scholarly journals FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples

Foods ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 827
Author(s):  
Najeeb Ur Rehman ◽  
Ahmed Al-Harrasi ◽  
Ricard Boqué ◽  
Fazal Mabood ◽  
Muhammed Al-Broumi ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
High Performance ◽  
Near Infrared ◽  
Pls Regression ◽  
Least Squares Regression ◽  
Daily Consumption ◽  
Limit Of Quantification ◽  
Scientific Attention

Daily consumption of caffeine in coffee, tea, chocolate, cocoa, and soft drinks has gained wide and plentiful public and scientific attention over the past few decades. The concentration of caffeine in vivo is a crucial indicator of some disorders—for example, kidney malfunction, heart disease, increase of blood pressure and alertness—and can cause some severe diseases including type 2 diabetes mellitus (DM), stroke risk, liver disease, and some cancers. In the present study, near-infrared spectroscopy (NIRS) coupled with partial least-squares regression (PLSR) was proposed as an alternative method for the quantification of caffeine in 25 commercially available tea samples consumed in Oman. This method is a fast, complementary technique to wet chemistry procedures as well as to high-performance liquid chromatography (HPLC) methods for the quantitative analysis of caffeine in tea samples because it is reagent-less and needs little or no pre-treatment of samples. In the current study, the partial least-squares (PLS) algorithm was built by using the near-infrared NIR spectra of caffeine standards prepared in tea samples scanned by a Frontier NIR spectrophotometer (L1280034) by PerkinElmer. Spectra were collected in the absorption mode in the wavenumber range of 10,000–4000 cm−1, using a 0.2 mm path length and CaF2 sealed cells with a resolution of 2 cm−1. The NIR results for the contents of caffeine in tea samples were also compared with results obtained by HPLC analysis. Both techniques provided good results for predicting the caffeine contents in commercially available tea samples. The results of the proposed study show that the suggested FT-NIRS coupled with PLS regression algorithun has a high potential to be routinely used for the quick and reproducible analysis of caffeine contents in tea samples. For the NIR method, the limit of quantification (LOQ) was estimated as 10 times the error of calibration (root mean square error of calibration (RMSECV)) of the model; thus, RMSEC was calculated as 0.03 ppm and the LOQ as 0.3 ppm.

Investigation of Noninvasive in vivo Blood Hematocrit Measurement Using NIR Reflectance Spectroscopy and Partial Least-Squares Regression

2000 ◽  
Vol 54 (2) ◽  
pp. 294-299 ◽  
Author(s):  
Songbiao Zhang ◽  
Babs R. Soller ◽  
Shubjeet Kaur ◽  
Kristen Perras ◽  
Thomas J. Vander Salm
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Study Groups ◽  
Patient Specific ◽  
Least Squares Regression ◽  
Fiber Optic Probe

Hematocrit (Hct), the volume percent of red cells in blood, is monitored routinely for blood donors, surgical patients, and trauma victims and requires blood to be removed from the patient. An accurate, noninvasive method for directly measuring hematocrit on patients is desired for these applications. The feasibility of noninvasive hematocrit measurement by using near-infrared (NIR) spectroscopy and partial least-squares (PLS) techniques was investigated, and methods of in vivo calibration were examined. Twenty Caucasian patients undergoing cardiac surgery on cardiopulmonary bypass were randomly selected to form two study groups. A fiber-optic probe was attached to the patient's forearm, and NIR spectra were continuously collected during surgery. Blood samples were simultaneously collected and reference Hct measurements were made with the spun capillary method. PLS multivariate calibration techniques were applied to investigate the relationship between spectral and Hct changes. Single patient calibration models were developed with good cross-validated estimation of accuracy (∼ 1 Hct%) and trending capability for most patients. Time-dependent system drift, patient temperature, and venous oxygen saturation were not correlated with the hematocrit measurements. Multi-subject models were developed for prediction of independent subjects. These models demonstrated a significant patient-specific offset that was shown to be partially related to spectrometer drift. The remaining offset is attributed to the large spectral variability of patient tissue, and a significantly larger set of patients would be required to adequately model this variability. After the removal of the offset, the cross-validated estimation of accuracy is 2 Hct%.

scholarly journals Wood species identification from Atlantic forest by near infrared spectroscopy

2019 ◽  
Vol 28 (3) ◽  
pp. e015
Author(s):  
José-Henrique Camargo Pace ◽  
João-Vicente De Figueiredo Latorraca ◽  
Paulo-Ricardo Gherardi Hein ◽  
Alexandre Monteiro de Carvalho ◽  
Jonnys Paz Castro ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Atlantic Forest ◽  
Wood Species ◽  
Native Species ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Planted Forests ◽  
Good Ability

Aim of study: Fast and reliable wood identification solutions are needed to combat the illegal trade in native woods. In this study, multivariate analysis was applied in near-infrared (NIR) spectra to identify wood of the Atlantic Forest species.Area of study: Planted forests located in the Vale Natural Reserve in the county of Sooretama (19 ° 01'09 "S 40 ° 05'51" W), Espírito Santo, Brazil.Material and methods: Three trees of 12 native species from homogeneous plantations. The principal component analysis (PCA) and partial least squares regression by discriminant function (PLS-DA) were performed on the woods spectral signatures.Main results: The PCA scores allowed to agroup some wood species from their spectra. The percentage of correct classifications generated by the PLS-DA model was 93.2%. In the independent validation, the PLS-DA model correctly classified 91.3% of the samples.Research highlights: The PLS-DA models were adequate to classify and identify the twelve native wood species based on the respective NIR spectra, showing good ability to classify independent native wood samples.Keywords: native woods; NIR spectra; principal components; partial least squares regression.

scholarly journals Hemodialysis monitoring using mid- and near-infrared spectroscopy with partial least squares regression

2018 ◽  
Vol 11 (7) ◽  
pp. e201700365 ◽  
Author(s):  
Raphael Henn ◽  
Christian G. Kirchler ◽  
Zora L. Schirmeister ◽  
Andreas Roth ◽  
Werner Mäntele ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Least Squares Regression

NIR Spectroscopy and Partial Least Squares Regression for the Determination of Phosphate Content and Viscosity Behaviour of Potato Starch

2001 ◽  
Vol 9 (2) ◽  
pp. 133-139 ◽  
Author(s):  
L.G. Thygesen ◽  
S.B. Engelsen ◽  
M.H. Madsen ◽  
O.B. Sørensen
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Potato Starch ◽  
Nir Spectroscopy ◽  
Peak Time ◽  
Phosphate Content ◽  
Least Squares Regression ◽  
Data Prediction

A set of 97 potato starch samples with a phosphate content corresponding to a phosphorus content between 0.029 and 0.11 g per 100 g dry matter was analysed using a Rapid Visco Analyzer (RVA) and near infrared (NIR) spectroscopy, (700–2498 nm). NIR-based prediction of phosphate content was possible with a root mean square error of cross-validation ( RMSECV) of 0.006% using PLSR (partial least squares regression). However, the NIR/PLSR model relied on weak spectral signals, and was highly sensitive to sample preparation. The best prediction of phosphate content from the RVA viscograms was a linear regression model based on the RVA variable Breakdown, which gave a RMSECV of 0.008%. NIR/PLSR prediction of the RVA variables Peak viscosity and Breakdown was successful, probably because they were highly related to phosphate content in the present data. Prediction of the other RVA variables from NIR/PLSR was mediocre (Through, Final Viscosity) or not possible (Setback, Peak time, Pasting temperature).

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