Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi...Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi166） and wild type （Zhonghua 11） rice. Furthermore, rice lines transformed with protein gene （OsTCTP） and regulation gene （Osmi166） were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.展开更多
Rapid and sensitive recognition of herbal pieces according to different concocted processing is crucial to quality control and pharmaceutical effect. Near-infrared （NIR） and mid-infrared （MIR） technology combined ...Rapid and sensitive recognition of herbal pieces according to different concocted processing is crucial to quality control and pharmaceutical effect. Near-infrared （NIR） and mid-infrared （MIR） technology combined with supervised pattern recognition based on partial least-squares discriminant analysis （PLSDA） was attempted to classify and recognize six different concocted processing pieces of 600 Areca catechu L. samples and the influence of fingerprint information preprocessing methods on recognition performance was also investigated in this work. Recognition rates of 99.24%, 100% and 99.49% for original fingerprint, multiple scatter correct （MSC） fingerprint and second derivative （2nd derivative） fingerprint of NIR spectra were achieved by PLSDA models, respectively. Meanwhile, a perfect recognition rate of 100% was obtained for the above three fingerprint models of MIR spectra. In conclusion, PLSDA can rapidly and effectively extract otherness of fingerprint information from NIR and MIR spectra to identify different concocted herbal pieces ofA. catechu.展开更多
For quality control purpose, an approach of fingerprinting and simultaneous quantification of five major bioactive constituents of Rhizoma Coptidis was established via a high-performance liquid chromatograph coupled w...For quality control purpose, an approach of fingerprinting and simultaneous quantification of five major bioactive constituents of Rhizoma Coptidis was established via a high-performance liquid chromatograph coupled with a photodiode array UV detector（HPLC-DAD） and an electrospray ionization mass spectrometer（HPLC-ESI/MS） The compounds were identified on the basis of the comparison of their mass spectra with literature data and those of standard samples and quantified by the HPLC-DAD method. Baseline separation was achieved on an XTerra C18 column（5 μm, 250 mm×4.6 mm i. d.） with linear gradient elution of formate buffer（consisting of 0.5% formic acid, adjusted to pH=4.5 with ammonia） and acetonitrile（consisting of 0.2% formic acid and 0.2% triethylamine）. The me- thod was validated for linearity（r^2〉0.9995）, repeatability（RSD〈3.1%）, intra- and inter-day precision（RSD〈1.8%） with recovery（99.9%-105.1%）, limits of detection（0.15-0.35 μg/mL）, and limits of quantification（0.53-0.82 μg/mL）. The similarities of 32 batches of Rhizoma Coptidis and their classification according to their manufacturers were based on the retention time and peak areas of the characteristic compounds. The five compounds were selected for quality assessment ofRhizoma coptidis via partial least squares analysis（PLS）.展开更多
In this study,multivariate analysis methods,including a principal component analysis(PCA)and partial least square(PLS)analysis,were applied to reveal the inner relationship of the key variables in the process of H_(2)...In this study,multivariate analysis methods,including a principal component analysis(PCA)and partial least square(PLS)analysis,were applied to reveal the inner relationship of the key variables in the process of H_(2)O_(2)-assisted Na_(2)CO_(3)(HSC)pretreatment of corn stover.A total of 120 pretreatment experiments were implemented at the lab scale under different conditions by varying the particle size of the corn stover and process variables.The results showed that the Na_(2)CO_(3) dosage and pretreatment temperature had a strong influence on lignin removal,whereas pulp refining instrument(PFI)refining and Na_(2)CO_(3) dosage played positive roles in the final total sugar yield.Furthermore,it was found that pretreatment conditions had a more significant impact on the amelioration of pretreatment effectiveness compared with the properties of raw corn stover.In addition,a prediction of the effectiveness of the corn stover HSC pretreatment based on a PLS analysis was conducted for the first time,and the test results of the predictability based on additional pretreatment experiments proved that the developed PLS model achieved a good predictive performance(particularly for the final total sugar yield),indicating that the developed PLS model can be used to predict the effectiveness of HSC pretreatment.Therefore,multivariate analysis can be potentially used to monitor and control the pretreatment process in future large-scale biorefinery applications.展开更多
The identification of soy sauce adulteration can avoid fraud, and protect the rights and interests of producers and consumers. Based on two measurement models (1 mm, 10 mm), the visible and near-infrared (Vis-NIR) spe...The identification of soy sauce adulteration can avoid fraud, and protect the rights and interests of producers and consumers. Based on two measurement models (1 mm, 10 mm), the visible and near-infrared (Vis-NIR) spectroscopy combined with standard normal variate-partial least squares-discriminant analysis (SNV-PLS-DA) was used to establish the discriminant analysis models for adulterated and brewed soy sauces. Chubang soy sauce was selected as an identification brand (negative, 70). The adulteration samples (positive, 72) were prepared by mixing Chubang soy sauce and blended soy sauce with different adulteration rates. Among them, the “blended soy sauce” sample was concocted of salt water (NaCl), monosodium glutamate (C<sub>5</sub>H<sub>10</sub>NNaO<sub>5</sub>) and caramel color (C<sub>6</sub>H<sub>8</sub>O<sub>3</sub>). The rigorous calibration-prediction-validation sample design was adopted. For the case of 1 mm, five waveband models (visible, short-NIR, long-NIR, whole NIR and whole scanning regions) were established respectively;in the case of 10 mm, three waveband models (visible, short-NIR and visible-short-NIR regions) for unsaturated absorption were also established respectively. In independent validation, the models of all wavebands in the cases of 1 mm and 10 mm have achieved good discrimination effects. For the case of 1 mm, the visible model achieved the optimal validation effect, the validation recognition-accuracy rate (RAR<sub>V</sub>) was 99.6%;while in the case of 10 mm, both the visible and visible-short-NIR models achieved the optimal validation effect (RAR<sub>V</sub> = 100%). The detection method does not require reagents and is fast and simple, which is easy to promote the application. The results can provide valuable reference for designing small dedicated spectrometers with different measurement modals and different spectral regions.展开更多
