR) transmission spectra information and mid-infrared (MIR) ATR spectra information of wines. This method improved the determination results by expanding the sources of analysis information. NIR spectra and MIR spectra of 153 wine samples from four different regions of grape growing were collected by near-infrared and mid-infrared Fourier transform spectrometer separately. These four different regions are Huailai, Yantai, Gansu and Changli, which are all typical geographical originals for Chinese wines. NIR and MIR discriminant models for wine regions were established using partial least squares discriminant analysis (PLS-DA) based on NIR spectra and MIR spectra separately. In PLS-DA, the regions of wine samples are presented in group of binary code. There are four wine regions in this paper, thereby using four nodes standing for categorical variables. The output nodes values for each sample in NIR and MIR models were normalized first. These values stand for the probabilities of each sample belonging to each category. They seemed as the input to the Bayesian discriminant formula as a priori probability value. The probabilities were substituteed into the Bayesian formula to get posterior probabilities, by which we can judge the new class characteristics of these samples. Considering the stability of PLS-DA models, all the wine samples were divided into calibration sets and validation sets randomly for ten times. The results of NIR and MIR discriminant models of four wine regions were as follows: the average accuracy rates of calibration sets were 78.21% (NIR) and 82.57% (MIR), and the average accuracy rates of validation sets were 82.50% (NIR) and 81.98% (MIR). After using the method proposed in this paper, the accuracy rates of calibration and validation changed to 87.11% and 90.87% separately, which all achieved better results of determination than individual spectroscopy. These results suggest that Bayesian information fusion of NIR and MIR spectra is feasible for fast identification of wine original regions.
		2014 Vol. 34 (10): 2662-2666 [Abstract] ( 871 ) PDF (1954 KB) ( 345 )

	2667	Application of Fourier Transform Infrared Spectroscopy in Identification of Wine Spoilage
		ZHAO Xian-de, DONG Da-ming^*, ZHENG Wen-gang, JIAO Lei-zi, LANG Yun
		DOI: 10.3964/j.issn.1000-0593(2014)10-2667-06
		In the present work, fresh and spoiled wine samples from three wines produced by different companies were studied using Fourier transform infrared (FTIR) spectroscopy. We analyzed the physicochemical property change in the process of spoilage, and then, gave out the attribution of some main FTIR absorption peaks. A novel determination method was explored based on the comparisons of some absorbance ratios at different wavebands although the absorbance ratios in this method were relative. Through the compare of the wine spectra before and after spoiled, the authors found that they were informative at the bands of 3 020～2 790, 1 760～1 620 and 1 550～800 cm^-1. In order to find the relation between these informative spectral bands and the wine deterioration and achieve the discriminant analysis, chemometrics methods were introduced. Principal compounds analysis (PCA) and soft independent modeling of class analogy (SIMCA) were used for classifying different-quality wines. And partial least squares discriminant analysis (PLS-DA) was applied to identify spoiled wines and good wines. Results showed that FTIR technique combined with chemometrics methods could effectively distinguish spoiled wines from fresh samples. The effect of classification at the wave band of 1 550～800 cm^-1 was the best. The recognition rate of SIMCA and PLS-DA were respectively 94% and 100%. This study demonstrates that Fourier transform infrared spectroscopy is an effective tool for monitoring red wine’s spoilage and provides theoretical support for developing early-warning equipments.
		2014 Vol. 34 (10): 2667-2672 [Abstract] ( 565 ) PDF (2302 KB) ( 369 )

	2673	Identification of Adulterants in Adulterated Milks by Near Infrared Spectroscopy Combined with Non-Linear Pattern Recognition Methods
		NI Li-jun¹, ZHONG Lin¹, ZHANG Xin², ZHANG Li-guo^1*, HUANG Shi-xin²
		DOI: 10.3964/j.issn.1000-0593(2014)10-2673-06
		In the present work, two hundred and eighty seven raw milks collected from pastures in Shanghai and surrounding areas of Shanghai were used as true milk samples and divided into three true milk sets. Five hundred and twenty six adulterated milk samples, which contained dextrin (or starch) mixed with melamine (or urea, or ammonium nitrate), were prepared as six different adulterated milk sets. The concentrations of these adulterants in the adulterated milks were designed to be 0.15%~0.45% (starch or dextrin), 700~2 100 mg·kg^-1 (ammonium nitrate), 524~1 572 mg·kg^-1 (urea), and 365.5~1 096.5 mg·kg^-1 (melamine) to guarantee the protein content of adulterated milks detected by Kjeldahl method not lower than 3%. All the near infrared spectra (NIR) of the samples should have a pretreatment of normal variable transformation (SNV) before they were used to build discriminating models. The three true milk sets and six adulterated milk sets were combined in different ways in order to build NIR models for discriminating different kinds of adulterants (i. e., dextrin, starch, melamine, urea and ammonium nitrate) based on simplified K-nearest neighbor classification algorithm (IS-KNN) and an improved and simplified of support vector machine (ν-SVM) method. The relationship between mass concentration of the adulterants and the rate of correct discrimination was also investigated. The results show that the average discrimination accuracy of IS-KNN and ν-SVM for identifying melamine, urea and ammonium nitrate were in the region of 49.55% to 51.01%, 61.78% to 68.79% and 68.25% to 73.51%, respectively. Therefore within the concentration regions designed in this study, it is difficult to distinguish different kinds of pseudo proteins by NIR spectroscopy. However, the average accuracy of IS-KNN and ν-SVM for identifying starch and dextrin are 92.33% and 93.66%, 77.29% and 85.08%, respectively. Most discrimination results of ν-SVM are better than those of IS-KNN. The correlative analysis between the discrimination accuracy rate and the content levels of the adulterants indicated that near infrared spectroscopy combined with non-linear pattern recognition methods can distinguish dextrin and starch in milks with higher concentration levels (>0.15%), but do not work well on identifying the adulterants with lower concentrations such as melamine (365.5 to 1 096.5 mg·kg^-1), urea (524 to 1 572 mg·kg^-1), ammonium nitrate (700 to 2 100 mg·kg^-1). Therefore near Infrared Spectroscopy is not suitable for identifying the adulterants with concentrations are below 0.1%.
		2014 Vol. 34 (10): 2673-2678 [Abstract] ( 598 ) PDF (3185 KB) ( 320 )

	2679	On-Site Evaluation of Raw Milk Qualities by Portable Vis/NIR Transmittance Technique
		WANG Jia-hua¹, ZHANG Xiao-wei¹, WANG Jun¹, HAN Dong-hai^2*
		DOI: 10.3964/j.issn.1000-0593(2014)10-2679-06
		To ensure the material safety of dairy products, visible (Vis)/near infrared (NIR) spectroscopy combined with chemometrics methods was used to develop models for fat, protein, dry matter (DM) and lactose on-site evaluation. A total of 88 raw milk samples were collected from individual livestocks in different years. The spectral of raw milk were measured by a portable Vis/NIR spectrometer with diffused transmittance accessory. To remove the scatter effect and baseline drift, the diffused transmittance spectra were preprocessed by 2nd order derivative with Savitsky-Golay (polynomial order 2, data point 25). Changeable size moving window partial least squares (CSMWPLS) and genetic algorithms partial least squares (GAPLS) methods were suggested to select informative regions for PLS calibration. The PLS and multiple linear regression (MLR) methods were used to develop models for predicting quality index of raw milk. The prediction performance of CSMWPLS models were similar to GAPLS models for fat, protein, DM and lactose evaluation, the root mean standard errors of prediction (RMSEP) were 0.115 6/0.103 3, 0.096 2/0.113 7, 0.201 3/0.123 7 and 0.077 4/0.066 8, and the relative standard deviations of prediction (RPD) were 8.99/10.06, 3.53/2.99, 5.76/9.38 and 1.81/2.10, respectively. Meanwhile, the MLR models were also calibrated with 8, 10, 9 and 7 variables for fat, protein, DM and lactose, respectively. The prediction performance of MLR models was better than or close to PLS models. The MLR models to predict fat, protein, DM and lactose yielded the RMSEP of 0.107 0, 0.093 0, 0.136 0 and 0.065 8, and the RPD of 9.72, 3.66, 8.53 and 2.13, respectively. The results demonstrated the usefulness of Vis/NIR spectra combined with multivariate calibration methods as an objective and rapid method for the quality evaluation of complicated raw milks. And the results obtained also highlight the potential of portable Vis/NIR instruments for on-site assessing quality indexes of raw milk.
		2014 Vol. 34 (10): 2679-2684 [Abstract] ( 629 ) PDF (2997 KB) ( 384 )

