Sensory adaptability of foods with added Lupin (Lupinus angustifolius) kernel fibre using pre-set criteria

Fiber-enriched white bread, muffin. pasta, orange juice, and breakfast bar were prepared with lupin (Lupin us angusti/olius) kernel fiber. Consumer panelists (n = 44) determined that all these fiber-enriched foods, except orange juice, fulfilled pre-set acceptability criteria. Fiber enrichment did not change overall acceptability (p> 0.05) of the bread and pasta, but reduced overall acceptability (p < 0.05) of the muffin, orange juice, and breakfast bar. In all fiber-enriched products, flavor was the attribute most highly correlated with overall acceptability (p < 0.05). The lupin kernel fiber used in this study therefore appears to have potential as a 'nonintrusive' ingredient in some processed cereal-based foods_ For other applications, fiber modification appears worthy of investigation to accomplish 'nonintrusive' fiber enrichment.

Lupin kernel fibre-enriched foods beneficially modify serum lipids in men

Objective: To examine the effect of a diet containing a novel legume food ingredient, Australian sweet lupin (Lupinus angustifolius) kernel fibre (LKFibre), compared to a control diet without the addition of LKFibre, on serum lipids in men.

Design: Randomized crossover dietary intervention study.

Setting: Melbourne, Australia — Free-living men.

Subjects: A total of 38 healthy males between the ages of 24 and 64 y completed the intervention.

Intervention: Subjects consumed an LKFibre and a control diet for 1 month each. Both diets had the same background menus with seven additional experimental foods that either contained LKFibre or did not. Depending on energy intake, the LKFibre diet was designed to contain an additional 17 to 30 g/day fibre beyond that of the control diet.

Results: Compared to the control diet, the LKFibre diet reduced total cholesterol (TC) (meanplusminuss.e.m.; 4.5plusminus1.7%; P=0.001), low-density lipoprotein cholesterol (LDL-C) (5.4plusminus2.2%; P=0.001), TC: high-density lipoprotein cholesterol (HDL-C) (3.0plusminus2.0%; P=0.006) and LDL-C:HDL-C (3.8plusminus2.6%; P=0.003). No effects on HDL-C, triacylglycerols, glucose or insulin were observed.

Conclusions: Addition of LKFibre to the diet provided favourable changes to some serum lipid measures in men, which, combined with its high palatability, suggest this novel ingredient may be useful in the dietary reduction of coronary heart disease risk.

Water-binding capacity and viscosity of Australian sweet lupin kernel fibre under in vitro conditions simulating the human upper gastrointestinal tract

There is currently little understanding of the physicochemical properties in the human gastrointestinal tract of Australian sweet lupin (Lupinus angustifolius) kernel fibre (LKF), a novel food ingredient with potential for the fibre enrichment of foods such as baked goods. Since physicochemical properties of dietary fibres have been related to beneficial physiological effects in vitro, this study compared water-binding capacity and viscosity of LKF with that of other fibres currently used for fibre-enrichment of baked goods, under in vitro conditions simulating the human upper gastrointestinal tract. At between 8.47 and 11.07g water/g dry solids, LKF exhibited water-binding capacities that were significantly higher (P<0.05) than soy fibre, pea hull fibre, cellulose and wheat fibre at all of the simulated gastrointestinal stages examined. Similarly, viscosity of LKF was significantly higher (P<0.05) than that of the other fibres at all simulated gastrointestinal stages. The relatively high water-binding capacity and viscosity of LKF identified in this study suggests that this novel fibre ingredient may elicit different and possibly more beneficial physiological effects in the upper human gastrointestinal tract than the conventional fibre ingredients currently used in fibre-enriched baked goods manufacture. We are now performing human studies to investigate the effect of LKF in the diet on health-related gastrointestinal events.

Lupin kernel fiber consumption modifies fecal microbiota in healthy men as determined by rRNA gene flourescent in situ by hybridization

Background Changes in the composition of gastrointestinal microbiota by dietary interventions using pro- and prebiotics provide opportunity for improving health and preventing disease. However, the capacity of lupin kernel fiber (LKFibre), a novel legume-derived food ingredient, to act as a prebiotic and modulate the colonic microbiota in humans needed investigation.

Aim of the study The present study aimed to determine the effect of LKFibre on human intestinal microbiota by quantitative fluorescent in situ hybridization (FISH) analysis.

Design A total of 18 free-living healthy males between the ages of 24 and 64 years consumed a control diet and a LKFibre diet (containing an additional 17–30 g/day fiber beyond that of the control—incorporated into daily food items) for 28 days with a 28-day washout period in a single-blind, randomized, crossover dietary intervention design.
Methods Fecal samples were collected for 3 days towards the end of each diet and microbial populations analyzed by FISH analysis using 16S rRNA gene-based oligonucleotide probes targeting total and predominant microbial populations.

Results Significantly higher levels of Bifidobacterium spp. (P = 0.001) and significantly lower levels of the clostridia group of C. ramosum, C. spiroforme and C. cocleatum (P = 0.039) were observed on the LKFibre diet compared with the control. No significant differences between the LKFibre and the control diet were observed for total bacteria, Lactobacillus spp., the Eubacterium spp., the C. histolyticum/C. lituseburense group and the Bacteroides–Prevotella group.
Conclusions Ingestion of LKFibre stimulated colonic bifidobacteria growth, which suggests that this dietary fiber may be considered as a prebiotic and may beneficially contribute to colon health.

