Perfume and Flavor Engineering: A Chemical Engineering Perspective

Rodrigues, Alı́rio E.; Nogueira, Idelfonso B. R.; Faria, Rui P. V.

doi:10.3390/molecules26113095

Cited by 14 publications

(12 citation statements)

References 63 publications

(57 reference statements)

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“…The oxidative degradation generates odorous compounds, highly valued in perfumery, known as ambroxides (Figure 8) such as: ambrafuran (19), also called ambroxide (AmbroxTM) and derivatives compounds: ambraoxide (20), methylambraoxide (21), ambracetal (22), ambrinol (23) [220]. One of the most interesting ambergris fragrant compounds is the terpene furan ambrafuran (19) or 3a,6,6,9a-tetramethyl-2,4,5,5a,7,8,9,9b-octahydro-1H-benzo[e] [1]benzofuran, which is regarded as the prototype of all ambergris fragrances. It has emerged as the main and most pertinent compound in ambergris, due to its sensitive odor, a typical animal note of the material, and its fixative properties [221,222].…”

Section: Fragrance Of Marine Animalsmentioning

confidence: 99%

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Occurrence of Marine Ingredients in Fragrance: Update on the State of Knowledge

et al. 2021

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The fragrance field of perfumes has attracted considerable scientific, industrial, cultural, and civilizational interest. The marine odor is characterized by the specific smell of sea breeze, seashore, algae, and oyster, among others. Marine odor is a more recent fragrance and is considered as one of the green and modern fragrances. The smells reproducing the marine environment are described due to their content of Calone 1951 (7-methyl-2H-1,5-benzodioxepin-3(4H)-one), which is a synthetic compound. In addition to the synthetic group of benzodioxepanes, such as Calone 51 and its derivatives, three other groups of chemical compounds seem to represent the marine smell. The first group includes the polyunsaturated cyclic ((+)-Dictyopterene A) and acyclic (giffordene) hydrocarbons, acting as pheromones. The second group corresponds to polyunsaturated aldehydes, such as the (Z,Z)-3,6-nonadienal, (E,Z)-2,6-nonadienal, which are most likely derived from the degradation of polyunsaturated fatty acids. The third group is represented by small molecules such as sulfur compounds and halogenated phenols which are regarded as the main flavor compounds of many types of seafood. This review exposes, most notably, the knowledge state on the occurrence of marine ingredients in fragrance. We also provide a detailed discussion on several aspects of essential oils, which are the most natural ingredients from various marine sources used in fragrance and cosmetics, including synthetic and natural marine ingredients.

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Section: Fragrance Of Marine Animalsmentioning

confidence: 99%

“…The world of perfumery brings to mind a fascinating industry, which has led to the birth of a wonderful perfume house. On the other side of this appearance, there is a modern industry combined with a strong scientific basis, whose basic foundation is chemistry [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Occurrence of Marine Ingredients in Fragrance: Update on the State of Knowledge

et al. 2021

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“…However, the creation and replication of flavor (the engineering behind it) are complex as it must evoke the smell and taste simultaneously, a multisensory experience [2]. In this scenario, flavor engineering has emerged as a field of product engineering that aims to fulfill the needs of the market and consumers by developing new flavors and flavor-based products [3]. This is a new field that needs more profound development to provide new tools to this industry.…”

Section: Introductionmentioning

confidence: 99%

Generating flavors using Scientific Machine Learning

Queiroz

Rebello

Costa

et al. 2022

Preprint

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The flavor is an essential component in developing numerous products in the market. The increasing consumption of processed and fast food and healthy packages has upraised the investment in new flavoring agents and, consequently, molecules with flavoring properties. In this context, this work brings a Scientific Machine Learning approach to address this product engineering need. Scientific Machine Learning in computational chemistry has opened paths in predicting a compound's properties without requiring synthesis. This work proposes a novel framework of deep generative models within this context to design new flavor molecules.

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“…The interest regarding natural products, especially in avor industry, has largely increased in the past two decades, alongside the rise of numerous natural products databases [3]. Consequently, a new area to acknowledge the avor industry challenges and concerns has arisen, developing innovative tools for the sector, the avor engineering [4].…”

Section: Introductionmentioning

confidence: 99%

A Transfer Learning approach to develop natural molecules with specific flavor requirements

Queiroz

Rebello

Costa

et al. 2022

Preprint

Self Cite

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In the past decades, the flavor industry's investment in research and development has increased to take innovative steps. Therefore, a new field to acknowledge the flavor industry challenges and concerns has arisen, developing innovative tools for the area of flavor engineering. Meanwhile, the lack of information and datasets regarding the flavored molecules and specific flavorings properties are obstacles to advances in this sector. In this context, this work presents the implementation of three Scientific Machine Learning techniques as an approach to specify flavoring characteristics in newly designed molecules. Therefore, this work brings an innovative methodology to design new natural flavor molecules with specific desired properties to product development. The Transfer Learning technique is presented, alongside a deep generative and a deep reinforcement learning models, to tackle the lack of data available when analyzing and studying flavor molecules and developing flavor-based products. This work brings as contributions the utilization of a web scrapper code to sample specific flavors’ databases, apply a generative model as well as a reinforcement learning one in a transfer learning context, integrates three Scientific Machine Learning techniques in a complex system as a framework, and approaches the transfer learning model training one-by-one keeping the parameters constant but training the neural networks specifically for each case. The deep transfer learning implementation in this purpose presented excellent results, regarding the generation of molecules based on specific flavor descriptors. Nine flavor descriptors were studied along this work and all of them presented more than 50% of new molecules generated within the outstanding results considered for the evaluation metric, Natural Product Likeness Score and Synthetic Accessibility Score. Finally, a discussion of the results is constructed based on the data availability, the presence in nature, and the multisensorial components of flavor impact for the specific flavors’ results.

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Perfume and Flavor Engineering: A Chemical Engineering Perspective

Cited by 14 publications

References 63 publications

Occurrence of Marine Ingredients in Fragrance: Update on the State of Knowledge

Occurrence of Marine Ingredients in Fragrance: Update on the State of Knowledge

Generating flavors using Scientific Machine Learning

A Transfer Learning approach to develop natural molecules with specific flavor requirements

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