Intelligence detection of oil absorption in French fries by surface profiles.

Surface profiles are important evaluation indices for oil absorption behavior of fried foods. This research established two intelligent models of partial least-squares regression (PLSR) and back propagation artificial neural network (BP-ANN) for monitoring the oil absorption behavior of French fries based on the surface characteristics. Surface morphology and texture of French fries by rapeseed oil (RO) and high-oleic peanut oil (HOPO) at different temperatures were investigated. Results showed that oil content of samples increased with frying temperature, accounting for 37.7% and 41.4% of samples fried by RO and HOPO respectively. The increase of crust ratio, roughness and texture parameters (Fm, Nwr, fwr, Wc) and the decrease of uniformity were observed with the frying temperature. Coefficients of prediction set of PLSR and BP-ANN models were more than 0.93, which indicated that surface features combined with chemometrics were rapid and precise methods for determining the oil content of French fries.

Integration of untargeted and pseudotargeted metabolomics reveals specific markers for authentication and adulteration detection of Fritillariae Bulbus using tandem mass spectrometry and chemometrics.

Authentication and adulteration detection of closely related herbal medicines is a thorny issue in the quality control and market standardization of traditional Chinese medicine. Taking Fritillariae Bulbus (FB) as a case study, we herein proposed a three-step strategy that integrates mass spectrometry-based metabolomics and multivariate statistical analysis to identify specific markers, thereby accurately identifying FBs and determining the adulteration level. First, an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based untargeted metabolomics method was employed to profile steroid alkaloids in five sorts of FB and screen potential differential markers. Then, the reliability of the screened markers was further verified by the distribution in different FB groups acquired from ultra-high performance liquid chromatography triple quadrupole mass spectrometry-based pseudotargeted metabolomics analysis. As a result, a total of 16 compounds were screened out to be the specific markers, which were successfully applied to distinguish five FBs by using discriminant analysis model. Besides, partial least squares regression models based on specific markers allowed accurate prediction of three sets of adulterated FBs. All the models afforded good linearity and good predictive ability with regression coefficient of prediction (R2p) > 0.9 and root mean square error of prediction (RMSEP) < 0.1. The reliable results of discriminant and quantitative analysis revealed that this proposed strategy could be potentially used to identify specific markers, which contributes to rapid chemical discrimination and adulteration detection of herbal medicines with close genetic relationship.

Aroma and taste analysis of pickled tea from spontaneous and yeast-enhanced fermentation by mass spectrometry and sensory evaluation.

Anaerobically fermented pickled tea (PT) can be produced by spontaneous fermentation (SF) or yeast-enhanced fermentation (YF). Aroma and taste characteristics of PT during YF and SF were investigated using sensory evaluation, odour activity, aroma character impact values, HS-SPME-GC-MS, UPLC-QQQ-MS/MS, and spectrophotometry, annotating 198 volatile and 115 non-volatile components. The main contributing volatile components were ß-ionone, and 1-octanol, promoted by YF and SF, and yielding floral and fruity aromas respectively. Additionally, compared with SF, YF promoted the formation of citronellol yielding a floral aroma, inhibited the stale aroma of methoxybenzenes, and reduced bitter, astringent, and sour tastes. Furthermore, partial least-squares regression analysis identified the main components related to the 'acides aroma' of PT as linalool oxide, n-decanoic acid, hexanoic acid, 3,7-dimethyl-2,6-octadienoic acid, 3-methyl-1-dodecyn-3-ol, and nerolidol. This application could be used as methodology for the comprehensive analysis of tea aroma and taste and these results can act as guidelines for PT production and quality control.

[Rapid detection technology of chemical component content in Lycii Fructus based on hyperspectral technology].

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The correlation between pharmacological activity and contents of eight constituents of Glycyrrhiza uralensis Fisch.

