Screening for Coffee Adulteration Using Digital Images and SPA-LDA

Souto, Urijatan Teixeira de Carvalho Polari; Barbosa, Mayara F.; Dantas, Hebertty Vieira; Pontes, Aline Santos de; Lyra, Wellington da Silva; Diniz, Paulo Henrique Gonçalves Dias; Araújo, Mário César Ugulino de; Silva, Edvan Cirino da

doi:10.1007/s12161-014-0020-7

Cited by 31 publications

(21 citation statements)

References 21 publications

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“…In addition, the accuracy of the classification was found to be greater in the prediction set than in the training set. Similar results were also observed in other studies (Costa et al., 2015, 2016; Souto et al., 2015a, b).…”

Section: Resultssupporting

confidence: 92%

Occurrence of wooden breast and white striping in Brazilian slaughtering plants and use of near‐infrared spectroscopy and multivariate analysis to identify affected chicken breasts

Carvalho

Madruga

Estévez

et al. 2020

Journal of Food Science

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White Striping (WS) and Wooden Breast (WB) are emerging poultry myopathies that occur worldwide, affecting the quality of meat. The aim of this study was to evaluate the occurrence of N, WS, WB, and WS/WB (myopathies combined) in chicken breast from Brazilian commercial plant, comparing (1) inspection based on visual aspect and palpation of Pectoralis major muscle, and (2) identification of these myopathies by near-infrared Spectroscopy (NIRS). Chickens slaughtered at Brazilian commercial plant at four age ranges (4 to 5, 6 to 7, 8 to 9, and 65 weeks) were inspected. Spectral information was acquired using a portable NIR spectrometer, and classification models were performed using and Successive Projection Algorithm-Linear Discriminant Analysis (SPA-LDA) and Soft Independent Modeling of Class Analogy (SIMCA) to distinguish normal and affected muscles. Results showed that occurrence of myopathies was aggravated by age of slaughter, as chicken slaughtered at 4 to 5 and 65 weeks exhibited 13.6 and 95% of myopathies, respectively. Birds slaughtered at 65 weeks showed no occurrence of WB, isolated or combined with WS. It was not possible to differentiate the WB and WS/WB classes; therefore, those samples were grouped (WB+WS/WB). SPA-LDA model showed greater accuracy (92 to 93%) in identifying Normal (N), WS, and WB+WS/WB groups, compared to SIMCA (89 to 91%). It can be concluded that the level of occurrence of myopathies in meat is directly related to the age of slaughter. This study demonstrated that NIRS combined with SPA-LDA model could be used as a tool to detect myopathies in chicken breast. This technique has potential for application in industrial processing lines as an alternative to the traditional methods of identification.

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Section: Resultssupporting

confidence: 92%

Occurrence of wooden breast and white striping in Brazilian slaughtering plants and use of near‐infrared spectroscopy and multivariate analysis to identify affected chicken breasts

Carvalho

Madruga

Estévez

et al. 2020

Journal of Food Science

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“…For illustration, Figure 5 shows examples of CACHAS architecture designed for some applications employing different image capturing devices, which is undoubtably its main component: (a) digital camera, 25 (b) webcam, 26 (c) smartphone, 27 and (d) scanner. 28 As can be seen, Petri plates, a Flow-Batch chamber, and a quartz cuvette have been used as sample holders, whereas fluorescent lamps or LEDs have been used as illumination sources. In the examples that employ digital camera, 25 webcam 26 and scanner, 28 histogram extraction, and data analysis are performed in the offline mode in a computer.…”

Section: Instrumentation For Cachasmentioning

confidence: 99%

“…3 An image capturing device to register the images of the chemical system under consideration. Simple commercial devices include digital cameras, 15,25,30,41,45,52,53,59,60 scanners, 21,28,34,35,39,43,46,[54][55][56][57]61 webcams, 26,29,32,33,[36][37][38]40,42,44,[47][48][49] and smartphones, 17,18,27,31,50,51,58 and the choice of one of them shall be according to its availability in each laboratory or specific need for a given application. However, it is important to note that the portability of systems through the use of smartphones has become a remarkable trend in the literature, mainly in applications involving univariate calibration for colorimetric analysis.…”

