Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis.

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.

Multivariate analysis of fresh-cut carambola slices stored under different temperatures.

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.

Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery

Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April-November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.

IUCN classification zones concord with, but underestimate, the population genetic structure of the zebra shark Stegostoma fasciatum in the Indo-West Pacific

The Indo-West Pacific (IWP), from South Africa in the western Indian Ocean to the western Pacific Ocean, contains some of the most biologically diverse marine habitats on earth, including the greatest biodiversity of chondrichthyan fishes. The region encompasses various densities of human habitation leading to contrasts in the levels of exploitation experienced by chondrichthyans, which are targeted for local consumption and export. The demersal chondrichthyan, the zebra shark, Stegostoma fasciatum, is endemic to the IWP and has two current regional International Union for the Conservation of Nature (IUCN) Red List classifications that reflect differing levels of exploitation: ‘Least Concern’ and ‘Vulnerable’. In this study, we employed mitochondrial ND4 sequence data and 13 microsatellite loci to investigate the population genetic structure of 180 zebra sharks from 13 locations throughout the IWP to test the concordance of IUCN zones with demographic units that have conservation value. Mitochondrial and microsatellite data sets from samples collected throughout northern Australia and Southeast Asia concord with the regional IUCN classifications. However, we found evidence of genetic subdivision within these regions, including subdivision between locations connected by habitat suitable for migration. Furthermore, parametric FST analyses and Bayesian clustering analyses indicated that the primary genetic break within the IWP is not represented by the IUCN classifications but rather is congruent with the Indonesian throughflow current. Our findings indicate that recruitment to areas of high exploitation from nearby healthy populations in zebra sharks is likely to be minimal, and that severe localized depletions are predicted to occur in zebra shark populations throughout the IWP region.

Isohydric and anisohydric characterisation of vegetable crops. The classification of vegetables by their physiological responses to water stress

Research on the physiological response of crop plants to drying soils and subsequent water stress has grouped plant behaviours as isohydric and anisohydric. Drying soil conditions, and hence declining soil and root water potentials, cause chemical signals—the most studied being abscisic acid (ABA)—and hydraulic signals to be transmitted to the leaf via xylem pathways. Researchers have attempted to allocate crops as isohydric or anisohydric. However, different cultivars within crops, and even the same cultivars grown in different environments/climates, can exhibit both response types. Nevertheless, understanding which behaviours predominate in which crops and circumstances may be beneficial. This paper describes different physiological water stress responses, attempts to classify vegetable crops according to reported water stress responses, and also discusses implications for irrigation decision-making.

Bovine cysticercosis-Development of a real-time PCR to enhance classification of suspect cysts identified at meat inspection

Laboratory confirmation methods are important in bovine cysticerosis diagnosis as other pathologies can result in morphologically similar lesions resulting in false identifications. We developed a probe-based real-time PCR assay to identify Taenia saginata in suspect cysts encountered at meat inspection and compared its use with the traditional method of identification, histology, as well as a published nested PCR. The assay simultaneously detects T. saginata DNA and a bovine internal control using the cytochrome c oxidase subunit 1 gene of each species and shows specificity against parasites causing lesions morphologically similar to those of T. saginata. The assay was sufficiently sensitive to detect 1 fg (Ct 35.09 +/- 0.95) of target DNA using serially-diluted plasmid DNA in reactions spiked with bovine DNA as well as in all viable and caseated positive control cysts. A loss in PCR sensitivity was observed with increasing cyst degeneration as seen in other molecular methods. In comparison to histology, the assay offered greater sensitivity and accuracy with 10/19 (53%) T. saginata positives detected by real-time PCR and none by histology. When the results were compared with the reference PCR, the assay was less sensitive but offered advantages of faster turnaround times and reduced contamination risk. Estimates of the assay's repeatability and reproducibility showed the assay is highly reliable with reliability coefficients greater than 0.94. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.

Prediction of breeding values for average fruit weight in mango using a multivariate individual mixed model

Mango is an important horticultural fruit crop and breeding is a key strategy to improve ongoing sustainability. Knowledge of breeding values of potential parents is important for maximising progress from breeding. This study successfully employed a mixed linear model methods incorporating a pedigree to predict breeding values for average fruit weight from highly unbalanced data for genotypes planted over three field trials and assessed over several harvest seasons. Average fruit weight was found to be under strong additive genetic control. There was high correlation between hybrids propagated as seedlings and hybrids propagated as scions grafted onto rootstocks. Estimates of additive genetic correlation among trials ranged from 0.69 to 0.88 with correlations among harvest seasons within trials greater than 0.96. These results suggest that progress from selection for broad adaptation can be achieved, particularly as no repeatable environmental factor that could be used to predict G x E could be identified. Predicted breeding values for 35 known cultivars are presented for use in ongoing breeding programs.

High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

BACKGROUND: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). RESULTS: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. CONCLUSION: Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.