Near-Infrared Mixture Identification by an Automated Library Searching Method: A Multivariate Approach

1992 ◽  
Vol 46 (5) ◽  
pp. 790-796 ◽  
Author(s):  
Su-Chin Lo ◽  
Chris W. Brown
Keyword(s):  
Near Infrared ◽  
Random Noise ◽  
Similarity Index ◽  
Principal Component ◽  
Liquid Mixtures ◽  
Poor Quality ◽  
Chemical Information ◽  
Infrared Spectral ◽  
Searching Method ◽  
Searching Performance

A mathematical technique for the identification of components in the near-infrared spectra of liquid mixtures without any prior chemical information is demonstrated. Originally, the technique was developed for searching mid-infrared spectral libraries. It utilizes principal component analysis to generate an orthonormal reference library and to compute the projections or scores of a mixture spectrum onto the principal space spanned by the orthonormal set. Both library and mixture spectra are analyzed and processed in Fourier domain to enhance the searching performance. A calibration matrix is calculated from library scores and is used to predict the mixture composition. Five liquid mixtures were correctly identified with the use of the calibration algorithm, whereas only one mixture was correctly characterized with a straight dot-product metric. The predictions were verified with the use of an adaptive filter to remove each of the resulting components from the library and the mixture spectra. In addition, a similarity index between the original mixture spectrum and a regenerated mixture spectrum is used as a final confirmation of the predictions. The effects of random noise on the searching method were also examined, and further enhancements of searching performance are suggested for identifying poor-quality mixture spectra.

Use of near infrared spectroscopy and spectral databases to assess the quality of pharmaceutical products and aid characterization of unknown components

2019 ◽  
Vol 27 (5) ◽  
pp. 379-390
Author(s):  
Mazlina Mohd Said ◽  
Simon Gibbons ◽  
Anthony Moffat ◽  
Mire Zloh
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Score Plot ◽  
Principal Component Analysis Score ◽  
Infrared Spectral ◽  
Herbal Formulations ◽  
Spectral Database

This research was initiated as part of the fight against public health problems of rising counterfeit, substandard and poor quality medicines and herbal products. An effective screening strategy using a two-step combination approach of an incremental near infrared spectral database (step 1) followed by principal component analysis (step 2) was developed to overcome the limitations of current procedures for the identification of medicines by near infrared spectroscopy which rely on the direct comparison of the unknown spectra to spectra of reference samples or products. The near infrared spectral database consisted of almost 4000 spectra from different types of medicines acquired and stored in the database throughout the study. The spectra of the test samples (pharmaceutical and herbal formulations) were initially compared to the reference spectra of common medicines from the database using a correlation algorithm. Complementary similarity assessment of the spectra was conducted based on the observation of the principal component analysis score plot. The validation of the approach was achieved by the analysis of known counterfeit Viagra samples, as the spectra did not fully match with the spectra of samples from reliable sources and did not cluster together in the principal component analysis score plot. Pre-screening analysis of an herbal formulation (Pronoton) showed similarity with a product containing sildenafil citrate in the database. This finding supported by principal component analysis has indicated that the product was adulterated. The identification of a sildenafil analogue, hydroxythiohomosildenafil, was achieved by mass spectrometry and Nuclear Magnetic Resonance (NMR) analyses. This approach proved to be a suitable technique for quick, simple and cost-effective pre-screening of products for guiding the analysis of pharmaceutical and herbal formulations in the quest for the identification of potential adulterants.

Inline biofilm monitoring based on near-infrared spectroscopy with ultracompact spectrometer technology

NIR news ◽  
2020 ◽  
Vol 31 (7-8) ◽  
pp. 9-13
Author(s):  
Robert Zimmerleiter ◽  
Elisabeth Leiss-Holzinger ◽  
Eva Maria Wagner ◽  
Kathrin Kober-Rychli ◽  
Martin Wagner ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spectral Response ◽  
Principal Component ◽  
Cost Effective ◽  
Sensor Surface ◽  
Chain Reaction ◽  
Microelectromechanical System ◽  
Infrared Spectral ◽  
Infrared Measurement ◽  
Polymerase Chain

In this article, we demonstrate a promising inline near-infrared measurement scheme for 24/7 biofilm monitoring based on cost-effective microelectromechanical system-based spectrometer technology. The shown near-infrared spectral data, acquired at a beer-canning line during a representative time span of 10 days, are analyzed by means of principal component analysis and the performance of the monitoring system and its capability to identify biofilms on its sensor surface are investigated by comparing spectral response with results of offline polymerase chain reaction measurements of smear samples. Correlations between presence of a biofilm and its thickness with scores on PC1 and PC2, respectively, were observed.

