Detection and measurement of aroma compounds with the electronic nose and a novel method for MOS sensor signal analysis during the wheat bread making process

The aim of this investigation was to incorporate mustard flour into bread in order to improve its quality. For this purpose, wheat flour 650 type for bread making was replaced with mustard seed flour at the level of 5, 7 and 10%. Bread quality through physico-chemical and sensory characteristics was analyzed and compared to those of bread without mustard flour. The protein content of mustard flour bread increased with 5.03% as a result of mustard flour incorporation, coupled with an increase in ash content. Sensory evaluation results revealed that the sample containing 7% mustard flour scored highest in most of the attributes including overall acceptability. The study reveals that incorporation of 7% mustard flour gave desirable results in terms of phisyco-chemical and sensory caracteristics of mustard flour fortified bread.

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Sensor, Signal and Image Informatics

Yearbook of Medical Informatics ◽

10.1055/s-0038-1638479 ◽

2006 ◽

Vol 15 (01) ◽

pp. 57-67 ◽

Cited By ~ 2

Author(s):

T. Aach ◽

H. Witte ◽

T.M. Lehmann

Keyword(s):

Data Acquisition ◽

Signal Analysis ◽

Emission Computed Tomography ◽

Biomedical Data ◽

Data Handling ◽

Sensor Signal ◽

Biomedical Signal ◽

Time Frequency ◽

Image Informatics ◽

Recent Trends

SummaryThe number of articles published annually in the fields of biomedical signal and image acquisition and processing is increasing. Based on selected examples, this survey aims at comprehensively demonstrating the recent trends and developments.Four articles are selected for biomedical data acquisition covering topics such as dose saving in CT, C-arm X-ray imaging systems for volume imaging, and the replacement of dose-intensive CTbased diagnostic with harmonic ultrasound imaging. Regarding biomedical signal analysis (BSA), the four selected articles discuss the equivalence of different time-frequency approaches for signal analysis, an application to Cochlea implants, where time-frequency analysis is applied for controlling the replacement system, recent trends for fusion of different modalities, and the role of BSA as part of a brain machine interfaces. To cover the broad spectrum of publications in the field of biomedical image processing, six papers are focused. Important topics are content-based image retrieval in medical applications, automatic classification of tongue photographs from traditional Chinese medicine, brain perfusion analysis in single photon emission computed tomography (SPECT), model-based visualization of vascular trees, and virtual surgery, where enhanced visualization and haptic feedback techniques are combined with a sphere-filled model of the organ.The selected papers emphasize the five fields forming the chain of biomedical data processing: (1) data acquisition, (2) data reconstruction and pre-processing, (3) data handling, (4) data analysis, and (5) data visualization. Fields 1 and 2 form the sensor informatics, while fields 2 to 5 form signal or image informatics with respect to the nature of the data considered.Biomedical data acquisition and pre-processing, as well as data handling, analysis and visualization aims at providing reliable tools for decision support that improve the quality of health care. Comprehensive evaluation of the processing methods and their reliable integration in routine applications are future challenges in the field of sensor, signal and image informatics.

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Analysis of Wheat Bread-Making Gene (wbm) Evolution and Occurrence in Triticale Collection Reveal Origin via Interspecific Introgression into Chromosome 7AL

Agronomy ◽

10.3390/agronomy9120854 ◽

2019 ◽

Vol 9 (12) ◽

pp. 854 ◽

Cited By ~ 1

Author(s):

Ilya Kirov ◽

Andrey Pirsikov ◽

Natalia Milyukova ◽

Maxim Dudnikov ◽

Maxim Kolenkov ◽

...

Keyword(s):

Orthologous Gene ◽

Wheat Breeding ◽

Wheat Bread ◽

Bread Making ◽

Pcr Screening ◽

Interspecific Introgression ◽

Unknown Species ◽

Long Interspersed Nuclear Elements ◽

Modern Genomic ◽

Gene Information

Bread-making quality is a crucial trait for wheat and triticale breeding. Several genes significantly influence these characteristics, including glutenin genes and the wheat bread-making (wbm) gene. World wheat collection screening showed that only a few percent of cultivars carry the valuable wbm variant, providing a useful source for wheat breeding. In contrast, no such analysis has been performed for triticale (wheat (AABB genome) × rye (RR) amphidiploid) collections. Despite the importance of the wbm gene, information about its origin and genomic organization is lacking. Here, using modern genomic resources available for wheat and its relatives, as well as PCR screening, we aimed to examine the evolution of the wbm gene and its appearance in the triticale genotype collection. Bioinformatics analysis revealed that the wheat Chinese Spring genome does not have the wbm gene but instead possesses the orthologous gene, called wbm-like located on chromosome 7A. The analysis of upstream and downstream regions revealed the insertion of LINE1 (Long Interspersed Nuclear Elements) retrotransposons and Mutator DNA transposon in close vicinity to wbm-like. Comparative analysis of the wbm-like region in wheat genotypes and closely related species showed low similarity between the wbm locus and other sequences, suggesting that wbm originated via introgression from unknown species. PCR markers were developed to distinguish wbm and wbm-like sequences, and triticale collection was screened resulting in the detection of three genotypes carrying wbm-specific introgression, providing a useful source for triticale breeding programs.

