RESUMEN
Smartphone-based assays to inspect pesticides in foods have attracted much attention as such assays can transform tedious laboratory-based assays into real-time, on-site, or even home-based assay and hence overcoming the limitations of conventional assays. Although an array of smartphone-based assays is available, information on the use of these assays for pesticides inspection is scattered. The purposes of this review are therefore to compile, summarize and discuss state-of-the-art as well as advantages and limitations of the relevant technologies. Suggestions are provided for further development of smartphone-based assays for rapid inspection of pesticides in foods. Smartphone-based assays relying on enzyme inhibitions are noted to be nonselective qualitative, capable of reporting results in a quantitative manner only when a sample contains an individual pesticide. Smartphone-based assays relying on chemical reactions also target only individual pesticides. Smartphone-based visible spectroscopy can, on the other hand, inspect individual and multiple pesticides with the aid of appropriate colorimetry-, luminescence-, or fluorescence-based assay. Smartphone-based visible-near infrared and Raman spectroscopies are suitable for simultaneous multiple pesticides inspection. Raman spectroscopy is of particular interest as it can detect pesticides even at lower concentrations. This spectroscopic technique can also serve as a real-time assay with the aid of cloud network computations.
RESUMEN
Soft material can undergo non-uniform deformation or change of shape upon processing. Identifying shape and its change is nevertheless not straightforward. In this study, novel image-based algorithm that can be used to identify shapes of input images and at the same time classify non-uniform deformation into various patterns, i.e., swelling/shrinkage, horizontal and vertical elongations/contractions as well as convexity and concavity, is proposed. The algorithm was first tested with computer-generated images and later applied to agar cubes, which were used as model shrinkable soft material, undergoing drying at different temperatures. Shape parameters and shape-parameter based algorithm as well as convolutional neural networks (CNNs) either incorrectly identified some complicated shapes or could only identify the point where non-uniform deformation started to take place; CNNs lacked ability to describe non-uniform deformation evolution. Shape identification accuracy of the newly developed algorithm against computer-generated images was 65.88%, while those of the other tested algorithms ranged from 34.76 to 97.88%. However, when being applied to the deformation of agar cubes, the developed algorithm performed superiorly to the others. The proposed algorithm could both identify the shapes and describe their changes. The interpretation agreed well with that via visual observation.
