scholarly journals Detection of Veterinary Antimicrobial Residues in Milk through Near-Infrared Absorption Spectroscopy

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Leandro da Conceição Luiz ◽  
Maria José Valenzuela Bell ◽  
Roney Alves da Rocha ◽  
Nayara Lizandra Leal ◽  
Virgílio de Carvalho dos Anjos
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Medicinal Products ◽  
Statistical Tool ◽  
Experimental Procedure ◽  
Milk Samples ◽  
Antimicrobial Residues ◽  
Maximum Residue Limits ◽  
Different Types ◽  
Ceftiofur Hydrochloride

This study focuses on detection of antimicrobial residues in milk through Fourier transform near-infrared spectroscopy. Simulated and real samples were considered. The simulated ones take into account veterinary drugs added in milk samples in the following concentrations: enrofloxacin 100 μg/L, terramycin 100 μg/L, and penicillin 4 μg/L. The statistical tool used to discriminate the samples was the principal component analysis (PCA). Our results show that, with this experimental procedure, it is possible to discriminate different types of antimicrobials dissolved in milk. Moreover, the methodology was able to detect real sample milked on different days after the injection of ceftiofur hydrochloride which is in principle a zero grace period antimicrobial. The methodology proved to be fast and accurate within the maximum residue limits allowed by European Agency for Medicinal Products and Ministry of Agriculture Livestock and Food Supply from Brazil.

scholarly journals Separation of Cultivars of Soybeans by Chemometric Methods Using Near Infrared Spectroscopy

2018 ◽  
Vol 10 (4) ◽  
pp. 351
Author(s):  
João S. Panero ◽  
Henrique E. B. da Silva ◽  
Pedro S. Panero ◽  
Oscar J. Smiderle ◽  
Francisco S. Panero ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Relevant Information ◽  
Whole Grains ◽  
Signal Correction ◽  
Different Types ◽  
Linear Fit

Near Infrared (NIR) Spectroscopy technique combined with chemometrics methods were used to group and identify samples of different soy cultivars. Spectral data, collected in the range of 714 to 2500 nm (14000 to 4000 cm-1), were obtained from whole grains of four different soybean cultivars and were submitted to different types of pre-treatments. Chemometrics algorithms were applied to extract relevant information from the spectral data, to remove the anomalous samples and to group the samples. The best results were obtained considering the spectral range from 1900.6 to 2187.7 nm (5261.4 cm-1 to 4570.9 cm-1) and with spectral treatment using Multiplicative Signal Correction (MSC) + Baseline Correct (linear fit), what made it possible to the exploratory techniques Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to separate the cultivars. Thus, the results demonstrate that NIR spectroscopy allied with de chemometrics techniques can provide a rapid, nondestructive and reliable method to distinguish different cultivars of soybeans.

Application of visible and near-infrared spectroscopy for evaluation of ewes milk with different feeds

2019 ◽  
Vol 59 (6) ◽  
pp. 1190 ◽  
Author(s):  
A. Bahri ◽  
S. Nawar ◽  
H. Selmi ◽  
M. Amraoui ◽  
H. Rouissi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Random Forest ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Principal Component ◽  
Least Squares Regression ◽  
Random Forest Regression ◽  
Milk Samples

Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.

scholarly journals Assessing the Spectral Characteristics of Dye- and Pigment-Based Inkjet Prints by VNIR Hyperspectral Imaging

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 603
Author(s):  
Lukáš Krauz ◽  
Petr Páta ◽  
Jan Kaiser
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Spectral Characteristics ◽  
Principal Component ◽  
Hyperspectral Image Processing ◽  
Different Types ◽  
The Difference ◽  
Hyperspectral Camera

Fine art photography, paper documents, and other parts of printing that aim to keep value are searching for credible techniques and mediums suitable for long-term archiving purposes. In general, long-lasting pigment-based inks are used for archival print creation. However, they are very often replaced or forged by dye-based inks, with lower fade resistance and, therefore, lower archiving potential. Frequently, the difference between the dye- and pigment-based prints is hard to uncover. Finding a simple tool for countrified identification is, therefore, necessary. This paper assesses the spectral characteristics of dye- and pigment-based ink prints using visible near-infrared (VNIR) hyperspectral imaging. The main aim is to show the spectral differences between these ink prints using a hyperspectral camera and subsequent hyperspectral image processing. Two diverse printers were exploited for comparison, a hobby dye-based EPSON L1800 and a professional pigment-based EPSON SC-P9500. The identical prints created via these printers on three different types of photo paper were recaptured by the hyperspectral camera. The acquired pixel values were studied in terms of spectral characteristics and principal component analysis (PCA). In addition, the obtained spectral differences were quantified by the selected spectral metrics. The possible usage for print forgery detection via VNIR hyperspectral imaging is discussed in the results.

