A wearable plasmonic-electronic sensor can noninvasively extract and “fingerprint” various molecules inside human body.

Meat is one of the most important foods in the human diet and is a major source of animal protein. It is essential to detect meat quality and safety for ensuring that high‐quality foods are delivered to consumers. Advanced spectroscopic techniques (near‐infrared spectroscopy, Raman spectroscopy, fluorescence spectroscopy, and terahertz spectroscopy) and imaging techniques (hyperspectral imaging, multispectral imaging, X‐ray imaging, and thermal imaging) provide feasible means for testing organoleptic properties, chemical composition, physicochemical properties, and safety indicators of meat. This review aimed to summarize the latest developments of spectroscopic and imaging techniques for meat quality and safety detection. The principles, multi‐scenario applications, advantages, disadvantages, and future prospects of these techniques are discussed. Spectroscopic techniques have been demonstrated to accurately detect changes in the chemical constituents and physical properties of meat, but can only perceive localized sample information. Imaging techniques provide visual information on the spatial distribution as well as physical and chemical characteristics of meat, but have a slower detection rate. Despite the remarkable outcomes attained by spectroscopy and imaging techniques in laboratory settings, their industrial applications remain encumbered by challenges, including substantial expenses and intricate data analysis procedures. In order to improve the comprehensiveness and accuracy of detection, future research directions might focus on the integration of multiple techniques combined with deep learning algorithms.

Lettuce is a globally important cash crop, valued by consumers for its nutritional content and pleasant taste. However, there is limited research on the changes in the growth indicators of lettuce during its growth period in domestic settings. Quality assessment primarily relies on subjective evaluations, resulting in significant variability. This study focused on hydroponically grown lettuce during the rosette stage and investigated the patterns of changes in the indicators and spectral curves over time. By employing spectral preprocessing and selecting characteristic wavelengths, three models were developed to predict the indicators. The results showed that the optimal model structures were S_G-UVE-PLSR (SSC and vitamin C) and Nor-CARS-PLSR (moisture content). The PLSR models achieved prediction set correlation coefficients of 0.8648, 0.8578, and 0.8047, with residual prediction deviations of 1.9685, 1.9568, and 1.6689, respectively. The optimal models were integrated into a portable device, using real-time analysis software written in Matlab2021a, for the prediction of the physicochemical indicators of lettuce during the rosette stage. The results demonstrated prediction set correlation coefficients of 0.8215, 0.8472, and 0.7671, with root mean square errors of prediction of 0.5348, 1.5813, and 2.3347 for a sample size of 180. The small discrepancies between the predicted and actual values indicate that the developed device can meet the requirements for real-time detection.

As hen eggs are a primary source of high-quality dietary protein, egg freshness is fundamentally linked to biochemical alterations during storage, including moisture redistribution, protein degradation, and fluctuating chemical profiles. Accurate assessment of these internal changes is paramount for quality control; nonetheless, conventional analytical techniques remain predominantly destructive, rendering them impractical for high-throughput industrial monitoring. While existing literature has explored individual spectroscopic methods, the synergistic potential of multi-sensor integration and advanced artificial intelligence (AI) algorithms remains insufficiently synthesized. This review systematically evaluates recent breakthroughs in integrating AI with diverse spectroscopic modalities for non-destructive freshness quantification, including Visible-Near-Infrared (VIS-NIR), Raman, Fluorescence, and Hyperspectral Imaging (HSI). We elucidate the underlying mechanisms of spectral response to internal quality degradation and discuss the evolution of data-driven modeling from traditional chemometrics to sophisticated deep learning architectures. Furthermore, this work identifies critical bottlenecks in real-time industrial implementation and proposes future research trajectories toward intelligent multi-sensor fusion platforms.

An analytical method has been developed and validated for the simultaneous determination of nine pharmaceuticals and personal care products (PPCPs) in water samples, including salicylic acid, naproxen, ibuprofen, paracetamol, clofibric acid, triclosan, diclofenac, ketoprofen, bisphenol A. The qualification and quantification of the target compounds were performed by gas chromatography-mass spectrometry in selected ion monitoring mode (GC-MS-SIM). The water samples were concentrated and purified through Oasis HLB cartridges after the pH value of the water was adjusted to 3, then derivatized with trimethyl sulfonium hydroxide (TMSH) at room temperature, and determined by GC-MS-SIM using 2,4,5-fenoprop as internal standard. The conditions for sample pretreatment (e. g. solid phase extraction and derivatization) were studied. Under the optimized conditions, the recoveries were ranged from 50.7% to 115.4% with the relative standard deviations lower than 10%. The limits of detection were in the range of 0.03-0.30 microg/L and the limits of quantification were in the range of 0.15-1.50 microg/L. The method has been successfully applied to monitor the occurrence of the PPCPs residues in agricultural irrigation water in Dongguan, Guangdong Province. The four compounds were detected at maximum mass concentration range of 0.176-0.998 microg/L. It proved that this analytical method is sensitive, reliable and acceptable.

The presence of fluoroquinolone antibiotic residues in food products poses a significant risk to public health and necessitates the development of rapid, sensitive, and reliable detection methods.This study introduces an integrated analytical platform that synergistically combines surface-enhanced Raman scattering (SERS) with a bespoke microfluidic chip and machine learning for the high-throughput screening of antibiotics.The SERS substrate employs Au@Ag core-shell nanocubes, which provide a high enhancement factor (approximately 2.14×106) and excellent signal uniformity. An integrated microfluidic chip, optimized through finite element simulation, ensures rapid and efficient mixing, leading to highly reproducible Raman measurements. Experimental results demonstrate successful detection of multiple antibiotics in standard solutions and egg white matrices. With machine learning algorithms (e.g., airPLS-UVE-PLSR), the limit of detection (LOD) for enrofloxacin, ciprofloxacin, and norfloxacin reached 10-7 M, and 10-6 M for oxytetracycline. This integrated platform offers a robust and practical solution for rapid food safety monitoring.