Classification of Bitter Orange Essential Oils According to Fruit Ripening Stage by Untargeted Chemical Profiling and Machine Learning

Taghadomi‐Saberi, Saeedeh; Garcia, Sílvia Mas; Masoumi, A A; Sadeghi, Morteza; Marco, S.

doi:10.3390/s18061922

Cited by 20 publications

(10 citation statements)

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“…The level of each EO in different lemon cultivars across ripening changed without significant correlations among all EOs. These results are in disagreement with the evidence reported by Taghadomi‐Saberi et al …”

Section: Resultscontrasting

confidence: 99%

Variations of peel essential oils during fruit ripening in four lemon (Citrus limon (L.) Burm. F.) cultivars

et al. 2019

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BACKGROUND: Lemon processing procedures yield a significant amount of waste as peels, which are 57% of processed lemons and represent a possible source of bioactive compounds (essential oils, EOs). EOs were extracted from lemon fruits belonging to four cultivars harvested at four different sampling times (25 October, 23 November, 20 December, 1 February), characterized, and quantified through gas chromatography-mass spectrometry. RESULTS: The chemical composition of EOs highlighted that 26 compounds of the four lemon cultivars at the different ripening stages were clearly identified. The compounds analysed belonged to four chemical classes: monoterpene hydrocarbons, oxygenated monoterpenes, sesquiterpenes, and fatty alcohol esters. Among the monoterpene hydrocarbons, D-limonene,-pinene, and -terpinene were the most abundant; and among the oxygenated monoterpenes, -terpineol, nerol, and geraniol were the most abundant. Quantitative gas chromatography-mass spectrometry analysis of the most abundant monoterpene hydrocarbons ( -pinene, -pinene, myrcene, D-limonene, and -terpinene) highlighted that the amount of EOs decreased during ripening stages. 'Ovale di Sorrento' and 'Sfusato Amalfitano' showed the highest level of EOs in December, whereas in 'Femminello Cerza' and 'Femminello Adamo' this occurred in November. EOs, as well as the phenolic compounds, were positively correlated with the antioxidant activity (2,2 ′ -azino-bis(3-ethylbenzothiazoline-6-sulphonic acid). CONCLUSIONS: EOs reached the highest level in the four lemon cultivars at different ripening stages. Campanian cultivars ('Ovale di Sorrento' and 'Sfusato Amalfitano') showed the greatest EO content in November, whereas in Sicilian cultivars ('Femminello Cerza' and 'Femminello Adamo') this occurred in December. Besides phenolic compounds, measured in lemon peel extracts, EOs can contribute to antioxidant activity, as demonstrated by the positive correlation. Composition and quantification of EOs Chemical composition of EOs during ripening stagesThe chemical composition of extracted EOs was analysed during ripening stages (Table 2). A total of 26 compounds were clearly identified in the fruit peel extracts of the four cultivars and at different ripening stages. Significant variations among cultivars and ripening stages were observed.The compounds analysed were assigned to four chemical classes: monoterpene hydrocarbons, oxygenated monoterpenes, sesquiterpenes, and fatty alcohol esters. Monoterpene hydrocarbons were the dominant fraction of EOs, with an average relative abundance of 94.5%. During the ripening they decreased from 95.2% to 93.3%, and among them the compounds represented most were D-

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Section: Resultscontrasting

confidence: 99%

Variations of peel essential oils during fruit ripening in four lemon (Citrus limon (L.) Burm. F.) cultivars

et al. 2019

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“…Insects in turn have evolved mechanisms for detecting these bitter compounds and avoiding them; these compounds thus deter feeding and oviposition ( Briscoe et al, 2013 ; Chen et al, 2019 ; Pontes et al, 2014 ; Salloum et al, 2011 ; Sellier et al, 2011 ; Wada-Katsumata et al, 2013 ). Since levels of bitter compounds differ among various stages of fruit ripening, it seems plausible that the sensitivity of an insect to different bitter compounds may influence its choice of a ripening stage on which to lay eggs ( Batista-Silva et al, 2018 ; Cheng and Breen, 1991 ; Taghadomi-Saberi et al, 2018 ). As a corollary, it seems conceivable that changes in bitter perception might contribute to the shift of oviposition preference in D. suzukii .…”