	2685	Potentiality of Synchronous Fluorescence Technology for Determination of Reconstituted Milk Adulteration in Fresh Milk
		LIU Huan, HAN Dong-hai^, WANG Shi-ping^
		DOI: 10.3964/j.issn.1000-0593(2014)10-2685-05
		In the present research, synchronous fluorescence technique was used for qualitative and quantitative detection of reconstituted milk mixed into two kinds of milk samples, raw milk and pasteurized milk, respectively. The total accuracy of sample was used to evaluate the performance of the qualitative discrimination models. The correlation coefficient (r), the root mean square error of correction (RMSEC) and the root mean square error of prediction (RMSEC) were used to evaluate the performance of the quantitative analysis models. The constant wavelength difference (Δλ) between the excitation and emission scanning was determined to be 80 nm from three-dimensional fluorescence spectrum of milk. The total discrimination accuracy was 100% by partial least squares discrimination analysis (PLS-DA) for raw milk, pasteurized milk and reconstituted milk samples. When checking whether the raw milk and pasteurized milk were mixed with reconstituted milk, the total accuracy of calibration samples was 100% and the accuracy of prediction samples was 75% and 81.25%, respectively. The effects of qualitative discrimination models were satisfactory. The PLS regression was used for quantitative analysis of the reconstituted milk content mixed in raw milk and pasteurized milk. The correlation coefficients of actual values versus predicted values were 0.911 2 and 0.911 2, respectively. The RMSEC was 0.042 2 and 0.038 4, respectively. The RMSEP was 0.054 8 and 0.057 5, respectively. The correlation coefficients of quantitative analysis models could reach up to 0.9. The results showed that synchronous fluorescence technology could be applied for rapid detection of reconstituted milk mixed in fresh milk.
		2014 Vol. 34 (10): 2685-2689 [Abstract] ( 629 ) PDF (2751 KB) ( 328 )

	2690	Authentication and Adulteration Analysis of Sesame Oil by FTIR Spectroscopy
		DING Qing-zhen¹, LIU Ling-ling¹, WU Yan-wen², LI Bing-ning², OUYANG Jie^1*
		DOI: 10.3964/j.issn.1000-0593(2014)10-2690-06
		It’s common in edible oil market that adulterating low price oils in high price oils. Sesame oil was often adulterated because of its high quality and price, so the authentication and adulteration of sesame oil were qualitatively and quantitatively analyzed by Fourier transform infrared (FTIR) spectroscopy combined with chemometrics. Firstly, FTIR spectra of sesame oil, soybean oil, and sunflower seed oil in 4 000～650 cm^-1 were analyzed. It was very difficult to detect the difference among the spectra of above edible oils, because they are all mixtures of triglyceride fatty acids and have similar spectra. However, the FTIR data of edible oils in the fingerprint region of 1 800～650 cm^-1 differed slightly because their fatty acid compositions are different, so the data could be classified and recognized by chemometric methods. The authenticity model of sesame oil was built by principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA). The recognition rate was 100%, and the built model was satisfactory. The classification limits of both soybean oil and sunflower seed oil adulterated in sesame oil were 10%, with the chemometric treatments of standard normal variation (SNV), partial least square (PLS) and PCA. In addition, the FTIR data processed by PCA and PLS were used to establish an analysis model of binary system of sesame oil mixed with soybean oil or sunflower oil, the prediction values had good corresponding relationship with true values, and the relative errors of prediction were between -6.87% and 8.07%, which means the quantitative model was practical. This method is very convenient and rapid after the models have been built, and can be used for rapid detection of authenticity and adulteration of sesame oil. The method is also practical and suitable for the daily analysis of large amount of samples.
		2014 Vol. 34 (10): 2690-2695 [Abstract] ( 928 ) PDF (2752 KB) ( 338 )

	2696	Raman Spectroscopy Combined with Pattern Recognition Methods for Rapid Identification of Crude Soybean Oil Adulteration
		LI Bing-ning, WU Yan-wen^*, WANG Yu, ZU Wen-chuan, CHEN Shun-cong
		DOI: 10.3964/j.issn.1000-0593(2014)10-2696-05
		In the present paper, a non-destructive, simple and rapid analytical method was proposed based on Raman spectroscopy (Raman) combined with principal component analysis (PCA) and support vector machine (SVM) as pattern recognition methods for adulteration of crude soybean oil (CSO). Based on fingerprint characteristics of Raman, the spectra of 28 CSOs, 46 refined edible oils (REOs) and 110 adulterated oil samples were analyzed and used for discrimination model establishment. The preprocessing methods include choosing spectral band of 780~1 800 cm^-1, Y-axis intensity correction, baseline correction and normalization in succession. After those series of spectral pretreatment, PCA was usually employed for extracting characteristic variables of all Raman spectral data and 7 principal components which were the highest contributions of all data were used as variables for SVM model. The SVM discrimination model was established by randomly picking 20 CSOs and 95 adulterated oils as calibration set, and 8 CSOs and 35 adulterated oils as validation set. There were 4 kinds of kernel function algorithm (linear, polynomial, RBF, sigmoid) respectively used for establishing SVM models and grid-search for optimization of parameters of all the SVM models. The classification results of 4 models were compared by their discrimination performances and the optimal SVM model was based on linear kernel classification algorithm with 100% accuracy rate of calibration set recognition, a zero misjudgment rate and the lowest detection limit of 2.5%. The above results showed that Raman combined PCA-SVM could discriminate CSO adulteration with refined edible oils. Since Raman spectroscopy is simple, rapid, non-destructive, environment friendly, and suitable for field testing, it will provide an alternative method for edible oil adulteration analysis.
		2014 Vol. 34 (10): 2696-2700 [Abstract] ( 587 ) PDF (2358 KB) ( 442 )

	2701	Vis-NIR Spectroscopic Pattern Recognition Combined with SG Smoothing Applied to Breed Screening of Transgenic Sugarcane
		LIU Gui-song¹, GUO Hao-song¹, PAN Tao^1*, WANG Ji-hua², CAO Gan²
		DOI: 10.3964/j.issn.1000-0593(2014)10-2701-06
		Based on Savitzky-Golay (SG) smoothing screening, principal component analysis (PCA) combined with separately supervised linear discriminant analysis (LDA) and unsupervised hierarchical clustering analysis (HCA) were used for non-destructive visible and near-infrared (Vis-NIR) detection for breed screening of transgenic sugarcane. A random and stability-dependent framework of calibration, prediction, and validation was proposed. A total of 456 samples of sugarcane leaves planting in the elongating stage were collected from the field, which was composed of 306 transgenic (positive) samples containing Bt and Bar gene and 150 non-transgenic (negative) samples. A total of 156 samples (negative 50 and positive 106) were randomly selected as the validation set; the remaining samples (negative 100 and positive 200, a total of 300 samples) were used as the modeling set, and then the modeling set was subdivided into calibration (negative 50 and positive 100, a total of 150 samples) and prediction sets (negative 50 and positive 100, a total of 150 samples) for 50 times. The number of SG smoothing points was expanded, while some modes of higher derivative were removed because of small absolute value, and a total of 264 smoothing modes were used for screening. The pairwise combinations of first three principal components were used, and then the optimal combination of principal components was selected according to the model effect. Based on all divisions of calibration and prediction sets and all SG smoothing modes, the SG-PCA-LDA and SG-PCA-HCA models were established, the model parameters were optimized based on the average prediction effect for all divisions to produce modeling stability. Finally, the model validation was performed by validation set. With SG smoothing, the modeling accuracy and stability of PCA-LDA, PCA-HCA were significantly improved. For the optimal SG-PCA-LDA model, the recognition rate of positive and negative validation samples were 94.3%, 96.0%; and were 92.5%, 98.0% for the optimal SG-PCA-LDA model, respectively. Conclusion: Vis-NIR spectroscopic pattern recognition combined with SG smoothing could be used for accurate recognition of transgenic sugarcane leaves, and provided a convenient screening method for transgenic sugarcane breeding.
		2014 Vol. 34 (10): 2701-2706 [Abstract] ( 814 ) PDF (1885 KB) ( 279 )

	2707	Application of Characteristic NIR Variables Selection in Portable Detection of Soluble Solids Content of Apple by Near Infrared Spectroscopy
		FAN Shu-xiang^{1, 2}, HUANG Wen-qian², LI Jiang-bo²，GUO Zhi-ming², ZHAO Chun-jiang^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2014)10-2707-06
		In order to detect the soluble solids content(SSC)of apple conveniently and rapidly, a ring fiber probe and a portable spectrometer were applied to obtain the spectroscopy of apple. Different wavelength variable selection methods, including uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were proposed to select effective wavelength variables of the NIR spectroscopy of the SSC in apple based on PLS. The back interval LS-SVM (BiLS-SVM) and GA were used to select effective wavelength variables based on LS-SVM. Selected wavelength variables and full wavelength range were set as input variables of PLS model and LS-SVM model, respectively. The results indicated that PLS model built using GA-CARS on 50 characteristic variables selected from full-spectrum which had 1512 wavelengths achieved the optimal performance. The correlation coefficient (R_p) and root mean square error of prediction (RMSEP) for prediction sets were 0.962, 0.403°Brix respectively for SSC. The proposed method of GA-CARS could effectively simplify the portable detection model of SSC in apple based on near infrared spectroscopy and enhance the predictive precision. The study can provide a reference for the development of portable apple soluble solids content spectrometer.
		2014 Vol. 34 (10): 2707-2712 [Abstract] ( 306 ) PDF (2213 KB) ( 329 )

