Quantitative study of impact damage on yellow peaches based on reflectance, absorbance and Kubelka–Munk spectral data

Li, Bin; Zhang, Feng; Liu, Yande; Yin, Hai; Zou, Jiping; Ouyang, Aiguo

doi:10.1039/d2ra04635k

Cited by 4 publications

(2 citation statements)

References 56 publications

(70 reference statements)

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“…Xu et al 16 established a PLSR model by combining the spectral data collected in the wavelength range of 900–1700 nm with the mechanical parameters measured by pressure sensitive film technology, and the quantitative prediction of mechanical parameters was realized. Li et al 17 established PLSR and SVR models by combining mechanical parameters obtained from pendulum collision device and intelligent data acquisition system with reflectance, absorbance, and Kubelka‐Munk spectra. The quantitative prediction of impact damage degree of yellow peaches was realized.…”

Section: Introductionmentioning

confidence: 99%

Quantitative study on impact damage of honey peaches based on reflection, absorbance, and Kubelka‐Munk spectrum combined with color characteristics

Li,

Zou,

et al. 2024

Journal of Chemometrics

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Impact damage is one of the key factors affecting the quality of honey peaches. Quantitative study of impact damage is of great significance for the sorting of postharvest quality of honey peaches. In order to realize the quantitative prediction of impact damage of honey peaches, the impact damage of honey peaches was quantitatively studied based on the fusion of color characteristics with reflection spectra (R), absorbance spectra (A), and Kubelka‐Munk spectra (K‐M). The mechanical parameters of honey peaches during collision were obtained using a single pendulum collision device. Reflectance spectra and color characteristics of damaged honey peaches were obtained by a hyperspectral imaging system. The R spectra were converted into A and K‐M spectra, and the partial least squares regression (PLSR) model was built based on the three spectra and the three spectra combined with color characteristics for quantitative prediction of mechanical parameters. The results show that the prediction performance of the PLSR model is improved by combining color features with spectral information. In order to eliminate the redundant information in the spectral data, the competitive adaptive reweighted sampling (CARS) algorithm was used to select the characteristic wavelengths of the three spectra, and the selected characteristic wavelengths were fused with the color features to establish the PLSR model. The results show that the PLSR model built by the characteristic wavelengths of the A spectrum combined with the color features has the best prediction performance for the mechanical parameters. The RP value for maximum force is 0.862, and the RP value for damage depth is 0.894. The results of this study not only provide the theoretical support for the quality sorting, packaging, and transportation of honey peaches but also provide the reference for the biomechanical properties of various agricultural products.

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Section: Introductionmentioning

confidence: 99%

Quantitative study on impact damage of honey peaches based on reflection, absorbance, and Kubelka‐Munk spectrum combined with color characteristics

Li,

Zou,

et al. 2024

Journal of Chemometrics

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“…In recent years, the combination of spectroscopy and computer vision technology has facilitated the advancement of hyperspectral imaging (HSI) and has garnered significant attention in various practical applications. Predicting crop growth and yield [ 7 ], detecting active ingredient content in drugs, and quality testing of fruits [ 8 ], meat [ 9 ], and fish products are some of the applications where HSI has proven to be valuable. Among these, HSI research in meat products has been particularly extensive.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Aging and Edible Safety of Pork in Postmortem Storage Based on HSI and Wavelet Transform

Xie,

Zhang,

et al. 2024

Foods

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The process of meat postmortem aging is a complex one, in which improved tenderness and aroma coincide with negative effects such as water loss and microbial growth. Determining the optimal postmortem storage time for meat is crucial but also challenging. A new visual monitoring technique based on hyperspectral imaging (HSI) has been proposed to monitor pork aging progress. M. longissimus thoracis from 15 pigs were stored at 4 °C for 12 days while quality indexes and HSI spectra were measured daily. Based on changes in physical and chemical indicators, 100 out of the 180 pieces of meat were selected and classified into rigor mortis, aged, and spoilt meat. Discrete wavelet transform (DWT) technology was used to improve the accuracy of classification. DWT separated approximate and detailed signals from the spectrum, resulting in a significant increase in classification speed and precision. The support vector machine (SVM) model with 70 band spectra achieved remarkable classification accuracy of 97.06%. The study findings revealed that the aging and microbial spoilage process started at the edges of the meat, with varying rates from one pig to another. Using HSI and visualization techniques, it was possible to evaluate and portray the postmortem aging progress and edible safety of pork during storage. This technology has the potential to aid the meat industry in making informed decisions on the optimal storage and cooking times that would preserve the quality of the meat and ensure its safety for consumption.

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Wavelength and texture feature selection for hyperspectral imaging: a systematic literature review

Rogers,

Blanc-Talon,

Urschler

et al. 2023

Food Measure

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Over the past two decades, hyperspectral imaging has become popular for non-destructive assessment of food quality, safety, and crop monitoring. Imaging delivers spatial information to complement the spectral information provided by spectroscopy. The key challenge with hyperspectral image data is the high dimensionality. Each image captures hundreds of wavelength bands. Reducing the number of wavelengths to an optimal subset is essential for speed and robustness due to the high multicollinearity between bands. However, there is yet to be a consensus on the best methods to find optimal subsets of wavelengths to predict attributes of samples. A systematic review procedure was developed and applied to review published research on hyperspectral imaging and wavelength selection. The review population included studies from all disciplines retrieved from the Scopus database that provided empirical results from hyperspectral images and applied wavelength selection. We found that 799 studies satisfied the defined inclusion criteria and investigated trends in their study design, wavelength selection, and machine learning techniques. For further analysis, we considered a subset of 71 studies published in English that incorporated spatial/texture features to understand how previous works combined spatial features with wavelength selection. This review ranks the wavelength selection techniques from each study to generate a table of the comparative performance of each selection method. Based on these findings, we suggest that future studies include spatial feature extraction methods to improve the predictive performance and compare them to a broader range of wavelength selection techniques, especially when proposing novel methods.

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Quantitative study of impact damage on yellow peaches based on reflectance, absorbance and Kubelka–Munk spectral data

Abstract: This study compared the quantitative predictive ability of three kinds of spectra for mechanical parameters. In summary, K–M spectra combined with the PLSR model can be used to accurately predict the mechanical parameters of impact damage.

Cited by 4 publications

References 56 publications

Quantitative study on impact damage of honey peaches based on reflection, absorbance, and Kubelka‐Munk spectrum combined with color characteristics

Quantitative study on impact damage of honey peaches based on reflection, absorbance, and Kubelka‐Munk spectrum combined with color characteristics

Monitoring the Aging and Edible Safety of Pork in Postmortem Storage Based on HSI and Wavelet Transform

Wavelength and texture feature selection for hyperspectral imaging: a systematic literature review

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