scholarly journals Feasibility of UV-Vis Spectral Fingerprinting Combined with Chemometrics for Rapid Detection of Phyllanthus niruri Adulteration with Leucaena leucocephala

2021 ◽  
Vol 50 (4) ◽  
pp. 997-1006
Author(s):  
Mohamad Rafi Mohamad Rafi ◽  
Bayu Nurcahyo Bayu Nurcahyo ◽  
Wulan Tri Wahyuni ◽  
Zulhan Arif ◽  
Dewi Anggraini Septaningsih ◽  
...  
Keyword(s):  
Leucaena Leucocephala ◽  
Raw Materials ◽  
Principal Component ◽  
Phyllanthus Niruri ◽  
Standard Normal Variate ◽  
Vis Spectroscopy ◽  
Standard Normal ◽  
Materials Used ◽  
First Time ◽  
Absorbance Data

Phyllanthus niruri is widely used in Indonesia as immunostimulant. The morphology of Leucaena leucocephala leaves is similar to that of P. niruri leaves. L. leucocephala is easy to find and collect because it is widely distributed in the world. Therefore, it is likely P. niruri could be adulterated with L. leucocephala. Therefore, identification and authentication of P. niruri is important to ensure the raw materials used are original without any substitution or mixture with other similar plants causing inconsistencies in their efficacy. In this paper, we described feasibility used of UV-Vis spectral fingerprinting and chemometrics for rapid method for the identification and detection of P. niruri leaves adulterated with L. leucocephala leaves. UV-Vis spectra of samples measured in the interval of 200-800 nm and signal smoothing followed by standard normal variate were used for pre-processing the spectral data. Principal component analysis (PCA)with the absorbance data from the pre-processed UV-Vis spectra in the range of 250-700 nm as variables could distinguish P. niruri from L. leucocephala. PCA followed by discriminant analysis (DA) could successfully classified P. niruri mixed with 5, 25, and 50% L. luecocephala into their respective groups (96.81%). We also employed soft independent modelling of class analogy (SIMCA) for authentication of P. niruri and found that 88.3% of the samples were also correctly classified into their respective groups. A combination of UV-Vis spectroscopy with chemometrics, such as PCA-DA and SIMCA, were used for the first time for the identification and detection of P. niruri adulterated with L. leucocephala.

Identification Performance of Different Types of Handheld Near-Infrared (NIR) Spectrometers for the Recycling of Polymer Commodities

2018 ◽  
Vol 72 (9) ◽  
pp. 1362-1370 ◽  
Author(s):  
Hui Yan ◽  
Heinz W. Siesler
Keyword(s):  
Near Infrared ◽  
Raw Materials ◽  
Discrimination Performance ◽  
Principal Component ◽  
Identification Performance ◽  
Score Plot ◽  
Discrimination Capability ◽  
Standard Normal Variate ◽  
Correct Assignment ◽  
Standard Normal

For sustainable utilization of raw materials and environmental protection, the recycling of the most common polymers—polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polystyrene (PS)—is an extremely important issue. In the present communication, the discrimination performance of the above polymer commodities based on their near-infrared (NIR) spectra measured with four real handheld (<200 g) spectrometers based on different monochromator principles were investigated. From a total of 43 polymer samples, the diffuse reflection spectra were measured with the handheld instruments. After the original spectra were pretreated by second derivative and standard normal variate (SNV), principal component analysis (PCA) was applied and unknown samples were tested by soft independent modeling of class analogies (SIMCA). The results show that the five polymer commodities cluster in the score plots of their first three principal components (PCs) and, furthermore, samples in calibration and test sets can be correctly identified by SICMA. Thus, it was concluded that on the basis of the NIR spectra measured with the handheld spectrometers the SIMCA analysis provides a suitable analytical tool for the correct assignment of the type of polymer. Because the mean distance between clusters in the score plot reflects the discrimination capability for each polymer pair the variation of this parameter for the spectra measured with the different handheld spectrometers was used to rank the identification performance of the five polymer commodities.

