Machine learning enabled assessment of the vacuum freeze-drying of the kiwifruit

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY
Uzair Sajjad , Farzana Bibi , Imtiyaz Hussain , Naseem Abbas , Muhammad Sultan , Hafiz Muhammad Asfahan , Muhammad Aleem , Wei-Mon Yan
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引用次数: 0

Abstract

Drying technologies have been essential for extending the shelf-life of perishable fruits and vegetables for over a century. Vacuum freeze-drying (VFD), though invented over a hundred years ago, remains one of the most advanced drying techniques, known for sustainably drying perishable products while maintaining quality indices and morphological properties comparable to their fresh state. The performance of the VFD system is sensitive to the operating conditions and features of the drying product which is assessed using experimental and/or numerical methods. However, the qualitative aspects of the dried product are not predictable. In this context, the present study aims to create a deep neural framework (DNF) that predicts the performance of a Vacuum Freeze Drying (VFD) system for kiwifruit, based on its morphology and nutritional value under varying conditions. This involves translating the fruit’s morphological features into trainable data and using a Generative Adversarial Network (GAN) to create diverse, unlabeled datasets. The framework is optimized using Gaussian Process (GP) for hyper-parameter tuning, focusing on minimizing errors like mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The maximum MSE of 1.243 is found in the prediction of rehydration rate, followed by color (0.725), energy consumption (0.426), moisture content (0.379), texture (0.320), sensory (0.250), and Brix (0.215), respectively. The maximum MAE and MAPE values are recorded 0.833 and 32.99 % while the minimum is observed 0.368 and 7.019 % in the case of rehydration rate and Brix, respectively. Overall, the R2 value was computed 0.863 which is reasonable for the quality assessment of kiwifruit dried by the VFD system.
机器学习使猕猴桃真空冷冻干燥的评估成为可能
一个多世纪以来,干燥技术一直是延长易腐水果和蔬菜保质期的关键。真空冷冻干燥(VFD)虽然是一百多年前发明的,但仍然是最先进的干燥技术之一,以可持续干燥易腐产品而闻名,同时保持其新鲜状态的质量指标和形态特性。VFD系统的性能对干燥产品的操作条件和特性很敏感,使用实验和/或数值方法进行评估。然而,干燥产品的定性方面是不可预测的。在此背景下,本研究旨在创建一个深度神经框架(DNF),根据猕猴桃在不同条件下的形态和营养价值来预测真空冷冻干燥(VFD)系统的性能。这包括将水果的形态特征转化为可训练的数据,并使用生成对抗网络(GAN)来创建各种未标记的数据集。该框架使用高斯过程(GP)进行超参数调优,重点是最小化均方误差(MSE),平均绝对误差(MAE)和平均绝对百分比误差(MAPE)等误差。再水化率预测的MSE最大,为1.243,其次是颜色(0.725)、能量消耗(0.426)、水分含量(0.379)、质地(0.320)、感官(0.250)和白利度(0.215)。复水化率和白糖度的最大MAE和MAPE值分别为0.833和32.99%,最小MAE和MAPE值分别为0.368和7.019%。综上所述,R2值为0.863,可用于VFD系统干燥猕猴桃的品质评价。
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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