scholarly journals Ultraviolet-visible/near infrared spectroscopy and hyperspectral imaging to study the different types of raw cotton

2020 ◽  
Author(s):  
Mohammad Al Ktash ◽  
Otto Hauler ◽  
Edwin Ostertag ◽  
Marc Brecht
Keyword(s):  
Hyperspectral Imaging ◽  
Large Scale ◽  
Near Infrared ◽  
Dominant Role ◽  
Principal Component ◽  
Industrial Applications ◽  
Acquisition Time ◽  
Hydroxyl Groups ◽  
Short Acquisition Time ◽  
Different Types

Different types of raw cotton were investigated by a commercial ultraviolet-visible/near infrared (UV-Vis/NIR) spectrometer (210–2200 nm) as well as on a home-built setup for NIR hyperspectral imaging (NIR-HSI) in the range 1100–2200 nm. UV-Vis/NIR reflection spectroscopy reveals the dominant role proteins, hydrocarbons and hydroxyl groups play in the structure of cotton. NIR-HSI shows a similar result. Experimentally obtained data in combination with principal component analysis (PCA) provides a general differentiation of different cotton types. For UV-Vis/NIR spectroscopy, the first two principal components (PC) represent 82 % and 78 % of the total data variance for the UV-Vis and NIR regions, respectively. Whereas, for NIR-HSI, due to the large amount of data acquired, two methodologies for data processing were applied in low and high lateral resolution. In the first method, the average of the spectra from one sample was calculated and in the second method the spectra of each pixel were used. Both methods are able to explain ≥90 % of total variance by the first two PCs. The results show that it is possible to distinguish between different cotton types based on a few selected wavelength ranges. The combination of HSI and multivariate data analysis has a strong potential in industrial applications due to its short acquisition time and low-cost development. This study opens a novel possibility for a further development of this technique towards real large-scale processes.

A Study on Utilization of Three-Dimensional Sensor Lip Image for Developing a Pronunciation Recognition System

2019 ◽  
Vol 63 (5) ◽  
pp. 50402-1-50402-9 ◽  
Author(s):  
Ing-Jr Ding ◽  
Chong-Min Ruan
Keyword(s):  
Principal Component Analysis ◽  
Automatic Speech Recognition ◽  
Feature Fusion ◽  
Three Dimensional ◽  
Principal Component ◽  
Recognition System ◽  
Geometrical Characteristics ◽  
3D Geometry ◽  
Different Types ◽  
The Disabled

Abstract The acoustic-based automatic speech recognition (ASR) technique has been a matured technique and widely seen to be used in numerous applications. However, acoustic-based ASR will not maintain a standard performance for the disabled group with an abnormal face, that is atypical eye or mouth geometrical characteristics. For governing this problem, this article develops a three-dimensional (3D) sensor lip image based pronunciation recognition system where the 3D sensor is efficiently used to acquire the action variations of the lip shapes of the pronunciation action from a speaker. In this work, two different types of 3D lip features for pronunciation recognition are presented, 3D-(x, y, z) coordinate lip feature and 3D geometry lip feature parameters. For the 3D-(x, y, z) coordinate lip feature design, 18 location points, each of which has 3D-sized coordinates, around the outer and inner lips are properly defined. In the design of 3D geometry lip features, eight types of features considering the geometrical space characteristics of the inner lip are developed. In addition, feature fusion to combine both 3D-(x, y, z) coordinate and 3D geometry lip features is further considered. The presented 3D sensor lip image based feature evaluated the performance and effectiveness using the principal component analysis based classification calculation approach. Experimental results on pronunciation recognition of two different datasets, Mandarin syllables and Mandarin phrases, demonstrate the competitive performance of the presented 3D sensor lip image based pronunciation recognition system.

scholarly journals Portable near Infrared Spectroscopy as a Tool for Fresh Tomato Quality Control Analysis in the Field

2021 ◽  
Vol 11 (7) ◽  
pp. 3209
Author(s):  
Karla R. Borba ◽  
Didem P. Aykas ◽  
Maria I. Milani ◽  
Luiz A. Colnago ◽  
Marcos D. Ferreira ◽  
...  
Keyword(s):  
Citric Acid ◽  
Near Infrared ◽  
Prediction Models ◽  
Quality Attributes ◽  
Internal Quality ◽  
Control Analysis ◽  
Tomato Quality ◽  
Wide Range ◽  
Different Types ◽  
Fresh Tomato

