Effect of functional group and carbon chain length on the odor detection threshold of aliphatic compounds

Determination of the appropriate extraction method for bound aroma compounds from strawberry and analysis of aroma substances in strawberry fruits of different varieties and developmental stages

Strawberries are rich in unique aroma substances, which include bound and free aroma compounds. Unlike the free aroma, the appropriate extraction and analytical methods for bound aroma compounds in strawberries remain unclear. In the present study, we compared three extraction methods for bound aroma compounds of strawberries and performed the single factor experiment for optimizing the hydrolysis method, process parameters, and response surface analysis. The following optimal process conditions were obtained for extracting bound aroma precursor compounds by the Cleanert PEP column: (1) column flow rate of 1 mL/min; (2) dichloromethane: pentane eluent ratio of 7:1; and (3) ethyl acetate: methanol retention solution ratio of 3:1. The bound aroma precursor compounds were enzymatically hydrolyzed at 38 °C for 48 h and finally detected by GC-MS. The results showed that HY strawberries at red fruit stages had the most abundant aroma content and types, and different varieties had different aroma types.

Utilisation of QSPR ODT modelling and odour vector modelling to predict Cannabis sativa odour

Cannabis flower odour is an important aspect of product quality as it impacts the sensory experience when administered, which can affect therapeutic outcomes in paediatric patient populations who may reject unpalatable products. However, the cannabis industry has a reputation for having products with inconsistent odour descriptions and misattributed strain names due to the costly and laborious nature of sensory testing. Herein, we evaluate the potential of using odour vector modelling for predicting the odour intensity of cannabis products. Odour vector modelling is proposed as a process for transforming routinely produced volatile profiles into odour intensity (OI) profiles which are hypothesised to be more informative to the overall product odour (sensory descriptor; SD). However, the calculation of OI requires compound odour detection thresholds (ODT), which are not available for many of the compounds present in natural volatile profiles. Accordingly, to apply the odour vector modelling process to cannabis, a QSPR statistical model was first produced to predict ODT from physicochemical properties. The model presented herein was produced by polynomial regression with 10-fold cross-validation from 1,274 median ODT values to produce a model with R2 = 0.6892 and a 10-fold R2 = 0.6484. This model was then applied to terpenes which lacked experimentally determined ODT values to facilitate vector modelling of cannabis OI profiles. Logistic regression and k-means unsupervised cluster analysis was applied to both the raw terpene data and the transformed OI profiles to predict the SD of 265 cannabis samples and the accuracy of the predictions across the two datasets was compared. Out of the 13 SD categories modelled, OI profiles performed equally well or better than the volatile profiles for 11 of the SD, and across all SD the OI data was on average 21.9% more accurate (p = 0.031). The work herein is the first example of the application of odour vector modelling to complex volatile profiles of natural products and demonstrates the utility of OI profiles for the prediction of cannabis odour. These findings advance both the understanding of the odour modelling process which has previously only been applied to simple mixtures, and the cannabis industry which can utilise this process for more accurate prediction of cannabis odour and thereby reduce unpleasant patient experiences.

Headspace Volatiles and Endogenous Extracts of Prunus mume Cultivars with Different Aroma Types

Prunus mume is a traditional ornamental plant, which owed a unique floral scent. However, the diversity of the floral scent in P. mume cultivars with different aroma types was not identified. In this study, the floral scent of eight P. mume cultivars was studied using headspace solid-phase microextraction (HS-SPME) and organic solvent extraction (OSE), combined with gas chromatography-mass spectrometry (GC-MS). In total, 66 headspace volatiles and 74 endogenous extracts were putatively identified, of which phenylpropanoids/benzenoids were the main volatile organic compounds categories. As a result of GC-MS analysis, benzyl acetate (1.55-61.26%), eugenol (0.87-6.03%), benzaldehyde (5.34-46.46%), benzyl alcohol (5.13-57.13%), chavicol (0-5.46%), and cinnamyl alcohol (0-6.49%) were considered to be the main components in most varieties. However, the volatilization rate of these main components was different. Based on the variable importance in projection (VIP) values in the orthogonal partial least-squares discriminate analysis (OPLS-DA), differential components of four aroma types were identified as biomarkers, and 10 volatile and 12 endogenous biomarkers were screened out, respectively. The odor activity value (OAV) revealed that several biomarkers, including (Z)-2-hexen-1-ol, pentyl acetate, (E)-cinnamaldehyde, methyl salicylate, cinnamyl alcohol, and benzoyl cyanide, contributed greatly to the strong-scented, fresh-scented, sweet-scented, and light-scented types of P. mume cultivars. This study provided a theoretical basis for the floral scent evaluation and breeding of P. mume cultivars.

