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.

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.

Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness

The robustness of multivariate calibration models, based on near infrared spectroscopy, for the assessment of total soluble solids (TSS) and dry matter (DM) of intact mandarin fruit (Citrus reticulata cv. Imperial) was assessed. TSS calibration model performance was validated in terms of prediction of populations of fruit not in the original population (different harvest days from a single tree, different harvest localities, different harvest seasons). Of these, calibration performance was most affected by validation across seasons (signal to noise statistic on root mean squared error of prediction of 3.8, compared with 20 and 13 for locality and harvest day, respectively). Procedures for sample selection from the validation population for addition to the calibration population (‘model updating’) were considered for both TSS and DM models. Random selection from the validation group worked as well as more sophisticated selection procedures, with approximately 20 samples required. Models that were developed using samples at a range of temperatures were robust in validation for TSS and DM.

Resistance of Brassicaceae plants to root-knot nematode (Meloidogyne spp.) in northern Australia

Brassicaceae plants have the potential as part of an integrated approach to replace fumigant nematicides, providing the biofumigation response following their incorporation is not offset by reproduction of plant-parasitic nematodes on their roots. Forty-three Brassicaceae cultivars were screened in a pot trial for their ability to reduce reproduction of three root-knot nematode isolates from north Queensland, Australia: M. arenaria (NQ1), M. javanica (NQ2) and M. arenaria race 2 (NQ5/7). No cultivar was found to consistently reduce nematode reproduction relative to forage sorghum, the current industry standard, although a commercial fodder radish (Raphanus sativus) and a white mustard (Sinapis alba) line were consistently as resistant to the formation of galls as forage sorghum. A second pot trial screened five commercially available Brassicaceae cultivars, selected for their biofumigation potential, for resistance to two nematode species, M. javanica (NQ2) and M. arenaria (NQ5/7). The fodder radish cv. Weedcheck, was found to be as resistant as forage sorghum to nematode reproduction. A multivariate cluster analysis using the resistance measurements, gall index, nematode number per g of root and multiplication for two nematode species (NQ2 and NQ5/7) confirmed the similarity in resistance between the radish cultivar and forage sorghum. A field trial confirmed the resistance of the fodder radish cv. Weedcheck, with a similar reduction in the number of Meloidogyne spp. juveniles recovered from the roots 8 weeks after planting. The use of fodder radish cultivars as biofumigation crops to manage root-knot nematodes in tropical vegetable production systems deserves further investigation.

Quantitative assessment of total phenol contents of European oak (Quercus petraea and Quercus robur) by diffuse reflectance NIR spectroscopy on solid wood surfaces

Near infrared spectroscopy (NIRS) combined with multivariate analysis techniques was applied to assess phenol content of European oak. NIRS data were firstly collected directly from solid heartwood surfaces: in doing so, the spectra were recorded separately from the longitudinal radial and the transverse section surfaces by diffuse reflectance. The spectral data were then pretreated by several pre-processing procedures, such as multiplicative scatter correction, first derivative, second derivative and standard normal variate. The tannin contents of sawmill collected from the longitudinal radial and transverse section surfaces were determined by quantitative extraction with water/methanol (1:4, by vol). Then, total phenol contents in tannin extracts were measured by the Folin-Ciocalteu method. The NIR data were correlated against the Folin-Ciocalteu results. Calibration models built with partial least squares regression displayed strong correlation - as expressed by high determination correlation coefficient (r2) and high ratio of performance to deviation (RPD) - between measured and predicted total phenols content, and weak calibration and prediction errors (RMSEC, RMSEP). The best calibration was provided with second derivative spectra (r2 value of 0.93 for the longitudinal radial plane and of 0.91 for the transverse section plane). This study illustrates that the NIRS technique when used in conjunction with multivariate analysis could provide reliable, quick and non-destructive assessment of European oak heartwood extractives.

NIR model development and robustness in prediction of melon fruit total soluble solids

Near infrared spectroscopy (NIRS) can be used for the on-line, non-invasive assessment of fruit for eating quality attributes such as total soluble solids (TSS). The robustness of multivariate calibration models, based on NIRS in a partial transmittance optical geometry, for the assessment of TSS of intact rockmelons (Cucumis melo) was assessed. The mesocarp TSS was highest around the fruit equator and increased towards the seed cavity. Inner mesocarp TSS levels decreased towards both the proximal and distal ends of the fruit, but more so towards the proximal end. The equatorial region of the fruit was chosen as representative of the fruit for near infrared assessment of TSS. The spectral window for model development was optimised at 695-1045 nm, and the data pre-treatment procedure was optimised to second-derivative absorbance without scatter correction. The 'global' modified partial least squares (MPLS) regression modelling procedure of WINISI (ver. 1.04) was found to be superior with respect to root mean squared error of prediction (RMSEP) and bias for model predictions of TSS across seasons, compared with the 'local' MPLS regression procedure. Updating of the model with samples selected randomly from the independent validation population demonstrated improvement in both RMSEP and bias with addition of approximately 15 samples.

