Self-propelled enzyme-controlled IR-mesoporous silica Janus nanomotor for smart delivery.

Here, we report the preparation of a novel Janus nanoparticle with opposite Ir and mesoporous silica nanoparticles through a partial surface masking with toposelective modification method. This nanomaterial was employed to construct an enzyme-powered nanomachine with self-propulsion properties for on-command delivery. The cargo-loaded nanoparticle was provided with a pH-sensitive gate and unit control at the mesoporous face by first attaching boronic acid residues and further immobilization of glucose oxidase through reversible boronic acid esters with the carbohydrate residues of the glycoenzyme. Addition of glucose leads to the enzymatic production of H2O2 and gluconic acid, being the first compound catalytically decomposed at the Ir nanoparticle face producing O2 and causing the nanomachine propulsion. Gluconic acid leads to a pH reduction at the nanomachine microenvironment causing the disruption of the gating mechanism with the subsequent cargo release. This work demonstrates that enzyme-mediated self-propulsion improved release efficiency being this nanomotor successfully employed for the smart release of Doxorubicin in HeLa cancer cells.

Deep quantification of a refined adulterant blended into pure avocado oil.

In this work, a new method has been developed to detect adulterations in avocado oil by combining optical images and their treatment with deep learning algorithms. For this purpose, samples of avocado oil adulterated with refined olive oil at concentrations from 1 % to 15 % (v/v) were prepared. Two groups of images of the different samples were obtained, one in conditions considered as bright and the other as dark, obtaining a total of 1,800 photographs. To obtain these images under both conditions, the exposure or shutter speed of the camera was modified (1/30 s for light conditions and 1/500 s for dark conditions). A residual neural network (ResNet34) was used to process and classify the images obtained. A different model was developed for each condition, and during blind validation of the models, ∼95 % of the images were correctly classified.

Is my food safe? - AI-based classification of lentil flour samples with trace levels of gluten or nuts.

An artificial intelligence-based method to rapidly detect adulterated lentil flour in real time is presented. Mathematical models based on convolutional neural networks and transfer learning (viz., ResNet34) have been trained to identify lentil flour samples that contain trace levels of wheat (gluten) or pistachios (nuts), aiding two relevant populations (people with celiac disease and with nut allergies, respectively). The technique is based on the analysis of photographs taken by a simple reflex camera and further classification into groups assigned to adulterant type and amount (up to 50 ppm). Two different algorithms were trained, one per adulterant, using a total of 2200 images for each neural network. Using blind sets of data (10% of the collected images; initially and randomly separated) to evaluate the performance of the models led to strong performances, as 99.1% of lentil flour samples containing ground pistachio were correctly classified, while 96.4% accuracy was reached to classify the samples containing wheat flour.

Distinct thermal patterns to detect and quantify trace levels of wheat flour mixed into ground chickpeas.

This paper combines intelligent algorithms based on a residual neural network (ResNet34) to process thermographic images. This integration is aimed at detecting traces of wheat flour, in concentrations from 1 to 50 ppm, mixed into chickpea flour. Using an image database of over 16 thousand samples to train the ResNet34, and 1712 images to blindly test it, the optimized intelligent algorithm is able to classify the thermographic images into 14 classes according to the concentration of wheat flour at a 99.0% correct classification rate. These results open the door to the development of a simple, fast, and inexpensive prototype that can be used during the entire distribution chain to help protect brands and consumers. The detection and quantification of trace amounts of wheat flour, or indirectly gluten, serves as a quality control and health safety application protecting, for example, people with celiac disease.

Convolutional capture of the expansion of extra virgin olive oil droplets to quantify adulteration.

In this research, more than 302,000 images of five different types of extra virgin olive oils (EVOOs) have been collected to train and validate a system based on convolutional neural networks (CNNs) to carry out their classification. Furthermore, comparable deep learning models have also been trained to detect and quantify the adulteration of these EVOOs with other vegetable oils. In this work, three groups of CNN models have been tested for (i) the classification of all EVOOs, (ii) the detection and quantification of adulterated samples for each individual EVOO, and (iii) a global version of the previous models combining all EVOOs into a single quantifying CNN. This last model was successfully validated using 30,195 images that were initially isolated from the initial database. The result was an algorithm capable of detecting and accurately classifying the five types of EVOO and their respective adulteration concentrations with an overall hit rate of >96%. Therefore, EVOO droplet analyses via CNNs have proven to be a convincing quality control tool for the evaluation of EVOO, which can be carried by producers, distributors, or even final consumers, to help locate adulterations.