Characterization and authentication of Spanish PDO wine vinegars using multidimensional fluorescence and chemometrics

Ríos-Reina, Rocío; Elcoroaristizabal, Saioa; Ocaña-González, Juan Antonio; García‐González, Diego L.; Amigo, José Manuel; Callejón, Raquel M.

doi:10.1016/j.foodchem.2017.02.118

Cited by 77 publications

(47 citation statements)

References 33 publications

(40 reference statements)

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“…SVM was first introduced by Boser et al in 1992 (Capron, Massart andSmeyers-Verbeke, 2007, Boser, Guyon andVapnik, 1992). Support vector machine is a powerful non-linear method to develop classification and regression models (RapidMiner GmbH, 2018, Ríos-Reina, Elcoroaristizabal, Ocaña-González, García-González, Amigo andCallejón, 2017). An SVM model used input data to constructs a hyperplane, or a group of hyperplanes, in a high-dimensional space (RapidMiner GmbH, 2018).…”

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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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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confidence: 99%

“…Another study compared SVM and PLS-DA in order to classify seventy-nine wine vinegar samples from three Spanish regions. The samples were classified according to their origin and their categories (aged and sweet), and the SVM classification models demonstrated a higher ability of prediction (between 92% and 100% correctly classified samples) than for the PLS-DA models [23].A group of sixty-four samples of white wine belonging to four different Spanish DO (wine with designation of origin) were classified, based on SVM and linear discriminant analysis (LDA) [24]. The authors achieved an accuracy of 100% with the SVM model when using five selected variables, according to the Kruskal-Wallis test, the PCA, and the backward stepwise LDA.…”

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confidence: 99%

Geographical Classification of Tannat Wines Based on Support Vector Machines and Feature Selection

et al. 2018

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Geographical product recognition has become an issue for researchers and food industries. One way to obtain useful information about the fingerprint of wines is by examining that fingerprint's chemical components. In this paper, we present a data mining and predictive analysis to classify Brazilian and Uruguayan Tannat wines from the South region using the support vector machine (SVM) classification algorithm with the radial basis kernel function and the F-score feature selection method. A total of 37 Tannat wines differing in geographical origin (9 Brazilian samples and 28 Uruguayan samples) were analyzed. We concluded that given the use of at least one anthocyanin (peon-3-glu) and the radical scavenging activity (DPPH), the Tannat wines can be classified with 94.64% accuracy and 0.90 Matthew's correlation coefficient (MCC). Furthermore, the combination of SVM and feature selection proved useful for determining the main chemical parameters that discriminate with regard to the origin of Tannat wines and classifying them with a high degree of accuracy. Additionally, to our knowledge, this is the first study to classify the Tannat wine variety in the context of two countries in South America. Beverages 2018, 4, 97 2 of 15Unsupervised techniques, such as principal component analysis and cluster analysis, have commonly been employed to differentiate wines [9][10][11][12][13]. However, unsupervised pattern recognition methods must not be confused with classification methods. In some cases, these techniques allow for the identification and visualization of certain patterns, while in other cases, they may be insufficient for finding patterns between food profiles and physicochemical attributes, due to the multitude of compounds and physical attributes that are present in food products [14,15].Other studies have used unsupervised techniques prior to using supervised techniques, such as linear discriminant analysis, partial least squares-discriminant analysis, and decision trees to classify wine samples [8,[16][17][18]. In addition, other advanced techniques available through current data mining methodology have been successfully used to classify wines and other food products according to their geographical origin and variety, such as support vector machines (SVM) [19]. In addition, SVM is a suitable classifier for balanced datasets [20].The SVM classification algorithm has been demonstrated to be useful for wine classification. For example, a total of 44 Polish white and red wine samples from different parts of Poland were classified [21]. The authors used principal component analysis (PCA) as an exploratory analysis and support vector machine method, with a radial basis function (RBF), to classify the data according to grape variety, geographic region, sugar content, alcohol content, type of yeast used, post-fermentation treatment, and the temperature in the fermentation process. The main results of this study of the classification of white and red wines, according to the variety of grapes used for production, showed 98...

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“…The detection of tea grade and type based on machine vision is attractive to the food industry for its convenience in system installation and fast response. Yet, its high requirements on sample positioning, lighting uniformity and focal distance still prevent its practical application [8][9][10].Application of fluorescence spectroscopy in food analysis is becoming increasingly attractive and has been demonstrated to be capable of classifying wine vinegars [11], fermented dairy products [12], camellia oil [13], cereals flours [14] and meat freshness [15,16] etc. Laser-induced fluorescence (LIF) can excite the characteristic fluorescence of the internal materials of leaves [17], which makes the detection more accurate [18], but there is still room for reduction in laser price and maintenance cost.…”

mentioning

confidence: 99%

“…Application of fluorescence spectroscopy in food analysis is becoming increasingly attractive and has been demonstrated to be capable of classifying wine vinegars [11], fermented dairy products [12], camellia oil [13], cereals flours [14] and meat freshness [15,16] etc. Laser-induced fluorescence (LIF) can excite the characteristic fluorescence of the internal materials of leaves [17], which makes the detection more accurate [18], but there is still room for reduction in laser price and maintenance cost.…”

mentioning

confidence: 99%

Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network

Lin

Lu³

et al. 2019

Sensors

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A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a CNN model, which is famous for its strong ability in pattern recognition. Principal component analysis combined with k-nearest-neighbor classification was also employed as a baseline for comparison. Six grades of green tea, two types of black tea and one kind of white tea were verified. The result proved a significant improvement in accuracy and showed that the proposed system and methodology provides a fast, compact and robust approach for tea classification.

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Characterization and authentication of Spanish PDO wine vinegars using multidimensional fluorescence and chemometrics

Cited by 77 publications

References 33 publications

Prediction Models to Control Aging Time in Red Wine

Prediction Models to Control Aging Time in Red Wine

Geographical Classification of Tannat Wines Based on Support Vector Machines and Feature Selection

Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network

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