Recent Advances in Emerging Techniques for Non-Destructive Detection of Food Quality and Safety (2nd Edition)

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Quality and Safety".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 198

Special Issue Editor


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Guest Editor
1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
2. High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang, China
Interests: nondestructive detection; hyperspectral imaging technology; spectroscopy; electronic nose; chemometrics; machine learning
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Special Issue Information

Dear Colleagues,

Currently, the issue of food safety and quality is a great public concern. The non-destructive detection technique (NDDT) has emerged as a powerful analytical tool in the food industries. In order to satisfy the demands of consumers and obtain superior food qualities, NDDT methods are required for quality evaluation. NDDT methods (such as near- and mid-infrared spectroscopy (NIRS), Raman spectroscopy, fluorescence spectroscopy (FS), hyperspectral imaging (HSI), terahertz spectroscopy, X-ray imaging methods, and thermal imaging) have provided interesting and promising results in detecting a variety of foods.

The NDDT allows for the simultaneous measurement of chemical data from food without destruction of the substance. Additionally, the NDDT can obtain both quantitative and qualitative data at the same time without separate analyses. This Special Issue aims to collect recent and novel applications of NDDT methods in relation to food quality and safety.

Prof. Dr. Xiaohong Wu
Guest Editor

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Keywords

  • non-destructive detection technique
  • near-infrared spectroscopy
  • mid-infrared spectroscopy
  • Raman spectroscopy
  • terahertz spectroscopy
  • hyperspectral imaging
  • X-ray imaging
  • thermal imaging
  • machine vision
  • electronic nose

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Published Papers (1 paper)

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Research

21 pages, 3947 KiB  
Article
Combining Feature Extraction Methods and Categorical Boosting to Discriminate the Lettuce Storage Time Using Near-Infrared Spectroscopy
by Xuan Zhou, Xiaohong Wu, Zhihang Cao and Bin Wu
Foods 2025, 14(9), 1601; https://doi.org/10.3390/foods14091601 - 1 May 2025
Abstract
Lettuce is a kind of nutritious leafy vegetable. The lettuce storage time has a significant impact on its nutrition and taste. Therefore, to classify lettuce samples with different storage times accurately and non-destructively, this study built classification models by combining several feature extraction [...] Read more.
Lettuce is a kind of nutritious leafy vegetable. The lettuce storage time has a significant impact on its nutrition and taste. Therefore, to classify lettuce samples with different storage times accurately and non-destructively, this study built classification models by combining several feature extraction methods and categorical boosting (CatBoost). Firstly, the near-infrared (NIR) spectral data of lettuce samples were collected using a NIR spectrometer, and then they were preprocessed using six preprocessing methods. Next, feature extraction was carried out on the spectral data using approximate linear discriminant analysis (ALDA), common-vector linear discriminant analysis (CLDA), maximum-uncertainty linear discriminant analysis (MLDA), and null-space linear discriminant analysis (NLDA). These four feature extraction methods can solve the problem of small sample sizes. Finally, the classification was achieved using classification and regression trees (CARTs) and CatBoost, respectively. The experimental results showed that the classification accuracy of NLDA combined with CatBoost could reach 97.67%. Therefore, the combination of feature extraction methods (NLDA) and CatBoost using NIR spectroscopy is an effective way to classify lettuce storage time. Full article
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