	2713	Detection of Citrus Greening Based on Vis-NIR Spectroscopy and Spectral Feature Analysis
		MA Hao¹, JI Hai-yan^1*, Won Suk Lee²
		DOI: 10.3964/j.issn.1000-0593(2014)10-2713-06
		In the present paper we discussed the methods of classification of citrus greening and extraction of spectral features based on the spectral reflectance of four different statuses of citrus leaves (healthy, HLB, iron deficiency and nitrogen deficiency). Between two classes of classification, the values of discriminability of different spectra were calculated to extract spectral features. The greater value of discriminability showed a bigger difference of the two spectra, which means it would be easier to distinguish the two classes. By the Fisher linear discriminant analysis, three classification models (HLB & healthy, HLB & iron deficiency and HLB & nitrogen deficiency) based on the spectral features yielded more than 90% accuracies, which were better than expected. And at last, we discussed the application of the classification tree in multi-class discriminant analysis and spectral features extraction. The models trained based on the original reflectance spectra, first derivative and selected spectral features yielded more than 88% average accuracy, and especially the model based on the spectral features yielded more than 94% average accuracies, which verified the feasibility of detection of citrus greening in multi-class discriminant analysis and the importance of the spectral feature extraction. The results were compared based on classification tree, k-NN and Bayesian classifiers. Adoption of spectral features as input variables was significantly superior to using the original spectrum, which confirmed the validity of spectral feature selection. Spectral features could be used well for developing a multi-spectral imaging system to detect the citrus greening.
		2014 Vol. 34 (10): 2713-2718 [Abstract] ( 661 ) PDF (2947 KB) ( 289 )

	2719	*Determination of Steviol in Stevia Rebaudiana* Leaves by Near Infrared Spectroscopy**
		TANG Qi-kun¹, WANG Yu^{1, 2*}, WU Yue-jin³, MIN Di¹, CHEN Da-wei¹, HU Tong-hua¹
		DOI: 10.3964/j.issn.1000-0593(2014)10-2719-04
		The objective of the present study is to develop a method for rapid determination of the content of stevioside (ST) and rebaudioside A (RA) in Stevia Rebaudiana leaves. One hundred and five samples of stevia from different areas containing ST of 0.27%～1.40% and RA of 0.61%～3.98% were used. The 105 groups’ NIRS diagram was processed by different methods including subtracting a straight line (SLS), multiplicative scatter correction (MSC), first derivative (FD), second derivative (SD) and so on, and then all data were analyzed by partial least square (PLS). The study showed that SLS can be used to extracted spectra information thoroughly to analyze the contents of ST, the correlation coefficients of calibration (R_c), the root-mean-square errors of calibration (RMSEC) and prediction (RMSEP), and the residual predictive deviation (RPD) were 0.986, 0.341, 1.00 and 2.8, respectively. The correlation coefficients of RA was 0.967, RMSEC was 1.50, RMSEP was 1.98 and RPD was 4.17. The results indicated that near infrared spectroscopy (NIRS) technique offers effective quantitative capability for ST and RA in Stevia Rebaudiana leaves. Then the model of stevia dried leaves was used to compare with the stevia powder near infrared model whose correlation coefficients of ST was 0.986, RMSEC was 0.32, RMSEP was 0.601 and RPD was 2.86 and the correlation coefficients of RA was 0.968, RMSEC was 1.50, RMSEP was 1.48 and RPD was 4.2. The result showed that there was no significant difference between the model of dried leaves and that of the powders. However, the dried leaves NIR model reduces the unnecessary the steps of drying and grinding in the actual detection process, saving the time and reducing the workload.
		2014 Vol. 34 (10): 2719-2722 [Abstract] ( 731 ) PDF (2205 KB) ( 780 )

	2723	Rapid Discriminating Hogwash Oil and Edible Vegetable Oil Using Near Infrared Optical Fiber Spectrometer Technique
		ZHANG Bing-fang^{1, 2}, YUAN Li-bo^1*, KONG Qing-ming³, SHEN Wei-zheng³, ZHANG Bing-xiu⁴, LIU Cheng-hai⁵
		DOI: 10.3964/j.issn.1000-0593(2014)10-2723-05
		In the present study, a new method using near infrared spectroscopy combined with optical fiber sensing technology was applied to the analysis of hogwash oil in blended oil. The 50 samples were a blend of frying oil and “nine three” soybean oil according to a certain volume ratio. The near infrared transmission spectroscopies were collected and the quantitative analysis model of frying oil was established by partial least squares (PLS) and BP artificial neural network. The coefficients of determination of calibration sets were 0.908 and 0.934 respectively. The coefficients of determination of validation sets were 0.961 and 0.952, the root mean square error of calibrations (RMSEC) was 0.184 and 0.136, and the root mean square error of predictions (RMSEP) was all 0.111 6. They conform to the model application requirement. At the same time, frying oil and qualified edible oil were identified with the principal component analysis (PCA), and the accurate rate was 100%. The experiment proved that near infrared spectral technology not only can quickly and accurately identify hogwash oil, but also can quantitatively detect hogwash oil. This method has a wide application prospect in the detection of oil.
		2014 Vol. 34 (10): 2723-2727 [Abstract] ( 692 ) PDF (2189 KB) ( 310 )

	2728	Rapid Determination of Beet Sugar Content Using Near Infrared Spectroscopy
		YANG Yong^1，2, REN Jian¹, ZHENG Xi-qun^1*, ZHAO Li-ying², LI Mao-mao¹
		DOI: 10.3964/j.issn.1000-0593(2014)10-2728-04
		In order to classify and set different prices on basis of difference of beet sugar content in the acquisition process and promote the development of beet sugar industry healthily, a fast, nondestructive, accurate method to detect sugar content of beet was determined by applying near infrared spectroscopy technology. Eight hundred twenty samples from 28 representative varieties of beet were collected as calibration set and 70 samples were chosen as prediction set. Then near infrared spectra of calibration set samples were collected by scanning, effective information was extracted from NIR spectroscopy, and the original spectroscopy data was optimized by data preprocessing methods appropriately. Then partial least square(PLS)regression was used to establish beet sugar quantitative prediction mathematical model. The performances of the models were evaluated by the root mean square of cross-validation (RMSECV), the coefficient of determination (R²) of the calibration model and the standard error of prediction (SEP), and the predicted results of these models were compared. Results show that the established mathematical model by using first derivative(FD) and standard normal variate transformation(SNV) coupled with partial least squares has good predictive ability. The R² of calibration models of sugar content of beet is 0.908 3, and the RMSECV is 0.376 7. Using this model to forecast the prediction set including 70 samples, the correlation coefficient is 0.921 4 between predicted values and measured values, and the standard error of prediction (SEP) is 0.439, without significant difference (p＞0.05) between predicted values and measured values. These results demonstrated that NIRS can take advantage of simple, rapid, nondestructive and environmental detection method and could be applied to predict beet sugar content. This model owned high accuracy and can meet the precision need of determination of beet sugar content. This detection method could be used to classify and set different prices on basis of difference of beet sugar content in the acquisition process.
		2014 Vol. 34 (10): 2728-2731 [Abstract] ( 638 ) PDF (1595 KB) ( 404 )

	2732	Freshwater Fish Freshness On-Line Detection Method Based on Near-Infrared Spectroscopy
		HUANG Tao¹, LI Xiao-yu^1*, PENG Yi², TAO Hai-long¹, LI Peng¹, XIONG Shan-bai³
		DOI: 10.3964/j.issn.1000-0593(2014)10-2732-05
		In the present study, the near infrared spectrum of freshwater fish was used to detect the freshness on line, and the near infrared spectra on-line acquisition device was built to get the fish spectrum. In the process of spectrum acquisition, experiment samples move at a speed of 0.5 m·s^-1, the near-infrared diffuse reflection spectrum (900~2 500 nm) could be got for the next analyzing, and SVM was used to build on-line detection model. Sample set partitioning based on joint X-Y distances algorithm (SPXY) was used to divide sample set, there were 111 samples in calibration set (57 fresh samples and 54 bad samples), and 37 samples in test set (19 fresh samples and 18 bad samples). Seven spectral preprocessing methods were utilized to preprocess the spectrum, and the influences of different methods were compared. Model results indicated that first derivative (FD) with autoscale was the best preprocessing method, the model recognition rate of calibration set was 97.96%, and the recognition rate of test set was 95.92%. In order to improve the modeling speed, it is necessary to optimize the spectra variables. Therefore genetic algorithm (GA), successive projection algorithm (SPA) and competitive adaptive reweighed sampling (CARS) were adopted to select characteristic variables respectively. Finally CARS was proved to be the optimal variable selection method, 10 characteristic wavelengths were selected to develop SVM model, recognition rate of calibration set reached 100%, and recognition rate of test set was 93.88%. The research provided technical reference for freshwater fish freshness online detection.
		2014 Vol. 34 (10): 2732-2736 [Abstract] ( 732 ) PDF (1800 KB) ( 399 )

