The non-invasive assessment of avocado maturity and quality

Horticultural products in today's modern market must have high quality standards. Consumer demand for consistent quality agricultural produce remains strong and continues to increase, this will lead to the development and subsequent increased availability of sophisticated techniques, sensors, and user-friendly non-invasive systems for measuring product quality indices. The inability to consistently guarantee internal fruit quality is a major factor not only for the Australian avocado industry but also the entire horticulture sector. Poor fruit quality is seen as a key factor affecting consumer confidence and impacts on supply chain efficiency and profitability. Removing fruit quality inconsistencies while providing the consumer with a consistent quality product is a vital commercial consideration of the Australian avocado industry for both domestic and export markets. Many fruit quality attributes affecting consumer acceptance are assessed using traditional methods that are generally subjective, labour intensive and costly. Commercially, avocado maturity is measured destructively by the determination of dry matter (DM) content, moisture content (MC) or oil content, all of which are highly correlated. Maturity is an important component in avocado fruit quality and a prime factor in palatability. A rapid, non-destructive measurement system that can accurately and simultaneously monitor external and internal attributes of every avocado fruit either in the field or in an in-line setting, is highly desirable for ensuring consistent product quality over an extended season, increasing industry marketability and profitability. The utility of near infrared (NIR) spectroscopy was investigated as a non-invasive assessment tool for estimating avocado maturity and thereby eating quality based on dry matter content of whole intact fruit primarily for the avocado variety 'Hass'. The technique was also assessed for detecting bruises and for predicting rot susceptibility as an indication of shelf-life for possible implementation in a commercial in-line application. The project also investigated the importance of the calibration model development process to incorporate seasonal and geographical variability to ensure model robustness. NIR spectroscopy has an obvious place in agriculture and environmental applications with its core strength in the analysis of biological materials, plus low cost of analysis, simplicity in sample preparation, no chemical reagent requirements, simultaneous analysis of multiple constituents, good repeatability and high throughput capability. The commercially available NIR spectroscopy systems assessed in this project highlighted the potential of NIR spectroscopy and its suitability for application in a commercial in-line setting for predicting avocado maturity and palatability of whole intact avocados, based on DM content. With horticultural products, the major challenge of implementing NIR spectroscopy is to ensure that the calibration model is robust, that is, that the calibration model holds across growing seasons and potentially across growing districts. The present project represents the first study to investigate the effect of seasonal variation on model robustness to be applied to avocado fruit. It found that seasonal variability has a significant effect on model predictive performance for DM in avocados. The robustness of the calibration model, which in general limits the commercial application for the technique, was found to increase across seasons when more seasonal variability was included in the calibration set. Across the seasons it achieved predictive performances in this case in the range of: validation coefficient of determination (Rᵥ²) of 0.76 – 0.89, root mean square error of prediction (RMSEP) of 1.43 - 1.97%, and standard deviation ratio's (SDR) of 2.0 to 3.1. Similarly, there are spectral differences between geographical regions and that specific regional models may have significantly reduced predictive performance when applied to samples containing biological variability from a different growing region. As with seasonal variability, this can be addressed by incorporating multiple geographical growing regions into the calibration model to account for the biological variability to improve model robustness as demonstrated in this study (i.e., Rᵥ² of 0.89, RMSEP of 1.51%, and SDR of 3.6). Furthermore, when models are constructed to include both season and geographical variability, model performance can be more robust when dealing with a broader range of future sample variability. This was demonstrated with calibration models constructed to incorporate 3 years of seasonal variability and encompassing 3 geographical regions, obtaining predictive performances ranging from Rᵥ ² 0.87 - 0.89; RMSEP of 1.42 - 1.64% and SDR of 2.7 - 3.1 across the various geographical regions. NIR spectroscopy shows great promise for the application in a commercial, in-line setting for the non-destructive evaluation of impact damage (bruising) and rot susceptibility of whole avocado fruit, although optimisation of the technology is required to address speed of throughput and environmental issues. The adoption of a rapid, non-invasive method to identify fruit that are less prone to rots and internal disorders would allow selection of fruit that could be sent to more distant markets with greater confidence that it will arrive in acceptable quality, thus ensuring maximum yield and higher returns for the producer and marketer. The ability of the NIR classification models to accurately predict rot development of hard green avocado fruit (stage 0 ripeness) into two classes, ≤10% and >10% of flesh affected, ranged from 65-84% over the three growing seasons. When the rot classes were defined as ≤30% and >30% the accuracy ranged from 69%-77%. In relation to impact damage (bruising), trials conducted over three growing seasons using an NIR spot assessment technique found hard green fruit at stage 2 ripeness, that were deliberately bruised could be correctly detected with 70-79% accuracy after 2-5 hours of impacting and with 83-89% accuracy after 24 hours. For eating ripe (stage 4) fruit, the accuracy was 60-100% after 2-5 hours of impacting and 66-100% after 24 hours across the three growing seasons. This indicates that in a commercial situation it would be an advantage to hold the fruit for 24 hours before undertaking NIR scanning