Fast inline tobacco classification by near-infrared hyperspectral imaging and support vector machine-discriminant analysis

2019 ◽  
Vol 11 (14) ◽  
pp. 1966-1975 ◽  
Author(s):  
Marcelo C. A. Marcelo ◽  
Frederico L. F. Soares ◽  
Jorge A. Ardila ◽  
Jailson C. Dias ◽  
Ricardo Pedó ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Classification Systems ◽  
Green Leaf ◽  
Support Vector ◽  
Chemical Characteristics ◽  
Tobacco Leaves

Classification systems are frequently used in tobacco Green Leaf Threshing (GLT) facilities to assess the chemical characteristics and quality of tobacco leaves.

Klasifikasi Anjing dan Kucing menggunakan Algoritma Linear Discriminant Analysis dan Support Vector Machine

2019 ◽  
Vol 11 (1) ◽  
pp. 46-51
Author(s):  
Alethea Suryadibrata ◽  
Suryadi Darmawan Salim
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Classification Systems ◽  
Training Data ◽  
Support Vector ◽  
Linear Discriminant ◽  
Medical Needs ◽  
Testing Data

One of the factors driving technological development is the increase in computers ability to complete various jobs. One of them is doing image processing, which is widely used in our daily life, such as the use of fingerprints, face/iris recognition barcodes, medical needs, and various other uses. Classification is one of the applications of image processing that is used the most. One algorithm that can be used for the development of image classification systems is Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). LDA is a feature extraction algorithm to find a subspace that separates classes well. SVM is a classification algorithm, based on the idea of finding a hyperplane that best divides a dataset into classes. In this study, LDA and SVM algorithms were tested on the dog and cat classification system, with the highest F- score calculation results being 0.69 with 200 training data and 50 testing data for cats and 0.64 with 200 training data and 30 testing data for dogs.

Hyperspectral Imaging and Support Vector Machine: A Powerful Combination to Differentiate Black Cohosh (Actaea racemosa) from Other Cohosh Species

Planta Medica ◽  
2017 ◽  
Vol 84 (06/07) ◽  
pp. 407-419 ◽  
Author(s):  
Sidonie Tankeu ◽  
Ilze Vermaak ◽  
Weiyang Chen ◽  
Maxleene Sandasi ◽  
Guy Kamatou ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Black Cohosh ◽  
Raw Material ◽  
Support Vector ◽  
Traditional Use ◽  
Data Set ◽  
Commercial Products ◽  
Actaea Racemosa

Abstract Actaea racemosa (black cohosh) has a history of traditional use in the treatment of general gynecological problems. However, the plant is known to be vulnerable to adulteration with other cohosh species. This study evaluated the use of shortwave infrared hyperspectral imaging (SWIR-HSI) in tandem with chemometric data analysis as a fast alternative method for the discrimination of four cohosh species (Actaea racemosa, Actaea podocarpa, Actaea pachypoda, Actaea cimicifuga) and 36 commercial products labelled as black cohosh. The raw material and commercial products were analyzed using SWIR-HSI and ultra-high-performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) followed by chemometric modeling. From SWIR-HSI data (920 – 2514 nm), the range containing the discriminating information of the four species was identified as 1204 – 1480 nm using Matlab software. After reduction of the data set range, partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) models with coefficients of determination (R2 ) of ≥ 0.8 were created. The novel SVM-DA model showed better predictions and was used to predict the commercial product content. Seven out of 36 commercial products were recognized by the SVM-DA model as being true black cohosh while 29 products indicated adulteration. Analysis of the UHPLC-MS data demonstrated that six commercial products could be authentic black cohosh. This was confirmed using the fragmentation patterns of three black cohosh markers (cimiracemoside C; 12-β,21-dihydroxycimigenol-3-O-L-arabinoside; and 24-O-acetylhydroshengmanol-3-O-β-D-xylopyranoside). SWIR-HSI in conjunction with chemometric tools (SVM-DA) could identify 80% adulteration of commercial products labelled as black cohosh.

Identification of Wine Grape Varieties Based on Near-infrared Hyperspectral Imaging

2019 ◽  
Vol 35 (6) ◽  
pp. 959-967
Author(s):  
Xu Liu ◽  
Enyu Zhang
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Spectral Data ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Variety Identification ◽  
Support Vector ◽  
Wine Grape ◽  
Full Spectrum ◽  
Grape Varieties

Abstract. Wine grape variety is one of the main determinants of wine quality. The objective of this study is to explore the feasibility of using hyperspectral imaging (HSI) to identify six red and six white wine grape cultivars during the ripening period. Abnormal spectral data were removed by the Mahalanobis distance, and six different methods were employed to preprocess the spectral data. Next, the effective wavelengths for the classification of grape varieties were selected using principal component analysis (PCA) loadings to improve the HSI processing speed. Finally, three methods were applied to classify grape samples: a support vector machine (SVM), a random forest (RF), and an AdaBoost model. The results indicated that the model established by Savitzky-Golay (S-G) Filter + PCA + SVM achieves the best classification result. The average calibration and validation accuracy for red grapes reached 93.06% and 90.01%, respectively, and for white grapes, they reached 83.77% and 81.09%, respectively, which are slightly lower than those achieved by the full-spectrum model. This study revealed that hyperspectral imaging has great potential for rapid variety discrimination of different wine grapes. Keywords: Hyperspectral imaging, Random forest, Support vector machine, Variety identification, Wine grape.

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