scholarly journals Classification of Sediment Quality according to Its Behavior in the Accelerated Particle Wear Test (APW)

2021 ◽  
Vol 13 (5) ◽  
pp. 2633
Author(s):  
José I. Pagán ◽  
Antonio J. Tenza-Abril ◽  
Luis Aragonés ◽  
Yolanda Villacampa ◽  
Isabel López
Keyword(s):  
Coastal Erosion ◽  
Sediment Quality ◽  
Principal Component ◽  
Wear Test ◽  
Wear Process ◽  
Simple Tool ◽  
Quality Classification ◽  
Classification Index

One of the main problems faced in coastal management is the loss or destruction of beaches due to erosion. A considerable diversity of factors is involved in coastal erosion, which makes it a complex system to study. The quality of the material that constitutes the beach, as well as the choice of appropriate materials for its nourishment are two of the main ones. Therefore, to make future nourishment projects more sustainable and durable, this work proposes a sediment quality classification based on the physical properties and wear process obtained through laboratory tests. The study of these variables, using principal component analysis, discriminant analysis and ANOVA, has divided the quality of 70 samples into three main groups. A Sediment Quality Classification Index (SQCI) is proposed, which categorizes the quality of the material into poor, regular or good, providing the coastal engineer with a simple tool to ensure more sustainable beach nourishments.

scholarly journals Electronic nose sensor development using ANN backpropagation for Lombok Agarwood classification

2020 ◽  
Vol 66 (No. 3) ◽  
pp. 97-103
Author(s):  
Farel Ahadyatulakbar Aditama ◽  
Lalu Zulfikri ◽  
Laili Mardiana ◽  
Tri Mulyaningsih ◽  
Nurul Qomariyah ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Gas Sensors ◽  
Poor Quality ◽  
Backpropagation Algorithm ◽  
The Poor ◽  
Quality Classification ◽  
Electronic Nose System ◽  
System Prototype

The aim of the present study is the development of an electronic nose system prototype for the classification of Gyrinops versteegii agarwood. The prototype consists of three gas sensors, i.e., TGS822, TGS2620, and TGS2610. The data acquisition and quality classification of the nose system are controlled by the Artificial Neural Network backpropagation algorithm in the Arduino Mega2650 microcontroller module. The testing result shows that an electronic nose can distinguish the quality of Gyrinops versteegii agarwood. The good-quality agarwood has an output of [1 –1], while the poor-quality agarwood has an output of [–1 1].

scholarly journals Metabolomics Approach for Discrimination and Quality Control of Natural and Commercial Fallopia multiflora Products in Vietnam

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Nguyen Thi Thoa ◽  
Nguyen Hai Dang ◽  
Do Hoang Giang ◽  
Nguyen Thi Thu Minh ◽  
Nguyen Tien Dat
Keyword(s):  
Clustering Analysis ◽  
Principal Component ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Fda Guidance ◽  
Promising Tool ◽  
Precision And Accuracy ◽  
And Control

A precise HPLC-DAD-based quantification together with the metabolomics statistical method was developed to distinguish and control the quality of Fallopia multiflora, a popular medicinal material in Vietnam. Multivariate statistical methods such as hierarchical clustering analysis and principal component analysis were utilized to compare and discriminate six natural and twelve commercial samples. 2,3,4′,5-Tetrahydroxystilbene 2-O-β-D-glucopyranoside (THSG) (1), emodin (4), and the new compound 6-hydroxymusizin 8-O-α-D-apiofuranosyl-(1⟶6)-β-D-glucopyranoside (5) could be considered as important markers for classification of F. multiflora. Furthermore, seven phenolics were quantified that the variation in the contents of selected metabolites revealed the differences in the quality of natural and commercial samples. Recovery of the compounds from the analytes was more than 98%, while the limits of detection (LOD) and the limits of quantitation (LOQ) ranged from 0.5 to 6.6 μg/ml and 1.5 to 19.8 μg/ml, respectively. The linearity, LOD, LOQ, precision, and accuracy satisfied the criteria FDA guidance on bioanalytical methods. Overall, this method is a promising tool for discrimination and quality assurance of F. multiflora products.

scholarly journals Contamination of bottom sediments by lead, zinc and cadmium in Rzeszow reservoir

