Automatic classification of tobacco leaves based on near infrared spectroscopy and nonnegative least squares

2018 ◽  
Vol 26 (2) ◽  
pp. 101-105 ◽  
Author(s):  
Zhang Jianqiang ◽  
Liu Weijuan ◽  
Zhang Huaihui ◽  
Hou Ying ◽  
Yang Panpan ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Spectral Data ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Nonnegative Least Squares ◽  
Tobacco Leaves ◽  
Infrared Spectral

A nonnegative least squares classifier was proposed in this paper to classify near infrared spectral data. The method used near infrared spectral data of training samples to make up a data dictionary of the sparse representation. By adopting the nonnegative least squares sparse coding algorithm, the near infrared spectral data of test samples would be expressed via the sparsest linear combinations of the dictionary. The regression residual of the test sample of each class was computed, and finally it was assigned to the class with the minimum residual. The method was compared with the other classifying approaches, including the well-performing principal component analysis–linear discriminant analysis and principal component analysis–particle swarm optimization–support vector machine. Experimental results showed that the approach was faster and generally achieved a better prediction performance over compared methods. The method can accurately recognize different classes of tobacco leaves and it provides a new technology for quality evaluation of tobacco leaf in its purchasing activities.

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.

Functional principal component analysis for near-infrared spectral data: a case study on Tricholoma matsutakeis

2020 ◽  
Vol 16 (8) ◽  
Author(s):  
Haoran Li ◽  
Tianhong Pan ◽  
Yuqiang Li ◽  
Shan Chen ◽  
Guoquan Li
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Detection Algorithm ◽  
Functional Principal Component Analysis ◽  
Support Vector ◽  
Edible Fungi ◽  
Functional Principal Component

AbstractTricholoma matsutakeis (TM) is the most expensive edible fungi in China. Given its price and exclusivity, some dishonest merchants will sell adulterated TM by combining it with cheaper fungi in an attempt to earn more profits. This fraudulent behavior has broken food laws and violated consumer trust. Therefore, there is an urgent need to develop a rapid, accurate, and nondestructive tool to discriminate TM from other edible fungi. In this work, a novel detection algorithm combined with near-infrared spectroscopy (NIR) and functional principal component analysis (FPCA) is proposed. Firstly, the raw NIR data were pretreated by locally weighted scatterplot smoothing (LOWESS) and multiplication scatter correction (MSC). Then, FPCA was used to extract valuable information from the preprocessed NIR data. Then, a classifier was designed by using the least-squares support-vector machine (LS-SVM) to distinguish categories of edible fungi. Furthermore, the one-versus-one (OVO) strategy was included and the binary LS-SVM was extended to a multi-class classifier. The 166 samples of four varieties of fungi were used to validate the proposed method. The results show that the proposed method has great capability in near infrared spectra classification, and the average accurate of FPCA-LSSVM is 97.3% which is greater than that of PCA-LSSVM (93.5%).

66 DEVELOPMENTAL HEALTH ASSESSMENT OF OFFSPRING PRODUCED FROM MAGNETIC NANOPARTICLES USING NEAR INFRARED ANALYSIS OF PLASMA

2017 ◽  
Vol 29 (1) ◽  
pp. 140
Author(s):  
K. R. Counsell ◽  
C. L. Durfey ◽  
J. M. Feugang ◽  
S. T. Willard ◽  
P. L. Ryan ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Blood Plasma ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Toxic Waste ◽  
Spectral Profiles ◽  
Developmental Health

