Forensic Classification of Paper with Infrared Spectroscopy and Principal Components Analysis

2005 ◽  
Vol 13 (4) ◽  
pp. 225-229 ◽  
Author(s):  
Ashwini Kher ◽  
Samantha Stewart ◽  
Mary Mulholland
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Infrared Region ◽  
Discriminating Power ◽  
Chemometric Technique ◽  
Ft Ir ◽  
Components Analysis ◽  
Processing Techniques ◽  
The Impact

Fourier transform infrared (FT-IR) spectra of six document papers were classified by the chemometric technique of soft independent modelling of class analogy (SIMCA) using principal component analysis (PCA). The data were split into two regions, mid infrared (MIR, 2500–4000 cm−1) and the near infrared region (NIR, 4000–9000 cm−1). The raw data failed to produce an appreciable separation; hence it was pre-processed using derivatives and the multiple wavenumber ratios technique. The aim of this research was to determine the spectral region with the best discriminating power and evaluate the impact of data pre-processing techniques on the classification of paper. It was found that MIR (both raw and ratios) had higher discriminating ability than the NIR. The first derivatives and two ratios produced the best classifications. These results indicate that IR spectroscopy coupled with chemometric analyses can be effectively employed for the forensic classification of paper.

scholarly journals DISCRIMINANT ANALYSIS OF SHODHANA (PROCESSING) ON BALIOSPERMUM MONTANUM MUELL (DANTI) ROOT SAMPLES BASED ON NEAR INFRARED SPECTROSCOPY AND MULTIVARIATE CHEMOMETRIC TECHNIQUE

2017 ◽  
Vol 9 (7) ◽  
pp. 130
Author(s):  
Siba Prasad Rout ◽  
Rabinarayan Acharya ◽  
Jayanta Kumar Maji
Keyword(s):  
Cluster Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Processing Technique ◽  
Identification System ◽  
Chemometric Technique ◽  
Spectral Detection ◽  
Preprocessing Technique ◽  
The Impact

Objective: To establish a noticeable and a justifiable identification system to assess the impact of shodhana (processing) on various levels of Baliospermum montanum (Danti) root samples obtained through shodhana (processing technique) in quality agreement based on near-infrared-spectroscopy.Methods: Authenticated raw Danti (R. D) root and various Danti root samples obtained after shodhana (processing) such as water processed Danti root (WPDR), Kusha processed Danti root (KPDR) and classical processed Danti root (CPDR), were dried, pulverized and shifted through eighty meshes. The samples were subjected to NIR spectral detection from 750 to 2500 nm at the interval of 1 nm. The multivariate analysis, principal component analysis (PCA) and hierarchical cluster analysis (HCA) analyzed with the help of Unscrambler and Matlab software.Results: Direct spectral analysis indicated the existence of significant numerical and graphical differences between Danti root samples containing different treatments during processing in respect to CH, OH and NH functional groups. The multivariate PCA algorithom plot allowed a clear segregation of the Danti root samples after various data preprocessing technique onto the hotelling T2 95% confidence limit for principal component 1 and 2. The cluster analysis had shown the extra information on the metabolite profiling of the complex purificatory environment. Conclusion: The present study demonstrates a generic, non-destructive solution to discriminate qualitatively in the sample matrix all the differently pretreated samples in favor of the NIR-sensitive functional group.

scholarly journals ANÁLISIS DE COMPONENTES PRINCIPALES DE IMÁGENES MULTIESPECTRALES EN EL ÁMBITO DEL ARTE RUPESTRE

2017 ◽  
Author(s):  
Berta Carrión-Ruiz ◽  
José Luis Lerma
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Rock Art ◽  
Visible Region ◽  
Principal Component ◽  
Optical Filters ◽  
Infrared Region ◽  
Spectral Behaviour ◽  
Near Infrared Region ◽  
Art Analysis

