scholarly journals Quantitative Detection of Components in Polymer-Bonded Explosives through Near-Infrared Spectroscopy with Partial Least Square Regression

ACS Omega ◽  
2021 ◽  
Vol 6 (36) ◽  
pp. 23163-23169
Author(s):  
Pengfei Su ◽  
Wenhao Liang ◽  
Gao Zhang ◽  
Xiaoyan Wen ◽  
Hai Chang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Quantitative Detection ◽  
Least Square Regression ◽  
Polymer Bonded Explosives

Determination of routine chemicals, physical indices and macromolecular substances in reconstituted tobacco using near infrared spectroscopy combined with sample set partitioning

2020 ◽  
Vol 28 (3) ◽  
pp. 153-162
Author(s):  
Lijun Wu ◽  
Baoxing Wang ◽  
Lei Zhang ◽  
Rumin Duan ◽  
Rui Gao ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Set Partitioning ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression ◽  
Sample Set

Near infrared spectroscopy coupled with sample set partitioning based on joint X-Y distances combined with partial least square regression was applied to the quantitative analysis of six routine chemicals, five physical indices and four macromolecular substances in reconstituted tobacco. The quantitative regression models of these indices were established by joint X-Y distances combined with partial least square regression. Results showed remarkable correlation between predicted and measured values of the 15 indices. The root mean square error of prediction of all the indices was low, and the correlation coefficients of these PLS models were all greater than 0.85. This was the first study in which NIR spectroscopy had been used to determine the macromolecular substances as well as certain physical indices in reconstituted tobacco. Results showed that this method could be feasibly applied for rapid detection of these properties of industrial products.

Near-Infrared Spectroscopy for Discrimination of Charcoal from Eucalyptus and Native Cerrado Species—Contribution to a Database for Forestry Supervision

2021 ◽  
Author(s):  
Silvana Nisgoski ◽  
Thaís A P Gonçalves ◽  
Júlia Sonsin-Oliveira ◽  
Adriano W Ballarin ◽  
Graciela I B Muñiz
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
New Technologies ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Eucalyptus Species ◽  
Linear Discriminant

Abstract The illegal charcoal trade is an internationally well-known forest crime. In Brazil, government agents try to control it using the document of forest origin (DOF). To confirm a load’s legality, the agents must compare it with the declared content of the DOF. However, to identify charcoal is difficult even for specialists in wood anatomy. Hence, new technologies would facilitate the agents’ work. Near-infrared spectroscopy (NIR) provides a rapid and precise response to differentiate carbonized species. Considering the rich Brazilian flora, NIR studies are still underdeveloped. Our work aimed to differentiate charcoals of seven eucalypts and 10 Cerrado species based on NIR analysis and to add information to a charcoal database. Data were collected with a spectrophotometer in reflectance mode. Partial least square regression with discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA) was applied to confirm the performance and potential of NIR spectra to distinguish native Cerrado species from eucalyptus species. Wavenumbers from 4,000 to 6,000 cm−1 and transversal surface presented the best results. NIR had the potential to distinguish eucalypt charcoals from Cerrado species and in comparison to reference samples. NIR is a potential tool for forestry supervision to guarantee the sustainability of the charcoal supply in Brazil and countries with similar conditions. Study Implications It is a challenge to protect the Cerrado biome against deforestation for charcoal production. The application of new technologies such as near-infrared spectroscopy (NIR) for charcoal identification might improve the work of government agents. In this article, we studied the spectra of Cerrado and eucalypt species. Our results present good separation between the analyzed groups. The main goal is to develop a reliable NIR database that would be useful in the practical work of agents. The database will be available for all control agencies, and future training will be done for a rapid initial evaluation in the field.

scholarly journals Detection of malaria in insectary-reared Anopheles gambiae using near-infrared spectroscopy

