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1.
Wei Sheng Yan Jiu ; 50(3): 495-500, 2021 May.
Artículo en Chino | MEDLINE | ID: mdl-34074375

RESUMEN

OBJECTIVE: Near-infrared(NIR) spectroscopy combined with partial least squares(PLS) were applied to establish a rapid method for green direct determination of mineral elements(calcium, phosphorus and potassium) in wheat flour samples. METHODS: NIR spectra and analytical measurements of calcium, phosphorus and potassium were collected from 117 wheat flour samples with different processing levels(whole grain wheat, special grade No. 1 wheat and wheat core flour). Principal components analysis(PCA) was developed to assign 81 wheat flour samples to build models and 36 samples as the validation set to evaluate the performance of the developed models. The influence of wavelength range and spectral preprocessing method on the predictive ability of the model were discussed, and the best models were selected. RESULTS: For calcium, the best NIR model showed a good prediction performance(r~2=0. 7907, RMSEP=5. 35, RPD=2. 19); the best NIR model for phosphorus gave an excellent prediction performance(r~2=0. 9777, RMSEP=15. 3, RPD=6. 71); the best model for potassium also gave an excellent prediction performance(r~2=0. 9777, RMSEP=18. 9, RPD=6. 84). CONCLUSION: NIR spectroscopy can realize the rapid prediction of mineral elements(calcium, phosphorus and potassium) in wheat flour. By selecting the wavelength range and spectral preprocessing method, the prediction ability of the NIR model can be significantly improved.


Asunto(s)
Harina , Espectroscopía Infrarroja Corta , Harina/análisis , Análisis de los Mínimos Cuadrados , Minerales , Triticum
2.
Sensors (Basel) ; 21(9)2021 May 09.
Artículo en Inglés | MEDLINE | ID: mdl-34065067

RESUMEN

This study innovatively proposes a feature fusion technique to determine fatty acid content during rice storage. Firstly, a self-developed olfactory visualization sensor was used to capture the odor information of rice samples at different storage periods and a portable spectroscopy system was employed to collect the near-infrared (NIR) spectra during rice storage. Then, principal component analysis (PCA) was performed on the pre-processed olfactory visualization sensor data and the NIR spectra, and the number of the best principal components (PCs) based on the single technique model was optimized during the backpropagation neural network (BPNN) modeling. Finally, the optimal PCs were fused at the feature level, and a BPNN detection model based on the fusion feature was established to achieve rapid measurement of fatty acid content during rice storage. The experimental results showed that the best BPNN model based on the fusion feature had a good predictive performance where the correlation coefficient (RP) was 0.9265, and the root mean square error (RMSEP) was 1.1005 mg/100 g. The overall results demonstrate that the detection accuracy and generalization performance of the feature fusion model are an improvement on the single-technique data model; and the results of this study can provide a new technical method for high-precision monitoring of grain storage quality.


Asunto(s)
Oryza , Algoritmos , Ácidos Grasos , Análisis de los Mínimos Cuadrados , Redes Neurales de la Computación , Espectroscopía Infrarroja Corta
3.
Sensors (Basel) ; 21(9)2021 May 07.
Artículo en Inglés | MEDLINE | ID: mdl-34067111

RESUMEN

It is very important for human health to supervise the use of food additives, because excessive use of food additives will cause harm to the human body, especially lead to organ failures and even cancers. Therefore, it is important to realize high-sensibility detection of benzoic acid, a widely used food additive. Based on the theory of electromagnetism, this research attempts to design a terahertz-enhanced metamaterial resonator, using a metamaterial resonator to achieve enhanced detection of benzoic acid additives by using terahertz technology. The absorption peak of the metamaterial resonator is designed to be 1.95 THz, and the effectiveness of the metamaterial resonator is verified. Firstly, the original THz spectra of benzoic acid aqueous solution samples based on metamaterial are collected. Secondly, smoothing, multivariate scattering correction (MSC), and smoothing combined with first derivative (SG + 1 D) methods are used to preprocess the spectra to study the better spectral pretreatment methods. Then, Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighted Sampling (CARS) are used to explore the optimal terahertz band selection method. Finally, Partial Least Squares (PLS) and Least square support vector machine (LS-SVM) models are established, respectively, to realize the enhanced detection of benzoic acid additives. The LS-SVM model combined with CARS has the best effect, with the correlation coefficient of prediction set (Rp) is 0.9953, the root mean square error of prediction set (RMSEP) is 7.3 × 10-6, and the limit of detection (LOD) is 2.3610 × 10-5 g/mL. The research results lay a foundation for THz spectral analysis of benzoic acid additives, so that THz technology-based detection of benzoic acid additives in food can reach requirements stipulated in the national standard. This research is of great significance for promoting the detection and analysis of trace additives in food, whose results can also serve as a reference to the detection of antibiotic residues, banned additives, and other trace substances.