The performance of different chemometric approaches was evaluated in the spectrophotometric determination of pharmaceutical mixtures characterized by having the amount of components with a very high ratio. Principal c...The performance of different chemometric approaches was evaluated in the spectrophotometric determination of pharmaceutical mixtures characterized by having the amount of components with a very high ratio. Principal component regression （PCR）, partial least squares with one dependent variable （PLS1） or multi-dependent variables （PLS2）, and multivariate curve resolution （MCR） were applied to the spectral data of a ternary mixture containing paracetamol, sodium ascorbate and chlorpheniramine （150：140：1, m/m/m）, and a quaternary mixture containing paracetamol, caffeine, phenylephrine and chlorpheniramine （125：6. 25：1.25：1, m/m/m/m）. The UV spectra of the calibration samples in the range of 200-320 nm were pre-treated by removing noise and useless data, and the wavelength regions having the most useful analytical information were selected using the regression coefficients calculated in the multivariate modeling. All the defined chemometric models were validated on external sample sets and then applied to commercial pharmaceutical formulations. Different data intervals, fixed at 0.5, 1.0, and 2.0 point/nm, were tested to optimize the prediction ability of the models. The best results were obtained using the PLSlcalibration models and the quantification of the species of a lower amount was sig- nificantly improved by adopting 0.5 data interval, which showed accuracy between 94.24% and 107.76%.展开更多
Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical str...Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.展开更多
The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectrosc...The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectroscopy and first-principles calculations are time-and power-consuming,not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space.In the present work,an artificial intelligence ensemble comprising two classifiers(with F1 scores of 0.9125 and 0.925)and a regressor(with mean squared error of 0.0014 eV)is constructed to achieve high-precision prediction of the bandgap.The bandgap perovskite dataset is established through highthroughput prediction of bandgaps by the ensemble.Based on the self-built dataset,partial dependence analysis(PDA)is developed to interpret the bandgap influential mechanism.Meanwhile,an interpretable mathematical model with an R^(2)of 0.8417 is generated using the genetic programming symbolic regression(GPSR)technique.The constructed PDA maps agree well with the Shapley Additive exPlanations,the GPSR model,and experiment verification.Through PDA,we reveal the boundary effect,the bowing effect,and their evolution trends with key descriptors.展开更多
In this study,we analyzed the characteristics of three-dimensional excitation-emission matrix spectra(EEMs)of 150 samples from five industrial wastewater types and domestic sewage to track water pollution sources effe...In this study,we analyzed the characteristics of three-dimensional excitation-emission matrix spectra(EEMs)of 150 samples from five industrial wastewater types and domestic sewage to track water pollution sources effectively.We then developed a recognition model for wastewater EEMs by establishing a feature dataset containing fluorescence peak values and parameters derived from EEMs,integrated with machine learning techniques.This model enables the rapid and precise identification of pollution sources.Our findings suggest that although the EEMs of the sixwastewater categories are distinct,visual differentiation is challenging.This was confirmed by cosine similarity assessments,showing some samples with low within-group(<0.8)and high between-group(>0.95)similarities.Despite significant variations in EEMs features acrosswastewater categories,identifying specific pollutants remains difficult,especially for pulp mills and leather effluents.Among the tested classification algorithms,Support Vector Machine(SVM)achieved the highest performance with91.7%accuracy,94%precision,91%recall,and 92%F_(1)-score,outperforming K-Nearest Neighbors and Partial Least Squares Discriminant Analysis.The SVM significantly improved identification accuracy for pulpmill and leather processing wastewaters compared to other models.To enhance identification accuracy,further exploration of EEMs features and expanding the training dataset are recommended.Combining EEMs features with machine learning presents a promising method for improvingwater pollution supervision and source tracing in environmental management practices.展开更多
As an important means regulating the relationship between human and natural ecosystem,ecological restoration program plays a key role in restoring ecosystem functions.The Grain-for-Green Program(GFGP,One of the world...As an important means regulating the relationship between human and natural ecosystem,ecological restoration program plays a key role in restoring ecosystem functions.The Grain-for-Green Program(GFGP,One of the world’s most ambitious ecosystem conservation set-aside programs aims to transfer farmland on steep slopes to forestland or grassland to increase vegetation coverage)has been widely implemented from 1999 to 2015 and exerted significant influence on land use and ecosystem services(ESs).In this study,three ecological models(In VEST,RUSLE,and CASA)were used to accurately calculate the three key types of ESs,water yield(WY),soil conservation(SC),and net primary production(NPP)in Karst area of southwestern China from 1982 to 2015.The impact of GFGP on ESs and trade-offs was analyzed.It provides practical guidance in carrying out ecological regulation in Karst area of China under global climate change.Results showed that ESs and trade-offs had changed dramatically driven by GFGP.In detail,temporally,SC and NPP exhibited an increasing trend,while WY exhibited a decreasing trend.Spatially,SC basically decreased from west to east;NPP basically increased from north to south;WY basically increased from west to east;NPP and SC,SC and WY developed in the direction of trade-offs driven by the GFGP,while NPP and WY developed in the direction of synergy.Therefore,future ecosystem management and restoration policy-making should consider trade-offs of ESs so as to achieve sustainable provision of ESs.展开更多
