Contamination is a critical issue that affects food consumption adversely. Therefore, efficient detection and classification of food contaminants are essential to ensure food safety. This study applied a visible and near-infrared (VNIR) hyperspectral imaging technique to detect and classify organic residues on the metallic surfaces of food processing machinery. The experimental analysis was performed by diluting both potato and spinach juices to six different concentration levels using distilled water. The 3D hypercube data were acquired in the range of 400–1000 nm using a line-scan VNIR hyperspectral imaging system. Each diluted residue in the spectral domain was detected and classified using six classification methods, including a 1D convolutional neural network (CNN-1D) and five pre-processing methods. Among them, CNN-1D exhibited the highest classification accuracy, with a 0.99 and 0.98 calibration result and a 0.94 validation result for both spinach and potato residues. Therefore, in comparison with the validation accuracy of the support vector machine classifier (0.9 and 0.92 for spinach and potato, respectively), the CNN-1D technique demonstrated improved performance. Hence, the VNIR hyperspectral imaging technique with deep learning can potentially afford rapid and non-destructive detection and classification of organic residues in food facilities.

Microbial electrolysis cells (MECs) have attracted significant interest as sustainable green hydrogen production devices because they utilize the environmentally friendly biocatalytic oxidation of organic wastes and electrochemical proton reduction with the support of relatively lower external power compared to that used by water electrolysis. However, the commercialization of MEC technology has stagnated owing to several critical technological challenges. Recently, many attempts have been made to utilize nanomaterials in MECs owing to the unique physicochemical properties of nanomaterials originating from their extremely small size (at least <100 nm in one dimension). The extraordinary properties of nanomaterials have provided great clues to overcome the technological hurdles in MECs. Nanomaterials are believed to play a crucial role in the commercialization of MECs. Thus, understanding the technological challenges of MECs, the characteristics of nanomaterials, and the employment of nanomaterials in MECs could be helpful in realizing commercial MEC technologies. Herein, the critical challenges that need to be addressed for MECs are highlighted, and then previous studies that used nanomaterials to overcome the technological difficulties of MECs are reviewed.

Power management systems (PMSs) are essential for the practical use of microbial fuel cell (MFC) technology, as they replace the unstable stacking of MFCs with step-up voltage conversion. Maximum-power extraction technology could improve the power output of MFCs; however, owing to the power consumption of the PMS operation, the maximum-power extraction point cannot deliver maximum power to the application load. This study proposes a practical power extraction for single MFCs, which reserves more electrical energy for an application load than conventional maximum power-point tracking (MPPT). When experimentally validated on a real MFC, the proposed method delivered higher output power during a longer PMS operation time than MPPT. The maximum power delivery enables more effective power conditioning of various micro-energy harvesting systems.

These studies were conducted to determine the effects of various concentrations of ammonium and nitrate on current generation using dual-cathode microbial fuel cells (MFCs). Current generation was not affected by ammonium up to 51.8+/-0.0 mg/l, whereas 103.5+/-0.0 mg/l ammonium chloride reduced the current slightly. On the other hand, when 60.0+/-0.0 and 123.3+/-0.1 mg/l nitrate were supplied, the current was decreased from 10.23+/-0.07 mA to 3.20+/-0.24 and 0.20+/-0.01 mA, respectively. Nitrate did not seem to serve as a fuel for current generation in these studies. At this time, COD and nitrate removal were increased except at 123+/-0.1 mg NO(3)(-)/l. These results show that proper management of ammonium and nitrate is very important for increasing the current in a microbial fuel cell.