2019 ◽  
Author(s):  
Marta F. Maia ◽  
Melissa Kapulu ◽  
Michelle Muthui ◽  
Martin G. Wagah ◽  
Heather M. Ferguson ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Anopheles Gambiae ◽  
Near Infrared Spectroscopy ◽  
Large Scale ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Human Malaria ◽  
The Impact

AbstractLarge-scale surveillance of mosquito populations is crucial to assess the intensity of vector-borne disease transmission and the impact of control interventions. However, there is a lack of accurate, cost-effective and high-throughput tools for mass-screening of vectors. This study demonstrates proof-of-concept that near-infrared spectroscopy (NIRS) is capable of rapidly identifying laboratory strains of human malaria infection in African mosquito vectors. By using partial least square regression models based on malaria-infected and uninfected Anopheles gambiae mosquitoes, we showed that NIRS can detect oocyst- and sporozoite-stage Plasmodium falciparum infections with 88% and 95% accuracy, respectively. Accurate, low-cost, reagent-free screening of mosquito populations enabled by NIRS could revolutionize surveillance and elimination strategies for the most important human malaria parasite in its primary African vector species. Further research is needed to evaluate how the method performs in the field following adjustments in the training datasets to include data from wild-caught infected and uninfected mosquitoes.

scholarly journals Multivariate method for prediction of fumonisins B1 and B2 and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR)

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244957
Author(s):  
Denize Tyska ◽  
Adriano Olnei Mallmann ◽  
Juliano Kobs Vidal ◽  
Carlos Alberto Araújo de Almeida ◽  
Luciane Tourem Gressler ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
External Validation ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Contamination Level ◽  
Internal Validation ◽  
Significant Difference

Fumonisins (FBs) and zearalenone (ZEN) are mycotoxins which occur naturally in grains and cereals, especially maize, causing negative effects on animals and humans. Along with the need for constant monitoring, there is a growing demand for rapid, non-destructive methods. Among these, Near Infrared Spectroscopy (NIR) has made great headway for being an easy-to-use technology. NIR was applied in the present research to quantify the contamination level of total FBs, i.e., fumonisin B1+fumonisin B2 (FB1+FB2), and ZEN in Brazilian maize. From a total of six hundred and seventy-six samples, 236 were analyzed for FBs and 440 for ZEN. Three regression models were defined: one with 18 principal components (PCs) for FB1, one with 10 PCs for FB2, and one with 7 PCs for ZEN. Partial least square regression algorithm with full cross-validation was applied as internal validation. External validation was performed with 200 unknown samples (100 for FBs and 100 for ZEN). Correlation coefficient (R), determination coefficient (R2), root mean square error of prediction (RMSEP), standard error of prediction (SEP) and residual prediction deviation (RPD) for FBs and ZEN were, respectively: 0.809 and 0.991; 0.899 and 0.984; 659 and 69.4; 682 and 69.8; and 3.33 and 2.71. No significant difference was observed between predicted values using NIR and reference values obtained by Liquid Chromatography Coupled to Tandem Mass Spectrometry (LC-MS/MS), thus indicating the suitability of NIR to rapidly analyze a large numbers of maize samples for FBs and ZEN contamination. The external validation confirmed a fair potential of the model in predicting FB1+FB2 and ZEN concentration. This is the first study providing scientific knowledge on the determination of FBs and ZEN in Brazilian maize samples using NIR, which is confirmed as a reliable alternative methodology for the analysis of such toxins.

scholarly journals Rapid Assessment of Fresh Beef Spoilage Using Portable Near-Infrared Spectroscopy

2021 ◽  
Vol 9 (3) ◽  
pp. 79-86
Author(s):  
Akeme Cyril Njume ◽  
Y. Aris Purwanto ◽  
Dewi Apri Astuti ◽  
Slamet Widodo
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Rapid Assessment ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Robust Model ◽  
Acceptable Model ◽  
Assess Quality