Asunto(s)
Ácido Benzoico , Máquina de Vectores de Soporte , Alimentos , Humanos , Análisis de los Mínimos Cuadrados
4.
Waste Manag ; 128: 132-141, 2021 Jun 01.
Artículo en Inglés | MEDLINE | ID: mdl-33989859

RESUMEN

This paper analyzes the effects of weight-based pricing on municipal waste generation in Flanders (Belgium) by applying the bias-corrected least squares dummy variables estimation model to account for the dynamic relationship. The study also considers yearly weight-based pricing dummy variables to clarify the annual effects after its introducing and price elasticities of weight-based pricing systems from the both viewpoints of short and long terms. The results by the dynamic panel estimations showed that the continuous participation of weight-based pricing decreases the amount of residual waste significantly by approximately 10.4%. However, the remarkable decrease (approximately 21.4%) was observed only in the first year after the introduction and the reduction effect gradually decreases following its introduction and it disappears in five years. The study also showed that price elasticities of weight-based pricing were smaller than that of volume-based pricing for five years after its introduction and consequently the difference between them disappeared in five years. In addition, the study indicated that the estimation results by the non-dynamic fixed effects model overestimated the long-term effects in weight-based pricing and price effects while underestimating the short-term effect by its introduction. The study suggested that we should consider dynamic effects and remove the bias from the least squares dummy variables estimators when we examine the effects of weight-based pricing.


Asunto(s)
Costos y Análisis de Costo , Bélgica , Análisis de los Mínimos Cuadrados
5.
Zhongguo Zhong Yao Za Zhi ; 46(10): 2565-2570, 2021 May.
Artículo en Chino | MEDLINE | ID: mdl-34047104

RESUMEN

Three cancer cell lines including gastric cancer SGC-7901, HGC-27, and MGC-803 cells were employed to evaluate the bioactivity of seven Dendrobium species. Simultaneously, these Dendrobium species were assessed with UPLC-Q-TOF-MS, and 504 common peaks were found. Based on the hypothesis that biological effects varied with differences in components, multivariate relevance analysis for chemical component-activity relationship of Dendrobium, including grey relation(GRA) and partial least squares(PLS) analysis were performed to evaluate the contribution of each identified component. The target peaks were identified by standards toge-ther with databases of Dendrobium, Nature Chemistry, MassBank, etc. Finally, four active components, including 3,5,9-trihydroxy-23-methylergosta-7,22-dien-6-one, diacylglycerol(14∶1/22∶6/0∶0), pipercitine, and 22-tricosenoic acid, might have negative effect on the growth of gastric cancer cells.


Asunto(s)
Dendrobium , Neoplasias Gástricas , Humanos , Análisis de los Mínimos Cuadrados , Análisis Multivariante , Neoplasias Gástricas/tratamiento farmacológico
6.
Zhongguo Zhong Yao Za Zhi ; 46(10): 2571-2577, 2021 May.
Artículo en Chino | MEDLINE | ID: mdl-34047105

RESUMEN

In order to establish a rapid and non-destructive evaluation method for the identification of Armeniacae Semen Amarum and Persicae Semen from different origins, the spectral information of Armeniacae Semen Amarum and Persicae Semen in the range of 898-1 751 nm was collected based on hyperspectral imaging technology. Armeniacae Semen Amarum and Persicae Semen from different origins were collected as research objects, and a total of 720 Armeniacae Semen Amarum samples and 600 Persicae Semen samples were used for authenticity discrimination. The region of interest(ROI) and the average reflection spectrum in the ROI were obtained, followed by comparing five pre-processing methods. Then, partial least squares discriminant analysis(PLS-DA), support vector machine(SVM), and random forest(RF) method were established for classification models, which were evaluated by the confusion matrix of prediction results and receiver operating characteristic curve(ROC). The results showed that in the three sample sets, the se-cond derivative pre-processing method and PLS-DA were the best model combinations. The classification accuracy of the test set under the 5-fold cross-va-lidation was 93.27%, 96.19%, and 100.0%, respectively. It was consistent with the confusion matrix of the predicted results. The area under the ROC curve obtained the highest values of 0.992 3, 0.999 6, and 1.000, respectively. The study revealed that the near-infrared hyperspectral imaging technology could accurately identify the medicinal materials of Armeniacae Semen Amarum and Persicae Semen from different origins and distinguish the authentication of these two varieties.


