A dataset on odor intensity and odor pleasantness of 222 binary mixtures of 72 key food odorants rated by a sensory panel of 30 trained assessors

Why do we like so much the smell of roses: The recipe for the perfect flower

The rose is the most cultivated ornamental plant in the world, and one of the reasons is that its fragrance is highly pleasant to humans. This raises the question of which volatile organic compounds (VOCs) emitted by flowers are involved in a rose odor-induced positive emotional response. Here, we invited participants to smell and rate the perceptual characteristics of roses whose VOCs were quantified. We revealed that (1) the more rose-specific the flower perception, the more pleasant the smell and (2) the rosy perception is driven by ionones and to a lesser extent by oxylipins while pleasantness by balanced proportion in the mixture of ionones, oxylipins, and 2-phenylethanol and derivatives. In the mixture, the proportion of some compounds, such as aliphatics and phenolic methyl esters, impact negatively the rose scent. Thus, the pleasure that roses bring to humans could be explained by the non-conscious perception of this unique mixture of compounds.

Pyrfume: A window to the world's olfactory data

Advances in theoretical understanding are frequently unlocked by access to large, diverse experimental datasets. Our understanding of olfactory neuroscience and psychophysics remain years behind the other senses, in part because rich datasets linking olfactory stimuli with their corresponding percepts, behaviors, and neural pathways remain scarce. Here we present a concerted effort to unlock and unify dozens of stimulus-linked olfactory datasets across species and modalities under a unified framework called Pyrfume. We present examples of how researchers might use Pyrfume to conduct novel analyses uncovering new principles, introduce trainees to the field, or construct benchmarks for machine olfaction.

An India soyabean dataset for identification and classification of diseases using computer-vision algorithms

Intelligent agriculture heavily relies on the science of agricultural disease image recognition. India is also responsible for large production of French beans, accounting for 37.25% of total production. In India from south region of Maharashtra state this crop is cultivated thrice in year. Soyabean plant is planted between the months of June through July, during the months of October and September during the rabi season, as well as in February. In the Maharashtrian regions of Pune, Satara, Ahmednagar, Solapur, and Nashik, among others, Soyabean plant is a common crop. In Maharashtra, Soyabean plant is grown over an area of around 31,050 hectares. This research presents a dataset of leaves from soyabean plants that are both insect-damaged and healthy. Images were taken over the course of fewer than two to three seasons on several farms. There are 3363 photos altogether in the seven folders that make up the dataset. Six categories comprise the dataset: I) Healthy plants II) Vein Necrosis III) Dry leaf IV) Septoria brown spot V) Root images VI) Bacterial leaf blight. This study's goal is to give academics and students accessibility to our dataset so they may use it for their studies and to build machine learning models.

Formation and Analysis of Volatile and Odor Compounds in Meat-A Review

The volatile composition and odor of meat and meat products is based on the precursors present in the raw meat. These are influenced by various pre-slaughter factors (species, breed, sex, age, feed, muscle type). Furthermore, post-mortem conditions (chiller aging, cooking conditions, curing, fermentation, etc.) determine the development of meat volatile organic compounds (VOCs). In this review, the main reactions leading to the development of meat VOCs such as the Maillard reaction; Strecker degradation; lipid oxidation; and thiamine, carbohydrate, and nucleotide degradation are described. The important pre-slaughter factors and post-mortem conditions influencing meat VOCs are discussed. Finally, the pros, cons, and future perspectives of the most commonly used sample preparation techniques (solid-phase microextraction, stir bar sorptive extraction, dynamic headspace extraction) and analytical methods (gas chromatography mass spectrometry and olfactometry, as well as electronic noses) for the analysis of meat VOCs are discussed, and the continued importance of sensorial analysis is pinpointed.

Evaluation of the Impact of Different Natural Zeolite Treatments on the Capacity of Eliminating/Reducing Odors and Toxic Compounds

Unlike odorants that mask odors, natural zeolite acts as a molecular sieve that captures and eliminates odors. Different treatment methods can be applied to influence the properties of the natural zeolites. To enhance the odor adsorption capacities of the natural zeolite two types of treatment methods were applied: chemical (acid, basic) and thermal. The initial natural zeolites and the activated one were characterized using X-ray diffraction (XRD) and scanning electron microscope (SEM-EDX). Two experiments were performed to establish the odor adsorption capacity of the activated natural zeolites. The best zeolite for the adsorption of humidity, ammonia and hydrogen sulfide was the 1-3 mm zeolite activated through thermal treatment. For the adsorption of PAHs, the best zeolite was the one activated through basic treatment, with an adsorption capacity of 89.6 ng/g.