Process innovation in the production of chelates for agricultural uses

2014 - 2015Analyzing the literature available on the production of iron chelates, it is clear as these extremely diffused products are produced according to processes developed in the second part of last century which involve the enormous use of organic solvents, chlorinated substances and water. Although these processes have been optimized, they are not environmentally sustainable and imply high costs of post processing operations and disposal of dangerous substances. Based on this scenario, it is clear the necessity to develop a production process environmentally friendly, based on the elimination of chlorinated substances and the reduction of organic solvents use... [edited by author]XIV n.s

Optimization of Chelates Production Process for Agricultural Administration of Inorganic Micronutrients

The iron chlorosis is one of the most diffused plant disease, which affects their growth and reduces the yield of harvests. This disease is caused by the iron deficiency and it is highlighted by the progressive yellowing of plants due to the reduction of chlorophyll production. The most efficient and diffused therapy against the iron chlorosis is the use of chelating agents and, among them, the o,o-EDDHA/Fe3+, the most stable isomer of EDDHA, is the most used due to its capacity to guarantee a prolonged treatment. The aim of this work is to develop a production process environment friendly, based on the recovering and recycling of organic solvents to minimize the waste produced. The feed organic solvents ratio has been varied evaluating the synthesis yield and the percentage of o,o-EDDHA/Fe3+ produced to identify the best feeding conditions. Several products have been then tested on lettuce plants to determine their usability

Influence of ultrasound-assisted par-frying on crust formation and browning during the production of French fries

Frying is a key processing step during the production of French fries and important for end product quality and sensory attributes. It is governed by heat and mass transfer between the frying oil and the potato strips. Crust and color of the French fries are key quality parameters and important in consumer perception. Crust formation is a result of combined heat and mass transfer effects. Convective heat transfer from frying oil to potato strips and heat conduction within the tissue cause water evaporation. Mass transfer occurs in the form of water vapor release to the frying oil and oil absorption in the outer layers of the potato strips. Browning of French fries is related to Maillard reactions between reducing sugars and amino acids. High contents of reducing sugars are often related to an undesired dark color and bitter taste of French fries. High-intensity ultrasound transmitted to liquid media causes cavitation and microstreaming, which can influence boundary layers and cell structures and result in improved heat and mass transfer. The influence of an ultrasound treatment of potato strips during the par-frying step was investigated in order to determine the effect on the resulting changes in product quality. Improved heat transfer at the product surface due to micro-streaming in the oil and a facilitated release of vapor from the product surface was observed. A faster crust formation was found at the initial phase of frying but the crust was found to become softer at longer sonication times due to persistent mechanical ultrasound effects. French fries from ultrasound assisted par-frying had a lighter color after finish-frying in comparison to the conventionally par-fried samples due to an improved release of reducing sugars from the tissue. Ultrasound-assisted par-frying showed to be effective in modifying heat and mass transfer with an impact on crust formation and browning of French fries. Further work is required regarding the optimization of parameters and sonication times

Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening.

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM-recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings

Do Parents Recognize Autistic Deviant Behavior Long before Diagnosis? Taking into Account Interaction Using Computational Methods

BACKGROUND: To assess whether taking into account interaction synchrony would help to better differentiate autism (AD) from intellectual disability (ID) and typical development (TD) in family home movies of infants aged less than 18 months, we used computational methods. METHODOLOGY AND PRINCIPAL FINDINGS: First, we analyzed interactive sequences extracted from home movies of children with AD (N = 15), ID (N = 12), or TD (N = 15) through the Infant and Caregiver Behavior Scale (ICBS). Second, discrete behaviors between baby (BB) and Care Giver (CG) co-occurring in less than 3 seconds were selected as single interactive patterns (or dyadic events) for analysis of the two directions of interaction (CG→BB and BB→CG) by group and semester. To do so, we used a Markov assumption, a Generalized Linear Mixed Model, and non negative matrix factorization. Compared to TD children, BBs with AD exhibit a growing deviant development of interactive patterns whereas those with ID rather show an initial delay of development. Parents of AD and ID do not differ very much from parents of TD when responding to their child. However, when initiating interaction, parents use more touching and regulation up behaviors as early as the first semester. CONCLUSION: When studying interactive patterns, deviant autistic behaviors appear before 18 months. Parents seem to feel the lack of interactive initiative and responsiveness of their babies and try to increasingly supply soliciting behaviors. Thus we stress that credence should be given to parents' intuition as they recognize, long before diagnosis, the pathological process through the interactive pattern with their child

Iron Chelates: Production Processes and Reaction Evolution Analysis

Nowadays, fertilization using synthetic chelates is the most common technique to address iron chlorosis, a disease that affects plant growth. Ethylenediamine-N,N'-bis(o-hydroxyphenyl) acetic acid (EDDHA) is among the most efficient iron chelating agents. To produce EDDHA, a reaction was performed using as reactants phenol, ethylenediamine, glyoxylic acid, and sodium hydroxide. To study the reaction kinetics, samples were withdrawn from the reactor during the reaction and the kinetics was quantified, evaluating the yield evolution during the reaction phase. This study was useful to optimize the reaction time. Then, a catalyst was added to the reaction mixture to analyze its effect on the reaction evolution. Comparing the reaction evolution of the non-catalyzed and the catalyzed reaction protocols, two main results have to be highlighted: the time to reach the final yield is lower than the one proposed in the literature and the used catalyst has a minimum effect on the reaction rate