	2737	Discrimination of Donkey Meat by NIR and Chemometrics
		NIU Xiao-ying¹, SHAO Li-min², DONG Fang¹, ZHAO Zhi-lei¹, ZHU Yan¹
		DOI: 10.3964/j.issn.1000-0593(2014)10-2737-06
		Donkey meat samples (n=167) from different parts of donkey body (neck, costalia, rump, and tendon), beef (n=47), pork (n=51) and mutton (n=32) samples were used to establish near-infrared reflectance spectroscopy (NIR) classification models in the spectra range of 4 000～12 500 cm^-1. The accuracies of classification models constructed by Mahalanobis distances analysis, soft independent modeling of class analogy (SIMCA) and least squares-support vector machine (LS-SVM), respectively combined with pretreatment of Savitzky-Golay smooth (5, 15 and 25 points) and derivative (first and second), multiplicative scatter correction and standard normal variate, were compared. The optimal models for intact samples were obtained by Mahalanobis distances analysis with the first 11 principal components (PCs) from original spectra as inputs and by LS-SVM with the first 6 PCs as inputs, and correctly classified 100% of calibration set and 98.96% of prediction set. For minced samples of 7 mm diameter the optimal result was attained by LS-SVM with the first 5 PCs from original spectra as inputs, which gained an accuracy of 100% for calibration and 97.53% for prediction. For minced diameter of 5 mm SIMCA model with the first 8 PCs from original spectra as inputs correctly classified 100% of calibration and prediction. And for minced diameter of 3 mm Mahalanobis distances analysis and SIMCA models both achieved 100% accuracy for calibration and prediction respectively with the first 7 and 9 PCs from original spectra as inputs. And in these models, donkey meat samples were all correctly classified with 100% either in calibration or prediction. The results show that it is feasible that NIR with chemometrics methods is used to discriminate donkey meat from the else meat.
		2014 Vol. 34 (10): 2737-2742 [Abstract] ( 837 ) PDF (4412 KB) ( 293 )

	2743	Principles and Applications of Hyperspectral Imaging Technique in Quality and Safety Inspection of Fruits and Vegetables
		ZHANG Bao-hua^1,2, LI Jiang-bo², FAN Shu-xiang², HUANG Wen-qian^2*, ZHANG Chi², WANG Qing-yan², XIAO Guang-dong²
		DOI: 10.3964/j.issn.1000-0593(2014)10-2743-09
		The quality and safety of fruits and vegetables are the most concerns of consumers. Chemical analytical methods are traditional inspection methods which are time-consuming and labor intensive destructive inspection techniques. With the rapid development of imaging technique and spectral technique, hyperspectral imaging technique has been widely used in the nondestructive inspection of quality and safety of fruits and vegetables. Hyperspectral imaging integrates the advantages of traditional imaging and spectroscopy. It can obtain both spatial and spectral information of inspected objects. Therefore, it can be used in either external quality inspection as traditional imaging system, or internal quality or safety inspection as spectroscopy. In recent years, many research papers about the nondestructive inspection of quality and safety of fruits and vegetables by using hyperspectral imaging have been published, and in order to introduce the principles of nondestructive inspection and track the latest research development of hyperspectral imaging in the nondestructive inspection of quality and safety of fruits and vegetables, this paper reviews the principles, developments and applications of hyperspectral imaging in the external quality, internal quality and safety inspection of fruits and vegetables. Additionally, the basic components, analytical methods, future trends and challenges are also reported or discussed in this paper.
		2014 Vol. 34 (10): 2743-2751 [Abstract] ( 868 ) PDF (2397 KB) ( 622 )

	2752	Hyperspectral Technology Combined with CARS Algorithm to Quantitatively Determine the SSC in Korla Fragrant Pear
		ZHAN Bai-shao, NI Jun-hui^*, LI Jun
		DOI: 10.3964/j.issn.1000-0593(2014)10-2752-06
		Hyperspectral imaging has large data volume and high dimensionality, and original spectra data includes a lot of noises and severe scattering. And, quality of acquired hyperspectral data can be influenced by non-monochromatic light, external stray light and temperature, which resulted in having some non-linear relationship between the acquired hyperspectral data and the predicted quality index. Therefore, the present study proposed that competitive adaptive reweighted sampling (CARS) algorithm is used to select the key variables from visible and near infrared hyperspectral data. The performance of CARS was compared with full spectra, successive projections algorithm (SPA), Monte Carlo-uninformative variable elimination (MC-UVE), genetic algorithm (GA) and GA-SPA (genetic algorithm-successive projections algorithm). Two hundred Korla fragrant pears were used as research object. SPXY algorithm was used to divided sample set to correction set with 150 samples and prediction set with 50 samples, respectively. Based on variables selected by different methods, linear PLS and nonlinear LS-SVM models were developed, respectively, and the performance of models was assessed using parameters r², RMSEP and RPD. A comprehensive comparison found that GA, GA-SPA and CARS can effectively select the variables with strong and useful information. These methods can be used for selection of Vis-NIR hyperspectral data variables, particularly for CARS. LS-SVM model can obtain the best results for SSC prediction of Korla fragrant pear based on variables obtained from CARS method. r², RMSEP and RPD were 0.851 2, 0.291 3 and 2.592 4, respectively. The study showed that CARS is an effectively hyperspectral variable selection method, and nonlinear LS-SVM model is more suitable than linear PLS model for quantitatively determining the quality of fragrant pear based on hyperspectral information.
		2014 Vol. 34 (10): 2752-2757 [Abstract] ( 644 ) PDF (3583 KB) ( 321 )

	2758	Analysis of Tobacco Color and Location Features Using Visible-Near Infrared Hyperspectral Data
		CAI Jia-yue¹, LIANG Miao¹, WEN Ya-dong², YU Chun-xia², WANG Luo-ping², WANG Yi², ZHAO Long-lian¹, LI Jun-hui^1*
		DOI: 10.3964/j.issn.1000-0593(2014)10-2758-06
		In the present paper, six categories of standard industrial grading tobacco provided by Hongta Group are taken as experimental samples, including three different tobacco locations-upper (B), middle(C) and lower(X) parts, with each part containing two kinds of tobacco colors-orange (O) and lemon yellow (L). Two methods including projection model method based on principal component and Fisher criterion (PPF) and support vector machine (SVM) method are used to analyze color and location features of tobacco based on visible-near infrared hyperspectral data. The results of projection model method indicate that in the projection and similarity analysis of tobacco color, location and six tobacco groups classified by color and location, two kinds of color can be fully differentiated, of which the similarity value is -1.000 8. Tobacco from upper and lower parts can also be fully differentiated with similarity value 0.405 3, but they both have intersections with tobacco from middle part. Six tobacco groups classified by color and location can be fully differentiated as well and their projection positions meet the actual external features of tobacco. The results of support vector machine method indicate that in the discriminant analysis of tobacco color, location and six tobacco groups classified by color and location, the average recognition rate of tobacco colors reaches 98%. The average recognition rate of tobacco location is 96%. The average recognition rate of six tobacco groups is 94%. Therefore, it’s feasible to analyze color and location features of tobacco using visible-near infrared hyperspectral data, which can provide reference for tobacco quality evaluation, computer-aided grading and tobacco intelligent acquisition, and also offers a new approach to the analysis of exterior features of other agricultural products.
		2014 Vol. 34 (10): 2758-2763 [Abstract] ( 733 ) PDF (1669 KB) ( 445 )

	2764	Analysis of Tobacco Style Features Using Near-Infrared Spectroscopy and Projection Model
		SHU Ru-xin¹, CAI Jia-yue², YANG Zheng-yu^1, YANG Kai¹, ZHAO Long-lian², ZHANG Lu-da³, ZHANG Ye-hui², LI Jun-hui^2
		DOI: 10.3964/j.issn.1000-0593(2014)10-2764-05
		In the present paper, a total of 4 733 flue-cured tobacco samples collected from 2003 to 2012 in 17 provincial origins and 5 ecological areas were tested by near infrared spectroscopy, including the NONG(Luzhou) flavor 1 580 cartons, QING(Fen) flavor 2004 cartons and Intermediate flavor 1 149 cartons. Using projection model based on principal component and Fisher criterion (PPF), Projection analysis models of tobacco ecological regions and style characteristics were established. Reasonableness of style flavor division is illustrated by the model results of tobacco ecological areas. With the Euclidean distance between the predicted sample projection values and the mean projection values of each class in style characteristics model, a description is given for the prediction samples to quantify the extent of the style features, and their first and second close categories. Using the dispersion of projected values in model and the given threshold value, prediction results can be refined into typical NONG, NONG to Intermediate,Intermediate to NONG, typical Intermediate, Intermediate to QING, QING to Intermediate, typical QING, QINGto NONG, NONG to QING, or super-model range. The model was validated by 35 tobacco samples obtained from the re-dryingprocess in 2012 with different origins and parts. This kind of analysis methods not only can achieve discriminant analysis, but also can get richer feature attribute information and provide guidance to raw tobacco processing and formulations.
		2014 Vol. 34 (10): 2764-2768 [Abstract] ( 315 ) PDF (1630 KB) ( 286 )