Asunto(s)
Medicamentos Herbarios Chinos , Análisis de los Mínimos Cuadrados , Semen , Máquina de Vectores de Soporte , Tecnología
7.
Spectrochim Acta A Mol Biomol Spectrosc ; 258: 119803, 2021 Sep 05.
Artículo en Inglés | MEDLINE | ID: mdl-33933939

RESUMEN

Facial creams are considered to be essential beauty items and are used by both females and males on an everyday basis. These can be encountered as an evidentiary material in criminal investigations, particularly in cases related to sexual and physical assaults against women. These are found in trace amounts and therefore their analysis is difficult and also, it must be through non-destructive methods. In the present work ATR-FTIR spectroscopy was employed for the discrimination of 57 samples of face creams out of which 31 were non-herbal and 26 were from herbal category. Visual analysis of the obtained Spectra was done for discrimination purposes but the method was prone to human error and laborious too. The spectroscopic results were analyzed with PCA (Principal Component Analysis) and PLS-DA (Partial least square discriminant analysis) methods. A segregation of samples was seen in the PCA plots to some extent. The class separation and prediction of the samples was performed using PLS-DA method. A good classification was achieved between herbal and non-herbal samples using PLS-DA method. Further, validation of the model was also performed by testing 10 unknown samples.


Asunto(s)
Espectroscopía Infrarroja por Transformada de Fourier , Proteínas de la Ataxia Telangiectasia Mutada , Análisis Discriminante , Femenino , Humanos , Análisis de los Mínimos Cuadrados , Masculino , Análisis de Componente Principal
8.
Spectrochim Acta A Mol Biomol Spectrosc ; 258: 119872, 2021 Sep 05.
Artículo en Inglés | MEDLINE | ID: mdl-33957443

RESUMEN

Due to the world-wide concern relating to herb quality and safety, there is a momentum to authenticate the geographical origin of herb with multi-platform techniques. This study attempted to assess multi-platform information as a practical strategy for the geographical traceability of the fruits of Amomum tsao-ko. To this aim, one hundred and eighty dried fruits of A. tsao-ko from five geographical regions were analyzed by near infrared (NIR) and ultraviolet visible (UV-Vis) spectroscopy. On this basis, two variable dimension reduction strategies, including principal component analysis (PCA) and sequential and orthogonalized partial-least squares (SO-PLS), and two variables selection strategies, including variable importance in projection (VIP) and sequential and orthogonalized covariance selection (SO-CovSel), were performed to extract the feature information in the two blocks. Partial least squares discriminant analysis (PLS-DA) classification algorithm combined with fused matrices was used to identify the geographical origins. The results of PLS-DA models indicated that SO-PLS and SO-CovSel, taking advantage of the sequential modeling coupled to orthogonalization, could not only identify the common information presented in the two blocks but also provide more concise methods without any loss of classification ability, which could be employed in authenticating the geographical regions of the fruits of A. tsao-ko, effectively.


Asunto(s)
Amomum , Frutas , Análisis Discriminante , Análisis de los Mínimos Cuadrados , Análisis Espectral
9.
Spectrochim Acta A Mol Biomol Spectrosc ; 258: 119870, 2021 Sep 05.
Artículo en Inglés | MEDLINE | ID: mdl-33957450

RESUMEN

As is known to all, the construction of calibration and validation sets is of great importance for how to select representative samples into subsets so that the calibration model can be built, evaluated and predicted effectively for model development. In this study, a method was proposed for the calibration and validation sets constructed by selecting samples maximally similar to the test samples based on the spectra data. Both the Euclidean distance and Mahalanobis distance were attempted to estimate the spectra similarity. The method to select samples for calibration is more suitable and specific to unknown test samples in practical applications, thus improving the measurement accuracy. In addition, the optimization of calibration set size was carried out to avoid the influence of unnecessary samples. Two data sets of Salvia miltiorrhiza (S. miltiorrhiza) and corn by near infrared spectroscopy (NIR) were used to test the performance of the proposed method compared with two typical sample-selection algorithms, Kennard-Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). The experimental results indicated that the proposed method could select a more targeted set of samples for the unknown test samples and had the superior predictive performance to the KS and SPXY methods.


