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

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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A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033520987315 ◽

2021 ◽

pp. 096703352098731

Author(s):

Adenilton C da Silva ◽

Lívia PD Ribeiro ◽

Ruth MB Vidal ◽

Wladiana O Matos ◽

Gisele S Lopes

Keyword(s):

Quality Control ◽

Discriminant Analysis ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Chemical Components ◽

Linear Discriminant ◽

Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

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Prediction of macro- and microelements content in Croatian common bean landraces (Phaseolus vulgaris L.) by NIR spectroscopy

Poljoprivreda ◽

10.18047/poljo.25.1.7 ◽

2019 ◽

Vol 25 (1) ◽

pp. 48-55

Author(s):

Marina Vranić ◽

Marko Petek ◽

Krešimir Bošnjak ◽

Boris Lazarević ◽

Klaudija Carović Stanko

Keyword(s):

Phaseolus Vulgaris ◽

Common Bean ◽

Near Infrared ◽

Model Development ◽

Nir Spectroscopy ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Potassium Oxide ◽

Phaseolus Vulgaris L

In this study, near-infrared spectroscopy (NIRS) was used to predict the contents of essential macro- and microelements in common bean (Phaseolus vulgaris L.) accessions of most widespread Croatian landraces. Total of 175 samples were used for the model development by modified partial least square (MPLS), principal component regression (PCR) and partial least square (PLS) techniques. Based on the coefﬁcients of determination (R2), standard error of calibration (SEC) and error of prediction (SEP) the models developed were (i) nearly applicable for nitrogen (N) (0.89, 0.12 and 0.45 respectively), (ii) poor for iron (Fe), cinc (Zn), potassium oxide (K2O) and potassium (K), (iii) usable for phosphorus pentoxide (P2O5), phosphorus (P), phytic acid (PA) and manganese (Mn). The MPLS regression statistics suggested the most accurate models developed comparing with PLS and PCR. It was concluded that a wider set of common bean samples needs to be used for macro- and microelements prediction by NIRS.

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Non-Destructive Quality Measurement for Three Varieties of Tomato Using VIS/NIR Spectroscopy

Sustainability ◽

10.3390/su131910747 ◽

2021 ◽

Vol 13 (19) ◽

pp. 10747

Author(s):

Khadija Najjar ◽

Nawaf Abu-Khalaf

Keyword(s):

Near Infrared ◽

Quality Measurement ◽

Nir Spectroscopy ◽

Principal Component ◽

Titratable Acidity ◽

Quality Parameters ◽

Partial Least Square ◽

Least Square ◽

Soluble Solids ◽

Non Destructive

The non-destructive visible/near-infrared (VIS/NIR) spectroscopy is a promising technique in determining the quality of agricultural commodities. Therefore, this study aimed to examine the ability of VIS/NIR spectroscopy (550–1100 nm) to distinguish between three different varieties of tomato (i.e., Ekram, Harver and Izmer), as well as to predict the quality parameters of tomato, such as soluble solids content (SSC), titratable acidity (TA), taste (SSC/TA) and firmness. Ninety intact samples from three tomato varieties were used. These samples were examined using VIS/NIR spectroscopy and quality parameters were also measured using traditional methods. Principal component analysis (PCA) and partial least square (PLS) were carried out. The results of PCA showed the ability of VIS/NIR spectroscopy to distinguish between the three varieties, where two PCs explained about 99% of the total variance in both calibration and validation sets. Moreover, PLS showed the possibility of modelling quality parameters. The correlation coefficient (R2) and the ratio of performance deviation (RPD) for all quality parameters (except for firmness) were found to be higher than 0.85 and 2.5, respectively. Thus, these results indicate that the VIS/NIR spectroscopy can be used to discriminate between different varieties of tomato and predict their quality parameters.

