Intelligent Classification of Cocoa Bean using E-nose

Cocoa bean (Theobrama cacao) is an essential raw material in the manufacture of chocolate, and their classification is crucial for the synthesis of good chocolate flavour. Cocoa beans appear to be very similar to one another when visualised. Hence, an electronic device named the electronic nose (E-Nose) is used to classify the odor of cocoa beans to give the best cocoa bean quality. E-nose is a set of an array of chemical sensors used to sense the gas vapours produced by the cocoa bean and the raw data collected was kept in Microsoft Excel, and the classification took place in Octave. They then underwent normalisation technique to increase classification accuracy, and their features were extracted using mean calculation. The features were classified using CBR, and the similarity value is obtained. The results show that CBR's classification accuracy, specificity and sensitivity are all 100%.

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Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽

10.3390/s19040916 ◽

2019 ◽

Vol 19 (4) ◽

pp. 916 ◽

Cited By ~ 2

Author(s):

Wen Cao ◽

Chunmei Liu ◽

Pengfei Jia

Keyword(s):

Feature Extraction ◽

Kernel Function ◽

Electronic Nose ◽

Classification Accuracy ◽

Extraction Methods ◽

Object Function ◽

Optimal Value ◽

Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

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Identification of Poisonous Mushrooms by Means of a Hand-Held Electronic Nose

Proceedings ◽

10.3390/proceedings2019014033 ◽

2019 ◽

Vol 14 (1) ◽

pp. 33

Author(s):

Francisco Portalo-Calero ◽

Jesús Lozano ◽

Félix Meléndez ◽

Patricia Arroyo ◽

José Ignacio Suárez

Keyword(s):

Electronic Nose ◽

Chemical Sensors ◽

Sampling Method ◽

Multivariate Methods ◽

Thin Sheets ◽

Practical Application ◽

Discrimination Capability ◽

Poisonous Mushrooms ◽

Amanita Mushrooms

This work presents a practical application of an electronic nose to fast and efficient discrimination of different species of Amanita mushrooms. The electronic nose instrument were utilized for investigation of discrimination capability with respect to odour profile of these fungi. The home-made prototype was based on MOS-type chemical sensors and headspace sampling method. Samples were cut into thin sheets, placed in glass vials and maintained at a constant temperature using a thermostatic bath, the headspace of which was subjected to analysis. The data were analysed using multivariate methods: PCA, LDA and Artificial Neural Networks. The obtained results confirmed legitimacy of application of the electronic nose technique to identification and discrimination of fungi species. Results show a correct classification of the fungi species at the level of 80–100%.

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Isolation and Characterization of Eugenia Aromatica Oil Extract Against Tropical Warehouse Moth Ephestia cautella [Lepidoptera: Pyralidae] In Cocoa Beans

Journal of Pediatrics & Neonatal Biology ◽

10.33140/jpnb.04.02.01 ◽

2019 ◽

Vol 4 (2) ◽

Keyword(s):

Adult Emergence ◽

Adult Mortality ◽

Raw Material ◽

Ephestia Cautella ◽

Cocoa Bean ◽

Phytochemical Analysis ◽

Cocoa Beans ◽

Egg Hatchability ◽

Isolation And Characterization ◽

Oil Extract

Cocoa bean is a raw material used for the production of chocolate and other confectionaries. Ephestia cautella is the major pest of dried cocoa beans in storage and synthetic insecticide like organochlorides and organophosphates are the major insecticides used to control this pest in storage which further post health hazard to man and his environment. This then necessitate the search for insecticide of plant origins which are bio-degradable and non-toxic to man. This study investigates the contact and fumigant efficacy of the powder and oil extract of Eugenia aromatica on the developmental stages of E. cautella. Powders of E. aromatica were administered at different concentrations (0.5g, 1.0g, 1.5g, 2.0g, and 2.5g). The oil from E.aromatica was extracted with ethanol using soxhlet extractor and redistilled using rotary evaporator and tested as fumigant insecticidal against development stages of E. cautellaat 0.5ml, 1.0ml, 1.5ml, 2.0ml, and 2.5ml. Egg hatchability, adult emergence, larvae and adult mortality of E.cautella were used as indices of insecticidal activities at 24hrs, 48hrs, 72hrs, and 96hrs post-treatment. Essential oil obtained from the plant was purified using thin layer chromatography and analysed by Gas Chromatography -Mass Spectrometer (GC-MS). Result obtained shown that E. aromatica powder and oil completely inhibited egg hatchability and adult emergence both as contact and fumigant. Except the 0.5g of E. aromatica powder that recorded 50.00% larva mortality and 51.67% adult mortality, other treatment concentrations recorded 90-100% larva and adult mortality. At 2.5ml oil extract tested as contact and fumigant larvicides after 96hrs recorded 92.98% and 98.23% mortality respectively. Results from phytochemical analysis of the oil showed that the major components were eugenol (82.044%) and Caryophyllene (11.716%). These findings suggested that E aromatica extract could be a potential source of insecticide which may be used for the production of biopesticide.

