Application of Multiple Classifier Fusion in the Discriminant Analysis of near Infrared Spectroscopy for Agricultural Products

2016 ◽  
Vol 24 (4) ◽  
pp. 363-372 ◽  
Author(s):  
Lili Luan ◽  
Yuheng Wang ◽  
Xueying Li ◽  
Wenyan Hu ◽  
Kai Li ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Agricultural Products ◽  
Classifier Fusion ◽  
Multiple Classifier

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.

Non-destructive screening method for detecting the presence of insects in sorghum grains using near infrared spectroscopy and discriminant analysis

2019 ◽  
Vol 149 ◽  
pp. 104057 ◽  
Author(s):  
Poliana Macedo Santos ◽  
Maria Lúcia Ferreira Simeone ◽  
Marco Aurélio Guerra Pimentel ◽  
Marcelo Martins Sena
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Screening Method ◽  
Sorghum Grains ◽  
Non Destructive

scholarly journals Use of near infrared spectroscopy and chemometrics to evaluate the shelf-life of cloudy sonicated apple juice

2019 ◽  
Vol 27 (1) ◽  
pp. 75-85 ◽  
Author(s):  
Lorenzo Serva ◽  
Stefania Balzan ◽  
Vittoria Bisutti ◽  
Filomena Montemurro ◽  
Giorgio Marchesini ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Shelf Life ◽  
Near Infrared Spectroscopy ◽  
Storage Time ◽  
Apple Juice ◽  
Near Infrared ◽  
Partial Least Square ◽  
Feasibility Trial ◽  
End Point

Fresh products, such as cloudy apple juice, could be preserved from early spoilage through the application of non-thermal processes such as sonication. However, shelf-life analyses based on microbiological and sensory evaluations are expensive and time consuming. Few studies have applied near infrared spectroscopy to evaluate the quality and decay of apple juices. Here, a feasibility trial was conducted to study the spectral behaviour at 1300–2500 nm combined with chemometric approaches. The shelf-life was monitored during two experiments, a challenge test with juices inoculated with spoilage yeasts (inoculated non-sonicated (INS)) and then submitted to sonication treatments (inoculated sonicated (IS)), and a storage test to evaluate the spoilage on non-inoculated juices (non-inoculated non-sonicated (NINS)) and sonicated non-inoculated juices (non-inoculated sonicated (NIS)). These experiments were investigated at six different refrigeration times 7, 14, 21, 28 and 60 days. Two functions were modelled to describe the behaviours of the first principal component according to the storage time. In agreement with a previous chemical and sensory evaluation, this approach allowed us to highlight shelf-life end points of 7 and 14 days for non-sonicated and sonicated samples, respectively. Three different models were evaluated for classification purposes: (1) sonicated versus non-treated samples, (2) end-point shelf-life evaluation at seven days for the NINS and INS juices and (3) end-point shelf-life discrimination at 14 days for IS and NIS samples. A partial least square-discriminant analysis enabled a group classification with accuracy values ranging from 0.63 to 1.00. The application of a variable importance in projection index to interpret the wavelengths of the spectral features suggests a contribution of organic acids and lipids to the prediction of decay. A canonical discriminant analysis provided a clearer separation of samples according to the storage time, especially in relation to the two time thresholds of 7 and 14 days.

Discriminant analysis of Mediterranean pine nuts ( Pinus pinea L.) from Chilean plantations by near infrared spectroscopy (NIRS)

Food Control ◽  
2017 ◽  
Vol 73 ◽  
pp. 634-643 ◽  
Author(s):  
Verónica Loewe ◽  
Rafael Maria Navarro-Cerrillo ◽  
Juan García-Olmo ◽  
Cecilia Riccioli ◽  
Rafael Sánchez-Cuesta
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Pinus Pinea ◽  
Mediterranean Pine
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