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

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

BACKGROUND

Coffee 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, that is, multiple linear regression and least-squares support vector machine. For that, 70 samples of whole and ground roasted coffees comprising the Agtron roasting range were prepared.

RESULTS

The 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 good determination coefficients and low errors as measured by external validation. For the whole bean coffee, the best model presented a determination coefficient (R2) of 0.99 and a root-mean-squared error (RMSE) of 1.91%, while R2 of 0.99 and RMSE of 0.87% was obtained for ground coffee, both using the colorimeter.

CONCLUSION

The 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. © 2024 Society of Chemical Industry.

Effect of photostimulation through red LED light radiation on natural fermentation of table olives: An innovative case study with Negrinha the Freixo variety

In this study, for the first time, red LED light radiation was applied to the fermentation process of table olives using the Negrinha de Freixo variety. Photostimulation using LED light emission (630 ± 10 nm) is proposed to shorten and speed up this stage and reduce time to market. Several physical-chemical characteristics and microorganisms (total microbial count of mesophilic aerobic, molds, yeasts, and lactic acid bacteria) and their sequence during fermentation were monitored. The fermentation occurred for 122 days, with two irradiation periods for red LED light. The nutritional composition and sensory analysis were performed at the end of the process. Fermentation under red LED light increased the viable yeast and lactic acid bacteria (LAB) cell counts and decreased the total phenolics in olives. Even though significant differences were observed in some color parameters, the hue values were of the same order of magnitude and similar for both samples. Furthermore, the red LED light did not play a relevant change in the texture profile, preventing the softening of the fruit pulp. Similarly, LED light did not modify the existing type of microflora but increased species abundance, resulting in desirable properties and activities. The species identified were yeasts - Candida boidinii, Pichia membranifaciens, and Saccharomyces cerevisiae, and bacteria - Lactobacillus plantarum and Leuconostoc mesenteroides, being the fermentative process dominated by S. cerevisiae and L. plantarum. At the end of fermentation (122 days), the irradiated olives showed less bitterness and acidity, higher hardness, and lower negative sensory attributes than non-irradiated. Thus, the results of this study indicate that red LED light application can be an innovative technology for table olives production.

A digital image-based colorimetric method for measuring free acidity in edible vegetable oils

The standard method used to quantify free acidity (FA) in vegetable oil is neutralization titration, which requires many toxic chemicals and depends on an analyst's experience in detecting endpoints. Here, a digital image colorimetry (DIC) method using a smartphone camera was developed to measure FA in vegetable oils. A cupric acetate solution was used to produce the colorimetric reaction. The coloured solutions were imaged, and R values (from the RGB colour system) were calibrated against the respective FAs in the standards. The FA values of the samples were determined by standard addition calibration. These results were compared to measurements of FA obtained by the standard titrimetric method. An excellent correlation was obtained, with an R2 of 0.98 and a mean absolute error of 0.06%. The chemicals needed for analysis were reduced by approximately 90%. Thus, DIC is a less subjective and more economical method for determining FA in vegetable oils.

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.

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

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.

Determination of phosphorus in water and chemical fertilizer samples using a simple drawing microfluidic paper-based analytical device

A simple one-step drawing for the cost-effective fabrication of microfluidic paper-based analytical devices (µPADs) for the determination of phosphate content in water and fertilizer samples is presented in this paper. The hydrophobic barrier of µPAD was patterned using a 2-mm tip marker pen using a transparent acrylic sheet template. The molybdenum blue reaction using ascorbic acid as a reducing agent was used. A pre-concentration step of samples is proposed to improve the sensitivity of the measurement. The blue complex produced on the µPADs was recorded using a smartphone camera. The color intensities (red, green, blue and gray) were analyzed using ImageJ program. The proposed µPAD method provides a linear calibration range from 0 to 100 mg L^-1 P. The limit of detection (LOD) was found to be 0.7 mg L^-1 P with a precision of 3.1%RSD for 50 mg L^-1 P (n = 10). The proposed method was successfully applied to the determination of phosphorus contents in water and liquid chemical fertilizer samples. The results obtained from µPAD agreed with a spectrophotometric method using paired t test at a 95% confidence level.