Asunto(s)
Algoritmos , Espectroscopía Infrarroja Corta , Calibración , Análisis de los Mínimos Cuadrados , Proyectos de Investigación
10.
Spectrochim Acta A Mol Biomol Spectrosc ; 259: 119768, 2021 Oct 05.
Artículo en Inglés | MEDLINE | ID: mdl-33971438

RESUMEN

The tuber development and nutrient transportation of potato crops are closely related to canopy photosynthesis dynamics. Chlorophyll fluorescence parameters of photosystem II, especially the maximum quantum yield of primary photochemistry (Fv/Fm), are intrinsic indicators for plant photosynthesis. Rapid detection of Fv/Fm of leaves by spectroscopy method instead of time-consuming pulse amplitude modulation technique could help to indicate potato development dynamics and guide field management. Accordingly, this study aims to extract fluorescence signals from hyperspectral reflectance to detect Fv/Fm. Hyperspectral imaging system and closed chlorophyll fluorescence imaging system were applied to collect the spectral data and values of Fv/Fm of 176 samples. The spectral data were decomposed by continuous wavelet transform (CWT) to obtain wavelet coefficients (WFs). Three mother wavelet functions including second derivative of Gaussian (gaus2), biorthogonal 3.3 (bior3.3) and reverse biorthogonal 3.3 (rbio3.3) were compared and the bior3.3 showed the best correlation with Fv/Fm. Two variable selection algorithms were used to select sensitive WFs of Fv/Fm including Monte Carlo uninformative variables elimination (MC-UVE) algorithm and random frog (RF) algorithm. Then the partial least squares (PLS) regression was used to establish detection models, which were labeled as bior3.3-MC-UVE-PLS and bior3.3-RF-PLS, respectively. The determination coefficients of prediction set of bior3.3-MC-UVE-PLS and bior3.3-RF-PLS were 0.8071 and 0.8218, respectively, and the root mean square errors of prediction set were 0.0181 and 0.0174, respectively. The bior3.3-RF-PLS had the best detection performance and the corresponding WFs were mainly distributed in the bands affected by fluorescence emission (650-800 nm), chlorophyll absorption and reflection. Overall, this study demonstrated the potential of CWT in fluorescence signals extraction and can serve as a guide in the quick detection of chlorophyll fluorescence parameters.


Asunto(s)
Solanum tuberosum , Análisis de Ondículas , Clorofila , Fluorescencia , Análisis de los Mínimos Cuadrados , Hojas de la Planta
11.
Int J Health Geogr ; 20(1): 22, 2021 05 19.
Artículo en Inglés | MEDLINE | ID: mdl-34011390

RESUMEN

BACKGROUND: Healthcare accessibility, a key public health issue, includes potential (spatial accessibility) and realized access (healthcare utilization) dimensions. Moreover, the assessment of healthcare service potential access and utilization should take into account the care provided by primary and secondary services. Previous studies on the relationship between healthcare spatial accessibility and utilization often used conventional statistical methods without addressing the scale effect and spatial processes. This study investigated the impact of spatial accessibility to primary and secondary healthcare services on length of hospital stay (LOS), and the efficiency of using a geospatial approach to model this relationship. METHODS: This study focused on the ≥ 75-year-old population of the Nord administrative region of France. Inpatient hospital spatial accessibility was computed with the E2SFCA method, and then the LOS was calculated from the French national hospital activity and patient discharge database. Ordinary least squares (OLS), spatial autoregressive (SAR), and geographically weighted regression (GWR) were used to analyse the relationship between LOS and spatial accessibility to inpatient hospital care and to three primary care service types (general practitioners, physiotherapists, and home-visiting nurses). Each model performance was assessed with measures of goodness of fit. Spatial statistical methods to reduce or eliminate spatial autocorrelation in the residuals were also explored. RESULTS: GWR performed best (highest R2 and lowest Akaike information criterion). Depending on global model (OLS and SAR), LOS was negatively associated with spatial accessibility to general practitioners and physiotherapists. GWR highlighted local patterns of spatial variation in LOS estimates. The distribution of areas in which LOS was positively or negatively associated with spatial accessibility varied when considering accessibility to general practitioners and physiotherapists. CONCLUSIONS: Our findings suggest that spatial regressions could be useful for analysing the relationship between healthcare spatial accessibility and utilization. In our case study, hospitalization of elderly people was shorter in areas with better accessibility to general practitioners and physiotherapists. This may be related to the presence of effective community healthcare services. GWR performed better than LOS and SAR. The identification by GWR of how these relationships vary spatially could bring important information for public healthcare policies, hospital decision-making, and healthcare resource allocation.


