Classification of amino resins and formaldehyde near infrared spectra using K-nearest neighbors

2019 ◽  
Vol 27 (5) ◽  
pp. 345-353
Author(s):  
M Gonçalves ◽  
NT Paiva ◽  
JM Ferra ◽  
J Martins ◽  
F Magalhães ◽  
...  
Keyword(s):  
Near Infrared ◽  
Urea Formaldehyde ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Nearest Neighbors ◽  
Ease Of Use ◽  
Synthesis Process ◽  
K Nearest Neighbors ◽  
Synthetic Adhesives

Amino resins are synthetic adhesives that can be divided into three major types: urea–formaldehyde (UF), melamine–urea–formaldehyde (MUF), or melamine–formaldehyde (MF). When less than 5% of melamine is added to a UF resin, the resin is called a melamine-fortified UF (mUF) resin. The extensive application of these resins in wood-based products is due to their many advantages: ease of use, strong bonding, resistance to wear and abrasion, heat resistance, and relatively low price. Several near infrared (NIR) models have been developed for this type of adhesives and have been used in industrial plants. However, the NIR spectroscopy is sensitive to the type of resin (UF, MUF, MF, or mUF) and even to the synthesis process, therefore different NIR models must be constructed per resin basis. This work presents two methods: (a) a method to distinguish the NIR spectra of formaldehyde from the NIR spectra of amino resins, and (b) a method to classify the NIR spectra of amino resins by class of resin. The method for the separation of formaldehyde from amino resins achieved 100% correct classification for the dataset used. This method was based on defining a baseline cutoff for the NIR spectra at which there were no amino resins bonds overlapping formaldehyde bonds. For the classification of amino resins, this work used the methodology of K-nearest neighbors, up to 91 neighbors, and principal component analysis, up to 10 principal components. The best classification method obtained an accuracy of 96.1% and can be used industrially to automatically select the most suitable NIR model for amino resins, helping to reduce the time taken for an NIR analysis and automatically preventing the wrong selection of NIR models by an operator.

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.

Lighting the Ivory Track: Are Near-Infrared and Chemometrics Up to the Job? A Proof of Concept

2019 ◽  
Vol 73 (7) ◽  
pp. 816-822 ◽  
Author(s):  
Aoife Power ◽  
Sandy Ingleby ◽  
James Chapman ◽  
Daniel Cozzolino
Keyword(s):  
Near Infrared ◽  
Mammalian Species ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Proof Of Concept ◽  
Classification Rate ◽  
Complementary Method ◽  
Infrared Reflection ◽  
Rapid Tool

A rapid tool to discriminate rhino horn and ivory samples from different mammalian species based on the combination of near-infrared reflection (NIR) spectroscopy and chemometrics was evaluated. In this study, samples from the Australian Museum mammalogy collection were scanned between 950 nm and 1650 nm using a handheld spectrophotometer and analyzed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). An overall correct classification rate of 73.5% was obtained for the classification of all samples. This study demonstrates the potential of NIR spectroscopy coupled with chemometrics as a means of a rapid, nondestructive classification technique of horn and ivory samples sourced from a museum. Near-infrared spectroscopy can be used as an alternative or complementary method in the detection of horn and ivory assisting in the combat of illegal trade and aiding the preservation of at-risk species.

scholarly journals Classification of chili powder (Capsicum annuum L.) antioxidant activity based on near infrared spectra

Food Research ◽  
2021 ◽  
Vol 5 (S2) ◽  
pp. 51-56
Author(s):  
Y.I. Aprilia ◽  
N. Khuriyati ◽  
A.C. Sukartiko
Keyword(s):  
Antioxidant Activity ◽  
Success Rate ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Near Infrared Spectra ◽  
Medium Level ◽  
Chili Powder ◽  
Pre Treatment

Testing the antioxidant activity of chili powder is often destructive; these methods are expensive, complicated, and lengthy analysis time. Meanwhile, information on antioxidant activity is needed by the industry to determine its quality class in rapid and uncomplicated handling. Therefore, this study was aimed to measure the antioxidant activity of chili powder and classify it into three quality classes, namely high, medium, and low, using Near Infrared (NIR) spectroscopy at spectral wavelengths of 1000-2500 nm and combined with chemometric techniques. The antioxidant activity of the sample was evaluated using the DPPH assay. Processing of the data started with outlier detection using Hotelling's T2 , then confirmed using leverage analysis and influence plot. The data were then processed with Smoothing-Savitzky Golay, SNV, and De-Trending. Principal Component Analysis (PCA) was performed for classifying the samples, which were validated with full crossvalidation. The results showed that the antioxidant activity was detected at a range of 1395 - 2390 nm. De-Trending was the best pre-treatment that successfully classified the low and high levels of antioxidant activity with a success rate of 100% and classified a medium level of antioxidant activity with a success rate of 96.97%.

scholarly journals Near-infrared spectroscopy for the distinction of wood and charcoal from Fabaceae species: comparison of ANN, KNN AND SVM models

2020 ◽  
Vol 29 (3) ◽  
pp. e020
Author(s):  
Helena Cristina Vieira ◽  
Joielan Xipaia dos Santos ◽  
Deivison Venicio Souza ◽  
Polliana D’ Angelo Rios ◽  
Graciela Inés Bolzon de Muñiz ◽  
...  
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Classification Models ◽  
K Nearest Neighbors ◽  
Araucaria Forest ◽  
Vector Machines ◽  
Wood Analysis ◽  
Artificial Neural Network Ann

