Nondestructive detection of total soluble solids in grapes using VMD‐RC and hyperspectral imaging

Abstract

Total soluble solids (TSS) are one of the most essential attributes determining the quality and price of fruit. This study aimed to use hyperspectral imaging (HSI) and wavelength selection for nondestructive detection of TSS in grape. A novel method involving variational mode decomposition and regression coefficients (VMD‐RC) was proposed to select feature wavelengths. VMD was introduced to decompose the hyperspectral images data of samples with bands of (400.68–1001.61 nm) to get a series of feature components. Afterward, these components were processed separately using regression analysis to obtain the stability values of RC of each component. Finally, a filter‐based selection strategy was used to screen key wavelengths. The least squares support vector machine (LSSVM) and partial least squares (PLS) were constructed under full and feature wavelengths for predicting TSS. The VMD‐RC‐LSSVM model obtained the best prediction accuracy for TSS, with Rp2$R_p^2$ of 0.93, with RMSEP$RMSEP$ of 0.6 %, with RER$RER$ of 18.14 and RPDp$RP{D_p}$ of 3.69. The overall results indicated that the VMD‐RC algorithm can be used as an alternative to handle high‐dimensional hyperspectral images data, and HSI has great potential for nondestructive and rapid evaluation of quality attributes in fruit. Practical Application Traditional methods of evaluating grape quality attributes are destructive, time‐consuming and laborious. Therefore, HSI was used to achieve rapid and nondestructive determination of TSS in grape. The results indicated that it was feasible to use HSI and variable selection for predicting TSS. It is of great significance to improve grape quality, guide postharvest handling and provide a valuable reference for noninvasively evaluating other internal attributes of fruit.

Comprehensive metabolomics-based analysis of sugar composition and content in berries of 18 grape varieties

“…Total soluble solid (TSS) is among the most essential attributes that determine the quality of fruits. TSS is dominated by total sugar content, with a minor contribution from soluble proteins, amino acids, and other organic materials (Xu et al, 2022). In this study, the TSS of 'Summer Black' and 'Flame Seedless' was found to be 25.12 and 24.71°Brix, respectively, which was higher than other grape varieties, while 'Victoria' had the lowest TSS of 14.93°Brix.…”

Monitorization of Varietal Aroma Composition Dynamics during Ripening in Intact Vitis vinifera L. Tempranillo Blanco Berries by Hyperspectral Imaging

“…In the case of the TSS, which is the classical and most well-known grape maturity parameter, values of R P 2 = 0.91 and SEP = 1.03 were obtained, with RPD well above 3.0 (8.19, Table ), clearly demonstrating the possibility of accurately estimating this variable using noninvasive HSI. TSS has been studied by HSI in red grapes. ,,− Good results were obtained in all of them, with good correlations and RPD between 1 and 4. On the other hand, there is a study in which HSI is used to estimate the TSS in red grapes and white grapes.…”

Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion

“…Currently, HSI technology has been widely used in the quality and safety detection of agricultural products ( Li et al., 2018 ; Yao et al., 2022 ). Some researchers measured the content of soluble solids in fruits by hyperspectral imaging technology ( Li et al., 2016a ; Gao et al., 2022 ; Xu et al., 2022b ). Furthermore, some researchers detected aflatoxin in corn, almond, and other crops by HSI technology ( Chu et al., 2017 ; Kimuli et al., 2018 ; Mishra et al., 2022 ).…”