A Comparison of near Infrared Method Development Approaches Using a Drug Product on Different Spectrophotometers and Chemometric Software Algorithms

2009 ◽  
Vol 17 (5) ◽  
pp. 233-244 ◽  
Author(s):  
Assad Kazeminy ◽  
Saeed Hashemi ◽  
Roger L. Williams ◽  
Gary E. Ritchie ◽  
Ronald Rubinovitz ◽  
...  
Keyword(s):  
Method Development ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Dosage Forms ◽  
Multivariate Models ◽  
Data Set ◽  
Pharmaceutical Dosage Forms ◽  
Pre Treatment ◽  
Instrument Configuration

It is well known that spectral variability in near infrared (NIR) spectroscopy can be attributed to the analyst, sample, sample positioning, instrument configuration and software (in both algorithm formats and structures used as well as in the execution of data pre-treatment and analysis). It is often acknowledged that the single largest factor impacting NIR results is sample presentation. However, what is obvious but not often acknowledged is that there are instrumental and software differences as well. These differences, evident in the quality of the spectra, may impact the chemometrics that are subsequently performed and, possibly, the results obtained from the multivariate statistical models. In order to investigate just what are these sources of variability, and just how much these variations may impact the results of the multivariate models for predicting the identification of pharmaceutical dosage forms, a study has been conducted. To the authors' knowledge, no other systematic study of this kind has been published. In this study, we are interested in learning what variability, if any, the choices for instrument and software have on the development of a NIR method for the identification of pharmaceutical dosage forms. Furthermore, we would like to learn what and how do the choices made early on in the experimental design impact the final quality of the spectra and the resulting multivariate models obtained from these spectra. A study protocol was designed, using a common data set consisting of four formulations of Ibuprofen, involving three investigating parties, namely, US FDA, USP and Irvine Pharmaceutical Services and using three NIR instruments, namely (listed in alphabetical order), a Bruker spectrometer, a Büchi spectrometer and a Foss spectrometer. Based on the results and despite differences in instrument configuration [dispersive or Fourier Transform (FT)], number of spectral data points, principal components analysis (PCA) or factorisation algorithms, and validation modelling approach, exact and accurate spectroscopic results can be achieved using NIR spectroscopy for discriminate analysis. More importantly, this study shows that the same NIR method spectral range and pre-treatment parameters can be used, and that nearly the same multivariate models can be obtained, despite instrumental and software differences, to accurately predict the identity of pharmaceutical dosage forms.

scholarly journals Textile Recognition and Sorting for Recycling at an Automated Line Using Near Infrared Spectroscopy

Recycling ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Kirsti Cura ◽  
Niko Rintala ◽  
Taina Kamppuri ◽  
Eetta Saarimäki ◽  
Pirjo Heikkilä
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Automated Recognition ◽  
Textile Materials ◽  
Input Material ◽  
Adequate Quality ◽  
Automated Line

In order to add value to recycled textile material and to guarantee that the input material for recycling processes is of adequate quality, it is essential to be able to accurately recognise and sort items according to their material content. Therefore, there is a need for an economically viable and effective way to recognise and sort textile materials. Automated recognition and sorting lines provide a method for ensuring better quality of the fractions being recycled and thus enhance the availability of such fractions for recycling. The aim of this study was to deepen the understanding of NIR spectroscopy technology in the recognition of textile materials by studying the effects of structural fabric properties on the recognition. The identified properties of fabrics that led non-matching recognition were coating and finishing that lead different recognition of the material depending on the side facing the NIR analyser. In addition, very thin fabrics allowed NIRS to penetrate through the fabric and resulted in the non-matching recognition. Additionally, ageing was found to cause such chemical changes, especially in the spectra of cotton, that hampered the recognition.

