4 resultados para Principal components analysis

em eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry


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Analysis of headspace volatiles by gas chromatography/mass spectrometry from king (Penaeus plebejus), banana (P. merguiensis), tiger (P. esculentus/semisulcatus) and greasy (Metapenaeus bennettae) prawns stored in ice or ice slurry, which is effectively an environment of low oxygen tension, indicated the presence of amines at the early stages of storage (less than 8 days) irrespective of the nature of the storage media. Esters were more prevalent in prawns stored on ice (normal oxygen conditions) at the latter stages of storage (more than 8 days) and were only produced by Pseudomonas fragi, whereas sulphides and amines occurred whether the predominant spoilage organism was Ps.fragi or Shewanella putrefaciens. The free amino acid profiles of banana and king prawns were high in arginine (12–14%) and low in cysteine (0.1–0.17%) and methionine (0.1–0.2%). Filter sterilised raw banana prawn broth inoculated with a total of 15 cultures of Ps. fragi and S. putrefaciens and incubated for two weeks at 5°C, showed the presence of 17 major compounds in the headspace volatiles analysed using gas chromatography/mass spectrometry (GC/MS). These were mainly amines, sulphides, ketones and esters. Principal Component Analysis of the results for the comparative levels of the volatiles produced by pure cultures, inoculated into sterile prawn broth, indicated three subgroupings of the organisms; I, Ps. fragi from a particular geographic location; II, S. putrefaciens from another geographic location; and III, a mixture of Ps. fragi and S. putrefaciens from different geographic locations. The sensory impression created by the cultures was strongly related to the chemical profile as determined by GC/MS. Organisms, even within the same subgrouping classified as identical by the usual tests, produced a different range of volatiles in the same uniform substrate.

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Quality of fresh-cut carambola (Averrhoa carambola L) is related to many chemical and biochemical variables especially those involved with softening and browning, both influenced by storage temperature. To study these effects, a multivariate analysis was used to evaluate slices packaged in vacuum-sealed polyolefin bags, and stored at 2.5 degrees C, 5 degrees C and 10 degrees C, for up to 16 d. The quality of slices at each temperature was correlated with the duration of storage, O(2) and CO(2) concentration in the package, physical chemical constituents, and activity of enzymes involved in softening (PG) and browning (PPO) metabolism. Three quality groups were identified by hierarchical cluster analysis, and the classification of the components within each of these groups was obtained from a principal component analysis (PCA). The characterization of samples by PCA clearly distinguished acceptable and non-acceptable slices. According to PCA, acceptable slices presented higher ascorbic acid content, greater hue angles ((o)h) and final lightness (L-5) in the first principal component (PC1). On the other hand, non-acceptable slices presented higher total pectin content. PPO activity in the PC1. Non-acceptable slices also presented higher soluble pectin content, increased pectin solubilisation and higher CO(2) concentration in the second principal component (PC2) whereas acceptable slices showed lower total sugar content. The hierarchical cluster and PCA analyses were useful for discriminating the quality of slices stored at different temperatures.

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The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images. PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.

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Increasing organic carbon inputs to agricultural soils through the use of pastures or crop residues has been suggested as a means of restoring soil organic carbon lost via anthropogenic activities, such as land use change. However, the decomposition and retention of different plant residues in soil, and how these processes are affected by soil properties and nitrogen fertiliser application, is not fully understood. We evaluated the rate and extent of decomposition of 13C-pulse labelled plant material in response to nitrogen addition in four pasture soils of varying physico-chemical characteristics. Microbial respiration of buffel grass (Cenchrus ciliaris L.), wheat (Triticum aestivum L.) and lucerne (Medicago sativa L.) residues was monitored over 365-days. A double exponential model fitted to the data suggested that microbial respiration occurred as an early rapid and a late slow stage. A weighted three-compartment mixing model estimated the decomposition of both soluble and insoluble plant 13C (mg C kg−1 soil). Total plant material decomposition followed the alkyl C: O-alkyl C ratio of plant material, as determined by solid-state 13C nuclear magnetic resonance spectroscopy. Urea-N addition increased the decomposition of insoluble plant 13C in some soils (≤0.1% total nitrogen) but not others (0.3% total nitrogen). Principal components regression analysis indicated that 26% of the variability of plant material decomposition was explained by soil physico-chemical characteristics (P = 0.001), which was primarily described by the C:N ratio. We conclude that plant species with increasing alkyl C: O-alkyl C ratio are better retained as soil organic matter, and that the C:N stoichiometry of soils determines whether N addition leads to increases in soil organic carbon stocks.