Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Application of Deep Learning in Spectral Analysis

Application of Deep Learning in Spectral Analysis

Jiang, Hui
Chen, Quansheng

Paperback

Technology & Engineering

ISBN10: 6208223814
ISBN13: 9786208223816
Publisher: LAP Lambert Academic Publishing
Published: Nov 5 2024
Pages: 284
Weight: 0.92
Height: 0.64 Width: 6.00 Depth: 9.00
Language: English
The deep learning approach combined with spectroscopic sensing techniques has shown great potential for quality evaluation of food and agro-products. Current advances in deep learning-based qualitative analysis include variety identification, geographical origin detection, adulteration recognition, and bruise detection, whereas quantitative analysis includes multiple component content prediction for fruits, grains, and crops. The main advantage of deep learning approach is the decreasing the dependence on human domain knowledge by end-to-end analysis and the improved precision and generalizability. This book discusses the current challenges of conventional chemometric methods and the emerging deep learning approach for spectral analysis. The research on exploring the learning mechanism of the 'black box' deep learning model is discussed. This book focuses on the application of deep learning approaches on quality evaluation of food and agro-products, lessons from current studies, and future perspectives.

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