Nondestructive testing methods for pesticide residue in food commodities: A review

Semi-invasive wearable clinic: Solution-processed smart microneedle electronics for next-generation integrated diagnosis and treatment

The integrated smart electronics for real-time monitoring and personalized therapy of disease-related analytes have been gradually gaining tremendous attention. However, human tissue barriers, including the skin barrier and brain-blood barrier, pose significant challenges for effective biomarker detection and drug delivery. Microneedle (MN) electronics present a promising solution to overcome these tissue barriers due to their semi-invasive structures, enabling effective drug delivery and target-analyte detection without compromising the tissue configuration. Furthermore, MNs can be fabricated through solution processing, facilitating large-scale manufacturing. This review provides a comprehensive summary of the recent three-year advancements in smart MNs development, categorized as follows. First, the solution-processed technology for MNs is introduced, with a focus on various printing technologies. Subsequently, smart MNs designed for sensing, drug delivery, and integrated systems combining diagnosis and treatment are separately summarized. Finally, the prospective and promising applications of next-generation MNs within mediated diagnosis and treatment systems are discussed.

A "turn-on" fluorescent sensor for herbicide quizalofop-p-ethyl based on cyanostilbene-pyridine macrocycle

Quizalofop-p-ethyl is a widely used herbicide that also poses a risk to human health and environmental safety. However, there is still a lack of simple and in-situ detecting method for quizalofop-p-ethyl so far. In this work, the fluorescent sensor was firstly developed on detection of quizalofop-p-ethyl based on cyanostilbene-pyridine macrocycle (CPM). CPM was prepared by the "1 + 1" condensation of pyridine-substituted cyanostilbene derivative with 4,4'-Bis(chloromethyl)biphenyl in 68 % yield. The weak fluorescence of CPM in aqueous media transferred to strong orange fluorescence after sensing quizalofop-p-ethyl. This sensing behavior exhibited high selectivity among 28 kinds of herbicides and ions. The limitation of detection (LOD) was 2.98 × 10-8 M and the limitation of quantification (LOQ) was 9.94 × 10-8 M (λex = 390 nm, λem = the maximum emission between 512 nm and 535 nm) with a dynamic range of 0.01-0.9 eq. The binding constant (Ka) of quizalofop-p-ethyl to the sensor CPM was 3.2 × 106 M-1. The 1:1 sensing mechanism was confirmed as that quizalofop-p-ethyl was located in the cavity of CPM, which enhanced aggregating effect and reduced the intramolecular rotation of aromatic groups for better AIE effect. The sensing ability of CPM for quizalofop-p-ethyl had been efficiently applied in test paper experiments, agricultural product tests and real water samples, revealing that CPM has good application prospect for simple and in-situ detection of quizalofop-p-ethyl in real environment.

A Wireless Network for Monitoring Pesticides in Groundwater: An Inclusive Approach for a Vulnerable Kenyan Population

Safe drinking water is essential to a healthy lifestyle and has been recognised as a human right by numerous countries. However, the realisation of this right remains largely aspirational, particularly in impoverished nations that lack adequate resources for water quality testing. Kenya, a Sub-Saharan country, bears the brunt of this challenge. Pesticide imports in Kenya increased by 144% from 2015 to 2018, with sales data indicating that 76% of these pesticides are classified as highly hazardous. This trend continues to rise. Over 70% of Kenya's population resides in rural areas, with 75% of the rural population engaged in agriculture and using pesticides. Agriculture is the country's main economic activity, contributing over 30% of its gross domestic product (GDP). The situation is further exacerbated by the lack of monitoring for pesticide residues in surface water and groundwater, coupled with the absence of piped water infrastructure in rural areas. Consequently, contamination levels are high, as agricultural runoff is a major contaminant of surface water and groundwater. The increased use of pesticides to enhance agricultural productivity exacerbates environmental degradation and harms water ecosystems, adversely affecting public health. This study proposes the development of a wireless sensor system that utilizes radio-frequency identification (RFID), Long-range (LoRa) protocol and a global system for mobile communications (GSM) for monitoring pesticide prevalence in groundwater sources. From the system design, individuals with limited literacy skills, advanced age, or non-expert users can utilize it with ease. The reliability of the LoRa protocol in transmitting data packets is thoroughly investigated to ensure effective communication. The system features a user-friendly interface for straightforward data input and facilitates broader access to information by employing various remote wireless sensing methods.

Probing the effect of protein corona on SERS signals: insights from melamine detection in milk matrix

Matrix effects limit the application of surface-enhanced Raman scattering (SERS) technology in the field of food safety. This study elucidated it from the perspective of protein corona by employing a model system for melamine SERS detection in milk. Compared with the melamine standard solution, higher detection limits (1 mg/L and 10 mg/L) are observed in milk matrix. The melamine signal exhibits an 80% reduction in whey protein solution, suggesting that protein has a significant impact on SERS signals. The changes in particle size, zeta potential and UV-vis spectra indicate the AuNPs interact with whey protein. Forming protein corona inhibits the melamine-induced AuNPs aggregation, reducing the number of 'hot spot' and the adsorption of melamine on AuNPs (from 0.28 mg/L to 0.07 mg/L), which may be responsible for signal loss. The found matrix effect from protein corona provides new insights for developing strategies about reducing matrix effect in SERS application.

