Quantitative methods to measure pigmentation variation in farmed Giant Tiger Prawns, Penaeus monodon, and the effects of different harvest methods on cooked colour

Cooked prawn colour is known to be a driver of market price and a visual indicator of product quality for the consumer. Although there is a general understanding that colour variation exists in farmed prawns, there has been no attempt to quantify this variation or identify where this variation is most prevalent. The objectives of this study were threefold: firstly to compare three different quantitative methods to measure prawn colour or pigmentation, two different colorimeters and colour quantification from digital images. Secondly, to quantify the amount of pigmentation variation that exists in farmed prawns within ponds, across ponds and across farms. Lastly, to assess the effects of ice storage or freeze-thawing of raw product prior to cooking. Each method was able to detect quantitative differences in prawn colour, although conversion of image based quantification of prawn colour from RGB to Lab was unreliable. Considerable colour variation was observed between prawns from different ponds and different farms, and this variation potentially affects product value. Different post-harvest methods prior to cooking were also shown to have a profound detrimental effect on prawn colour. Both long periods of ice storage and freeze thawing of raw product were detrimental to prawn colour. However, ice storage immediately after cooking was shown to be beneficial to prawn colour. Results demonstrated that darker prawn colour was preserved by holding harvested prawns alive in chilled seawater, limiting the time between harvesting and cooking, and avoiding long periods of ice storage or freeze thawing of uncooked product.

A digital imaging technique to quantify the incidence of internal browning in potato (Solanum tuberosum) tubers

Internal browning disorders, including brown fleck (BF), in potato (Solanum tuberosum) tubers greatly reduce tuber quality, but the causes are not well understood. This is due, in part, to the highly variable data provided by visual value-based rating systems. A digital imaging technique was developed to quantify accurately the incidence of internal browning in potato tubers. Images of tuber sections were scanned using a flatbed scanner and digitally enhanced to highlight tuber BF lesions, and the area of affected tissue calculated using pixel quantification software. Digital imaging allowed for the determination of previously unused indices of the incidence and severity of internal browning in potato tubers. Statistical analysis of the comparison between digitally derived and visual-rating BF data from a glasshouse experiment showed that digital data greatly improved the delineation of treatment effects. The F-test probability was further improved through square root or logarithmic data transformations of the digital data, but not of the visual-rating data. Data from a field experiment showed that the area of tuber affected by BF and the number of small BF lesions increased with time and with increase in tuber size. The results from this study indicate that digital imaging of internal browning disorders of potato tubers holds much promise in determining their causes that heretofore have proved elusive.

Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform

A low-altitude platform utilising a 1.8-m diameter tethered helium balloon was used to position a multispectral sensor, consisting of two digital cameras, above a fertiliser trial plot where wheat (Triticum spp.) was being grown. Located in Cecil Plains, Queensland, Australia, the plot was a long-term fertiliser trial being conducted by a fertiliser company to monitor the response of crops to various levels of nutrition. The different levels of nutrition were achieved by varying nitrogen application rates between 0 and 120 units of N at 40 unit increments. Each plot had received the same application rate for 10 years. Colour and near-infrared images were acquired that captured the whole 2 ha plot. These images were examined and relationships sought between the captured digital information and the crop parameters imaged at anthesis and the at-harvest quality and quantity parameters. The statistical analysis techniques used were correlation analysis, discriminant analysis and partial least squares regression. A high correlation was found between the image and yield (R2 = 0.91) and a moderate correlation between the image and grain protein content (R2 = 0.66). The utility of the system could be extended by choosing a more mobile platform. This would increase the potential for the system to be used to diagnose the causes of the variability and allow remediation, and/or to segregate the crop at harvest to meet certain quality parameters.