Smartphone-Assisted Nanozyme Colorimetric Sensor Array Combined "Image Segmentation-Feature Extraction" Deep Learning for Detecting Unsaturated Fatty Acids.

Conventional methods for detecting unsaturated fatty acids (UFAs) pose challenges for rapid analyses due to the need for complex pretreatment and expensive instruments. Here, we developed an intelligent platform for facile and low-cost analysis of UFAs by combining a smartphone-assisted colorimetric sensor array (CSA) based on MnO2 nanozymes with "image segmentation-feature extraction" deep learning (ISFE-DL). Density functional theory predictions were validated by doping experiments using Ag, Pd, and Pt, which enhanced the catalytic activity of the MnO2 nanozymes. A CSA mimicking mammalian olfactory system was constructed with the principle that UFAs competitively inhibit the oxidization of the enzyme substrate, resulting in color changes in the nanozyme-ABTS substrate system. Through linear discriminant analysis coupled with the smartphone App "Quick Viewer" that utilizes multihole parallel acquisition technology, oleic acid (OA), linoleic acid (LA), α-linolenic acid (ALA), and their mixtures were clearly discriminated; various edible vegetable oils, different camellia oils (CAO), and adulterated CAOs were also successfully distinguished. Furthermore, the ISFE-DL method was combined in multicomponent quantitative analysis. The sensing elements of the CSA (3 × 4) were individually segmented for single-hole feature extraction containing information from 38,868 images of three UFAs, thereby allowing for the extraction of more features and augmenting sample size. After training with the MobileNetV3 small model, the determination coefficients of OA, LA, and ALA were 0.9969, 0.9668, and 0.7393, respectively. The model was embedded in the smartphone App "Intelligent Analysis Master" for one-click quantification. We provide an innovative approach for intelligent and efficient qualitative and quantitative analysis of UFAs and other compounds with similar characteristics.

Nanozyme-based colorimetric sensor arrays coupling with smartphone for discrimination and "segmentation-extraction-regression" deep learning assisted quantification of flavonoids.

Achieving rapid, cost effective, and intelligent identification and quantification of flavonoids is challenging. For fast and uncomplicated flavonoid determination, a sensing platform of smartphone-coupled colorimetric sensor arrays (electronic noses) was developed, relying on the differential competitive inhibition of hesperidin, nobiletin, and tangeretin on the oxidation reactions of nanozymes with a 3,3',5,5'-tetramethylbenzidine substrate. First, density functional theory calculations predicted the enhanced peroxidase-like activities of CeO2 nanozymes after doping with Mn, Co, and Fe, which was then confirmed by experiments. The self-designed mobile application, Quick Viewer, enabled a rapid evaluation of the red, green, and blue values of colorimetric images using a multi-hole parallel acquisition strategy. The sensor array based on three channels of CeMn, CeFe, and CeCo was able to discriminate between different flavonoids from various categories, concentrations, mixtures, and the various storage durations of flavonoid-rich Citri Reticulatae Pericarpium through a linear discriminant analysis. Furthermore, the integration of a "segmentation-extraction-regression" deep learning algorithm enabled single-hole images to be obtained by segmenting from a 3 × 4 sensing array to augment the featured information of array images. The MobileNetV3-small neural network was trained on 37,488 single-well images and achieved an excellent predictive capability for flavonoid concentrations (R2 = 0.97). Finally, MobileNetV3-small was integrated into a smartphone as an application (Intelligent Analysis Master), to achieve the one-click output of three concentrations. This study developed an innovative approach for the qualitative and simultaneous multi-ingredient quantitative analysis of flavonoids.

Novel portable photoelectrochemical sensor based on CdS/Au/TiO2 nanotube arrays for sensitive, non-invasive, and instantaneous uric acid detection in saliva.

Uric acid (UA) monitoring is the most effective method for diagnosis and treatment of gout, hyperuricemia, hypertension, and other diseases. However, challenges remain regarding detection efficiency and rapid on-site detection. Here, we first synthesized a CdS/Au/TiO2-NTAs Z-scheme heterojunction material using a titanium dioxide nanotube array (TiO2-NTAs) as the substrate and modified with gold nanoparticles (Au) and cadmium sulfide particles (CdS). This material achieves bandgap alignment to generate a large number of electron-hole pairs under illumination. Then, using CdS/Au/TiO2-NTAs as the working electrode and molecularly imprinted polymers (MIP) as the recognition unit, we constructed a portable photoelectrochemical (PEC) sensor for non-invasive instant detection of UA concentration in human saliva, which has unique advantages in the field of high-sensitivity PEC instant detection. The portable MIP-PEC sensor achieves a linear range of 0.01-50 µM and a detection limit as low as 5.07 nM (S/N = 3). At the same time, the portable MIP-PEC sensor exhibits excellent sensitivity, specificity as well as stability, and shows no statistically significant difference compared to traditional high-performance liquid chromatography (HPLC) in practical sample detection. Compared to traditional PEC modes, this work demonstrates a novel and universal method for high-sensitivity instant detection in the field of PEC.