This paper tackles principal component analysis (PCA) in images that include wavelengths between 380-1000 nm. Our approach is focussed on taking advantage of the potencial of ultraviolet and infrarred images, in combination with the visible ones, to improve documentation process and rock art analysis. In this way, we want to improve the discrimination between pigment and support rock, and analyse the spectral behaviour of rock art paintings in the ultraviolet and infrared regions. Three images were used, one image from the ultraviolet (UV) region, one from the visible region (VIS) and another one from the near infrared region (NIR). Optical filters coupled to the camera optics were used to take the images. These filters capture specific wavelengths excluding radiation that we are not interested in registering. Finally, PCA is applied to the acquired images. The results obtained demonstrate the PCA usefulness with imagery in this field and also it is possible to extract some conclusions about the correspondent paint pigments.http://dx.doi.org/10.4995/CIGeo2017.2017.6597

Chemometric Approach to a Rapid Attenuated Total Reflection Fourier Transform Infrared Analysis of Complex Heroin-Based Mixtures

2020 ◽  
pp. 000370282096971
Author(s):  
Nataša Radosavljević Stevanović ◽  
Milena Jovanović ◽  
Federico Marini ◽  
Slavica Ražić
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Illegal Market ◽  
Ft Ir

Heroin is one of the most frequently seized drugs in Southeastern Europe. Due to the position in the Balkan route, the Republic of Serbia keeps important role in suppression of the trafficking of heroin for domestic and foreign illegal market. This research is aimed to provide a good scientific approach in the field of seized heroin analysis. Two different forms of heroin are present in the illegal market, mostly in mixtures with typical “cutting” agents: caffeine, paracetamol, and sugars. It was observed that the quantity of pure heroin in seized samples slightly increases from year to year. The aim of this study was to produce a reliable and fast procedure for classification of illicit heroin samples and determination of the concentration range of heroin in the samples. For that purpose, the attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) technique was used and combined with such chemometric methods as principal component analysis, cluster analysis, and partial least squares. Principal component analysis (PCA) as an unsupervised model was used for exploratory purposes to identify trends, similarities, and differences between samples by reducing the dimensionality of the data. The cluster classification of examined samples turned out to be extremely useful to evaluate the possibilities of the ATR FT-IR technique to classify the samples appropriately into the patterns, the constituted clusters. Additionally, partial least square was the suitable method for the purpose of determination of the heroin hydrochloride concentration range in examined samples. It is proved that the joined application of spectroscopy and chemometrics can be extremely convenient and useful for forensic and drugs control laboratories.

Classification of Vegetable Oils by FT-IR

1997 ◽  
Vol 51 (8) ◽  
pp. 1118-1124 ◽  
Author(s):  
Donald B. Dahlberg ◽  
Shawn M. Lee ◽  
Seth J. Wenger ◽  
Julie A. Vargo
Keyword(s):  
Ir Spectra ◽  
Principal Component ◽  
Fast Detection ◽  
Signal Correction ◽  
Ft Ir ◽  
Ir Detection ◽  
The Fourier Transform ◽  
Cooking Oils ◽  
Cis And Trans

The Fourier transform infrared (FT-IR) spectra of 27 brands of 10 types of cooking oils and margarines were measured without temperature control. Attempts to predict the vegetable source and physical properties of these oils failed until wavelength selection and multiplicative signal correction (MSC) were applied to the FT-IR spectra. After pretreatment of the data, principal component analysis (PCA) was totally successful at oil identification, and partial least-squares (PLS) models were able to predict both the refractive indices [standard error of estimation (SEE) 0.0002] and the viscosities (SEE 0.52 cP) of the oils. These models were based predominately on the FT-IR detection of the cis and trans double-bond content of the oils, as well as small amounts of defining impurities in sesame oils. Efforts to use selected wavelengths to discriminate oil sources were only partially successful. These results show the potential utility of FT-IR in the fast detection of substitution or adulteration of products like cooking oils.

scholarly journals Pengembangan Metode Pengujian Keaslian Beras Aceh Menggunakan Nirs Dengan Metode PCA

2019 ◽  
Vol 4 (1) ◽  
pp. 578-587
Author(s):  
Masyitah Masyitah ◽  
Syahrul Syahrul ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Principal Component Analysis ◽  
Protein Content ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Ft Ir

