Rapid Discrimination of Apple Varieties via Near-Infrared Reflectance Spectroscopy and Fast Allied Fuzzy C-Means Clustering

2015 ◽  
Vol 11 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Xiaohong Wu ◽  
Bin Wu ◽  
Jun Sun ◽  
Min Li
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Clustering Algorithm ◽  
Near Infrared ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Fuzzy C Means ◽  
Apple Varieties ◽  
Possibilistic Clustering

Abstract Discrimination of apple varieties plays an important role in apple post-harvest commercial processing. A fast allied fuzzy c-means (FAFCM) clustering algorithm was proposed to classify the apple varieties using near-infrared reflectance (NIR) spectroscopy technology and orthogonal linear discriminant analysis (OLDA) which was used as feature extraction and dimensionality reduction method. Our classification method: the high-dimensional NIR data were reduced to three-dimensional data by OLDA at first, and the FAFCM clustering algorithm was implemented to classify the reduced data. Furthermore, the principal component analysis (PCA) and linear discriminant analysis (LDA) combined with k-nearest neighbor classifier (KNNC), fuzzy c-means (FCM) clustering and unsupervised possibilistic clustering algorithm (UPCA), formed the other four classification methods to classify apple samples in comparison with our proposed method. The experimental results showed that FAFCM achieved the best performance of classification.

Classification of freshwater fish species by linear discriminant analysis based on near infrared reflectance spectroscopy

2017 ◽  
Vol 25 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Hao Lv ◽  
Wenjie Xu ◽  
Juan You ◽  
Shanbai Xiong
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Freshwater Fish ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Infrared Reflectance Spectroscopy

Near infrared reflectance spectroscopy was used to discriminate different species of freshwater fish samples. Samples from seven freshwater fish species of the family Cyprinidae (black carp ( Mylopharyngodon piceus), grass carp ( Ctenopharyngodon idellus), silver carp ( Hypophthalmichthys molitrix), bighead carp ( Aristichthys nobilis), common carp ( Cyprinus carpio), crucian ( Carassius auratus), and bream ( Parabramis pekinensis)) were scanned by near infrared reflectance spectroscopy from 1000 nm to 1799 nm. Linear discriminant analysis models were built for the classification of species. We inspected the effect of partial least square, principal component analysis, competitive adaptive reweighted sampling, and fast Fourier transform on linear discriminant analysis. The results showed that the dimension reduction methods worked very well for this example. Linear discriminant analysis models which were combined with principal component analysis and fast Fourier transform could classify accurately all the samples under multiplicative scatter correction pre-processing. According to the loadings in principal component analysis, spectra wavelengths 1000, 1001, 1154, 1208, 1284, 1288, 1497, 1665, and 1770 nm were selected as effective wavelengths in linear discriminant analysis. The discriminant analysis was simplified by using effective wavelengths as independent variables in a linear discriminant analysis model. This study indicated that linear discriminant analysis combined with near infrared reflectance spectroscopy could be an effective strategy for the classification of freshwater fish species.

Near infrared reflectance spectrometry classification of lettuce using linear discriminant analysis

2015 ◽  
Vol 7 (5) ◽  
pp. 1890-1895 ◽  
Author(s):  
Anna Luiza Bizerra Brito ◽  
Dimitri Albuquerque Araújo ◽  
Márcio José Coelho Pontes ◽  
Liliana Fátima Bezerra Lira Pontes
Keyword(s):  
Discriminant Analysis ◽  
Variable Selection ◽  
Linear Discriminant Analysis ◽  
Near Infrared ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Reflectance Spectrometry

This study proposes a methodology for lettuce classification employing near infrared reflectance spectrometry and variable selection.

Research on the Variety Discrimination of Apple Using a Hybrid Possibilistic Clustering

2013 ◽  
Vol 710 ◽  
pp. 768-771 ◽  
Author(s):  
Xiao Hong Wu ◽  
Tong Xiang Cai ◽  
Bin Wu ◽  
Jun Sun
Keyword(s):  
Clustering Algorithm ◽  
Near Infrared ◽  
Learning Algorithm ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Simple Computation ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Possibilistic Clustering ◽  
Discriminant Information

Near infrared reflectance (NIR) spectroscopy has been used to obtain NIR spectra of two varieties of apple samples. The dimensionality of NIR spectra was reduced by principal component analysis (PCA), and discriminant information was extracted by linear discriminant analysis (LDA). Last, a hybrid possibilistic clustering algorithm (HPCA) was utilized as classifier to discriminate the apple samples of different varieties. HPCA integrates possibilistic clustering algorithm (PCA) and improved possibilistic c-means (IPCM) clustering algorithm, and produces not only the membership values but also typicality values by simple computation of the sample co-variance. Experimental results showed that HPCA, as an unsupervised learning algorithm, could quickly and easily discriminate the apple varieties.