Licorice (Glycyrrhiza uralensis Fisch. (GUF), Leguminosae) has been extensively applied in traditional Chinese medicine (TCM) to treat diseases, exactly, in almost half of Chinese herbal prescription. However, the relationship between chemical contents and efficacy has not been established, which could evaluate GUF quality. To create a simple and effective quality-evaluation method, 33 batches of GUF from different habitats in China were collected. The correlation between eight constituents (liquiritin, isoliquiritin, liquiritigenin, isoliquiritigenin, glycyrrhizic acid, licochalcone A, glabridin and glycyrrhetinic acid) and pharmacological activities (anti-inflammatory, antioxidant and immunoregulatory) was analyzed per the partial least squares regression method. Results showed that eight constituents correlated significantly with the pharmacological activity. The correlation equation modes between pharmacological activity and contents of eight constituents were constructed and verified to be reliable. In GUF extract, the main constituents liquiritin, isoliquiritin and glycyrrhizic acid exhibited positive influence on anti-inflammatory and antioxidant effect with different potent, while the metabolites liquiritigenin and isoliquiritigenin exhibited positive effect on the immunoregulatory activity and glycyrrhetinic acid exhibited positive effect on all the tested activities. Thus, our chemical-efficacy correlation method is reliable and feasible to predict the pharmacological activity based on its eight constituents. It could be powerful in quality control of GUF and provides a useful way for quality evaluation of other medicinal herbs.

Dynamic Zanthoxylum pungency characteristics and their correlation with sanshool composition and chemical structure.

Huajiao (Zanthoxylum) from different regions varies in pungency features. The objective of this study was to explore the reasons for the differences. Temporal check-all-that-apply (TCATA) and time-intensity (TI)) were used to determine time-related pungency features of huajiao and sanshools. The compositions of sanshools in huajiao were measured by high-performance liquid chromatograph (HPLC). TI results revealed that hydroxy-γ-sanshool tingling and numbing duration (1332.00 ± 50.91 and 1020.00 ± 61.19 s, respectively) were about twice that of hydroxy-α-sanshool (720.00 ± 25.92 and 584.00 ± 22.63 s, respectively). Tingling and numbing were not perceived by hydroxy-ß-sanshool and hydroxy-γ-isosanshool. HPLC results showed that HαSS was the main component of huajiao sanshools, representing 71.06 % to 92.90 %. TCATA results revealed the pungency sensations appearance sequence: tingling, salivating, cooling, and burning appeared first, followed by vibrating, and numbing was perceived last. These findings revealed the relationship between the compositions of sanshool and the pungency features of huajiao.

Assessing the Information Potential of MIR Spectral Signatures for Prediction of Multiple Soil Properties Based on Data from the AfSIS Phase I Project.

The aim of the study was to assess the predictive potential of mid-infrared (MIR) spectral response in the estimation of 60 soil properties. It is important to know the accuracy limitations in estimating various soil characteristics using various models in conditions of high spatial variability of the environment. To fully assess this potential, three types of algorithms were used in modeling, i.e., partial least squares (PLSR), one-dimensional convolutional neural network (1DCNN), and generalized regression neural network (GRNN). The research used data from 19 sub-Saharan African countries collected as part of the Africa Soil Information Service (AfSIS) Phase I project. The repositories provide 18,250 MIR reflectance recordings and nearly two thousand analytical data records from the determination of many soil properties by reference methods. The modeled subset of these properties included texture (three variables), bulk density, moisture content at soil water characteristic curves (SWCC, 4 variables), total and organic C and total N content (3 variables), total elemental content (32 variables), elemental content in bioavailable forms (12 variables), electrical conductivity, exchangeable acidity, exchangeable bases, pH, and phosphorus sorption index. It is not possible to indicate a universal optimal prediction model for all soil variables. The best prediction results are provided by all regression models for total and organic C, total Fe, total Al and bioavailable Al content, and pH. For bulk density, total N and total K content satisfactory results are provided by specific model type. Many other properties, i.e., texture, SWCC, total Ga, Rb, Na, Ca, Cu, Pb, Hg content, and bioavailable Ca and K content, can be predicted with accuracies sufficient for some less demanding tasks.

Rapid determination of the total content of oleanolic acid and ursolic acid in Chaenomelis Fructus using near-infrared spectroscopy.