The Three-River Source Region(TRSR)in China holds a vital position and exhibits an irreplaceable strategic importance in ecological preservation at the national level.On the basis of an in-depth study of the vegetatio...The Three-River Source Region(TRSR)in China holds a vital position and exhibits an irreplaceable strategic importance in ecological preservation at the national level.On the basis of an in-depth study of the vegetation evolution in the TRSR from 2000 to 2022,we conducted a detailed analysis of the feedback mechanism of vegetation growth to climate change and human activity for different vegetation types.During the growing season,the spatiotemporal variations of normalized difference vegetation index(NDVI)for different vegetation types in the TRSR were analyzed using the Moderate Resolution Imaging Spectroradiometer(MODIS)-NDVI data and meteorological data from 2000 to 2022.In addition,the response characteristics of vegetation to temperature,precipitation,and human activity were assessed using trend analysis,partial correlation analysis,and residual analysis.Results indicated that,after in-depth research,from 2000 to 2022,the TRSR's average NDVI during the growing season was 0.3482.The preliminary ranking of the average NDVI for different vegetation types was as follows:shrubland(0.5762)>forest(0.5443)>meadow(0.4219)>highland vegetation(0.2223)>steppe(0.2159).The NDVI during the growing season exhibited a fluctuating growth trend,with an average growth rate of 0.0018/10a(P<0.01).Notably,forests displayed a significant development trend throughout the growing season,possessing the fastest rate of change in NDVI(0.0028/10a).Moreover,the upward trends in NDVI for forests and steppes exhibited extensive spatial distributions,with significant increases accounting for 95.23%and 93.80%,respectively.The sensitivity to precipitation was significantly enhanced in other vegetation types other than highland vegetation.By contrast,steppes,meadows,and highland vegetation demonstrated relatively high vulnerability to temperature fluctuations.A further detailed analysis revealed that climate change had a significant positive impact on the TRSR from 2000 to 2022,particularly in its northwestern areas,accounting for 85.05%of the total area.Meanwhile,human activity played a notable positive role in the southwestern and southeastern areas of the TRSR,covering 62.65%of the total area.Therefore,climate change had a significantly higher impact on NDVI during the growing season in the TRSR than human activity.展开更多
Nondestructive method of measuring soluble solids content (SSC) of citrus fruits was developed using Fourier transform near infrared reflectance (FT-NIR) measurements collected through optics fiber. The models describ...Nondestructive method of measuring soluble solids content (SSC) of citrus fruits was developed using Fourier transform near infrared reflectance (FT-NIR) measurements collected through optics fiber. The models describing the relationship between SSC and the NIR spectra of citrus fruits were developed and evaluated. Different spectra correction algorithms (standard normal variate (SNV), multiplicative signal correction (MSC)) were used in this study. The relationship between laboratory SSC and FT-NIR spectra of citrus fruits was analyzed via principle component regression (PCR) and partial least squares (PLS) re- gression method. Models based on the different spectral ranges were compared in this research. The first derivative and second derivative were applied to all spectra to reduce the effects of sample size, light scattering, instrument noise, etc. Different baseline correction methods were applied to improve the spectral data quality. Among them the second derivative method after baseline correction produced best noise removing capability and yielded optimal calibration models. A total of 170 NIR spectra were acquired; 135 NIR spectra were used to develop the calibration model; the remaining spectra were used to validate the model. The developed PLS model describing the relationship between SSC and NIR reflectance spectra could predict SSC of 35 samples with correlation coefficient of 0.995 and RMSEP of 0.79 °Brix.展开更多
To evaluate the potential of FT-NIR spectroscopy and the influence of the distance between the light source/detection probe and the fruit for measuring the sugar content (SC) of Fuji apples, diffuse reflectance spectr...To evaluate the potential of FT-NIR spectroscopy and the influence of the distance between the light source/detection probe and the fruit for measuring the sugar content (SC) of Fuji apples, diffuse reflectance spectra were measured in the spectral range from 12500 to 4000 cm^-1 at 0 mm, 2 mm, 4 mm and 6 mm distances. Four calibration models at four distances were established between diffused reflectance spectra and sugar content by partial least squares (PLS) analysis. The correlation coefficients (R) of calibrations ranged from 0.982 to 0.997 with SEC values from 0.138 to 0.453 and the SECV values from 0.74 to 1.58. The best model of original spectra at 0 mm distance yielded high correlation determination of 0.918, a SEC of 0.092, and a SEP of 0.773. The results showed that different light/detection probe-fruit distances influence the apple reflective spectra and SC predictions.展开更多
This study is intended to explore the chemical differences of Acori Tatarinowii Rhizoma （ATR） samples collected from two habitats, Sichuan and Anhui provinces, China. Gas chromatography-mass spectrometry （GC-MS） w...This study is intended to explore the chemical differences of Acori Tatarinowii Rhizoma （ATR） samples collected from two habitats, Sichuan and Anhui provinces, China. Gas chromatography-mass spectrometry （GC-MS） was applied to establishing the quantitative chemical fingerprints of ATRs. A total of 104 volatile compounds were identified and quantified with the information of mass spectra and retention index （RI）. Furthermore, least absolute shrinkage and selection operator （LASSO）, a sparse regularization method, combined with subsampling was employed to improve the classification ability of partial least squares-discriminant analysis （PLS-DA）. After variable selection by LASSO, three chemical markers,β-elemene, α-selinene and α-asarone, were identified for the discrimination of ATRs from two habitats, and the total classification correct rate was increased from 82.76% to 96.55%. The proposed LASSO-PLS-DA method can serve as an efficient strategy for screening marked chemical components and geo-herbalism research of traditional Chinese medicines.展开更多
