scholarly journals Electronic Eye Based on RGB Analysis for the Identification of Tequilas

Biosensors ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 68
Author(s):  
Anais Gómez ◽  
Diana Bueno ◽  
Juan Manuel Gutiérrez
Keyword(s):  
Low Cost ◽  
Principal Component ◽  
Image Sensor ◽  
Raspberry Pi ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Qualitative Classification ◽  
Analytical System ◽  
Leave One Out ◽  
Electronic Eye

The present work reports the development of a biologically inspired analytical system known as Electronic Eye (EE), capable of qualitatively discriminating different tequila categories. The reported system is a low-cost and portable instrumentation based on a Raspberry Pi single-board computer and an 8 Megapixel CMOS image sensor, which allow the collection of images of Silver, Aged, and Extra-aged tequila samples. Image processing is performed mimicking the trichromatic theory of color vision using an analysis of Red, Green, and Blue components (RGB) for each image’s pixel. Consequently, RGB absorbances of images were evaluated and preprocessed, employing Principal Component Analysis (PCA) to visualize data clustering. The resulting PCA scores were modeled with a Linear Discriminant Analysis (LDA) that accomplished the qualitative classification of tequilas. A Leave-One-Out Cross-Validation (LOOCV) procedure was performed to evaluate classifiers’ performance. The proposed system allowed the identification of real tequila samples achieving an overall classification rate of 90.02%, average sensitivity, and specificity of 0.90 and 0.96, respectively, while Cohen’s kappa coefficient was 0.87. In this case, the EE has demonstrated a favorable capability to correctly discriminated and classified the different tequila samples according to their categories.

scholarly journals Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4479 ◽  
Author(s):  
Xavier Cetó ◽  
Núria Serrano ◽  
Miriam Aragó ◽  
Alejandro Gámez ◽  
Miquel Esteban ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Reversed Phase ◽  
Classification Rate ◽  
Phenolic Profile ◽  
Linear Discriminant ◽  
Sample Extraction

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

scholarly journals Discriminant Analysis of Traditional Chinese Medicinal Properties Based on Holistic Chemical Profiling by 1H-NMR Spectrometry

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Jie Zhang ◽  
Wenna Guo ◽  
Qiao Li ◽  
Faxin Sun ◽  
Xiaomeng Xu ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Chinese Medicine ◽  
Principal Component ◽  
Linear Discriminant ◽  
Chemical Profiling ◽  
H Nmr ◽  
Nmr Spectrometry ◽  
Cinnamomi Cortex ◽  
Medicinal Properties ◽  
Leave One Out

Medicinal property, which is closely related to drug chemical profiling, is the essence of traditional Chinese medicine (TCM) theory and has always been the focus of modern Chinese medicine. Based on dozens of classic and commonly used TCM herbs with recognized medicinal properties, the present study just aimed to investigate the feasibility and reliability of medicinal property discriminant by using 1H-NMR spectrometry, which provided a mass of spectral data showing holistic chemical profile for multivariate analysis and data mining, including principal component analysis (PCA), Fisher linear discriminant analysis (FLDA), and canonical discriminant analysis (CDA). By using FLDA for two-class recognition, a large majority of test herbs (59/61) were properly discriminated as cold or hot group, and the only two exceptions were Chuanbeimu (Fritillariae Cirrhosae Bulbus) and Rougui (Cinnamomi Cortex), suggesting that medicinal properties interrelate with flavor and body tropism, and all these factors together bring up medicinal property and efficacy. While by performing CDA, 98.4% of the original grouped herbs and 77.0% of the leave-one-out cross-validated grouped cases were correctly classified. The findings demonstrated that discriminant analysis based on holistic chemical profiling data by 1H-NMR spectrometry may provide a powerful alternative to have a deeper understanding of TCM medicinal property.

scholarly journals ADAPTIVE VARIABLE EXTRACTIONS WITH LDA FOR CLASSIFICATION OF MIXED VARIABLES, AND APPLICATIONS TO MEDICAL DATA

2021 ◽  
Vol 20 (Number 3) ◽  
pp. 305-327
Author(s):  
Hashibah Hamid ◽  
Nor Idayu Mahat ◽  
Safwati Ibrahim
Keyword(s):  
Multiple Correspondence Analysis ◽  
Principal Component ◽  
Medical Data ◽  
Significant Shift ◽  
Continuous Variables ◽  
Linear Discriminant ◽  
Mixed Variables ◽  
Constant Change ◽  
Leave One Out ◽  
Direct Use