Lupin kernel fibre foods improve bowel function and beneficially modify some putative faecal risk factors for colon cancer in men.

Consumption of some dietary fibres may benefit bowel health; however, the effect of Australian sweet lupin (Lupinus angustifolius) kernel fibre (LKFibre) is unknown. The present study examined the effect of a high-fibre diet containing LKFibre on bowel function and faecal putative risk factors for colon cancer compared to a control diet without LKFibre. Thirty-eight free-living, healthy men consumed an LKFibre and a control diet for 1 month each in a single-blind, randomized, crossover study. Depending on subject energy intake, the LKFibre diet was designed to provide 17–30 g/d fibre (in experimental foods) above that of the control diet. Bowel function self-perception, frequency of defecation, transit time, faecal output, pH and moisture, faecal levels of SCFA and ammonia, and faecal bacterial [ß]-glucuronidase activity were assessed. In comparison to the control diet, the LKFibre diet increased frequency of defecation by 0·13 events/d (P = 0·047), increased faecal output by 21 % (P = 0·020) and increased faecal moisture content by 1·6 % units (P = 0·027), whilst decreasing transit time by 17 % (P = 0·012) and decreasing faecal pH by 0·26 units (P < 0·001). Faecal butyrate concentration was increased by 16 % (P = 0·006), butyrate output was increased by 40 % (P = 0·002) and [ß]-glucuronidase activity was lowered by 1·4 µmol/h per g wet faeces compared to the control diet (P < 0·001). Addition of LKFibre to the diet incorporated into food products improved some markers of healthy bowel function and colon cancer risk in men.

Parameter optimization of kernel-based one-class classifier on imbalance learning

Compared with conventional two-class learning schemes, one-class classification simply uses a single class in the classifier training phase. Applying one-class classification to learn from unbalanced data set is regarded as the recognition based learning and has shown to have the potential of achieving better performance. Similar to twoclass learning, parameter selection is a significant issue, especially when the classifier is sensitive to the parameters. For one-class learning scheme with the kernel function, such as one-class Support Vector Machine and Support Vector Data Description, besides the parameters involved in the kernel, there is another one-class specific parameter: the rejection rate v. In this paper, we proposed a general framework to involve the majority class in solving the parameter selection problem. In this framework, we first use the minority target class for training in the one-class classification stage; then we use both minority and majority class for estimating the generalization performance of the constructed classifier. This generalization performance is set as the optimization criteria. We employed the Grid search and Experiment Design search to attain various parameter settings. Experiments on UCI and Reuters text data show that the parameter optimized one-class classifiers outperform all the standard one-class learning schemes we examined.

Flowchart and kernel software for modular robot

Flowcharting is a common method of setting out the requirements for a piece of code. It is simple with few rules to follow. Rarely however, s it used as the code itself. This paper describes the outline of a software package that uses the flowchart as the code for a small, autonomous, modular robot, designed for use in High Schools. It also describe the code used by the robot to complement the flowchart software creating a system that can be used by students and their teachers to design, build and program a robot without previous programming experience.

Kernel-based naive bayes classifier for breast cancer prediction

The classification of breast cancer patients is of great importance in cancer diagnosis. Most classical cancer classification methods are clinical-based and have limited diagnostic ability. The recent advances in machine learning technique has made a great impact in cancer diagnosis. In this research, we develop a new algorithm: Kernel-Based Naive Bayes (KBNB) to classify breast cancer tumor based on memography data. The performance of the proposed algorithm is compared with that of classical navie bayes algorithm and kernel-based decision tree algorithm C4.5. The proposed algorithm is found to outperform in the both cases. We recommend the proposed algorithm could be used as a tool to classify the breast patient for early cancer diagnosis.

Microarray data classification using automatic SVM kernel selection

Microarray data classification is one of the most important emerging clinical applications in the medical community. Machine learning algorithms are most frequently used to complete this task. We selected one of the state-of-the-art kernel-based algorithms, the support vector machine (SVM), to classify microarray data. As a large number of kernels are available, a significant research question is what is the best kernel for patient diagnosis based on microarray data classification using SVM? We first suggest three solutions based on data visualization and quantitative measures. Different types of microarray problems then test the proposed solutions. Finally, we found that the rule-based approach is most useful for automatic kernel selection for SVM to classify microarray data.

A meta-learning approach to automatic kernel selection for support vector machines

Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods such as support vector machine (SVM). Automatic kernel selection is a key issue given the number of kernels available, and the current trial-and-error nature of selecting the best kernel for a given problem. This paper introduces a new method for automatic kernel selection, with empirical results based on classification. The empirical study has been conducted among five kernels with 112 different classification problems, using the popular kernel based statistical learning algorithm SVM. We evaluate the kernels’ performance in terms of accuracy measures. We then focus on answering the question: which kernel is best suited to which type of classification problem? Our meta-learning methodology involves measuring the problem characteristics using classical, distance and distribution-based statistical information. We then combine these measures with the empirical results to present a rule-based method to select the most appropriate kernel for a classification problem. The rules are generated by the decision tree algorithm C5.0 and are evaluated with 10 fold cross validation. All generated rules offer high accuracy ratings.