Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

Abstract

The grape is a very well-liked fruit that is valued for its distinct flavor and several health benefits, including antioxidants, anthocyanins, soluble sugars, minerals, phenolics, flavonoids, organic acids, and vitamins, which significantly improve the product’s overall quality. Today’s supply chain as a whole needs quick and easy methods for evaluating fruit quality. Thus, the objective of this study was to estimate the quality attributes of Flame Seedless grape berries cultivated under various agronomical management and other practices using color space coordinates (berry L*, berry a*, and berry b*) as inputs in an artificial neural network (ANN) model with the best topology of (3-20-11). Satisfactory predictions based on the R2 range, which was 0.9817 to 0.9983, were obtained for physical properties (i.e., berry weight, berry length, and berry diameter as well as berry adherence strength) and chemical properties (i.e., anthocyanin, total soluble solids (TSS), TSS/titratable acidity, total sugars, titratable acidity, reducing sugars, and non-reducing sugars). Meanwhile, we also performed a contribution analysis to analyze the relative importance of CIELab colorimeter parameters of berries L*, a*, and b* to determine the main fruit quality. In terms of relative contribution, berry b* contributed relatively largely to berry weight, berry adherence strength, TSS, TSS/titratable acidity, titratable acidity, total sugars, reducing sugars, and non-reducing sugars and a* contributed relatively largely to anthocyanin, berry length, and berry diameter. The developed ANN prediction model can aid growers in enhancing the quality of Flame Seedless grape berries by selecting suitable agronomical management and other practices to avoid potential quality issues that could affect consumers of them. This research demonstrated how color space coordinates and ANN model may well be utilized to evaluate the Flame seedless grape berries’ quality.

Implementation of information and communication technologies in fruit and vegetable supply chain: a systematic literature review

“…Non-invasive sensors namely Fourier transform infrared (FTIR) spectroscopy, near-infrared (NIR) spectroscopy and visible (VIS) spectroscopy have been used for the assessment of the freshness of processed foods, prediction of ripeness and fast grading of FVs (Ferrara et al ., 2022; Minas et al ., 2023; Tsakanikas et al ., 2018). Artificial Neural Network (ANN) can estimate FV temperature, offering superior insight and improved SCM in the absence of appropriate hardware for food waste minimization (Al-Saif et al ., 2022; Chitikela et al ., 2021; Erukainure et al ., 2022). The utilization of image processing techniques and machine learning models to assess the visual quality and forecast the interior features of packed FV will help in food waste reduction (Van De Looverbosch et al ., 2020; Palumbo et al ., 2022).…”