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"METHODS OF ANALYSIS"
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Food forensics: using DNA technology to combat misdescription and fraud
The fraudulent misdescription of food contents on product labels is a widespread problem, particularly with high added-value products commanding a premium price. Proving conclusively that fraud has occurred requires the detection and quantification of food constituents. These are often biochemically similar to the materials they replace, making their identification and measurement extremely difficult. Despite the fact that food matrices are extremely complex and variable, a variety of the molecular markers used to physically map genomes have now been successfully adapted for detection of food substitution. These successes include the speciation of meats, fish and fruit in processed food products, the identification of the geographical origin of olive oil, the detection of dilution of Basmati rice with non-Basmati varieties and the quantitative detection of neuronal tissue and offal in processed meat.
Journal Article
Training cognition : optimizing efficiency, durability, and generalizability
\"This book describes research on training using cognitive psychology to build a complete empirical and theoretical picture of the training process. It includes a review of relevant cognitive psychological literature, a summary of recent laboratory experiments, a presentation of original theoretical ideas, and a discussion of possible applications to real-world training settings\"--Provided by publisher.
Book
Detection of non-milk fat in milk fat by gas chromatography and linear discriminant analysis
Gas chromatography was utilized to determine triacylglycerol profiles in milk and non-milk fat. The values of triacylglycerol were subjected to linear discriminant analysis to detect and quantify non-milk fat in milk fat. Two groups of milk fat were analyzed: A) raw milk fat from the central region of Mexico (n=216) and B) ultrapasteurized milk fat from 3 industries (n=36), as well as pork lard (n=2), bovine tallow (n=2), fish oil (n=2), peanut (n=2), corn (n=2), olive (n=2), and soy (n=2). The samples of raw milk fat were adulterated with non-milk fats in proportions of 0, 5, 10, 15, and 20% to form 5 groups. The first function obtained from the linear discriminant analysis allowed the correct classification of 94.4% of the samples with levels <10% of adulteration. The triacylglycerol values of the ultrapasteurized milk fats were evaluated with the discriminant function, demonstrating that one industry added non-milk fat to its product in 80% of the samples analyzed.
Journal Article
Prehistoric adaptation in the American Southwest
This resource is about post-Pleistocene adaptive change among the aboriginal cultures of the mountains and deserts of Arizona and New Mexico.
Book
Pathogen detection in milk samples by ligation detection reaction-mediated universal array method
This paper describes a new DNA chip, based on the use of a ligation detection reaction coupled to a universal array, developed to detect and analyze, directly from milk samples, microbial pathogens known to cause bovine, ovine, and caprine mastitis or to be responsible for foodborne intoxication or infection, or both. Probes were designed for the identification of 15 different bacterial groups: Staphylococcus aureus, Streptococcus agalactiae, nonaureus staphylococci, Streptococcus bovis, Streptococcus equi, Streptococcus canis, Streptococcus dysgalactiae, Streptococcus parauberis, Streptococcus uberis, Streptococcus pyogenes, Mycoplasma spp., Salmonella spp., Bacillus spp., Campylobacter spp., and Escherichia coli and related species. These groups were identified based on the 16S rRNA gene. For microarray validation, 22 strains from the American Type Culture Collection or other culture collections and 50 milk samples were tested. The results demonstrated high specificity, with sensitivity as low as 6 fmol. Moreover, the ligation detection reaction-universal array assay allowed for the identification of Mycoplasma spp. in a few hours, avoiding the long incubation times of traditional microbiological identification methods. The universal array described here is a versatile tool able to identify milk pathogens efficiently and rapidly.
Journal Article
Chromatomass-Spectrometric Method for the Quantitative Determination of Amino- and Carboxylic Acids in Biological Samples
A highly sensitive method for the qualitative and quantitative determination of amino- and carboxylic acids, as well as a number of urea and methionine cycle metabolites in the studied solutions, is presented. Derivatives (esterification) were obtained for amino acids by their reaction in a solution of 3 N of hydrochloric acid in n-butanol for 15 min at 65 °C and for carboxylic acids by their reaction with phenol in ethyl acetate with 3 N of hydrochloric acid for 20 min at 65 °C. Experimental work on the determination of individual metabolites was carried out using the HPLC-MS/MS method and included the creation of a library of spectra of the analyzed compounds and their quantitative determination. Multiplex methods have been developed for the quantitative analysis of the desired metabolites in a wide range of concentrations of 3–4 orders of magnitude. The approach to the analysis of metabolites was developed based on the method of the dynamic monitoring of multiple reactions of the formation of fragments for a mass analyzer with a triple quadrupole (QQQ). The effective chromatographic separation of endogenous metabolites was carried out within 13 min. The calibration curves of the analyzed compounds were stable throughout the concentration range and had the potential to fit below empirical levels. The developed methods and obtained experimental data are of interest for a wide range of biomedical studies, as well as for monitoring the content of endogenous metabolites in biological samples under various pathological conditions. The sensitivity limit of the methods for amino acids was about 4.8 nM and about 0.5 μM for carboxylic acids. Up to 19 amino- and up to 12 carboxy acids and about 10 related metabolites can be tested in a single sample.
