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Biosensors and Spectroscopic Techniques for Agricultural Product Safety and Quality Monitoring

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Prof. Dr. Lijuan Xie

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College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
Interests: nondestructive detection of internal quality and safety of agricultural products and food; detection of transgenic protein, heavy metal residue, antibiotic residual, biological materials and food internal quality based on terahertz and near-infrared spectroscopy

Special Issue Information

Dear Colleagues,

In recent years, researchers have started to pursue research on new techniques for agricultural products and food detection since understanding that argo-food is essential to human life and the development of the food industry. Compared to traditional rigid devices, biosensing and optical approaches are more flexible, lightweight, and non-invasive, which significantly helps lessen damage to fragile products. They offer excellent sensitivity and a wider range of functions. The emergence of new functional materials and sensing techniques aids in the more accurate and effective detection of targets via extensive indicators.

This Special Issue, therefore, aims to gather original research, review and patent analysis articles on recent advances, technologies, solutions, applications, trends and challenges in the field of techniques for agricultural product safety and quality monitoring.

Prof. Dr. Lijuan Xie
Guest Editor

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Keywords

sensing
biosensors
spectroscopy
agricultural product quality
agricultural product safety
food
fruit
nondestructive detection

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13 pages, 2300 KiB

Open AccessArticle

Prediction of Soluble-Solid Content in Citrus Fruit Using Visible–Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm

by Min-Jee Kim, Woo-Hyeong Yu, Doo-Jin Song, Seung-Woo Chun, Moon S. Kim, Ahyeong Lee, Giyoung Kim, Beom-Soo Shin and Changyeun Mo

Sensors 2024, 24(5), 1512; https://doi.org/10.3390/s24051512 - 26 Feb 2024

Cited by 1 | Viewed by 758

Abstract

Citrus fruits were sorted based on external qualities, such as size, weight, and color, and internal qualities, such as soluble solid content (SSC), acidity, and firmness. Visible and near-infrared (VNIR) hyperspectral imaging techniques were used as rapid and nondestructive techniques for determining the internal quality of fruits. The applicability of the VNIR hyperspectral imaging technique for predicting the SSC in citrus fruits was evaluated in this study. A VNIR hyperspectral imaging system with a wavelength range of 400–1000 nm and 100 W light source was used to acquire hyperspectral images from citrus fruits in two orientations (i.e., stem and calyx ends). The SSC prediction model was developed using partial least-squares regression (PLSR). Spectrum preprocessing, effective wavelength selection through competitive adaptive reweighted sampling (CARS), and outlier detection were used to improve the model performance. The performance of each model was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). In the present study, the PLSR model was developed using only a citrus cultivar. The SSC prediction CARS-PLSR model with outliers removed exhibited R² and RMSE values of approximatively 0.75 and 0.56 °Brix, respectively. The results of this study are expected to be useful in similar fields such as agricultural and food post-harvest management, as well as in the development of an online system for determining the SSC of citrus fruits. Full article

(This article belongs to the Special Issue Biosensors and Spectroscopic Techniques for Agricultural Product Safety and Quality Monitoring)

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