Abstrak. Tujuan dari penelitian ini adalah membangun model pendugaan untuk menilai keaslian beras Aceh berdasarkan spektrum NIRS yang dihasilkan. Pendeteksian keaslian beras Aceh secara cepat dan efesien dapat diwujudkan melalui pengembangan teknologi Near Infrared Reflectance Spectroscopy (NIRS). Penelitian ini menggunakan beras varietas Sigupai (Aceh Barat Daya), varietas  Sanbay (Simeulue) dan varietas Ciherang. Jumlah sampel yang digunakan pada penelitian ini adalah 45 sampel. Pengukuran spektrum beras menggunakan Self developed FT-IR IPTEK T-1516. Klasifikasi data spektrum beras menggunakan Principal Component Analysis (PCA) dengan dua  pretreatment yaitu De-trending dan Multiplicative Scatter Correction. Hasil penelitian ini diperoleh yaitu: Spektrum NIRS beras menunjukkan keberadaan kandungan lemak pada panjang gelombang 2355 nm - 2462 nm. Kandungan karbohidrat pada panjang gelombang 2256 nm - 2321 nm.  Kandungan protein pada panjang gelombang 2056 nm - 2166 nm. Kandungan kadar air pada panjang gelombang 1910 nm-1980 nm dan panjag gelombang 1411 nm - 1492 nm menunjukkan kandungan protein dan kadar air. NIRS dengan metode PCA mampu membedakan pencampuran beras Sigupai dengan beras Ciherang dimana pembedaan terbaik terjadi dalam bentuk dua macam pengelompokan yaitu beras  Sigupai ≥ 75 dan beras Sigupai ≤50 dan pretreatment de-trending merupakan pretreatment terbaik dalam mengklasifikasi beras Aceh (Sigupai dan Sanbay) dengan beras Nasional (Ciherang).Development of Methods for Testing the Authenticity of Aceh Rice Using NIRS with the PCA MethodAbstract. The purpose of this study is to develop a prediction model to assess the authenticity of Aceh rice based on the NIRS spectrum produced. The detection of the authenticity of Aceh rice quickly and efficiently can be realized through technological development Near Infrared Reflectance Spectroscopy (NIRS). This study uses Sigupai rice varieties (Aceh Barat Daya), Sanbay (Simeulue) and Ciherang. The number of samples used in this study was 45 samples. Measurement of rice spectrum  using Self developed FT-IR IPTEK T-1516. Rice spectrum data classification uses the Principal Component Analysis (PCA) with two pretreatments, namely De-trending and Multiplicative Scatter Correction. The results of this study were obtained: NIRS spectrum of rice showed the presence of fat content at a wavelength of 2355 nm - 2462 nm. Carbohydrate content at wavelength 2256 nm - 2321 nm. Protein content at wavelength 2056 nm - 2166 nm. The content of water content at a wavelength of 1910 nm-1980 nm and wave length of 1411 nm - 1492 nm shows the protein content and water content. NIRS with the PCA method was able to distinguish the mixing of Sigupai rice from Ciherang rice where the best differentiation occurred in the form of two types of grouping namely Sigupai rice ≥ 75 and Sigupai rice ≤ 50 and de-trending pretreatment was the best pretreatment in classifying Aceh rice (Sigupai and Sanbay) with National rice (Ciherang).

scholarly journals Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jaweria Kainat ◽  
Syed Sajid Ullah ◽  
Fahd S. Alharithi ◽  
Roobaea Alroobaea ◽  
Saddam Hussain ◽  
...  
Keyword(s):  
Image Processing ◽  
Feature Fusion ◽  
Principal Component ◽  
Features Extraction ◽  
Support Vector ◽  
Features Fusion ◽  
Extraction Step ◽  
Processing Techniques ◽  
Feature Score

Existing plant leaf disease detection approaches are based on features of extracting algorithms. These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. Diseases of a plant can be classified from their symptoms. We proposed a cucumber leaf recognition approach, consisting of five steps: preprocessing, normalization, features extraction, features fusion, and classification. Otsu’s thresholding is implemented in preprocessing and Tan–Triggs normalization is applied for normalizing the dataset. During the features extraction step, texture and shape features are extracted. In addition, increasing the instances improves some characteristics. Through a principal component analysis approach, serial feature fusion is employed to provide a feature score. Fused features can be classified through a support vector machine. The accuracy of the Fine KNN is 94.30%, which is higher than the previous work in past papers.

scholarly journals Application of Spectroscopic UV-Vis and FT-IR Screening Techniques Coupled with Multivariate Statistical Analysis for Red Wine Authentication: Varietal and Vintage Year Discrimination