The strategy surrounding the extraction of a number of mixed variables is examined in this paper in building a model for Linear Discriminant Analysis (LDA). Two methods for extracting crucial variables from a dataset with categorical and continuous variables were employed, namely, multiple correspondence analysis (MCA) and principal component analysis (PCA). However, in this case, direct use of either MCA or PCA on mixed variables is impossible due to restrictions on the structure of data that each method could handle. Therefore, this paper executes some adjustments including a strategy for managing mixed variables so that those mixed variables are equivalent in values. With this, both MCA and PCA can be performed on mixed variables simultaneously. The variables following this strategy of extraction were then utilised in the construction of the LDA model before applying them to classify objects going forward. The suggested models, using three real sets of medical data were then tested, where the results indicated that using a combination of the two methods of MCA and PCA for extraction and LDA could reduce the model’s size, having a positive effect on classifying and better performance of the model since it leads towards minimising the leave-one-out error rate. Accordingly, the models proposed in this paper, including the strategy that was adapted was successful in presenting good results over the full LDA model. Regarding the indicators that were used to extract and to retain the variables in the model, cumulative variance explained (CVE), eigenvalue, and a non-significant shift in the CVE (constant change), could be considered a useful reference or guideline for practitioners experiencing similar issues in future.

scholarly journals Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA and SVM)

2014 ◽  
Vol 32 (No. 6) ◽  
pp. 538-548 ◽  
Author(s):  
A. Sanaeifar ◽  
S.S. Mohtasebi ◽  
M. Ghasemi-Varnamkhasti ◽  
H. Ahmadi ◽  
J. Lozano
Keyword(s):  
Electronic Nose ◽  
Low Cost ◽  
Principal Component ◽  
Oxide Semiconductor ◽  
Support Vector ◽  
Computational Techniques ◽  
Laboratory Equipment ◽  
Linear Discriminant ◽  
Ripening Stages ◽  
Non Destructive

Potential application of a metal oxide semiconductor based electronic nose (e-nose) as a non-destructive instrument for monitoring the change in volatile production of banana during the ripening process was studied. The proposed e-nose does not need any advanced or expensive laboratory equipment and proved to be reliable in recording meaningful differences between ripening stages. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Soft Independent Modelling of Class Analogy (SIMCA) and Support Vector Machines (SVM) techniques were used for this purpose. Results showed that the proposed e-nose can distinguish between different ripening stages. The e-nose was able to detect a clear difference in the aroma fingerprint of banana when using SVM analysis compared with PCA and LDA, SIMCA analysis. Using SVM analysis, it was possible to differentiate and to classify the different banana ripening stages, and this method was able to classify 98.66% of the total samples in each respective group. Sensor array capabilities in the classification of ripening stages using loading analysis and SVM and SIMCA were also investigated, which leads to develop the application of a specific e-nose system by applying the most effective sensors or ignoring the redundant sensors.  

scholarly journals Nondestructive Detection of Blackheart in Potato by Visible/Near Infrared Transmittance Spectroscopy

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Zhu Zhou ◽  
Songwei Zeng ◽  
Xiaoyu Li ◽  
Jian Zheng
Keyword(s):  
Near Infrared ◽  
Discrimination Performance ◽  
Principal Component ◽  
Spectroscopic Technique ◽  
Chemometric Methods ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Infrared Transmittance ◽  
Validation Set ◽  
Weight Correction

The possibility of using visible/near infrared (Vis/NIR) transmission spectroscopic technique in the 513–850 nm region coupled with partial least squares-linear discriminant analysis (PLS-LDA) and other chemometric methods to classify potatoes with blackheart was investigated. The discrimination performance of different morphological correction methods, including weight correction, height correction, and volume correction, was compared. The results showed that height corrected transmittance has the best performance, with both calibration and validation sets having a success rate of 97.11%. Out of 1800 wavelengths, only six wavelengths (711, 817, 741, 839, 678, and 698 nm) were selected as the optimum wavelengths for the discrimination of blackheart tubers based on principal component analysis (PCA). The data analysis showed that the overall classification rate by PLS-LDA method decreased from 97.11% to 96.82% in calibration set and from 97.11% to 96.53% in validation set, which was acceptable. The importance of these conclusions may be helpful to transfer Vis/NIR transmission technology from laboratory to industrial application in nondestructive, real-time, or portable measurement of potatoes quality.