Near-Infrared Spectral Analysis of Gas Mixtures

2002 ◽  
Vol 56 (5) ◽  
pp. 593-598 ◽  
Author(s):  
Maria A. Van Agthoven ◽  
Go Fujisawa ◽  
Philip Rabbito ◽  
Oliver C. Mullins
Keyword(s):  
Principal Component Analysis ◽  
Spectral Analysis ◽  
Gas Flow ◽  
Near Infrared ◽  
Mass Density ◽  
Principal Component ◽  
Gas Mixtures ◽  
Complex Mixtures ◽  
Natural Gases ◽  
Infrared Spectral

The analysis by near-infrared spectroscopy (NIR) of a series of gas mixtures approximating natural gases is reported. Wide variations of gas pressure and temperature are used in accord with conditions found in various utilitarian gas flow streams. The NIR analysis of CH4 and CO2 composition is found to be straightforward and depends only on compound mass density, but not explicitly on temperature, pressure, or composition. Linearity of the spectra of more complex mixtures is maintained, but the NIR analysis is more complex. Principal component analysis is shown to resolve composition for those gas mixtures.

Effect of Random Noise on the Performance of NIR Calibrations

1998 ◽  
Vol 6 (1) ◽  
pp. 77-87 ◽  
Author(s):  
Jing Lu ◽  
W.F. McClure ◽  
F.E. Barton ◽  
D.S. Himmelsbach
Keyword(s):  
Least Squares ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Simple Procedure ◽  
Random Noise ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Optical Data ◽  
Spectra Analysis ◽  
Calibration Methods

The proliferation of applications for near infrared (NIR) spectroscopy has been fostered by advances in instrumentation and statistics. NIR analytical instrumentation is becoming more stable and reliable. Chemometrics is playing an important role in qualitative and quantitative NIR spectra analysis. The objective of this study was to evaluate the performances of four commonly used calibration models: (1) stepwise multiple linear regression (SMLR); (2) classical least-squares (CLS); (3) principal component regression (PCR); and (4) partial least-squares (PLS) in NIR spectroscopy analysis when random noise is present in the optical data. A conceptually simple procedure for comparing the performance of the four calibration methods in the presence of different levels of random noise in spectra data has been introduced here. This procedure, using the computer simulation data and real spectra of tobacco, has provided useful information for understanding the effects of random noise on the performance of multivariate calibration methods. Both numerical and graphical results will be shown.

scholarly journals A Near Infrared Spectroscopic Discrimination of Noodle Flours Using a Principal-Component Analysis Coupled with Chemical Information.

2002 ◽  
Vol 18 (10) ◽  
pp. 1145-1150 ◽  
Author(s):  
Masanori KUMAGAI ◽  
Kikuko KARUBE ◽  
Tomoaki SATO ◽  
Naganori OHISA ◽  
Toshio AMANO ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Chemical Information ◽  
Infrared Spectroscopic

Infrared Spectral Search for Mixtures in Large-Size Libraries

1991 ◽  
Vol 45 (10) ◽  
pp. 1628-1632 ◽  
Author(s):  
Su-Chin Lo ◽  
Chris W. Brown
Keyword(s):  
Adaptive Filter ◽  
Similarity Index ◽  
Principal Component Regression ◽  
Principal Component ◽  
Target Component ◽  
Infrared Spectral ◽  
Original Size ◽  
Unknown Mixture ◽  
Spectral Libraries ◽  
Chemical Groups

A routine for searching large spectral libraries with spectra of mixtures is presented. The dimensionality of a 3169-compound library is reduced to 12% of its original size by using Fourier transform compression and principal component analysis. A principal component regression is performed and used as a prefilter in selecting spectra having features (and chemical groups) similar to those of the unknown mixture. A dot-product metric is then used to identify a target component from the subgroup formed by the prefilter. This is followed by the application of an adaptive filter to remove the similarity of the target component from the subgroup and from the unknown mixture; the search is repeated on the modified data. Successive applications of the adaptive filter will produce minimum residuals if the correct identifications are made. Once the residuals are minimized, a similarity index is calculated to determine the closeness of the unknown mixture spectrum to a spectrum reconstructed from the library spectra. Four out of five two- and three-component spectra were correctly identified. One of the two components in the fifth mixture was correctly identified, and the residual values flagged the improper identification of the second component. After the adaptive filter was applied to the entire library, the second component was correctly identified. Results for this new algorithm are compared to those from four more traditional search routines, which were only completely successful on one of the unknown mixtures.

