scholarly journals A non-destructive NIR spectroscopic method combined with chemometry for simultaneous assay of paracetamol and caffeine in tablets

2021 ◽  
Vol 57 (2) ◽  
pp. 68-75
Author(s):  
Dana MUNTEAN ◽  
◽  
Alina PORFIRE ◽  
Cristian ALECU ◽  
Sonia IURIAN ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Spectroscopic Method ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Hplc Method ◽  
Fixed Dose ◽  
Calibration Model ◽  
Dose Combination ◽  
The One

The use of near infrared (NIR) spectroscopy to predict the concentration of two active pharmaceutical ingredients (APIs), paracetamol and caffeine, in intact tablets, has been evaluated in this study. A partial least squares (PLS) regression model was developed using spectral data obtained on a calibration set consisting of 28 formulations containing 80, 90, 100, 110 and 120% of each API. Regression models were developed for each API, both using un-processed spectral data as well as after applying various spectra pre-processing methods. Cross-validation was used to select best calibration model. The selected model was validated in terms of precision, trueness, accuracy and linearity in a concentration ranging from 90 to 110% of the targeted APIs concentration. The applicability of the method was tested on tablets containing 300 mg paracetamol and 30 mg caffeine as targeted composition, and the API content predicted by the proposed NIR-chemometric method was not statistically different from the one obtained by HPLC method, used as a reference method. Thus, the method presented in the current paper is a step forward towards the implementation NIR as useful tool for monitoring the manufacturing process of fixed-dose combination tablets with paracetamol and caffeine.

Quantitative Evaluation of Glycyrrhizic Acid That Affects the Product Quality of Kakkonto Extract, a Traditional Herbal Medicine, by a Chemometric near Infrared Spectroscopic Method

2009 ◽  
Vol 17 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Yoshifumi Mohri ◽  
Yukoh Sakata ◽  
Makoto Otsuka
Keyword(s):  
Acid Content ◽  
Glycyrrhizic Acid ◽  
Near Infrared ◽  
Spectroscopic Method ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Wave Number ◽  
Coefficient Of Determination ◽  
Calibration Model

The purpose of this study was to construct a calibration model for the prediction of glycyrrhizic acid content in Kakkonto extracts using near infrared (NIR) spectroscopy. The NIR spectra of the Kakkonto extracts were obtained using a Fourier transform NIR spectrometer in transmission mode and chemometric analysis was performed using partial least-square (PLS) regression. The calibration model was constructed by the selection of wave number regions and by the first derivative pre-treatment of NIR spectra. The glycyrrhizic acid content could be predicted using a calibration model comprising three principal components (PCs) obtained by the PLS method. The calibration model was theoretically analysed by investigating the standard error of prediction values, the loading vectors of each PC and the regression vector. The relationship between the actual and predicted glycyrrhizic acid contents in the Kakkonto extract exhibited a straight line with a coefficient of determination of 0.966 (calibration) and 0.945 (validation), respectively. The predicted glycyrrhizic acid content in the Kakkonto extract was within the 95% predictive intervals.

Prediction of Fatty Acid Content in Beef Neck Lean by near Infrared Reflectance Analysis

1998 ◽  
Vol 6 (1) ◽  
pp. 229-234 ◽  
Author(s):  
William R. Windham ◽  
W.H. Morrison
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Near Infrared ◽  
Fatty Acid Content ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Longissimus Dorsi ◽  
Near Infrared Reflectance ◽  
Total Fatty Acids

Near infrared (NIR) spectroscopy in the prediction of individual and total fatty acids of bovine M. Longissimus dorsi neck muscles has been studied. Beef neck lean was collected from meat processing establishments using advanced meat recovery systems and hand-deboning. Samples ( n = 302) were analysed to determine fatty acid (FA) composition and scanned from 400 to 2498 nm. Total saturated and unsaturated FA values ranged from 43.2 to 62.0% and 38.3 to 56.2%, respectively. Results of partial least squares (PLS) modeling shown reasonably accurate models were attained for total saturate content [standard error of performance ( SEP = 1.10%); coefficient of determination on the validation set ( r2 = 0.77)], palmitic ( SEP = 0.94%; r2 = 0.69), unsaturate ( SEP = 1.13%; r2 = 0.77), and oleic ( SEP = 0.97; r2 = 0.78). Prediction of other individual saturated and unsaturated FAs was less accurate with an r2 range of 0.10 to 0.53. However, the sum of individual predicted saturated and unsaturated FA was acceptable compared with the reference method ( SEP = 1.10 and 1.12%, respectively). This study shows that NIR can be used to predict accurately total fatty acids in M. Longissimus dorsi muscle.

Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

2021 ◽  
Author(s):  
Iva Hrelja ◽  
Ivana Šestak ◽  
Igor Bogunović
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Data ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Least Squares Regression ◽  
Macro Scale ◽  
Sentinel 2

<p>Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.</p><p>The study is performed in Mediterranean Croatia (44° 05’ N; 15° 22’ E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed <em>Quercus ssp.</em> and <em>Juniperus ssp.</em> forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22<sup>nd</sup> 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31<sup>st</sup> and September 5<sup>th </sup>2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSE<sub>p</sub>) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.</p><p>The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSE<sub>p </sub>(Spectroscopy dataset RMSE<sub>p</sub> = 1.91; Sentinel-2 dataset RMSE<sub>p</sub> = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.</p><p><strong>Keywords:</strong> remote sensing, soil spectroscopy, wildfires, soil organic matter</p><p><strong>Acknowledgment: </strong>This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Perčin is acknowledged for her cooperation during the laboratory work.</p>

OPTIMISATION AND VALIDATION OF HPLC METHOD FOR SIMULTANEOUS QUANTIFICATION OF RIFAMPICIN, ISONIAZID, PYRAZINAMIDE, AND ETHAMBUTOL HYDROCHLORIDE IN ANTI-TUBERCULOSIS 4-FDC TABLET

2015 ◽  
Vol 77 (1) ◽  
Author(s):  
Sholihul Khoiri ◽  
Sudibyo Martono ◽  
Abdul Rohman
Keyword(s):  
High Performance ◽  
Phosphate Buffer Solution ◽  
Gradient Elution ◽  
Hplc Method ◽  
Fixed Dose ◽  
Buffer Solution ◽  
Solution Ph ◽  
Combination Tablet ◽  
Simultaneous Quantification ◽  
Dose Combination

High-performance liquid chromatography (HPLC) method has been developed and validated for the simultaneous quantification of four components, namely rifampicin (RIF), isoniazid (INH), pyrazinamide (PYR), and ethambutol hydrochloride (ETM), contained in anti-tuberculosis drugs in fixed dose combination tablet (4-FDC). In order to increase the sensitivity of EMB, the pre-column derivatization technique with phenethyl isocyanate (PEIC) was carried out. The separation was accomplished using Waters Symmetry C8 (250× 4.6 mm i.d.; 5 μm) at 30oC. The mobile phase used was a mixture of acetonitrile and 20 mM phosphate buffer solution (pH 6.8) containing triethylamine and delivered at 1.5 mL/minute using gradient elution. TheUV detector was set at 210 nm. The method was validated in terms of selectivity, linearity, accuracy, precision, detection limit, quantification limit, and robustness according to International Conference on Harmanization (ICH). The optimized method is succcesfully used for quantitative analysis of RIF, INH, PYR and ETM in 4-FDC tablets. The level of these drugs in 4-FDC tablets were in accordance to that specified in Indonesian pharmacopeia.

scholarly journals Separation of Cultivars of Soybeans by Chemometric Methods Using Near Infrared Spectroscopy

2018 ◽  
Vol 10 (4) ◽  
pp. 351
Author(s):  
João S. Panero ◽  
Henrique E. B. da Silva ◽  
Pedro S. Panero ◽  
Oscar J. Smiderle ◽  
Francisco S. Panero ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Relevant Information ◽  
Whole Grains ◽  
Signal Correction ◽  
Different Types ◽  
Linear Fit