Portable spectrometers are promising tools that can be an alternative way, for various purposes, of analyzing food quality, such as monitoring in a few seconds the internal quality during fruit ripening in the field. A portable/handheld (palm-sized) near-infrared (NIR) spectrometer (Neospectra, Si-ware) with spectral range of 1295–2611 nm, equipped with a micro-electro-mechanical system (MEMs), was used to develop prediction models to evaluate tomato quality attributes non-destructively. Soluble solid content (SSC), fructose, glucose, titratable acidity (TA), ascorbic, and citric acid contents of different types of fresh tomatoes were analyzed with standard methods, and those values were correlated to spectral data by partial least squares regression (PLSR). Fresh tomato samples were obtained in 2018 and 2019 crops in commercial production, and four fruit types were evaluated: Roma, round, grape, and cherry tomatoes. The large variation in tomato types and having the fruits from distinct years resulted in a wide range in quality parameters enabling robust PLSR models. Results showed accurate prediction and good correlation (Rpred) for SSC = 0.87, glucose = 0.83, fructose = 0.87, ascorbic acid = 0.81, and citric acid = 0.86. Our results support the assertion that a handheld NIR spectrometer has a high potential to simultaneously determine several quality attributes of different types of tomatoes in a practical and fast way.

scholarly journals Principal component analysis tomography in near-infrared integral field spectroscopy of young stellar objects – I. Revisiting the high-mass protostar W33A

2021 ◽  
Vol 503 (1) ◽  
pp. 270-291
Author(s):  
F Navarete ◽  
A Damineli ◽  
J E Steiner ◽  
R D Blum
Keyword(s):  
Large Scale ◽  
Near Infrared ◽  
Rotation Axis ◽  
Principal Component ◽  
Young Stellar Objects ◽  
High Mass ◽  
Continuum Emission ◽  
Rotating Disc ◽  
Stellar Objects ◽  
K Band

ABSTRACT W33A is a well-known example of a high-mass young stellar object showing evidence of a circumstellar disc. We revisited the K-band NIFS/Gemini North observations of the W33A protostar using principal components analysis tomography and additional post-processing routines. Our results indicate the presence of a compact rotating disc based on the kinematics of the CO absorption features. The position–velocity diagram shows that the disc exhibits a rotation curve with velocities that rapidly decrease for radii larger than 0.1 arcsec (∼250 au) from the central source, suggesting a structure about four times more compact than previously reported. We derived a dynamical mass of 10.0$^{+4.1}_{-2.2}$ $\rm {M}_\odot$ for the ‘disc + protostar’ system, about ∼33 per cent smaller than previously reported, but still compatible with high-mass protostar status. A relatively compact H2 wind was identified at the base of the large-scale outflow of W33A, with a mean visual extinction of ∼63 mag. By taking advantage of supplementary near-infrared maps, we identified at least two other point-like objects driving extended structures in the vicinity of W33A, suggesting that multiple active protostars are located within the cloud. The closest object (Source B) was also identified in the NIFS field of view as a faint point-like object at a projected distance of ∼7000 au from W33A, powering extended K-band continuum emission detected in the same field. Another source (Source C) is driving a bipolar $\rm {H}_2$ jet aligned perpendicular to the rotation axis of W33A.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible–near-infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Elise A. Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Diagnostic Methods ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces. Methods Visible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores. Results Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion This study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

scholarly journals STATISTICAL MULTIVARIATE ANALYSIS APPLIED TO ENVIRONMENTAL CHARACTERIZATION OF SOIL IN SEMIARID REGION1

2019 ◽  
Vol 32 (1) ◽  
pp. 200-210
Author(s):  
Antônio Italcy de Oliveira Júnior ◽  
Luiz Alberto Ribeiro Mendonça ◽  
Sávio de Brito Fontenele ◽  
Adriana Oliveira Araújo ◽  
Maria Gorethe de Sousa Lima Brito
Keyword(s):  
Organic Matter Content ◽  
Principal Component ◽  
Matter Content ◽  
Multivariate Statistical ◽  
Statistical Tool ◽  
Environmental Characterization ◽  
Hierarchical Grouping ◽  
Two Factors ◽  
Total Data

ABSTRACT Soil is a dynamic and complex system that requires a considerable number of samples for analysis and research purposes. Using multivariate statistical methods, favorable conditions can be created by analyzing the samples, i.e., structural reduction and simplification of the data. The objective of this study was to use multivariate statistical analysis, including factorial analysis (FA) and hierarchical groupings, for the environmental characterization of soils in semiarid regions, considering anthropic (land use and occupation) and topographic aspects (altitude, moisture, granulometry, PR, and organic-matter content). As a case study, the São José Hydrographic Microbasin, which is located in the Cariri region of Ceará, was considered. An FA was performed using the principal component method, with normalized varimax rotation. In hierarchical grouping analysis, the “farthest neighbor” method was used as the hierarchical criterion for grouping, with the measure of dissimilarity given by the “square Euclidean distance.” The FA indicated that two factors explain 75.76% of the total data variance. In the analysis of hierarchical groupings, the samples were agglomerated in three groups with similar characteristics: one with samples collected in an area of the preserved forest and two with samples collected in areas with more anthropized soils. This indicates that the statistical tool used showed sensitivity to distinguish the most conserved soils and soils with different levels of anthropization.

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

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