2018 ◽  
Vol 29 (4) ◽  
pp. 10-15
Author(s):  
Katarzyna Maj-Zajezierska ◽  
Piotr Koszelnik
Keyword(s):  
Heavy Metals ◽  
Bottom Sediments ◽  
Sediment Quality ◽  
Environmental Risks ◽  
Sediment Classification ◽  
Quality Classification ◽  
Lead Zinc ◽  
Aquatic Sediment ◽  
Positive Effect

Abstract The aim of the present study was to determine the level of contamination of the bottom sediments in the Rzeszow reservoir by the selected heavy metals Pb, Cd and Zn, and to identify the potential environmental risks of heavy metals content basing on available assessments and classification of bottom sediments. The Rzeszow reservoir is situated on the Wisłok River in the Podkarpackie Voivodeship, southeaster Poland, was constructed on 1974. Nowadays, as a result of silting, the reservoir reduced its surface and depth, which does not have a positive effect on the assumed functions it is to perform. The study was conducted in 2016. The samples of sediment were collected in five locations. Samples were taken twice: in June and in October. The following concentrations have been determined: cadmium - 0.01 ÷ 0.92 mg•kg–1, zinc - 54.39 ÷ 128 mg•kg–1, lead - 2.98 ÷ 25.42 mg•kg–1. The decline trend in the sediment is following: Zn > Pb > Cd. For the assessment of sediment contamination, following methods: aquatic sediment quality classification used by the Polish Geological Institute - I class, Regulation of the Minister of Environment of April 16 2002 on the types and concentrations of substances contaminating the excavated material – unpolluted and LAW sediment classification – Pb - I/I-II, Cd - I/I-II/II and Zn - I/I-II. The obtained results were compared with the results obtained by the other authors in earlier years, which led to the estimated changes in the concentration of the tested metals.

scholarly journals MICROSCOPIC EXAMINATION OF WOOL FIBERS IN PRAMENKA FOR THE QUALITY CLASSIFICATION OF RAW WOOL

2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Nadžida MLAĆO ◽  
Amela KATICA ◽  
Velija KATICA ◽  
Almira SOFTIĆ ◽  
Vedad ŠAKIĆ ◽  
...  
Keyword(s):  
Textile Industry ◽  
Organic Waste ◽  
Environmental Problem ◽  
The Body ◽  
Raw Material ◽  
Wool Fibers ◽  
Quality Classification ◽  
Selection Of

In Bosnia and Herzegovina, Montenegro, as well as in most Balkan countries, wool is a major environmental problem. After sheep shearing, farmers usually leave the wool at the shear sites, providing poorly degradable organic waste. The purchase price of such untreated wool is as low as its quality. By this research, we have tried to draw attention, from another aspect, to the quality of wool fibers of certain parts of the body, which is ultimately very important in the textile industry and in the selection of wool for further processing. The cuticle is made from cornfied cells, flakes, located on the surface of wool fibers. One of the significant roles of the cuticle is the protective. Namely, the cuticle protects the wool fibers from various external factors, whether mechanical or physic-chemical (such as ammonia evaporation in poorly maintained facilities, etc.), which can damage the fleece and thus make it less quality. We have found some differences in the flakes position and shape in the wool fibers we investigated, depending on part of the body from which they were sampled. However, by microscopic analyses of samples taken from the root of the tail, we have found that the flakes were much smaller and finer in structure than the arrangement and appearance of the cornified flakes from the rump. In this study, we have compared the appearance and arrangement of flakes of cuticle, which is very important in assessing the quality of wool and its further use as a raw material.

scholarly journals Implementasi Metode Feature Extraction pada Klasifikasi Kualitas Daun Tembakau Madura

Rekayasa ◽  
2017 ◽  
Vol 10 (2) ◽  
pp. 71
Author(s):  
Kunto Aji Wibisono ◽  
Achmad Fiqhi Ibadillah
Keyword(s):  
Economic Value ◽  
Raw Material ◽  
Grading System ◽  
Leaf Quality ◽  
Tobacco Leaf ◽  
Quality Detection ◽  
Quality Classification ◽  
Accuracy Level