Laser-induced breakdown spectroscopy （LIBS） has become a powerful technology in geological applications. The correct identification of rocks and soils is critical to many geological projects. In this study, LIBS dat...Laser-induced breakdown spectroscopy （LIBS） has become a powerful technology in geological applications. The correct identification of rocks and soils is critical to many geological projects. In this study, LIBS database software with a user-friendly and intuitive interface is developed based on Windows, consisting of a database module and a sample identification module. The database module includes a basic database containing LIBS persistent lines for elements and a dedicated geological database containing LIBS emission lines for several rock and soil reference standards. The module allows easy use of the data. A sample identification module based on partial least squares discriminant analysis （PLS-DA） or support vector machine （SVM） algorithms enables users to classify groups of unknown spectra. The developed system was used to classify rock and soil data sets in a dedicated database and the results demonstrate that the system is capable of fast and accurate classification of rocks and soils, and is thus useful for the detection of geological materials.展开更多
AIM: To study the metabolic profiling of serum samples from compensated and decompensated cirrhosis patients. METHODS: A pilot metabolic profiling study was conducted using three groups: compensated cirrhosis patients...AIM: To study the metabolic profiling of serum samples from compensated and decompensated cirrhosis patients. METHODS: A pilot metabolic profiling study was conducted using three groups: compensated cirrhosis patients (n = 30), decompensated cirrhosis patients (n = 30) and healthy controls (n = 30). A 1H nuclear magnetic resonance (NMR)-based metabonomics approach was used to obtain the serum metabolic profiles of the samples. The acquired data were processed by multivariate principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA). RESULTS: The OPLS-DA model was capable of distinguishing between decompensated and compensated cirrhosis patients, with an R2Y of 0.784 and a Q2Y of 0.598. Twelve metabolites, such as pyruvate, phenylala-nine and succinate, were identified as the most influential factors for the difference between the two groups. The validation of the diagnosis prediction showed that the accuracy of the OPLS-DA model was 85% (17/20). CONCLUSION: 1H NMR spectra combined with pattern recognition analysis techniques offer a new way to diagnose compensated and decompensated cirrhosis in the future.展开更多
The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classifi...The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classification of complex organics,three kinds of fresh leaves were measured by LIBS.100 spectra from 100 samples of each kind of leaves were measured and then they were divided into a training set and a test set in a ratio of 7:3.Two algorithms of chemometric methods including the partial least squares discriminant analysis(PLS-DA) and principal component analysis Mahalanobis distance(PCA-MD) were used to identify these leaves.By using 23 lines from 16 elements or molecules as input data,these two methods can both classify these three kinds of leaves successfully.The classification accuracies of training sets are both up to 100% by PCA-MD and PLS-DA.The classification accuracies of the test set are 93.3% by PCA-MD and 97.8% by PLS-DA.It means that PLS-DA is better than PCA-MD in classifying plant leaves.Because the components in PLS-DA process are more suitable for classification than those in PCA-MD process.We think that this work can provide a reference for plant traceability using LIBS.展开更多
The Fraction of Absorbed Photosynthetically Active Radiation(FPAR) is an important indicator of the primary productivity of vegetation. FPAR is often used to estimate the assimilation of carbon dioxide in vegetation. ...The Fraction of Absorbed Photosynthetically Active Radiation(FPAR) is an important indicator of the primary productivity of vegetation. FPAR is often used to estimate the assimilation of carbon dioxide in vegetation. Based on MOD15 A2 H/FPAR data product, the temporal and spatial variation characteristics and variation trend of FPAR in different vegetation types in 2001 to 2018 were analyzed in the Hengduan Mountains. The response of FPAR to climate change was investigated by using Pearson correlation analytical method and partial least squares regression analysis. Results showed that the FPAR in Hengduan Mountains presented an increasing trend with time. Spatially, it was high in the south and low in the north, and it also showed obvious vertical zonality by elevation gradient.The vegetation FPAR was found to be positively correlated with air temperature and sunshine duration but negatively correlated with precipitation. Partial least squares regression analysis showed that the influence of sunshine duration on vegetation FPAR in Hengduan Mountains was stronger than that of air temperature and precipitation.展开更多
Based on the data of 1950 – 1999 monthly global SST from Hadley Center, NCAR/NCEP reanalysis data and rainfall over 160 weather stations in China, investigation is conducted into the difference of summer rainfall in ...Based on the data of 1950 – 1999 monthly global SST from Hadley Center, NCAR/NCEP reanalysis data and rainfall over 160 weather stations in China, investigation is conducted into the difference of summer rainfall in China (hereafter referred to as the "CS rainfall") between the years with the Indian Ocean Dipole (IOD) occurring independently and those with IOD occurring along with ENSO so as to study the effects of El Ni?o - Southern Oscillation (ENSO) on the relationship between IOD and the CS rainfall. It is shown that CS rainfall will be more than normal in South China (centered in Hunan province) in the years of positive IOD occurring independently; the CS rainfall will be less (more) than normal in North China (Southeast China) in the years of positive IOD occurring together with ENSO. The effect of ENSO is offsetting (enhancing) the relationship between IOD and summer rainfall in Southwest China, the region joining the Yangtze River basin with the Huaihe River basin (hereafter referred to as the "Yangtze-Huaihe basin") and North China (Southeast China). The circulation field is also examined for preliminary causes of such an influence.展开更多
基金supported by the projects under the Innovation Team of the Safety Standards and Testing Technology for Agricultural Products of Zhejiang Province, China (Grant No.2010R50028)the National Key Technologies R&D Program of China during the 11th Five-Year Plan Period (Grant No.2006BAK02A18)
文摘Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi166） and wild type （Zhonghua 11） rice. Furthermore, rice lines transformed with protein gene （OsTCTP） and regulation gene （Osmi166） were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.
