Centre for Plant & Water Science - Central Queensland University

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Centre for Plant & Water Science 2008 Annual Report NEAR INFRARED SPECTROSCOPY FOR PREDICTING B74 MANGO MATURITY AND EATING QUALITY SUMMARY This projects represents the second year of a three year funded activity within a larger DPI led, HAL-Harvest Company funded project “Development of Best Practice Pre- and Post- Harvest Protocols for Production of B74 Mango, Phase II”. In a previous season, we have demonstrated that handheld unit can be used to predict DM and internal flesh colour in intact and just-harvested fruit, ºBrix in ripe fruit, and ºBrix of ripened fruit from spectra collected at the just-harvested stage. In the current project, we aim to extend this work done in previous years on Calypso mango, to confirm the robustness of the handheld unit in field conditions, advancement of handheld technology, and develop agronomic protocols for uniform and better mango maturity prediction. The previous season’s work was done testing both a portable laboratory-based NIRS unit connected to a laptop computer (the Shoebox system) and a portable handgun. During this season a portable handgun was utilised throughout the season and in some instances an online Insight unit was trialed along with the handheld unit. Trials were conducted near Darwin, Katherine, Mareeba and Bundaberg. Key findings Accuracy of the handheld NIR (iQ unit) under field conditions Based on two seasons results, the unit can be used with fruit over a range of temperature and ambient light conditions, however the instrument should be kept as cool as possible (e.g. storage in shade rather than in sun). Use of the instrument on exceptionally hot days (> 40º C) is not recommended. Predicting flesh colour and % DM Results indicate that models could be used across varieties through the growing season and between growing seasons if the model calibration population adequately represented the variation present in the new populations. A quality control procedure is recommended for the next few seasons at least. This would involve confirming the accuracy of the unit’s estimates of % DM and flesh colour by scanning about 20 fruit then measuring actual flesh colour and % DM for each fruit and averaging the results from the unit estimates and actual measurements. This should be done several times during the season and for each region. Prediction of flesh colour was not as robust, with large bias values skewing RMSEP (Root Mean Square Error of Prediction) values. It is recommended that a single model cannot be used across all growing districts, and that a model updating or bias adjustment procedure is required. Model performance should be checked on each farm, as outlined above. Predicting ripe fruit ºBrix As expected, the on-line InSight unit performed slightly better than the handheld iQ unit. However, DM content was well predicted by the iQ unit in fruit at the hard green stage, and the ripe stage TSS is well correlated to % DM at the hard green stage. Therefore NIRS should be able to predict ripe fruit ºBrix by scanning fruit at harvest. It is recommended that this issue be re-considered using a model developed using a wider range of DM and thus ripe fruit ºBrix values. Alternatively, as the link between DM and Page 53
Centre for Plant & Water Science 2008 Annual Report final stage ºBrix is well established, it may be appropriate to simply adopt specifications on DM. A B Figure 1: A: Mango cultivar Calypso fruit sorted at harvest by SWNIRS PLSR DM model into three DM grades (left) 16%DM. B: Using the technology for quality control in packing shed. Application issues Testing manager and picker estimation of maturity Our data suggest that select harvesting based on fruit external appearance not be used as a standard practice. In preference identifying the most mature trees/areas of the farm should be identified using NIRS, and these trees/areas be strip-picked. Assessing block or maturity zone fruit maturity Trees in each maturity zone could be marked by colour-coded paint on the trunk, or similar. Using maturity zones may enable fewer trees to be sampled to accurately determine maturity zone because of less fruit maturity variation between trees. Flesh colour and DM changes during fruit growth We suggest tagging of about 10 fruit per tree on eight trees per management area and assessing their quality with the iQ every week from 4-6 weeks before the expected start of harvest. Graphing the results will help predict the start of commercial harvest for each maturity zone. PROJECT STAFF Principal Investigator: Co-Principal Investigator: FUNDING HAL Australia INCOME $123,490 Dr. Phul Subedi Professor Kerry Walsh Page 54
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Centre for Plant & Water Science - Central Queensland University

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