In vitro fertilization is optimized when there is a homogenous population of viable spermatozoa, not subjected toxic waste products of apoptotic cells. In a previous study, we developed a “nanopurification” technique to magnetically target and remove non-viable spermatozoa from a boar insemination dose. Nanopurified semen has successfully been studied with IVF in swine and bovine but lacks health data regarding offspring produced from exposed semen. Developmental health performance in mammals is typically assessed through measurements of immune related biomolecules in plasma (e.g. immunoglobulins), quantifying each variable with a specific analytical assay. Recent developments in aqueous based near infrared spectroscopy (NIR), aquaphotomics, have been shown to distinguish reproductive stages (e.g. oestrus, diestrus) in blood serum. Thus, application of aquaphotomics may be ideal for analysis of offspring resulting from fertilization with nanopurified semen, using serum or plasma. Our study objective was to identify holistic differences in blood plasma by characterising NIR spectral profiles in offspring produced from nanopurified semen. Extended boar semen doses were mixed with or without specific nanoparticles to target non-viable spermatozoa. Semen doses were exposed to an electromagnetic field, noninvasively separating non-viable spermatozoa from the insemination dose. Six gilts were bred with (n = 3) or without (n = 3) nanopurified semen. Following birth and weaning, 20 offspring of equal sexes were randomly selected from control and nanopurified litters (10/group) for growth and developmental measurements up until market weight. Blood plasma was collected from offspring at market weight for NIR analysis. Spectral data were collected with a quartz cuvette and ASD FieldSpec® 3 spectrophotometer (ASD Inc., Boulder, CO, USA). Chemometric analysis (Unscrambler® X version 10.4; CAMO Software, Oslo, Norway) included a Savistsky-Golay 1st and 2nd derivative for detection of distinct spectral features. Principal component analysis and partial least-squares block-discrimination were used to examine treatment effects, in a blind experiment. Plasma spectral profiles from control and nanopurified offspring contained 6 shared peaks at 1360, 1373, 1402, 1404, 1422, and 1428 nm. Principle components 1 and 2 accounted for 96.26% of the total variance, with no separation of principal component analysis scores for plasma spectra between groups. Partial least-squares discriminant analysis metrics (slope = 0.026, SECV = 0.52) and Students t-test showed no significant difference (P = 0.57) between groups. Results indicate blood plasma content is not influenced in nanopurified offspring when compared with the control. In addition, solute NIR has shown to be a valuable promising tool for assessing complex aqueous solutions in swine. Further effects on growth and development from offspring born from nanopurified continue to be investigated. This work was supported by USDA-ARS Biophotonics Initiative grant #58–6402–3-018.

Study of Vis-NIR and NIR spectral data of black inkjet prints using principal component analysis for forensic discrimination

NIR news ◽  
2017 ◽  
Vol 28 (2) ◽  
pp. 7-12 ◽  
Author(s):  
Michal Oravec ◽  
Lukáš Gál ◽  
Michal Čeppan
Keyword(s):  
Principal Component Analysis ◽  
Spectral Data ◽  
Near Infrared ◽  
Molecular Spectroscopy ◽  
Principal Component ◽  
Invasive Procedure ◽  
Component Analysis ◽  
Infrared Region ◽  
Forensic Analysis ◽  
Fibre Optics

The aim of this work was to prepare spectral data for principal component analysis and to examine 19 samples of six different brands. Samples consisted of the same type of office paper with black areas printed in black ink only. The spectral data were acquired by fibre optics reflection spectroscopy in Vis-NIR and only NIR (Vis-NIR FORS) directly on paper. The black inkjet-printed samples were analysed with regard to the forensic analysis of documents. The method used is based on the combination of molecular spectroscopy in the visible (Vis) and near infrared region (NIR) combined with a chemometric method, – principal component analysis (PCA). The PCA method divides the inkjet inks sample into clusters. It was found out that by a combination of spectrum pre-processing methods and principal component analysis, it is possible to separate inks containing carbon black from the other inks using other organic colourants. This method appears to be a useful tool for forensic examination of printed documents containing inkjet inks. Spectra of inkjet inks were acquired without any destructive or invasive procedure, for example cutting sample or for extraction with the possibility to measure out of the laboratory.

scholarly journals Pre-processing of inkjet prints NIR spectral data for principal component analysis

2015 ◽  
Vol 8 (2) ◽  
pp. 191-196 ◽  
Author(s):  
Michal Oravec ◽  
Lukáš Gál ◽  
Michal Čeppan
Keyword(s):  
Principal Component Analysis ◽  
Spectral Data ◽  
Near Infrared ◽  
Molecular Spectroscopy ◽  
High Reliability ◽  
Principal Component ◽  
Component Analysis ◽  
Infrared Region ◽  
Ocean Optics ◽  
Novel Approach

Abstract This paper presents a novel approach in non-destructive analysis of inkjet-printed documents. Our method is based on the combination of molecular spectroscopy in the Near Infrared Region (NIR) and a chemometric method - principal component analysis (PCA). The aim of this work was to prepare spectral data for the analysis of the interrelationships between 19 samples consisting of the same type of office paper on which black squares were full printed in black ink only. The spectra were obtained separately using the Ocean Optics System in two spectral regions, i.e., overtones: 1000-1600 nm and combination bands: 1600-2300 nm, with the paper base. Experimental results confirmed the high reliability of the proposed approach despite the sparse dataset.

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