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Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres

Author

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  • Maged Mohammed

    (Date Palm Research Center of Excellence, King Faisal University, Al-Ahsa 31982, Saudi Arabia
    Agricultural and Biosystems Engineering Department, Faculty of Agriculture, Menoufia University, Shebin El Koum 32514, Egypt)

  • Ramasamy Srinivasagan

    (Computer Engineering Department, College of Computer Sciences & Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

  • Ali Alzahrani

    (Computer Engineering Department, College of Computer Sciences & Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

  • Nashi K. Alqahtani

    (Date Palm Research Center of Excellence, King Faisal University, Al-Ahsa 31982, Saudi Arabia
    Department of Food and Nutrition Sciences, College of Agricultural and Food Sciences, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

Abstract

The safety and quality of fresh fruits deserve the greatest attention, and are a priority for producers and consumers alike. Modern technologies are crucial to accurately estimating and predicting fresh fruits’ quality and shelf life, to optimize supply chain management. Modified atmosphere packaging (MAP) is an essential method that maintains quality parameters and increases the shelf life of fresh fruits by reducing their ripening rates. This study aimed to develop a cost-effective, non-destructive technique using tiny machine learning (TinyML) and a multispectral sensor to predict/estimate the quality parameters and shelf life of packaged fresh dates under the natural atmosphere (Control), vacuum-sealed bags (VSBs), and MAP with different gas combinations: 20% CO 2 + N balance (MAP1), and 20% CO 2 + 10% O 2 + N balance (MAP2). The shelf life and quality parameters of the packaged fresh dates (pH, total soluble solids (TSSs), sugar content (SC), moisture content (MC), and tannin content (TC)) were evaluated under different storage temperatures and times. A multispectral sensor (AS7265x) was utilized to correlate the fruit quality parameters with spectrum analysis under the same storage conditions, to prepare the dataset to train the prediction models. The prediction models were trained in the Edge Impulse Platform, and deployed to an Arduino Nano 33 BLE sense microcontroller unit (MCU) for inference. The findings indicated that the vacuum and MAP1 efficiently increased the shelf life and maintained the quality parameters of the packaged fresh fruit to 43 ± 2.39 and 39 ± 3.34 days, respectively, at 5 °C. The optimal neural network consisted of the input layer with 20 nodes (the packaging type, storage temperature, and 18 channels of the spectral sensor data at 410 to 940 nm wavelengths), two hidden layers with 20 and 12 nodes, and an output layer with one node for the target quality parameter or shelf life. These optimal prediction models efficiently predicted the shelf life with R 2 = 0.951, pH with R 2 = 0.854, TSSs with R 2 = 0.893, SC with R 2 = 0.881, MC with R 2 = 0.941, and TC with R 2 = 0.909. The evaluation of the developed prediction models under each packaging condition indicated that these models serve as powerful tools for accurately predicting fruit quality parameters, and estimating the shelf life of fresh dates.

Suggested Citation

  • Maged Mohammed & Ramasamy Srinivasagan & Ali Alzahrani & Nashi K. Alqahtani, 2023. "Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres," Sustainability, MDPI, vol. 15(17), pages 1-24, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12871-:d:1225207
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    References listed on IDEAS

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    1. Khalied Albarrak & Yonis Gulzar & Yasir Hamid & Abid Mehmood & Arjumand Bano Soomro, 2022. "A Deep Learning-Based Model for Date Fruit Classification," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
    2. Chen, Assaf & Orlov-Levin, Valerie & Meron, Moshe, 2019. "Applying high-resolution visible-channel aerial imaging of crop canopy to precision irrigation management," Agricultural Water Management, Elsevier, vol. 216(C), pages 196-205.
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    1. Aristeidis Karras & Anastasios Giannaros & Christos Karras & Leonidas Theodorakopoulos & Constantinos S. Mammassis & George A. Krimpas & Spyros Sioutas, 2024. "TinyML Algorithms for Big Data Management in Large-Scale IoT Systems," Future Internet, MDPI, vol. 16(2), pages 1-29, January.

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