The objective of this study was to develop a prediction model to assess fresh beef spoilage directly with the use of a portable near-infrared spectroscopy (NIRS), without conducting a chemical method. Three fresh beef samples were bought from a slaughterhouse and traditional market on separate days. Spectra were acquired using a portable Scio spectrometer with wavelength 740-1070 nm, and two-third was used for calibration sets and one-third for validation sets. Partial least square regression and cross-validation were used to develop a model and equation for predicting beef spoilage. The changes observed were changed in color, water loss, and muscle hardness. The best predictive model was obtained from the original spectra (no pre-process) results as follows (R2C = 0.9, Rp = 0.86, SEC = 0.61, SEP = 0.69 and RPD = 3.53). Multiple Scattered Correlation (MSC) pre-processing method gave a good and acceptable model with results as follows; Rc = 0.89, SEC = 0.66, SEP = 0.83 and RPD = 2.91. NIRS showed variability of the samples and rate of spoilage, hence, can be used to assess quality and safety. Further studies are needed to develop a robust model to predict fresh beef spoilage using a portable NIRS Scio.

scholarly journals Proximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS)

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8619
Author(s):  
Isadora Kaline Camelo Pires de Oliveira Galdino ◽  
Hévila Oliveira Salles ◽  
Karina Maria Olbrich dos Santos ◽  
Germano Veras ◽  
Flávia Carolina Alonso Buriti
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Proximate Composition ◽  
Near Infrared ◽  
Prediction Models ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Composition Analysis ◽  
Reference Methods

Background In Brazil, over the last few years there has been an increase in the production and consumption of goat cheeses. In addition, there was also a demand to create options to use the whey extracted during the production of cheeses. Whey can be used as an ingredient in the development of many products. Therefore, knowing its composition is a matter of utmost importance, considering that the reference methods of food analysis require time, trained labor and expensive reagents for its execution. Methods Goat whey samples produced in winter and summer were submitted to proximate composition analysis (moisture, total solids, ashes, proteins, fat and carbohydrates by difference) using reference methods and near infrared spectroscopy (NIRS). The spectral data was preprocessed by baseline correction and the Savitzky–Golay derivative. The models were built using Partial Least Square Regression (PLSR) with raw and preprocessed data for each dependent variable (proximate composition parameter). Results The average whey composition values obtained using the referenced methods were in accordance with the consulted literature. The composition did not differ significantly (p > 0.05) between the summer and winter whey samples. The PLSR models were made available using the following figures of merit: coefficients of determination of the calibration and prediction models (R2cal and R2pred, respectively) and the Root Mean Squared Error Calibration and Prediction (RMSEC and RMSEP, respectively). The best models used raw data for fat and protein determinations and the values obtained by NIRS for both parameters were consistent with their referenced methods. Consequently, NIRS can be used to determine fat and protein in goat whey.

scholarly journals Gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopy

2018 ◽  
Vol 192 ◽  
pp. 03021 ◽  
Author(s):  
Jetsada Posom ◽  
Jirawat phuphanutada ◽  
Ravipat Lapcharoensuk
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Ash Content ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mushroom Cultivation

The aim of this study was to use the near infrared spectroscopy for predicting the gross calorific value (GCV) and ash content (AC) of recycled sawdust from mushroom cultivation. The wavenumber was in range of 12500-4000 cm-1 with the diffuse reflection mode was used. The NIR models was established using partial least square regression (PLSR) and was validated via using full cross validation. GCV model provided the coefficient of determination (R2), root mean square error of cross validation (RMSECV), ratio of prediction to deviation (RPD), and bias of 0.90, 445 J/g, 3.19 and 4 J/g, respectively. The AC model gave the R2, RMSECV, RPD and bias of 0.83, 1.7000 %wt, 2.44 and 0.0059 %wt, respectively. For prediction of unknow samples, GCV model provided the standard error of prediction (SEP) and bias of 670 J/g and -654 J/g, respectively. The AC model gave the SEP and bias of 1.84 %wt and 0.912 %wt, respectively. The result represented that the GCV and AC model probably used as the rapid method and non-destructive method.

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