Shock Sterilization of Dry Powder Foods

2003 ◽  
Author(s):  
Kazuhito Fujiwara ◽  
Tetsuyuki Hiroe ◽  
Makio Asakawa
Keyword(s):  
Principal Component ◽  
Experimental Results ◽  
Volatile Components ◽  
Dry Powder ◽  
Industrial Use ◽  
Sterilization Equipment

The sterilization for fungi and bacteria in some kinds of dry powder foods is limited to keep their grade. Especially in spices the heat sterilization is restricted to minimum use, because the heat reduces the hot-taste that is a principal component in spices. The operation for the sterilization has to act only on fungi and bacteria, not to degrade the volatile components contained in flavor elements. In this paper, the instance of shock sterilization is shown, and the development of the idea to the sterilization equipment and its performance are presented. Experimental results showed the potential of the shock for the sterilization and the feasibility for the industrial use.

scholarly journals GC-MS Based Identification of the Volatile Components of Six Astragalus Species from Uzbekistan and Their Biological Activity

Plants ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 124
Author(s):  
Haidy A. Gad ◽  
Nilufar Z. Mamadalieva ◽  
Stefan Böhmdorfer ◽  
Thomas Rosenau ◽  
Gokhan Zengin ◽  
...  
Keyword(s):  
Radical Scavenging ◽  
Reducing Power ◽  
Principal Component ◽  
Inhibitory Effect ◽  
Maximum Activity ◽  
Volatile Components ◽  
Drug Candidates ◽  
Predominant Component ◽  
Clear Differentiation

The compositions of volatile components in the aerial parts of six Astragalus species, namely A. campylotrichus (Aca), A. chiwensis (Ach), A. lehmannianus (Ale), A. macronyx (Ama), A. mucidus (Amu) and A. sieversianus (Asi), were investigated using gas chromatograph-mass spectrometry (GC-MS) analysis. Ninety-seven metabolites were identified, accounting for 73.28, 87.03, 74.38, 87.93, 85.83, and 91.39% of Aca, Ach, Ale, Ama, Amu and Asi whole oils, respectively. Sylvestrene was the most predominant component in Asi, Amu and Ama, with highest concentration in Asi (64.64%). In addition, (E)-2-hexenal was present in a high percentage in both Ale and Ach (9.97 and 10.1%, respectively). GC-MS based metabolites were subjected to principal component analysis (PCA) and hierarchal cluster analysis (HCA) to explore the correlations between the six species. The PCA score plot displayed clear differentiation of all Astragalus species and a high correlation between the Amu and Ama species. The antioxidant activity was evaluated in vitro using various assays, phosphomolybdenum (PM), 2,2 diphenyl-1-picryl-hydrazyl-hydrate (DPPH), 2,2-azino bis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), cupric reducing antioxidant capacity (CUPRAC), ferric reducing power (FRAP) and ferrous ion chelation (FIC) assays. In addition, the potential for the volatile samples to inhibit both acetyl/butyrylcholinesterases (AChE, BChE), α- amylase, α-glucosidase and tyrosinase was assessed. Most of the species showed considerable antioxidant potential in the performed assays. In the DPPH assay, Ama exhibited the maximum activity (24.12 ± 2.24 mg TE/g sample), and the volatiles from Amu exhibited the highest activity (91.54 mgTE/g oil) in the ABTS radical scavenging assay. The effect was more evident in both CUPRAC and FRAP assays, where both Ale and Ama showed the strongest activity in comparison with the other tested species (84.06, 80.28 mgTE/g oil for CUPRAC and 49.47, 49.02 mgTE/g oil for FRAP, respectively). Asi demonstrated the strongest AChE (4.55 mg GALAE/g oil) and BChE (3.61 mg GALAE/g oil) inhibitory effect. Furthermore, the best tyrosinase inhibitory potential was observed for Ale (138.42 mg KAE/g). Accordingly, Astragalus species can be utilized as promising natural sources for many medicinally important components that could be tested as drug candidates for treating illnesses such as Alzheimer’s disease, diabetes mellitus and oxidative stress-related diseases.

scholarly journals Fractional Wavelet-Based Generative Scattering Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Jiasong Wu ◽  
Xiang Qiu ◽  
Jing Zhang ◽  
Fuzhi Wu ◽  
Youyong Kong ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Reduction Method ◽  
Gaussian White Noise ◽  
Principal Component ◽  
Experimental Results ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Adversarial Networks ◽  
Dimensionality Reduction Method