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Chemical profile and bioprospecting of cocoa beans analyzed by paper spray mass spectrometry

Research Society and Development ◽

10.33448/rsd-v9i8.6882 ◽

2020 ◽

Vol 9 (8) ◽

pp. e975986882

Author(s):

Afonso Henrique de Oliveira Júnior ◽

Ana Luiza Coeli Cruz Ramos ◽

Mayara Neves Santos Guedes ◽

Miriã Cristina Pereira Fagundes ◽

Rodinei Augusti ◽

...

Keyword(s):

Mass Spectrometry ◽

Raw Material ◽

Cocoa Bean ◽

Chemical Profile ◽

Cocoa Beans ◽

Paper Spray ◽

Methanolic Extracts ◽

Bioactive Properties ◽

Ionization Mode ◽

Negative Ionization

The quality cocoa derived products have increasingly received greater recognition and relevance both by consumers and producers. Cocoa beans are the main components responsible for much of the cocoa agro-industrial chain being currently valued for the bioactive properties found in the species' by-products, creating a great interest in exploring the potentials of cocoa. Much of the work that aims to evaluate the compounds found in the fruit's beans employ HPLC, UHPLC and LC-MS. In this work Paper Spray Mass Spectrometry (PS-MS) was employed as a method for characterizing and bioprospecting the chemical profile of cocoa beans (Theobroma cacao) of the forrasteiro variety grown in the Trans-Amazonian region of the Brazilian State of Pará. Methanolic extracts were prepared from samples of cocoa beans and evaluated in the negative and positive ionization modes. In the positive ionization mode it was possible to identify 11 compounds, comprising the classes of methylxanthines (18.2%), phenylpropanoids (9.1%), steroids (27.3%) and flavonoids (45.5%), while in the negative ionization mode, it was possible to identify 55 compounds among hydroxybenzoic acids (16.4%), phenylpropanoids (20.0%), flavonoids (52.7%), sugars and glycosides (10.9%). PS-MS proved to be an effective method for the evaluation of cocoa bean samples, being able to identify a total of sixty-six compounds. The bioactive properties attributed to cocoa were confirmed in the samples analyzed by the compounds identified through PS-MS whilst also indicating the quality of the raw material and describing its chemical profile, contributing to a greater understanding of its attributes.

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Characterization and Classification of Cocoa Bean Shells from Different Regions of Venezuela Using HPLC-PDA-MS/MS and Spectrophotometric Techniques Coupled to Chemometric Analysis

Foods ◽

10.3390/foods10081791 ◽

2021 ◽

Vol 10 (8) ◽

pp. 1791

Author(s):

Letricia Barbosa-Pereira ◽

Simona Belviso ◽

Ilario Ferrocino ◽

Olga Rojo-Poveda ◽

Giuseppe Zeppa

Keyword(s):