Aim of study: The objective of this work was to evaluate the potential of NIR spectroscopy to differentiate Fabaceae species native to Araucaria forest fragments.Area of study; Trees of the evaluated species were collected from an Araucaria forest stand in the state of Santa Catarina, southern Brazil, in the region to be flooded by the São Roque hydroelectric project.Material and methods: Discs of three species (Inga vera, Machaerium paraguariense and Muellera campestris) were collected at 1.30 meters from the ground. They were sectioned to cover radial variation of the wood (regions near bark, intermediate and near pith). After wood analysis, the same samples were carbonized. Six spectra were obtained from each specimen of wood and charcoal. The original and second derivative spectra, principal component statistics and classification models (Artificial Neural Network: ANN, Support Vector Machines with kernel radial basis function: SVM and k-Nearest Neighbors: k-NN) were investigated.Main results: Visual analysis of spectra was not efficient for species differentiation, so three NIR classification models for species discrimination were tested. The best results were obtained with the use of k-NN for both wood and charcoal and ANN for wood analysis. In all situations, second derivative NIR spectra produced better results.Research highlights: Correct discrimination of wood and charcoal species for control of illegal logging was achieved. Fabaceae species in an Araucaria forest stand were correctly identified.Keywords: Araucaria forest; identification of species; classification models.Abbreviations used: Near infrared: NIR, Lages Herbarium of Santa Catarina State University: LUSC, Principal component analysis: PCA, artificial neural network: ANN, support vector machines with kernel radial basis function: SVM, k-nearest neighbors: k-NN.

Classification of Apple Varieties Using FT-NIR Spectroscopy and Possibilistic Learning Vector Quantization

2014 ◽  
Vol 644-650 ◽  
pp. 1405-1408
Author(s):  
Bin Wu ◽  
Xiang Li ◽  
Sheng Wei Qiu ◽  
Xiao Hong Wu ◽  
Min Li
Keyword(s):  
Vector Quantization ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Learning Vector Quantization ◽  
High Dimensional ◽  
Fcm Clustering ◽  
Sensitivity Problem ◽  
Fuzzy Learning

Red Fuji, Huaniu and Gala were classified by Fourier transform near infrared (FT-NIR) spectroscopy and possibilistic learning vector quantization (PLVQ) which was proposed to solve the noise sensitivity problem of fuzzy learning vector quantization (PLVQ). Firstly, apple NIR spectra were measured by FT-NIR spectrophotometer. Secondly, principal component analysis (PCA) was used to compress the dimensionality of NIR spectra which was high dimensional. Thirdly, fuzzy c-means (FCM) clustering was run to termination to obtain the cluster vectors for PLVQ. Finally, PLVQ was performed to classify the data. Experimental results showed that this classification method was fast, nondestructive and effective for classifying the variety of apples.

scholarly journals Classification of softwoods using wood extract information and near infrared spectroscopy

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 5301-5312
Author(s):  
Se-Yeong Park ◽  
Jong-Hwa Kim ◽  
Jong-Chan Kim ◽  
Sang-Yun Yang ◽  
Hwanmyeong Yeo ◽  
...  
Keyword(s):  
Wood Species ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Radiata Pine ◽  
Korean Pine ◽  
Layer Chromatography ◽  
Extract Information ◽  
Alcohol Benzene

Three kinds of softwoods (Douglas fir, radiata pine, and Sugi) were used to test the possibility of their classification via near infrared (NIR) spectroscopy. In a previous study, the authors presented that the content of Korean softwood extractives (larix, red pine, Korean pine, cedar, and cypress) influenced wood classification. For expanding the extent of wood species to be considered in the analysis, three foreign wood species were newly introduced. Prior to comparing the NIR spectra obtained from the three softwoods, principal component analysis (PCA) was conducted to evaluate the possibility of discriminating the three foreign softwoods. The three species were also divided into three groups based on PCA, and a thin-layer chromatography (TLC) test improved the reliability of NIR-based wood classification via extractive contents. A similar pattern was obtained for alcohol-benzene eluted extractive compounds between same wood species.

Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning

2018 ◽  
Vol 72 (12) ◽  
pp. 1774-1780 ◽  
Author(s):  
Irene Marivel Nolasco Perez ◽  
Amanda Teixeira Badaró ◽  
Sylvio Barbon ◽  
Ana Paula AC Barbon ◽  
Marise Aparecida Rodrigues Pollonio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Support Vector ◽  
Processing Industry ◽  
Chemical Attributes ◽  
Physical And Chemical

Identification of different chicken parts using portable equipment could provide useful information for the processing industry and also for authentication purposes. Traditionally, physical–chemical analysis could deal with this task, but some disadvantages arise such as time constraints and requirements of chemicals. Recently, near-infrared (NIR) spectroscopy and machine learning (ML) techniques have been widely used to obtain a rapid, noninvasive, and precise characterization of biological samples. This study aims at classifying chicken parts (breasts, thighs, and drumstick) using portable NIR equipment combined with ML algorithms. Physical and chemical attributes (pH and L*a*b* color features) and chemical composition (protein, fat, moisture, and ash) were determined for each sample. Spectral information was acquired using a portable NIR spectrophotometer within the range 900–1700 nm and principal component analysis was used as screening approach. Support vector machine and random forest algorithms were compared for chicken meat classification. Results confirmed the possibility of differentiating breast samples from thighs and drumstick with 98.8% accuracy. The results showed the potential of using a NIR portable spectrophotometer combined with a ML approach for differentiation of chicken parts in the processing industry.

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

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.

Sign in / Sign up

Export Citation Format

Share Document