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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Determination of Fat, Protein, and Total Solids in Cheese by Near-Infrared Reflectance Spectroscopy

Journal of AOAC International ◽

10.1093/jaoac/78.3.802 ◽

1995 ◽

Vol 78 (3) ◽

pp. 802-806 ◽

Cited By ~ 46

Author(s):

José Louis Rodriguez-Otero ◽

Maria Hermida ◽

Alberto Cepeda

Keyword(s):

Near Infrared ◽

Independent Set ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Sample Treatment ◽

Infrared Reflectance ◽

Near Infrared Reflectance ◽

Total Solids

Abstract Near-infrared reflectance (NIR) spectroscopy was used to analyze fat, protein, and total solids in cheese without any sample treatment. A set of 92 samples of cow’s milk cheese was used for instrument calibration by principal components analysis and modified partial least-square regression. The following statistical values were obtained: standard error of calibration (SEC) = 0.388 and squared correlation coefficient (R2) = 0.99 for fat, SEC = 0.397 and R2 = 0.98 for protein, and SEC = 0.412 and R2 = 0.99 for total solids. To validate the calibration, an independent set of 25 cheese samples of the same type was used. Standard errors of validation were 0.47,0.50, and 0.61 for fat, protein, and total solids, respectively, and hf for the regression of measurements by reference methods versus measurements by NIR spectroscopy was 0.98 for the 3 components.

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Analysis of protein glycation in human fingernail clippings with near-infrared (NIR) spectroscopy as an alternative technique for the diagnosis of diabetes mellitus

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2018-0239 ◽

2018 ◽

Vol 56 (9) ◽

pp. 1551-1558 ◽

Cited By ~ 10

Author(s):

Tinne Monteyne ◽

Renaat Coopman ◽

Antoine S. Kishabongo ◽

Jonas Himpe ◽

Bruno Lapauw ◽

...

Keyword(s):

Diabetes Mellitus ◽

Healthy Subjects ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Protein Glycation ◽

Patients With Diabetes ◽

Diagnosis Of Diabetes Mellitus

Abstract Background: Glycated keratin allows the monitoring of average tissue glucose exposure over previous weeks. In the present study, we wanted to explore if near-infrared (NIR) spectroscopy could be used as a non-invasive diagnostic tool for assessing glycation in diabetes mellitus. Methods: A total of 52 patients with diabetes mellitus and 107 healthy subjects were enrolled in this study. A limited number (n=21) of nails of healthy subjects were glycated in vitro with 0.278 mol/L, 0.556 mol/L and 0.833 mol/L glucose solution to study the effect of glucose on the nail spectrum. Consequently, the nail clippings of the patients were analyzed using a Thermo Fisher Antaris II Near-IR Analyzer Spectrometer and near infrared (NIR) chemical imaging. Spectral classification (patients with diabetes mellitus vs. healthy subjects) was performed using partial least square discriminant analysis (PLS-DA). Results: In vitro glycation resulted in peak sharpening between 4300 and 4400 cm−1 and spectral variations at 5270 cm−1 and between 6600 and 7500 cm−1. Similar regions encountered spectral deviations during analysis of the patients’ nails. Optimization of the spectral collection parameters was necessary in order to distinguish a large dataset. Spectra had to be collected at 16 cm−1, 128 scans, region 4000–7500 cm−1. Using standard normal variate, Savitsky-Golay smoothing (7 points) and first derivative preprocessing allowed for the prediction of the test set with 100% correct assignments utilizing a PLS-DA model. Conclusions: Analysis of protein glycation in human fingernail clippings with NIR spectroscopy could be an alternative affordable technique for the diagnosis of diabetes mellitus.

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Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry

International Journal of Analytical Chemistry ◽

10.1155/2020/9830685 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Mohd Yusop Nurida ◽

Dolmat Norfadilah ◽

Mohd Rozaiddin Siti Aishah ◽

Chan Zhe Phak ◽

Syafiqa M. Saleh

Keyword(s):

Fourier Transform ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Infrared Spectrometry ◽

Calibration Model ◽

Co2 Absorption ◽

Processing Unit

The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO2 loading, and water content in CO2 absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R2) of 0.9955 and RMSECV of 0.75%. CO2 calibration model achieved R2 of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R2 of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO2, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO2 solvent monitoring to replace the time-consuming and labor-intensive conventional methods.

