Evaluation of method for simultaneous determination of pesticide residues in fruits and vegetables by collaborative study

A collaborative study involving 8 laboratories was conducted to evaluate a method for the simultaneous determination of pesticide residues in 6 types of fruits and vegetables (spinach, tomato, apple, radish, cabbage and carrot). The method of analysis was the same as reported by Kakimoto et al. in 2003. One hundred and thirty-nine pesticides were spiked by each of 8 laboratories at levels of 0.1 microg/g (pesticides analyzed by GC/MS) or 0.5 microg/g (pesticides analyzed by HPLC) into the 6 kinds of samples. Statistical analysis showed that 111 pesticides could be analyzed with practical precision by this method. For screening purposes, the method could analyze 118 pesticides. The median values of the limits of detection were 0.001-0.041 microg/g. The calibration curves were linear in the range of 0.5-5 microg/mL for most pesticides with median correlation coefficients of 0.983-1.000.     

Deep-ultraviolet Raman microspectroscopy: characterization of wide-gap semiconductors

We have developed a high-throughput deep-ultraviolet (DUV) Raman microspectrometer with excitation from a continuous wave (cw) laser operated at 244 nm that enables us to characterize thin surface layers of wide-gap semiconductors. This spectrometer system consists of a filter spectrometer for the rejection of stray light and a high-dispersion spectrograph combined with a liquid nitrogen cooled charge-coupled device (CCD) detector and extends the low-frequency limit of the observable spectral range down to 170 cm(-1). In the microscope we use a Cassegrain reflective objective for the collection of the scattered light and an off-axis mirror for introduction of the excitation laser light. DUV Raman spectroscopy has been applied for studying wide-gap semiconductors including SiC and AlGaN epitaxial films and shallow implanted layers of these materials. Raman spectra of various crystals have also been measured for examining the performance of this system. Resonance enhancement of Raman bands has been observed for several semiconductors, and the results are discussed.     

Performance evaluation system for probabilistic neural network hardware

The probabilistic neural network (PNN) is one of the most promising neural networks, and is now applied to some real-world applications. In order to speed up the PNN calculation considerably, we have developed a PNN hardware system for video image recognition. The performance of the PNN hardware cannot be evaluated precisely until the evaluation system is completed. In this study, we developed a performance evaluation system for the PNN hardware and demonstrated it using the developed evaluation system.This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

Selection of manipulator system for multiple-goal task by evaluating task completion time and cost with computational time constraints

The focus of this study is on the problem of manipulator system selection for a multiple-goal task by evaluating task completion time and cost with computational time constraints. An approach integrating system selection, structural configuration design, layout design, motion planning, and relative cost calculation is proposed to solve this problem within a reasonable computational time. In the proposed approach, multiple-objective particle swarm optimization (MOPSO) is utilized to search for the appropriate manipulator system with appropriate structural configuration from a set of candidate systems. Particle swarm optimization (PSO) and the nearest neighborhood algorithm are employed in layout design and motion planning due to their high convergence speed. Three methods involving a random search algorithm are compared to the proposed approach through a simulation. The simulation is done with a set of tasks and the result shows the effectiveness of the proposed approach.     

A new Landsat 8 cloud discrimination algorithm using thresholding tests

ABSTRACT

In this work, we propose a Cloud Discrimination Algorithm for Landsat 8 (CDAL8) to improve a high-frequency automatic land change detection system developed at the National Institute of Advanced Industrial Science and Technology (AIST), Japan for large-scale satellite image analysis. Although the land change detection system can process several kinds of satellite remote sensing data, improvements are needed to enable practical applications using Landsat 8 data. Cloud discrimination is a necessary pre-processing step for land cover change detection. Currently, most of the prediction errors on land change detection are caused by the false cloud discrimination results as a pre-processing step. Therefore, we introduce an improved cloud discrimination algorithm (CDAL8) in this study to improve the overall performance of our land change detection system. The algorithm was developed based on a Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm and Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA). CDAL8 is distinct in that it switches judgment tests and their thresholds using a threshold brightness temperature and uses separate features in cloud judgment and clear-sky judgment. To evaluate the accuracy of the proposed algorithm, we compared it with the Automated Cloud-Cover Assessment algorithm (ACCA) and Function of Mask (Fmask) version 3.3 using US Geological Survey Landsat 8 cloud cover assessment validation data, which contain 96 cloud masks. Our proposed cloud discrimination algorithm (CDAL8) have promising results with an accuracy of 88.1%, which was greater than that of the ACCA (82.5%) and Fmask (84.6%). Furthermore, we also confirmed that the average accuracy of CDAL8 was approximately 91.2% when low solar elevation scenes were removed.     