	2769	Fourier Transform Infrared Spectroscopy Combined with Attenuated Total Reflection Applied to Reagent-Free Quantitative Analysis of Thalassemia Screening Indicators
		LONG Xiao-li¹, LIU Gui-song², XIAO Qing-qing³, CHEN Jie-mei^3*
		DOI: 10.3964/j.issn.1000-0593(2014)10-2769-06
		A simultaneous quantitative analysis method for the thalassemia screening indicators mean corpuscular hemoglobin (MCH), mean corpuscular volume (MCV), and hemoglobin (Hb) was developed with Fourier transform infrared (FTIR) spectrometers and attenuated total reflection (ATR) combined with partial least squares (PLS). A total of 380 human peripheral blood samples were collected, which were composed of 180 positive samples and 200 negative samples according to the criteria of hematological indicator screening for thalassemia. One hundred fifty samples (64 negative, 86 positive) were randomly selected from all samples as the validation set, the remaining 230 samples (136 negative, 94 positive) were used as modeling samples; and then the modeling set was further subdivided into calibration set (68 negative, 47 positive, and 115 in total) and prediction set (68 negative, 47 positive, and 115 in total) for 200 times. Comparison of experimental results show that the prediction effect of PLS models in mid-infrared (MIR) fingerprint region (1 600～900 cm^-1) was significantly better those of PLS models in the full scanning region (4 000～600 cm^-1), and model complexity is significantly reduced. Based on PLS model in MIR fingerprint region, the optimal numbers of PLS factors for MCH, MCV and Hb were 10, 10 and 6, respectively, and the root mean square error (M_SEP_Ave) and the correlation coefficient (M_R_{P, Ave}) of prediction in the modeling set were 2.19 pg, 0.902 for MCH, 5.13 fL, 0.898 for MCV and 8.0 g·L^-1, 0.922 for Hb, respectively. The root mean square error (V_SEP) and the correlation coefficient (V_R_P) of prediction in the validation set were 2.22 pg, 0.900 for MCH, 5.38 fL, 0.895 for MCV and 7.7 g·L^-1, 0.929 for Hb, respectively. The sensitivity and specificity for thalassemia screening achieved 100.0% and 95.3%, respectively. Conclusion: FTIR/ATR spectroscopy combined with PLS method could provide a new reagent-free and rapid technique for thalassemia screening for large populations.
		2014 Vol. 34 (10): 2769-2774 [Abstract] ( 543 ) PDF (1868 KB) ( 273 )

	2775	Study on Detection Methods of Interstitial Fluid Glucose Concentration Based on Infrared Attenuated Total Reflection
		SUN Chang-yue¹, CAO Yu-zhen¹, YU Song-lin², YU Hai-xia¹, XU Ke-xin², LI Da-chao^2*
		DOI: 10.3964/j.issn.1000-0593(2014)10-2775-04
		Measuring the glucose concentrations in the interstitial fluid is currently the main method to achieve the continuous blood glucose monitoring. The MIR-ATR(Mid-infrared，Attenuated Total Reflection)Spectroscopy has prominent advantage on the analysis of small biological molecule for composition information like the glucose, but it is still an unresolved problem that how to detect the subcutaneous glucose concentration by using the MIR-ATR Spectroscopy. In the present paper, we carry out the experiment based on MIR-ATR for the detection of subcutaneous glucose information on both the natural state and the penetration state based on the theoryanalysis of MIR penetration depth. Firstly, collect spectral data of the subcutaneous glucose concentration of human finger on the natural state were collected as the light shined the skin directly, and it was discussed whether the MIR can penetrate the skin to get the information of subcutaneous glucose. On this basis, collect spectral data of the subcutaneous glucose concentration of human finger at the penetration state were collected when the Interstitial fluid is permeated to the surface layer by using low-frequency ultrasound and vacuum, then it analyzed that whether it can detect the glucose-specific information or not. As the two-dimensional correlation spectroscopy has high resolution and good versatility, it is widely used to analyze the inter-molecular reaction and judge the absorption peaks information in many fields including the MIR spectroscopy field, so we choose the Two-dimensional correlation spectroscopy to analyze the information of subcutaneous glucose concentration at the natural state and the penetration state. The experiment result shows that the MIR-ATR spectroscopy can’t be applied in the detection of subcutaneous glucose concentrationdirectly, and it is a promising direction to make the Interstitial fluid permeated to the surface layer by the physical methods or chemical methods.
		2014 Vol. 34 (10): 2775-2778 [Abstract] ( 747 ) PDF (1917 KB) ( 314 )

	2779	Analyzing and Modeling Methods of Near Infrared Spectroscopy for In-situ Prediction of Oil Yield from Oil Shale
		LIU Jie, ZHANG Fu-dong, TENG Fei, LI Jun, WANG Zhi-hong^*
		DOI: 10.3964/j.issn.1000-0593(2014)10-2779-06
		In order to in-situ detect the oil yield of oil shale, based on portable near infrared spectroscopy analytical technology, with 66 rock core samples from No.2 well drilling of Fuyu oil shale base in Jilin, the modeling and analyzing methods for in-situ detection were researched. By the developed portable spectrometer, 3 data formats (reflectance, absorbance and K-M function) spectra were acquired. With 4 different modeling data optimization methods: principal component-mahalanobis distance(PCA-MD) for eliminating abnormal samples, uninformative variables elimination (UVE) for wavelength selection and their combinations: PCA-MD+UVE and UVE+PCA-MD, 2 modeling methods: partial least square(PLS)and back propagation artificial neural network (BPANN), and the same data pre-processing, the modeling and analyzing experiment were performed to determine the optimum analysis model and method. The results show that the data format, modeling data optimization method and modeling method all affect the analysis precision of model. Results show that whether or not using the optimization method, reflectance or K-M function is the proper spectrum format of the modeling database for two modeling methods. Using two different modeling methods and four different data optimization methods, the model precisions of the same modeling database are different. For PLS modeling method, the PCA-MD and UVE+PCA-MD data optimization methods can improve the modeling precision of database using K-M function spectrum data format. For BPANN modeling method, UVE, UVE+PCA-MD and PCA-MD+UVE data optimization methods can improve the modeling precision of database using any of the 3 spectrum data formats. In addition to using the reflectance spectra and PCA-MD data optimization method, modeling precision by BPANN method is better than that by PLS method. And modeling with reflectance spectra, UVE optimization method and BPANN modeling method, the model gets the highest analysis precision, its correlation coefficient (R_P) is 0.92, and its standard error of prediction (SEP) is 0.69%.
		2014 Vol. 34 (10): 2779-2784 [Abstract] ( 607 ) PDF (3048 KB) ( 352 )

	2785	The Establishment and External Validation of NIR Qualitative Analysis Model for Waste Polyester-Cotton Blend Fabrics
		LI Feng¹, LI Wen-xia^1*, ZHAO Guo-liang¹, TANG Shi-jun², LI Xue-jiao¹, WU Hong-mei¹
		DOI: 10.3964/j.issn.1000-0593(2014)10-2785-07
		A series of 354 polyester-cotton blend fabrics were studied by the near-infrared spectra (NIRS) technology, and a NIR qualitative analysis model for different spectral characteristics was established by partial least squares (PLS) method combined with qualitative identification coefficient. There were two types of spectrum for dying polyester-cotton blend fabrics: normal spectrum and slash spectrum. The slash spectrum loses its spectral characteristics, which are effected by the samples’ dyes, pigments, matting agents and other chemical additives. It was in low recognition rate when the model was established by the total sample set, so the samples were divided into two types of sets: normal spectrum sample set and slash spectrum sample set, and two NIR qualitative analysis models were established respectively. After the of models were established the model’s spectral region, pretreatment methods and factors were optimized based on the validation results, and the robustness and reliability of the model can be improved lately. The results showed that the model recognition rate was improved greatly when they were established respectively, the recognition rate reached up to 99% when the two models were verified by the internal validation. RC (relation coefficient of calibration) values of the normal spectrum model and slash spectrum model were 0.991 and 0.991 respectively, RP (relation coefficient of prediction) values of them were 0.983 and 0.984 respectively, SEC (standard error of calibration) values of them were 0.887 and 0.453 respectively, SEP (standard error of prediction) values of them were 1.131 and 0.573 respectively. A series of 150 bounds samples reached used to verify the normal spectrum model and slash spectrum model and the recognition rate reached up to 91.33% and 88.00% respectively. It showed that the NIR qualitative analysis model can be used for identification in the recycle site for the polyester-cotton blend fabrics.
		2014 Vol. 34 (10): 2785-2791 [Abstract] ( 646 ) PDF (2778 KB) ( 463 )

	2792	Model Research of Electric Coal Calorific Value Based on Near Infrared Frequency Domain Self-Adaption Analysis Method
		LI Zhi², WANG Sheng-hao^1,2*, ZHAO Yong¹, WANG Xiang-feng³, LI Yao-zheng⁴
		DOI: 10.3964/j.issn.1000-0593(2014)10-2792-07
		At present, because the blending coal was taken in some power stations as the major fuel which has too complex physical and chemical characters to build accurate normal near infrared quantitative models in some cases, which brought difficulties for on-line electric coal calorific value detection. For this reason, it was carefully studied that the time domain and frequency domain properties of the power generation coal near infrared spectra, and was proposed that a new quantitative near infrared method named frequency domain self-adaption analysis. The first step, time domain near infrared spectra are converted into frequency domain near infrared signal by Fast Fourier Transform; The second step, the suitable frequency information range by means of valid spectra energy parameter η_E was obtained by this method; The third step, it was constructed that an information volume parameter which is formed by correlation coefficient, standard deviation spectra and coordinate of harmonic in frequency domain to initialize the regression model input parameters’ position; Finally, the optimal model is established by way of discrete frequency domain scooping and synthesized performance function. At the same time, compared with the principle component regression, partial least squares regression, back propagation artificial network, support vector regression and partial least squares regression optimized by genetic algorithm models, it is acquired that a more accurate method which can effectively avoid over fitting and virtual effective models and has a very useful application prospect by verifying the electric coal calorific value. Additionally, this method can be used in other quantitative spectra analysis.
		2014 Vol. 34 (10): 2792-2798 [Abstract] ( 528 ) PDF (3533 KB) ( 229 )

