High blood pressure early screening remains a challenge due to the lack of symptoms associated with it. Accordingly, noninvasive methods based on photoplethysmography (PPG) or clinical data analysis and the training of machine learning techniques for hypertension detection have been proposed in the literature. Nevertheless, several challenges arise when analyzing PPG signals, such as the need for high-quality signals for morphological feature extraction from PPG related to high blood pressure. On the other hand, another popular approach is to use deep learning techniques to avoid the feature extraction process. Nonetheless, this method requires high computational power and behaves as a black-box approach, which impedes application in a medical context. In addition, considering only the socio-demographic and clinical data of the subject does not allow constant monitoring. This work proposes to use the wavelet scattering transform as a feature extraction technique to obtain features from PPG data and combine it with clinical data to detect early hypertension stages by applying Early and Late Fusion. This analysis showed that the PPG features derived from the wavelet scattering transform combined with a support vector machine can classify normotension and prehypertension with an accuracy of 71.42% and an F1-score of 76%. However, classifying normotension and prehypertension by considering both the features extracted from PPG signals through wavelet scattering transform and clinical variables such as age, body mass index, and heart rate by either Late Fusion or Early Fusion did not provide better performance than considering each data type separately in terms of accuracy and F1-score.

Berries are highly perishable and susceptible to spoilage, resulting in significant food and economic losses. The use of chemicals in traditional postharvest protection techniques can harm both human health and the environment. Consequently, there is an increasing interest in creating environmentally friendly solutions for postharvest protection. This article discusses various approaches, including the use of “green” chemical compounds such as ozone and peracetic acid, biocontrol agents, physical treatments, and modern technologies such as the use of nanostructures and molecular tools. The potential of these alternatives is evaluated in terms of their effect on microbial growth, nutritional value, and physicochemical and sensorial properties of the berries. Moreover, the development of nanotechnology, molecular biology, and artificial intelligence offers a wide range of opportunities to develop formulations using nanostructures, improving the functionality of the coatings by enhancing their physicochemical and antimicrobial properties and providing protection to bioactive compounds. Some challenges remain for their implementation into the food industry such as scale-up and regulatory policies. However, the use of sustainable postharvest protection methods can help to reduce the negative impacts of chemical treatments and improve the availability of safe and quality berries.