scholarly journals Electronic Eye for Identification of Tequila Samples

Proceedings ◽  
2020 ◽  
Vol 60 (1) ◽  
pp. 44
Author(s):  
Anais Gómez ◽  
Diana Bueno ◽  
Juan Manuel Gutiérrez
Keyword(s):  
Color Index ◽  
Low Cost ◽  
Image Data ◽  
Ccd Camera ◽  
Raspberry Pi ◽  
Imaging Method ◽  
Surface Color ◽  
Portable Instrumentation ◽  
Sample Concentration ◽  
Electronic Eye

The present work reports the potential of a bio-inspired system based on spectrometry, also known as Electronic Eye (EE), capable of detecting different Tequila samples. The reported system analyzes small volumes of Tequila Reposado and Blanco by calculating samples’ absorbances, using a low cost and portable instrumentation employing a CCD camera. The absorbance imaging method consisted of exciting samples with light passes through an 8MP camera connected to a Raspberry Pi Card. The camera’s image data are analyzed using MATLAB 2018b to be represented in Red, Green and Blue (RGB) components for each pixel, in order to get an approximation of the absorbance and the Surface Color Index (Isc) associated with sample concentration. Using the developed EE, it was possible to identify seven different kinds and brands of Tequila. From the obtained results, it was observed that the average absorbance of the Tequila Reposado was greater than the absorbance of the Tequila Blanco. Otherwise, with the Isc, the Tequila Blanco color index is lower concerning the Tequila Reposado’s. Finally, the EE allowed the identification of Tequila samples with reproducibility and repeatability.

scholarly journals Classification of Lampung robusta Specialty Coffee According to Differences in Cherry Processing Methods Using UV Spectroscopy and Chemometrics

Agriculture ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 109
Author(s):  
Diding Suhandy ◽  
Meinilwita Yulia
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Uv Spectroscopy ◽  
Low Cost ◽  
Principal Component ◽  
Support Vector ◽  
Processing Methods ◽  
Linear Discriminant ◽  
Specialty Coffee ◽  
Supervised Classification Methods

The postharvest processing factors including cherry processing methods highly influence the final quality of coffee beverages, especially in the composition of several coffee metabolites such as glucose, fructose, the amino acid (glutamic acid), and chlorogenic acids (CGA) as well as trigonelline contents. In this research, UV spectroscopy combined with chemometrics was used to classify a ground roasted Lampung robusta specialty coffee according to differences in the cherry processing methods. A total of 360 samples of Lampung robusta specialty coffee with 1 g of weight for each sample from three different cherry processing methods were prepared as samples: 100 samples of pure dry coffee (DRY), 100 samples of pure semi-dry coffee (SMD), 100 samples of pure wet coffee (WET) and 60 samples of adulterated coffee (ADT) (SMD coffee was adulterated with DRY and WET coffee). All samples were extracted using a standard protocol as explained by previous works. A low-cost benchtop UV-visible spectrometer (Genesys™ 10S UV-Vis, Thermo Scientific, Waltham, MA, USA) was utilized to obtain UV spectral data in the interval of 190–400 nm using the fast scanning mode. Using the first three principal components (PCs) with a total of 93% of explained variance, there was a clear separation between samples. The samples were clustered into four possible groups according to differences in cherry processing methods: dry, semi-dry, wet, and adulterated. Four supervised classification methods, partial least squares–discriminant analysis (PLS-DA), principal component analysis–linear discriminant analysis (PCA-LDA), linear discriminant analysis (LDA) and support vector machine classification (SVMC) were selected to classify the Lampung robusta specialty coffee according to differences in the cherry processing methods. PCA-LDA is the best classification method with 91.7% classification accuracy in prediction. PLS-DA, LDA and SVMC give an accuracy of 56.7%, 80.0% and 85.0%, respectively. The present research suggested that UV spectroscopy combining with chemometrics will be highly useful in Lampung robusta specialty coffee authentication.

scholarly journals PEMANFAATAN RASPBERRY PI SEBAGAI PROCESSOR PADA PENDETEKSIAN DAN PENGENALAN POLA WAJAH

2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Fadli Sirait ◽  
Yoserizal Yoserizal
Keyword(s):  
Discriminant Analysis ◽  
Embedded System ◽  
Face Detection ◽  
Principal Component ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Linear Discriminant ◽  
Face Detection And Recognition ◽  
Haar Cascade ◽  
Detection And Recognition

