Spectroscopy Applications in Plant and Plant-Based Foods

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Food Science and Technology".

Deadline for manuscript submissions: 10 August 2024 | Viewed by 1171

Special Issue Editors

Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
Interests: plant phenotyping; quality inspection; spectroscopy and spectral analysis; machine learning
Dr. Wei Wang
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Guest Editor
Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Science, Xiamen 361021, China
Interests: environmental pollutant detection; plant phenotyping; heavy metal detection; laser-induced breakdown spectroscopy
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
Interests: deep learning; hyperspectral imaging; plant phenotyping
School of Information Engineering, Huzhou University, Huzhou 313000, China
Interests: spectroscopy; spectral imaging; agricultural engineering; food
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Special Issue Information

Dear Colleagues,

Under the effect of the plant-based diet trend, plant-based foods have gained widespread popularity. Plant-based foods refer to a variety of products made from fruits, vegetables, grains, legumes and pulses, etc. In recent years, spectroscopy technology has received extensive attention in the fields of plants and food. In addition, the development of imaging technology, microscopic equipment, and artificial intelligence has promoted the further application of spectroscopy technology in rapid and non-destructive detection. Noting the rapid expansion in spectroscopy, we are announcing a Special Issue of Applied Sciences entitled "Spectroscopy Applications in Plant and Plant-Based Foods". The aim is to explore the latest progress in applying spectroscopy technology to the quality and safety assessment of plant raw materials, food processing, and food products. Phenotypic information analysis, sensory quality evaluation, nutrient composition detection, microbial metabolism analysis, food functional assessment, contamination detection, etc., are encouraged. The optimization of analytical equipment and platforms, as well as innovative applications of analytical techniques and methods, are also welcomed.

Dr. Yiying Zhao
Dr. Wei Wang
Dr. Lei Zhou
Dr. Chu Zhang
Guest Editors

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Keywords

  • visible and near-infrared spectroscopy
  • mid-infrared spectroscopy
  • Raman spectroscopy
  • laser spectroscopic detection
  • multispectral and/or hyperspectral imaging
  • artificial intelligence and machine learning
  • quality and safety evaluation
  • novel analytical techniques and methods

Published Papers (1 paper)

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Research

16 pages, 2992 KiB  
Article
Rapid Discrimination of Organic and Non-Organic Leafy Vegetables (Water Spinach, Amaranth, Lettuce, and Pakchoi) Using VIS-NIR Spectroscopy, Selective Wavelengths, and Linear Discriminant Analysis
Appl. Sci. 2023, 13(21), 11830; https://doi.org/10.3390/app132111830 - 29 Oct 2023
Viewed by 829
Abstract
Organic leafy vegetables face challenges related to potential substitution with non-organic products and vulnerability to dehydration and deterioration. To address these concerns, visible and near-infrared spectroscopy (VIS-NIR) combined with linear discriminant analysis (LDA) was employed in this study to rapidly distinguish between organic [...] Read more.
Organic leafy vegetables face challenges related to potential substitution with non-organic products and vulnerability to dehydration and deterioration. To address these concerns, visible and near-infrared spectroscopy (VIS-NIR) combined with linear discriminant analysis (LDA) was employed in this study to rapidly distinguish between organic and non-organic leafy vegetables. The organic category includes organic water spinach (Ipomoea aquatica Forsskal), amaranth (Amaranthus tricolor L.), lettuce (Lactuca sativa var. ramosa Hort.), and pakchoi (Brassica rapa var. chinensis (Linnaeus) Kitamura), while the non-organic category consists of their four non-organic counterparts. Binary classification was performed on the reflectance spectra of these vegetables’ leaves and stems, respectively. Given the broad range of the VIS-NIR spectrum, stability selection (SS), random forest (RF), and analysis of variance (ANOVA) were used to evaluate the importance of the wavelengths selected by genetic algorithm (GA). According to the GA-selected wavelengths and their SS-evaluated values and locations, the significant bands for leaf spectra classification were identified as 550–910 nm and 1380–1500 nm, while 750–900 nm and 1700–1820 nm were important for stem spectra classification. Using these selected bands in the LDA classification, classification accuracies of over 95% were achieved, showcasing the effectiveness of utilizing the proposed method to rapidly identify organic leafy vegetables and the feasibility and potential of using a cost-effective spectrometer that only contains necessary bands for authenticating. Full article
(This article belongs to the Special Issue Spectroscopy Applications in Plant and Plant-Based Foods)
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