scholarly journals Near-infrared chemical imaging for quantitative analysis of chlorpheniramine maleate and distribution homogeneity assessment in pharmaceutical formulations

2016 ◽  
Vol 09 (06) ◽  
pp. 1650002 ◽  
Author(s):  
Manfei Xu ◽  
Luwei Zhou ◽  
Qiao Zhang ◽  
Zhisheng Wu ◽  
Xinyuan Shi ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spatial Information ◽  
Nir Spectroscopy ◽  
Pharmaceutical Formulations ◽  
Chemical Imaging ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Chemical Information ◽  
Chlorpheniramine Maleate ◽  
Homogeneity Assessment

Near infrared chemical imaging (NIR-CI) combines conventional near infrared (NIR) spectroscopy with chemical imaging, thus provides spectral and spatial information simultaneously. It could be utilized to visualize the spatial distribution of the ingredients in a sample. The data acquired using NIR-CI instrument are hyperspectral data cube (hypercube) containing thousands of spectra. Chemometric methodologies are necessary to transform spectral information into chemical information. Partial least squares (PLS) method was performed to extract chemical information of chlorpheniramine maleate in pharmaceutical formulations. A series of samples which consisted of different CPM concentrations (w/w) were compressed and hypercube data were measured. The spectra extracted from the hypercube were used to establish the PLS model of CPM. The results of the model were [Formula: see text] 0.981, RMSEC 0.384%, RMSECV 0.483%, RMSEP 0.631%, indicating that this model was reliable.

Industrial Applications of Near-Infrared Chemical Imaging Microscopy

2001 ◽  
Vol 7 (S2) ◽  
pp. 162-163
Author(s):  
EN Lewis ◽  
LH Kidder ◽  
KS Haber
Keyword(s):  
Spatial Distribution ◽  
Near Infrared ◽  
Imaging System ◽  
Raw Materials ◽  
Single Point ◽  
Nir Spectroscopy ◽  
Chemical Imaging ◽  
Industrial Applications ◽  
Chemical Information ◽  
Spatially Resolved

Single point near-infrared (NIR) spectroscopy is used extensively for characterizing raw materials and finished products in a wide variety of industries: polymers, paper, film, pharmaceuticals, paintings and coatings, food and beverages, agricultural products. As advanced industrial materials become more complex, their functionality is often determined by the spatial distribution of their discrete sample constituents. However, conventional single point NIR spectroscopy cannot adequately probe the interrelationship between the spatial distribution of sample components with the physical properties of the sample. to fully characterize these samples, it is necessary to probe simultaneously spatial and chemical heterogeneity and correlate these properties with sample characteristics.Recently, we have developed a novel NIR imaging spectrometer that can deliver spatially resolved chemical information very rapidly. in contrast to conventional, single point NIR spectrometers, the imaging system uses an infrared focal-plane array (FPA) to collect up to 76,800 complete spectra, one for each pixel on the array, in approximately one minute.

scholarly journals Chemometric Tools for NIRS and NIR Hyperspectral Imaging

2012 ◽  
Vol 69 (1) ◽  
Author(s):  
Laura M. DALE ◽  
André THEWIS ◽  
Ioan ROTAR ◽  
Juan A. FERNANDEZ PIERNA ◽  
Christelle BOUDRY ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Agricultural Sector ◽  
Spatial Information ◽  
Imaging System ◽  
Agricultural Products ◽  
Chemical Information ◽  
Special Focus ◽  
Large Area ◽  
Analytical Tools

Nowadays in agriculture, new analytical tools based on spectroscopic technologies are developed. Near Infrared Spectroscopy (NIRS) is a well known technology in the agricultural sector allowing the acquisition of chemical information from the samples with a large number of advantages, such as: easy to use tool, fast and simultaneous analysis of several components, non-polluting, noninvasive and non destructive technology, and possibility of online or field implementation. Recently, NIRS system was combined with imaging technologies creating the Near Infrared Hyperspectral Imaging system (NIR-HSI). This technology provides simultaneously spectral and spatial information from an object. The main differences between NIR-HSI and NIRS is that many spectra can be recorded simultaneously from a large area of an object with the former while with NIRS only one spectrum was recorded for analysis on a small area. In this work, both technologies are presented with special focus on the main spectrum and images analysis methods. Several qualitative and quantitative applications of NIRS and NIR-HSI in agricultural products are listed. Developments of NIRS and NIR-HSI will enhance progress in the field of agriculture by providing high quality and safe agricultural products, better plant and grain selection techniques or compound feed industry’s productivity among others.