Predictive evaluation of multicomponent direct compress model tablets by integrating sphere UV-Vis spectroscopy and chemometrics

2021 ◽  
pp. 1-12
Author(s):  
Yuta Otsuka ◽  
Suvra Pal
Keyword(s):  
Regression Models ◽  
Process Analytical Technology ◽  
Compaction Pressure ◽  
Principal Component ◽  
Magnesium Stearate ◽  
Dicalcium Phosphate ◽  
Integrating Sphere ◽  
Pls Regression ◽  
Standard Normal Variate ◽  
Vis Spectroscopy

BACKGROUND: Control of the pharmaceutical manufacturing process and active pharmaceutical ingredients (API) is essential to product formulation and bioavailability. OBJECTIVE: The aim of this study is to predict tablet surface API concentration by chemometrics using integrating sphere UV-Vis spectroscopy, a non-destructive and contact-free measurement method. METHODS: Riboflavin, pyridoxine hydrochloride, dicalcium phosphate anhydrate, and magnesium stearate were mixed and ground with a mortar and pestle, and 100 mg samples were subjected to direct compression at a compaction pressure of 6 MPa at 7 mm diameter. The flat surface tablets were then analyzed by integrating sphere UV-Vis spectrometry. Standard normal variate (SNV) normalization and principal component analysis were applied to evaluate the measured spectral dataset. The spectral ranges were prepared at 300–800 nm and 500–700 nm with SNV normalization. Partial least squares (PLS) regression models were constructed to predict the API concentrations based on two previous datasets. RESULTS: The regression vector of constructed PLS regression models for each API was evaluated. API concentration prediction depends on riboflavin absorbance at 550 nm and the excipient dicalcium phosphate anhydrate. CONCLUSION: Integrating sphere UV-Vis spectrometry is a useful tool to process analytical technology.

scholarly journals Tekstylia i pieczęcie. O relacjach pomiędzy produkcją włókienniczą a praktykami stemplowania w Grecji epoki brązu oraz o zupełnie nowych danych z odcisków glinianych pieczęci

2021 ◽  
Vol 69 ◽  
pp. 111-135
Author(s):  
Agata Ulanowska
Keyword(s):  
Comparative Analysis ◽  
Bronze Age ◽  
Raw Materials ◽  
Textile Production ◽  
Site Specific ◽  
Textile Manufacturing ◽  
Organic Products ◽  
Materials Used ◽  
First Time ◽  
New Evidence

This contribution discusses the evidence of textile impressions preserved on the undersides of clay sealings from Bronze Age Greece. A collection of modern casts taken from these sealings, stored in the Corpus der minoischen und mykenischen Siegel in Heidelberg, is currently being analyzed by the author. The assumed reliability of textile impressions as a source of knowledge about the qualities of actual textiles and raw materials used to produce them has been verified by a series of archaeological experiments and comparative analysis of modern raw materials of various origin. Results ofthe analysis of 199 casts from two Aegean sites: Lerna in Argolid and Phaistos on Crete, have provided new evidence for technical uses of textile and organic products in the daily storage routine and sealing practices, as well as for the specific parameters of threads, cords, and fabrics impressed on clay. Due to the relatively large number of textile imprints, it is possible, for the first time, to make site-specific comparisons of textile production on the basis of products and to tracktechnical developments in textile manufacturing throughout the Aegean Bronze Age.

scholarly journals FTIR spectroscopy for quality evaluation of sports supplements on the Polish market

2020 ◽  
pp. 177-185
Author(s):  
Krzysztof Wójcicki
Keyword(s):  
Ftir Spectroscopy ◽  
Principal Component ◽  
Ftir Spectra ◽  
Protein Supplements ◽  
Sports Supplements ◽  
Calibration And Validation ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Spectrum Region ◽  
Branched Chain

Introduction. Our study aimed to apply medium infrared (MIR/FTIR) spectroscopy to evaluate the quality of various sports supplements available in the Polish shops and gyms. Study objects and methods. The study objects included forty-eight sports supplements: whey (15 samples), branched-chain amino acids (12 samples), creatine (3 samples), mass gainers (6 samples), and pre-workouts (12 samples). First, we determined the protein quantity in individual whey supplements by the Kjeldahl method and then correlated the results with the measured FTIR spectra by chemometric methods. The principal component analysis (PCA) was used to distinguish the samples based on the measured spectra. The samples were grouped according to their chemical composition. Further, we correlated the spectra with the protein contents using the partial least squares (PLS) regression method and mathematic transformations of the FTIR spectral data. Results and discussion. The analysis of the regression models confirmed that we could use FTIR spectra to estimate the content of proteins in protein supplements. The best result was obtained in a spectrum region between 1160 and 2205 cm–1 and after the standard normal variate normalization. R2 for the calibration and validation models reached 0.85 and 0.76, respectively, meaning that the models had a good capability to predict protein content in whey supplements. The RMSE for the calibration and validation models was low (2.7% and 3.7%, respectively). Conclusion. Finally, we proved that the FTIR spectra applied together with the chemometric analysis could be used to quickly evaluate the studied products.