Chaenomelis Fructus is a widely used traditional Chinese medicine with a long history in China. The total content of oleanolic acid (OA) and ursolic acid (UA) is taken as an important quality marker of Chaenomelis Fructus. In this study, quantitative models for the prediction total content of OA and UA in Chaenomelis Fructus were explored based on near-infrared spectroscopy (NIRS). The content of OA and UA in each sample was determined using high-performance liquid chromatography (HPLC), and the data was used as a reference. In the partial least squares (PLS) model, both leave one out cross validation (LOOCV) of the calibration set and external validation of the validation set were used to screen spectrum preprocessing methods, and finally the multiplicative scatter correction (MSC) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLS regression, and the characteristic spectrum range was determined as 7,500-4,250 cm-1, with 14 optimal ranks. In the back propagation artificial neural network (BP-ANN) model, the scoring data of 14 ranks obtained from PLS regression analysis were taken as input variables, and the total content of OA and UA reference values were taken as output values. The number of hidden layer nodes of BP-ANN was screened by full-cross validation (Full-CV) of the calibration set and external validation of the validation set. The result shows that both PLS model and PLS-BP-ANN model have strong prediction ability. In order to evaluate and compare the performance and prediction ability of models, the total content of OA and UA in each sample of the test set were detected under the same HPLC conditions, the NIRS data of the test set were input, respectively, to the optimized PLS model and PLS-BP-ANN model. By comparing the root-mean-square error (RMSEP) and determination coefficient (R 2) of the test set and ratio of performance to deviation (RPD), the PLS-BP-ANN model was found to have better performance with RMSEP of 0.59 mg·g-1, R 2 of 95.10%, RPD of 4.53 and bias of 0.0387 mg·g-1. The results indicated that NIRS can be used for the rapid quality control of Chaenomelis Fructus.

LC-MS/MS combined with blood-brain dual channel microdialysis for simultaneous determination of active components of astragali radix-safflower combination and neurotransmitters in rats with cerebral ischemia reperfusion injury: Application in pharmacokinetic and pharmacodynamic study.

BACKGROUND: Astragali Radix-Safflower combination (ARSC) is widely utilized in clinic to treat cerebral ischemia/reperfusion injury (CI/RI). Whereas, there is no in-depth research of the pharmacokinetics (PK) and pharmacodynamics (PD) analysis of ARSC after intragastric administration in rats with CI/RI. PURPOSE: The purpose of this research is to investigate the PK characteristics of eight active ingredients (astragaloside IV, calycosin, calycosin-7-O-ß-glucoside, formononetin, ononin, hydroxysafflor yellow A, syringin and vernine) of ARSC, and the regulation of neurotransmitters disorders, revealing the pharmacodynamic substance basis and the mechanism of ARSC in treating CI/RI from the molecular level. METHODS: We established a new method which based on blood-brain dual channel microdialysis (MD) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to continuously gather, and determine the components of ARSC and neurotransmitters related to CI/RI in vivo. The collected data were analyzed by sigmoid-Emax function. The neurotransmitters primarily regulated in CI/RI rat were discussed by principal component analysis and the compound most associated with total pharmacodynamics was chosen by partial least squares regression. RESULTS: The validated LC-MS/MS method had specificity and selectivity to simultaneously analyze the concentration of eight active components of ARSC extract and five neurotransmitters of CI/RI rats. The recovery rates of brain MD probe and blood MD probe were stable within six hours. The MD probes recovery rates decreased with the increase of flow rates, but the solution concentration had little effect on the probes recovery rates. It was feasible to correct the recovery rates of probes in vivo by using reverse dialysis method. All eight active ingredients of ARSC could pass across the blood brain barrier after CI/RI. ARSC regulated the release of glutamate (Glu), γ-aminobutyric acid (GABA), dopamine (DA), 5-hydroxytryptamine (5-HT) and aspartic acid (Asp). Notably, astragaloside IV and hydroxysafflor yellow A might have better regulatory effect on neurotransmitters in comparison with other six measured components of ARSC, and Glu was the neurotransmitter mainly regulated in CI/RI rats. CONCLUSION: The ARSC was able to treat CI/RI through ameliorating neurotransmitters disorders. There was a hysteresis between the peaked drug concentration and maximum therapeutic effect of ARSC. The drug effective concentrations range of ASIV, calycosin, calycosin-7-O-ß-glucoside, syringin and vernine in blood microdialysate and calycosin, syringin, vernine in brain microdialysate were narrow, which need be paid attention in clinical use.

Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach.

