Rapid Determination of Nutrient Concentrations in Hass Avocado Fruit by Vis/NIR Hyperspectral Imaging of Flesh or Skin

Abstract

Fatty acid composition and mineral nutrient concentrations can affect the nutritional and postharvest properties of fruit and so assessing the chemistry of fresh produce is important for guaranteeing consistent quality throughout the value chain. Current laboratory methods for assessing fruit quality are time-consuming and often destructive. Non-destructive technologies are emerging that predict fruit quality and can minimise postharvest losses, but it may be difficult to develop such technologies for fruit with thick skin. This study aimed to develop laboratory-based hyperspectral imaging methods (400–1000 nm) for predicting proportions of six fatty acids, ratios of saturated and unsaturated fatty acids, and the concentrations of 14 mineral nutrients in Hass avocado fruit from 219 flesh and 194 skin images. Partial least squares regression (PLSR) models predicted the ratio of unsaturated to saturated fatty acids in avocado fruit from both flesh images (R2 = 0.79, ratio of prediction to deviation (RPD) = 2.06) and skin images (R2 = 0.62, RPD = 1.48). The best-fit models predicted parameters that affect postharvest processing such as the ratio of oleic:linoleic acid from flesh images (R2 = 0.67, RPD = 1.63) and the concentrations of boron (B) and calcium (Ca) from flesh images (B: R2 = 0.61, RPD = 1.51; Ca: R2 = 0.53, RPD = 1.71) and skin images (B: R2 = 0.60, RPD = 1.55; Ca: R2 = 0.68, RPD = 1.57). Many quality parameters predicted from flesh images could also be predicted from skin images. Hyperspectral imaging represents a promising tool to reduce postharvest losses of avocado fruit by determining internal fruit quality of individual fruit quickly from flesh or skin images.

A Rapid Non-Destructive Hyperspectral Imaging Data Model for the Prediction of Pungent Constituents in Dried Ginger

“…Two different multivariate regression approaches were used in this study. Partial least squares regression (PLSR) was chosen for its excellent performance and broad use in previous studies [ 34 , 43 , 44 , 45 , 46 , 47 ]. The second method, least absolute shrinkage and selection operator (LASSO) regression, was selected for its power to handle multiple collinear features [ 48 ].…”

Boron Effects on Fruit Set, Yield, Quality and Paternity of Hass Avocado

“…One half of the flesh from each of the eight ripe fruit per tree was mashed finely to extract oil. We derivatised fatty acid methyl esters from the extracted oil and determined the fatty acid composition via gas chromatography-mass spectrometry (PerkinElmer Clarus 580 GC coupled to a PerkinElmer Clarus SQ8S MS) using the methods described previously [68,69]. We calculated the relative abundance of individual fatty acids in each sample by dividing the peak area of each individual fatty acid by the total peak area of all fatty acids in the sample and multiplying it by 100%.…”

Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat

“…PLSR models were trained to correlate the spectral reflectance of the samples, measured using NIRS, VNIR and SWIR HSI cameras, with their reference C and N concentrations (measured using wet chemistry analysis) in the calibration datasets. Data transformation techniques, including Savitzky-Golay derivatives, multiple scatter corrections, orthogonal signal correction and standard normal variate, were used (individually or combined) to increase the signal to noise ratio where needed [27]. The number of latent variables (LV) for the PLSR models was selected at the lowest predicted residual error sum of squares (PRESS) in the cross-validation set (Equation (2)).…”