Huirong Xu’s research while affiliated with Zhejiang University and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (69)


Characterize Firmness Changes of Nectarine and Peach Fruit Associated With Harvest Maturity and Storage Duration Using Parameters of Force–Displacement Curves
  • Article

January 2025

·

13 Reads

Journal of Texture Studies

Xuan Luo

·

Yiran Zhao

·

Shijie Tian

·

[...]

·

Huirong Xu

Fruit firmness is a critical attribute for evaluating the quality of peaches and nectarines. The precise measurement of fruit firmness plays a key role in maturity assessment, determining harvest periods, and predicting shelf‐life. Texture analyzers are increasingly employed for accurate fruit firmness measurement, offering advantages in reducing operator errors compared to the traditional Magness–Taylor test. However, the parameters defining firmness vary across the literatures. To optimize the parameter(s) for characterizing the firmness of peaches and nectarines, we evaluated 31 parameters derived from force–displacement curves using fruit with various maturity levels and storage durations. Our findings affirmed that the conventional Magness–Taylor measurement effectively delineated firmness changes associated with varying maturity levels, while its ability to capture firmness changes due to storage duration was constrained. On the contrary, parameters extracted from the steady phase (P2), which depict flesh properties after penetrating to a specified depth, exhibited strong performance across diverse maturity stages and storage durations. As effective firmness characterization parameters should differentiate various sample groups based on both maturity and storage duration criteria—pivotal factors influencing softening, the P2‐derived parameters are thus deemed more appropriate for firmness characterization. Given the stability of the steady phase (P2) within the force–displacement curve and the high correlation among the P2‐derived parameters, it is more recommended to use the end force value of P2, corresponding to the force value at 10 mm depth to represent the firmness of peaches and nectarines.







Figure 4 Spectra profile of raw data and preprocessing with SNV in different batches
Figure 5 Monte Carlo Sampling (MCS) outlier detection of the in-line detection set
Figure 8 Results of wavelength selection by CARS-PLSR of in-line data.
Descriptive statistics of the pomelo datasets in three years
Model performance of PLSR with different wavelength selection methods in the range of 500-1000 nm
Modeling method for SSC prediction in pomelo using Vis-NIRS with wavelength selection and latent variable updating
  • Article
  • Full-text available

March 2024

·

6 Reads

International Journal of Agricultural and Biological Engineering

Download




Citations (47)


... Technologies such as near-infrared spectroscopy [8], hyperspectral imaging [9], electrical property detection [10], and acoustic property detection [11] had all been applied to the rapid, nondestructive testing of quality indicators in fruits and vegetables, demonstrating significant potential. Among them, hyperspectral imaging showed strong detection capabilities and could capture comprehensive spectral information. ...

Reference:

The Study on Nondestructive Detection Methods for Internal Quality of Korla Fragrant Pears Based on Near-Infrared Spectroscopy and Machine Learning
Transmittance spectra and acoustic properties of durians with different ripening: An exploration of application for complex-structured and large-sized fruit
  • Citing Article
  • November 2024

Postharvest Biology and Technology

... RAN components that are open to the public speed up the delivery of new services to users. Intelligent RAN improves accuracy in managing network complexity and decreases human intervention in the loop shown in Figure 2 [33]. The optimal solution was determined from a given range of values using DRL for congestion control, which may have been a local optimum as opposed to the intended global optimum [34]. ...

Biomimetic leaves with immobilized catalase for machine learning-enabled validating fresh produce sanitation processes
  • Citing Article
  • January 2024

... A flexible gripper has obvious advantages in grasping flexible objects, and the force sensing function is required for the complete flexible grasping of soft objects. Jin [22] designed a two-finger gripper based on fin-shaped flexible gripper. The pressure sensor and bending sensor were placed on the inner and outer surface of each flexible gripper, and the force-sensitive resistance sensor was pasted on the inner surface of the flexible gripper to detect pressure and deformation thus realizing the evaluation of kiwi fruit hardness. ...

