Cachaça Classification Using Chemical Features and Computer Vision

Rodrigues, Bruno Urbano; Costa, Ronaldo Martins da; Salvini, Rogerio; Soares, Anderson da Silva; Silva, Flávio Alves da; Caliari, Márcio; Cardoso, Karla Cristina Rodrigues; Ribeiro, Tânia Isabel Monteiro

doi:10.1016/j.procs.2014.05.186

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…14,16,17,19 Although the models developed by Rodrigues co-workers exhibited better qualitative performance parameters, commercial samples were not used for their construction and validation, significantly reducing the data variance, and consequently the applicability and robustness of these models. 17,19 Moreover, one more disadvantage presented by the models developed by Rodrigues et al was the need for several steps of complex image-processing methods. 17 The model developed in this work showed performance parameters similar to the models by Fernandes et al, 14 and better performance parameters than those by Bernardes and Barbeira.…”

Section: Discussionmentioning

confidence: 99%

“…[9][10][11][12][13] A brief description of works that present the separation of aged cachaças by the wood used in the maturation process using different analytical and chemometric methods is shown in Table 1. [14][15][16][17][18][19][20][21][22][23] The following unsupervised exploratory methods: principal component analysis (PCA) and hierarchical cluster analysis have been used in combination with high-performance liquid chromatography coupled with diode array detection, 15,18,20 sensorial analysis, 21 electrospray ionization mass spectrometry, 22 and electronic spectroscopy. 23 Supervised classification methods have also been used: Rodrigues and co-workers presented models using artificial neural networks, k-nearest neighbor, linear discriminant analysis, quadratic discriminant analysis, multilayer perceptron, and support vector machine coupled to a computer vision system and chemical information, obtaining accuracy up to 100%.…”

Section: Introductionmentioning

confidence: 99%

“…23 Supervised classification methods have also been used: Rodrigues and co-workers presented models using artificial neural networks, k-nearest neighbor, linear discriminant analysis, quadratic discriminant analysis, multilayer perceptron, and support vector machine coupled to a computer vision system and chemical information, obtaining accuracy up to 100%. 17,19 Fernandes et al used digital images decomposed into color histograms associated with k-nearest neighbor, linear discriminant analysis, quadratic discriminant analysis, and partial least squares discriminant analysis (PLS-DA), reaching accuracy, sensitivity, and specificity rates higher than 90%. 14 Bernardes and Barbeira presented data fusion PLS2-DA models using electronic spectroscopy and spectrofluorimetry with reliability rates ranging from 56 to 100%.…”

Section: Introductionmentioning

confidence: 99%

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A fast and low‐cost approach for the discrimination of commercial aged cachaças using synchronous fluorescence spectroscopy and multivariate classification

Silveira

Barbeira

2022

J Sci Food Agric

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BACKGROUND: Cachaça is the distilled beverage typical of Brazil and can be subjected to the aging process in wooden barrels. In addition to oak barrels, cachaça is also aged in barrels of different Brazilian native woods, resulting in a wide variety of its sensory characteristics. In this work, 172 cachaças aged in bálsamo, jequitibá, oak, and umburana barrels were analyzed by synchronous fluorescence spectroscopy and by the classification methods of principal component analysis and partial least squares discriminant analysis. Spectra were preprocessed by the first derivative by Savitzky-Golay smoothing, using a filter width and polynomial order determined through face-centered central composite designs. Multivariate analysis was realized using the spectra recorded at different wavelength differences, and models were compared by the classification errors in the test sets. RESULTS:The principal component analysis applied to the synchronous fluorescence spectra presented a tendency of separation by the wood used in the aging process, and the partial least squares discriminant analysis model constructed using the fluorescence spectra recorded at a wavelength difference of 30 nm provided better performance parameters (efficiency 91-97%, sensitivity 81-100%, and specificity 91-100%).CONCLUSION: Synchronous fluorescence spectroscopy offers a promising approach for the classification of cachaças aged in bálsamo, oak, jequitibá, and umburana barrels, and the discriminant model can be used for routine analysis as a screening method.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A fast and low‐cost approach for the discrimination of commercial aged cachaças using synchronous fluorescence spectroscopy and multivariate classification

Silveira

Barbeira

2022

J Sci Food Agric

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“…There have not been many CV methods developed for spirits. However, in beverages such as cachaça, which is a Brazilian distilled drink made from sugarcane, a method to assess color from image analysis was developed using a lighting source below the glass as a background and a digital camera placed above the glass, obtaining results in both RGB and CIELab color scales [53]. Pessoa et al [54] developed a method based on color assessment to determine copper in sugarcane spirits using images captured with a digital camera with a Bayer RGB mosaic filter, which consists of a grid of color filters and photosensors, and a charge-coupled device, apart from an illuminated black box, and a porcelain plaque.…”

Section: Computer Vision In Alcoholic Beveragesmentioning

confidence: 99%

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

et al. 2019

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Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

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“…According to Pathare et al (2013), alternatives to chemometric techniques include the use of a colorimeter, which is a light-sensitive instrument that measures how much color is absorbed by an object or substance. Recently, Rodrigues et al (2014) showed that colorimeters can obtain useful information about types of Brazilian rum. However, chemometric and colorimetric approaches are expensive in actual practice.…”

Section: Introductionmentioning

confidence: 99%

A feasibility cachaca type recognition using computer vision and pattern recognition

Rodrigues

Soares

Costa

et al. 2016

Computers and Electronics in Agriculture

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a b s t r a c tBrazilian rum (also known as cachaça) is the third most commonly consumed distilled alcoholic drink in the world, with approximately 2.5 billion liters produced each year. It is a traditional drink with refined features and a delicate aroma that is produced mainly in Brazil but consumed in many countries. It can be aged in various types of wood for 1-3 years, which adds aroma and a distinctive flavor with different characteristics that affect the price. A research challenge is to develop a cheap automatic recognition system that inspects the finished product for the wood type and the aging time of its production. Some classical methods use chemical analysis, but this approach requires relatively expensive laboratory equipment. By contrast, the system proposed in this paper captures image signals from samples and uses an intelligent classification technique to recognize the wood type and the aging time. The classification system uses an ensemble of classifiers obtained from different wavelet decompositions. Each classifier is obtained with different wavelet transform settings. We compared the proposed approach with classical methods based on chemical features. We analyzed 105 samples that had been aged for 3 years and we showed that the proposed solution could automatically recognize wood types and the aging time with an accuracy up to 100.00% and 85.71% respectively, and our method is also cheaper.

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Cachaça Classification Using Chemical Features and Computer Vision

Cited by 7 publications

References 11 publications

A fast and low‐cost approach for the discrimination of commercial aged cachaças using synchronous fluorescence spectroscopy and multivariate classification

A fast and low‐cost approach for the discrimination of commercial aged cachaças using synchronous fluorescence spectroscopy and multivariate classification

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

A feasibility cachaca type recognition using computer vision and pattern recognition

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