Rapid Assessment Unit, Centre for Tropical Agri-tech Research, and Innovative Food Technologies, Department of Employment, Economic Development and Innovation, Cairns 4870, QLD, Australia.
J Sci Food Agric. 2011 Jan 30;91(2):233-8. doi: 10.1002/jsfa.4175.
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.
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%.
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.
园艺产品无法持续保证内在质量，这对初级生产者、营销者，最终对消费者来说都非常重要。目前，商业鳄梨成熟度的评估主要基于干物质百分比（%DM）的破坏性评估，有时还基于油分百分比，这两者都与成熟度高度相关。在这项研究中，傅里叶变换（FT）近红外光谱（NIRS）技术首次被用作一种非侵入性技术，用于估计整个完整的“哈斯”鳄梨果实的%DM。偏最小二乘回归模型是从漫反射光谱中开发出来的，用于预测%DM，同时考虑了季节内变化和果园条件的影响。
发现从昆士兰州中部主要生产区的一个单一农场中组合三个收获期（早期、中期和晚期），可以为%DM 生成一个预测模型，验证集的决定系数为 0.76，预测 DM 值的均方根误差为 1.53%，DM 值范围为 19.4-34.2%。
研究结果表明，FT-NIRS 在漫反射模式下具有非侵入性预测整个“哈斯”鳄梨果实%DM 的潜力。当 FT-NIRS 系统用于整个鳄梨时，与文献中确定的其他已用于鳄梨研究应用的 NIRS 系统的数据相比，结果相当有利。