Asunto(s)
Accesibilidad a los Servicios de Salud , Regresión Espacial , Anciano , Francia/epidemiología , Humanos , Análisis de los Mínimos Cuadrados , Análisis Espacial
12.
Zhongguo Zhong Yao Za Zhi ; 46(7): 1592-1597, 2021 Apr.
Artículo en Chino | MEDLINE | ID: mdl-33982456

RESUMEN

For the field detection problems of critical quality attribute(CQA) of moisture content in traditional Chinese medicine(TCM) manufacturing process, big brand TCM Tongren Niuhuang Qingxin Pills were used as the carrier, to establish a moisture content NIR field detection model with or without cellophane in real world production with use of near infrared(NIR) spectroscopy combined with stoichiometry. With the moisture content determined by drying method as reference value, the partial least square method(PLS) was used to analyze the correlation between the spectrum and the moisture reference value. Then the spectral pretreatment methods were screened and optimized to further improve the accuracy and stability of the model. The results showed that the best quantitative model was developed by the spectral data pretreatment of standard normal variate(SNV) with the latent variable factor number of 2 and 7 of Tongren Niuhuang Qingxin Pills with or without cellophane samples. The prediction coefficient of determination(R_(pre)~2) and standard deviation of prediction(RMSEP) of the model with cellophane samples were 0.765 7 and 0.157 2%; R_(pre)~2 and RMSEP of the model without cellophane samples were 0.772 2 and 0.207 8%. The NIR quantitative models of moisture content of Tongren Niuhuang Qingxin Pills with and without cellophane both showed good predictive performance to realize the rapid, accurate and non-destructive quantitative analysis of moisture content in such pills, and provide a method for the field quality control of the critical chemical attributes of moisture in the manufacturing of big brand TCM.


Asunto(s)
Medicamentos Herbarios Chinos , Medicina China Tradicional , Análisis de los Mínimos Cuadrados , Espectroscopía Infrarroja Corta
13.
Zhongguo Zhong Yao Za Zhi ; 46(7): 1616-1621, 2021 Apr.
Artículo en Chino | MEDLINE | ID: mdl-33982459

RESUMEN

Spatial distribution uniformity is the critical quality attribute(CQA) of Ginkgo Leaves Tablets, a variety of big brand traditional Chinese medicine. The evaluation of the spatial distribution uniformity of active pharmaceutical ingredients(APIs) in Ginkgo Leaves Tablets is important in ensuring their stable and controllable quality. In this study, hyperspectral imaging technology was used to construct the spatial distribution map of API concentration based on three prediction models, further to realize the visualization research on the spatial distribution uniformity of Ginkgo Leaves Tablets. The region of interest(ROI) was selected from each Ginkgo Leaves Tablet, with length and width of 50 pixels, and a total of 2 500 pixels. Each pixel had 288 spectral channels, and the number of content prediction data could reach 1×10~5 for a single sample. The results of the three models showed that the Partial Least Squares(PLS) model had the highest prediction accuracy, with calibration set determination coefficient R_(pre)~2 of 0.987, prediction set determination coefficient R_(pre)~2 of 0.942, root mean square error of calibration(RMSEC) of 0.160%, and root mean square error of prediction(RMSEP) of 0.588%. The classical least-squares(CLS) model had a greater prediction error, with the RMSEP of 0.867%. Multivariate Curve Resolution-Alternating Least Square(MCR-ALS) model showed the worst predictive ability among the three models, and it couldn't realize content prediction. Based on the prediction results of PLS and CLS models, the spatial distribution map of APIs concentration was obtained through three-dimensional data reconstruction. Furthermore, histogram method was used to evaluate the spatial distribution uniformity of API. The data showed that the spatial distribution of APIs in Ginkgo Leaves Tablets was relatively uniform. The study explored the feasibility of visualization of spatial distribution of Ginkgo Leaves Tablets based on three models. The results showed that PLS model had the highest prediction accuracy, and MCR-ALS model had the lowest prediction accuracy. The research results could provide a new strategy for the visualization method of quality control of Ginkgo Leaves Tablets.