	2799	Near Infrared Spectroscopy Quantitative Analysis Model Based on Incremental Neural Network with Partial Least Squares
		CAO Hui¹, LI Da-hang¹, LIU Ling^1*, ZHOU Yan²
		DOI: 10.3964/j.issn.1000-0593(2014)10-2799-05
		This paper proposes an near infrared spectroscopy quantitative analysis model based on incremental neural network with partial least squares. The proposed model adopts the typical three-layer back-propagation neural network (BPNN), and the absorbance of different wavelengths and the component concentration are the inputs and the outputs, respectively. Partial least square (PLS) regression is performed on the history training samples firstly, and the obtained history loading matrices of the independent variables and the dependent variables are used for determining the initial weights of the input layer and the output layer, respectively. The number of the hidden layer nodes is set as the number of the principal components of the independent variables. After a set of new training samples is collected, PLS regression is performed on the combination dataset consisting of the new samples and the history loading matrices to calculate the new loading matrices. The history loading matrices and the new loading matrices are fused to obtain the new initial weights of the input layer and the output layer of the proposed model. Then the new samples are used for training the proposed mode to realize the incremental update. The proposed model is compared with PLS, BPNN, the BPNN based on PLS (PLS-BPNN) and the recursive PLS (RPLS) by using the spectra data of flue gas of natural gas combustion. For the concentration prediction of the carbon dioxide in the flue gas, the root mean square error of prediction (RMSEP) of the proposed model are reduced by 27.27%, 58.12%, 19.24% and 14.26% than those of PLS, BPNN, PLS-BPNN and RPLS, respectively. For the concentration prediction of the carbon monoxide in the flue gas, the RMSEP of the proposed model are reduced by 20.65%, 24.69%, 18.54% and 19.42% than those of PLS, BPNN, PLS-BPNN and RPLS, respectively. For the concentration prediction of the methane in the flue gas, the RMSEP of the proposed model are reduced by 27.56%, 37.76%, 8.63% and 3.20% than those of PLS, BPNN, PLS-BPNN and RPLS, respectively. Experiments results show that the proposed model could optimize the construction and the initial weights of BPNN by PLS and has higher prediction effectiveness. Moreover, based on the information of the built model, the proposed model uses the new samples for incremental update without accessing the history samples. Hence, the proposed model has better robustness and generalization.
		2014 Vol. 34 (10): 2799-2803 [Abstract] ( 755 ) PDF (1844 KB) ( 375 )

	2804	Rapid Determination of COD in Aquaculture Water Based on LS-SVM with Ultraviolet/Visible Spectroscopy
		LIU Xue-mei, ZHANG Hai-liang^*
		DOI: 10.3964/j.issn.1000-0593(2014)10-2804-04
		Ultraviolet/visible (UV/Vis) spectroscopy was studied for the rapid determination of chemical oxygen demand (COD), which was an indicator to measure the concentration of organic matter in aquaculture water. In order to reduce the influence of the absolute noises of the spectra, the extracted 135 absorbance spectra were preprocessed by Savitzky-Golay smoothing (SG), EMD, and wavelet transform (WT) methods. The preprocessed spectra were then used to select latent variables (LVs) by partial least squares (PLS) methods. Partial least squares (PLS) was used to build models with the full spectra, and back-propagation neural network (BPNN) and least square support vector machine (LS-SVM) were applied to build models with the selected LVs. The overall results showed that BPNN and LS-SVM models performed better than PLS models, and the LS-SVM models with LVs based on WT preprocessed spectra obtained the best results with the determination coefficient (r²) and RMSE being 0.83 and 14.78 mg·L^-1 for calibration set, and 0.82 and 14.82 mg·L^-1 for the prediction set respectively. The method showed the best performance in LS-SVM model. The results indicated that it was feasible to use UV/Vis with LVs which were obtained by PLS method, combined with LS-SVM calibration could be applied to the rapid and accurate determination of COD in aquaculture water. Moreover, this study laid the foundation for further implementation of online analysis of aquaculture water and rapid determination of other water quality parameters.
		2014 Vol. 34 (10): 2804-2807 [Abstract] ( 591 ) PDF (1291 KB) ( 295 )

	2808	*Absolutely Nondestructive Discrimination of Huoshan Dendrobium nobile* Species with Miniature Near-Infrared (NIR) Spectrometer Engine**
		HU Tian^1,3, YANG Hai-long^1,3, TANG Qing⁴, ZHANG Hui², NIE Lei¹, LI Lian^1,3, WANG Jin-feng^1,3, LIU Dong-ming^1,3, JIANG Wei^1,3, WANG Fei^1,3, ZANG Heng-chang^1,3*
		DOI: 10.3964/j.issn.1000-0593(2014)10-2808-07
		As one very precious traditional Chinese medicine (TCM), Huoshan Dendrobium has not only high price, but also significant pharmaceutical efficacy. However, different species of Huoshan Dendrobium exhibit considerable difference in pharmaceutical efficacy, so rapid and absolutely non-destructive discrimination of Huoshan Dendrobium nobile according to different species is crucial to quality control and pharmaceutical effect. In this study, as one type of miniature near-infrared (NIR) spectrometer, MicroNIR 1700 was used for absolutely nondestructive determination of NIR spectra of 90 batches of Dendrobium from five species of different commodity grades. The samples were intact and not smashed. Soft independent modeling of class analogy (SIMCA) pattern recognition based on principal component analysis (PCA) was used to classify and recognize different species of Dendrobium samples. The results indicated that the SIMCA qualitative models established with pretreatment method of standard normal variate transformation (SNV) in the spectra range selected by Qs method had 100% recognition rates and 100% rejection rates. This study demonstrated that a rapid and absolutely non-destructive analytical technique based on MicroNIR 1700 spectrometer was developed for successful discrimination of five different species of Huoshan Dendrobium with acceptable accuracy.
		2014 Vol. 34 (10): 2808-2814 [Abstract] ( 926 ) PDF (4628 KB) ( 373 )

	2815	Drug Discrimination by Near Infrared Spectroscopy Based on Summation Wavelet Extreme Learning Machine
		LIU Zhen-bing¹, JIANG Shu-jie^1*, YANG Hui-hua¹, ZHANG Xue-bo²
		DOI: 10.3964/j.issn.1000-0593(2014)10-2815-06
		As an effective technique to identify counterfeit drugs, Near Infrared Spectroscopy has been successfully used in the drug management of grass-roots units, with classifier modeling of Pattern Recognition. Due to a major disadvantage of the characteristic overlap and complexity, the wide bandwidth and the weak absorption of the Spectroscopy signals, it seems difficult to give a satisfactory solutions for the modeling problem. To address those problems, in the present paper, a summation wavelet extreme learning machine algorithm (SWELM(CS)) combined with Cuckoo research was adopted for drug discrimination by NIRS. Specifically, Extreme Learning Machine (ELM) was selected as the classifier model because of its properties of fast learning and insensitivity, to improve the accuracy and generalization performances of the classifier model; An inverse hyperbolic sine and a Morlet-wavelet are used as dual activation functions to improve convergence speed, and a combination of activation functions makes the network more adequate to deal with dynamic systems; Due to ELM’s weights and hidden layer threshold generated randomly, it leads to network instability, so Cuckoo Search was adapted to optimize model parameters; SWELM(CS) improves stability of the classifier model. Besides, SWELM(CS) is based on the ELM algorithm for fast learning and insensitivity; the dual activation functions and proper choice of activation functions enhances the capability of the network to face low and high frequency signals simultaneously; it has high stability of classification by Cuckoo Research. This compact structure of the dual activation functions constitutes a kernel framework by extracting signal features and signal simultaneously, which can be generalized to other machine learning fields to obtain a good accuracy and generalization performances. Drug samples of near infrared spectroscopy produced by Xian-Janssen Pharmaceutical Ltd were adopted as the main objects in this paper. Experiments for binary classification and multi-label classification were conducted, and the conclusion proved that the proposed method has more stable performance, higher classification accuracy and lower sensitivity to training samples than the existing ones, such as the BP neural network, ELM and ELM by particle swarm optimization.
		2014 Vol. 34 (10): 2815-2820 [Abstract] ( 596 ) PDF (2788 KB) ( 307 )

	2821	Research on NIR-CI Parameters Optimization of Chlorpheniramine Maleate Tablets Based on Binary Image and Statistical Measurement
		ZHOU Lu-wei, WU Zhi-sheng^, SHI Xin-yuan, XU Man-fei, ZHANG Qiao, QIAO Yan-jiang^
		DOI: 10.3964/j.issn.1000-0593(2014)10-2821-06
		The optimization method was established to investigate the effect of near infrared chemical imaging (NIR-CI) detection parameters on hyperspectral data quality. In order to optimize the detection parameters, chlorpheniramine maleate (CPM) tablets were chosen as examples and the L9(3⁴) orthogonal-test design was adopted to research the effects of spectral resolution, spatial resolution, scan times and scan height. Binary image coupled with statistical measurement was proposed to quantitatively analyze hyperspectral data and determine the content of CPM on the tablet surface. High-performance liquid chromatography (HPLC) was used as reference method for accurate CPM determination. The absolute value of the difference between CPM contents obtained from NIR-CI and HPLC was chosen as index. The result demonstrated that the optimum parameters for acquiring hyperspectral data were: 25 μm×25 μm (spatial resolution), 5340 (scan height, the value of Z, precise focus), 16 cm^-1(spectral resolution) and 16 (scan times). The influence of scan height on hyperspectral data was firstly investigated. The optimized parameters could be applied to CPM tablets and other drugs for NIR-CI data acquisition and methodology establishment.
		2014 Vol. 34 (10): 2821-2826 [Abstract] ( 881 ) PDF (5263 KB) ( 222 )

