Detecting urine metabolites related to training performance in swimming athletes by means of Raman spectroscopy and principal component analysis

Moreira, Letícia Parada; Silveira, Landulfo; Pacheco, Marcos Tadeu Tavares; Silva, Alexandre Galvão da; Rocco, Débora Dias Ferraretto Moura

doi:10.1016/j.jphotobiol.2018.06.013

Cited by 26 publications

(17 citation statements)

References 58 publications

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“…The corresponding spectral profiles are shown in Figure 1 , where the average spectral curves of the seven temperatures are collected before and after the basketball players' exercise. It can be found that there are several obvious absorption peaks at 7800 cm −1 , 9300 cm −1 , and 10800 cm −1 , which may be caused by function groups of C-H, H-O, N-H, and C-O band in water, glucose, and protein from the urine [ 2 ]. Obviously, there is no significant difference in spectral profiles between before and after exercise of basketball player by eye-naked judgement.…”

Section: Resultsmentioning

confidence: 99%

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Rapid Assessment of Exercise State through Athlete’s Urine Using Temperature-Dependent NIRS Technology

Ding

Yuan

Sun

et al. 2020

Journal of Analytical Methods in Chemistry

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Athletes usually take nutritional supplements and perform the specialized training to improve the performance of sport. A quick assessment of their athletic status will help to understand the current physical function of athletes’ status and the effect of nutritional supplementation. Human urine, as one of the most important body indicators, is composed of many metabolites, which can provide effective monitoring information for physical conditions. In this study, temperature-dependent near-infrared spectroscopy (NIRS) technology was used to collect the spectra of athlete’s urine for evaluating the feasibility of rapidly detecting the exercise state of the basketball player. To obtain the detection results accurately, several chemometrics methods including principal component analysis (PCA), variables selection method of variable importance in projection (VIP), continuous 1D wavelet transform (CWT), and partial least square-discriminant analysis (PLS-DA) were employed to develop a classifier to distinguish the physical status of athletes. The optimal classifying results were obtained by wavelet-PLS-DA classifier, whose average precision, sensitivity, and specificity are all above 0.95, and the overall accuracy of all samples is 0.97. These results demonstrate that temperature-dependent NIRS can be used to rapidly assess the physical function of athlete’s status and the effect of nutritional supplementation is feasible. It can be believed that temperature-dependent NIR spectroscopy will obtain applications more widely in the future.

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Section: Resultsmentioning

confidence: 99%

“…Body metabolites contain a variety of primary and secondary metabolites, which can reflect athlete's physical function and state and provide the most intuitive information for human health and exercise states [ 2 ]. Urine of human body is one of the most important outputs composed of many metabolites.…”

Section: Introductionmentioning

confidence: 99%

Rapid Assessment of Exercise State through Athlete’s Urine Using Temperature-Dependent NIRS Technology

Ding

Yuan

Sun

et al. 2020

Journal of Analytical Methods in Chemistry

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“…2 Additional uses have been demonstrated in the years since, including sparse reconstruction (using only the PCs contributing most to the dataset) for denoising, 23 exploiting PCA for baseline correction, [24][25][26][27] intensity standardisation, 28,29 and interferent subtraction. 28 PCA has been found to be useful in a range of analytical techniques including mass spectrometry 30 as well as Raman, 31 nearinfrared, 32 infrared, 33 nuclear magnetic resonance, 34 laser induced breakdown spectroscopy (LIBS), 35 ultraviolet-visible (UV-Vis), 36 fluorescence, 37 and X-ray absorption spectroscopies. 38 The Math Behind PCA…”

Section: Goalsmentioning

confidence: 99%

Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra

Beattie¹,

Esmonde-White²

2021

Appl Spectrosc

149

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Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal component analysis is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning principal component analysis is not well understood by many applied analytical scientists and spectroscopists who use principal component analysis. The meaning of features identified through principal component analysis is often unclear. This manuscript traces the journey of the spectra themselves through the operations behind principal component analysis, with each step illustrated by simulated spectra. Principal component analysis relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of principal component analysis , such as the scores representing “concentration” or “weights". The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a principal component analysis model shows how to interpret application specific chemical meaning of the principal component analysis loadings and how to analyze scores. A critical benefit of principal component analysis is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

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“…In the case of cleaning the noisy data via biomedical sensors, most researchers use principal component analysis, such as a reduction in dimensionality or feature space [22][23][24][25][26], feature extraction in further data visualisation [27][28][29], and feature selection tools in machine and deep learning applications [30][31][32][33][34] for machine learning applications.…”

Section: Data-cleaning Applicationsmentioning

confidence: 99%

Sample Reduction for Physiological Data Analysis Using Principal Component Analysis in Artificial Neural Network

et al. 2021

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With its potential, extensive data analysis is a vital part of biomedical applications and of medical practitioner interpretations, as data analysis ensures the integrity of multidimensional datasets and improves classification accuracy; however, with machine learning, the integrity of the sources is compromised when the acquired data pose a significant threat in diagnosing and analysing such information, such as by including noisy and biased samples in the multidimensional datasets. Removing noisy samples in dirty datasets is integral to and crucial in biomedical applications, such as the classification and prediction problems using artificial neural networks (ANNs) in the body’s physiological signal analysis. In this study, we developed a methodology to identify and remove noisy data from a dataset before addressing the classification problem of an artificial neural network (ANN) by proposing the use of the principal component analysis–sample reduction process (PCA–SRP) to improve its performance as a data-cleaning agent. We first discuss the theoretical background to this data-cleansing methodology in the classification problem of an artificial neural network (ANN). Then, we discuss how the PCA is used in data-cleansing techniques through a sample reduction process (SRP) using various publicly available biomedical datasets with different samples and feature sizes. Lastly, the cleaned datasets were tested through the following: PCA–SRP in ANN accuracy comparison testing, sensitivity vs. specificity testing, receiver operating characteristic (ROC) curve testing, and accuracy vs. additional random sample testing. The results show a significant improvement in the classification of ANNs using the developed methodology and suggested a recommended range of selectivity (Sc) factors for typical cleaning and ANN applications. Our approach successfully cleaned the noisy biomedical multidimensional datasets and yielded up to an 8% increase in accuracy with the aid of the Python language.

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Detecting urine metabolites related to training performance in swimming athletes by means of Raman spectroscopy and principal component analysis

Cited by 26 publications

References 58 publications

Rapid Assessment of Exercise State through Athlete’s Urine Using Temperature-Dependent NIRS Technology

Rapid Assessment of Exercise State through Athlete’s Urine Using Temperature-Dependent NIRS Technology

Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra

Sample Reduction for Physiological Data Analysis Using Principal Component Analysis in Artificial Neural Network

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