Integrating Wireless Remote Sensing and Sensors for Monitoring Pesticide Pollution in Surface and Groundwater

Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays an integral role in various sectors such as agriculture, industrial processes, and domestic consumption. Even though water covers 71% of the global land surface, governments have been grappling with the challenge of ensuring the provision of safe water for domestic use. A contributing factor to this situation is the persistent contamination of available water sources rendering them unfit for human consumption. A common contaminant, pesticides are not frequently tested for despite their serious effects on biodiversity. Pesticide determination in water quality assessment is a challenging task because the procedures involved in the extraction and detection are complex. This reduces their popularity in many monitoring campaigns despite their harmful effects. If the existing methods of pesticide analysis are adapted by leveraging new technologies, then information concerning their presence in water ecosystems can be exposed. Furthermore, beyond the advantages conferred by the integration of wireless sensor networks (WSNs), the Internet of Things (IoT), Machine Learning (ML), and big data analytics, a notable outcome is the attainment of a heightened degree of granularity in the information of water ecosystems. This paper discusses methods of pesticide detection in water, emphasizing the possible use of electrochemical sensors, biosensors, and paper-based sensors in wireless sensing. It also explores the application of WSNs in water, the IoT, computing models, ML, and big data analytics, and their potential for integration as technologies useful for pesticide monitoring in water.

Application of imaging and spectroscopy techniques for grading of bovine embryos - a review

Although embryo transfers have grown considerably in the cattle industry, the selection of embryos required for successful pregnancies remains a challenging task. Visual inspection of 7th-day embryos using a stereomicroscope, followed by classification based on morphological features is the most commonly practiced procedure. However, there are inaccuracies and inconsistencies in the manual grading of bovine embryos. The objective of this review was to evaluate the potential of imaging and spectroscopic techniques in the selection of bovine embryos. Digital analysis of microscopic images through extracting visual features in the embryo region, and classification using machine learning methods have yielded about 88-96% success in pregnancies. The Raman spectral pattern provides valuable information regarding developmental stages and quality of the embryo. The Raman spectroscopy approach has also been successfully used to determine various parameters of bovine oocytes. Besides, Fourier Transform Infrared (FTIR) spectroscopy has the ability to assess embryo quality through analyzing embryo composition, including nucleic acid and amides present. Hyperspectral Imaging has also been used to characterize metabolite production during embryo growth. Although the time-lapse imaging approach is beneficial for morphokinetics evaluation of embryo development, optimized protocols are required for successful implementation in bovine embryo transfers. Most imaging and spectroscopic findings are still only at an experimental stage. Further research is warranted to improve the repeatability and practicality to implement in commercial facilities.

Identification of liquor adulteration by Raman spectroscopy method based on ICNAFS

The health of consumers can be impacted by the additives placed into the liquor. To address the issues of poor accuracy, low reliability, and complex operational procedures in identifying adulteration in existing liquor, an improved convex non-negative matrix factorization (ICNAFS) with an adaptive graph constraint for unsupervised feature extraction is proposed in this paper, with the goal of achieving rapid identification of adulteration in liquor by Raman spectroscopy through dimensionality reduction. For the sake to streamline the calculation process for effective feature extraction and increase the accuracy of the analyzed model, the proposed ICNAFS method incorporates two fundamental models, such as ridge regression and convex non-negative matrix factorization (NMF). In particular, dimensionality reduction of the original spectrum is initially conducted using Principal Component Analysis (PCA), Sequential Projection Algorithm (SPA), Convex Non-Negative Matrix Factorization with an Adaptive Graph Constraint (CNAFS), and ICNAFS respectively. k-means is subsequently employed to merge the four models for clustering analysis. The results suggest that the accuracy of the presented ICNAFS-assisted k-means model is higher than the other techniques, with a clustering accuracy of 98.67%, exhibiting a 4% improvement over the existing CNAFS, through examination of 150 sets of tainted liquor data from five categories of samples. This demonstrates the potency of the proposed ICNAFS-assisted k-means clustering model in conjunction with Raman spectroscopy as a method for detecting tainted liquor.

Effect of Aspect Ratio of a Gold-Nanorod-Modified Screen-Printed Carbon Electrode for Carbaryl Detection in Three Different Samples of Vegetables