Identification and Evaluation of Aromatic Volatile Compounds in 26 Cultivars and 8 Hybrids of Freesia hybrida

Freesia hybrida is a group of cultivars in the genus Freesia with a strong floral scent composed of diverse volatile organic compounds (VOCs). In this study, the VOCs of 34 F. hybrida were extracted and analyzed by headspace solid phase microextraction and gas chromatography mass spectrometry (HS-SPME-GC-MS). A total of 164 VOCs whose relative contents were higher than 0.05% were detected. The numbers of VOCs in all germplasms differed between 11 to 38, and the relative contents ranged from 32.39% to 94.28%, in which most germplasms were higher than 80%. Terpenoids, especially monoterpenes, were the crucial type of VOCs in most germplasms, of which linalool and D-limonene were the most frequently occurring. Principal component analysis (PCA) clearly separated samples based on whether linalool was the main component, and hierarchical clustering analysis (HCA) clustered samples into 4 groups according to the preponderant compounds linalool and (E)-β-ocimene. Comparison of parental species and hybrids showed heterosis in three hybrids, and the inherited and novel substances suggested that monoterpene played an important role in F. hybrida floral scent. This study established a foundation for the evaluation of Freesia genetic resources, breeding for the floral aroma and promoting commercial application.

Changes in the Electroencephalographic Activity in Response to Odors Produced by Organic Compounds

Abstract. Volatile organic compounds are widely used to manufacture various products in addition to research purposes. They play an important role in the air quality of outdoor and indoor with a pleasant or unpleasant odor. It is well known that the odor of chemicals with different structures can affect brain functions differently. In general, organic compounds are mainly characterized by their functional groups. Acetic acid, acetaldehyde, acetone, and acetonitrile are widely used laboratory chemicals with the same methyl group, but different functional groups. Hence, the present study was aimed to investigate whether the exposure of these four chemicals (10%) exhibits the same electroencephalographic (EEG) activity or different. For this purpose, the EEG was recorded in 20 male healthy volunteers. The EEG was recorded from 32 electrodes located on the scalp, based on the International 10–20 system with modified combinatorial nomenclature. The results indicated that tested subjects are less sensitive to acetic acid odor than other three chemicals. The absolute theta activity significantly increased at Cp5 and F8 regions, and the relative mid-beta (RMB) significantly decreased at Fc1 region during the exposure of acetic acid. On the other hand, acetaldehyde, acetone, and acetonitrile produced EEG changes in many indices such as relative theta, relative gamma, relative high beta, relative beta, relative slow beta, the ratio of alpha to high beta, and spectral edge frequencies. However, there was no significant change in the absolute wave activity. Although acetaldehyde, acetone, and acetonitrile odors affected almost similar EEG indices, they exhibited changes in different brain regions. The variations in the EEG activity of these chemicals may be due to the activation of different olfactory receptors, odor characteristics, and structural arrangements.

Is It Possible to Predict the Odor of a Molecule on the Basis of its Structure?

The olfactory sense is the dominant sensory perception for many animals. When Richard Axel and Linda B. Buck received the Nobel Prize in 2004 for discovering the G protein-coupled receptors’ role in olfactory cells, they highlighted the importance of olfaction to the scientific community. Several theories have tried to explain how cells are able to distinguish such a wide variety of odorant molecules in a complex context in which enantiomers can result in completely different perceptions and structurally different molecules. Moreover, sex, age, cultural origin, and individual differences contribute to odor perception variations that complicate the picture. In this article, recent advances in olfaction theory are presented, and future trends in human olfaction such as structure-based odor prediction and artificial sniffing are discussed at the frontiers of chemistry, physiology, neurobiology, and machine learning.

