Visualization of Sugar Content Distribution of White Strawberry by Near-Infrared Hyperspectral Imaging.

Abstract

In this study, an approach to visualize the spatial distribution of sugar content in white strawberry fruit flesh using near-infrared hyperspectral imaging (NIR-HSI; 913-2166 nm) is developed. NIR-HSI data collected from 180 samples of "Tochigi iW1 go" white strawberries are investigated. In order to recognize the pixels corresponding to the flesh and achene on the surface of the strawberries, principal component analysis (PCA) and image processing are conducted after smoothing and standard normal variate (SNV) pretreatment of the data. Explanatory partial least squares regression (PLSR) analysis is performed to develop an appropriate model to predict Brix reference values. The PLSR model constructed from the raw spectra extracted from the flesh region of interest yields high prediction accuracy with an RMSEP and R2p values of 0.576 and 0.841, respectively, and with a relatively low number of PLS factors. The Brix heatmap images and violin plots for each sample exhibit characteristics feature of sugar content distribution in the flesh of the strawberries. These findings offer insights into the feasibility of designing a noncontact system to monitor the quality of white strawberries.

Plant phenotyping relevance

近赤外ハイパースペクトル画像からイチゴ果実の糖含量分布を推定・可視化する画像解析および回帰モデルが研究の中心であり、植物器官の品質形質を直接取得するフェノタイピング手法に該当する。

Code and data availability

The paper describes NIR hyperspectral imaging of 180 white strawberries with PCA/PLSR analysis in MATLAB, but no blocks contain a data availability statement, deposited hyperspectral images, Brix phenotype data, or author analysis code. The only referenced URLs (MATLAB, Violinplot-Matlab) are generic third-party tools,

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