Adaptation of the Folin-Ciocalteu and Fast Blue BB spectrophotometric methods to digital image analysis for the determination of total phenolic content: Reduction of reaction time, interferences and sample analysis

Zugazua-Ganado, Maider; Bordagaray, Ane; Ezenarro, Jokin; Garcia-Arrona, Rosa; Ostra, Miren; Vidal, Maider

doi:10.1016/j.lwt.2024.115756

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…Similar precision (%RSD < 2.4) was achieved by Lucas et al [28], indicating the reliability and accuracy (98.4 to 103.8%) of the developed smartphone-based method for analyzing TPC in plant extracts using the HSV color model, specifically the S color parameter. It is noted that the %RSD for spectrophotometry reported in the literature varies between 0.1 and 4.8% [18,[47][48][49]. However, the %RSD for the method developed in this study was notably lower.…”

Section: Validation Of a Smartphone Analytical Methods For Total Phen...mentioning

confidence: 62%

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Revolutionizing Phenolic Content Determination in Vegetable Oils: A Cutting-Edge Approach Using Smartphone-Based Image Analysis

Vucane,

Cinkmanis,

Juhnevica-Radenkova

et al. 2024

Foods

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This study addressed the need for a more accessible and efficient method of analyzing phenolic content in vegetable oils. The research aimed to develop a method that could be widely adopted by both researchers and industry professionals, ultimately revolutionizing the way phenolic content in vegetable oils is analyzed. This study developed a method of determining the total phenolic content (TPC) in vegetable oils using smartphone image analysis in the RGB color model. The method employed a gallic acid calibration solution and demonstrated exceptional determination coefficients for the RGB colors. The R—red color was selected as the basis for the analyses, and the method was statistically equivalent to standard UV/Vis spectrophotometry. The highest TPC was determined in hemp and olive oils, while the lowest was found in rice bran, grapeseed, and macadamia nut oils. This study concluded that smartphone image analysis, mainly using the R component of the RGB color model, was a superior alternative to traditional spectrophotometric methods for determining the TPC in vegetable oils. This innovative approach could revolutionize phenolic content analysis by providing researchers and industry professionals with a cost-effective, safe, and efficient tool. The estimated limit of detection (LOD) of 1.254 mg L−1 and limit of quantification (LOQ) of 3.801 mg L−1 further confirmed the reliability and comparability of the method. With these findings, it was expected that the method would be widely adopted in the future.

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Section: Validation Of a Smartphone Analytical Methods For Total Phen...mentioning

confidence: 62%

“…It is noted that the %RSD for spectrophotometry reported in the literature varies between 0.1 and 4.8% [ 18 , 47 , 48 , 49 ]. However, the %RSD for the method developed in this study was notably lower.…”

Section: Resultsmentioning

confidence: 99%

Revolutionizing Phenolic Content Determination in Vegetable Oils: A Cutting-Edge Approach Using Smartphone-Based Image Analysis

Vucane,

Cinkmanis,

Juhnevica-Radenkova

et al. 2024

Foods

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“…Spreadsheet LWT contains the total phenolic content (mg GAE/g sample) of tea and juice samples determined using four different methods …”

Section: Methodsmentioning

confidence: 99%

Enhancing Statistical Education in Chemistry and STEAM Using JAMOVI. Part 2. Comparing Dependent Groups and Principal Component Analysis (PCA)

Sequeira,

Borges

2024

J. Chem. Educ.

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“…The absorbance of n/m BP was measured using a UV–VIS spectrophotometer (XD7500, LOVIBOND, Dortmund, Germany) at 760 nm. After preparing standard solutions of gallic acid, a calibration curve was established in the concentration range of 5–30 μg/mL (Zugazua‐Ganado et al., 2024 ). The results were reported in mg of gallic acid per g of n/m BP.…”

Section: Methodsmentioning

confidence: 99%

The effect of nano/microparticles of bee pollen on the shelf life of high‐fat cooked sausage during refrigerated storage

Mashhadi,

Davati,

Emamifar

et al. 2024

Food Science & Nutrition

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Sausage is susceptible to oxidative changes in lipids and microbial spoilage due to the presence of water, fat, protein, and vitamins. Bee pollen (BP) as a source of potential antioxidants and antibacterial compounds can effectively prevent lipid peroxidation and microbial spoilage in meat products. The aim of the present study was to investigate the antibacterial and antioxidant activities of BP and the effects of nano/microparticles of bee pollen extract (n/m BP) at a concentration of 125 and 250 mg/100 g meat on the oxidative stability and microbial growth of high‐fat sausage during 30 days of storage at 4°C. The formation of BP particles in the nano/micro range was confirmed by scanning electron microscopy. High concentrations of total phenolic compounds (28.26 ± 0.10 mg GAE/g BP) with antioxidant activity (EC50 = 5.4 ± 0.07 mg/mL) were detected in BP. Based on the microdilution assay, the minimum inhibitory concentration of n/m BP for all test bacteria was 1000 (μg/mL) and the minimum bactericidal concentration of n/m BP was 2000 (μg/mL) for Staphylococcus aureus and Bacillus cereus and 4000 (μg/mL) for Escherichia coli and Pseudomonas aeruginosa. The n/m BP treatment (250 mg/100 g meat) showed a higher pH value (p < .05) and lower TBARS values (p < .05) than the ascorbic acid treatment (100 mg/100 g meat) and the control during the storage period. The microbial analysis showed that the addition of n/m BP led to a significant decrease (p < .05) in the total bacterial count, coliforms, S. aureus, and fungal population compared to the other samples. The results show that the addition of n/m BP (125 mg/100 g) can improve the texture, taste, and overall acceptability of the sausage compared to the control sample. In conclusion, this study suggests that BP can replace synthetic antioxidants in high‐fat sausages at the nano/microparticle level.

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Adaptation of the Folin-Ciocalteu and Fast Blue BB spectrophotometric methods to digital image analysis for the determination of total phenolic content: Reduction of reaction time, interferences and sample analysis

Cited by 4 publications

References 22 publications

Revolutionizing Phenolic Content Determination in Vegetable Oils: A Cutting-Edge Approach Using Smartphone-Based Image Analysis

Revolutionizing Phenolic Content Determination in Vegetable Oils: A Cutting-Edge Approach Using Smartphone-Based Image Analysis

Enhancing Statistical Education in Chemistry and STEAM Using JAMOVI. Part 2. Comparing Dependent Groups and Principal Component Analysis (PCA)

The effect of nano/microparticles of bee pollen on the shelf life of high‐fat cooked sausage during refrigerated storage

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