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Model Optimization for near-Infrared Spectroscopy Analysis of Chemical Oxygen Demand of Wastewater

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.500.832 ◽

2012 ◽

Vol 500 ◽

pp. 832-837 ◽

Cited By ~ 4

Author(s):

Tao Pan ◽

Wei Wei Chen ◽

Wen Jie Huang ◽

Rui Tuo Qu

Keyword(s):

Chemical Oxygen Demand ◽

Near Infrared ◽

Oxygen Demand ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Simultaneous Optimization ◽

Model Optimization ◽

Spectroscopy Analysis ◽

Smoothing Parameters

A directly rapid quantitative analysis method for chemical oxygen demand (COD) of wastewater samples was established by near-infrared (NIR) spectroscopy and partial least square (PLS) method. The optimization of Savitzky-Golay (SG) smoothing modes combined with PLS factor was applied to optimize the model of NIR spectroscopy analysis here. The waveband used for modeling was the combination of 400-1878 nm and 2088-2338 nm. The optimal smoothing parameters were the 5th derivative smoothing, 5th degree polynomial, 17 smoothing points, the optimal PLS factor, root mean squared error of predication (RMSEP) and correlation coefficient of predication (RP) were 7, 33.2 (mg/L) and 0.929 respectively, which was obviously superior to the direct PLS model without SG smoothing and ones based on the whole spectral collecting region 400-2500 nm. This demonstrated that NIR spectroscopy can be applied to the rapid determination of COD of wastewater, large-scale simultaneous optimization selection of SG smoothing parameters and PLS factor can be effectively applied to the model optimization of NIR analysis.

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Classification of Automobile Lubricant by Near-Infrared Spectroscopy Combined with Machine Classification

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.460-461.667 ◽

2011 ◽

Vol 460-461 ◽

pp. 667-672

Author(s):

Yun Zhao ◽

Xing Xu ◽

Yong He

Keyword(s):

Near Infrared ◽

Prediction Models ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Support Vector ◽

Training Set ◽

Test Set ◽

Svm Model

The main objective of this paper is to classify four kinds of automobile lubricant by near-infrared (NIR) spectral technology and to observe whether NIR spectroscopy could be used for predicting water content. Principle component analysis (PCA) was applied to reduce the information from the spectral data and first two PCs were used to cluster the samples. Partial least square (PLS), least square support vector machine (LS-SVM), and Gaussian processes classification (GPC) were employed to develop prediction models. There were 120 samples for training set and test set. Two LS-SVM models with first five PCs and first six PCs were built, respectively, and accuracy of the model with five PCs is adequate with less calculation. The results from the experiment indicate that the LS-SVM model outperforms the PLS model and GPC model outperforms the LS-SVM model.

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Prediction of Soil Texture Distributions by using PLSR and Reflectance Spectroscopy

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6558.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 7876-7881

Keyword(s):

Statistical Analysis ◽

Soil Texture ◽

Near Infrared ◽

Diffuse Reflectance Spectroscopy ◽

Principal Component ◽

Reflectance Spectroscopy ◽

Soil Samples ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression

The texture of soil i.e. Sand, Silt and Clay are the most important physical properties of soil for agricultural management. In the agricultural practices to increase the productivity of soil, moisture-holding capacity, aeration and to support the agronomic decisions the knowledge of soil texture is an essential task. For this purpose, the present research gives better results and fast acquisition of soil information with the use of Visible and Near Infrared (Vis- NIR) Diffuse Reflectance Spectroscopy. A total of 30 soil samples from two different locations from Aurangabad, Maharashtra, India were collected and analyzed for soil texture. To detect the soil texture the Vis-NIR DRS has shown levels of accurate results compared to the traditional laboratory method with less time, cost and effort. To measure the reflectance of soil the ASD FieldSpec4 Spectroradiometer (350-2500nm) was used. By the observation of captured spectra by using Spectroradiometer it showed that on the basis of different textural classes the soil samples could be spectrally separable. For database collection and pre-processing, we have used RS3 and ViewSpec Pro software respectively. The statistical analysis by using the combination of Principal Component Analysis (PCA) and Partial Least Square Regression method gives accurate results. To determine the texture of soil sample thirteen features were calculated. The main goal of this research was to determine the soil texture by using statistical methods and to test the performance of VNIR-SWIR reflectance spectroscopy by using the ASD FieldSpec4 Spectroradiometer for estimation of the texture of the soil. The results showed that R2 = 0.99 gives maximum accuracy for clay content and R2 = 0.988 for silt content and R2 = 0.989 for sand. The Root Mean Square Values (RMSE) for clay, silt, and sand are 0.02392, 0.02399 and 0.02289 respectively. With the use of reflectance spectroscopy and statistical analysis by using regression models we can determine the soil properties accurately in very less time.

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