Generative adversarial networks and variational autoencoders (VAEs) provide impressive image generation from Gaussian white noise, but both are difficult to train, since they need a generator (or encoder) and a discriminator (or decoder) to be trained simultaneously, which can easily lead to unstable training. To solve or alleviate these synchronous training problems of generative adversarial networks (GANs) and VAEs, researchers recently proposed generative scattering networks (GSNs), which use wavelet scattering networks (ScatNets) as the encoder to obtain features (or ScatNet embeddings) and convolutional neural networks (CNNs) as the decoder to generate an image. The advantage of GSNs is that the parameters of ScatNets do not need to be learned, while the disadvantage of GSNs is that their ability to obtain representations of ScatNets is slightly weaker than that of CNNs. In addition, the dimensionality reduction method of principal component analysis (PCA) can easily lead to overfitting in the training of GSNs and, therefore, affect the quality of generated images in the testing process. To further improve the quality of generated images while keeping the advantages of GSNs, this study proposes generative fractional scattering networks (GFRSNs), which use more expressive fractional wavelet scattering networks (FrScatNets), instead of ScatNets as the encoder to obtain features (or FrScatNet embeddings) and use similar CNNs of GSNs as the decoder to generate an image. Additionally, this study develops a new dimensionality reduction method named feature-map fusion (FMF) instead of performing PCA to better retain the information of FrScatNets,; it also discusses the effect of image fusion on the quality of the generated image. The experimental results obtained on the CIFAR-10 and CelebA datasets show that the proposed GFRSNs can lead to better generated images than the original GSNs on testing datasets. The experimental results of the proposed GFRSNs with deep convolutional GAN (DCGAN), progressive GAN (PGAN), and CycleGAN are also given.

Potentiometric Taste Sensing Using Reduced Graphene Oxide Screen Printed Electrodes

2020 ◽  
Vol 18 (12) ◽  
pp. 881-888
Author(s):  
Anil B. Patil ◽  
Umesh. J. Tupe ◽  
Vikas V. Deshmane ◽  
Arun V. Patil
Keyword(s):  
Graphene Oxide ◽  
Reduced Graphene Oxide ◽  
Principal Component ◽  
Experimental Results ◽  
Statistical Tool ◽  
Sensing Applications ◽  
Screen Printed Electrodes ◽  
Reduced Graphene ◽  
Pharmaceutical Industries ◽  
Screen Printed

This paper reports the development of simple and economical reduced graphene oxide (rGO) based screen-printed electrodes (SPE) for five basic taste sensing applications. Twenty different test solutions for the five tastes of salty, sour, sweet, umami, and bitter at 1 ppm, 10 ppm, 100 ppm, 1000 ppm concentration levels were tested with the fabricated SPEs. From experimental results, electrical signals generated between the electrode and test solution interface were measured using the potentiometric method. Satisfactory potentiometric responses of SPEs to different ppm concentrations for each sample were used to analyze the sample data. Histogram using the statistical tool was used to analyze the changes in the conductivity response. A multivariate Principal Component Analysis (PCA) statistical tool correlated using loading plots between variables and factors of all the five basic tastes. The plot showed the interrelation between variables and test samples. The obtained experimental results from these rGO based SPEs make them suitable for their use in taste sensing applications such as for any taste disorder disability, food-producing industry, pharmaceutical industries, etc.

scholarly journals Effect of Spices on the Formation of VOCs in Roasted Mutton Based on GC-MS and Principal Component Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Jiapei Xi ◽  
Ping Zhan ◽  
Honglei Tian ◽  
Peng Wang
Keyword(s):  
Principal Component Analysis ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Principal Component ◽  
Component Analysis ◽  
Volatile Components ◽  
Volatile Profile ◽  
Asian Countries ◽  
Volatile Organic ◽  
Chinese Prickly Ash