This study uses Raman and IR spectroscopic methods for the detection of adulterants in marine oils. These techniques are used individually and as low-level fused spectroscopic data sets. We used cod liver oil (CLO) and salmon oil (SO) as the valuable marine oils mixed with common adulterants, such as palm oil (PO), omega-3 concentrates in ethyl ester form (O3C), and generic fish oil (FO). We showed that support vector machines (SVM) can classify the adulterant present in both CLO and SO samples. Furthermore, partial least squares regression (PLSR) may be used to quantify the adulterants present. For example, PO and O3C adulterated samples could be detected with a RMSEP value less than 4%. However, the FO adulterant was more difficult to quantify because of its compositional similarity to CLO and SO. In general, data fusion improved the RMSEP for PO and O3C detection. This shows that Raman and IR spectroscopy can be used in concert to provide a useful analytical test for common adulterants in CLO and SO.

An emerging strategy for tracing the anti-erectile dysfunction components of raw and salt-processed semens of Astragali Complanati by combinatory liquid chromatography-mass spectrometry-based quantitative analysis, efficacy assessment on impotent rats, and partial least squares regression.

Semens of Astragali Complanati own anti-erectile dysfunction effect; however, the components which contribute to the anti-erectile dysfunction effect remain unclear. This work raised a strategy that integrates liquid chromatography coupled mass spectrometry-based quantitative analysis, anti-erectile dysfunction assessment on impotent rats, and their relationship analysis for pinpointing anti-erectile dysfunction components from semens of Astragali Complanati. For simultaneous quantification of seven major components in raw and salt-processed semens of Astragali Complanati, an accurate and reliable liquid chromatography-mass spectrometry method was developed under multiple reaction monitoring mode. Of note, chloramphenicol was employed as the internal standard. The method showed good linearity and repeatability, where the recovery rates of each component ranged from 98.1 to 104.7%, and the precisions of intra- and interday were all within 3.4%. The method has been used for quantification of the seven major components in 10 batches of raw and salt-processed semens of Astragali Complanati. Then, the anti-erectile dysfunction effects of raw and salt-processed semens of Astragali Complanati were evaluated on impotent rats. Gray relationship analysis and partial least squares regression were combined for elucidating the relationship. As a result, complanatuside, astragalin, complanatoside B, and kaempferol were found to be responsible for anti-erectile dysfunction effect of Astragali Complanati.

Application of Fourier transform mid-infrared photoacoustic spectroscopy for rapid assessment of phosphorus availability in digestates and digestate-amended soils.

Digestate is the anaerobic digestion by-product of biogas production that can be used as a phosphorus (P) fertilizer. To achieve the efficient utilization of digestate as a P fertilizer and evaluate P availability in digestate-amended soils, it is necessary to assess both available P in different digestates and digestate-amended soils. In this study, Fourier transform mid-infrared photoacoustic spectroscopy (FTIR-PAS) combined with multivariate analysis was applied to predict water-extractable P (WEP) in digestates and plant-available P in digestate-amended soils. The plant-available P was determined by the diffusive gradients in thin films (DGT) technique. 45 digestate samples were collected both from laboratory-scale digesters (26 samples) and operating biogas plants (19 samples) in Denmark for WEP determination. Three soils amended with the collected 19 digestate samples from biogas plants (that results to 57 digestate-amended soil samples in total) were deployed for DGT measurement of plant- available P. The WEP predicting model had a coefficient of determination (R2) of 0.80 and a root mean square error of 0.78 g kg-1 while the plant-available P predicting model exhibited an R2 of 0.70 and a root mean square error of 134.09 µg P L-1. Furthermore, regression coefficients with a significant contribution of the plant-available P predicting model were identified, indicating that FTIR-PAS is capable for correlating spectra information with plant-available P related chemical bonds. In conclusion, FTIR-PAS can be used as a faster and non-destructive alternative for the assessment of both WEP in digestates and plant-available P in digestate-amended soils.

Highly efficient authentication of edible oils by FTIR spectroscopy coupled with chemometrics.

A novel improved method for the authentication of edible oil samples based on Fourier-transform infrared (FTIR) spectroscopy coupled with chemometrics has been developed. A discrimination analysis model has been developed. On this basis, 100% correct classification of 135 samples from eleven species has been achieved. Recognition rates with respect to external validation for 91 pure oil samples and 231 blend samples were 100% and 92.6%, respectively. A general quantitative model for detecting edible oil adulteration (taking Camellia oil as an example) has also been built. An optimal backward interval partial least-squares model, based on the spectral regions ν = 3100-2900, 1800-1700, 1500-1400, and 1200-1100 cm-1, has been determined, giving good performances. A specific sub-model using a single adulterant oil has also been constructed, which showed higher prediction accuracy. Based on the developed qualitative and quantitative FTIR methods, adulterant oils in Camellia blends could be rapidly detected, effectively differentiated, and accurately quantified.

