Prediction of Protein Content in Pea (<i>Pisum sativum</i> L.) Seeds Using Artificial Neural Networks

Pea (Pisum sativum L.) is a legume valued mainly for its high seed protein content. The protein content of pea is characterized by a high lysine content and low allergenicity. This has made consumers appreciate peas increasingly in recent years, not only for their taste, but also for their nutritional value. An important element of pea cultivation is the ability to predict protein content, even before harvest. The aim of this research was to develop a linear and a non-linear model for predicting the percentage of protein content in pea seeds and to perform a comparative analysis of the effectiveness of these models. The analysis also focused on identifying the variables with the greatest impact on protein content. The research included the method of machine learning (artificial neural networks) and multiple linear regression (MLR). The input parameters of the models were weather, agronomic and phytophenological data from 2016–2020. The predictive properties of the models were verified using six ex-post forecast measures. The neural model (N1) outperformed the multiple regression (RS) model. The N1 model had an RMS error magnitude of 0.838, while the RS model obtained an average error value of 2.696. The MAPE error for the N1 and RS models was 2.721 and 8.852, respectively. The sensitivity analysis performed for the best neural network showed that the independent variables most influencing the protein content of pea seeds were the soil abundance of magnesium, potassium and phosphorus. The results presented in this work can be useful for the study of pea crop management. In addition, they can help preserve the country’s protein security

Recognition of Chiral Carboxylates by Synthetic Receptors

Recognition of anionic species plays a fundamental role in many essential chemical, biological, and environmental processes. Numerous monographs and review papers on molecular recognition of anions by synthetic receptors reflect the continuing and growing interest in this area of supramolecular chemistry. However, despite the enormous progress made over the last 20 years in the design of these molecules, the design of receptors for chiral anions is much less developed. Chiral recognition is one of the most subtle types of selectivity, and it requires very precise spatial organization of the receptor framework. At the same time, this phenomenon commonly occurs in many processes present in nature, often being their fundamental step. For these reasons, research directed toward understanding the chiral anion recognition phenomenon may lead to the identification of structural patterns that enable increasingly efficient receptor design. In this review, we present the recent progress made in the area of synthetic receptors for biologically relevant chiral carboxylates

H-Bond Mediated Phase-Transfer Catalysis: Enantioselective Generating of Quaternary Stereogenic Centers in β-Keto Esters

In this work, we would like to present the development of a highly optimized method for generating the quaternary stereogenic centers in β-keto esters. This enantioselective phase-transfer alkylation catalyzed by hybrid Cinchona catalysts allows for the efficient generation of the optically active products with excellent enantioselectivity, using only 1 mol% of the catalyst. The vast majority of phase-transfer catalysts in asymmetric synthesis work by creating ionic pairs with the nucleophile-attacking anionic substrate. Therefore, it is a sensible approach to search for new methodologies capable of introducing functional groups into the precursor’s structure, maintaining high yields and enantiomeric purity

Controlling transport of ion pairs by a light-responsive heteroditopic azobenzene carrier

Precise and stimuli-controllable transport of charged and neutral guests is a hallmark of cellular processes. Although ion transport has been mimicked with artificial carriers, no such systems are known for electrically neutral ion pairs. We have engineered an artificial carrier (1) that demonstrates a regulated binding and transport of ion pairs dependant on the photo-controlled translocation of the cation and anion binding domains. The NMR and electrochemical experiments supported by DFT calculations show that UVA-generated V-shaped cis-1, featuring ion binding domains close to each other, exhibits an unprecedented 74-fold higher extraction rate of ion pairs relative to the native trans-1

Engineering Light-Mediated Bistable Azobenzene Switches Bearing Urea d‑Aminoglucose Units for Chiral Discrimination of Carboxylates

The symmetrical molecular receptors <b>1a</b> and <b>1b</b> consisting of a photochemically addressable azobenzene tether functionalized with urea hydrogen-bonding groups and d-carbohydrates as chiral selectors were developed to achieve control over the chiral recognition of α-amino acid-derived carboxylates. The photo- and thermally interconvertible planar <i>E</i>-<b>1</b> and concaved <i>Z</i>-<b>1</b> were found to exhibit different affinities, selectivities, and binding modes toward these biologically important anions in a highly polar medium (DMSO + 0.5% H₂O). Binding affinity for the same enantiomerically pure guest was up to 3 times higher for <i>E</i>-<b>1</b> than for <i>Z</i>-<b>1</b> (cf. parameter β). In addition, the rate of thermal <i>Z</i> → <i>E</i> isomerization was found to depend on the chiral binding ability of <i>Z-</i><b>1</b>, i.e., more strongly bound carboxylate enantiomer as well as higher enantiomer concentration caused faster relaxation to <i>E-</i><b>1</b>