Near Infrared (NIR) Spectroscopy technique combined with chemometrics methods were used to group and identify samples of different soy cultivars. Spectral data, collected in the range of 714 to 2500 nm (14000 to 4000 cm-1), were obtained from whole grains of four different soybean cultivars and were submitted to different types of pre-treatments. Chemometrics algorithms were applied to extract relevant information from the spectral data, to remove the anomalous samples and to group the samples. The best results were obtained considering the spectral range from 1900.6 to 2187.7 nm (5261.4 cm-1 to 4570.9 cm-1) and with spectral treatment using Multiplicative Signal Correction (MSC) + Baseline Correct (linear fit), what made it possible to the exploratory techniques Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to separate the cultivars. Thus, the results demonstrate that NIR spectroscopy allied with de chemometrics techniques can provide a rapid, nondestructive and reliable method to distinguish different cultivars of soybeans.

Factors Influencing the Calibration of near Infrared Reflectometry Applied to the Assessment of Total Nitrogen in Potato. II. Operator, Moisture and Maturity Class

1995 ◽  
Vol 3 (3) ◽  
pp. 167-174 ◽  
Author(s):  
Mark W. Young ◽  
Donald K.L. MacKerron ◽  
Howard V. Davies
Keyword(s):  
Field Experiment ◽  
Spectral Data ◽  
Total Nitrogen ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Moisture Contents ◽  
Factors Influencing ◽  
Maturity Class ◽  
The One ◽  
Low Nitrogen

Oven dried samples of leaf stem and tuber material taken from a nitrogen field experiment were analysed by Dumas combustion when fresh and by near infrared (NIR) then, and in the next two years, by a number of operators who made estimates of nitrogen concentration, [N]NIR, with differing degrees of error. The errors differed between years in the case of the one operator who made estimates in two years. Leaf, stem and tuber material of high and low nitrogen concentration were treated to produce samples at various moisture contents. These samples were scanned by NIR and the spectral data were examined. Higher moisture was found to decrease the reflectance at all the wavelengths used and would, therefore, introduce error into [N]NIR estimates. The NIR calibration used was found to be applicable to cultivars in a range of maturity classes. Several recommendations are made that will help to minimise the error introduced into [N]NIR estimates from various sources.

scholarly journals Biodiesel Synthesis Monitoring using Near Infrared Spectroscopy

2018 ◽  
Vol 12 (1) ◽  
pp. 95-110 ◽  
Author(s):  
Estela Kamile Gelinski ◽  
Fabiane Hamerski ◽  
Marcos Lúcio Corazza ◽  
Alexandre Ferreira Santos
Keyword(s):  
Fossil Fuels ◽  
Near Infrared ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Transesterification Reaction ◽  
Biodiesel Production ◽  
Molar Ratios ◽  
External Data ◽  
Biodiesel Synthesis ◽  
Biodiesel Production Monitoring

Objective: Biodiesel is a renewable fuel considered as the main substitute for fossil fuels. Its industrial production is mainly made by the transesterification reaction. In most processes, information on the production of biodiesel is essentially done by off-line measurements. Methods: However, for the purpose of control, where online monitoring of biodiesel conversion is required, this is not a satisfactory approach. An alternative technique to the online quantification of conversion is the near infrared (NIR) spectroscopy, which is fast and accurate. In this work, models for biodiesel reactions monitoring using NIR spectroscopy were developed based on the ester content during alkali-catalyzed transesterification reaction between soybean oil and ethanol. Gas chromatography with flame ionization detection was employed as the reference method for quantification. FT-NIR spectra were acquired with a transflectance probe. The models were developed using Partial Least Squares (PLS) regression with synthetic samples at room temperature simulating reaction composition for different ethanol to oil molar ratios and conversions. Model predictions were then validated online for reactions performed with ethanol to oil molar ratios of 6 and 9 at 55ºC. Standard errors of prediction of external data were equal to 3.12%, hence close to the experimental error of the reference technique (2.78%), showing that even without using data from a monitored reaction to perform calibration, proper on-line predictions were provided during transesterification runs. Results: Additionally, it is shown that PLS models and NIR spectra of few samples can be combined to accurately predict the glycerol contents of the medium, making the NIR spectroscopy a powerful tool for biodiesel production monitoring.