Ability of Vis-PIR spectroscopy to monitor changes in organic carbon of loamy soils at two depths

2021 ◽  
Author(s):  
Hayfa Zayani ◽  
Youssef Fouad ◽  
Didier Michot ◽  
Zeineb Kassouk ◽  
Zohra Lili-Chabaane ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Near Infrared ◽  
Full Range ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Calibration Data ◽  
Least Squares Regression ◽  
Data Set ◽  
Soil Carbon Stocks ◽  
The Mean

<p>Visible-Near Infrared (Vis-NIR) spectroscopy has proven its efficiency in predicting several soil properties such as soil organic carbon (SOC) content. In this preliminary study, we explored the ability of Vis-NIR to assess the temporal evolution of SOC content. Soil samples were collected in a watershed (ORE AgrHys), located in Brittany (Western France). Two sampling campaigns were carried out 5 years apart: in 2013, 198 soil samples were collected respectively at two depths (0-15 and 15-25 cm) over an area of 1200 ha including different land use and land cover; in 2018, 111 sampling points out of 198 of 2013 were selected and soil samples were collected from the same two depths. Whole samples were analyzed for their SOC content and were scanned for their reflectance spectrum. Spectral information was acquired from samples sieved at 2 mm fraction and oven dried at 40°C, 24h prior to spectra acquisition, with a full range Vis-NIR spectroradiometer ASD Fieldspec®3. Data set of 2013 was used to calibrate the SOC content prediction model by the mean of Partial Least Squares Regression (PLSR). Data set of 2018 was therefore used as test set. Our results showed that the variation ∆SOC<sub>obs</sub><sub></sub>obtained from observed values in 2013 and 2018 (∆SOC<sub>obs</sub> = Observed SOC (2018) - Observed SOC (2013)) is ranging from 0.1 to 25.9 g/kg. Moreover, our results showed that the prediction performance of the calibrated model was improved by including 11 spectra of 2018 in the 2013 calibration data set (R²= 0.87, RMSE = 5.1 g/kg and RPD = 1.92). Furthermore, the comparison of predicted and observed ∆SOC between 2018 and 2013 showed that 69% of the variations were of the same sign, either positive or negative. For the remaining 31%, the variations were of opposite signs but concerned mainly samples for which ∆SOCobs is less than 1,5 g/kg. These results reveal that Vis-NIR spectroscopy was potentially appropriate to detect variations of SOC content and are encouraging to further explore Vis-NIR spectroscopy to detect changes in soil carbon stocks.</p>

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation

2022 ◽  
pp. 096703352110572
Author(s):  
Nicholas T Anderson ◽  
Kerry B Walsh
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Short Wave ◽  
Least Squares Regression ◽  
Machine Learning Methods ◽  
Transmission Geometry ◽  
Modelling Techniques ◽  
Test Sets ◽  
Model Robustness

Short wave near infrared (NIR) spectroscopy operated in a partial or full transmission geometry and a point spectroscopy mode has been increasingly adopted for evaluation of quality of intact fruit, both on-tree and on-packing lines. The evolution in hardware has been paralleled by an evolution in the modelling techniques employed. This review documents the range of spectral pre-treatments and modelling techniques employed for this application. Over the last three decades, there has been a shift from use of multiple linear regression to partial least squares regression. Attention to model robustness across seasons and instruments has driven a shift to machine learning methods such as artificial neural networks and deep learning in recent years, with this shift enabled by the availability of large and diverse training and test sets.

Near infrared (NIR)-spectroscopy and in-vitro dissolution absorption system 2 (IDAS2) can help detect changes in the quality of generic drugs

2020 ◽  
Vol 46 (1) ◽  
pp. 80-90
Author(s):  
Carlos Jiménez-Romero ◽  
Johayra Simithy ◽  
Anthony Severdia ◽  
Daniel Álvarez ◽  
Manuel Grosso ◽  
...  
Keyword(s):  
Near Infrared ◽  
Generic Drugs ◽  
Nir Spectroscopy ◽  
In Vitro Dissolution ◽  
Absorption System ◽  
System 2

Analysis of Black Wattle (Acacia mearnsii De Wild) Bark by Near Infrared Spectroscopy

Holzforschung ◽  
2003 ◽  
Vol 57 (5) ◽  
pp. 527-532 ◽  
Author(s):  
L. R. Schimleck ◽  
Y. Yazaki
Keyword(s):  
Near Infrared ◽  
Rapid Assessment ◽  
Nir Spectroscopy ◽  
Hot Water ◽  
Soluble Solids ◽  
Tannin Content ◽  
Acacia Mearnsii ◽  
Considerable Potential ◽  
Black Wattle

