This study presents a novel method to apply the RGB-D (Red Green Blue–Depth) sensors and fuse aligned RGB and NIR images with deep convolutional neural networks (CNN) for fruit detection. It aims to build a more accurate, faster, and more reliable fruit detection system, which is a vital element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). A common Faster R-CNN network VGG16 was adopted through transfer learning, for the task of kiwifruit detection using imagery obtained from two modalities: RGB (red, green, blue) and Near-Infrared (NIR) images. Kinect v2 was used to take a bottom view of the kiwifruit canopy’s NIR and RGB images. The NIR (1 channel) and RGB images (3 channels) were aligned and arranged side by side into a 6-channel image. The input layer of the VGG16 was modified to receive the 6-channel image. Two different fusion methods were used to extract features: Image-Fusion (fusion of the RGB and NIR images on input layer) and Feature-Fusion (fusion of feature maps of two VGG16 networks where the RGB and NIR images were input respectively). The improved networks were trained end-to-end using back-propagation and stochastic gradient descent techniques and compared to original VGG16 networks with RGB and NIR image input only. Results showed that the average precision (APs) of the original VGG16 with RGB and NIR image input only were 88.4% and 89.2% respectively, the 6-channel VGG16 using the Feature-Fusion method reached 90.5%, while that using the Image-Fusion method reached the highest AP of 90.7% and the fastest detection speed of 0.134 s/image. The results indicated that the proposed kiwifruit detection approach shows a potential for better fruit detection.

Sartans are a group of pharmaceuticals widely used to regulate blood pressure. Their concentration levels were monitored in 80 wastewater treatment plants (WWTP) in the Baltic Sea Region, reached from limit of detection up to 6 µg/L. The concentrations were significantly different in different countries, but consistent within the respective country. The degradation of sartans (losartan, valsartan, irbesartan) in moving bed biofilm reactors (MBBRs) that utilize biofilms grown on mobile carriers to treat wastewater was investigated for the first time, and compared with the degradation in a conventional activated sludge (CAS) treatment plant. The results showed the formation of six microbial transformation products (TPs) of losartan, four of valsartan, and four of irbesartan in biological wastewater treatment. Four of these metabolites have not been described in the literature before. Chemical structures were suggested and selected TPs were verified and quantified depending on availability of true standards. Valsartan acid was a common TP of losartan, valsartan, and irbesartan. Losartan and irbesartan also shared one TP: losartan/irbesartan TP335. Based on the mass balance analysis, losartan carboxylic acid is the main TP of losartan, and valsartan acid is the main TP of valsartan during the biotransformation process. For irbesartan, TP447 is likely to be the main TP, as its peak areas were two orders of magnitude higher than those of all the other detected TPs of this compound. The effects of adapting biofilms to different biological oxygen demand (BOD) loading on the degradation of sartans as well as the formation of their TPs were investigated. Compared to feeding a poor substrate (pure effluent wastewater from a CAS), feeding with richer substrate (1/3 raw and 2/3 effluent wastewater) promoted the metabolism of most compounds (co-metabolization). However, the addition of raw wastewater inhibited some metabolic pathways of other compounds, such as from losartan/irbesartan to TP335 (competitive inhibition). The formation of irbesartan TP447 did not change with or without raw wastewater. Finally, the sartans and their TPs were investigated in a full-scale CAS wastewater treatment plant (WWTP). The removal of losartan, valsartan, and irbesartan ranged from 3.0 % to 72% and some of the transformation products (TPs) from human metabolism were also removed in the WWTP. However, some of the sartan TPs, i.e., valsartan acid, losartan carboxylic acid, irbesartan TP443 and losartan TP453, were formed in the WWTP. Relative high amounts of especially losartan carboxylic acid, which was detected with concentrations up to 2.27 µg/L were found in the effluent.

Knowledge of bacteria’s heat resistance is essential for developing effective thermal treatments. Choosing an appropriate test method is important to accurately determine bacteria’s heat resistances. Although being a major factor to influence the thermo-tolerance of bacteria, the heating rate in samples cannot be controlled in water or oil bath methods due to main dependence on sample’s thermal properties. A heating block system (HBS) was designed to regulate the heating rates in liquid, semi-solid and solid foods using a temperature controller. Distilled water, apple juice, mashed potato, almond powder and beef were selected to evaluate the HBS’s performance by experiment and computer simulation. The results showed that the heating rates of 1, 5 and 10 °C/min with final set-point temperatures and holding times could be easily and precisely achieved in five selected food materials. A good agreement in sample central temperature profiles was obtained under various heating rates between experiment and simulation. The experimental and simulated results showed that the HBS could provide a sufficiently uniform heating environment in food samples. The effect of heating rate on bacterial thermal resistance was evaluated with the HBS. The system may hold potential applications for rapid and accurate assessments of bacteria’s thermo-tolerances.