scholarly journals New evaluation method to detect physiological stress in fruit trees by airborne hyperspectral image spectroscopy

2010 ◽  
Vol 16 (1) ◽  
Author(s):  
J. Tamás
Keyword(s):  
Precision Agriculture ◽  
Near Infrared ◽  
Evaluation Method ◽  
Hyperspectral Image ◽  
Physiological Stress ◽  
Incident Light ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Infrared Range ◽  
Image Spectroscopy

Nowadays airborne remote sensing data are increasingly used in precision agriculture. The fast space-time dependent localization of stresses in orchards, which allows for a more efficient application of horticultural technologies, could lead to improved sustainable precise management. The disadvantage of the near field multi and hyper spectroscopy is the spot sample taking, which can apply independently only for experimental survey in plantations. The traditional satellite images is optionally suitable for precision investigation because of the low spectral and ground resolution on field condition. The presented airborne hyperspectral image spectroscopy reduces above mentioned disadvantages and at the same time provides newer analyzing possibility to the user. In this paper we demonstrate the conditions of data base collection and some informative examination possibility. The estimating of the board band vegetation indices calculated from reflectance is well known in practice of the biomass stress examinations. In this method the N-dimension spectral data cube enables to calculate numerous special narrow band indexes and to evaluate maps. This paper aims at investigating the applied hyperspectral analysis for fruit tree stress detection. In our study, hyperspectral data were collected by an AISADUAL hyperspectral image spectroscopy system, with high (0,5-1,5 m) ground resolution. The research focused on determining of leaves condition in different fruit plantations in the peach orchard near Siófok. Moreover the spectral reflectance analyses could provide more information about plant condition due to changes in the absorption of incident light in the visible and near infrared range of the spectrum.

A Brief Review of near Infrared in Petroleum Product Analysis

1996 ◽  
Vol 4 (1) ◽  
pp. 69-74 ◽  
Author(s):  
Jerome Workman
Keyword(s):  
Functional Groups ◽  
Petroleum Product ◽  
Octane Number ◽  
Near Infrared ◽  
Scientific Literature ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Hydrocarbon Fuel ◽  
Chemical Information ◽  
Product Analysis

The use of infrared spectroscopy [including near infrared (NIR) spectroscopy] for the analysis of petroleum product analysis has become an essential component of hydrocarbon processing and refining since the mid-1940s. Early scientific literature identified absorption band positions for a variety of hydrocarbon functional groups from pure compounds to complex mixtures. The short wavelength NIR region (generally designated as 750–1100 nm), and the long-wavelength NIR region (1100–2500 nm) have been explored for their relative chemical information content with respect to hydrocarbon fuel mixtures. The functional groups of methyl, methylene, carbon–carbon, carbon–oxygen (including carbonyl), and aromatic (C–H) are measured directly using NIR spectroscopy. NIR spectroscopy combined with multivariate calibration has resulted in the reported analysis of numerous fuel components. The scientific literature has reported varied success for the measurement of RON (research octane number), MON (motor octane number), PON (pump octane number), cetane, cloud point, MTBE ( tert-Butyl methyl ether), RVP (Reid vapour pressure), ethanol, API, bromine number, lead, sulphur, aromatics, olefins and saturates content in such products as gasoline, diesel fuels, and jet fuels. This review paper summarises the foundational work using near-infrared for hydrocarbon fuels measurement beginning in 1938.

scholarly journals An Overview of Hyperspectral Remote Sensing and its applications in various Disciplines

2016 ◽  
Vol 5 (2) ◽  
pp. 85 ◽  
Author(s):  
Alpana Shukla ◽  
Rajsi Kot
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Spatial Information ◽  
Coastal Zone Management ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Data Sets ◽  
Imaging Data ◽  
Advantages And Disadvantages

<div><p><em>Recent advances in remote sensing and geographic information has opened new directions for the development of hyperspectral sensors. Hyperspectral remote sensing, also known as imaging spectroscopy is a new technology. Hyperspectral imaging is currently being investigated by researchers and scientists for the detection and identification of vegetation, minerals, different objects and background.</em><em> Hyperspectral remote sensing combines imaging and spectroscopy in a single system which often includes large data sets and requires new processing methods. Hyperspectral data sets are generally made of about 100 to 200 spectral bands of relatively narrow bandwidths (5-10 nm), whereas, multispectral data sets are usually composed of about 5 to 10 bands of relatively large bandwidths (70-400 nm). Hyperspectral imagery is collected as a data cube with spatial information collected in the X-Y plane, and spectral information represented in the Z-direction. </em><em>Hyperspectral remote sensing is applicable in many different disciplines. It was originally developed for mining and geology; it has now spread into fields such as agriculture and forestry, ecology, coastal zone management, geology and mineral exploration. This paper presents an overview of hyperspectral imaging, data exploration and analysis, applications in various disciplines, advantages and disadvantages and future aspects of the technique.</em></p></div>

scholarly journals Investigation of NIR spectroscopy for identifying and sorting wood with respect to species, moisture content, and weathering