Asunto(s)
Ginkgo biloba , Medicina China Tradicional , Calibración , Análisis de los Mínimos Cuadrados , Hojas de la Planta , Control de Calidad , Espectroscopía Infrarroja Corta , Comprimidos
14.
Environ Monit Assess ; 193(6): 363, 2021 May 27.
Artículo en Inglés | MEDLINE | ID: mdl-34041601

RESUMEN

Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in a river system. The accuracy of traditional water quality prediction methods is generally low, and the prediction results have serious autocorrelation. To overcome nonstationarity, randomness, and nonlinearity of the water quality parameter data, an improved least squares support vector machine (LSSVM) model was proposed to improve the model's performance at two gaging stations, namely Panzhihua and Jiujiang, in the Yangtze River, China. In addition, a hybrid model that recruits variational mode decomposition (VMD) to denoise the input data was adopted. A novel metaheuristic optimization algorithm, the sparrow search algorithm (SSA) was also implemented to compute the optimal parameter values for the LSSVM model. To validate the proposed hybrid model, standalone LSSVM, SSA-LSSVM, VMD-LSSVM, support vector regression (SVR), as well as back propagation neural network (BPNN) were considered as the benchmark models. The results indicated that the VMD-SSA-LSSVM model exhibited the best forecasting performance among all the peer models at Panzhihua station. Furthermore, the model forecasting results applied at Jiujiang were consistent with those at Panzhihua station. This result further verified the accuracy and stability of the VMD-SSA-LSSVM model. Thus, the proposed hybrid model was effective method for forecasting nonstationary and nonlinear water quality parameter series and can be recommended as a promising model for water quality parameter forecasting.


Asunto(s)
Gorriones , Máquina de Vectores de Soporte , Algoritmos , Animales , China , Monitoreo del Ambiente , Análisis de los Mínimos Cuadrados , Ríos , Calidad del Agua
15.
Medicine (Baltimore) ; 100(16): e25542, 2021 Apr 23.
Artículo en Inglés | MEDLINE | ID: mdl-33879701

RESUMEN

ABSTRACT: The disease progression of gouty arthritis (GA) is relatively clear, with the 4 stages of hyperuricemia (HUA), acute gouty arthritis (AGA), gouty arthritis during the intermittent period (GIP), and chronic gouty arthritis (CGA). This paper attempts to construct a clinical diagnostic model based on blood routine test data, in order to avoid the need for bursa fluid examination and other tedious steps, and at the same time to predict the development direction of GA.Serum samples from 579 subjects were collected within 3 years in this study and were divided into a training set (n = 379) and validation set (n = 200). After a series of multivariate statistical analyses, the serum biochemical profile was obtained, which could effectively distinguish different stages of GA. A clinical diagnosis model based on the biochemical index of the training set was established to maximize the probability of the stage as a diagnosis, and the serum biochemical data from 200 patients were used for validation.The total area under the curve (AUC) of the clinical diagnostic model was 0.9534, and the AUCs of the 5 models were 0.9814 (Control), 0.9288 (HUA), 0.9752 (AGA), 0.9056 (GIP), and 0.9759 (CGA). The kappa coefficient of the clinical diagnostic model was 0.80.This clinical diagnostic model could be applied clinically and in research to improve the accuracy of the identification of the different stages of GA. Meanwhile, the serum biochemical profile revealed by this study could be used to assist the clinical diagnosis and prediction of GA.


Asunto(s)
Artritis Gotosa/diagnóstico , Reglas de Decisión Clínica , Pruebas Hematológicas/estadística & datos numéricos , Adulto , Área Bajo la Curva , Artritis Gotosa/etiología , Biomarcadores/sangre , Sedimentación Sanguínea , Nitrógeno de la Urea Sanguínea , Proteína C-Reactiva/análisis , Estudios de Casos y Controles , Progresión de la Enfermedad , Femenino , Humanos , Hiperuricemia/sangre , Hiperuricemia/complicaciones , Análisis de los Mínimos Cuadrados , Recuento de Leucocitos , Lipoproteínas HDL/sangre , Masculino , Persona de Mediana Edad , Análisis Multivariante , Oportunidad Relativa , Valor Predictivo de las Pruebas , Análisis de Componente Principal , Pronóstico , Análisis de Regresión , Reproducibilidad de los Resultados , Ácido Úrico/sangre
16.
Sensors (Basel) ; 21(9)2021 Apr 28.
Artículo en Inglés | MEDLINE | ID: mdl-33925152