Annual forecasting of the Australian macadamia crop - integrating tree census data with statistical climate-adjustment models

To facilitate marketing and export, the Australian macadamia industry requires accurate crop forecasts. Each year, two levels of crop predictions are produced for this industry. The first is an overall longer-term forecast based on tree census data of growers in the Australian Macadamia Society (AMS). This data set currently accounts for around 70% of total production, and is supplemented by our best estimates of non-AMS orchards. Given these total tree numbers, average yields per tree are needed to complete the long-term forecasts. Yields from regional variety trials were initially used, but were found to be consistently higher than the average yields that growers were obtaining. Hence, a statistical model was developed using growers' historical yields, also taken from the AMS database. This model accounted for the effects of tree age, variety, year, region and tree spacing, and explained 65% of the total variation in the yield per tree data. The second level of crop prediction is an annual climate adjustment of these overall long-term estimates, taking into account the expected effects on production of the previous year's climate. This adjustment is based on relative historical yields, measured as the percentage deviance between expected and actual production. The dominant climatic variables are observed temperature, evaporation, solar radiation and modelled water stress. Initially, a number of alternate statistical models showed good agreement within the historical data, with jack-knife cross-validation R2 values of 96% or better. However, forecasts varied quite widely between these alternate models. Exploratory multivariate analyses and nearest-neighbour methods were used to investigate these differences. For 2001-2003, the overall forecasts were in the right direction (when compared with the long-term expected values), but were over-estimates. In 2004 the forecast was well under the observed production, and in 2005 the revised models produced a forecast within 5.1% of the actual production. Over the first five years of forecasting, the absolute deviance for the climate-adjustment models averaged 10.1%, just outside the targeted objective of 10%.

An identification tool for the Australian weedy Sporobolus species based on random amplified polymorphic DNA (RAPD) profiles

Based on morphological features alone, there is considerable difficulty in identifying the 5 most economically damaging weed species of Sporobolus [viz. S. pyramidalis P. Beauv., S. natalensis (Steud.) Dur and Schinz, S. fertilis (Steud.) Clayton, S. africanus (Poir.) Robyns and Tourney, and S. jacquemontii Kunth.] found in Australia. A polymerase chain reaction (PCR)-based random amplified polymorphic DNA (RAPD) technique was used to create a series of genetic markers that could positively identify the 5 major weeds from the other less damaging weedy and native Sporobolus species. In the initial RAPD profiling experiment, using arbitrarily selected primers and involving 12 species of Sporobolus, 12 genetic markers were found that, when used in combination, could consistently identify the 5 weedy species from all others. Of these 12 markers, the most diagnostic were UBC51490 for S. pyramidalis and S. natalensis; UBC43310.2000.2100 for S. fertilis and S. africanus; and ORA20850 and UBC43470 for S. jacquemontii. Species-specific markers could be found only for S. jacquemontii. In an effort to understand why there was difficulty in obtaining species-specific markers for some of the weedy species, a RAPD data matrix was created using 40 RAPD products. These 40 products amplified by 6 random primers from 45 individuals belonging to 12 species, were then subjected to numerical taxonomy and multivariate system (NTSYS pc version 1.70) analysis. The RAPD similarity matrix generated from the analysis indicated that S. pyramidalis was genetically more similar to S. natalensis than to other species of the 'S. indicus complex'. Similarly, S. jacquemontii was more similar to S. pyramidalis, and S. fertilis was more similar to S. africanus than to other species of the complex. Sporobolus pyramidalis, S. jacquemontii, S. africanus, and S. creber exhibited a low within-species genetic diversity, whereas high genetic diversity was observed within S. natalensis, S. fertilis, S. sessilis, S. elongates, and S. laxus. Cluster analysis placed all of the introduced species (major and minor weedy species) into one major cluster, with S. pyramidalis and S. natalensis in one distinct subcluster and S. fertilis and S. africanus in another. The native species formed separate clusters in the phenograms. The close genetic similarity of S. pyramidalis to S. natalensis, and S. fertilis to S. africanus may explain the difficulty in obtaining RAPD species-specific markers. The importance of these results will be within the Australian dairy and beef industries and will aid in the development of integrated management strategy for these weeds.

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.