Evaluation of the bony repair in rat cranial defect using near infrared reflectance spectroscopy and discriminant analysis

2017 ◽  
Vol 33 (4) ◽  
pp. 1160-1168 ◽  
Author(s):  
Leomir A. S. de Lima ◽  
Kássio M. G. Lima ◽  
Lana S. S. de Oliveira ◽  
Aurigena A. Araújo ◽  
Raimundo Fernandes de Araújo Junior
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Cranial Defect

scholarly journals Evaluation of Visible-Near Infrared Reflectance Spectra of Avocado Leaves as a Non-destructive Sensing Tool for Detection of Laurel Wilt

Plant Disease ◽  
2012 ◽  
Vol 96 (11) ◽  
pp. 1683-1689 ◽  
Author(s):  
Sindhuja Sankaran ◽  
Reza Ehsani ◽  
Sharon A. Inch ◽  
Randy C. Ploetz
Keyword(s):  
Discriminant Analysis ◽  
Vegetation Index ◽  
Near Infrared ◽  
Principal Component ◽  
Persea Americana ◽  
Near Infrared Reflectance ◽  
Laurel Wilt ◽  
Linear Discriminant ◽  
Raffaelea Lauricola ◽  
Non Destructive

Laurel wilt, caused by the fungus Raffaelea lauricola, affects the growth, development, and productivity of avocado, Persea americana. This study evaluated the potential of visible-near infrared spectroscopy for non-destructive sensing of this disease. The symptoms of laurel wilt are visually similar to those caused by freeze damage (leaf necrosis). In this work, we performed classification studies with visible-near infrared spectra of asymptomatic and symptomatic leaves from infected plants, as well as leaves from freeze-damaged and healthy plants, both of which were non-infected. The principal component scores computed from principal component analysis were used as input features in four classifiers: linear discriminant analysis, quadratic discriminant analysis (QDA), Naïve-Bayes classifier, and bagged decision trees (BDT). Among the classifiers, QDA and BDT resulted in classification accuracies of higher than 94% when classifying asymptomatic leaves from infected plants. All of the classifiers were able to discriminate symptomatic-infected leaves from freeze-damaged leaves. However, the false negatives mainly resulted from asymptomatic-infected leaves being classified as healthy. Analyses of average vegetation indices of freeze-damaged, healthy (non-infected), asymptomatic-infected, and symptomatic-infected leaves indicated that the normalized difference vegetation index and the simple ratio index were statistically different.

scholarly journals Discrimination of E uterpe oleracea  Mart. (Açaí) and E uterpe edulis  Mart. (Juçara) Intact Fruit Using Near-Infrared (NIR) Spectroscopy and Linear Discriminant Analysis

2015 ◽  
Vol 39 (6) ◽  
pp. 2856-2865 ◽  
Author(s):  
Yara Gurgel Dall' Acqua ◽  
Luis Carlos Cunha Júnior ◽  
Viviani Nardini ◽  
Valquira Garcia Lopes ◽  
José Dalton da Cruz Pessoa ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Linear Discriminant

Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef

2021 ◽  
pp. 096703352110495
Author(s):  
Cassius EO Coombs ◽  
Robert R Liddle ◽  
Luciano A González
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Consumer Confidence ◽  
Spectroscopic Methods ◽  
Marbling Score ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Beef Steak

The present study analysed the ability for portable near infrared reflectance (NIR) and Raman spectroscopy sensors to differentiate between grass-fed and grain-fed beef. Scans were made on lean and fat surfaces of 108 beef steak samples labelled as grass-fed ( n = 54) and grain-fed ( n = 54), with partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) used to develop discrimination models which were tested on independent datasets. Furthermore, PLS-DA was used to predict visual marbling score and days on feed (DOF). The NIR spectra accurately discriminated between grass- and grain-fed beef on both fat (91.7%, n = 92) and lean (88.5%, n = 96), as did Raman (fat 95.2%, n = 82; lean 69.6%, n = 68). Fat scanning using NIR spectroscopy moderately predicted DOF (r2val = 0.53), though Raman and NIR spectroscopy lean prediction models for DOF and marbling were less precise (r2val < 0.50). It can be concluded that portable NIR and Raman spectrometers can be used successfully to differentiate grass-fed from grain-fed beef and therefore aid retail and consumer confidence.

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