Using a simple digital camera and SPA-LDA modeling to screen teas

Diniz, Paulo Henrique Gonçalves Dias; Dantas, Hebertty Vieira; Melo, Karla D. T.; Barbosa, Mayara F.; Harding, David P.; Nascimento, Elaine Cristina Lima do; Pistonesi, Marcelo Fabián; Band, Beatriz Susana Fernández; Araújo, Mário César Ugulino de

doi:10.1039/c2ay25481f

Cited by 41 publications

(24 citation statements)

References 54 publications

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“…Glass, quartz or prismatic PTFE cuvettes 17,[29][30][31] and flow cells 32,33 have been used similarly to spectrophotometry involving specific colorimetric reactions, replacing exclusively the detector. Other common holders have included glass, plastic and lab-made polytetrafluorethylene (PTFE) Petri plates 25,27,28,[34][35][36][37][38][39][40][41][42][43][44][45][46] have been the most employed holder type to dispose the samples directly without pretreatment. PTFE chambers of automated Flow-Batch Analyzers 26,47 have been also used to prepare samples and calibration solutions in flow into the mixing chamber.…”

Section: Instrumentation For Cachasmentioning

confidence: 99%

Chemometrics‐assisted color histogram‐based analytical systems

Diniz

2020

Journal of Chemometrics

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This review systematizes for the first time the here called "Chemometricsassisted color histogram-based analytical systems" under the acronym CACHAS. A comprehensive discussion of the fundamentals, general characteristics, and applications are presented in order to direct practical aspects for developing methods that use color histograms as input data for the construction of chemometric models. For this, analytical information is rapidly acquired using a simple image acquisition device (such as digital camera, webcam, scanner, or smartphone), without need for any image processing, other than extraction of the histograms, which are constructed by counting the frequency distribution of the different color tones (conveniently specified through a color space) of all pixels delimited by a region of interest in a digital image. As a consequence, CACHAS have been increasingly used for qualitative and quantitative purposes in food, agricultural, forensic, biomedical, and microbiological analysis, reinforcing the principles of green analytical chemistry.

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Section: Instrumentation For Cachasmentioning

confidence: 99%

Chemometrics‐assisted color histogram‐based analytical systems

Diniz

2020

Journal of Chemometrics

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“…Diniz et al [32] used a color histogram (CH) with the software ImageJ 1.44p as an analytical information to screen teas. Yu et al [33] combined CH and texture information to classify green teas.…”

Section: Color Histogrammentioning

confidence: 99%

Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm

Zhang

Yang

Cattani

et al. 2016

Entropy

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This work proposes a tea-category identification (TCI) system, which can automatically determine tea category from images captured by a 3 charge-coupled device (CCD) digital camera. Three-hundred tea images were acquired as the dataset. Apart from the 64 traditional color histogram features that were extracted, we also introduced a relatively new feature as fractional Fourier entropy (FRFE) and extracted 25 FRFE features from each tea image. Furthermore, the kernel principal component analysis (KPCA) was harnessed to reduce 64 + 25 = 89 features. The four reduced features were fed into a feedforward neural network (FNN). Its optimal weights were obtained by Jaya algorithm. The 10ˆ10-fold stratified cross-validation (SCV) showed that our TCI system obtains an overall average sensitivity rate of 97.9%, which was higher than seven existing approaches. In addition, we used only four features less than or equal to state-of-the-art approaches. Our proposed system is efficient in terms of tea-category identification.

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“…On the other hand, color histograms describe the statistical distribution of the pixels in a digital image as a function of the recorded color component, and not a feature or a physicalchemical behavior directly [22]. Color histograms have been successfully used as input data for classification of teas [22], honeys [23], and edible vegetable oils [24].…”

Section: Introductionmentioning

confidence: 99%

Using color histograms and SPA-LDA to classify bacteria

et al. 2014

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In this work, a new approach is proposed to verify the differentiating characteristics of five bacteria (Escherichia coli, Enterococcus faecalis, Streptococcus salivarius, Streptococcus oralis, and Staphylococcus aureus) by using digital images obtained with a simple webcam and variable selection by the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). In this sense, color histograms in the red-green-blue (RGB), hue-saturation-value (HSV), and grayscale channels and their combinations were used as input data, and statistically evaluated by using different multivariate classifiers (Soft Independent Modeling by Class Analogy (SIMCA), Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Partial Least Squares Discriminant Analysis (PLS-DA) and Successive Projections Algorithm-Linear Discriminant Analysis (SPA-LDA)). The bacteria strains were cultivated in a nutritive blood agar base layer for 24 h by following the Brazilian Pharmacopoeia, maintaining the status of cell growth and the nature of nutrient solutions under the same conditions. The best result in classification was obtained by using RGB and SPA-LDA, which reached 94 and 100 % of classification accuracy in the training and test sets, respectively. This result is extremely positive from the viewpoint of routine clinical analyses, because it avoids bacterial identification based on phenotypic identification of the causative organism using Gram staining, culture, and biochemical proofs. Therefore, the proposed method presents inherent advantages, promoting a simpler, faster, and low-cost alternative for bacterial identification.

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Using a simple digital camera and SPA-LDA modeling to screen teas

Cited by 41 publications

References 54 publications

Chemometrics‐assisted color histogram‐based analytical systems

Chemometrics‐assisted color histogram‐based analytical systems

Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm

Using color histograms and SPA-LDA to classify bacteria

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