Application of near Infrared Spectroscopy On-Combine in Corn Grain Breeding

2005 ◽  
Vol 13 (2) ◽  
pp. 69-75 ◽  
Author(s):  
Roland Welle ◽  
Willi Greten ◽  
Thomas Müller ◽  
Gary Weber ◽  
Hartwig Wehrmann
Keyword(s):  
Grain Yield ◽  
Dry Matter ◽  
Near Infrared ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Successful Implementation ◽  
Grain Moisture ◽  
Yield Determination ◽  
Corn Grain

Improving maize ( Zea mays L.) grain yield and agronomic properties are major goals for corn breeders in northern Europe. In order to facilitate field grain yield determination we measured corn grain moisture content with near infrared (NIR) spectroscopy directly on a harvesting machine. NIR spectroscopy, in combination with harvesting, significantly improved quality and speed of yield determination within the very narrow harvest time window. Moisture calibrations were developed with 2117 samples from the 2001 to 2003 crop seasons using six diode array spectrometers mounted on combines. These models were derived from databases containing spectra from all instruments. Spectrometer-specific calibrations cannot be used to predict samples measured on other instruments of the same type. Standard error of cross-validation ( SECV) and coefficient of determination ( R2) were 0.56 and 0.99%, respectively. Moisture standard errors of prediction ( SEPs) for the six instruments, using varying independent sample sets from the 2004 harvest, ranged between 0.59% and 0.99% with R2 values between 0.92 to 0.98. The six instruments produced the same dry matter predictions on a common sample set as indicated by high R2 and low biases among them, hence there was no need to apply specific standardisation algorithms. Moisture NIR spectroscopy determinations were significantly more precise than those obtained using the reference method. Analysis of variance revealed low least significant differences and high heritabilities. High precision and heritability demonstrate successful implementation of on-combine NIR spectroscopy for routine dry matter (yield) measurements.

Assessment of the Pulpwood Quality of Standing Trees Using near Infrared Spectroscopy

1998 ◽  
Vol 6 (A) ◽  
pp. A117-A123 ◽  
Author(s):  
L. R. Schimleck ◽  
A. J. Michell ◽  
C. A. Raymond ◽  
A. Muneri
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Tree Height ◽  
Tree Breeding ◽  
Pulp Yield ◽  
Calibration Model ◽  
Standing Trees ◽  
Sampling Height ◽  
Individual Trees ◽  
Whole Tree

In Australia, considerable effort has been directed at improving the pulp yield of plantation grown trees through tree breeding programs. However, an improvement in pulp yield relies on the assessment of large numbers of trees. Traditional methods of assessment are expensive, time consuming and destructive, inhibiting their use. Cores can be extracted non-destructively from standing trees using TRECOR, a handheld motor driven drill. The cores are milled, their near-infrared spectra obtained and pulp yield estimated using an appropriate calibration model. The height at which the core is taken is very important. It must represent the whole tree and sampling must be easy and practical. The longitudinal and radial (within-tree) variation of pulp yield for 15 Eucalyptus nitens trees was examined using near-infrared (NIR) spectroscopy. The trees were taken from three families (five trees per family) selected for giving high, medium and low pulp yields respectively. Three trees (one from each family) were examined in detail. Maps of within-tree variation of pulp yield were developed. Pulp yield was found to be highly variable within individual trees and between trees of the same family. The yield of samples from 10% of tree height (approximately 2.2 m) gave the best correlation with whole-tree yield. Samples from 5% of tree height (approximately 1.1 m) gave a slightly lower correlation but provided a more convenient sampling height. Ten Eucalyptus globulus and ten E. nitens trees growing on five sites in Australia were used to examine the longitudinal variation of pulp yield. Trees from sites in Tasmania, Western Australia and Victoria were sampled. The optimal sampling height for E. globulus was 1.1 m. No single sampling height could be recommended for E. nitens due to large site effects.

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