基金supported by the National Natural Science Foundation of China(Nos.21205145,21276006,21036009)the Open Funds of State Key Laboratory of Chemo/Biosensing and Chemometrics of Hunan University(No.201111)+1 种基金the Special Fund for Basic Scientific Research of Central Colleges,South-Central University for Nationalities(Nos.CZZ10005 and CZQ11012)the 'Five-twelfth' National Science and Technology Support Program (No.2012BAI27B00)
文摘Rapid and sensitive recognition of herbal pieces according to different concocted processing is crucial to quality control and pharmaceutical effect. Near-infrared （NIR） and mid-infrared （MIR） technology combined with supervised pattern recognition based on partial least-squares discriminant analysis （PLSDA） was attempted to classify and recognize six different concocted processing pieces of 600 Areca catechu L. samples and the influence of fingerprint information preprocessing methods on recognition performance was also investigated in this work. Recognition rates of 99.24%, 100% and 99.49% for original fingerprint, multiple scatter correct （MSC） fingerprint and second derivative （2nd derivative） fingerprint of NIR spectra were achieved by PLSDA models, respectively. Meanwhile, a perfect recognition rate of 100% was obtained for the above three fingerprint models of MIR spectra. In conclusion, PLSDA can rapidly and effectively extract otherness of fingerprint information from NIR and MIR spectra to identify different concocted herbal pieces ofA. catechu.
基金Supported by the National Natural Science Foundation of China(No.30725045)Shanghai Leading Academic Discipline Project (No.B906)in part by the Scientific Foundation of Shanghai China(Nos.07DZ19728, 06DZ19717 and 06DZ19005)
文摘For quality control purpose, an approach of fingerprinting and simultaneous quantification of five major bioactive constituents of Rhizoma Coptidis was established via a high-performance liquid chromatograph coupled with a photodiode array UV detector（HPLC-DAD） and an electrospray ionization mass spectrometer（HPLC-ESI/MS） The compounds were identified on the basis of the comparison of their mass spectra with literature data and those of standard samples and quantified by the HPLC-DAD method. Baseline separation was achieved on an XTerra C18 column（5 μm, 250 mm×4.6 mm i. d.） with linear gradient elution of formate buffer（consisting of 0.5% formic acid, adjusted to pH=4.5 with ammonia） and acetonitrile（consisting of 0.2% formic acid and 0.2% triethylamine）. The me- thod was validated for linearity（r^2〉0.9995）, repeatability（RSD〈3.1%）, intra- and inter-day precision（RSD〈1.8%） with recovery（99.9%-105.1%）, limits of detection（0.15-0.35 μg/mL）, and limits of quantification（0.53-0.82 μg/mL）. The similarities of 32 batches of Rhizoma Coptidis and their classification according to their manufacturers were based on the retention time and peak areas of the characteristic compounds. The five compounds were selected for quality assessment ofRhizoma coptidis via partial least squares analysis（PLS）.
基金This work was financially supported by the National Natural Science Foundation of China(No.31870568)Shandong Provincial Natural Science Foundation for Distinguished Young Scholars(China)(No.ZR2019JQ10)+1 种基金the Major Program of the Shandong Province Natural Science Foundation(No.ZR2018ZB0208)the"Transformational Technologies for Clean Energy and Demonstration"Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA21060201).
文摘In this study,multivariate analysis methods,including a principal component analysis(PCA)and partial least square(PLS)analysis,were applied to reveal the inner relationship of the key variables in the process of H_(2)O_(2)-assisted Na_(2)CO_(3)(HSC)pretreatment of corn stover.A total of 120 pretreatment experiments were implemented at the lab scale under different conditions by varying the particle size of the corn stover and process variables.The results showed that the Na_(2)CO_(3) dosage and pretreatment temperature had a strong influence on lignin removal,whereas pulp refining instrument(PFI)refining and Na_(2)CO_(3) dosage played positive roles in the final total sugar yield.Furthermore,it was found that pretreatment conditions had a more significant impact on the amelioration of pretreatment effectiveness compared with the properties of raw corn stover.In addition,a prediction of the effectiveness of the corn stover HSC pretreatment based on a PLS analysis was conducted for the first time,and the test results of the predictability based on additional pretreatment experiments proved that the developed PLS model achieved a good predictive performance(particularly for the final total sugar yield),indicating that the developed PLS model can be used to predict the effectiveness of HSC pretreatment.Therefore,multivariate analysis can be potentially used to monitor and control the pretreatment process in future large-scale biorefinery applications.
文摘The identification of soy sauce adulteration can avoid fraud, and protect the rights and interests of producers and consumers. Based on two measurement models (1 mm, 10 mm), the visible and near-infrared (Vis-NIR) spectroscopy combined with standard normal variate-partial least squares-discriminant analysis (SNV-PLS-DA) was used to establish the discriminant analysis models for adulterated and brewed soy sauces. Chubang soy sauce was selected as an identification brand (negative, 70). The adulteration samples (positive, 72) were prepared by mixing Chubang soy sauce and blended soy sauce with different adulteration rates. Among them, the “blended soy sauce” sample was concocted of salt water (NaCl), monosodium glutamate (C<sub>5</sub>H<sub>10</sub>NNaO<sub>5</sub>) and caramel color (C<sub>6</sub>H<sub>8</sub>O<sub>3</sub>). The rigorous calibration-prediction-validation sample design was adopted. For the case of 1 mm, five waveband models (visible, short-NIR, long-NIR, whole NIR and whole scanning regions) were established respectively;in the case of 10 mm, three waveband models (visible, short-NIR and visible-short-NIR regions) for unsaturated absorption were also established respectively. In independent validation, the models of all wavebands in the cases of 1 mm and 10 mm have achieved good discrimination effects. For the case of 1 mm, the visible model achieved the optimal validation effect, the validation recognition-accuracy rate (RAR<sub>V</sub>) was 99.6%;while in the case of 10 mm, both the visible and visible-short-NIR models achieved the optimal validation effect (RAR<sub>V</sub> = 100%). The detection method does not require reagents and is fast and simple, which is easy to promote the application. The results can provide valuable reference for designing small dedicated spectrometers with different measurement modals and different spectral regions.
