Determination of peroxide value in edible oils based on Digital Image Colorimetry

Anconi, Amanda Carolina Souza Andrada; Brito, Nathália Cristina Silva; Nunes, Cleiton Antônio

doi:10.1016/j.jfca.2022.104724

Cited by 17 publications

(7 citation statements)

References 30 publications

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“…Moreover, observing the results of the y-randomization test, it can be seen that all models presented a cR 2 p > 0.5, which indicates no random adjustment. 17 As for the cross-validation and external validation parameters agree with the calibration step, where the colorimetric sensor model presented the highest RMSE and lowest R 2 among them. However, the other models presented low RMSE values for the validation test set.…”

Section: Resultsmentioning

confidence: 80%

“…The intermediate option, the use of smartphone digital image colorimetry, could cost 15–50 times more depending on the model. Reports in the literature can be found on the use of digital images for the classification of coffee fruit maturation stages, 14 the determination of total phenols and antioxidant activity in tomato, strawberry, and coffee, 15 determination of total sterols, 16 and peroxide 17 in edible oils.…”

Section: Introductionmentioning

confidence: 99%

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Feasibility of using colorimetric devices for whole and ground coffee roasting degrees prediction

de Carvalho Pires,

da Silva Mutz,

de Carvalho

et al. 2024

J Sci Food Agric

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BACKGROUNCoffee roasting is one of the crucial steps in obtaining a high‐quality product as it forms the product's color and flavor characteristics. Roast control is made by visual inspection or traditional instruments such as the Agtron spectrophotometer, which can have high implementation costs. Therefore, the present study evaluated colorimetric approaches (a bench colorimeter, smartphone digital images, and a colorimetric sensor) to predict the Agtron roasting degrees of whole and ground coffee. Two calibration approaches were assessed, i.e., Multiple Linear Regression (MLR) and Least‐Squares Support Vector Machine (LS‐SVM). For that, 70 samples of whole and ground roasted coffees comprising the Agtron roasting range were prepared.RESULTSThe results showed that all three colorimetric acquisition types were efficient for the model building, but the bench colorimeter and the smartphone digital images generally performed with great determination coefficients and low errors as measured by external validation. For the whole bean coffee, the best model presented an R2 of 0.99 and RMSE of 1.91%, while R2 of 0.99 and RMSE of 0.87% was obtained for ground coffee, both using the colorimeter.CONCLUSIONThe obtained models presented good prediction capability, as assessed by external validation and randomization tests. The obtained findings point to an alternative for coffee roasting monitoring that can lead to higher digitalization and local control of the process, even for smaller producers, due to its lower costs.This article is protected by copyright. All rights reserved.

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Section: Resultsmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Feasibility of using colorimetric devices for whole and ground coffee roasting degrees prediction

de Carvalho Pires,

da Silva Mutz,

de Carvalho

et al. 2024

J Sci Food Agric

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“…Perez-Calabuig et al (2023) used optical images and artificial neural networks to achieve a 95% accuracy classification of avocado oil blended with a range of 1 to 15% refined olive oil [ 34 ]. A higher number of reports exist regarding the successful quantification of quality parameters in avocado oil using smartphone images, such as peroxide values [ 21 ], chlorophyll and carotenoids [ 22 ], and total sterols [ 20 ].…”

Section: Resultsmentioning

confidence: 99%

“…Among the alternatives, digital image colorimetry (DIC) is a promising candidate, and a recent focus of the scientific community in studies regarding oil quality [ 19 , 20 , 21 , 22 , 23 ]. The technique involves extracting color variables in a pre-defined color space (e.g., RGB), quantifying them, and constructing desired models [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring Strategies to Mitigate the Lightness Effect on the Prediction of Soybean Oil Content in Blends of Olive and Avocado Oil Using Smartphone Digital Image Colorimetry

de Carvalho,

da Silva Mutz,

Machado

et al. 2023

Foods

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Extra virgin olive oil (EVOO) and avocado oil (AVO) are recognized for their unique sensory characteristics and bioactive compounds. Declared blends with other vegetable oils are legal, but undeclared mixing is a common type of fraud that can affect product quality and commercialization. In this sense, this study explored strategies to mitigate the influence of lighting in order to make digital image colorimetry (DIC) using a smartphone more robust and reliable for predicting the soybean oil content in EVOO and AVO blends. Calibration models were obtained by multiple linear regression using the images’ RGB values. Corrections based on illuminance and white reference were evaluated to mitigate the lightness effect and improve the method’s robustness and generalization capability. Lastly, the prediction of the built model from data obtained using a distinct smartphone was assessed. The results showed models with good predictive capacities, R2 > 0.9. Generally, models solely based on GB values showed better predictive performances. The illuminance corrections and blank subtraction improved the predictions of EVOO and AVO samples, respectively, for image acquisition from distinct smartphones and lighting conditions as evaluated by external validation. It was concluded that adequate data preprocessing enables DIC using a smartphone to be a reliable method for analyzing oil blends, minimizing the effects of variability in lighting and imaging conditions and making it a potential technique for oil quality assurance.

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“…In this platform, the image is divided into red, green, and blue channels, and a whole number is assigned for each of the three colors in a given tone of the image [13]. Due to its advantages, DIC has been applied in the colorimetric analysis of various analytes in different sensing platforms such as hydrogen peroxide in aqueous solutions [16], iron in dietary supplements [17], aspirin and salicylic acid [18], and peroxide value in edible oils [19]. For the visual indicator, leaf extracts of Cordyline fruticosa were used as it is reported to contain pigments [20] that can be used for the digital image analysis of water alkalinity.…”

Section: Introductionmentioning

confidence: 99%

RGB-based colorimetric method for the analysis of water alkalinity using Cordyline fruticosa extracts as visual indicator

Parungao,

Reyes

2024

IJPHS

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Leaf extracts of Cordyline fruticosa was used as a natural visual indicator for a digital image-based analytical procedure for the measurement of water alkalinity. A smartphone and ImageJ were used to capture and process the images, respectively. This work sought to develop and evaluate a greener strategy of using of Cordyline fruticosa leaf extract as natural indicator, in a digital image-based colorometric set-up using smartphone. Under optimized conditions, the linear working range was from 40 to 160 ppm of CaCO3, with a calibration sensitivity of 5 x 10-3 ppm and a coefficient of correlation (r2) = 0.998. The limit of detection and quantification were 39 ppm and 117 ppm, respectively. A 10,000 ppm CaCO3 solution was tested using the proposed method and the results statistically agree with the results obtained from titrimetric methods. The study presented a simple analytical procedure that can be develop and apply for the assessment of pollution that could pose serious human and environmental health hazards.

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Determination of peroxide value in edible oils based on Digital Image Colorimetry

Cited by 17 publications

References 30 publications

Feasibility of using colorimetric devices for whole and ground coffee roasting degrees prediction

Feasibility of using colorimetric devices for whole and ground coffee roasting degrees prediction

Exploring Strategies to Mitigate the Lightness Effect on the Prediction of Soybean Oil Content in Blends of Olive and Avocado Oil Using Smartphone Digital Image Colorimetry

RGB-based colorimetric method for the analysis of water alkalinity using Cordyline fruticosa extracts as visual indicator

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