Molecules ◽  
2019 ◽  
Vol 24 (22) ◽  
pp. 4166 ◽  
Author(s):  
Elisabeta-Irina Geană ◽  
Corina Teodora Ciucure ◽  
Constantin Apetrei ◽  
Victoria Artem
Keyword(s):  
Discriminant Analysis ◽  
Principal Component ◽  
Total Variance ◽  
Red Wines ◽  
Linear Discriminant ◽  
Vis Spectroscopy ◽  
Ft Ir ◽  
Wine Authentication ◽  
Analytical Approaches

One of the most important issues in the wine sector and prevention of adulterations of wines are discrimination of grape varieties, geographical origin of wine, and year of vintage. In this experimental research study, UV-Vis and FT-IR spectroscopic screening analytical approaches together with chemometric pattern recognition techniques were applied and compared in addressing two wine authentication problems: discrimination of (i) varietal and (ii) year of vintage of red wines produced in the same oenological region. UV-Vis and FT-IR spectra of red wines were registered for all the samples and the principal features related to chemical composition of the samples were identified. Furthermore, for the discrimination and classification of red wines a multivariate data analysis was developed. Spectral UV-Vis and FT-IR data were reduced to a small number of principal components (PCs) using principal component analysis (PCA) and then partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were performed in order to develop qualitative classification and regression models. The first three PCs used to build the models explained 89% of the total variance in the case of UV-Vis data and 98% of the total variance for FR-IR data. PLS-DA results show that acceptable linear regression fits were observed for the varietal classification of wines based on FT-IR data. According to the obtained LDA classification rates, it can be affirmed that UV-Vis spectroscopy works better than FT-IR spectroscopy for the discrimination of red wines according to the grape variety, while classification of wines according to year of vintage was better for the LDA based FT-IR data model. A clear discrimination of aged wines (over six years) was observed. The proposed methodologies can be used as accessible tools for the wine identity assurance without the need for costly and laborious chemical analysis, which makes them more accessible to many laboratories.

Comparative non-destructive classification of partial waxy wheats using near-infrared and Raman spectroscopy

2019 ◽  
Vol 70 (5) ◽  
pp. 437 ◽  
Author(s):  
Dongli Liu ◽  
Yixuan Wu ◽  
Zongmei Gao ◽  
Yong-Huan Yun
Keyword(s):  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Triticum Aestivum L ◽  
Waxy Wheat ◽  
Waxy Proteins ◽  
Sample Set

Waxy proteins play a key role in amylose synthesis in wheat. Eight lines of common wheat (Triticum aestivum L.) carrying mutations in the three homoeologous waxy loci, Wx-A1, Wx-B1 and Wx-D1, have been classified by near-infrared (NIR) and Raman spectroscopy combined with chemometrics. Sample spectra from wheat seeds were collected by using a NIR spectrometer in the wave rage 1600–2400 nm, and then Raman spectrometer in the wave range 700–2000 cm–1. All samples were split randomly into a calibration sample set containing 284 seeds (~35 seeds per line) and a validation sample set containing the remaining 92 seeds. Classification of these samples was undertaken by discriminant analysis combined with principal component analysis (PCA) based on the raw spectra processed by appropriate pre-treatment methods. The classification results by discriminant analysis indicated that the percentage of correctly identified samples by NIR spectroscopy was 84.2% for the calibration set and 84.8% for the validation set, and by Raman spectroscopy 94.4% and 94.6%, respectively. The results demonstrated that Raman spectroscopy combined with chemometrics as a rapid method is superior to NIR spectroscopy in classifying eight partial waxy wheat lines with different waxy proteins.

Effects of chemical substitutions on the properties of azacalixphyrins: a first-principles study

2016 ◽  
Vol 18 (39) ◽  
pp. 27308-27316 ◽  
Author(s):  
Gabriel Marchand ◽  
Olivier Siri ◽  
Denis Jacquemin
Keyword(s):  
First Principles ◽  
Near Infrared ◽  
Infrared Region ◽  
First Principles Study ◽  
Near Infrared Region ◽  
The Impact

Azacalixphyrins are particularly stable macrocycles absorbing light up to the near infrared region, and we present here the first investigation of the impact of substitutions on their properties.

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