Effect of fuzzy-controlled slow freezing on pumpkin (Cucurbita Moschata Duch) cell disintegration and phenolics

Freezing has been widely used to preserve vegetables including seasonal pumpkins. This work aimed to investigate the effects of freezing on pumpkin cell disintegration and phenolics. A fuzzy logic control (FLC) system was built to obtain better temperature control of the freezing system. Changes in cellular disintegration, electrical conductivity and phenolics content were evaluated. The angle measure technique and principal component analysis were used to delineate the surface texture changes of the frozen pumpkin cells. The results showed that FLC offered reliable temperature control performance. Freezing at -18 °C for 7 h caused the highest cell degradation of 0.467 on the disintegration scale. Decomposition was also indicated by an almost double increase in electrical conductivity. The changes in texture were accurately reflected in the mean angle spectra and 81.3% and 7.4% of the variability due to treatments could be explained by two principal components respectively. Freezing pumpkin at -18 °C for 6 h correlated to the maximum increase in total phenolics of 70.44%. The increased phenolics were dominated by caffeic acid, chlorogenic acid and p-coumaric acid. In conclusion, as the freezing system exhibits positive effects on the phenolics content of pumpkin, it may be employed to process seasonal pumpkin to obtain higher value from the produce.

Image texture analysis and colorimetry for the classification of uranium ore concentrate powders

In the context of nuclear security, uranium ore concentrates (UOCs) play an important role: they are traded in large quantities and this makes their use “out of regulatory control” a possible scenario. Once an incident of illicit trafficking o f n uclear m aterial is detected, an understanding of its origin and production process is required; this implies the necessity to use analytical techniques able to measure characteristic parameters (e.g. physical, chemical, isotopic characteristics of the nuclear materials) which are referred to, in the field o f t he n uclear f orensics, a s signatures. The present study investigates the potential of image texture analysis (i.e. the angle measure technique), combined with the spectrophotometric determination of colours for the evaluation of the origin of several UOCs. The use of different multivariate statistical techniques allows the categorization of about 80 different samples into a few groups of UOCs powders, which makes this approach a promising method complementing the already established methods in nuclear forensics.

Texture Feature Extraction and Classification of SEM Images of Wheat Straw/Polypropylene Composites in Accelerated Aging Test

Wheat straw/polypropylene composites are green recycled and biomass-based material. After accelerated aging test of the composite was done, practical and effective methods for characterization and extraction of texture feature of microscopic Scanning Electron Microscopy (SEM) images of composites were investigated in this paper, and involved data compression and classification recognition were studied as well. Through Angle Measure Technique (AMT) method, the complexity spectra, MA spectra, of the preprocessed SEM images of the composites were derived and then the first four principal components of MA spectra using Principal Component Analysis (PCA) were extracted accordingly. Two kinds of classifiers based on Extreme Learning Machine (ELM) and Support Vector Machine (SVM) were introduced to classify the SEM images into five different aging periods in this paper. The research results indicate that AMT method is a very novel and effective approach in texture feature characterization and analysis of SEM images of composites and high classification accuracy of SEM images in different aging periods by using intelligent recognition can be reached.

The AMT Features Extraction from Batch Drilling Step Monitor Signals and its Applications

Angle Measure Technique (AMT) algorithm is proposed to analyze the quality of batch drilling process in this paper. The groups spindle power (SP) signals of batch drilling were preprocessed by the normalization method, and then the AMT was used to extract the scale features of monitor signals from local to global. By the method of Principal Component Analysis (PCA), the dimensionality of these scale features was reduced to obtain a distribution of the step quality of batch drilling. The results indicate that this method can be used for observation of high precision cutting step quality.