Teknologi biometrik adalah teknologi untuk mengindetifikasi mahluk hidup. Tujuan perancangan  adalah  untuk  membangun  sistem  pendeteksi  wajah  dari  objek  citra  yang  didapat dari  gambar  frame  video  melalui  kamera.  Kemudian  dilakukan  pendeteksi  pola  wajah  yang dikenali  dan  mencari  kemiripan  terhadap  database  model  wajah  menggunakan  Raspberri  Pi berbasis  penggunaan  perangkat  lunak  Free  dan  Opensource.  Perancangan  ini  menggunakan metode  pengenalan  objek  citra  wajah  dengan  Haar  Cascade  Classifier  yang  diimplentasikan pada libarary OpenCV, sedangkan metode pengenalan pola wajah dengan menggunakan analisa PCA (Principal Component Analysist) dan LDA (Linear Discriminant Analysis) menggunakan pemograman  prerangkat  lunak  yang  dibuat  berbasis  Python.  Perangkat  lunak  yang dikembangkan  juga  dijalankan  pada  sistem  operasi  berbasis  Linux  Raspbian  (Jessie  dan Wheezy)  yang  diinstal  di  Raspberry  Pi.  Proses  input  citra  menggunakan  USB  kamera  yang dipasang  pada  Raspberry  Pi  2  Model  B  yang  dilengkapi  dengan  LCD  3,5  inchi.  Berdasarkan data pengujian terhadap 127 input didapat tingkat akurasi untuk pendeteksian satu objek wajah 84-97% sementara performa penggunaan CPU pada Raspberry Pi 41.87-46.25%.Kata kunci: face detection and recognition, Raspberry pi, pengolahan citra, embedded system.

scholarly journals Discrimination of Nasopharyngeal Carcinoma from NoncancerousEx VivoTissue Using Reflectance Spectroscopy

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Zhihong Xu ◽  
Wei Huang ◽  
Duo Lin ◽  
Shanshan Wu ◽  
Maowen Chen ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Ex Vivo ◽  
Low Cost ◽  
Principal Component ◽  
Diagnostic Algorithm ◽  
Reflectance Spectroscopy ◽  
Reflectance Spectra ◽  
Clinical Value ◽  
Linear Discriminant

Reflectance spectroscopy is a low-cost, nondestructive, and noninvasive method for detection of neoplastic lesions of mucosal tissue. This study aims to evaluate the capability of reflectance spectroscopy system under white light (400–700 nm) with a multivariate statistical analysis for distinguishing nasopharyngeal carcinoma (NPC) from nasopharyngeal benignex vivotissues. High quality reflectance spectra were acquired from nasopharyngealex vivotissues belonging to 18 noncancerous and 19 cancerous subjects, and the combination of principal component analysis-linear discriminant analysis (PCA-LDA) along with leave-one-spectrum-out cross-validation (LOOCV) diagnostic algorithm was subsequently employed to classify different types of tissue group, achieving a diagnostic sensitivity of 73.7% and a specificity of 72.2%. Furthermore, in order to distinguish NPC from nasopharyngeal benignex vivotissues based on reflectance spectra simply, spectral intensity ratios of oxyhemoglobin (R540/R576) were used as an indicator of the carcinogenesis associated transformation in the hemoglobin oxygenation. This tentative work demonstrated the potential of reflectance spectroscopy for NPC detection usingex vivotissue and has significant experimental and clinical value for furtherin vivoNPC detection in the future.

An Improved Document Image Classification using Deep Transfer Learning and Feature Reduction

2021 ◽  
Vol 10 (2) ◽  
pp. 549-557
Keyword(s):  
Transfer Learning ◽  
Principal Component ◽  
Document Image ◽  
Feature Reduction ◽  
Document Management ◽  
Support Vector ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Erp System ◽  
Deep Model

Electronic Document Management is an essential workflow within every successful ERP implementation. The integration of these documents in their respective pipelines (e.g. OCR, data extraction) inside the ERP system for processing usually requires a previous classification step to improve the success rate. Unfortunately, due to the variation in type, size, and layout of business documents (i.e. invoices, checks, delivery forms), their classification is a challenging computer task and may need an additional data for model training. This paper investigates the Transfer Learning paradigm using different pre-trained deep models to extract useful features from scanned document images. In fact, the machine learning classifiers, such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB) process the extracted features for classification. The authors compared the constructed models performances based on various metrics. To overcome the over-fitting issue and dataset imbalance, we run a crossvalidation procedure at different folds sizes (4, 6, and 8) to assess the models’ generalization ability. We also inspected the effect of dimensionality reduction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) on the overall performances and execution time. We found that the best classification rate is 97.83% achieved by combining LR, LDA, and the DenseNet121 deep model. Despite the small used dataset (546 images), this excellent performance encourages the integration of this approach in an ERP system as a separate module for document preprocessing for ERP users

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