Section: Instrumentation For Cachasmentioning

confidence: 99%

“…An exception is when using scanners, which already have a shield cover to prevent external interferences, or a protective box 39 can be also adapted for this same purpose. An image capturing device to register the images of the chemical system under consideration. Simple commercial devices include digital cameras, 15,25,30,41,45,52,53,59,60 scanners, 21,28,34,35,39,43,46,54–57,61 webcams, 26,29,32,33,36–38,40,42,44,47–49 and smartphones, 17,18,27,31,50,51,58 and the choice of one of them shall be according to its availability in each laboratory or specific need for a given application. However, it is important to note that the portability of systems through the use of smartphones has become a remarkable trend in the literature, mainly in applications involving univariate calibration for colorimetric analysis. A computer containing a software for extraction of the color histograms from the registered images and other for construction of the chemometric models.…”

Section: Instrumentation For Cachasmentioning

confidence: 99%

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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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“…With the considerable increase in the coffee industry, it is necessary to develop techniques to identify the adulterants in the product [ 4 ]. Several analytical techniques to identify and quantify adulterants in coffee have been developed, for example, anion exchange chromatography [ 7 ], capillary electrophoresis [ 8 ], near infrared Fourier transform spectroscopy (FT-NIR) [ 9 ], high-performance liquid chromatography (HPLC) [ 10 ] and UV-vis spectroscopy [ 11 ]. Although these techniques are sensitive and precise, they can be performed only by trained personnel, and the sample preparation time may be extremely large.…”

Section: Introductionmentioning

confidence: 99%

Identification and Quantification of Adulterants in Coffee (Coffea arabica L.) Using FT-MIR Spectroscopy Coupled with Chemometrics

Flores-Valdez

Meza-Márquez

Osorio-Revilla

et al. 2020

Foods

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Food adulteration is an illegal practice performed to elicit economic benefits. In the context of roasted and ground coffee, legumes, cereals, nuts and other vegetables are often used to augment the production volume; however, these adulterants lack the most important coffee compound, caffeine, which has health benefits. In this study, the mid-infrared Fourier transform spectroscopy (FT-MIR) technique coupled with chemometrics was used to identify and quantify adulterants in coffee (Coffea arabica L.). Coffee samples were adulterated with corn, barley, soy, oat, rice and coffee husks, in proportions ranging from 1–30%. A discrimination model was developed using the soft independent modeling of class analogy (SIMCA) framework, and quantitative models were developed using such algorithms as the partial least squares algorithms with one variable (PLS1) and multiple variables (PLS2) and principal component regression (PCR). The SIMCA model exhibited an accuracy of 100% and could discriminate among all the classes. The quantitative model with the highest performance corresponded to the PLS1 algorithm. The model exhibited an R2c: ≥ 0.99, standard error of calibration (SEC) of 0.39–0.82, and standard error of prediction (SEP) of 0.45–0.94. The developed models could identify and quantify the coffee adulterants, and it was considered that the proposed methodology can be applied to identify and quantify the adulterants used in the coffee industry.

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Screening for Coffee Adulteration Using Digital Images and SPA-LDA

Cited by 31 publications

References 21 publications

Occurrence of wooden breast and white striping in Brazilian slaughtering plants and use of near‐infrared spectroscopy and multivariate analysis to identify affected chicken breasts

Occurrence of wooden breast and white striping in Brazilian slaughtering plants and use of near‐infrared spectroscopy and multivariate analysis to identify affected chicken breasts

Chemometrics‐assisted color histogram‐based analytical systems

Identification and Quantification of Adulterants in Coffee (Coffea arabica L.) Using FT-MIR Spectroscopy Coupled with Chemometrics

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