Provenance Study of Famous Chinese Greenware Bodies Using Principal Component Analysis

1994 ◽  
Vol 49 (7-8) ◽  
pp. 759-766
Author(s):  
C. T. Yap ◽  
Younan Hua
Keyword(s):  
Principal Component Analysis ◽  
Raw Materials ◽  
Chemical Constituents ◽  
Analytical Data ◽  
Principal Component ◽  
Black Body ◽  
Component Analysis ◽  
Provenance Studies ◽  
Southern Song ◽  
Materials Used

Abstract This work is based on published analytical data of 69 pieces of Chinese greenware bodies from seven famous wares: Yue, Longquan, Southern Song Guan, Ru, Jun, Yaozhou, and Ge. It is possible to do provenance studies of these wares by the application of principal component analysis using major and minor chemical constituents (Si02, Al2O3, Fe2O3, CaO, MgO, K2O , Na2O , and TiO2). The results show that not only are northern and southern greenwares completely distinct in the raw materials used, but in addition the southern greenwares are reasonably well segreated into groups belonging to Yue ware, Longquan ware (white body), Longquan ware (black body) and Southern Song Guan ware. Our analysis also confirms the production of Ge ware in the Longquan area.

Replacing nano-clay for kaolin in ultramarine pigments

2013 ◽  
Vol 43 (1) ◽  
pp. 1-7
Author(s):  
Vahideh Tajer Kajinebaf ◽  
Fereshteh Rezaeian ◽  
Masoud Rajabi ◽  
Saeid Baghshahi
Keyword(s):  
Raw Materials ◽  
X Ray Diffraction ◽  
Arabic Gum ◽  
Content Type ◽  
Calcined Clay ◽  
X Ray ◽  
Vis Spectroscopy ◽  
Nano Clay ◽  
Ultramarine Pigments ◽  
First Time

Purpose – Replacing nano-clay for kaolin in ultramarine pigments was investigated. The paper aims to discuss these issues. Design/methodology/approach – Ultramarine pigments with both kaolin and nano-clay were synthesized by traditional method. For this purpose, mixing of the raw materials consisted of calcined clay, sulfur, sodium hydroxide and Arabic gum was milled and then calcined at 800°C for 9 h under controlled atmosphere. The characterization was carried out by X-ray diffraction (XRD), Fourier transform infrared spectroscopy, scanning electron microscopy (SEM), UV-vis spectroscopy, colorimetery (CIELab method) and dynamic light scattering (DLS) techniques. Findings – The investigations show that using nano-clay results in richer pigments. XRD results reveal that the ultramarine phase formation is enhanced by using nano-clay. SEM and DLS results also confirm that the ultramarine pigment synthesized by using nano-clay has smaller particles than that prepared by kaolin. Originality/value – In this research, for the first time, nano-clay was substituted for kaolin to synthesized ultramarine pigment.

scholarly journals Automatic and Rapid Discrimination of Cotton Genotypes by Near Infrared Spectroscopy and Chemometrics

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Hai-Feng Cui ◽  
Zi-Hong Ye ◽  
Lu Xu ◽  
Xian-Shu Fu ◽  
Cui-Wen Fan ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Standard Normal Variate ◽  
Pattern Recognition Methods ◽  
Standard Normal ◽  
Cotton Genotypes ◽  
Rapid Discrimination

This paper reports the application of near infrared (NIR) spectroscopy and pattern recognition methods to rapid and automatic discrimination of the genotypes (parent, transgenic, and parent-transgenic hybrid) of cotton plants. Diffuse reflectance NIR spectra of representative cotton seeds (n=120) and leaves (n=123) were measured in the range of 4000–12000 cm−1. A practical problem when developing classification models is the degradation and even breakdown of models caused by outliers. Considering the high-dimensional nature and uncertainty of potential spectral outliers, robust principal component analysis (rPCA) was applied to each separate sample group to detect and exclude outliers. The influence of different data preprocessing methods on model prediction performance was also investigated. The results demonstrate that rPCA can effectively detect outliers and maintain the efficiency of discriminant analysis. Moreover, the classification accuracy can be significantly improved by second-order derivative and standard normal variate (SNV). The best partial least squares discriminant analysis (PLSDA) models obtained total classification accuracy of 100% and 97.6% for seeds and leaves, respectively.