Section: Introductionmentioning

confidence: 99%

Evolutionary shifts in taste coding in the fruit pest Drosophila suzukii

Dweck

Talross

Wang

et al. 2021

eLife

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Although most Drosophila species lay eggs in overripe fruit, the agricultural pest Drosophila suzukii lays eggs in ripe fruit. We found that changes in bitter taste perception have accompanied this adaptation. We show that bitter-sensing mutants of Drosophila melanogaster undergo a shift in egg laying preference toward ripe fruit. D. suzukii has lost 20% of the bitter-sensing sensilla from the labellum, the major taste organ of the head. Physiological responses to various bitter compounds are lost. Responses to strawberry purées are lost from two classes of taste sensilla. Egg laying is not deterred by bitter compounds that deter other species. Profiling of labellar transcriptomes reveals reduced expression of several bitter Gr genes (gustatory receptors). These findings support a model in which bitter compounds in early ripening stages deter egg laying in most Drosophila species, but a loss of bitter response contributes to the adaptation of D. suzukii to ripe fruit.

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“…Recently machine learning (ML) has been proved as a tool able to deeply investigate the modulatory role of EOs’ chemical components on Pseudomonas aeruginosa biofilm production [ 21 , 22 , 23 , 24 ]. In particular, 89 EOs extracted in different periods and times of extractions from three different plants were analyzed: 13 EOs (RSEOs) from Ridolfia segetum Moris (RS); 32 EOs (FVEOs) from Foeniculum vulgare Miller (FV) and 44 EOs (CGEOs) from Calamintha nepeta (L.) Savi subsp.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Analyses on Data including Essential Oil Chemical Composition and In Vitro Experimental Antibiofilm Activities against Staphylococcus Species

et al. 2019

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Biofilm resistance to antimicrobials is a complex phenomenon, driven not only by genetic mutation induced resistance, but also by means of increased microbial cell density that supports horizontal gene transfer across cells. The prevention of biofilm formation and the treatment of existing biofilms is currently a difficult challenge; therefore, the discovery of new multi-targeted or combinatorial therapies is growing. The development of anti-bioﬁlm agents is considered of major interest and represents a key strategy as non-biocidal molecules are highly valuable to avoid the rapid appearance of escape mutants. Among bacteria, staphylococci are predominant causes of biofilm-associated infections. Staphylococci, especially Staphylococcus aureus (S. aureus) is an extraordinarily versatile pathogen that can survive in hostile environmental conditions, colonize mucous membranes and skin, and can cause severe, non-purulent, toxin-mediated diseases or invasive pyogenic infections in humans. Staphylococcus epidermidis (S. epidermidis) has also emerged as an important opportunistic pathogen in infections associated with medical devices (such as urinary and intravascular catheters, orthopaedic implants, etc.), causing approximately from 30% to 43% of joint prosthesis infections. The scientific community is continuously looking for new agents endowed of anti-biofilm capabilities to fight S. aureus and S epidermidis infections. Interestingly, several reports indicated in vitro efficacy of non-biocidal essential oils (EOs) as promising treatment to reduce bacterial biofilm production and prevent the inducing of drug resistance. In this report were analyzed 89 EOs with the objective of investigating their ability to modulate bacterial biofilm production of different S. aureus and S. epidermidis strains. Results showed the assayed EOs to modulated the biofilm production with unpredictable results for each strain. In particular, many EOs acted mainly as biofilm inhibitors in the case of S. epidermidis strains, while for S. aureus strains, EOs induced either no effect or stimulate biofilm production. In order to elucidate the obtained experimental results, machine learning (ML) algorithms were applied to the EOs’ chemical compositions and the determined associated anti-biofilm potencies. Statistically robust ML models were developed, and their analysis in term of feature importance and partial dependence plots led to indicating those chemical components mainly responsible for biofilm production, inhibition or stimulation for each studied strain, respectively.

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Classification of Bitter Orange Essential Oils According to Fruit Ripening Stage by Untargeted Chemical Profiling and Machine Learning

Cited by 20 publications

References 50 publications

Variations of peel essential oils during fruit ripening in four lemon (Citrus limon (L.) Burm. F.) cultivars

Variations of peel essential oils during fruit ripening in four lemon (Citrus limon (L.) Burm. F.) cultivars

Evolutionary shifts in taste coding in the fruit pest Drosophila suzukii

Machine Learning Analyses on Data including Essential Oil Chemical Composition and In Vitro Experimental Antibiofilm Activities against Staphylococcus Species

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