Estimating the catechin concentrations of new shoots in green tea fields using ground-based hyperspectral imagery.

Hyperspectral imagery was applied to estimating non-galloyl (EC, EGC) and galloyl (ECG, EGCG) types of catechins in new shoots of green tea. Partial least squares regression models were developed to consider the effects of commercial fertilizer (CF) and organic fertilizer (OF). The models could explain each type of catechin with a precision of more than 0.79, with a few exceptions. When the CF model was applied to the OF hyperspectral reflectance and the OF model was applied to the CF hyperspectral reflectance for mutual prediction, the prediction accuracy was better with the OF models than CF models. The prediction models using both CF and OF data (hyperspectral reflectances, and concentrations of catechins) had a precision of more than 0.76 except for the non-galloyl-type catechins as a group and EGC alone. These results provide useful data for maintaining and improving the quality of green tea.

Using UAV image data to monitor the effects of different nitrogen application rates on tea quality.

BACKGROUND: Accurate and efficient evaluation of the effect of nitrogen application rate on tea quality is of great significance for nitrogen management in a tea garden. However, previous methods were all through soil or leaf sampling, using biochemical methods for laboratory testing. These methods are not only less one-time detection samples, but also time-consuming, laborious and inefficient. Therefore, the development of fast, efficient and non-destructive diagnostic methods is an important goal in this field. RESULTS: We obtained spectral information on the tea canopy using a multispectral camera carried by an unmanned aerial vehicle (UAV), and extracted the average DN value of the experimental plot by environmental visual imagery (ENVI); we finally obtained 28 spectral parameters. By analyzing the correlation between spectral parameters and ground parameters measured synchronously, five spectral parameters with high correlation were selected. Finally, the prediction models of tea nitrogen, polyphenol and amino acid content were established by using support vector machine (SVM), partial least squares and backpropagation neural network. Through modeling comparison and coefficient verification, the results show that the ground parameters measured in the laboratory were in good agreement with the results estimated by the model. The SVM model had the best performance in predicting nitrogen and tea polyphenol content, with R2  = 0.7583 and 0.7533, root mean square error of prediction (RMSEP) = 0.4086 and 0.3392, and normalized RMSEP (NRMSEP) = 1.23 and 1.28, respectively. The partial least squares regression model had the best performance in predicting amino acid content, with R2  = 0.7597, RMSEP = 0.1176 and NRMSEP = 4.10. CONCLUSION: The results show that the model based on UAV image data and machine learning algorithm can effectively detect the main biochemical components of the tea plant, which provides an important basis for tea garden management. © 2021 Society of Chemical Industry.

GC-IMS and olfactometry analysis on the tea aroma of Yingde black teas harvested in different seasons.

This study aims to investigate the influence of different harvesting seasons on the aroma of black tea and the trend in the tea aroma variation. A total of 68 volatile substances was identified by gas chromatography coupled with ion-mobility spectrometry (GC-IMS), and 20 characteristic aroma-active compounds were quantitatively analyzed by gas chromatography-olfactometry coupled with aroma extract dilution analysis (GC-O AEDA) and odor activity value (OAV) analysis. These aroma-active compounds are mainly linalool, ß-damascenone, and benzeneacetaldehyde. Both methods confirmed that the aroma of tea changes with the harvesting seasons, showing a downward trend followed by an upward trend. Besides, black teas harvested in different seasons have their characteristic volatile compounds and metabolism precursors. The degradation of glycosides, carotenes, and amino acids are the most important degradation pathways for the formation of tea aroma. The PLSR results of GC-O-AEDA, OAV, and DSA data agree with each other, showing that five aroma attributes of the autumn tea have strong correlations. The autumn tea has the richest aroma, followed by the spring tea and the summer tea.

Improving Prediction of Peroxide Value of Edible Oils Using Regularized Regression Models.

We present four unique prediction techniques, combined with multiple data pre-processing methods, utilizing a wide range of both oil types and oil peroxide values (PV) as well as incorporating natural aging for peroxide creation. Samples were PV assayed using a standard starch titration method, AOCS Method Cd 8-53, and used as a verified reference method for PV determination. Near-infrared (NIR) spectra were collected from each sample in two unique optical pathlengths (OPLs), 2 and 24 mm, then fused into a third distinct set. All three sets were used in partial least squares (PLS) regression, ridge regression, LASSO regression, and elastic net regression model calculation. While no individual regression model was established as the best, global models for each regression type and pre-processing method show good agreement between all regression types when performed in their optimal scenarios. Furthermore, small spectral window size boxcar averaging shows prediction accuracy improvements for edible oil PVs. Best-performing models for each regression type are: PLS regression, 25 point boxcar window fused OPL spectral information RMSEP = 2.50; ridge regression, 5 point boxcar window, 24 mm OPL, RMSEP = 2.20; LASSO raw spectral information, 24 mm OPL, RMSEP = 1.80; and elastic net, 10 point boxcar window, 24 mm OPL, RMSEP = 1.91. The results show promising advancements in the development of a full global model for PV determination of edible oils.

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV-Vis Spectroscopy and Chemometrics.

In this present research, a spectroscopic method based on UV-Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10-50% level of adulteration. UV-Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83-0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPDp and RERp in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV-Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.

Identification of Antiprotozoal Compounds from Buxus sempervirens L. by PLS-Prediction.

Various nor-triterpene alkaloids of Buxus (B.) sempervirens L. have shown remarkable in vitro activity against the causative agents of tropical malaria and East African sleeping sickness. To identify further antiprotozoal compounds of this plant, 20 different fractions of B. sempervirens L., exhibiting a wide range of in vitro bioactivity, were analyzed by UHPLC/+ESI-QqTOF-MS/MS. The analytical profiles were investigated by partial least squares regression (PLS) for correlations between the intensity of LC/MS signals, bioactivity and cytotoxicity. The resulting models highlighted several compounds as mainly responsible for the antiprotozoal activity and thus, worthwhile for subsequent isolation. These compounds were dereplicated based on their mass spectra in comparison with isolated compounds recently reported by us and with literature data. Moreover, an estimation of the cytotoxicity of the highlighted compounds was derived from an additional PLS model in order to identify plant constituents with strong selectivity. In conclusion, high levels of antitrypanosomal and antiplasmodial activity were predicted for eight and four compounds, respectively. These include three hitherto unknown constituents of B. sempervirens L., presumably new natural products.

Spatiotemporal variation of nitrogen and phosphorus and its main influencing factors in Huangshui River basin.

The foundation of managing excess nutrients in river is the identification of key physical processes and the control of decisive influencing factors. The existing studies seldom consider the influence of rainfall-runoff relationship and only focus on a few anthropogenic activities and natural attributes factors. To address this issue, a comprehensive set of influencing factors including rainfall-runoff relationship (represented by runoff coefficient), basic physical and chemical parameters of water quality, land use types, landscape patterns, topography, and socioeconomic development was constructed in this study. M-K test and cluster analysis were conducted to identify the temporal mutation and spatial clustering characteristics of NH3-N and TP in Huangshui River basin, respectively. Partial least squares regression was used to elucidate the linkages between water contaminants and the factors. As shown in the results, the temporal mutations of NH3-N and TP were obvious in the middle reaches, with 4 out of 7 catchments in the middle reaches have a larger number of mutations of NH3-N than other catchments. The cluster analysis results of NH3-N and TP among catchments were similar. This study also indicated that although the Huangshui River basin was located in the upper reaches of the Yellow River, the influences of rainfall-runoff relationship on spatiotemporal changes of NH3-N and TP in its sub-basins were limited. Only the temporal change of NH3-N in Jintan catchment in the upstream area was significantly affected by runoff coefficient. The indexes of proportion of water area (PWA), proportion of impervious area (PIA), and proportion of primary industry (PPI) were the top three influencing factors of temporal variation of NH3-N and TP for most catchments in the middle reaches. The temporal change of NH3-N in Jintan catchment in the upstream area was obviously affected by runoff coefficient. The spatial variation of NH3-N and TP were all affected by PWA and proportion of secondary industry significantly. The results of this study can provide theoretical basis and technical support for the control and management of nitrogen and phosphorus pollution in upper reaches of rivers.