In this study, three different sizes of gold nanorods (AuNRs) were synthesized using the seed-growth method by adding various volumes of AgNO3 as 400, 600, and 800 μL into the growth solution of gold nanoparticles. Three different sizes of AuNRs were then characterized using UV-vis spectroscopy, high-resolution transmission electron microscopy (HRTEM), selected area electron diffraction (SAED) patterns, and atomic force microscopy (AFM) to investigate the surface morphology, topography, and aspect ratios of each synthesized AuNR. The aspect ratios from the histogram of size distributions of three AuNRs as 2.21, 2.53, and 2.85 can be calculated corresponding to the addition of AgNO3 volumes of 400, 600, and 800 μL. Moreover, each AuNR in three different aspect ratios was drop-cast onto the surface of a commercial screen-printed carbon electrode (SPCE) to obtain three different SPCE-modified AuNRs (SPCE-A400, SPCE-A600, and SPCE-A800, respectively). All SPCE-modified AuNRs were then evaluated for their electrochemical behavior using cyclic voltammetry and electrochemical impedance spectroscopy (EIS) techniques and the highest electrochemical performance was shown as the order of magnitude of SPCE-A400 > SPCE-A600/SPCE-A800. The reason for the highest electrocatalytic activity of SPCE-A400 might be due to the smallest particle size and uniform distribution of AuNRs ∼ 2.2, which enhanced the charge transfer, thus providing the highest electroactive surface area (0.6685 cm2) compared to other electrodes. These results also confirm that the sensing mechanism for all SPCE-modified AuNRs is controlled by diffusion phenomena. In addition, the optimum pH was obtained as 4 for carbaryl detection for all SPCE-modified AuNRs with the highest current shown by SPCE-A400. Furthermore, SPCE-A400 has the highest fundamental parameters (surface coverage, catalytic rate constant, electron transfer rate constant, and adsorption capacity) for carbaryl detection, which were investigated using cyclic voltammetry and chronoamperometric techniques. The electroanalytical performances of all SPCE-modified AuNRs for carbaryl detection were also investigated with SPCE-A400 displaying the best performance among other electrodes in terms of its linearity (0.2-100 μM), limit of detection (LOD) ∼ 0.07 μM, and limit of quantification (LOQ) ∼ 0.2 μM. All SPCE-modified AuNRs were also subsequently evaluated for their stability, reproducibility, and selectivity in the presence of interfering species such as NaNO2, NH4NO3, Zn(CH3CO2)2, FeSO4, diazinon, and glucose and show reliable results as depicted from %RSD values less than 3%. At last, all SPCE-modified AuNRs have been employed for carbaryl detection using a standard addition technique in three different samples of vegetables (cabbage, cucumber, and Chinese cabbage) with its results (%recovery ≈ 100%) within the acceptable analytical range. In conclusion, this work demonstrates the great potential of a disposable device based on an AuNR-modified SPCE for rapid detection and high sensitivity in monitoring the concentration of carbaryl as a residual pesticide in vegetable samples.

Rapid detection of residual chlorpyrifos and pyrimethanil on fruit surface by surface-enhanced Raman spectroscopy integrated with deep learning approach

Chlorpyrifos and pyrimethanil are widely used insecticides/fungicides in agriculture. The residual pesticides/fungicides remaining in fruits and vegetables may do harm to human health if they are taken without notice by the customers. Therefore, it is important to develop methods and tools for the rapid detection of pesticides/fungicides in fruits and vegetables, which are highly demanded in the current markets. Surface-enhanced Raman spectroscopy (SERS) can achieve trace chemical detection, while it is still a challenge to apply SERS for the detection and identification of mixed pesticides/fungicides. In this work, we tried to combine SERS technique and deep learning spectral analysis for the determination of mixed chlorpyrifos and pyrimethanil on the surface of fruits including apples and strawberries. Especially, the multi-channel convolutional neural networks-gate recurrent unit (MC-CNN-GRU) classification model was used to extract sequence and spatial information in the spectra, so that the accuracy of the optimized classification model could reach 99% even when the mixture ratio of pesticide/fungicide varied considerably. This work therefore demonstrates an effective application of using SERS combined deep learning approach in the rapid detection and identification of different mixed pesticides in agricultural products.

Multiscale Deepspectra Network: Detection of Pyrethroid Pesticide Residues on the Hami Melon

The problem of pyrethroid residues has become a topical issue, posing a potential food safety concern. Pyrethroid pesticides are widely used to prevent and combat pests in Hami melon cultivation. Due to its high sensitivity and accuracy, gas chromatography (GC) is used most frequently for detecting pyrethroid pesticide residues. However, GC has a high cost and complex operation. This study proposed a deep-learning approach based on the one-dimensional convolutional neural network (1D-CNN), named Deepspectra network, to detect pesticide residues on the Hami melon based on visible/near-infrared (380-1140 nm) spectroscopy. Three combinations of convolution kernels were compared in the single-scale Deepspectra network. The convolution group of "5 × 1" and "3 × 1" kernels obtained a better overall performance. The multiscale Deepspectra network was compared to three single-scale Deepspectra networks on the preprocessing spectral data and obtained better results. The coefficient of determination (R2) for lambda-cyhalothrin and beta-cypermethrin was 0.758 and 0.835, respectively. The residual predictive deviation (RPD) for lambda-cyhalothrin and beta-cypermethrin was 2.033 and 2.460, respectively. The Deepspectra networks were compared with two conventional regression models: partial least square regression (PLSR) and support vector regression (SVR). The results showed that the multiscale Deepspectra network outperformed the other models. It was found that the multiscale Deepspectra network could be a novel approach for the quantitative estimation of pyrethroid pesticide residues on the Hami melon. These findings can also provide an effective strategy for spectral analysis.