Enhancing Cutin Extraction Efficiency from Industrially Derived Tomato Processing Residues by High-Pressure Homogenization.

This study primarily aimed to enhance the extraction of cutin from industrial tomato peel residues. Initially, the conventional extraction process was optimized using response surface methodology (RSM). Subsequently, high-pressure homogenization (HPH) was introduced to improve extraction efficiency and sustainability. The optimization process focused on determining the optimal conditions for conventional extraction via chemical hydrolysis, including temperature (100-130 °C), time (15-120 min), and NaOH concentration (1-3%). The optimized conditions, determined as 130 °C, 120 min, and 3% NaOH solution, yielded a maximum cutin extraction of 32.5%. Furthermore, the results indicated that applying HPH pre-treatment to tomato peels before alkaline hydrolysis significantly increased the cutin extraction yield, reaching 46.1%. This represents an approximately 42% increase compared to the conventional process. Importantly, HPH pre-treatment enabled cutin extraction under milder conditions using a 2% NaOH solution, reducing NaOH usage by 33%, while still achieving a substantial cutin yield of 45.6%. FT-IR analysis confirmed that cutin obtained via both conventional and HPH-assisted extraction exhibited similar chemical structures, indicating that the main chemical groups and structure of cutin remained unaltered by HPH treatment. Furthermore, cutin extracts from both conventional and HPH-assisted extraction demonstrated thermal stability up to approximately 200 °C, with less than 5% weight loss according to TGA analysis. These findings underscore the potential of HPH technology to significantly enhance cutin extraction yield from tomato peel residues while utilizing milder chemical hydrolysis conditions, thereby promoting a more sustainable and efficient cutin extraction process.

The effect of a lifestyle training package on physical activity and nutritional status in obese and overweight pregnant women: A randomized controlled clinical trial.

AIM: The aim of this study was to determine the effect of a lifestyle training package that consisted of a 60- to 90-min session of group training, educational booklet and text messages on physical activity and nutritional status in obese and overweight pregnant women. METHODS: In this randomized controlled trial, 140 obese or overweight women (gestational age: 16-20 weeks) covered by health centres in Tehran, Iran, were equally randomized into two parallel groups using block randomization and stratified by the body mass index. Participants completed the Food Frequency Questionnaire and International Physical Activity Questionnaire at baseline and fourth and eighth weeks after intervention. Participants were 38 obese and 102 overweight women who were later followed-up. RESULTS: After the intervention, the mean weekly intake of the vegetable and fruit subgroups was significantly higher; and intake of fats and oils and the confections subgroups were significantly lower in the intervention compared with the control group (p < 0.05). Although physical activity was higher in the intervention group, 8 weeks after the intervention, this difference was not statistically significant (p > 0.05). CONCLUSIONS: The training package appears to offer a suitable strategy for adjusting the intake of the recommended food subgroups in obese and overweight pregnant women.

A Comprehensive Overview of Tomato Processing By-Product Valorization by Conventional Methods versus Emerging Technologies.

The tomato processing industry can be considered one of the most widespread food manufacturing industries all over the world, annually generating considerable quantities of residue and determining disposal issues associated not only with the wasting of invaluable resources but also with the rise of significant environmental burdens. In this regard, previous studies have widely ascertained that tomato by-products are still rich in valuable compounds, which, once recovered, could be utilized in different industrial sectors. Currently, conventional solvent extraction is the most widely used method for the recovery of these compounds from tomato pomace. Nevertheless, several well-known drawbacks derive from this process, including the use of large quantities of solvents and the difficulties of utilizing the residual biomass. To overcome these limitations, the recent advances in extraction techniques, including the modification of the process configuration and the use of complementary novel methods to modify or destroy vegetable cells, have greatly and effectively influenced the recovery of different compounds from plant matrices. This review contributes a comprehensive overview on the valorization of tomato processing by-products with a specific focus on the use of "green technologies", including high-pressure homogenization (HPH), pulsed electric fields (PEF), supercritical fluid (SFE-CO2), ultrasounds (UAE), and microwaves (MAE), suitable to enhancing the extractability of target compounds while reducing the solvent requirement and shortening the extraction time. The effects of conventional processes and the application of green technologies are critically analyzed, and their effectiveness on the recovery of lycopene, polyphenols, cutin, pectin, oil, and proteins from tomato residues is discussed, focusing on their strengths, drawbacks, and critical factors that contribute to maximizing the extraction yields of the target compounds. Moreover, to follow the "near zero discharge concept", the utilization of a cascade approach to recover different valuable compounds and the exploitation of the residual biomass for biogas generation are also pointed out.

Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images.

Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task remains challenging due to the high variation of defects in shapes and sizes, demanding a better incorporation of contextual information into deep networks. In this paper, we show that an attention-based multi-scale convolutional neural network (A+MCNN) improves the automated classification of common distress and non-distress objects in pavement images by (i) encoding contextual information through multi-scale input tiles and (ii) employing a mid-fusion approach with an attention module for heterogeneous image contexts from different input scales. A+MCNN is trained and tested with four distress classes (crack, crack seal, patch, pothole), five non-distress classes (joint, marker, manhole cover, curbing, shoulder), and two pavement classes (asphalt, concrete). A+MCNN is compared with four deep classifiers that are widely used in transportation applications and a generic CNN classifier (as the control model). The results show that A+MCNN consistently outperforms the baselines by 1∼26% on average in terms of the F-score. A comprehensive discussion is also presented regarding how these classifiers perform differently on different road objects, which has been rarely addressed in the existing literature.