	2827	NIR Spectroscopy Combined with Stability and Equivalence MW-PLS Method Applied to Analysis of Hyperlipidemia Indexes
		CHEN Jie-mei¹, XIAO Qing-qing¹, PAN Tao^1*, YAN Xia^{1, 2}, WANG Da-wei^{1, 2}, YAO Li-jun¹
		DOI: 10.3964/j.issn.1000-0593(2014)10-2827-06
		Moving window partial least square (MW-PLS) method was improved by considering the stability and equivalence, and was used for the wavelength optimization of reagent-free near-infrared (NIR) spectroscopic analysis of total cholesterol (TC) and triglycerides (TG) for hyperlipidemia. A random and stability-dependent framework of calibration, prediction, and validation was proposed. From all human serum samples (negative 145 and positive 158, a total of 303 sample), 103 samples (negative 44 and positive 59) were randomly selected for the validation set, the remaining samples (negative 101 and positive 99, a total of 200 sample) were used as modeling set; then the modeling set was randomly divided into calibration set (negative 51 and positive 49, a total of 100 sample) and prediction set (negative 50 and positive 50, a total of 100 sample) by 50 times. To produce modeling stability, the model parameters were optimized based on the average prediction effect for all divisions; the optimized models were validated by using the validation samples. The obtained optimal MW-PLS wavebands were 1 556～1 852 nm for TC and 1 542～1 866 nm for TG. In order to solve the problem that instrument design typically involves some limitations of position and number of wavelengths because of cost and material properties, the equivalent model sets were proposed, and a unique public waveband 1 542～1 852 nm of the equivalent model sets for TC, TG was found. The validation results show that: using the optimal MW-PLS wavebands, validation samples’ root mean square error of prediction (V_SEP) for TC, TG were 0.177, 0.100 mmol·L^-1, the correlation coefficient of prediction (V_R_P) for TC, TG were 0.988, 0.996, and the sensitivity and specificity for hyperlipidemia achieved 95.0%, 90.5%, respectively; using the public equivalent wavebands, the V_SEP for TC, TG were 0.177, 0.101 mmol·L^-1), the V_R_P for TC, TG were 0.988, 0.996, and the sensitivity and specificity achieved 92.7%, 90.3%, respectively. Conclusion: NIR spectroscopy combined with the stability and equivalence-improvement MW-PLS method can provide a potential tool for detecting hyperlipidemia for large population.
		2014 Vol. 34 (10): 2827-2832 [Abstract] ( 601 ) PDF (2401 KB) ( 293 )

	2833	*Tracking Analysis of Three Extraction Processes of Arenaria Polytrichoides* by Fourier Transform Infrared Spectrocopy**
		MA Jing¹, WU Xian-xue^1, TAI Xi¹, XU Liu-xian¹, ZHU Jin-lan¹, QIN Yao¹, ZHOU Qun², SUN Su-qin^2
		DOI: 10.3964/j.issn.1000-0593(2014)10-2833-06
		In order to develop a process analysis method to guide extraction process of Arenaria polytrichoides (AP) based on tracking analysis by Fourier transform infrared (FTIR), IR spectra of petroleum ether extracts (PE-E), ethyl acetate extracts (EtOAc-E), n-butanol extracts (n-BuOH-E) and water extracts (H₂O-E) of AP from three extraction methods were recorded. The FTIR and corresponding second derivative infrared (SDIR) spectra were analyzed comparatively from two aspects, namely, different extracts from a same extraction process and the same extracts from different methods. The spectral analysis results show that different extracts obtained from a same extraction process have distinctly different spectral absorbance character. Although the IR spectral absorption characteristics of the same extracts from different methods are rather similar in holistic, some explicit spectral differences still could be found among each other. In extraction process one (M1), main flavonoids and their glycosides of AP migrated to EtOAc-E and the rest part of them shift to n-BuOH-E according to FTIR peaks such as 1 603 and 1 123 cm^-1. However, the circumstances in method two (M2) and method three (M3) were just the reverse. Moreover, a few flavonoid glycosides got into H₂O-E. The relative content of all kinds of aglycones and higher saturated alkyl are much higher in EtOAc-E of M2 than that of M1 and M3 according to the relative absorption intensive of peak at 2 850 cm^-1. Similarly, n-BuOH-E of M3 has relative rich contents of glycosides and polysaccharides than those of M1 and M2 by peaks such as 1 066 and 2 927 cm^-1. These results demonstrate that the migration rules of AP components are not always same in different extraction process. The substance migration information during the extraction process could be recorded and disclosed in an intuitive way by FTIR tracking analysis of corresponding extracts. Consequently, FTIR tracking analysis is a fast, efficient, low-carbon and environment-friendly process analysis method. The method has important macro guiding significance for quality control and process optimization of extraction and isolation process of medicinal plant including AP.
		2014 Vol. 34 (10): 2833-2838 [Abstract] ( 554 ) PDF (4716 KB) ( 282 )

	2839	Carbon Monoxide Gas Detection System Based on Mid-Infrared Spectral Absorption Technique
		LI Guo-lin, DONG Ming, SONG Nan, SONG Fang, ZHENG Chuan-tao^, WANG Yi-ding^
		DOI: 10.3964/j.issn.1000-0593(2014)10-2839-06
		Based on infrared spectral absorption technique, a carbon monoxide (CO) detection system was developed using the fundamental absorption band at the wavelength of 4.6 μm of CO molecule and adopting pulse-modulated wideband incandescence and dual-channel detector. The detection system consists of pulse-modulated wideband incandescence, open ellipsoid light-collector gas-cell, dual-channel detector, main-control and signal-processing module. By optimizing open ellipsoid light-collector gas-cell, the optical path of the gas absorption reaches 40 cm, and the amplitude of the electrical signal from the detector is 2 to 3 times larger than the original signal. Therefore, by using the ellipsoidal condenser, the signal-to-noise ratio of the system will be to some extent increased to improve performance of the system. With the prepared standard CO gas sample, sensing characteristics on CO gas were investigated. Experimental results reveal that, the limit of detection (LOD) is about 10 ppm; the relative error at the LOD point is less than 14%, and that is less than 7.8% within the low concentration range of 20~180 ppm; the maximum absolute error of 50 min long-term measurement concentration on the 0 ppm gas sample is about 3 ppm, and the standard deviation is as small as 0.18 ppm. Compared with the CO detection systems utilizing quantum cascaded lasers (QCLs) and distributed feedback lasers (DFBLs), the proposed sensor shows potential applications in CO detection under the circumstances of coal-mine and environmental protection, by virtue of high performance-cost ratio, simple optical-path structure, etc.
		2014 Vol. 34 (10): 2839-2844 [Abstract] ( 639 ) PDF (4066 KB) ( 359 )

	2845	Fast and Accurate Extraction of Ring-Down Time in Cavity Ring-Down Spectroscopy
		WANG Dan¹, HU Ren-zhi¹, XIE Pin-hua^1*, QIN Min¹, LING Liu-yi^1，2, DUAN Jun¹
		DOI: 10.3964/j.issn.1000-0593(2014)10-2845-06
		Research is conducted to accurate and efficient algorithms for extracting ring-down time (τ) in cavity ring-down spectroscopy (CRDS) which is used to measure NO₃ radical in the atmosphere. Fast and accurate extraction of ring- down time guarantees more precise and higher speed of measurement. In this research, five kinds of commonly used algorithms are selected to extract ring-down time which respectively are fast Fourier transform (FFT) algorithm, discrete Fourier transform (DFT) algorithm, linear regression of the sum (LRS) algorithm, Levenberg-Marquardt (LM) algorithm and least squares (LS) algorithm. Simulated ring-down signals with various amplitude levels of white noises are fitted by using five kinds of the above-mentioned algorithms, and comparison and analysis is conducted to the fitting results of five kinds of algorithms from four respects: the vulnerability to noises, the accuracy and precision of the fitting, the speed of the fitting and preferable fitting ring-down signal waveform length. The research results show that Levenberg-Marquardt algorithm and linear regression of the sum algorithm are able to provide more precise results and prove to have higher noises immunity, and by comparison, the fitting speed of Levenberg-Marquardt algorithm turns out to be slower. In addition, by analysis of simulated ring-down signals, five to ten times of ring-down time is selected to be the best fitting waveform length because in this case, standard deviation of fitting results of five kinds of algorithms proves to be the minimum. External modulation diode laser and cavity which consists of two high reflectivity mirrors are used to construct a cavity ring-down spectroscopy detection system. According to our experimental conditions, in which the noise level is 0.2%, linear regression of the sum algorithm and Levenberg-Marquardt algorithm are selected to process experimental data. The experimental results show that the accuracy and precision of linear regression of the sum algorithm is considerably close to those of Levenberg-Marquardt algorithm, and on the other hand, the fitting speed of linear regression of the sum algorithm is faster than that of Levenberg-Marquardt algorithm about five times. The experimental results are consistent with the simulation analysis, and it indicates that linear regression of the sum algorithm is the desirable fitting method, as far as our experimental conditions are concerned.
		2014 Vol. 34 (10): 2845-2850 [Abstract] ( 919 ) PDF (2424 KB) ( 700 )

