Classification of Pernambuco (Caesalpinia EchinataLam.) Wood Quality by near Infrared Spectroscopy and Linear Discriminant Analysis

2010 ◽  
Vol 18 (6) ◽  
pp. 435-442 ◽  
Author(s):  
Monica Casale ◽  
Laurence R. Schimleck ◽  
Charles Espey
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Wood Quality ◽  
Linear Discriminant

scholarly journals Classification of cereal bars using near infrared spectroscopy and linear discriminant analysis

2013 ◽  
Vol 51 (2) ◽  
pp. 924-928 ◽  
Author(s):  
Anna Luiza Bizerra Brito ◽  
Lívia Rodrigues Brito ◽  
Fernanda Araújo Honorato ◽  
Márcio José Coelho Pontes ◽  
Liliana Fátima Bezerra Lira Pontes
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Linear Discriminant ◽  
Cereal Bars

Classification of Apple Using near Infrared Spectroscopy Based on Fuzzy Discriminant Analysis

2013 ◽  
Vol 710 ◽  
pp. 524-528 ◽  
Author(s):  
Xiao Hong Wu ◽  
Xing Xing Wan ◽  
Bin Wu ◽  
Fan Wu
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
K Nearest Neighbor ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Discriminant Information

Classification of apple is an important link in postharvest commercialization processing. To realize the non-destructive, rapid and effective discrimination of apple fruits, the near infrared reflectance spectra of four varieties of apples were collected using near infrared spectroscopy, reduced by principal component analysis (PCA) and used to extract the discriminant information by linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), fuzzy discriminant analysis (FDA) and Foley-Sammon discriminant analysis. Finally k-nearest neighbor finished the classification. The classification results showed that FDA could extract the discriminant information of NIR spectra more effectively, and achieved the highest classification accuracy.

scholarly journals Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Noman Naseer ◽  
Nauman Khalid Qureshi ◽  
Farzan Majeed Noori ◽  
Keum-Shik Hong
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Continuous Wave ◽  
Mental Arithmetic ◽  
Statistical Significance ◽  
Support Vector ◽  
Functional Near Infrared Spectroscopy ◽  
Linear Discriminant

We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA),k-nearest neighbour (kNN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that thepvalues were statistically significant relative to all of the other classifiers (p< 0.005) using HbO signals.

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 Classification of blue pen ink using infrared spectroscopy and linear discriminant analysis

2013 ◽  
Vol 109 ◽  
pp. 122-127 ◽  
Author(s):  
Carolina Santos Silva ◽  
Flávia de Souza Lins Borba ◽  
Maria Fernanda Pimentel ◽  
Marcio José Coelho Pontes ◽  
Ricardo Saldanha Honorato ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Linear Discriminant

Classification of malignant gliomas by infrared spectroscopy and linear discriminant analysis

Biopolymers ◽  
2006 ◽  
Vol 82 (4) ◽  
pp. 301-305 ◽  
Author(s):  
Christoph Krafft ◽  
Katja Thümmler ◽  
Stephan B. Sobottka ◽  
Gabriele Schackert ◽  
Reiner Salzer
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Malignant Gliomas ◽  
Linear Discriminant

scholarly journals A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1586
Author(s):  
Pao Li ◽  
Xinxin Zhang ◽  
Shangke Li ◽  
Guorong Du ◽  
Liwen Jiang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
Nondestructive Method ◽  
Linear Discriminant ◽  
Outer Skin ◽  
Data Pretreatment ◽  
Pca Method

Citri Reticulatae Pericarpium (CRP), has been used in China for hundreds of years as a functional food and medicine. However, some short-age CRPs are disguised as long-age CRPs by unscrupulous businessmen in order to obtain higher profits. In this paper, a rapid and nondestructive method for the classification of different-age CRPs was established using portable near infrared spectroscopy (NIRS) in diffuse reflectance mode combination with appropriate chemometric methods. The spectra of outer skin and inner capsule of CRPs at different storage ages were obtained directly without destroying the samples. Principal component analysis (PCA) with single and combined spectral pretreatment methods was used for the classification of different-age CRPs. Furthermore, the data were pretreated with the PCA method, and Fisher linear discriminant analysis (FLD) with optimized pretreatment methods was discussed for improving the accuracy of classification. Data pretreatment methods can be used to eliminate the noise and background interference. The classification accuracy of inner capsule is better than that of outer skin data. Furthermore, the best results with 100% prediction accuracy can be obtained with FLD method, even without pretreatment.

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