Grasping perception and prediction model of kiwifruit firmness based on flexible sensing claw
  • Citing Article
  • December 2023

Computers and Electronics in Agriculture

... To validate the performance of the candy defect recognition detection method proposed in this paper, we trained five advanced object recognition detection algorithms under the same dataset and parameter settings conditions, including Faster R-CNN, YOLOv5, YOLOX, YOLOv7, and the improved YOLOv7 proposed in this paper [33][34][35][36]. During the training phase, the parameters of these models were uniform and are shown in Table 2. ...

MSDD-YOLOX: An enhanced YOLOX for real-time surface defect detection of oranges by type
  • Citing Article
  • September 2023

European Journal of Agronomy

... Among the 30 test samples, 25 were correctly predicted, resulting in an overall classification accuracy of 83.33%, which is generally sufficient for the rapid, nondestructive field detection of longan SSC classification. Among the five misclassified samples (numbers 8,11,17,21,26), three samples had SSC values of 21.20% (sample 11), 20.75% (sample 17), and 18.80% (sample 26), which are very close to the classification thresholds of 21% and 19%, respectively. This proximity to the critical thresholds may have been the primary cause of misclassification. ...

Improving the prediction performance of soluble solids content (SSC) in kiwifruit by means of near-infrared spectroscopy using slope/bias correction and calibration updating
  • Citing Article
  • May 2023

Food Research International

... It serves as a fundamental dietary component in numerous developed and developing nations, contributing to its status as a staple food. Potatoes are ingested in their uncooked state as a fundamental sustenance or vegetable, transformed into French fries, crisps, and additional culinary enhancements, and employed in the production of potato flour, starch, and alcohol [1]. According to the data provided by the Food and Agriculture Organization (FAO), the global production of potatoes amounted to a significant quantity of 376 million metric tons. ...

Evaluation of dry matter content in intact potatoes using different optical sensing modes

Journal of Food Measurement and Characterization

... These methods are often time-consuming, costly in terms of equipment and reagents, prone to human error, have a limited throughput, and are laborious and limited in obtaining real-time data, hindering rapid and efficient research analysis [4]. Additionally, conventional techniques usually entail destructive sampling, which makes it difficult to study the same sample again or to monitor changes over time [5]. ...

Vis/NIR model development and robustness in prediction of potato dry matter content with influence of cultivar and season
  • Citing Article
  • March 2023

Postharvest Biology and Technology

... Moreover, like other fruits, the postharvest ripening process within the Actinidia genus exhibits botanical diversity, as noted by Garcia et al. [16]. Although numerous studies have delved into kiwifruit ripening, particularly in varieties with red hearts or green flesh, there has been a dearth of research into the regulatory mechanisms governing the ripening and senescence of yellow-fleshed kiwifruit [17,18]. Thus, elucidating the key molecular factors and metabolites that regulate postharvest ripening and senescence in fruits is crucial. ...

Establishment of evaluation criterion based on starch dyeing method and implementation of optical and acoustic techniques for postharvest determination of “HongYang” kiwifruit ripeness
  • Citing Article
  • January 2023

European Journal of Agronomy

... The remaining 40 samples were placed in cold storage at 0 • C ± 1 • C for 30 days, and then at room temperature for 3 days. Ten of these samples were selected to determine the post-ripening quality (SSC, TA, and peel color) and the edible rate [33]. The remaining 30 samples were kept at room temperature for observation, and the rot rate was calculated on day 7 [2]. ...

Non-destructive evaluation of the edible rate for pomelo using X-ray imaging method
  • Citing Article
  • September 2022

Food Control

... Various machine learning methods have been developed and combined with other methods to identify the geographical origin of food such as peaches, Chinese Longjing tea, and Pu'er tea, including VIS-NIR, fluorescence spectroscopy, image-processing technology, 1 H nuclear magnetic resonance spectroscopy, hyperspectral imaging (HIS) technology [16][17][18]. Chen et al. [19] have proposed a method for identifying the adulteration of camellia oil and quantifying the level of adulteration using excitation-emission matrix spectroscopy and a CNN. Wu et al. [20] have proposed a method for detecting the adulteration of nine types of vegetable oils using three-dimensional (3D) fluorescence spectroscopy and a CNN. ...

Identification of the geographic origin of peaches by VIS-NIR spectroscopy, fluorescence spectroscopy and image processing technology
  • Citing Article
  • August 2022

Journal of Food Composition and Analysis