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.

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.

Combination of RT-LAMP and fluorescence spectroscopy using chemometric techniques for an ultra-sensitive and rapid alternative for the detection of SARS-CoV-2.

The increased spread of COVID-19 caused by SARS-CoV-2 has made it necessary to develop more efficient, fast, accurate, specific, sensitive and easy-to-use detection platforms to overcome the disadvantages of gold standard methods (RT-qPCR). Here an approach was developed for the detection of the SARS-CoV-2 virus using the loop-mediated isothermal amplification (LAMP) technique for SARS-CoV-2 RNA target amplification in samples of nasopharyngeal swabs. The discrimination between positive and negative SARS-CoV-2 samples was achieved by using fluorescence spectra generated by the excitation of the LAMP's DNA intercalator dye at λ497 nm in a fluorescence spectrophotometer and chemometric tools. Exploratory analysis of the 83 sample spectra using principal component analysis (PCA) indicated a trend in differentiation between positive and negative samples resulting from the peak emission of the fluorescent dye. The classification was performed by partial least squares discriminant analysis (PLS-DA) achieving a sensitivity, a specificity and an accuracy of 100%, 95% and 89%, respectively for the discrimination between negative and positive samples from 1.58 to 0.25 ng L-1 after LAMP amplification. Therefore, this study indicates that the use of the LAMP technique in fluorescence spectroscopy may offer a fast (<1 hour), sensitive and low-cost method.

Everybody loves cheese: crosslink between persistence and virulence of Shiga-toxin Escherichia coli.

General cheese manufacturing involves high temperatures, fermentation and ripening steps that function as hurdles to microbial growth. On the other hand, the application of several different formulations and manufacturing techniques may create a bacterial protective environment. In cheese, the persistent behavior of Shiga toxin-producing Escherichia coli (STEC) relies on complex mechanisms that enable bacteria to respond to stressful conditions found in cheese matrix. In this review, we discuss how STEC manages to survive to high and low temperatures, hyperosmotic conditions, exposure to weak organic acids, and pH decreasing related to cheese manufacturing, the cheese matrix itself and storage. Moreover, we discuss how these stress responses interact with each other by enhancing adaptation and consequently, the persistence of STEC in cheese. Further, we show how virulence genes eae and tir are affected by stress response mechanisms, increasing either cell adherence or virulence factors production, which leads to a selection of more resistant and virulent pathogens in the cheese industry, leading to a public health issue.

Ultrasound improves chemical reduction of natural contaminant microbiota and Salmonella enterica subsp. enterica on strawberries.

New sanitization methods have been evaluated to improve food safety and food quality and to replace chlorine compounds. However, these new methods can lead to physicochemical and sensory changes in fruits and vegetables. The present study evaluated the effects of acetic acid, peracetic acid, and sodium dodecylbenzenesulfonate isolated or combined with 5min of ultrasound treatment (40kHz, 500W) on strawberry quality over 9days of storage at 8°C. The strawberry natural contaminant microbiota (molds and yeasts, mesophilic aerobic and lactic acid bacteria), physicochemical quality (pH, total titratable acidity, total soluble solids, vitamin C, and color), sensory quality (triangle test) and inactivation of Salmonella enterica subsp. enterica intentionally inoculated onto strawberries were analyzed. Ultrasound increased the effect of all chemical compounds in the reduction of aerobic mesophilic, molds and yeasts. The best treatment for those groups of microorganisms was ultrasound combined with peracetic acid (US+PA) that reduced 1.8 and 2.0logcfu/g during 9days of storage. Bactericidal effect of peracetic acid was also improved by ultrasound inactivation of S. enterica, reaching a decimal reduction of 2.1logcfu/g. Moreover, synergistic effects were observed in contaminant natural microbiota inactivation for all tested compounds during storage, without any major physicochemical or sensory alteration to the strawberries. Therefore, ultrasound treatment can improve the effect of sanitizers that are substitutes of chlorine compounds without altering the quality of strawberries during storage. Acetic acid (PubChem CID: 176); Peracetic acid (PubChem CID: 6585); Sodium dodecylbenzenesulfonate (PubChem CID: 18372154).