基金Ministero dell'Istruzione,dell'Universitàe della Ricerca(MIUR),Italy,for the financial support to this work,grant 60%2014
文摘The performance of different chemometric approaches was evaluated in the spectrophotometric determination of pharmaceutical mixtures characterized by having the amount of components with a very high ratio. Principal component regression （PCR）, partial least squares with one dependent variable （PLS1） or multi-dependent variables （PLS2）, and multivariate curve resolution （MCR） were applied to the spectral data of a ternary mixture containing paracetamol, sodium ascorbate and chlorpheniramine （150：140：1, m/m/m）, and a quaternary mixture containing paracetamol, caffeine, phenylephrine and chlorpheniramine （125：6. 25：1.25：1, m/m/m/m）. The UV spectra of the calibration samples in the range of 200-320 nm were pre-treated by removing noise and useless data, and the wavelength regions having the most useful analytical information were selected using the regression coefficients calculated in the multivariate modeling. All the defined chemometric models were validated on external sample sets and then applied to commercial pharmaceutical formulations. Different data intervals, fixed at 0.5, 1.0, and 2.0 point/nm, were tested to optimize the prediction ability of the models. The best results were obtained using the PLSlcalibration models and the quantification of the species of a lower amount was sig- nificantly improved by adopting 0.5 data interval, which showed accuracy between 94.24% and 107.76%.
基金Supported by the National Natural Science Foundation of China(61374166,6153303)the Doctoral Fund of Ministry of Education of China(20120010110010)the Fundamental Research Funds for the Central Universities(YS1404,JD1413,ZY1502)
文摘Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.
基金supported by the National Research Foundation of Korea(NRF)funded by the Korean government(MSIT)(Grant number:RS-2025-02316700,and RS-2025-00522430)the China Scholarship Council Program。
文摘The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectroscopy and first-principles calculations are time-and power-consuming,not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space.In the present work,an artificial intelligence ensemble comprising two classifiers(with F1 scores of 0.9125 and 0.925)and a regressor(with mean squared error of 0.0014 eV)is constructed to achieve high-precision prediction of the bandgap.The bandgap perovskite dataset is established through highthroughput prediction of bandgaps by the ensemble.Based on the self-built dataset,partial dependence analysis(PDA)is developed to interpret the bandgap influential mechanism.Meanwhile,an interpretable mathematical model with an R^(2)of 0.8417 is generated using the genetic programming symbolic regression(GPSR)technique.The constructed PDA maps agree well with the Shapley Additive exPlanations,the GPSR model,and experiment verification.Through PDA,we reveal the boundary effect,the bowing effect,and their evolution trends with key descriptors.
基金supported by the Leading Talent of the Science and Technology Nova Program of Zhejiang(No.2020R52039)the Outstanding Innovative Team Supporting Plan of Jiaxing City(No.2022-LHYJ-02-0503-02)+1 种基金the Key Research Project of Yangtze Delta Region Institute of Tsinghua University(No.2023ZQZ005)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCX22-1386).
文摘In this study,we analyzed the characteristics of three-dimensional excitation-emission matrix spectra(EEMs)of 150 samples from five industrial wastewater types and domestic sewage to track water pollution sources effectively.We then developed a recognition model for wastewater EEMs by establishing a feature dataset containing fluorescence peak values and parameters derived from EEMs,integrated with machine learning techniques.This model enables the rapid and precise identification of pollution sources.Our findings suggest that although the EEMs of the sixwastewater categories are distinct,visual differentiation is challenging.This was confirmed by cosine similarity assessments,showing some samples with low within-group(<0.8)and high between-group(>0.95)similarities.Despite significant variations in EEMs features acrosswastewater categories,identifying specific pollutants remains difficult,especially for pulp mills and leather effluents.Among the tested classification algorithms,Support Vector Machine(SVM)achieved the highest performance with91.7%accuracy,94%precision,91%recall,and 92%F_(1)-score,outperforming K-Nearest Neighbors and Partial Least Squares Discriminant Analysis.The SVM significantly improved identification accuracy for pulpmill and leather processing wastewaters compared to other models.To enhance identification accuracy,further exploration of EEMs features and expanding the training dataset are recommended.Combining EEMs features with machine learning presents a promising method for improvingwater pollution supervision and source tracing in environmental management practices.
基金Under the auspices of National Key Technology Research and Development Project of China(No.2018YFC0507301-02)Chinese Academy of Sciences,Strategic Pilot Science and Technology Project(Class A)(No.XDA2002040201)Shaanxi Province Natural Science Basic Research Project(No.2018JM4016)
文摘As an important means regulating the relationship between human and natural ecosystem,ecological restoration program plays a key role in restoring ecosystem functions.The Grain-for-Green Program(GFGP,One of the world’s most ambitious ecosystem conservation set-aside programs aims to transfer farmland on steep slopes to forestland or grassland to increase vegetation coverage)has been widely implemented from 1999 to 2015 and exerted significant influence on land use and ecosystem services(ESs).In this study,three ecological models(In VEST,RUSLE,and CASA)were used to accurately calculate the three key types of ESs,water yield(WY),soil conservation(SC),and net primary production(NPP)in Karst area of southwestern China from 1982 to 2015.The impact of GFGP on ESs and trade-offs was analyzed.It provides practical guidance in carrying out ecological regulation in Karst area of China under global climate change.Results showed that ESs and trade-offs had changed dramatically driven by GFGP.In detail,temporally,SC and NPP exhibited an increasing trend,while WY exhibited a decreasing trend.Spatially,SC basically decreased from west to east;NPP basically increased from north to south;WY basically increased from west to east;NPP and SC,SC and WY developed in the direction of trade-offs driven by the GFGP,while NPP and WY developed in the direction of synergy.Therefore,future ecosystem management and restoration policy-making should consider trade-offs of ESs so as to achieve sustainable provision of ESs.
