Fruits and Vegetables

Schulz, Hartwig; Barańska, Małgorzata

doi:10.1016/b978-0-12-374136-3.00012-2

Cited by 7 publications

(3 citation statements)

References 113 publications

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“…The absorption peaks between 1790 and 1706 cm −1 are related to C_O groups, whereas those at 1000-1200 cm −1 are attributed to C\ \O groups. These peaks have been associated with polysaccharides and essential oils [37].…”

Section: Pca Analysismentioning

confidence: 98%

Non-invasive spectroscopic methods to estimate orange firmness, peel thickness, and total pectin content

Bizzani

Flores

Colnago

et al. 2017

Microchemical Journal

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Orange firmness, peel thickness, and total pectin content are associated with fruit quality and denote important parameters for the food industry. These attributes are usually determined through destructive methods that can be time-consuming and also unable to monitor fruit quality over time. Therefore, non-invasive methods such time-domain nuclear magnetic resonance (TD-NMR), near-infrared (NIR), and mid-infrared (MIR) spectroscopies may represent efficient alternatives to evaluate these quality attributes. In this work, partial least square regression (PLSR) models of TD-NMR relaxometry as well as NIR and MIR spectroscopic data were used to predict firmness, peel thickness, and total pectin content of fresh Valencia oranges. Principal component analyses (PCA) were applied to explain the correlations of orange ripening stage, flowering, and crop season with its physicochemical parameters. Data obtained through standard destructive methods were used to calibrate and validate the PLSR models. NIR and MIR showed the best PLSR models for orange firmness, with Pearson correlation coefficients (r) of 0.92 and 0.84 and squared errors of prediction (SEP) equal to 6.22 and 9.05 N, respectively. Orange peel thickness PLSR model was validated only by TD-NMR (r = 0.72; SEP = 0.49 mm). TD-NMR and NIR also presented potential to predict total pectin orange in orange (r = 0.76 and 0.70; SEP = 5.76% and 5.04%, respectively). Therefore, NIR presented a higher potential to predict orange firmness than MIR and TD-NMR. On the other hand, TD-NMR showed a higher prediction power concerning peel thickness than NIR and MIR. Both NIR and TD-NMR methods showed similar prediction powers for total pectin content.

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Section: Pca Analysismentioning

confidence: 98%

Non-invasive spectroscopic methods to estimate orange firmness, peel thickness, and total pectin content

Bizzani

Flores

Colnago

et al. 2017

Microchemical Journal

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“…γ-terpinene, or 1,4-p-menthadiene, is an isoprenoid with a strong lemon smell and herbal taste. The GC-MS technique detected γ-terpinene in the leaves of Origanum vulgare [37] and citrus fruits, where the recovery percentage was 3-5% [38]. Besides its role as a flavoring agent, γ-terpinene has an antioxidant effect, making it an ideal candidate in medicine [39].…”

Section: Voc Flavor Profile Of Carbonated Lemon Drinkmentioning

confidence: 99%

Analysis of Volatile Flavor Compounds in Four Commercial Beverages Using Static Headspace Gas Chromatography/Mass Spectrometry: A Qualitative Approach

Petre,

Cristea,

Cojocaru

et al. 2024

Applied Sciences

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Understanding the volatile flavor compounds present in commercial beverages is crucial for the food and beverage industries as these compounds significantly influence product perception and consumer acceptability. This paper aims to comprehensively analyze the composition of volatile organic compounds in various commercial beverages using targeted gas chromatographic–mass spectrometry (GC/MS) approaches. The study employs a screening method based on the Headspace-GC/MS technique to efficiently extract and identify volatile organic compounds found in commercial beverages. This methodology provides a rapid and reliable system for assessing the volatile profile of these beverages. The analysis reveals the presence of a total of 40 volatile organic compounds, including esters, terpenes/terpenoids, alkenes, aldehydes, aromatic hydrocarbons, and alcohols. Medium-chain esters were found to dominate the volatile profile of the beverages, suggesting a significant contribution to their fragrance and taste. This study offers valuable insights into the volatile profile of commercial beverages, creating awareness among consumers about the flavor components present in these products. By understanding the volatile composition, consumers can make informed choices regarding their beverage consumption. The comprehensive analysis provided by this research serves as a foundation for further studies related to flavor characterization and optimization in commercial beverages.

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“…To our knowledge, Raman spectroscopy has not previously been employed in fragrance analysis, although numerous studies report the use of Raman spectroscopy for the analysis of essential oils . Because essential oils are commonly found in fragrances, and because synthetic analogs of their main compounds are frequently used, previous studies on essential oils can further the development and interpretation of new analytical methods for the fragrance industry …”

Section: Introductionmentioning

confidence: 99%

Quality control of fragrances using Raman spectroscopy and multivariate analysis

Santos¹,

Poppi

2015

J Raman Spectroscopy

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An analytical methodology using Raman spectroscopy and chemometrics was developed for direct, fast and non‐destructive discrimination and prediction of the properties of fragrances according to their composition. The soft independent modeling of class analogies was used as a supervised classification method for fragrances classification, and partial least squares regression as a multivariate calibration method for the prediction of physicochemical properties of fragrances, such as density and refractive index. From 155 fragrance samples, the model exhibited a high success rate for all of the studied fragrance classes, with 100% correct classification. In the multivariate calibration model, adequate correlation was observed between the measured and partial least squares regression‐predicted data for refractive index and density, with a relative standard error of prediction between 0.02% and 0.07%, respectively. This study demonstrates the wide applicability of the methodology for the discrimination, classification, and prediction of complex olfactory mixtures in quality control of fragrances. Copyright © 2015 John Wiley & Sons, Ltd.

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Fruits and Vegetables

Cited by 7 publications

References 113 publications

Non-invasive spectroscopic methods to estimate orange firmness, peel thickness, and total pectin content

Non-invasive spectroscopic methods to estimate orange firmness, peel thickness, and total pectin content

Analysis of Volatile Flavor Compounds in Four Commercial Beverages Using Static Headspace Gas Chromatography/Mass Spectrometry: A Qualitative Approach

Quality control of fragrances using Raman spectroscopy and multivariate analysis

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