Formation and characterization of copper tetrakis (4-cumylphenoxy) phthalocyanine:perylene solar cells

Perylene-based bulk heterojunction solar cells with copper tetrakis (4-cumylphenoxy) phthalocyanine (Tc-CuPc) and heterojunction solar cells with copper phthalocyanine (CuPc) were produced and characterized. A device based on CuPc and perylene provided better efficiency, fill factor, short-circuit current and open-circuit voltage compared to those of a device based on Tc-CuPc and perylene. The Tc-CuPc:perylene structure was examined by transmission electron microscopy, which indicated mixtured microstructure. Energy levels of the molecules were calculated and discussed.     

Covalently cross-linked multilayer thin films composed of diazoresin and brilliant yellow for an optical pH sensor

The multilayered thin films composed diazoresin (DR) and brilliant yellow (BY) were fabricated by a layer-by-layer (LbL) deposition technique, and the ionic bonds between diazonium ion in DR and sulfonate residues in BY were converted to covalent bonds by UV light irradiation. The cross-linking between BY and DR prevented BY from desorption even in a pH 13 solution. The cross-linked (DR/BY)₁₀DR film exhibited pH-dependent absorption spectra in pH 9-13, and the response was repeatable and quick. The pH response time of the films was within a few seconds upon change from pH 9.0 to 13.0, and ca. 40 s upon change from pH 13.0 to 9.0.     

Site‐Selective Iron(III) Chloride‐Catalyzed Arylation of 4‐Aryl‐4‐methoxy‐2,5‐cyclohexadienones for the Synthesis of Polyarylated Phenols

The iron(III) chloride‐catalyzed Friedel–Crafts arylation of 4‐aryl‐4‐methoxy‐2,5‐cyclohexadienones, which were easily prepared by the phenyliodine(III) diacetate (PIDA)‐mediated oxidation of 4‐arylphenols in methanol, proceeded site‐selectively to form meta‐terphenyl (2,4‐diarylphenol) derivatives in good yields. The subsequent PIDA‐mediated oxidation and iron(III) chloride‐catalyzed Friedel–Crafts arylation of the resulting products gave the corresponding 2,4,6‐triarylphenol derivatives. The present method provides useful highly substituted polyarylated compounds.

     

Identification of yeast strains isolated from marcha in Sikkim, a microbial starter for amylolytic fermentation

Marcha or murcha is a traditional amylolytic starter used to produce sweet-sour alcoholic drinks, commonly called jaanr in the Himalayan regions of India, Nepal, Bhutan, and Tibet (China). The aim of this study was to examine the microflora of marcha collected from Sikkim in India, focusing on yeast flora and their roles. Twenty yeast strains were isolated from six samples of marcha and identified by genetic and phenotypic methods. They were first classified into four groups (Group I, II, III, and IV) based on physiological features using an API test. Phylogenetic, morphological, and physiological characterization identified the isolates as Saccharomyces bayanus (Group I); Candida glabrata (Group II); Pichia anomala (Group III); and Saccharomycopsis fibuligera, Saccharomycopsis capsularis, and Pichia burtonii (Group IV). Among them, the Group I, II, and III strains produced ethanol. The isolates of Group IV had high amylolytic activity. Because all marcha samples tested contained both starch degraders and ethanol producers, it was hypothesized that all four groups of yeast (Group I, II, III, and IV) contribute to starch-based alcohol fermentation.