基金supported by the National Natural Science Foundation of China (42377472, 42174055)the Jiangxi Provincial Social Science "Fourteenth Five-Year Plan" (2024) Fund Project (24GL45)+1 种基金the Research Center of Resource and Environment Economics (20RGL01)the Provincial Finance Project of Jiangxi Academy of Sciences-Young Talent Cultivation Program (2023YSBG50010)
文摘The Three-River Source Region(TRSR)in China holds a vital position and exhibits an irreplaceable strategic importance in ecological preservation at the national level.On the basis of an in-depth study of the vegetation evolution in the TRSR from 2000 to 2022,we conducted a detailed analysis of the feedback mechanism of vegetation growth to climate change and human activity for different vegetation types.During the growing season,the spatiotemporal variations of normalized difference vegetation index(NDVI)for different vegetation types in the TRSR were analyzed using the Moderate Resolution Imaging Spectroradiometer(MODIS)-NDVI data and meteorological data from 2000 to 2022.In addition,the response characteristics of vegetation to temperature,precipitation,and human activity were assessed using trend analysis,partial correlation analysis,and residual analysis.Results indicated that,after in-depth research,from 2000 to 2022,the TRSR's average NDVI during the growing season was 0.3482.The preliminary ranking of the average NDVI for different vegetation types was as follows:shrubland(0.5762)>forest(0.5443)>meadow(0.4219)>highland vegetation(0.2223)>steppe(0.2159).The NDVI during the growing season exhibited a fluctuating growth trend,with an average growth rate of 0.0018/10a(P<0.01).Notably,forests displayed a significant development trend throughout the growing season,possessing the fastest rate of change in NDVI(0.0028/10a).Moreover,the upward trends in NDVI for forests and steppes exhibited extensive spatial distributions,with significant increases accounting for 95.23%and 93.80%,respectively.The sensitivity to precipitation was significantly enhanced in other vegetation types other than highland vegetation.By contrast,steppes,meadows,and highland vegetation demonstrated relatively high vulnerability to temperature fluctuations.A further detailed analysis revealed that climate change had a significant positive impact on the TRSR from 2000 to 2022,particularly in its northwestern areas,accounting for 85.05%of the total area.Meanwhile,human activity played a notable positive role in the southwestern and southeastern areas of the TRSR,covering 62.65%of the total area.Therefore,climate change had a significantly higher impact on NDVI during the growing season in the TRSR than human activity.
基金Project supported by New Century Excellent Talents in University(No. NCET-04-0524), and the Research Fund for the Doctoral Pro-gram of Higher Education (No. 20030335060) of China
文摘Nondestructive method of measuring soluble solids content (SSC) of citrus fruits was developed using Fourier transform near infrared reflectance (FT-NIR) measurements collected through optics fiber. The models describing the relationship between SSC and the NIR spectra of citrus fruits were developed and evaluated. Different spectra correction algorithms (standard normal variate (SNV), multiplicative signal correction (MSC)) were used in this study. The relationship between laboratory SSC and FT-NIR spectra of citrus fruits was analyzed via principle component regression (PCR) and partial least squares (PLS) re- gression method. Models based on the different spectral ranges were compared in this research. The first derivative and second derivative were applied to all spectra to reduce the effects of sample size, light scattering, instrument noise, etc. Different baseline correction methods were applied to improve the spectral data quality. Among them the second derivative method after baseline correction produced best noise removing capability and yielded optimal calibration models. A total of 170 NIR spectra were acquired; 135 NIR spectra were used to develop the calibration model; the remaining spectra were used to validate the model. The developed PLS model describing the relationship between SSC and NIR reflectance spectra could predict SSC of 35 samples with correlation coefficient of 0.995 and RMSEP of 0.79 °Brix.
基金Project (No. 30270763) supported by the National Natural Science Foundation of China
文摘To evaluate the potential of FT-NIR spectroscopy and the influence of the distance between the light source/detection probe and the fruit for measuring the sugar content (SC) of Fuji apples, diffuse reflectance spectra were measured in the spectral range from 12500 to 4000 cm^-1 at 0 mm, 2 mm, 4 mm and 6 mm distances. Four calibration models at four distances were established between diffused reflectance spectra and sugar content by partial least squares (PLS) analysis. The correlation coefficients (R) of calibrations ranged from 0.982 to 0.997 with SEC values from 0.138 to 0.453 and the SECV values from 0.74 to 1.58. The best model of original spectra at 0 mm distance yielded high correlation determination of 0.918, a SEC of 0.092, and a SEP of 0.773. The results showed that different light/detection probe-fruit distances influence the apple reflective spectra and SC predictions.