New determinants of olfactory habituation

Habituation is a filter that optimizes the processing of information by our brain in all sensory modalities. It results in an unconscious reduced responsiveness to continuous or repetitive stimulation. In olfaction, the main question is whether habituation works the same way for any odorant or whether we habituate differently to each odorant? In particular, whether chemical, physical or perceptual cues can limit or increase habituation. To test this, the odour intensity of 32 odorants differing in physicochemical characteristics was rated by 58 participants continuously during 120s. Each odorant was delivered at a constant concentration. Results showed odorants differed significantly in habituation, highlighting the multifactoriality of habituation. Additionally habituation was predicted from 15 physico-chemical and perceptual characteristics of the odorants. The analysis highlighted the importance of trigeminality which is highly correlated to intensity and pleasantness. The vapour pressure, the molecular weight, the Odor Activity Value (OAV) and the number of double bonds mostly contributed to the modulation of habituation. Moreover, length of the carbon chain, number of conformers and hydrophobicity contributed to a lesser extent to the modulation of habituation. These results highlight new principles involved in the fundamental process of habituation, notably trigeminality and the physicochemical characteristics associated.

Research on odor interaction between aldehyde compounds via a partial differential equation (PDE) model

In order to explore the odor interaction of binary odor mixtures, a series of odor intensity evaluation tests were performed using both individual components and binary mixtures of aldehydes. Based on the linear relation between the logarithm of odor activity value and odor intensity of individual substances, the relationship between concentrations of individual constituents and their joint odor intensity was investigated by employing a partial differential equation (PDE) model. The obtained results showed that the binary odor interaction was mainly influenced by the mixing ratio of two constituents, but not the concentration level of an odor sample. Besides, an extended PDE model was also proposed on the basis of the above experiments. Through a series of odor intensity matching tests for several different binary odor mixtures, the extended PDE model was proved effective at odor intensity prediction. Furthermore, odorants of the same chemical group and similar odor type exhibited similar characteristics in the binary odor interaction. The overall results suggested that the PDE model is a more interpretable way of demonstrating the odor interactions of binary odor mixtures.

Diverse applications of electronic-nose technologies in agriculture and forestry

Electronic-nose (e-nose) instruments, derived from numerous types of aroma-sensor technologies, have been developed for a diversity of applications in the broad fields of agriculture and forestry. Recent advances in e-nose technologies within the plant sciences, including improvements in gas-sensor designs, innovations in data analysis and pattern-recognition algorithms, and progress in material science and systems integration methods, have led to significant benefits to both industries. Electronic noses have been used in a variety of commercial agricultural-related industries, including the agricultural sectors of agronomy, biochemical processing, botany, cell culture, plant cultivar selections, environmental monitoring, horticulture, pesticide detection, plant physiology and pathology. Applications in forestry include uses in chemotaxonomy, log tracking, wood and paper processing, forest management, forest health protection, and waste management. These aroma-detection applications have improved plant-based product attributes, quality, uniformity, and consistency in ways that have increased the efficiency and effectiveness of production and manufacturing processes. This paper provides a comprehensive review and summary of a broad range of electronic-nose technologies and applications, developed specifically for the agriculture and forestry industries over the past thirty years, which have offered solutions that have greatly improved worldwide agricultural and agroforestry production systems.

The swipe card model of odorant recognition

Just how we discriminate between the different odours we encounter is not completely understood yet. While obviously a matter involving biology, the core issue isa matter for physics: what microscopic interactions enable the receptors in our noses-small protein switches—to distinguish scent molecules? We survey what is and is not known about the physical processes that take place when we smell things, highlighting the difficulties in developing a full understanding of the mechanics of odorant recognition. The main current theories, discussed here, fall into two major groups. One class emphasises the scent molecule's shape, and is described informally as a "lock and key" mechanism. But there is another category, which we focus on and which we call "swipe card" theories:the molecular shape must be good enough, but the information that identifies the smell involves other factors. One clearly-defined "swipe card" mechanism that we discuss here is Turin's theory, in which inelastic electron tunnelling is used to discern olfactant vibration frequencies. This theory is explicitly quantal, since it requires the molecular vibrations to take in or give out energy only in discrete quanta. These ideas lead to obvious experimental tests and challenges. We describe the current theory in a form that takes into account molecular shape as well as olfactant vibrations. It emerges that this theory can explain many observations hard to reconcile in other ways. There are still some important gaps in a comprehensive physics-based description of the central steps in odorant recognition. We also discuss how far these ideas carry over to analogous processes involving other small biomolecules, like hormones, steroids and neurotransmitters. We conclude with a discussion of possible quantum behaviours in biology more generally, the case of olfaction being just one example. This paper is presented in honour of Prof. Marshall Stoneham who passed away unexpectedly during its writing.