Summary The analysis of two sets of Acacia mearnsii De Wild (Black Wattle) samples by near infrared (NIR) spectroscopy is reported. Set 1 samples were characterised in terms of hot water extractives, Stiasny value and polyflavanoid content. Set 2 samples were characterised by nine different parameters, including tannin content. NIR spectra were obtained from the milled bark of all samples and calibrations developed for each parameter. Calibrations developed for hot water extractives and polyflavanoid content (set 1) gave very good coefficients of determination (R2) and performed well in prediction. Set 2 calibrations were generally good with total and soluble solids, tannin content, Stiasny value-2 and UV-2, all having R2 greater than 0.8. Owing to the small number of set 2 samples, no predictions were made using the calibrations. The strong relationships obtained for many parameters in this study indicates that NIR spectroscopy has considerable potential for the rapid assessment of the quality of extractives in A. mearnsii bark.

Application of near-infrared spectroscopy for the detection of internal insect infestation inPiceaabiesseed lots

2004 ◽  
Vol 34 (1) ◽  
pp. 76-84 ◽  
Author(s):  
Mulualem Tigabu ◽  
Per Christer Odén ◽  
Tong Yun Shen
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Absorption Bands ◽  
Classification Rate ◽  
Data Set ◽  
Storage Lipids ◽  
Nir Absorption ◽  
Seed Origin ◽  
Single Seeds

The use of near-infrared (NIR) spectroscopy to discriminate between uninfested seeds of Picea abies (L.) Karst and seeds infested with Plemeliella abietina Seitn (Hymenoptera, Torymidae) larva is sensitive to seed origin and year of collection. Five seed lots collected during different years from Sweden, Finland, and Belarus were used in this study. Initially, seeds were classified as infested or uninfested with X-radiography, and then, NIR spectra from single seeds were collected with a NIR spectrometer from 1100 to 2498 nm with a resolution of 2 nm. Discriminant models were derived by partial least squares regression using raw and orthogonal signal corrected spectra (OSC). The resulting OSC model developed on a pooled data set was more robust than the raw model and resulted in 100% classification accuracy. Once irrelevant spectral variations were removed by using OSC pretreatment, single-lot calibration models resulted in similar classification rates for the new samples irrespective of origin and year of collection. Dis criminant analyses performed with selected NIR absorption bands also gave nearly 100% classification rate for new samples. The origin of spectral differences between infested and uninfested seeds was attributed to storage lipids and proteins that were completely depleted in the former by the feeding larva.

scholarly journals Effect of pneumatic compression therapy on lymph movement in lymphedema-affected extremities, as assessed by near-infrared fluorescence lymphatic imaging

2017 ◽  
Vol 10 (02) ◽  
pp. 1650049 ◽  
Author(s):  
Melissa B. Aldrich ◽  
Deborah Gross ◽  
John Rodney Morrow ◽  
Caroline E. Fife ◽  
John C. Rasmussen
Keyword(s):  
Near Infrared ◽  
Contractile Activity ◽  
Lymphatic Vessels ◽  
Compression Therapy ◽  
Direct Visualization ◽  
Near Infrared Fluorescence ◽  
Pre Treatment ◽  
And Function ◽  
Lymphatic Imaging

Previous studies have shown cost effectiveness and quality-of-life benefit of pneumatic compression therapy (PCT) for lymphedema (LE). Insurers, such as the Centers for Medicare/Medicaid (CMS), however, desire visual proof that PCT moves lymph. Near-infrared fluorescence lymphatic imaging (NIRFLI) was used to visualize lymphatic anatomy and function in four subjects with primary and cancer treatment-related LE of the lower extremities before, during, and after PCT. Optically transparent and windowed PCT garments allowed visualization of lymph movement during single, 1[Formula: see text]h PCT treatment sessions. Visualization revealed significant extravascular and lymphatic vascular movement of intradermally injected dye in all subjects. In one subject with sufficient patent lymphatic vessels to allow quantification of lymph pumping velocities and frequencies, these values were significantly increased during and after PCT as compared to pre-treatment values. Lymphatic contractile activity in patent lymphatic vessels occurred in concert with the sequential cycling of PCT. Direct visualization revealed increased lymphatic function, during and after PCT therapy, in all LE-affected extremities. Further studies are warranted to assess the effects of PCT pressure and sequences on lymph uptake and movement.

Sign in / Sign up

Export Citation Format

Share Document