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.

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.

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.

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.

Identification of textiles by handheld near infrared spectroscopy: Protecting customers against product counterfeiting

2018 ◽  
Vol 26 (5) ◽  
pp. 311-321 ◽  
Author(s):  
Hui Yan ◽  
Heinz W Siesler
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Discrimination Performance ◽  
Rapid Test ◽  
Principal Component ◽  
Component Analysis ◽  
Textile Materials ◽  
Discrimination Capability ◽  
Near Infrared Spectra

Textiles are extremely important materials for everyday life with a broad range of applications and properties. Due to the large variations in quality on the one hand and the increasing quality awareness and price consciousness of customers on the other hand, the availability of a simple tool for a rapid test of the correct identity of the purchased textile article would be a significant progress in customer protection. Miniaturization of near infrared spectrometers has advanced to the point where handheld instruments could provide reliable and affordable means to serve this purpose. One objective of the present communication was to scrutinize the identification and discrimination performance for textile materials for four real-handheld (<200 g) near infrared spectrometers based on different monochromator principles. The second focus was to show that in the near future these handheld instruments can be used by a non-expert user community to protect themselves against fraud in textile purchase situations. For this purpose, diffuse reflection spectra of 72 textile samples of synthetic and natural origin were measured. While in simple situations, test samples can readily be authenticated by visual inspection of their near infrared spectra only, for a more comprehensive identification of unknown samples principal component analysis in combination with soft independent modeling of class analogies was applied. In the present work, this approach provided a suitable analytical tool for the correct assignment of the investigated different types of textile materials. Moreover, the evaluation of the mean Euclidian distances in the principal component analysis score plots derived from the near infrared spectra of the textile classes under investigation allowed to compare the identification performance and discrimination capability of the different handheld instruments.

scholarly journals Machine Learning-Based Prediction of Selected Parameters of Commercial Biomass Pellets Using Line Scan Near Infrared-Hyperspectral Image

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 316
Author(s):  
Lakkana Pitak ◽  
Kittipong Laloon ◽  
Seree Wongpichet ◽  
Panmanas Sirisomboon ◽  
Jetsada Posom
Keyword(s):  
Near Infrared ◽  
Hyperspectral Image ◽  
Thermal Conversion ◽  
High Energy ◽  
Volatile Matter ◽  
Successive Projections Algorithm ◽  
Color Distribution ◽  
Standard Normal Variate ◽  
Distribution Mapping ◽  
Standard Normal

Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R2 values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.

On the Scales Associated with Near-Infrared Reflectance Difference Spectra

1995 ◽  
Vol 49 (6) ◽  
pp. 765-772 ◽  
Author(s):  
M. S. Dhanoa ◽  
S. J. Lister ◽  
R. J. Barnes
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
The Other ◽  
Difference Spectra ◽  
Near Infrared Reflectance ◽  
Standard Normal Variate ◽  
Near Infrared Spectra ◽  
Standard Normal ◽  
Relationship Of ◽  
The Relationship

Scale differences of individual near-infrared spectra are identified when set-independent standard normal variate (SNV) and de-trend (DT) transformations are applied in either SNV followed by DT or DT then SNV order. The relationship of set-dependent multiplicative scatter correction (MSC) to SNV is also referred to. A simple correction factor is proposed to convert derived spectra from one order to the other. It is suggested that the suitable order for the study of changes using difference spectra (when removing baselines) should be DT followed by SNV, which leads to all derived spectra on the scale of mean zero and variance equal to one. If baselines are identical, then SNV scale spectra can be used to calculate differences.

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.

scholarly journals The separation of softwood and hardwood in historical wooden statues of the Nazenji-temple in Japan using NIR spectroscopy

IAWA Journal ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 740-750 ◽  
Author(s):  
Hisashi Abe ◽  
Yohei Kurata ◽  
Ken Watanabe ◽  
Atsuko Ishikawa ◽  
Shuichi Noshiro ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Forest Products ◽  
Chamaecyparis Obtusa ◽  
Score Plot ◽  
Hardwood Species ◽  
Cercidiphyllum Japonicum ◽  
Sciadopitys Verticillata ◽  
Second Derivative Spectra

Abstract The applicability of near-infrared (NIR) spectroscopy to the identification of wood species of archaeologically/historically valuable wooden artifacts in a non-invasive manner was investigated using reference wood samples from the xylarium of the Forestry and Forest Products Research Institute (TWTw) and applied to several wooden statues carved about 1000 years ago. Diffuse-reflectance NIR spectra were obtained from five standard wood samples each of five softwood species (Chamaecyparis obtusa, Cryptomeria japonica, Sciadopitys verticillata, Thujopsis dolabrata, Torreya nucifera) and five hardwood species (Aesculus turbinata, Cercidiphyllum japonicum, Cinnamomum camphora, Prunus jamasakura, Zelkova serrata). A principal component analysis (PCA) model was developed from the second derivative spectra. The score plot of the first two components clearly showed separation of the wood sample data into softwood and hardwood clusters. The developed PCA model was applied to 370 spectra collected from 21 wooden statues preserved in the Nazenji-temple in Shizuoka Prefecture in Japan, including 14 made from Torreya spp. and 7 made from Cinnamomum spp. In the score plot, the statue spectra were also divided into two clusters, corresponding to either softwood (Torreya spp.) or hardwood (Cinnnamomum spp.) species. These results show that NIR spectroscopy combined with PCA is a powerful technique for determining whether archaeologically/historically valuable wooden artifacts are made of softwood or hardwood.

Use of Difference near Infrared Reflectance Spectra to Extract Relevant Information from the Spectra of Agro–Food Products

1998 ◽  
Vol 6 (1) ◽  
pp. 89-95 ◽  
Author(s):  
Ana Garrido-Varo ◽  
Ronald Carrete ◽  
Víctor Fernández-Cabanás
Keyword(s):  
Near Infrared ◽  
Food Products ◽  
Relevant Information ◽  
Near Infrared Reflectance ◽  
Standard Normal Variate ◽  
Corn Gluten ◽  
Corn Gluten Feed ◽  
Standard Normal ◽  
Gluten Feed ◽  
By Products

This paper compares the use of log 1/ R versus standard normal variate (SNV) and Detrending (DT) transformations calculated either of two forms, SNV followed by DT (SNV+DT) or DT then SNV (DT+SNV) for their abilities to enhance interpretation of spectra and to detect areas of maximum differences in composition of two agro–food products (sunflower seed and corn) and their corresponding by-products (sunflower meal and corn gluten feed). The results obtained show that the SNV+DT and the DT+SNV transformations of the raw data make the existing chemical differences between scattering agro–food products more easily interpretable.

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