A Combined System of Paper Device and Portable Spectrometer for the Detection of Pesticide Residues

Affordable Portable Platform for Classic Photometry and Low-Cost Determination of Cholinesterase Activity

Excessive use of pesticides could potentially harm the environment for a long time. The reason for this is that the banned pesticide is still likely to be used incorrectly. Carbofuran and other banned pesticides that remain in the environment may also have a negative effect on human beings. In order to provide a better chance for effective environmental screening, this thesis describes a prototype of a photometer tested with cholinesterase to potentially detect pesticides in the environment. The open-source portable photodetection platform uses a color-programmable red, green and blue light-emitting diode (RGB LED) as a light source and a TSL230R light frequency sensor. Acetylcholinesterase from Electrophorus electricus (AChE) with high similarity to human AChE was used for biorecognition. The Ellman method was selected as a standard method. Two analytical approaches were applied: (1) subtraction of the output values after a certain period of time and (2) comparison of the slope values of the linear trend. The optimal preincubation time for carbofuran with AChE was 7 min. The limits of detection for carbofuran were 6.3 nmol/L for the kinetic assay and 13.5 nmol/L for the endpoint assay. The paper demonstrates that the open alternative for commercial photometry is equivalent. The concept based on the OS3P/OS3P could be used as a large-scale screening system.

Machine Learning-Based Analytical Systems: Food Forensics

Despite a large amount of money being spent on both food analyses and control measures, various food-borne illnesses associated with pathogens, toxins, pesticides, adulterants, colorants, and other contaminants pose a serious threat to human health, and thus food safety draws considerable attention in the modern pace of the world. The presence of various biogenic amines in processed food have been frequently considered as the primary quality parameter in order to check food freshness and spoilage of protein-rich food. Various conventional detection methods for detecting hazardous analytes including microscopy, nucleic acid, and immunoassay-based techniques have been employed; however, recently, array-based sensing strategies are becoming popular for the development of a highly accurate and precise analytical method. Array-based sensing is majorly facilitated by the advancements in multivariate analytical techniques as well as machine learning-based approaches. These techniques allow one to solve the typical problem associated with the interpretation of the complex response patterns generated in array-based strategies. Consequently, the machine learning-based neural networks enable the fast, robust, and accurate detection of analytes using sensor arrays. Thus, for commercial applications, most of the focus has shifted toward the development of analytical methods based on electrical and chemical sensor arrays. Therefore, herein, we briefly highlight and review the recently reported array-based sensor systems supported by machine learning and multivariate analytics to monitor food safety and quality in the field of food forensics.

The application of rapid test paper technology for pesticide detection in horticulture crops: a comprehensive review

Background

The ever increasing pests and diseases occurring during vegetable crop production is a challenge for agronomists and farmers. One of the practices to avoid or control the attack of the causal agents is the use of pesticides, including herbicides, insecticides nematicides, and molluscicides. However, the use of these products can result in the presence of harmful residues in horticultural crops, which cause several human diseases such as weakened immunity, splenomegaly, renal failure, hepatitis, respiratory diseases, and cancer. Therefore, it was necessary to find safe and effective techniques to detect these residues in horticultural crops and to monitor food security.

Main body

The review discusses the use of conventional methods to detect pesticide residues on horticultural crops, explain the sensitivity of nanoparticle markers to detect a variety of pesticides, discuss the different methods of rapid test paper technology and highlight recent research on rapid test paper detection of pesticides.

Conclusions

The methodologies discussed in the current review can be used in a certain situation, and the variety of methods enable detection of different types of pesticides in the environment. Notably, the highly sensitive immunoassay, which offers the advantages of being low cost, highly specific and sensitive, allows it to be integrated into many detection fields to accurately detect pesticides.

Machine-Learning-Assisted Analysis of Colorimetric Assays on Paper Analytical Devices

Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary, resulting in less accurate results. Recently, machine-learning (ML)-assisted models have been used in image analysis. We evaluated a combination of four ML models-logistic regression, support vector machine (SVM), random forest, and artificial neural network (ANN)-as well as three image color spaces, RGB, HSV, and LAB, for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras and users for food color and enzyme inhibition assays to create training and test datasets. The prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML models and color space combinations. All models better predicted coarse-level classifications than fine-grained concentration classes. ML models using the sample color along with a reference color increased the models' ability to predict the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration class prediction accuracy obtained for food color was 0.966 when using the ANN model and LAB color space. The accuracy for enzyme inhibition assay was 0.908 when using the SVM model and LAB color space. Appropriate models and color space combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool.