Effects of seasonal variability on FT-NIR prediction of dry matter content for whole Hass avocado fruit

Exploring genetic variation, oil and α-tocopherol content in avocado (Persea americana) from northwestern Mexico

“…Oil percentages of our local samples are comparable or higher than previously reported for genotypes of the West Indian race (Gómez-López 2000, 2002. Variation in total lipid content between fruits might be associated with maturity stage but also with environmental conditions, postharvest management, geographical conditions and genetic background (Wedding et al 2013;Hurtado-Fernández et al 2014;Yu et al 2016).…”

Effect of cultivar and season on the robustness of PLS models for soluble solid content prediction in apricots using FT-NIRS

“…It is worth mentioning that although it contains the atypical data of 'Kabaaşı' from 2016, the global model constructed with the data of three successive years and two cultivars gave very good results (R p 2 = 0.830, RMSEP = 1.90 and RPD = 2.43) (Table 4) which clearly indicates the robustness of the prediction model. Similar results were also reported for some other fruits such as Japanese plums (Louw and Theron 2010), apples (Peirs et al 2003;Bobelyn et al 2010), mangos (Rungpichayapichet et al 2016), avocado (Wedding et al 2013) and mandarins (Guthrie et al 2005) it was found that the multi-cultivar The calibration models were built on the different dual combinations of data from different harvest seasons and the model validation was carried out either on the internal test set corresponding to the one-third of the whole data (TSV 1/3) or on the data set of the remaining season. For the global model (last row in the table) the data from three successive harvest seasons were combined and validated with internal test set (TSV 1/3).…”

“…However, in order to develop effective grading lines for practical applications, developed models should be robust enough to predict SSC of the fruit of different cultivars, maturity stages and seasons (Nicolaï et al 2007). The results of the studies carried out on peaches (Peiris et al 1998), apples (Peirs et al 2003;Bobelyn et al 2010), mandarins (Guthrie et al 2005), avocado (Wedding et al 2013), Japanese plums (Louw and Theron 2010) and mango (Rungpichayapichet et al 2016) showed that the prediction accuracy of the FT-NIR models can exhibit considerable seasonal variations. In all these quoted studies improvement in model robustness was observed when spectral data of several seasons were incorporated in the calibration model.…”

Modelling postharvest quality of blueberry affected by biological variability using image and spectral data

“…NIR models using eight apple cultivars from three orchards and two picking dates were developed to obtain more robust models for practical use . Model performance for avocado dry matter content was found to improve as more seasonal variability was incorporated . In addition, cultivar, seasonal, storage and origin effects on predicting apple SSC and firmness were investigated through the use of functional analysis of variance .…”