Quantitative Data ◽

High Performance ◽

Functional Foods ◽

Radical Scavenging Activity ◽

Radical Scavenging ◽

Cocoa Bean ◽

Screening Methods ◽

Cocoa Beans ◽

Food Ingredient

The cocoa bean shell (CBS) is one of the main cocoa byproducts with a prospective to be used as a functional food ingredient due to its nutritional and sensory properties. This study aims to define the chemical fingerprint of CBSs obtained from cocoa beans of diverse cultivars and collected in different geographical areas of Venezuela assessed using high-performance liquid chromatography coupled to photodiodes array and mass spectrometry (HPLC-PDA-MS/MS) and spectrophotometric assays combined with multivariate analysis for classification purposes. The study provides a comprehensive fingerprint and quantitative data for 39 compounds, including methylxanthines and several polyphenols, such as flavan-3-ols, procyanidins, and N-phenylpropenoyl amino acids. Several key cocoa markers, such as theobromine, epicatechin, quercetin-3-O-glucoside, procyanidin_A pentoside_3, and N-coumaroyl-l-aspartate_2, were found suitable for the classification of CBS according to their cultivar and origin. Despite the screening methods required a previous purification of the sample, both methodologies appear to be suitable for the classification of CBS with a high correlation between datasets. Finally, preliminary findings on the identification of potential contributors for the radical scavenging activity of CBS were also accomplished to support the valorization of this byproduct as a bioactive ingredient in the production of functional foods.

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Classification of Cocoa Beans Based on their Level of Fermentation using Spectral Information

TecnoLógicas ◽

10.22430/22565337.1654 ◽

2021 ◽

Vol 24 (50) ◽

pp. e1654

Author(s):

Karen Sánchez ◽

Jorge Bacca ◽

Laura Arévalo-Sánchez ◽

Henry Arguello ◽

Sergio Castillo

Keyword(s):

Hyperspectral Image ◽

Agricultural Practices ◽

Quality Parameters ◽

Raw Material ◽

Hyperspectral Images ◽

Cocoa Bean ◽

Cocoa Beans ◽

Chemical Evaluation ◽

Chocolate Industry ◽

Processing Techniques

Cocoa beans are the most important raw material for the chocolate industry and an essential product for the economy of tropical countries such as Colombia. Their price mainly depends on their quality, which is determined by various aspects, such as good agricultural practices, their harvest point, and level of fermentation. The entities that regulate the international marketing of cocoa beans have been encouraging the development of new classification methods that, compared to current techniques, could save time, reduce waste, and increase the number of evaluated beans. In particular, hyperspectral images are a novel tool for food quality control. However, studies that have examined some quality parameters of cocoa using spectroscopy also involve the chemical evaluation of cocoa powder and liquor and the interior of the beans, which implies an invasive analysis, longer times, and waste generation. Therefore, in this paper, we assess the quality of cocoa beans based on their level of fermentation using a noninvasive system to obtain hyperspectral information, as well as fast image processing and spectral classification techniques. We obtained hyperspectral images of 90 cocoa beans in the range between 350 and 950 nm in an optical laboratory. In addition, each cocoa bean was classified according to its fermentation level: slightly fermented (SF), correctly fermented (CF), and highly fermented (HF). We compared this classification with that carried out by experts from the Colombia National Federation of Cocoa Growers and reported in the Colombian technical standard No. 1252. The results show that the level of fermentation of dried cocoa beans can be estimated using noninvasive hyperspectral image acquisition and processing techniques.

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Determination of the Prospective Processed Cacao Product and Calculation of the Added Value in Agro-Tourism Based on Cacao Agroindustry in Pidie Jaya Regency

Acta Universitatis Cibiniensis Series E Food Technology ◽

10.2478/aucft-2018-0004 ◽

2018 ◽

Vol 22 (1) ◽

pp. 33-42

Author(s):

◽

Tajuddin Bantacut ◽

Sapta Raharja

Keyword(s):

System Approach ◽

Added Value ◽

Assessment Criteria ◽

Cocoa Bean ◽

Cocoa Beans ◽

Bayes Method ◽

Cocoa Products ◽

Processed Products ◽

Derivative Products

Abstract Utilization of cocoa bean to be a derivative products in industrial is wide enough, that it is necessary to determine the priority of the processed products development. This study aimed to determine the prospective processed cocoa products with a system approach using Bayes method and assessed the potential of added value by using Hayami method. Based on several assessment criteria indicated that chocolate bar is the priority product that needs to be developed and followed by several other processed products. This development was able to produce the added value of Rp 135.000 per kg of cocoa beans. Result indicated that by processing the cocoa beans into chocolate bar could provide a considerable income for the businessman.

