Classification of the fragrance properties of chemical compounds based on support vector machine and linear discriminant analysis

Luan, Feng; Liu, Huming; Wen, Yingying; Zhang, X. Y.

doi:10.1002/ffj.1876

Cited by 12 publications

(4 citation statements)

References 26 publications

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“…Recently, SVMs have been used in a range of areas like classification of fragrance properties [74], dynamic classification for video stream [9], classification of forest fire types [66], consumer churn prediction [32], text categorization [53], spam classification [120] and estimating production levels [25].…”

Section: Support Vector Machinesmentioning

confidence: 99%

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

Mostafa

Toksarı

2011

Neural Comput & Applic

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First performed in 1954, organ transplantation is a universally practiced clinical procedure. This study uses ant colony optimization (ACO), radial basis function neural network (RBFNN), Kohonen's self-organizing maps (SOM), and support vector machines (SVMs) to examine the effect of various cognitive, psychographic, and attitudinal factors on organ donation. ACO, RBFNN, SOM, and SVMs are compared to a standard statistical method (linear discriminant analysis [LDA]). The variable sets considered are altruistic values, perceived risks/benefits, knowledge, attitudes toward organ donation, and intention to donate organs. The paper shows how it is possible to identify various dimensions of organ donation behavior by uncovering complex patterns in the dataset and also shows the classification and clustering abilities of machine-learning systems.

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Section: Support Vector Machinesmentioning

confidence: 99%

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

Mostafa

Toksarı

2011

Neural Comput & Applic

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“…These mixtures generate a combined response in the sensors and create an odor pattern. The use of data analysis, such as principal component analysis (PCA), cluster analysis and classification techniques such as artificial neural networks (ANN) or support vector machines (SVM), have the potential to classify samples based on their aroma with a proper level of accuracy (between 70 and 100%) [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Electronic Nose for the Determination of Urinary Infections

de la Rica-Martinez,

Martínez-Muñoz,

Sanjuan

et al. 2023

Sensors

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Currently, urine samples for bacterial or fungal infections require a long diagnostic period (48 h). In the present work, a point-of-care device known as an electronic nose (eNose) has been designed based on the “smell print” of infections, since each one emits various volatile organic compounds (VOC) that can be registered by the electronic systems of the device and recognized in a very short time. Urine samples were analyzed in parallel using urine culture and eNose technology. A total of 203 urine samples were analyzed, of which 106 were infected and 97 were not infected. A principal component analysis (PCA) was performed using these data. The algorithm was initially capable of correctly classifying 49% of the total samples. By using SVM-based models, it is possible to improve the accuracy of the classification up to 74% when randomly using 85% of the data for training and 15% for validation. The model is evaluated as having a correct classification rate of 74%. In conclusion, the diagnostic accuracy of the eNose in urine samples is high, promising and amenable for further improvement, and the eNose has the potential to become a feasible, reproducible, low-cost and high-precision device to be applied in clinical practice for the diagnosis of urinary tract infections.

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“…Nevertheless, with developments in the field of machine learning, recent years have seen a growing interest in using a data-driven approach for prediction of structure-odor relationships. Many have attempted to demonstrate the feasibility of predicting olfactory perception with structural parameters of the odorant molecules as features 32 – 35 . However, the approaches used differ greatly with respect to the data used for the predictions, where some utilize perceptual data obtained from untrained individuals while others use qualitative data from trained experts thus making comparison difficult.…”

Section: Introductionmentioning

confidence: 99%

Data based predictive models for odor perception

Chacko

Jain

Patwardhan

et al. 2020

Sci Rep

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Machine learning and data analytics are being increasingly used for quantitative structure property relation (QSPR) applications in the chemical domain where the traditional Edisonian approach towards knowledge-discovery have not been fruitful. The perception of odorant stimuli is one such application as olfaction is the least understood among all the other senses. In this study, we employ machine learning based algorithms and data analytics to address the efficacy of using a data-driven approach to predict the perceptual attributes of an odorant namely the odorant characters (OC) of “sweet” and “musky”. We first analyze a psychophysical dataset containing perceptual ratings of 55 subjects to reveal patterns in the ratings given by subjects. We then use the data to train several machine learning algorithms such as random forest, gradient boosting and support vector machine for prediction of the odor characters and report the structural features correlating well with the odor characters based on the optimal model. Furthermore, we analyze the impact of the data quality on the performance of the models by comparing the semantic descriptors generally associated with a given odorant to its perception by majority of the subjects. The study presents a methodology for developing models for odor perception and provides insights on the perception of odorants by untrained human subjects and the effect of the inherent bias in the perception data on the model performance. The models and methodology developed here could be used for predicting odor characters of new odorants.

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Classification of the fragrance properties of chemical compounds based on support vector machine and linear discriminant analysis

Cited by 12 publications

References 26 publications

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

Low-Cost Electronic Nose for the Determination of Urinary Infections

Data based predictive models for odor perception

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