Approaches to selecting native plant replacements for fleshy-fruited invasive species

Invasive plants are a serious threat to biodiversity. Yet, in some cases, they may play an important ecological role in heavily modified landscapes, such as where fleshy-fruited invasive plants support populations of native frugivores. How can such conservation conflicts be managed? We advocate an approach in which native fleshy-fruited plants are ranked on their ability to provide the fruit food resources for native frugivores currently being provided by invasive plants. If these native taxa are preferentially used, where ecologically appropriate, in plantings for restoration and in park and garden settings, they could help support native frugivore populations in the event of extensive invasive plant control. We develop and critically examine six approaches to selecting candidate native plant taxa: a multivariate approach based on the frugivore assemblage, a scoring model, and several multivariate approaches (including trait combinations having the greatest correlation with the diet of the native frugivore assemblage) based on the functional traits of fruit morphology, phenology, conspicuousness, and accessibility. To illustrate these approaches, we use a case study with Bitou bush (Chrysanthemoides monilifera subsp. rotundata) (Asteraceae), an Australian Weed of National Significance. The model using a dissimilarity value generated from all available traits identified a set of species used by the frugivores of C. monilifera more than null models. A replacement approach using species ranked by either all traits available or the frugivore community appears best suited to guide selection of plants in restoration practice.

The stock structure of grey mackerel Scomberomorus semifasciatus in Australia as inferred from its parasite fauna

The scombrid Scomberomorus semifasciatus is an important component of inshore fisheries in tropical Australia. Data on the parasite fauna of 593 fish from areas off northern and eastern Australia were examined for evidence of discrete fish populations. The parasites used were juveniles of Pterobothrium pearsoni, Callitetrarhynchus gracilis, Anisakis simplex (sensu latu) and Terranova sp. Tukey Kramer pairwise comparisons gave significant differences in the abundances of two or more parasites between fish from the east coast, the eastern Gulf of Carpentaria and the remainder of northern Australia. Multivariate analysis gave further evidence of differences and the results suggest that at least 4 populations or stocks of grey mackerel occur along the northern and eastern coastline of Australia.

The role of fruit traits of bird-dispersed plants in invasiveness and weed risk assessment

Aim: Birds play a major role in the dispersal of seeds of many fleshy-fruited invasive plants. The fruits that birds choose to consume are influenced by fruit traits. However, little is known of how the traits of invasive plant fruits contribute to invasiveness or to their use by frugivores. We aim to gain a greater understanding of these relationships to improve invasive plant management. Location: South-east Queensland, Australia. Methods: We measure a variety of fruit morphology, pulp nutrient and phenology traits of a suite of bird-dispersed alien plants. Frugivore richness of these aliens was derived from the literature. Using regressions and multivariate methods, we investigate relationships between fruit traits, frugivore richness and invasiveness. Results: Plant invasiveness was negatively correlated to fruit size, and all highly invasive species had quite similar fruit morphology [smaller fruits, seeds of intermediate size and few (<10) seeds per fruit]. Lower pulp water was the only pulp nutrient trait associated with invasiveness. There were strong positive relationships between the diversity of bird frugivores and plant invasiveness, and in the diversity of bird frugivores in the study region and another part of the plants' alien range. Main conclusions: Our results suggest that weed risk assessments (WRA) and predictions of invasive success for bird-dispersed plants can be improved. Scoring criteria for WRA regarding fruit size would need to be system-specific, depending on the fruit-processing capabilities of local frugivores. Frugivore richness could be quantified in the plant's natural range, its invasive range elsewhere, or predictions made based on functionally similar fruits.

Cat's claw creeper vine, Macfadyena unguis-cati (Bignoniaceae), invasion impacts: comparative leaf nutrient content and effects on soil physicochemical properties

Macfadyena unguis-cati (L.) Gentry (Bignoniaceae) is a major environmental weed in coastal Queensland, Australia. There is a lack of quantitative data on its leaf chemistry and its impact on soil properties. Soils from infested vs uninfested areas, and leaves of M. unguis-cati and three co-occurring vine species (one exotic, two native) were collected at six sites (riparian and non-riparian) in south-eastern Queensland. Effects of invasion status, species, site and habitat type were examined using univariate and multivariate analyses. Habitat type had a greater effect on soil nutrients than on leaf chemistry. Invasion effect of M. unguis-cati on soil chemistry was more pronounced in non-riparian than in riparian habitat. Significantly higher values were obtained in M. unguis-cati infested (vs. uninfested) soils for ~50% of traits. Leaf ion concentrations differed significantly between exotic and native vines. Observed higher leaf-nutrient load (especially nitrogen, phosphorus and potassium) in exotic plants aligns with the preference of invasive plant species for disturbed habitats with higher nutrient input. Higher load of trace elements (aluminium, boron, cadmium and iron) in its leaves suggests that cycling of heavy-metal ions, many of which are potentially toxic at excess level, could be accelerated in soils of M. unguis-cati-invaded landscape. Although inferences from the present study are based on correlative data, the consistency of the patterns across many sites suggests that M. unguis-cati may improve soil fertility and influence nutrient cycling, perhaps through legacy effects of its own litter input.