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Artificial Intelligence in Fractured Dental Implant Detection and Classification: Evaluation Using Dataset from Two Dental Hospitals

Diagnostics ◽

10.3390/diagnostics11020233 ◽

2021 ◽

Vol 11 (2) ◽

pp. 233

Author(s):

Dong-Woon Lee ◽

Sung-Yong Kim ◽

Seong-Nyum Jeong ◽

Jae-Hong Lee

Keyword(s):

Dental Implant ◽

Classification Accuracy ◽

Reliability And Validity ◽

Mechanical Complication ◽

Radiographic Images ◽

Acceptable Accuracy ◽

Reliable Detection ◽

Classification Evaluation ◽

Accuracy Performance

Fracture of a dental implant (DI) is a rare mechanical complication that is a critical cause of DI failure and explantation. The purpose of this study was to evaluate the reliability and validity of a three different deep convolutional neural network (DCNN) architectures (VGGNet-19, GoogLeNet Inception-v3, and automated DCNN) for the detection and classification of fractured DI using panoramic and periapical radiographic images. A total of 21,398 DIs were reviewed at two dental hospitals, and 251 intact and 194 fractured DI radiographic images were identified and included as the dataset in this study. All three DCNN architectures achieved a fractured DI detection and classification accuracy of over 0.80 AUC. In particular, automated DCNN architecture using periapical images showed the highest and most reliable detection (AUC = 0.984, 95% CI = 0.900–1.000) and classification (AUC = 0.869, 95% CI = 0.778–0.929) accuracy performance compared to fine-tuned and pre-trained VGGNet-19 and GoogLeNet Inception-v3 architectures. The three DCNN architectures showed acceptable accuracy in the detection and classification of fractured DIs, with the best accuracy performance achieved by the automated DCNN architecture using only periapical images.

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An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽

10.3390/info12060249 ◽

2021 ◽

Vol 12 (6) ◽

pp. 249

Author(s):

Xin Jin ◽

Yuanwen Zou ◽

Zhongbing Huang

Keyword(s):

Cell Cycle ◽

Deep Learning ◽

Image Classification ◽

Classification Accuracy ◽

Data Augmentation ◽

Cycle Phase ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

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An effective technique for diabetic retinopathy using hybrid machine learning technique

Statistical Methods in Medical Research ◽

10.1177/0962280220983541 ◽

2021 ◽

pp. 096228022098354

Author(s):

N Satyanarayana Murthy ◽

B Arunadevi

Keyword(s):

Diabetic Retinopathy ◽

Short Term Memory ◽

Early Recognition ◽

Hybrid Techniques ◽

Specificity And Sensitivity ◽

Retinal Blood Vessels ◽

Segmented Images ◽

Fuzzy C Means Clustering ◽

Working Region

Diabetic retinopathy (DR) stays as an eye issue that has continuously developed in individuals who experienced diabetes. The complexities in diabetes cause harm to the vein at the back of the retina. In outrageous cases, DR could swift apparition disaster or visual impairment. This genuine impact had the option to charge through convenient treatment and early recognition. As of late, this issue has been spreading quickly, particularly in the working region, which in the end constrained the interest of an analysis of this disease from the most prompt stage. Therefore, that are castoff to protect the progressions of this disorder, revealing of the retinal blood vessels (RBVs) play a foremost role. The growth of an abnormal vessel leads to the development steps of DR, where it can be well known by extracting the RBV. The recognition of the BV for DR by developing an automatic approach is a major aim of our research study. In the proposed method, there are two major steps: one is segmentation and the second one is classification of affected retinal BV. The proposed method uses the Kinetic Gas Molecule Optimization based on centroid initialization used for the Fuzzy C-means Clustering. In the classification step, those segmented images are given as input to hybrid techniques such as a convolution neural network with bidirectional-long short-term memory (CNN with Bi-LSTM). The learning degree of Bi-LSTM is revised by using the self-attention mechanism for refining the classification accuracy. The trial consequences disclosed that the mixture algorithm achieved higher accuracy, specificity, and sensitivity than existing techniques.

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