Smartphone-based method for the determination of chlorophyll and carotenoid contents in olive and avocado oils: An approach with calibration transfer

Carvalho, Thaís Cristina Lima de; Nunes, Cleiton Antônio

doi:10.1016/j.jfca.2021.104164

Cited by 18 publications

(14 citation statements)

References 29 publications

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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%

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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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Section: Resultsmentioning

confidence: 99%

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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“…Color space transformation, demosaicing [50], denoising [50] calibration [51], illumination and sensor property normalization [51]…”

Section: Conditions During Data Acquisitionmentioning

confidence: 99%

“…Currently, most ML models learn to recognize both low and high-level features, i.e., directly identifiable features in the data (e.g., object classification), and process these features from the training data without explicit coding [51,67]. Therefore, ML allows SbSs to interpret biosensor signals, even in complex sample matrices and uncontrolled scene background conditions, assuming the AI model has been adequately trained [36].…”

Section: Ai For Feature Selectionmentioning

confidence: 99%

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Best practices and current implementation of emerging smartphone-based (bio)sensors – Part 1: Data handling and ethics

Ross

Zhao

Bosman

et al. 2023

TrAC Trends in Analytical Chemistry

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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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Smartphone-based method for the determination of chlorophyll and carotenoid contents in olive and avocado oils: An approach with calibration transfer

Cited by 18 publications

References 29 publications

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

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

Best practices and current implementation of emerging smartphone-based (bio)sensors – Part 1: Data handling and ethics

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

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