Duplex Detection of Antibiotics in Milk Powder Using Lateral-Flow Assay Based on Surface-Enhanced Raman Spectroscopy

Development of a highly sensitive ampicillin sensor utilizing functionalized aptamers

To develop a sensitive and simple ampicillin (AMP) sensor for trace antibiotic residue detection, the influencing factors of the modification effect of nanogold-functionalized nucleic acid sequences (Adenine: A, Thymine: T) were comprehensively analyzed in this study, including the modification method, base length and type. It was found that under the same base concentration, longer chains are more likely to reach saturation than shorter chains; and when the base concentration and length are both the same, A exhibits a higher saturation modification level compared to T. Based on these research findings, a highly sensitive fluorescence aptamer sensor for detecting ampicillin was constructed using the optimized functionalized sequence (ployA6-aptamer) and experimental conditions (6 hours binding time between nucleic acid aptamer and complementary strand, pH 7 working solution, 20 minutes detection time) based on the principle of fluorescence resonance energy transfer. The sensor has a detection range of 0.18 ng ml-1 to 3.11 ng ml-1 for ampicillin, with a detection limit of 0.04 ng ml-1. It exhibits significant selectivity and achieves an average recovery rate of 98.71% in tap water and 91.83% in milk. This method can be used not only for residual ampicillin detection, but also for highly sensitive detection of various antibiotics and small biological molecules by replacing the aptamer type. It provides a research basis for the design of highly sensitive fluorescence aptamer sensors and further applications of nanogold@DNA composite structures.

Progress and Challenge of Sensors for Dairy Food Safety Monitoring

One of the most consumed foods is milk and milk products, and guaranteeing the suitability of these products is one of the major concerns in our society. This has led to the development of numerous sensors to enhance quality controls in the food chain. However, this is not a simple task, because it is necessary to establish the parameters to be analyzed and often, not only one compound is responsible for food contamination or degradation. To attempt to address this problem, a multiplex analysis together with a non-directed (e.g., general parameters such as pH) analysis are the most relevant alternatives to identifying the safety of dairy food. In recent years, the use of new technologies in the development of devices/platforms with optical or electrochemical signals has accelerated and intensified the pursuit of systems that provide a simple, rapid, cost-effective, and/or multiparametric response to the presence of contaminants, markers of various diseases, and/or indicators of safety levels. However, achieving the simultaneous determination of two or more analytes in situ, in a single measurement, and in real time, using only one working 'real sensor', remains one of the most daunting challenges, primarily due to the complexity of the sample matrix. To address these requirements, different approaches have been explored. The state of the art on food safety sensors will be summarized in this review including optical, electrochemical, and other sensor-based detection methods such as magnetoelastic or mass-based sensors.

Spectral encoder to extract the efficient features of Raman spectra for reliable and precise quantitative analysis

Raman spectroscopy has become a powerful analytical tool highly demanded in many applications such as microorganism sample analysis, food quality control, environmental science, and pharmaceutical analysis, owing to its non-invasiveness, simplicity, rapidity and ease of use. Among them, quantitative research using Raman spectroscopy is a crucial application field of spectral analysis. However, the entire process of quantitative modeling largely relies on the extraction of effective spectral features, particularly for measurements on complex samples or in environments with poor spectral signal quality. In this paper, we propose a method of utilizing a spectral encoder to extract effective spectral features, which can significantly enhance the reliability and precision of quantitative analysis. We built a latent encoded feature regression model; in the process of utilizing the autoencoder for reconstructing the spectrometer output, the latent feature obtained from the intermediate bottleneck layer is extracted. Then, these latent features are fed into a deep regression model for component concentration prediction. Through detailed ablation and comparative experiments, our proposed model demonstrates superior performance to common methods on single-component and multi-component mixture datasets, remarkably improving regression precision while without needing user-selected parameters and eliminating the interference of irrelevant and redundant information. Furthermore, in-depth analysis reveals that latent encoded feature possesses strong nonlinear feature representation capabilities, low computational costs, wide adaptability, and robustness against noise interference. This highlights its effectiveness in spectral regression tasks and indicates its potential in other application fields. Sufficient experimental results show that our proposed method provides a novel and effective feature extraction approach for spectral analysis, which is simple, suitable for various methods, and can meet the measurement needs of different real-world scenarios.

Quantitative analysis of Raman spectra for glucose concentration in human blood using Gramian angular field and convolutional neural network

In this study, convolutional neural network based on Gramian angular field (GAF-CNN) was firstly proposed. The 1-D Raman spectral data was converted into images and used for predicting the biochemical value of blood glucose. 106 sets of blood spectrums were acquired by Fourier transform (FT) Raman spectroscopy. Spectral data ranging from 800 cm^-1 to 1800 cm^-1 were selected for quantitative analysis of the blood glucose. Data augmentation was used to train neural networks and normalize the Raman spectra. And, we applied principal component analysis (PCA) for dimension reduction and information extraction. The root mean squared error of prediction (RMSEP) are 0.06570 (GADF) and 0.06774 (GASF), the determination coefficient of prediction (R²) are 0.99929 (GADF) and 0.99925 (GASF), and the residual predictive deviation of prediction (RPD) are 37.56324 (GADF) and 36.43362 (GASF). GAF-CNN model performed better for predicting of glucose concentration. The GAF-CNN model can be used to establish a calibration model to predict blood glucose concentration.

Electrochemical Methods for the Analysis of Milk

The milk and dairy industries are some of the most profitable sectors in many countries. This business requires close control of product quality and continuous testing to ensure the safety of the consumers. The potential risk of contaminants or degradation products and undesirable chemicals necessitates the use of fast, reliable detection tools to make immediate production decisions. This review covers studies on the application of electrochemical methods to milk (i.e., voltammetric and amperometric) to quantify different analytes, as reported over the last 10 to 15 years. The review covers a wide range of analytes, including allergens, antioxidants, organic compounds, nitrogen- and aldehyde containing compounds, biochemicals, heavy metals, hydrogen peroxide, nitrite, and endocrine disruptors. The review also examines pretreatment procedures applied to milk samples and the use of novel sensor materials. Final perspectives are provided on the future of cost-effective and easy-to-use electrochemical sensors and their advantages over conventional methods.

A shallow convolutional neural network with elastic nets for blood glucose quantitative analysis using Raman spectroscopy

In this paper, a one-dimensional shallow convolutional neural network structure combined with elastic nets (1D-SCNN-EN) was firstly proposed to predict the glucose concentration of blood by Raman spectroscopy. A total of 106 different blood glucose spectra were obtained by Fourier transform (FT) Raman spectroscopy. The one-dimensional shallow convolutional neural network, with elastic nets added to the full connected layer, was presented to capture multiple deep features and reduce the complexity of the model. The 1D-SCNN-EN model has a better performance than conventional approaches (partial least squares and support vector machine). The root mean squared error of calibration (RMSEC), the root mean squared error of prediction (RMSEP), the determination coefficient of prediction (R_P²), and the residual predictive deviation of prediction (RPD) were 0.10262, 0.11210, 0.99403, and 12.94601, respectively. The experiment results showed that the 1D-SCNN-EN model has a higher prediction accuracy and stronger robustness than the other regression models. The overall studies indicated that the 1D-SCNN-EN model looked promising for predict the glucose concentration of blood by Raman spectroscopy when the sample size is small.