A predictive approach to the antioxidant capacity assessment of green and black tea infusions

Abstract

Contemporary consumers drink significant amounts of tea because of its health–benefits mainly associated to the presence of polyphenols with high antioxidant activity. Therefore, the information how to obtain tea infusion of the highest quality, i.e. with high antioxidant capacity is needed. In this study, the various models for the prediction of total polyphenols and antioxidant activity of green and black tea infusions were developed and compared. Three mathematical equations: Spiro’s, Peleg’s and logarithmic, and two data mining techniques: multivariate adaptive regression splines (MARS) and artificial neural networks (ANNs) were used to build the predictive models. The results obtained show that Spiro’s model could be used for the prediction of green tea quality expressed as total phenolic content or the antioxidant activity (determination coefficients above 0.99), whereas Peleg’s model is more suitable for black tea quality prediction (determination coefficients above 0.99). Data mining techniques (MARS and ANNs) enable to create models fast and of simple application with very good acceptability (determination coefficients above 0.99).

Predictive Modeling of Changes in TBARS in the Intramuscular Lipid Fraction of Raw Ground Beef Enriched with Plant Extracts

“…ANNs were also applied to monitor chicken meat authenticity [ 22 ], to distinguish volatile compounds from meat cuts using a GC-MS analysis [ 23 ] and to predict the multiple quality of dry-cured ham based on protein degradation [ 24 ]. The ANNs, known as “black-box” models, do not make any assumption on the relations between variables and could be applied for the huge data matrix, providing very complex functions including non-linear multiple regressions [ 25 , 26 ]. The main advantage of ANNs is the ability to learn and adapt to the changing experimental conditions and to generalize, which enables the usage of the model to the unlearnt (new) data [ 21 , 26 ].…”

Global perspective on food fraud with special emphasis on the prevalence of food fraud practices and policies in Pakistan

“…Tea, an infusion prepared from the leaves of Camellia sinensis , is arguably the second most consumed beverage in the world (Prasanth et al, 2019). Only about 20% of the tea leaves worldwide are used as green tea (non‐fermented), whereas the rest 80% are consumed as black tea (produced by fermentation‐induced enzymatic oxidation of the tea leaves) (Muzolf‐Panek et al, 2021). In unregulated markets, such as Pakistan, and similar to the scenario involving spices, whereby both branded and non‐branded tea products are available, the substandard tea leaves are often subjected to treatment with extraneous matter such as artificial colors (color adulteration), including Bismarck brown Y and plumbago (black lead), for increasing the color content of tea leaves (Tea Board India, 2021).…”

Kinetic modeling of valuable phenolic compounds extraction from strawberry and apple agro‐industrial by‐products

“…The frequent consumption of fruits and vegetables in the human diet is vital because they provide many essential and nonessential nutrients, including vitamins, proteins, minerals, carbohydrates, and secondary metabolites, which have numerous health benefits (Muzolf-Panek et al, 2021). However, it is concerning that around one-third of the fruit and vegetable world production is lost or wasted after harvest (FAO, 2021), and households can generate up to 101 kg/person/ year (De Laurentiis et al, 2018).…”