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Buckwheat and quinoa seeds as supplements in wheat bread production

Hemijska industrija ◽

10.2298/hemind120314048d ◽

2013 ◽

Vol 67 (1) ◽

pp. 115-121 ◽

Cited By ~ 7

Author(s):

Mirjana Demin ◽

Biljana Vucelic-Radovic ◽

Nebojsa Banjac ◽

Neli Nikolajevna-Tipsina ◽

Mirjana Milovanovic

Keyword(s):

Nutritive Value ◽

Chenopodium Quinoa ◽

Control Sample ◽

Nutritional Composition ◽

Fiber Content ◽

Wheat Bread ◽

Bread Making ◽

Hydrothermal Preparation ◽

Crude Fiber Content ◽

Bread Production

The aim of this work was to compare the nutritional characteristics of wheat bread with the bread produced of wheat flour supplemented with quinoa and buckwheat seeds. Bread making properties of these blends were analyzed in order to investigate their ability to make moulded bread. Quinoa (Chenopodium quinoa Will.) and buckwheat seeds were grown in the vicinity of Belgrade, Serbia. The addition of pseudocereal seeds (at levels of 30% and 40%) and a selected technological process, which included hydrothermal preparation of supplements, resulted with a valuable effect on nutritive value of breads. In comparison with the wheat bread that was used as control sample, the protein increase of 2% and the increase of crude fiber content at around 0.5% in 30% supplemented breads were registered. Furthermore, the incorporation of both seeds mixture at the level of 40%, increased the content of protein for 2.5% and fiber content for 0.4%. In regard to the starch, fat, and ash contents there were no major differences. The investigated breads were nutritionally superior to the wheat bread. Chemical composition of the selected seeds was also investigated. The results showed that the blends containing either 30% or 40% of selected seeds expressed high potential for the production of molded breads, as new baking products with enhanced nutritional composition. The applied technological procedure was modified in such way that for all blended combination of supplements it changed rheological properties of dough. Furthermore, it resulted in a good volume of breads with excellent sensory properties of aroma-odor and taste.

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Optimization of enzymes addition to improve whole wheat bread making quality by response surface methodology and optimization technique

Journal of Food Science and Technology ◽

10.1007/s13197-019-03629-5 ◽

2019 ◽

Vol 56 (3) ◽

pp. 1454-1461 ◽

Cited By ~ 1

Author(s):

Koki Matsushita ◽

Ayaka Terayama ◽

Daisuke Goshima ◽

Dennis Marvin Santiago ◽

Takao Myoda ◽

...

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Optimization Technique ◽

Wheat Bread ◽

Bread Making ◽

Whole Wheat ◽

Whole Wheat Bread

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DIMENSIONALITY REDUCTION FOR SENSORY DATASETS BASED ON MASTER–SLAVE SYNCHRONIZATION OF LORENZ SYSTEM

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127413300139 ◽

2013 ◽

Vol 23 (05) ◽

pp. 1330013 ◽

Cited By ~ 1

Author(s):

REZA GHAFFARI ◽

IOAN GROSU ◽

DACIANA ILIESCU ◽

EVOR HINES ◽

MARK LEESON

Keyword(s):

Breast Cancer ◽

Artificial Intelligence ◽

Electronic Nose ◽

Lorenz System ◽

Nearest Neighbor ◽

Performance Testing ◽

K Nearest Neighbor ◽

Classification Rate ◽

Benchmark Datasets ◽

Novel Method

In this study, we propose a novel method for reducing the attributes of sensory datasets using Master–Slave Synchronization of chaotic Lorenz Systems (DPSMS). As part of the performance testing, three benchmark datasets and one Electronic Nose (EN) sensory dataset with 3 to 13 attributes were presented to our algorithm to be projected into two attributes. The DPSMS-processed datasets were then used as input vector to four artificial intelligence classifiers, namely Feed-Forward Artificial Neural Networks (FFANN), Multilayer Perceptron (MLP), Decision Tree (DT) and K-Nearest Neighbor (KNN). The performance of the classifiers was then evaluated using the original and reduced datasets. Classification rate of 94.5%, 89%, 94.5% and 82% were achieved when reduced Fishers iris, crab gender, breast cancer and electronic nose test datasets were presented to the above classifiers.

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