<p>Madura merupakan salah satu daerah penghasil tembakau di Indonseia. Tembakau Madura  merupakan jenis komoditi perkebunan yang memiliki nilai ekonomi tinggi. Sebagian besar tembakau madura diserap oleh pabrik rokok sebagai bahan baku utama rokok maupun sebagai racikan atau campuran kretek. Secara umum tembakau Madura sendiri dibagi menjadi tiga bagian: tembakau gunung, tembakau tegal, dan tembakau sawah. Jenis tembakau gunung adalah yang paling diburu oleh pabrik rokok, meski produktivitasnya terbilang sangat rendah dibanding tembakau sawah. Terdapat banyak jenis  varietas tembakau gunung yang ditanam petani di Madura, namun  yang memiliki karakteristik khas adalah tembaku Prancak – 95. Hal ini disebabkan  <a href="http://global-news.co.id/2016/04/tembakau-varietas-prancak-95-madura-diam-diam-dikembangkan-tiongkok/">Aroma tembakau Prancak-95 Madura tidak bisa ditiru oleh</a> jenis varietas tembaku lain di Indonesia. Hal lain yang membedakan yaitu terjadi karena kontur atau struktur tanah Madura yang memang khas, yang merupakan kelebihan dari tembakau Madura.Pada penelitian ini didesain sebuah sistem gradding untuk mendeteksi kualitas tembakau Prancak – 95 madura. Deteksi kualitas daun tembakau ini didasarkan pada dua ekstraksi fitur yaitu tekstur dan aromatik. Berdasarkan kedua fitur tersebut nantinya akan diklasifikasikan dengan menggunakan standard kualifikasi SNI. Sehingga  level akurasi deteksi kualitas daun tembakau Madura menjadi lebih optimal</p><p class="Normal1"><em>Kata Kunci: </em><em>Image extraction, Sensor Gas, Tembakau Madura</em><em>.</em></p><p class="Normal1" align="center">Implementation of Feature Image Extraction on Quality Classification of Maduraness Tobacco<strong></strong></p><p class="Normal1"><strong>ABSTRACT</strong></p><p><em>Madura is one of the tobacco producing areas in Indonesian. Madura tobacco is a type of plantation commodity that has high economic value. Most tobacco Madura is absorbed by cigarette manufacturers as the main raw material of cigarettes as well as as a concoction or clove mixture. In general Madura tobacco itself is divided into three parts: mountain tobacco, tobacco tegal, and tobacco sawah. Types of mountain tobacco are the most hunted by cigarette manufacturers, although the productivity is very low compared to tobacco. There are many types of varieties of mountain tobacco grown by farmers in Madura, but which has a distinctive characteristic is the Prancak-95 tobacco. This is because the Prancak-95 Madura tobacco aroma cannot be imitated by other types of copious varieties in Indonesia. Another thing that distinguishes that occurs due to the contour or structure of Madura land that is typical, which is the advantage of Madura tobacco. In this study designed a grading system to detect the quality of Prancak tobacco - 95 madura. The tobacco leaf quality detection is based on two feature extractions, namely texture and aromatics. Based on these two features will be classified using SNI qualification standards. So that the accuracy level of Madura tobacco leaf quality detection becomes more optimal</em><em></em></p><em>Keywords: Image extraction, Gas Sensor, Maduraness Tobacco </em>

scholarly journals A Frequency-Based Approach for the Detection and Classification of Structural Changes Using t-SNE †

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5097 ◽  
Author(s):  
David Agis ◽  
Francesc Pozo
Keyword(s):  
Structural Changes ◽  
Dimensional Space ◽  
Principal Component ◽  
Majority Voting ◽  
Data Set ◽  
Centroid Distance ◽  
Low Dimensional ◽  
Reduced Data

This work presents a structural health monitoring (SHM) approach for the detection and classification of structural changes. The proposed strategy is based on t-distributed stochastic neighbor embedding (t-SNE), a nonlinear procedure that is able to represent the local structure of high-dimensional data in a low-dimensional space. The steps of the detection and classification procedure are: (i) the data collected are scaled using mean-centered group scaling (MCGS); (ii) then principal component analysis (PCA) is applied to reduce the dimensionality of the data set; (iii) t-SNE is applied to represent the scaled and reduced data as points in a plane defining as many clusters as different structural states; and (iv) the current structure to be diagnosed will be associated with a cluster or structural state based on three strategies: (a) the smallest point-centroid distance; (b) majority voting; and (c) the sum of the inverse distances. The combination of PCA and t-SNE improves the quality of the clusters related to the structural states. The method is evaluated using experimental data from an aluminum plate with four piezoelectric transducers (PZTs). Results are illustrated in frequency domain, and they manifest the high classification accuracy and the strong performance of this method.

scholarly journals Evaluation of Ground Surface Pregrouting in a Mountain Tunnel Based on FAHP