基金Project(21465016)supported by the National Natural Foundation of China
文摘This study is intended to explore the chemical differences of Acori Tatarinowii Rhizoma （ATR） samples collected from two habitats, Sichuan and Anhui provinces, China. Gas chromatography-mass spectrometry （GC-MS） was applied to establishing the quantitative chemical fingerprints of ATRs. A total of 104 volatile compounds were identified and quantified with the information of mass spectra and retention index （RI）. Furthermore, least absolute shrinkage and selection operator （LASSO）, a sparse regularization method, combined with subsampling was employed to improve the classification ability of partial least squares-discriminant analysis （PLS-DA）. After variable selection by LASSO, three chemical markers,β-elemene, α-selinene and α-asarone, were identified for the discrimination of ATRs from two habitats, and the total classification correct rate was increased from 82.76% to 96.55%. The proposed LASSO-PLS-DA method can serve as an efficient strategy for screening marked chemical components and geo-herbalism research of traditional Chinese medicines.
基金supported by National Major Scientific Instruments and Equipment Development Special Funds,China(No.2011YQ030113)
文摘Laser-induced breakdown spectroscopy （LIBS） has become a powerful technology in geological applications. The correct identification of rocks and soils is critical to many geological projects. In this study, LIBS database software with a user-friendly and intuitive interface is developed based on Windows, consisting of a database module and a sample identification module. The database module includes a basic database containing LIBS persistent lines for elements and a dedicated geological database containing LIBS emission lines for several rock and soil reference standards. The module allows easy use of the data. A sample identification module based on partial least squares discriminant analysis （PLS-DA） or support vector machine （SVM） algorithms enables users to classify groups of unknown spectra. The developed system was used to classify rock and soil data sets in a dedicated database and the results demonstrate that the system is capable of fast and accurate classification of rocks and soils, and is thus useful for the detection of geological materials.
文摘AIM: To study the metabolic profiling of serum samples from compensated and decompensated cirrhosis patients. METHODS: A pilot metabolic profiling study was conducted using three groups: compensated cirrhosis patients (n = 30), decompensated cirrhosis patients (n = 30) and healthy controls (n = 30). A 1H nuclear magnetic resonance (NMR)-based metabonomics approach was used to obtain the serum metabolic profiles of the samples. The acquired data were processed by multivariate principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA). RESULTS: The OPLS-DA model was capable of distinguishing between decompensated and compensated cirrhosis patients, with an R2Y of 0.784 and a Q2Y of 0.598. Twelve metabolites, such as pyruvate, phenylala-nine and succinate, were identified as the most influential factors for the difference between the two groups. The validation of the diagnosis prediction showed that the accuracy of the OPLS-DA model was 85% (17/20). CONCLUSION: 1H NMR spectra combined with pattern recognition analysis techniques offer a new way to diagnose compensated and decompensated cirrhosis in the future.
基金supported by the Fundamental Research Funds for the Central Universities of Ministry of Education of China(No.JB190501)Science and Technology Innovation Team of Shaanxi Province(No.2019TD-002)National Natural Science Foundation of China(No.11774277)。
文摘The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classification of complex organics,three kinds of fresh leaves were measured by LIBS.100 spectra from 100 samples of each kind of leaves were measured and then they were divided into a training set and a test set in a ratio of 7:3.Two algorithms of chemometric methods including the partial least squares discriminant analysis(PLS-DA) and principal component analysis Mahalanobis distance(PCA-MD) were used to identify these leaves.By using 23 lines from 16 elements or molecules as input data,these two methods can both classify these three kinds of leaves successfully.The classification accuracies of training sets are both up to 100% by PCA-MD and PLS-DA.The classification accuracies of the test set are 93.3% by PCA-MD and 97.8% by PLS-DA.It means that PLS-DA is better than PCA-MD in classifying plant leaves.Because the components in PLS-DA process are more suitable for classification than those in PCA-MD process.We think that this work can provide a reference for plant traceability using LIBS.
基金supported by the National Natural Science Foundation of China (41801099)the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0307, 2019QZKK0301)。
文摘The Fraction of Absorbed Photosynthetically Active Radiation(FPAR) is an important indicator of the primary productivity of vegetation. FPAR is often used to estimate the assimilation of carbon dioxide in vegetation. Based on MOD15 A2 H/FPAR data product, the temporal and spatial variation characteristics and variation trend of FPAR in different vegetation types in 2001 to 2018 were analyzed in the Hengduan Mountains. The response of FPAR to climate change was investigated by using Pearson correlation analytical method and partial least squares regression analysis. Results showed that the FPAR in Hengduan Mountains presented an increasing trend with time. Spatially, it was high in the south and low in the north, and it also showed obvious vertical zonality by elevation gradient.The vegetation FPAR was found to be positively correlated with air temperature and sunshine duration but negatively correlated with precipitation. Partial least squares regression analysis showed that the influence of sunshine duration on vegetation FPAR in Hengduan Mountains was stronger than that of air temperature and precipitation.
基金National Science Foundation of China (40475028)a project from Key Laboratory of Meteorological Disaster of Jiangsu Province (KLME060210)
文摘Based on the data of 1950 – 1999 monthly global SST from Hadley Center, NCAR/NCEP reanalysis data and rainfall over 160 weather stations in China, investigation is conducted into the difference of summer rainfall in China (hereafter referred to as the "CS rainfall") between the years with the Indian Ocean Dipole (IOD) occurring independently and those with IOD occurring along with ENSO so as to study the effects of El Ni?o - Southern Oscillation (ENSO) on the relationship between IOD and the CS rainfall. It is shown that CS rainfall will be more than normal in South China (centered in Hunan province) in the years of positive IOD occurring independently; the CS rainfall will be less (more) than normal in North China (Southeast China) in the years of positive IOD occurring together with ENSO. The effect of ENSO is offsetting (enhancing) the relationship between IOD and summer rainfall in Southwest China, the region joining the Yangtze River basin with the Huaihe River basin (hereafter referred to as the "Yangtze-Huaihe basin") and North China (Southeast China). The circulation field is also examined for preliminary causes of such an influence.