RESUMEN

Leaf pigment content retrieval is an essential research field in remote sensing. However, retrieval studies on anthocyanins are quite rare compared to those on chlorophylls and carotenoids. Given the critical physiological significance of anthocyanins, this situation should be improved. In this study, using the reflectance, partial least squares regression (PLSR) and Gaussian process regression (GPR) were sought to retrieve the leaf anthocyanin content. To our knowledge, this is the first time that PLSR and GPR have been employed in such studies. The results showed that, based on the logarithmic transformation of the reflectance (log(1/R)) with 564 and 705 nm, the GPR model performed the best (R2/RMSE (nmol/cm2): 0.93/2.18 in the calibration, and 0.93/2.20 in the validation) of all the investigated methods. The PLSR model involved four wavelengths and achieved relatively low accuracy (R2/RMSE (nmol/cm2): 0.87/2.88 in calibration, and 0.88/2.89 in validation). GPR apparently outperformed PLSR. The reason was likely that the non-linear property made GPR more effective than the linear PLSR in characterizing the relationship for the absorbance vs. content of anthocyanins. For GPR, selected wavelengths around the green peak and red edge region (one from each) were promising to build simple and accurate two-wavelength models with R2 > 0.90.


Asunto(s)
Antocianinas , Hojas de la Planta , Clorofila , Análisis de los Mínimos Cuadrados , Modelos Lineales
17.
Sensors (Basel) ; 21(9)2021 Apr 26.
Artículo en Inglés | MEDLINE | ID: mdl-33925882

RESUMEN

In this study, the possibility of non-destructive detection of tomato pesticide residues was investigated using Vis/NIRS and prediction models such as PLSR and ANN. First, Vis/NIR spectral data from 180 samples of non-pesticide tomatoes (used as a control treatment) and samples impregnated with pesticide with a concentration of 2 L per 1000 L between 350-1100 nm were recorded by a spectroradiometer. Then, they were divided into two parts: Calibration data (70%) and prediction data (30%). Next, the prediction performance of PLSR and ANN models after processing was compared with 10 spectral preprocessing methods. Spectral data obtained from spectroscopy were used as input and pesticide values obtained by gas chromatography method were used as output data. Data dimension reduction methods (principal component analysis (PCA), Random frog (RF), and Successive prediction algorithm (SPA)) were used to select the number of main variables. According to the values obtained for root-mean-square error (RMSE) and correlation coefficient (R) of the calibration and prediction data, it was found that the combined model SPA-ANN has the best performance (RC = 0.988, RP = 0.982, RMSEC = 0.141, RMSEP = 0.166). The investigational consequences obtained can be a reference for the development of internal content of agricultural products, based on NIR spectroscopy.


Asunto(s)
Lycopersicon esculentum , Residuos de Plaguicidas , Calibración , Análisis de los Mínimos Cuadrados , Espectroscopía Infrarroja Corta
18.
Spectrochim Acta A Mol Biomol Spectrosc ; 258: 119798, 2021 Sep 05.
Artículo en Inglés | MEDLINE | ID: mdl-33892304

RESUMEN

Geographical origin is an important factor affecting the quality of traditional Chinese medicine. In this paper, the identification of geographical origin of Gastrodia elata was performed by using excitation-emission matrix fluorescence and chemometric methods. Firstly, excitation-emission matrix (EEM) fluorescence spectra of Gastrodia elata samples from different geographical origins were obtained. And then three chemometric methods, including multilinear partial least squares discriminant analysis (N-PLS-DA), unfold partial least squares discriminant analysis (U-PLS-DA), and k-nearest neighbor (kNN) method, were applied to build discriminant models. Finally, 45 Gastrodia elata samples could be differentiated from each other by these classification models according to their geographical origins. The results showed that all models obtained good classification results. Compared with the N-PLS-DA and U-PLS-DA, kNN got more accurate and reliable classification results and could identify Gastrodia elata samples from different geographical origins with 100% accuracy on the training and test set. Therefore, the proposed method was available for easily and quickly distinguishing the geographical origin of Gastrodia elata, which can be considered as a promising alternative method for determining the geographic origin of other traditional Chinese medicines.


