Classification of wines according to their production regions with the contained trace elements using laser-induced breakdown spectroscopy

Tian, Ye; Yan, Chun‐Hua; Zhang, Tianlong; Tang, Hongsheng; Li, Hua; Yu, Jialu; Bernard, J.; Chen, Li; Martin, Serge; Gilon, Nicole; Bocková, Jana; Veis, P.; Chen, Yanping; Yu, Jin

doi:10.1016/j.sab.2017.07.003

Cited by 56 publications

(23 citation statements)

References 42 publications

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“…On the other hand, Vors et al have shown that optimal data preprocessing and variable-selection methods have a great influence on classification performances [ 51 ]. Some other studies have also indicated that these are also influenced by the selection of optimal variables [ 52 , 53 , 54 ]. It was therefore important to select variables or regions that were related to the elements that contribute the most to coffee-variety identification.…”

Section: Discussionmentioning

confidence: 99%

Identification of Coffee Varieties Using Laser-Induced Breakdown Spectroscopy and Chemometrics

Chu

Shen

Liu

et al. 2017

Sensors

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We linked coffee quality to its different varieties. This is of interest because the identification of coffee varieties should help coffee trading and consumption. Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to identify coffee varieties. Wavelet transform (WT) was used to reduce LIBS spectra noise. Partial least squares-discriminant analysis (PLS-DA), radial basis function neural network (RBFNN), and support vector machine (SVM) were used to build classification models. Loadings of principal component analysis (PCA) were used to select the spectral variables contributing most to the identification of coffee varieties. Twenty wavelength variables corresponding to C I, Mg I, Mg II, Al II, CN, H, Ca II, Fe I, K I, Na I, N I, and O I were selected. PLS-DA, RBFNN, and SVM models on selected wavelength variables showed acceptable results. SVM and RBFNN models performed better with a classification accuracy of over 80% in the prediction set, for both full spectra and the selected variables. The overall results indicated that it was feasible to use LIBS and chemometric methods to identify coffee varieties. For further studies, more samples are needed to produce robust classification models, research should be conducted on which methods to use to select spectral peaks that correspond to the elements contributing most to identification, and the methods for acquiring stable spectra should also be studied.

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Section: Discussionmentioning

confidence: 99%

Identification of Coffee Varieties Using Laser-Induced Breakdown Spectroscopy and Chemometrics

Chu

Shen

Liu

et al. 2017

Sensors

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“…forest have been used to classify wines according to their production regions using trace elements (Tian et al, 2017), to model the impact of climate change on the wine regions from Hungary (Gaál, Moriondo and Bindi, 2012) or in different European wine regions (Moriondo et al, 2013) where the predicted value is generated (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…Random forest is a learning method for classification, or regression (Alhaj andMaghari, 2017, Tian et al, 2017) that was proposed by Breiman in 2001(Tian et al, 2017, Breiman, Leo, 2001).…”

Section: Introductionmentioning

confidence: 99%

Prediction Models to Control Aging Time in Red Wine

Astray

Mejuto

Martínez-Martínez

et al. 2019

Preprint

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A combination of physical-chemical analysis has been used to monitor the aging of red wines from D.O. Toro (Spain). The changes in the chemical composition of wines that occur along aging time can be permitted to discriminate wine samples collected after one, four, seven and ten months of aging. Different computational models were used to develop a good authenticity tool to certificate wines. In this research different models have developed: Artificial Neural Network models (ANNs), Support Vector Machine (SVM) and Random Forest (RF) models. The results obtained for the ANN model developed with sigmoidal function in the output neuron and the RF model permit to determine the aging time, with an average absolute percentage deviation below 1% and it can conclude that these two models have demonstrated its capacity as a valid tool to predict the wine age.

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“…The origin and production methods of the foods significantly impact the health, social, and environmental domains since consumers have shown increasing awareness regarding these aspects. Protected designation of origin is, in fact, very important also for processed products as wines [14][15][16][17], olive oil [18,19], flour [20], and tomato sauce [21], for which different studies have proved the potentiality of the method. As for other horticultural products and seeds, a wide range of products have been tested, starting from seeds as Arabica coffee and grains up to fruits, as shown in Table 1.…”

Section: Nirs Technique and Application For Fruit And Vegetable Authementioning

confidence: 99%

Spectral and Hyperspectral Technologies as an Additional Tool to Increase Information on Quality and Origin of Horticultural Crops

2019

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Nowadays, consumer awareness of the impact of site of origin and method of production on the quality and safety of foods, and particularly of fresh produce, is driving the research towards developing various techniques to assist present certifications, traceability, and audit procedures. With regard to horticultural produce, consumer preferences have shifted to fruit and vegetables, which are healthy and ecologically produced, and toward processed foods having sustainable or social certifications and with sites of origin clearly reported on the label. Some recent studies demonstrate the potentiality of near infrared (NIR) technology (including hyperspectral imaging) for discriminating fresh and processed horticultural products based on their composition, quality attributes, and origin. These studies principally mention that each biological tissue possesses a fingerprint NIR spectrum, which consists of a unique and characteristic pattern of radiation, distinguishing a particular biological tissue from physically and/or chemically different samples. Particularly, recent studies discriminated apples, wine, wheat kernels, and derived flours based on their geographical origins. Spectral information allowed discrimination among growing methods (organic and conventional) for asparagus and strawberry fruits, and among harvest dates for fennels, table grapes, and artichokes. Moreover, information about freshness and storage days after minimal processing can be obtained. Recent literature and original results will be discussed. From our perspective, present results suggest that these techniques may have a potentiality to increase information about product history, but if and only if the variability captured by the classification models is vast in terms of diverse samples belonging to various cultivars, varieties, harvest times, cultural practices, geographical origins, storage conditions, and maturity stages, while being used as a complementary method to the conventional ones―either to make an initial screening of critical features, or to add to the amount of available information. Lacking the inclusion of these parameters could result in good classification results, but the reliability of the classification in this case would be dubious in terms of assessment of the factor contributing towards correct classification.

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Classification of wines according to their production regions with the contained trace elements using laser-induced breakdown spectroscopy

Cited by 56 publications

References 42 publications

Identification of Coffee Varieties Using Laser-Induced Breakdown Spectroscopy and Chemometrics

Identification of Coffee Varieties Using Laser-Induced Breakdown Spectroscopy and Chemometrics

Prediction Models to Control Aging Time in Red Wine

Spectral and Hyperspectral Technologies as an Additional Tool to Increase Information on Quality and Origin of Horticultural Crops

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