Journal Article
Principal component analysis for the early detection of mastitis and lameness in dairy cows
This investigation analysed the applicability of principal component analysis (PCA), a latent variable method, for the early detection of mastitis and lameness. Data used were recorded on the Karkendamm dairy research farm between August 2008 and December 2010. For mastitis and lameness detection, data of 338 and 315 cows in their first 200 d in milk were analysed, respectively. Mastitis as well as lameness were specified according to veterinary treatments. Diseases were defined as disease blocks. The different definitions used (two for mastitis, three for lameness) varied solely in the sequence length of the blocks. Only the days before the treatment were included in the blocks. Milk electrical conductivity, milk yield and feeding patterns (feed intake, number of feeding visits and time at the trough) were used for recognition of mastitis. Pedometer activity and feeding patterns were utilised for lameness detection. To develop and verify the PCA model, the mastitis and the lameness datasets were divided into training and test datasets. PCA extracted uncorrelated principle components (PC) by linear transformations of the raw data so that the first few PCs captured most of the variations in the original dataset. For process monitoring and disease detection, these resulting PCs were applied to the Hotelling's T2 chart and to the residual control chart. The results show that block sensitivity of mastitis detection ranged from 77·4 to 83·3%, whilst specificity was around 76·7%. The error rates were around 98·9%. For lameness detection, the block sensitivity ranged from 73·8 to 87·8% while the obtained specificities were between 54·8 and 61·9%. The error rates varied from 87·8 to 89·2%. In conclusion, PCA seems to be not yet transferable into practical usage. Results could probably be improved if different traits and more informative sensor data are included in the analysis.
Journal Article
Improvement of Mannitol-Yolk-Polymyxin B Agar by Supplementing with Trimethoprim for Quantitative Detection of Bacillus cereus in Foods
Mannitol-yolk-polymyxin B agar (MYPA) was modified by supplementation with trimethoprim. The ability of the supplemented medium to select for and recover Bacillus cereus from pure cultures and food samples with high background microflora was compared with MYPA. For evaluation of the modified MYPA (mMYPA) in food samples with high background microflora, B. cereus was experimentally spiked into red pepper powder, fermented soybean paste, vegetable salad, and radish sprouts, and then it was recovered on MYPA and mMYPA for comparison. In all food samples, there was no difference in recoverability (P > 0.05) between mMYPA (red pepper powder, 3.34 ± 0.24 log CFU/g; fermented soybean paste, 3.52 ± 0.47 log CFU/g; vegetable salad, 3.51 ± 0.23 log CFU/g; radish sprouts, 3.32 ± 0.40 log CFU/g) and MYPA (red pepper powder, 3.18 ± 0.20 log CFU/g; fermented soybean paste, 3.33 ± 0.43 log CFU/g; vegetable salad, 3.36 ± 0.19 log CFU/g; radish sprouts, 3.33 ± 0.31 log CFU/g). However, mMYPA exhibited better selectivity than MYPA, because additional trimethoprim made the differentiation of suspected colonies easier by inhibiting competing flora. The addition of trimethoprim to conventional media could be a useful option to improve selectivity in foods with high background microflora.
Journal Article
Detection of cow milk in donkey milk by chemometric procedures on triacylglycerol stereospecific analysis results
Stereospecific analysis is an important tool for the characterization of lipid fraction of food matrices, and also of milk samples. The results of a chemical-enzymatic-chromatographic analytical method were elaborated by chemometric procedures such as linear discriminant analysis (LDA) and artificial neural network (ANN). According to the total composition and intrapositional fatty acid distribution in the triacylglycerol (TAG) backbone, the obtained results were able to characterize pure milk samples and milk mixtures with 1, 3, 5% cow milk added to donkey milk. The resulting score was very satisfactory. Totally correct classified samples were obtained when the TAG stereospecific results of all the considered milk mixtures (donkey-cow) were elaborated by LDA and ANN chemometric procedures.
Journal Article