2021 ◽  
Author(s):  
Ekaterina Tounis
Keyword(s):  
Infrared Spectroscopy ◽  
Moisture Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Surface Preparation ◽  
Nir Spectroscopy ◽  
Species Group ◽  
Chemical Information ◽  
Calibration Techniques

Near-infrared spectroscopy can characterize wood surfaces fast and without significant surface preparation. It is based on molecular overtone and combination vibrations which are typically very broad, leading to complex spectra. Multivariate calibration techniques are often employed to extract the desired chemical information. This study focused on the potential of near-infrared spectroscopy combined with partial least squares for identifying and sorting wood with respect to species and physical properties and on the effects of wood preparation and weathering on the precision of analysis. It was shown that a range of moisture content values and artificial weathering periods could be well predicted indepenedently of wood species analyzed. Species within the spruce-pine-fir species group could be predicted reasonably well when tested under similar conditions. When different moisture contents and weathering exposure periods were introduced, species prediction was still possible, but, with decreased prediciton ability.

Near-Infrared Spectroscopic Determination of Acetate, Ammonium, Biomass, and Glycerol in an Industrial Escherichia Coli Fermentation

1996 ◽  
Vol 50 (1) ◽  
pp. 102-108 ◽  
Author(s):  
Jeffrey W. Hall ◽  
Brian McNeil ◽  
Malcolm J. Rollins ◽  
Indira Draper ◽  
Brad G. Thompson ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Least Squares ◽  
Near Infrared ◽  
Control Strategies ◽  
Nir Spectroscopy ◽  
Time Frame ◽  
Chemical Information ◽  
Bioprocess Monitoring ◽  
Prediction Errors ◽  
Least Squares Regression

By use of near-infrared (NIR) spectroscopy, simultaneous, multiple-constituent estimation of important bioprocess parameters can be obtained in a time frame (<1 min assay) that was previously unattainable. Therefore, with NIR spectroscopy the opportunity exists to incorporate real-time chemical information into bioprocess monitoring or control strategies which will lead to significant bioprocess improvements. The NIR spectroscopic analysis of unmodified whole broth samples for acetate, ammonium, biomass, and glycerol is described for an industrial Escherichia coli fed-batch fermentation bioprocess. For acetate and glycerol, suitable results were obtained from multiple linear least-squares regression (MLR) analysis. A more sophisticated partial least-squares (PLS) regression analysis was necessary to adequately model ammonium and biomass. The respective prediction errors (1σ) of 0.7 g/L, 1.4 g/L, 0.7 g/L, and 7 mmol/L for acetate, biomass, glycerol, and ammonium compare well with the error of the wet chemical reference methods used to derive the calibration algorithms.

scholarly journals A Hyperspectral Imaging Approach for Classifying Geographical Origins of Rhizoma Atractylodis Macrocephalae Using the Fusion of Spectrum-Image in VNIR and SWIR Ranges (VNIR-SWIR-FuSI)

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2045 ◽  
Author(s):  
Chenlei Ru ◽  
Zhenhao Li ◽  
Renzhong Tang
Keyword(s):  
Data Fusion ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Image Features ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Projection Algorithm ◽  
Support Vector ◽  
Gray Level ◽  
Biomedical Analysis

Hyperspectral data processing technique has gained increasing interests in the field of chemical and biomedical analysis. However, appropriate approaches to fusing features of hyperspectral data-cube are still lacking. In this paper, a new data fusion approach was proposed and applied to discriminate Rhizoma Atractylodis Macrocephalae (RAM) slices from different geographical origins using hyperspectral imaging. Spectral and image features were extracted from hyperspectral data in visible and near-infrared (VNIR, 435–1042 nm) and short-wave infrared (SWIR, 898–1751 nm) ranges, respectively. Effective wavelengths were extracted from pre-processed spectral data by successive projection algorithm (SPA). Meanwhile, gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) were employed to extract textural variables. The fusion of spectrum-image in VNIR and SWIR ranges (VNIR-SWIR-FuSI) was implemented to integrate those features on three fusion dimensions, i.e., VNIR and SWIR fusion, spectrum and image fusion, and all data fusion. Based on data fusion, partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) were utilized to establish calibration models. The results demonstrated that VNIR-SWIR-FuSI could achieve the best accuracies on both full bands (97.3%) and SPA bands (93.2%). In particular, VNIR-SWIR-FuSI on SPA bands achieved a classification accuracy of 93.2% with only 23 bands, which was significantly better than those based on spectra (80.9%) or images (79.7%). Thus it is more rapid and possible for industry applications. The current study demonstrated that hyperspectral imaging technique with data fusion holds the potential for rapid and nondestructive sorting of traditional Chinese medicines (TCMs).