Peppertree prickly ash, Amomum tsao-ko, cumin, and ginger have long been used in Asian countries to modify the flavor and to partially neutralize any unpleasant odors present in roast lamb. The purpose of this study was to evaluate the change in the amount of volatile components present in roast lamb compared to meat added with peppertree prickly ash, Amomum tsao-ko, cumin, and ginger. Principal component analysis was carried out on the 27 initially selected from 88 volatile substances, and 15 substances with a projection of more than 0.25 in the load matrix were used as indicators to study the different contents in roasted mutton and lamb prepared by adding peppertree prickly ash, Amomum tsao-ko, cumin, and ginger. The types of VOCs (volatile organic compounds) detected in roast meat without adding spices were the least. Roast meat with the addition of cumin leads to the strongest content of aldehydes, followed by the addition of Amomum tsao-ko. Additionally, roast meat with the addition of Chinese prickly ash leads to the strongest content of terpenes, followed by the addition of ginger. Moreover, with the addition of spices, the content of volatiles responsible for the presence of a mutton odor (such as hexanal, heptanal, pentanal, (z)-4-decenal, benzaldehyde, p-propyl-anisole, and dimethyl ether) was not significantly decreased, and in fact some volatiles increased in amount such as pentanal, hexanal, octanal, and (z)-4-decenal. In conclusion, the effect of addition of spices on the volatile profile of roasted mutton and lamb can be attributed to the generation of flavor volatiles mainly derived from raw spices’ hot action, with few additional volatiles formed during boiling.

scholarly journals Detecting Shilling Attacks with Automatic Features from Multiple Views

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yaojun Hao ◽  
Fuzhi Zhang ◽  
Jian Wang ◽  
Qingshan Zhao ◽  
Jianfang Cao
Keyword(s):  
Principal Component Analysis ◽  
Recommender Systems ◽  
Detection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Experimental Results ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Multiple Views ◽  
Fine Grained

Due to the openness of the recommender systems, the attackers are likely to inject a large number of fake profiles to bias the prediction of such systems. The traditional detection methods mainly rely on the artificial features, which are often extracted from one kind of user-generated information. In these methods, fine-grained interactions between users and items cannot be captured comprehensively, leading to the degradation of detection accuracy under various types of attacks. In this paper, we propose an ensemble detection method based on the automatic features extracted from multiple views. Firstly, to collaboratively discover the shilling profiles, the users’ behaviors are analyzed from multiple views including ratings, item popularity, and user-user graph. Secondly, based on the data preprocessed from multiple views, the stacked denoising autoencoders are used to automatically extract user features with different corruption rates. Moreover, the features extracted from multiple views are effectively combined based on principal component analysis. Finally, according to the features extracted with different corruption rates, the weak classifiers are generated and then integrated to detect attacks. The experimental results on the MovieLens, Netflix, and Amazon datasets indicate that the proposed method can effectively detect various attacks.

scholarly journals Assessing the Groundwater Quality in the Liwa Area, the United Arab Emirates

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2816 ◽  
Author(s):  
Alina Barbulescu ◽  
Yousef Nazzal ◽  
Fares Howari
Keyword(s):  
Groundwater Quality ◽  
United Arab Emirates ◽  
Quality Index ◽  
Principal Component ◽  
Water Parameters ◽  
The World ◽  
Abu Dhabi Emirate ◽  
Agricultural Use ◽  
Industrial Use ◽  
Drinkable Water

Last period groundwater quality raises big concerns all over the world since it is a limited source of drinkable water and for agricultural and industrial use. While the suitability of the groundwater of Liwa aquifer (Abu Dhabi Emirate) for agricultural use has been previously partially studied, not all the water parameters have been taken into account. Therefore, in this paper, we propose the study of 42 concentrations series of 19 groundwater parameters. We test the hypothesis that the water parameters series recorded at different locations are similar and group the samples in clusters. The main parameters that determine the differences between the clusters are determined by Principal Component Analysis (PCA). Finally, we use a quality index for assessing the water suitability for drinking. The conclusions emphasize the necessity of using more than one technique to evaluate water quality for different purposes and to cross-validate the results.

scholarly journals Sciadopitys verticillata Resin: Volatile Components and Impact on Plant Pathogenic and Foodborne Bacteria

Molecules ◽  
2019 ◽  
Vol 24 (20) ◽  
pp. 3767
Author(s):  
David I. Yates ◽  
Bonnie H. Ownley ◽  
Nicole Labbé ◽  
Joseph J. Bozell ◽  
William E. Klingeman ◽  
...  
Keyword(s):  
Bacterial Growth ◽  
Foodborne Pathogens ◽  
Antimicrobial Properties ◽  
Principal Component ◽  
Gas Chromatography Mass Spectrometry ◽  
Volatile Components ◽  
Sample Treatment ◽  
Bacterial Populations ◽  
Sciadopitys Verticillata ◽  
The Impact

