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The classification of carambola, also known as starfruit, according to quality parameters is usually conducted by trained human evaluators through visual inspections. This is a costly and subjective method that can generate high variability in results. As an alternative, computer vision systems (CVS) combined with deep learning (DCVS) techniques have been introduced in the industry as a powerful and an innovative tool for the rapid and non-invasive classification of fruits. However, validating the learning capability and trustworthiness of a DL model, aka black box, to obtain insights can be challenging. To reduce this gap, we propose an integrated eXplainable Artificial Intelligence (XAI) method for the classification of carambolas at different maturity stages. We compared two Residual Neural Networks (ResNet) and Visual Transformers (ViT) to identify the image regions that are enhanced by a Random Forest (RF) model, with the aim of providing more detailed information at the feature level for classifying the maturity stage. Changes in fruit colour and physicochemical data throughout the maturity stages were analysed, and the influence of these parameters on the maturity stages was evaluated using the Gradient-weighted Class Activation Mapping (Grad-CAM), the Attention Maps using RF importance. The proposed approach provides a visualization and description of the most important regions that led to the model decision, in wide visualization follows the models an importance features from RF. Our approach has promising potential for standardized and rapid carambolas classification, achieving 91 % accuracy with ResNet and 95 % with ViT, with potential application for other fruits., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The objective of the present study was to evaluate the technological properties of red mombin seed flour (RMSF) and its effect on the nutritional composition and physical properties of chocolate brownies. RMSF was incorporated at various levels (25%, 50%, 75%, and 100%) into the formulation, with control brownies produced using refined (white) wheat flour (WWF). Results demonstrated that the addition of RMSF was beneficial to dietary fiber and ash content. Mineral profiles were shown to be an important factor for recommended daily intake values. Moreover, the antioxidant activity in this dessert increased approximately 13% due to the addition of RMSF, thus allowing longer preservation of the product in face of oxidation. Regarding textural parameters, the lowest concentration (25%) was sufficient to improve chewiness, gumminess, cohesiveness and resilience. Therefore, the incorporation of RMSF in chocolate brownies was capable of completely replacing WWF, improving physical and nutritional characteristics., (© Association of Food Scientists & Technologists (India) 2020.)