scholarly journals Establishment of a comprehensive indicator to nondestructively analyze watermelon quality at different ripening stages

2014 ◽  
Vol 07 (04) ◽  
pp. 1350034 ◽  
Author(s):  
Shuye Qi ◽  
Shuhui Song ◽  
Shengnan Jiang ◽  
Yingrui Chen ◽  
Wu Li ◽  
...  
Keyword(s):  
Near Infrared ◽  
Linear Equations ◽  
Nir Spectroscopy ◽  
Growth Period ◽  
Economic Benefits ◽  
Chemical Information ◽  
X Ray ◽  
Ripening Stages ◽  
Physical Information ◽  
Comprehensive Indicator

Two nondestructive methods based on visible and near-infrared (VIS-NIR) spectroscopy and X-ray image have been used for the evaluation of watermelon quality. The prediction performance based on partial least squares (PLS) by diffuse transmittance measurement (500–1010 nm) was evaluated for chemical quality attributes SSC (Rc = 0.903; RMSEC = 0.572% Brix; Rp = 0.862; RMSEP = 0.717% Brix; RPD = 1.83), lycopene (Rc = 0.845; RMSEC = 0.266 mg/100 gFW; Rp = 0.751; RMSEP = 0.439 mg/100 gFW; RPD = 1.13) and moisture (Rc = 0.917; RMSEC = 0.280%; Rp = 0.937; RMSEP = 0.276%; RPD = 2.79). The X-ray calibration linear equations developed by extracting the appropriate gray threshold were sufficiently precise for volume (R2 = 0.986) and weight (R2 = 0.993). In order to optimize prediction model of watermelon quality in growth period, multivariate multi-block technique factor analysis enabled integration of these traits: chemical information is related to physical information. Applying principle component analysis to extract common factors and varimax with Kaiser normalization to improve explanatory, the comprehensive indicator based on variances was established satisfactorily with Rc = 0.94, RMSEC = 0.244, Rp = 0.93, RMSEP = 0.344 and RPD = 2.00. A comparison of these models indicates that the comprehensive indicator determined only by portable VIS-NIR spectrometer appears as a suitable method for appraising watermelon quality nondestructively on the plant at different ripen stages. This method contributes to infer the picking date of watermelon with higher accuracy and bigger economic benefits than that by experience.

scholarly journals Concentration monitoring with near infrared chemical imaging in a tableting press

2018 ◽  
Author(s):  
Himmat Dalvi ◽  
Clémence Fauteux-Lefebvre ◽  
Jean-Maxime Guay ◽  
Nicolas Abatzoglou ◽  
Ryan Gosselin
Keyword(s):  
Near Infrared ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Chemical Imaging ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Monitoring Methods ◽  
Comparative Results ◽  
Analytical Tools

Monitoring powder potency and homogeneity is important in achieving real-time release testing in a continuous tablet manufacturing operation. If quality related issues are encountered, monitoring powder potency inside a feed frame offers a last opportunity to intervene in the process before tablet compression. Feed frame monitoring methods based on near infrared (NIR) spectroscopy have been increasingly reported in recent years. New process analytical tools with the potential of being deployed alone or in combination with NIR spectroscopy for feed frame monitoring are now available commercially. The present study evaluated the potential of near infrared chemical imaging (NIR CI) for in-line monitoring of a prototype pharmaceutical composition containing ascorbic acid (AA), microcrystalline cellulose and dicalcium phosphate. NIR spectroscopy was the reference method. In-line calibration models based on partial least square regression were developed and validated with a range of AA concentrations. The ability of NIR spectroscopy and NIR CI to predict concentrations in test runs was ascertained both independently and in combination. NIR CI, with a single bandpass filter, predicted AA concentrations—present at commercially relevant concentrations—with acceptable accuracy. Comparative results showed that NIR CI has the potential for in-line monitoring of blend concentrations inside feed frames. In addition to the advantage of increased sample size, it also has the potential to detect segregation inside feed frames.

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