Parameter Selection Based on Support Vector Machines for Tongue Spectral Identification Model

2014 ◽  
Vol 678 ◽  
pp. 242-251
Author(s):  
Wen Juan Yan ◽  
Guo Quan He ◽  
Shi Jian Huang ◽  
Lin Qin
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Kernel Functions ◽  
Support Vector ◽  
Data Sets ◽  
Spectral Identification ◽  
Kernel Parameter ◽  
Two Samples ◽  
Infrared Spectral ◽  
Identification Model

Support Vector Machine (SVM) method is suitable for machine learning. In order to detect pathological information from tongue diagnosis rapidly, noninvasively and objectively, a near infrared spectral identification model is proposed based on SVM. The tongue spectral data of healthy people and hepatitis patients were collected. Twenty two samples were obtained for individual groups, and for each group, fifteen samples were randomly selected and used as the training sets, while the other seven were taken as the prediction sets. For the data sets, The effects of the principal component number, kernel parameters, and kernel functions on the identification model were investigated respectively. The results showed that the penalty parameter c was always 0.25, not related to the values of the principal component number and kernel parameter g. The kernel parameter g decreased along with the increased number of principal components, and ultimately reached a relatively stable value. When the Radial Basis Function (RBF) was applied, the established model was the best, indicating that the SVM approach is feasible to classify and recognize tongue near infrared spectroscopy, as along as right parameters are selected. This can provide a novel tongue spectral analysis method to distinguish healthy individuals from hepatitis patients.

scholarly journals Near infrared spectra features of cocoa pod husk used for feedstuff

2021 ◽  
Vol 922 (1) ◽  
pp. 012011
Author(s):  
Samadi ◽  
S Wajizah ◽  
Z Zulfahrizal
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Animal Feed ◽  
Raw Materials ◽  
Chemical Properties ◽  
Principal Component ◽  
Chemical Information ◽  
Crude Fibre ◽  
Aceh Province ◽  
Cocoa Pod Husk

Abstract This presented study aimed to study the near infrared spectroscopic features of cocoa pod husk samples used as raw materials for animal feedstuff. Spectral data of organic material samples contains chemical properties information that can be revealed through modelling, Thus, the study of this features is essential to assess and reveal buried respective information. Cocoa pod husk samples were obtained from several districts in Aceh Province, grinded and prepared as bulk samples. Diffuse reflectance spectral data for a total of 30 bulk cocoa pod husk samples were acquired and recorded in wavelength range from 1000 to 2500 nm. Spectral data were firstly projected onto principal component analysis to observe similarities among samples. Spectra correction, namely mean normalization was employed to enhance spectra features. The results showed that several chemical information related to cocoa properties can be revealed such as dry matter, crude protein, crude fibre, ether extract, nitrogen-free extract and ash content due to the second and third overtones pf combination bands O-H, C-O-H and N-H. Optimum wavelength for estimating cocoa pod husk attributes are in 1217, 1405-1474 nm, 1629 nm, 1906-1979 nm, and 2283 nm. Based on obtained study, it may conclude that several quality attributes of animal feed samples further can be determined by means of near infrared spectroscopy approach.

Tanshinone IIA Rapid Analysis of Jingutongxiao Pills by Near-Infrared Spectroscopy

2013 ◽  
Vol 807-809 ◽  
pp. 2075-2078
Author(s):  
Jing Wei Lei ◽  
Hai Yan Gong ◽  
Lei Li ◽  
Cai Xia Xie ◽  
Xiao Yan Duan ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Near Infrared ◽  
Evaluation Method ◽  
Principal Component ◽  
Tanshinone Iia ◽  
Calibration Model ◽  
Analysis Model ◽  
Infrared Spectral ◽  
Traditional Chinese Medicine Preparation

Traditional Chinese medicine(TCM) preparation has a long history in China，and now it is an important direction at the development of hospital preparations, because lacking of quality control technology, quality of the different production batch is not quite stable in the process of production. Therefore, to establish perfect, accurate, rapid quality evaluation method is the key to realize modernization of the traditional Chinese medicine preparation[1,2]. The paper establishes the quantitative analysis model for Tanshinone IIA in Jingutongxiao Pills by near-infrared spectral technology. Through the discussion of spectral wavelength, spectral pretreatment and principal component count, it was found that the second derivative combined with partial least squares (PLS) establishes a best quantitative calibration model. The correlation coefficient of calibration (R2)was 0.975 37, the Root Mean Square Error of Cross Validation (RMSECV) is 0.001 17 and the external prediction deviation (RMSEP) is 0.00174. It indicated that the near-infrared spectral technology could be used for rapid, accurate, nondestructive determination of Tanshinone IIA in Jingutongxiao Pills[3,4].

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