ChemSensor classification of red wines

Dirinck, I.; Leuven, Isabelle Van; Dirinck, P.

doi:10.1016/s0167-4501(06)80123-8

Cited by 7 publications

(4 citation statements)

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“…Strategies utilized to authenticate coffee have traditionally relied on wet chemistry. Some of the chemical data used to characterize the arabica and robusta coffees are the contents of minerals ( − ), volatile substances ( − ), chlorogenic acids, and caffeine ( − ). The analysis of the lipid fraction is also an usual approach, in particular, the determination of fatty acids ( , ), sterols ( , ), diterpenes ( − ), and tocopherols ().…”

Section: Introductionmentioning

confidence: 99%

Chemical Discrimination of Arabica and Robusta Coffees by Fourier Transform Raman Spectroscopy

Rubayiza

Meurens

2005

J. Agric. Food Chem.

129

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This article deals with the potential of Fourier transform (FT) Raman spectroscopy in discrimination of botanical species of green and roasted coffees. There are two species of commercial importance: Coffea arabica (arabica) and Coffea canephora (robusta). It is recognized that they differ in their lipid fraction, especially in the content of the diterpene kahweol, which is present at 0.1-0.3% dry matter basis in arabica beans and only in traces (<0.01%) in robusta. The visual examination of the Raman spectra of the lipid fraction extracted from arabica, robusta and liberica samples shows differences in the mid-wavenumbers region: arabica spectra have two characteristic scattering bands at 1567 and 1478 cm(-1). The spectrum of the pure kahweol shows the same bands. Principal component analysis is applied to the spectra and reveals clustering according to the coffee species. The first principal component (PC1) explains 93% of the spectral variation and corresponds to the kahweol concentration. Using the PC1 score plot, two groups of arabica can be distinguished as follows: one group with high kahweol content and another group with low kahweol content. The first group includes samples coming from Kenya and Jamaica; the second group includes samples from Australia. The main difference between these coffees is that those from Kenya and Jamaica are well-known for growing at a high altitude whereas those ones from Australia are grown at a low altitude. To our knowledge, the application of Raman spectroscopy has never been used in coffee analysis.

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Section: Introductionmentioning

confidence: 99%

Chemical Discrimination of Arabica and Robusta Coffees by Fourier Transform Raman Spectroscopy

Rubayiza

Meurens

2005

J. Agric. Food Chem.

129

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“…The use of chemometrics for interpretation of patterns from these multivariate data allows fast and precise classification of samples according to different criteria. Thus, DS-MS has been used for classification of different olive oil classes (Peña, Cárdenas, Gallego, & Valcárcel, 2002) and of wines (Dirinck, Van Leuven, & Dirinck, 2006), among others.…”

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confidence: 99%

Vitamin C content and sensorial properties of dehydrated carrots blanched conventionally or by ultrasound

et al. 2013

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“…Flavour can be considered the signature of a product [5][6][7][8], and defining a relationship between chemical profile and aroma sensory impact is an important challenge for both the analytical and industrial fields, as they aim to achieve an objective and fast routine evaluation of a product with an automatic analytical procedure [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

HS-SPME-MS-Enose Coupled with Chemometrics as an Analytical Decision Maker to Predict In-Cup Coffee Sensory Quality in Routine Controls: Possibilities and Limits

et al. 2019

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The quality assessment of the green coffee that you will go to buy cannot be disregarded from a sensory evaluation, although this practice is time consuming and requires a trained professional panel. This study aims to investigate both the potential and the limits of the direct headspace solid phase microextraction, mass spectrometry electronic nose technique (HS-SPME-MS or MS-EN) combined with chemometrics for use as an objective, diagnostic and high-throughput technique to be used as an analytical decision maker to predict the in-cup coffee sensory quality of incoming raw beans. The challenge of this study lies in the ability of the analytical approach to predict the sensory qualities of very different coffee types, as is usual in industry for the qualification and selection of incoming coffees. Coffees have been analysed using HS-SPME-MS and sensory analyses. The mass spectral fingerprints (MS-EN data) obtained were elaborated using: (i) unsupervised principal component analysis (PCA); (ii) supervised partial least square discriminant analysis (PLS-DA) to select the ions that are most related to the sensory notes investigated; and (iii) cross-validated partial least square regression (PLS), to predict the sensory attribute in new samples. The regression models were built with a training set of 150 coffee samples and an external test set of 34. The most reliable results were obtained with acid, bitter, spicy and aromatic intensity attributes. The mean error in the sensory-score predictions on the test set with the available data always fell within a limit of ±2. The results show that the combination of HS-SPME-MS fingerprints and chemometrics is an effective approach that can be used as a Total Analysis System (TAS) for the high-throughput definition of in-cup coffee sensory quality. Limitations in the method are found in the compromises that are accepted when applying a screening method, as opposed to human evaluation, in the sensory assessment of incoming raw material. The cost-benefit relationship of this and other screening instrumental approaches must be considered and weighed against the advantages of the potency of human response which could thus be better exploited in modulating blends for sensory experiences outside routine.

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ChemSensor classification of red wines

Cited by 7 publications

References 0 publications

Chemical Discrimination of Arabica and Robusta Coffees by Fourier Transform Raman Spectroscopy

Chemical Discrimination of Arabica and Robusta Coffees by Fourier Transform Raman Spectroscopy

Vitamin C content and sensorial properties of dehydrated carrots blanched conventionally or by ultrasound

HS-SPME-MS-Enose Coupled with Chemometrics as an Analytical Decision Maker to Predict In-Cup Coffee Sensory Quality in Routine Controls: Possibilities and Limits

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