Evaluation of convolutional neural network for non-destructive detection of imidacloprid and acetamiprid residues in chili pepper (Capsicum frutescens L.) based on visible near-infrared spectroscopy.

Consumption of agricultural products with pesticide residue is risky and can negatively affect health. This study proposed a nondestructive method of detecting pesticide residues in chili pepper based on the combination of visible and near-infrared (VIS/NIR) spectroscopy (400-2498 nm) and deep learning modeling. The obtained spectra of chili peppers with two types of pesticide residues (acetamiprid and imidacloprid) were analyzed using a one-dimensional convolutional neural network (1D-CNN). Compared with the commonly used partial least squares regression model, the 1D-CNN approach yielded higher prediction accuracy, with a root mean square error of calibration of 0.23 and 0.28 mg/kg and a root mean square error of prediction of 0.55 and 0.49 mg/kg for the acetamiprid and imidacloprid data sets, respectively. Overall, the results indicate that the combination of the 1D-CNN model and VIS/NIR spectroscopy is a promising nondestructive method of identifying pesticide residues in chili pepper.

The role of online game usage in the relationship between initial daily negative moods and subsequent positive moods: The moderating role of hedonistic motivation.

This study explores the relationship between initial daily negative mood, online game usage and subsequent positive mood; and examine the moderating roles of hedonistic motivation on the perspective of mood regulation theory. To gather data over five consecutive workdays, this study used the experience sampling method. We then obtained 800 valid daily data from 160 participants. The results of multilevel path analysis show that: (i) initial daily negative mood increases the usage of online games and further enhances subsequent positive mood; (ii) students with higher hedonic motivation will show a stronger positive correlation between initial daily negative mood and online game usage; (iii) students with higher hedonic motivation will show a stronger positive correlation between online game usage and subsequent positive mood. Theoretical and practical implications are also investigated in this study.

Visualization and (Semi-)quantification of submicrometer plastics through scanning electron microscopy and time-of-flight secondary ion mass spectrometry.

Increasing numbers of studies have demonstrated the existence of nanoplastics (1-999 nm) in the environment and commercial products, but the current technologies for detecting and quantifying nanoplastics are still developing. Herein, we present a combination of two techniques, e.g., scanning electron microscopy (SEM) and time-of-flight secondary ion mass spectrometry (ToF-SIMS), to analyze submicron-sized plastics. A drop-casting of a 20-nL particle suspension on a Piranha solution-cleaned silicon wafer with dry ice incubation and subsequent freeze-drying was used to suppress the coffee-ring effect. SEM images were used to quantify particles, and this technique is applicable for 0.195-1.04-µm polystyrene (PS), 0.311-µm polyethylene terephthalate (PET), and 0.344-µm polyethylene (PE) at a minimum concentration of 2.49 × 109 particles/mL. ToF-SIMS could not quantify the particle number, while it could semi-quantitatively estimate number ratios of submicron PE, PET, polyvinyl chloride (PVC), and PS particles in the mixture. Analysis of submicron plastics released from three hot water-steeped teabags (respectively made of PET/PE, polylactic acid (PLA), and PET) was revisited. The SEM-derived sizes and particle numbers were comparable to those measured by a nanoparticle tracking analysis (NTA) regardless of whether or not the hydro-soluble oligomers were removed. ToF-SIMS further confirmed the number ratios of different particles from a PET/PE composite teabag leachate. This method shows potential for application in analyzing more-complex plastic particles released from food contact materials.