Asunto(s)
Gastrodia , Geografía , Análisis de los Mínimos Cuadrados , Medicina China Tradicional
19.
Molecules ; 26(8)2021 Apr 15.
Artículo en Inglés | MEDLINE | ID: mdl-33921119

RESUMEN

Moringa oleifera Lam. is one of the world's most useful medicinal plants. Different parts of the M. oleifera tree contain a rich profile of important minerals, proteins, vitamins, and various important bioactive compounds. However, there are differences in the phytochemical composition of the medicinal plant's raw materials due to seasonal variation, cultivation practices, and post-harvest processing. The main objective of this study was therefore to determine the effect of harvesting frequencies on selected bioactive compounds of a M. oleifera cultivar (PKM1) grown in a hydroponic system under a shade net structure. Three harvesting frequency treatments were applied in the study, with the plants harvested at every 30 days (high frequency), 60 days (intermediate frequency), and 90 days (low frequency) respectively. 1H-NMR was used for data acquisition, and multivariate data analysis by means of principal component analysis (PCA), partial least square discriminatory analysis (PLS-DA), and orthogonal partial least square discriminatory analysis (OPLS-DA) were applied to determine the changes in the leaf metabolite profile, and also to identify the spectral features contributing to the separation of samples. Targeted metabolite analysis was used to match the NMR peaks of the compounds with the NMR chemical shifts of the contribution plot. The contribution plot showed that the increase in concentration of some compounds in aliphatic, sugar and aromatic regions contributed to the separation of the samples. The results revealed that intermediate and low harvesting frequencies resulted in a change in the leaf metabolite profile. Compounds such as chlorogenic acid, ferulic acid, vanillic acid, wogonin, esculetin, niazirin, and gamma-aminobutyric acid (GABA) showed an increase under intermediate and low harvesting frequencies. These results provide insight into the effect of harvesting frequencies on the metabolite profile and associated medicinal activity of M. oleifera.


Asunto(s)
Espectroscopía de Resonancia Magnética/métodos , Moringa oleifera/química , Análisis de los Mínimos Cuadrados , Plantas Medicinales/química , Análisis de Componente Principal , Ácido Vanílico/química , Ácido gamma-Aminobutírico/química
20.
J Chromatogr A ; 1646: 462093, 2021 Jun 07.
Artículo en Inglés | MEDLINE | ID: mdl-33853038

RESUMEN

Enhancement of chromatograms, such as the reduction of baseline noise and baseline drift, is often essential to accurately detect and quantify analytes in a mixture. Current methods have been well studied and adopted for decades and have assisted researchers in obtaining reliable results. However, these methods rely on relatively simple statistics of the data (chromatograms) which in some cases result in significant information loss and inaccuracies. In this study, a deep one-dimensional convolutional autoencoder was developed that simultaneously removes baseline noise and baseline drift with minimal information loss, for a large number and great variety of chromatograms. To enable the autoencoder to denoise a chromatogram to be almost, or completely, noise-free, it was trained on data obtained from an implemented chromatogram simulator that generated 190.000 representative simulated chromatograms. The trained autoencoder was then tested and compared to some of the most widely used and well-established denoising methods on testing datasets of tens of thousands of simulated chromatograms; and then further tested and verified on real chromatograms. The results show that the developed autoencoder can successfully remove baseline noise and baseline drift simultaneously with minimal information loss; outperforming methods like Savitzky-Golay smoothing, Gaussian smoothing and wavelet smoothing for baseline noise reduction (root mean squared error of 1.094 mAU compared to 2.074 mAU, 2.394 mAU and 2.199 mAU) and Savitkzy-Golay smoothing combined with asymmetric least-squares or polynomial fitting for baseline noise and baseline drift reduction (root mean absolute error of 1.171 mAU compared to 3.397 mAU and 4.923 mAU). Evidence is presented that autoencoders can be utilized to enhance and correct chromatograms and consequently improve and alleviate downstream data analysis, with the drawback of needing a carefully implemented simulator, that generates realistic chromatograms, to train the autoencoder.


Asunto(s)
Cromatografía/métodos , Algoritmos , Humanos , Análisis de los Mínimos Cuadrados , Redes Neurales de la Computación
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