Chinese Greenware Raw Materials: Principal Component Analysis of Major and Minor Chemical Constituents

1995 ◽  
Vol 49 (7) ◽  
pp. 981-986 ◽  
Author(s):  
Choon-Teck Yap ◽  
Younan Hua
Keyword(s):  
Principal Component Analysis ◽  
Raw Materials ◽  
Chemical Constituents ◽  
Principal Component ◽  
Component Analysis ◽  
Southern Song ◽  
Materials Used

Principal component analysis is used in the study of 24 raw materials for the production of Chinese greenware porcelains, known as Yue ware, Longquan ware, Southern Song Guan ware, Ge ware, Ru ware, Linru ware, Jun ware, and Yaozhou ware. The results of this study give reasonably good indications of the possible raw materials used in the production of these greenwares.

scholarly journals Spectroscopic analysis of chia seeds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Monica Mburu ◽  
Olivier Paquet-Durand ◽  
Bernd Hitzmann ◽  
Viktoria Zettel
Keyword(s):  
Fluorescence Spectroscopy ◽  
Fluorescence Spectra ◽  
Nir Spectroscopy ◽  
South American ◽  
Prediction Errors ◽  
Nutritional Values ◽  
Standard Normal Variate ◽  
Spectral Evaluation ◽  
Standard Normal ◽  
First Time

AbstractChia seeds are becoming more and more popular in modern diets. In this contribution NIR and 2D-fluorescence spectroscopy were used to determine their nutritional values, mainly fat and protein content. 25 samples of chia seeds were analysed, whereof 9 samples were obtained from different regions in Kenya, 16 samples were purchased in stores in Germany and originated mostly from South America. For the purchased samples the nutritional information of the package was taken in addition to the values obtained for fat and protein, which were determined at the Hohenheim Core Facility. For the first time the NIR and fluorescence spectroscopy were used for the analysis of chia. For the spectral evaluation two different pre-processing methods were tested. Baseline correction with subsequent mean-centring lead to the best results for NIR spectra whereas SNV (standard normal variate transformation) was sufficient for the evaluation of fluorescence spectra. When combining NIR and fluorescence spectra, the fluorescence spectra were also multiplied with a factor to adjust the intensity levels. The best prediction results for the evaluation of the combined spectra were obtained for Kenyan samples with prediction errors below 0.2 g/100 g. For all other samples the absolute prediction error was 0.51 g/100 g for fat and 0.62 g/100 g for protein. It is possible to determine the amount of protein and fat of chia seeds by fluorescence and NIR spectroscopy. The combination of both methods is beneficial for the predictions. Chia seeds from Kenya had similar protein and lipid contents as South American seeds.

A Chemometrics Approach for Distinguishing between Beers Using near Infrared Spectroscopy

2009 ◽  
Vol 17 (2) ◽  
pp. 69-76 ◽  
Author(s):  
Hua Li ◽  
Yutaka Takahashi ◽  
Masanori Kumagai ◽  
Kazuhiko Fujiwara ◽  
Ryoei Kikuchi ◽  
...  
Keyword(s):  
Quality Evaluation ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Total Polyphenol ◽  
Standard Normal Variate ◽  
Production Processes ◽  
Standard Normal ◽  
Akita Prefecture ◽  
Beer Production

Thirty-eight beers from different producing areas and/or makers were distinguished by principal component analysis (PCA) of the near infrared (NIR) spectra acquired by a portable NIR spectrometer. Classsification of Akita beers: beers locally produced in Akita prefecture, Japan, from other famous brand beers could be successfully performed, especially when the PCA was calculated on the standard normal variate (SNV) spectra. The classification equations use information related to water and CH2 absorption that reflected the differences in chemical com position of beers due to different production processes. In addition, the compositions of total polyphenol and total nitrogen were estimated from NIR spectra by multiple linear regression (MLR). This study showed that NIR spectroscopy is promising for beer quality evaluation, both for identifying multifarious beers including Akita beers using PCA and for rapid in-line quality control and inspection for beer production using the quantitative MLR analysis.

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