Transmittance spectra and acoustic properties of durians with different ripening: An exploration of application for complex-structured and large-sized fruit

An, Changqing; Yu, Yida; Qu, Maozhen; Zhao, Yiran; Tian, Shijie; Tao, Kun; Xu, Huirong

doi:10.1016/j.postharvbio.2024.113103

Postharvest Biology and Technology

2024

DOI: 10.1016/j.postharvbio.2024.113103

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Transmittance spectra and acoustic properties of durians with different ripening: An exploration of application for complex-structured and large-sized fruit

Changqing An,

Yida Yu,

Maozhen Qu

et al.

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2024

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The Study on Nondestructive Detection Methods for Internal Quality of Korla Fragrant Pears Based on Near-Infrared Spectroscopy and Machine Learning

Che,

Liang,

Xia

et al. 2024

Foods

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Quality control and grading of Korla fragrant pears significantly impact their commercial value. Rapid and non-destructive detection of soluble solids content (SSC) and firmness is crucial to improving this. This study proposes a method combining near-infrared spectroscopy (NIRS) with machine learning for the rapid, non-destructive detection of SSC and firmness in Korla pears. By analyzing absorbance in the 900–1800 nm range, six preprocessing methods—Savitzky–Golay derivative (SGD), standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky–Golay smoothing (SGS), vector normalization (VN), and min-max normalization (MMN)—were applied to the raw spectral data. uninformative variable elimination (UVE) and successive projections algorithm (SPA) were then used to extract effective wavelengths. Partial least squares regression (PLSR) models were developed for SSC and firmness based on the extracted data. The results showed that all preprocessing and wavelength-extraction methods improved model accuracy. The optimal SSC prediction model was MSC-SPA-PLSR (R = 0.93, RMSE = 0.195), and the best hardness prediction model was MSC-UVE-PLSR (R = 0.83, RMSE = 0.249). This research aids in establishing a non-destructive testing system, offering producers a rapid and accurate quality assessment tool, and provides the food industry with better production control measures to enhance standardization and market competitiveness of Korla pears.

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The Study on Nondestructive Detection Methods for Internal Quality of Korla Fragrant Pears Based on Near-Infrared Spectroscopy and Machine Learning

Che,

Liang,

Xia

et al. 2024

Foods

View full text Add to dashboard Cite

show abstract

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Transmittance spectra and acoustic properties of durians with different ripening: An exploration of application for complex-structured and large-sized fruit

Cited by 1 publication

References 49 publications

The Study on Nondestructive Detection Methods for Internal Quality of Korla Fragrant Pears Based on Near-Infrared Spectroscopy and Machine Learning

The Study on Nondestructive Detection Methods for Internal Quality of Korla Fragrant Pears Based on Near-Infrared Spectroscopy and Machine Learning

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