Efficient identification of Bachu mushroom by flourier transform infrared (FT-IR) spectroscopy coupled with PLS-GS-SVM

Ma, Ling; Gao, Rui; Han, Huijie; Chen, Cheng; Yan, Ziwei; Zhao, Jianyong; Lv, Xiaoyi; Chen, Chen; Xie, Lei

doi:10.1016/j.ijleo.2020.165712

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…PLS-DA is a multivariate statistical analysis method that reduces dimensionality by extracting features ( Ma et al, 2020 ). PLS-DA successfully divides the space into two regions by a straight line to classify samples.…”

Section: Machine Learningmentioning

confidence: 99%

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

Zhang,

Wang

2023

Food Chemistry: X

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Section: Machine Learningmentioning

confidence: 99%

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

Zhang,

Wang

2023

Food Chemistry: X

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“…Machine learning (ML) can be applied to the analysis of FTIR data to improve classification performance [ 38 , [57] , [58] , [59] ]. The Convolutional Neural Network (CNN) deep learning algorithm is a popular machine learning approach for analyzing spectral data.…”

Section: Introductionmentioning

confidence: 99%

Development of a machine learning model for systematics of Aspergillus section Nigri using synchrotron radiation-based fourier transform infrared spectroscopy

Nuankaew,

Boonyuen,

Thumanu

et al. 2024

Heliyon

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“…This method can eliminate the worthless spectral information in the original data, improve the modeling speed while retaining the original data to the maximum extent information contained in. Therefore, PLS is widely used in spectral analysis 9 – 11 .…”

Section: Introductionmentioning

confidence: 99%

Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey

Chen

et al. 2022

Sci Rep

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Zhejiang Suichang native honey, which is included in the list of China’s National Geographical Indication Agricultural Products Protection Project, is very popular. This study proposes a method of Raman spectroscopy combined with machine learning algorithms to accurately detect low-concentration adulterated Suichang native honey. In this study, the native honey collected by local beekeepers in Suichang was selected for adulteration detection. The spectral data was compressed by Savitzky–Golay smoothing and partial least squares (PLS) in sequence. The PLS features taken for further analysis were selected according to the contribution rate. In this study, three classification modeling methods including support vector machine, probabilistic neural network and convolutional neural network were adopted to correctly classify pure and adulterated honey samples. The total accuracy was 100%, 100% and 99.75% respectively. The research result shows that Raman spectroscopy combined with machine learning algorithms has great potential in accurately detecting adulteration of low-concentration honey.

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Efficient identification of Bachu mushroom by flourier transform infrared (FT-IR) spectroscopy coupled with PLS-GS-SVM

Cited by 12 publications

References 24 publications

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

Development of a machine learning model for systematics of Aspergillus section Nigri using synchrotron radiation-based fourier transform infrared spectroscopy

Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey

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