	2851	A Review of Mixed Gas Detection System Based on Infrared Spectroscopic Technique
		DANG Jing-min¹, FU Li¹, YAN Zi-hui², ZHENG Chuan-tao¹, CHANG Yu-chun¹, CHEN Chen^3, WANG Yi-ding^1
		DOI: 10.3964/j.issn.1000-0593(2014)10-2851-07
		In order to provide the experiences and references to the researchers who are working on infrared(IR) mixed gas detection field. The proposed manuscript reviews two sections of the aforementioned field, including optical multiplexing structure and detection method. At present, the coherent light sources whose representative are quantum cascade laser (QCL) and interband cascade laser(ICL) become the mainstream light source in IR mixed gas detection, which replace the traditional non-coherent light source, such as IR radiation source and IR light emitting diode. In addition, the photon detector which has a super high detectivity and very short response time is gradually beyond thermal infrared detector, dominant in the field of infrared detector. The optical multiplexing structure is the key factor of IR mixed gas detection system, which consists of single light source multiplexing detection structure and multi light source multiplexing detection structure. Particularly, single light source multiplexing detection structure is advantages of small volume and high integration, which make it a plausible candidate for the portable mixed gas detection system; Meanwhile, multi light source multiplexing detection structure is embodiment of time division multiplex, frequency division multiplexing and wavelength division multiplexing, and become the leading structure of the mixed gas detection system because of its wider spectral range, higher spectral resolution, etc. The detection method applied to IR mixed gas detection includes non-dispersive infrared (NDIR) spectroscopy, wavelength and frequency-modulation spectroscopy, cavity-enhanced spectroscopy and photoacoustic spectroscopy, etc. The IR mixed gas detection system designed by researchers after recognizing the whole sections of the proposed system, which play a significant role in industrial and agricultural production, environmental monitoring, and life science, etc.
		2014 Vol. 34 (10): 2851-2857 [Abstract] ( 907 ) PDF (2967 KB) ( 441 )

	2858	Design and Implementation of a Long Wavelength Near InfraRed Spectrometer Based on MEMS Scanning Mirror
		YE Kun-tao¹, DONG Tai-yuan¹, HE Wen-xi¹, LI Yu-xiao¹, CHENG Xian-ming¹, LI Guang-yong¹, LI Hao-yu², XU Xiao-xuan^2*
		DOI: 10.3964/j.issn.1000-0593(2014)10-2858-05
		Long Wavelength Near InfraRed(LW-NIR) spectrometer has wide applications. Miniaturization and low-cost are two major goals of the development of LW-NIR spectrometer in the industrial or research community. Under the background that having a trend of spectrometer miniaturization and integration, method and main problems involved in miniaturization of LW-NIR spectrometer through MEMS scanning mirror, such as the design strategy of the light-splitting optical system, selection considerations of the MEMS scanning mirror, design method of the preamplifier circuit, etc, have been presented in detail. A prototype of miniaturized LW-NIR spectrometer, with the spectrum range of detection of 900～2 055 nm, is designed and implemented using MEMS scanning mirror, InGaAs single detector unit with high sensitivity. Littrow optical layout is used for its light-splitting optical system, and the spectral resolution is between 9.4～16 nm at 1 000～1 965 nm detection wavelength range. The prototype is successfully applied in LW-NIR spectrum measurement on pure water and ethanol aqueous solution, and a forecast analysis on ethanol aqueous solution concentration is also demonstrated. Through adopting MEMS scanning mirror into the spectrometer system, the complexity of the mechanical scanning fixtures and its controlling mechanism is greatly reduced therefore the size of the spectrometer is reduced. Furthermore, due to MEMS scanning mirror technology, LW-NIR spectrometer with single InGaAs detector is achieved, thus the cost reduction of the NIR spectrometer system is also realized because the expensive InGaAs arrays are avoided.
		2014 Vol. 34 (10): 2858-2862 [Abstract] ( 669 ) PDF (2710 KB) ( 457 )

	2863	Research on Error Reduction of Path Change of Liquid Samples Based on Near Infrared Trans-Reflective Spectra Measurement
		WANG Ya-hong^1,2, DONG Da-ming^1*, ZHOU Ping², ZHENG Wen-gang¹, YE Song²，WANG Wen-zhong^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2014)10-2863-05
		Based on sucrose solution as the research object, this paper measured the trans-reflective spectrum of sucrose solution of different concentration by the technique of near infrared spectrum in three optical path (4, 5, 6 mm). Five kinds of pretreatment method (vector normalization, baseline offset correction, multiplicative scatter correction, standard normal variate transformation, a derivative) were used to eliminate the influence of the optical path difference, and to establish model of the calibration set in combination with the PLS(Partial Least Squares)method. Five kinds of pretreatment method could restrain the interference of light path in varying degrees. Compared with the PLS model of original spectra, the model of multiple scattering correction combined with PLS method is the optimal model. The results of quantitative analysis of original spectra: the number of principal component PC=6, the determination coefficient R²=0.891 278, the determination coefficient of cross validation R²_CV=0.888 374, root mean square error of calibration RMSEC=1.704%, root mean square error of cross validation RMSECV=1.827%; The results of quantitative analysis of spectra after MSC pretreatment: the number of principal component PC=3, the determination coefficient R²=0.987 535, the determination coefficient of cross validation R²_CV=0.983 343, root mean square error of calibration RMSEC=0.89%, root mean square error of cross validation RMSECV=1.05%. The correlation coefficient of the prediction set is as much as 0.976 22. root mean square error of prediction is 0.01, lesser than 0.014 36. The results show that the MSC can eliminate the influence of optical path difference, improve the prediction precision and improve the stability.
		2014 Vol. 34 (10): 2863-2867 [Abstract] ( 541 ) PDF (3210 KB) ( 252 )

	2868	A Novel Metabolomic Data Scaling Method Based on K-L Divergence
		DENG Ling-li^{1, 2}, Cheng Kian-Kai³, SHEN Gui-ping¹, ZHOU Ling¹, LIU Xin-zhuo¹, DONG Ji-yang^1*, CHEN Zhong¹
		DOI: 10.3964/j.issn.1000-0593(2014)10-2868-05
		A new scaling method in the current study based on Kullback-Leibler (K-L) divergence is proposed for NMR metabolomic data. The proposed method (called K-L scaling) is a supervised scaling method as group information is incorporated in the scaling procedure. Notably, K-L divergence measures the difference between two different datasets by their probability distributions, it can be used for the analysis of data that either follows Gaussian or non-Gaussian distributions. In K-L scaling, all variables were first standardized to unit variance, then their variance was adjusted using Kullback-Leibler divergence to highlight the significant variables. K-L scaling can tell effectively the difference in spectral data points between two experimental groups, and then enhances the weights of biological-relevant variables, and at the same time reduces the weight of noise and uninformative variables. The developed method was applied to a ¹H-NMR metabolomic dataset acquired from human urine. Analysis results of the dataset showed that this new scaling method is efficient in suppressing the contribution of noise in the resulting multivariate model. In addition, it can increase the weights of important variables, and improve the interpretability and predictability of subsequent principal component regression (PCR) and partial least squares discriminant analysis (PLS-DA). Furthermore, the scaling method facilitated the identification of metabolic signatures. The current result suggested that the developed K-L scaling method may become a useful alternative for the preprocessing of NMR-based metabolomic data.
		2014 Vol. 34 (10): 2868-2872 [Abstract] ( 850 ) PDF (2353 KB) ( 349 )

	2873	The Research of the Relationship Between Snow Properties and the Bidirectional Polarized Reflectance from Snow Surface
		SUN Zhong-qiu, WU Zheng-fang, ZHAO Yun-sheng
		DOI: 10.3964/j.issn.1000-0593(2014)10-2873-05
		In the context of remote sensing, the reflectance of snow is a key factor for accurate inversion for snow properties, such as snow grain size, albedo, because of it is influenced by the change of snow properties. The polarized reflectance is a general phenomenon during the reflected progress in natural incident light. In this paper, based on the correct measurements for the multiple-angle reflected property of snow field in visible and near infrared wavelength (from 350 to 2 500 nm), the influence of snow grain size and wet snow on the bidirectional polarized property of snow was measured and analyzed. Combining the results measured in the field and previous conclusions confirms that the relation between polarization and snow grain size is obvious in infrared wavelength (at about 1 500 nm), which means the degree of polarization increasing with an increase of snow grain size in the forward scattering direction, it is because the strong absorption of ice near 1 500 nm leads to the single scattering light contributes to the reflection information obtained by the sensor; in other word, the larger grain size, the more absorption accompanying the larger polarization in forward scattering direction; we can illustrate that the change from dry snow to wet snow also influences the polarization property of snow, because of the water on the surface of snow particle adheres the adjacent particles, that means the wet snow grain size is larger than the dry snow grain size. Therefore, combining the multiple-angle polarization with reflectance will provide solid method and theoretical basis for inversion of snow properties.
		2014 Vol. 34 (10): 2873-2877 [Abstract] ( 551 ) PDF (1814 KB) ( 377 )

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