Sciadopitys verticillata (Sv) produces a white, sticky, latex-like resin with antimicrobial properties. The aims of this research were to evaluate the effects of this resin (Sv resin) on bacterial populations and to determine the impact of its primary volatile components on bioactivity. The impact of sample treatment on chemical composition of Sv resin was analyzed using Fourier transform infrared spectroscopy (FTIR) coupled with principal component analysis. The presence and concentration of volatiles in lyophilized resin were determined using gas chromatography/mass spectrometry (GC/MS). Changes in bacterial population counts due to treatment with resin or its primary volatile components were monitored. Autoclaving of the samples did not affect the FTIR spectra of Sv resin; however, lyophilization altered spectra, mainly in the CH and C=O regions. Three primary bioactive compounds that constituted >90% of volatiles (1R-α-pinene, tricyclene, and β-pinene) were identified in Sv resin. Autoclaved resin impacted bacterial growth. The resin was stimulatory for some plant and foodborne pathogens (Pseudomonas fluorescens, P. syringae, and Xanthomonas perforans) and antimicrobial for others (Escherichia coli, Bacillus cereus, Agrobacterium tumefaciens, and Erwinia amylovora). Treatment with either 1R-α-pinene or β-pinene reduced B. cereus population growth less than did autoclaved resin. The complex resin likely contains additional antimicrobial compounds that act synergistically to inhibit bacterial growth.

CORRIDOR-SCENE CLASSIFYING METHODS FOR MOBILE ROBOT BASED ON MULTI-SONAR-SENSOR INFORMATION FUSION

2007 ◽  
Vol 04 (01) ◽  
pp. 15-26 ◽  
Author(s):  
XIUQING WANG ◽  
ZENG-GUANG HOU ◽  
LONG CHENG ◽  
MIN TAN ◽  
FEI ZHU
Keyword(s):  
Principal Component Analysis ◽  
Mobile Robot ◽  
Principal Component ◽  
Component Analysis ◽  
Experimental Results ◽  
Scene Analysis ◽  
Kernel Principal Component Analysis ◽  
Kernel Pca ◽  
Sonar Sensor ◽  
Pca Method

The ability of cognition and recognition for complex environment is very important for a real autonomous robot. A new scene analysis method using kernel principal component analysis (kernel-PCA) for mobile robot based on multi-sonar-ranger data fusion is put forward. The principle of classification by principal component analysis (PCA), kernel-PCA, and the BP neural network (NN) approach to extract the eigenvectors which have the largest k eigenvalues are introduced briefly. Next the details of PCA, kernel-PCA and the BP NN method applied in the corridor scene analysis and classification for the mobile robots based on sonar data are discussed and the experimental results of those methods are given. In addition, a corridor-scene-classifier based on BP NN is discussed. The experimental results using PCA, kernel-PCA and the methods based on BP neural networks (NNs) are compared and the robustness of those methods are also analyzed. Such conclusions are drawn: in corridor scene classification, the kernel-PCA method has advantage over the ordinary PCA, and the approaches based on BP NNs can also get satisfactory results. The robustness of kernel-PCA is better than that of the methods based on BP NNs.

JPEG Steganalysis Based on Feature Fusion by Principal Component Analysis

2012 ◽  
Vol 263-266 ◽  
pp. 2933-2938 ◽  
Author(s):  
Feng Ying He ◽  
Shang Ping Zhong ◽  
Kai Zhi Chen
Keyword(s):  
Principal Component Analysis ◽  
Image Classification ◽  
Detection Rate ◽  
Feature Fusion ◽  
Principal Component ◽  
Component Analysis ◽  
Experimental Results ◽  
Jpeg Images

Aiming to the problems in the existing JPEG steganalysis schemes, such as high redundancy in features and failure to make good use of the complementarities among them, this study proposed a JPEG steganalysis approach based on feature fusion by the principal component analysis (PCA) and analysis of the complementarities among features. The study fused complementary features and isolated redundant components by PCA, and finally used RBaggSVM classifier for classification. Experimental results show that this scheme effectively improves the detection rate of steganalysis in JPEG images and achieves faster speed of image classification.

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