Artificial Neural Networks and Thermal Image for Temperature Prediction in Apples

Badia-Melis, Ricardo; Qian, Jianping; Fan, Beilei; Hoyos-Echevarria, Pedro; Ruiz-García, Luis; Yang, Xinting

doi:10.1007/s11947-016-1700-7

Cited by 36 publications

(13 citation statements)

References 24 publications

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“…ANNs can use input-output data to recognize and guess patterns by training and learning, bringing unique benefits to process control including control of complex processes and unknown (Kondakci and Zhou 2017). This tool is reported in the literature as efficient for assessing and predicting the influence of various factors on the processing of foods (Erdős et al 2018;Espinosa-Sandoval et al 2019;Sun et al 2019) and storage (Badia-Melis et al 2016;Kodogiannis 2017;Shi et al 2018). However, none of those works used this tool for data grouping, according to their similarities, to corroborate sensory analysis results, evaluated by other statistical tools.…”

Section: Introductionmentioning

confidence: 99%

Nutritional Clustering of Cookies Developed with Cocoa Shell, Soy, and Green Banana Flours Using Exploratory Methods

Barros

Natarelli

Tavares

et al. 2020

Food Bioprocess Technol

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The aim of this presented study was to develop and cluster cookies made with cocoa shell, a by-product of the chocolate industry, and nutritionally rich raw materials, soy and green banana flours, according to their nutritional characteristics and to evaluate them sensorially, through exploratory methods. The results of proximate composition, phenolic compounds, antioxidant activity, hardness and color were submitted to Kohonen's self-organizing maps (KSOMs) and the samples trend to form three groups. All treatments showed functional properties due to their high protein content, dietary fiber, phenolic compounds, and antioxidants. All formulations had good sensory acceptance (grades between 6.71 and 7.11) and the simplex-centroid design used to optimize the acceptance was effective. The Check-All-That-Apply (CATA) analysis indicated sensory differences between the treatments. It is concluded that the KSOMs is effective to nutritionally describe and classify samples and cocoa shell, soy, and green banana flours provided nutritional and sensory characteristics acceptable to consumers, making them viable for inclusion in human nutrition.

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Section: Introductionmentioning

confidence: 99%

Nutritional Clustering of Cookies Developed with Cocoa Shell, Soy, and Green Banana Flours Using Exploratory Methods

Barros

Natarelli

Tavares

et al. 2020

Food Bioprocess Technol

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show abstract

“…Artificial neural networks (ANNs), which have excellent self-learning ability and can adjust their parameters online to handle variations of the controlled system, have received increasing attention for industrial applications. With advancements in machine learning methods, ANNs has been applied to a number of industrial applications, such as the development of an occupancy prediction model [18], improvement of thermal comfort indices [19], and data measurement in temperature control systems [20][21][22][23][24]. For a thermal process system with strong nonlinearly, large lag and strong coupling, an adaptive system can improve control performance in terms of the transient response and overshoot [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A Reference-Model-Based Artificial Neural Network Approach for a Temperature Control System

Hashimoto

Jiang

et al. 2020

Processes

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Artificial neural networks (ANNs), which have excellent self-learning performance, have been applied to various applications, such as target detection and industrial control. In this paper, a reference-model-based ANN controller with integral-proportional-derivative (I-PD) compensation has been proposed for temperature control systems. To improve the ANN self-learning efficiency, a reference model is introduced for providing the teaching signal for the ANN. System simulations were carried out in the MATLAB/SIMULINK environment and experiments were carried out on a digital-signal-processor (DSP)-based experimental platform. The simulation and experimental results were compared with those for a conventional I-PD control system. The effectiveness of the proposed method was verified.

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“…To study the temperature distribution on a pallet of fruits during plastic boxes and cardboard packaging Apples ANN Thermal imaging showed the cardboard boxes to be a better packaging material for apples compared to plastic boxes [93] The statistical results demonstrated significant changes in the reference quality properties of samples before and after storage [70] 18.…”

Section: Imaging Technology Thermography Packagingmentioning

confidence: 99%

Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

et al. 2020

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Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, pre-cooling, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins for multiple shipments in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables.

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Artificial Neural Networks and Thermal Image for Temperature Prediction in Apples

Cited by 36 publications

References 24 publications

Nutritional Clustering of Cookies Developed with Cocoa Shell, Soy, and Green Banana Flours Using Exploratory Methods

Nutritional Clustering of Cookies Developed with Cocoa Shell, Soy, and Green Banana Flours Using Exploratory Methods

A Reference-Model-Based Artificial Neural Network Approach for a Temperature Control System

Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

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