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Changan Zhu ◽  
Runfang Sun ◽  
Hua Xu ◽  
Yiwei Liu ◽  
Zhuang Chen
Keyword(s):  
Quality Evaluation ◽  
Ground Surface ◽  
Field Tests ◽  
Analytic Hierarchy ◽  
Quality Classification ◽  
Final Score ◽  
Hierarchy Process ◽  
Mountain Tunnel

The quality of ground surface pregrouting (GSPG) is commonly qualitatively evaluated using single factor; however, the quality evaluation involves numerous facets, and a quantitative evaluation is rare. The aim of this study was to quantitatively evaluate the quality of a GSPG based on three aspects. The fuzzy analytic hierarchy process (FAHP) was adopted to obtain the final score and quality classification of the GSPG. Based on three aspects, namely, integrity, continuity, and sturdiness, a series of field tests was also conducted, qualitatively evaluating the quality of the GSPG preliminary, to obtain the required test data and verify the FAHP results. The results of the FAHP showed that the quality of the GSPG was 86.42, which could be classified as “Good”, whereas the field tests exhibited that the GSPG was effective, thereby verifying that the FAHP was reliable. In addition, the proposed method provided a detailed comprehension of the quality evaluation of GSPG and a frame of reference for analogous engineering.

scholarly journals Classification of soybean tempe quality using deep learning

2021 ◽  
Vol 924 (1) ◽  
pp. 012022
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
I Prakoso ◽  
V Liana ◽  
M R Fauzy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Low Cost ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Traditional Food ◽  
Quality Classification ◽  
Non Destructive

Abstract Tempe is a traditional food originating from Indonesia, which is made from the fermentation process of soybean using Rhizopus mold. The purpose of this study was to classify three quality levels of soybean tempe i.e., fresh, consumable, and non-consumable using a convolutional neural network (CNN) based deep learning. Four types of pre-trained networks CNN were used in this study i.e. SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The sensitivity analysis showed the highest quality classification accuracy of soybean tempe was 100% can be achieved when using AlexNet with SGDm optimizer and learning rate of 0.0001; GoogLeNet with Adam optimizer and learning rate 0.0001, GoogLeNet with RMSProp optimizer, and learning rate 0.0001, ResNet50 with Adam optimizer and learning rate 0.00005, ResNet50 with Adam optimizer and learning rate 0.0001, and SqueezeNet with RSMProp optimizer and learning rate 0.0001. In further testing using testing-set data, the classification accuracy based on the confusion matrix reached 98.33%. The combination of the CNN model and the low-cost digital commercial camera can later be used to detect the quality of soybean tempe with the advantages of being non-destructive, rapid, accurate, low-cost, and real-time.

scholarly journals Comparison of different criteria used to categorize technological quality of pork

2016 ◽  
Vol 46 (12) ◽  
pp. 2241-2248 ◽  
Author(s):  
Henrique Pereira Cazedey ◽  
Robledo de Almeida Torres Filho ◽  
Paulo Rogério Fontes ◽  
Alcinéia de Lemos Souza Ramos ◽  
Eduardo Mendes Ramos
Keyword(s):  
Pork Quality ◽  
Post Mortem ◽  
International Consensus ◽  
Frequency Distributions ◽  
Quality Classification ◽  
Initial Ph ◽  
Technological Quality ◽  
Quality Categories

ABSTRACT: Pork may be classified into quality categories according to its color, texture and exudation, though no international consensus criterion has been reached yet. Thus, the aim of the present paper was to examine the relation between important meat quality traits, evaluating pork quality classification of a same data by different criteria proposed in the literature. In 60 pork loins (Longissimus thoracis muscle), initial pH (pH45min) and R-value were evaluated after 45min post mortem between the 9th and 10th ribs, and ultimate pH (pH24h), objective color and water-holding capacity were evaluated 24h post mortem in two 2.54cm thick steaks cut between the 9th and 11th ribs to be classified into PSE (pale, soft and exudative), RSE (reddish-pink, soft and exudative), RFN (reddish-pink, firm and non-exudative) or DFD (dark, firm and dry) quality. Frequency distributions of quality categories differed (P<0.001) among criteria, which resulted in large variations: 3 to 68% PSE; 0 to 73% RSE; 5 to 68% RFN; 0 to 22% DFD; and 0 to 33% unclassified samples. A same sample may be classified into different quality categories according to the criterion utilized, which results in large variations in frequency distributions and also in quality attributes. Therefore, the classification of pork quality depends on the adopted criterion, which indicated the need for international standardization, so that pork quality can be determined efficiently and effectively.

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