This study reports on the wavelength dependence of near infrared (NIR) radiation penetration depth into 'Hass' avocado fruit for both transmission and reflectance modes in the wavelength range from 12,820-4,000 cm-1 (780-2,500 nm). For transmission mode, depths of penetration less than 3 mm occurred in the 5,369 to 7,413 cm-1 (1,862-1,348 nm) range, while the maximum depth of penetration of approximately 12 mm occurred in the region 11,293 to 11,987 cm-1 (885-834 nm). Two other wavelength regions were identified with significant depth of penetration in transmission mode, one around 9,272 cm-1 (1,078 nm) and the second at approximately 7,899 cm-1 (1,265 nm) with approximately 10 and 4.5 mm penetration respectively. In reflectance mode, despite the difference in the optical paths sampled, qualitatively similar trends in wavelength dependence of the depth of penetration were observed, with a lower penetration depth of 3.3 mm occurring around 9,311 cm-1 (1,074 nm) and maximum depth of penetration of approximately 4.8 mm occurring in the 11,625 to 11,987 cm-1 (860-834 nm) region.

Fish stocking occurs in aquatic systems for conservation purposes, to create or enhance recreational fisheries and to enhance wild-catch commercial fisheries. Identifying and quantifying the contribution of stocking efforts to wild populations is crucial to informing these management objectives. Provenance determination methods trade off accuracy, replicability, and costeffectiveness at fishery-relevant scales. We present and assess multiple methods for provenance determination using a case study of barramundi (Lates calcarifer) in the Dry Tropics region of northern Australia. A novel application of near-infrared spectroscopy (NIRS) is compared to two established methods for fish provenance, otolith microchemistry and genetic parentage analysis using microsatellites. The otolith microchemistry method was able to provide extremely high provenance resolution (>99% accuracy). The microsatellite parentage analysis method had a slightly lower overall accuracy (95%), likely as a result of genetic introgression in this region. Provenance determination using otolith NIRS had the lowest overall accuracy (76%). Once limitations regarding spectral noise, image resolution, and sample size are addressed, NIRS may have potential for costeffectively determining provenance in fish.

BACKGROUND:A rapid non-destructive in-line grading system that can rapidly and accurately assess individual avocado fruit for internal quality attributes, including bruises and rots, would allow the avocado industry to provide a more consistent fruit quality to the consumer, optimise market distribution and ensure maximum yield for the producer and retailer. Fourier transform-near-infrared (FT-NIR) spectroscopy was investigated to detect bruises and rot susceptibility as an indication of shelf-life in avocado fruit at both the sprung stage of ripeness and eating ripe fruit.RESULTS:The classification models (principal component linear discriminant analysis, partial least squares discriminant analysis and support vector machine) for each of three growing seasons found hard green fruit that were deliberately bruised could be correctly detected with 70-78% accuracy after 2-5 h following impact damage and with 83-89% accuracy after 24 h. For eating ripe fruit, the accuracy was 60-100% after 2-5 h following impact damage and 66-100% after 24 h. The ability of the classification models to accurately predict rot development into two classes, ≤10% and >10% of flesh affected, ranged from 65% to 84% over the three growing seasons. When the rot classes were defined as ≤30% and >30% the accuracy was 69-77%.CONCLUSIONS:The results of the study highlight the potential of FT-NIR reflectance spectroscopy for application in a commercial, in-line setting for the non-destructive evaluation of impact damage and rot susceptibility of whole avocado fruit. The study indicates that fruit should be held for approximately 24 h prior to scanning to allow bruise development to occur, particularly in hard fruit (i.e., stage 2) prior to bruise assessment. © 2018 Society of Chemical Industry.

[Extract] Quality and safety evaluation of agricultural products has become an increasingly important consideration in market/commercial viability and systems for such evaluations are now demanded by customers, including distributors and retailers. Unfortunately, most horticultural products struggle with delivering adequate and consistent quality to the consumer. Removing inconsistencies and providing what the consumer expects is a key factor for retaining and expanding both domestic and international markets. Most commercial quality classification systems for fruit and vegetables are based on external features of the product, for example: shape, colour, size, weight and blemishes. However, the external appearance of most fruit is generally not an accurate guide to the internal or eating quality of the fruit. Internal quality of fruit is currently subjectively judged on attributes such as volatiles, firmness, and appearance. Destructive subjective measures such as internal flesh colour, or objective measures such as extraction of juice to measure sweetness (°Brix) or assessment of dry matter (DM) content are also used, although obviously not for every fruit – just a sample to represent the whole consignment.

BACKGROUND:The inability to consistently guarantee internal quality of horticulture produce is of major importance to the primary producer, marketers and ultimately the consumer. Currently, commercial avocado maturity estimation is based on the destructive assessment of percentage dry matter (%DM), and sometimes percentage oil, both of which are highly correlated with maturity. In this study the utility of Fourier transform (FT) near-infrared spectroscopy (NIRS) was investigated for the first time as a non-invasive technique for estimating %DM of whole intact 'Hass' avocado fruit. Partial least squares regression models were developed from the diffuse reflectance spectra to predict %DM, taking into account effects of intra-seasonal variation and orchard conditions.RESULTS:It was found that combining three harvests (early, mid and late) from a single farm in the major production district of central Queensland yielded a predictive model for %DM with a coefficient of determination for the validation set of 0.76 and a root mean square error of prediction of 1.53% for DM in the range 19.4-34.2%.CONCLUSION:The results of the study indicate the potential of FT-NIRS in diffuse reflectance mode to non-invasively predict %DM of whole 'Hass' avocado fruit. When the FT-NIRS system was assessed on whole avocados, the results compared favourably against data from other NIRS systems identified in the literature that have been used in research applications on avocados.