Rapid detection of adulteration of minced beef using Vis/NIR reflectance spectroscopy with multivariate methods

Weng, Shizhuang; Guo, Bingqing; Tang, Peipei; Yin, Xun; Pan, Fangfang; Zhao, Jing; Huang, Liusheng; Zhang, Dongyan

doi:10.1016/j.saa.2019.118005

Cited by 59 publications

(27 citation statements)

References 37 publications

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“…The near-infrared (NIR) region covers wavelengths from 780 to 2500 nm, which is consistent with the overtone and combination band of hydrogen-containing groups (O-H, C-H, and N-H) [10,11]. As a rapid and nondestructive analysis technique [12,13], NIRS has been widely applied to explore the inner information of samples.…”

Section: Introductionmentioning

confidence: 76%

An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy

Zou

Liu

Lei

et al. 2021

Entropy

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Effective and rapid assessment of pork freshness is significant for monitoring pork quality. However, a traditional sensory evaluation method is subjective and physicochemical analysis is time-consuming. In this study, the near-infrared spectroscopy (NIRS) technique, a fast and non-destructive analysis method, is employed to determine pork freshness. Considering that commonly used statistical modeling methods require preprocessing data for satisfactory performance, this paper presents a one-dimensional squeeze-and-excitation residual network (1D-SE-ResNet) to construct the complex relationship between pork freshness and NIRS. The developed model enhances the one-dimensional residual network (1D-ResNet) with squeeze-and-excitation (SE) blocks. As a deep learning model, the proposed method is capable of extracting features from the input spectra automatically and can be used as an end-to-end model to simplify the modeling process. A comparison between the proposed method and five popular classification models indicates that the 1D-SE-ResNet achieves the best performance, with a classification accuracy of 93.72%. The research demonstrates that the NIRS analysis technique based on deep learning provides a promising tool for pork freshness detection and therefore is helpful for ensuring food safety.

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Section: Introductionmentioning

confidence: 76%

An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy

Zou

Liu

Lei

et al. 2021

Entropy

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“…Principal component analysis (PCA) is a commonly used multivariate statistical method [ 13 ] that is performed by generating a set of principal components that are linear transformations of the original variables; these new principal components are orthogonal to each other and ranked according to the explained variance [ 31 ]. PCA scoring plots are often used for visualization and can provide a clear view of the sample distribution.…”

Section: Methodsmentioning

confidence: 99%

Distinguishing Different Varieties of Oolong Tea by Fluorescence Hyperspectral Technology Combined with Chemometrics

Sun

et al. 2022

Foods

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Oolong tea is a semi-fermented tea that is popular among people. This study aims to establish a classification method for oolong tea based on fluorescence hyperspectral technology(FHSI) combined with chemometrics. First, the spectral data of Tieguanyin, Benshan, Maoxie and Huangjingui were obtained. Then, standard normal variation (SNV) and multiple scatter correction (MSC) were used for preprocessing. Principal component analysis (PCA) was used for data visualization, and with tolerance ellipses that were drawn according to Hotelling, outliers in the spectra were removed. Variable importance for the projection (VIP) > 1 in partial least squares discriminant analysis (PLS–DA) was used for feature selection. Finally, the processed spectral data was entered into the support vector machine (SVM) and PLS–DA. MSC_VIP_PLS–DA was the best model for the classification of oolong tea. The results showed that the use of FHSI could accurately distinguish these four types of oolong tea and was able to identify the key wavelengths affecting the tea classification, which were 650.11, 660.29, 665.39, 675.6, 701.17, 706.31, 742.34 and 747.5 nm. In these wavelengths, different kinds of tea have significant differences (p < 0.05). This study could provide a non-destructive and rapid method for future tea identification.

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“…Yang et al (2018) classified beef, pork, and mutton samples with 100% accuracy using SVM. Weng et al (2020) used a portable NIR spectrometer and deep convolutional neural network (DCNN) to successfully identify beef adulterated with pork (R 2 p = 0.94). Revilla et al (2020) correctly classified 84% of dry-cured beef samples of a Protected Geographic Indication quality label and 100% of non-Protected Geographic Indication, using a benchtop NIR spectrometer and artificial neural network.…”

Section: Near Infrared Spectroscopymentioning

confidence: 99%

Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review

Leighton

Marcoux

Wei

et al. 2022

Meat and Muscle Biology

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Consumer demand for high-quality healthy food is increasing,  thus meat processors require the means toassess these rapidly, accurately, and inexpensively. Traditional methods forquality assessments are time-consuming, expensive, invasive, and have potentialto negatively impact the environment. Consequently, emphasis has been put onfinding non-destructive, fast, and accurate technologies for productcomposition and quality evaluation. Research in this area is advancing rapidlythrough recent developments in the areas of portability, accuracy, and machinelearning. The present review, therefore, critically evaluates and summarizes developmentsof popular non-invasive technologies (i.e., from imaging to spectroscopicsensing technologies) for estimating beef, pork, and lamb composition andquality, which will hopefully assist in the implementation of thesetechnologies for rapid evaluation/real-timegrading of livestock products in the nearfuture.

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Rapid detection of adulteration of minced beef using Vis/NIR reflectance spectroscopy with multivariate methods

Cited by 59 publications

References 37 publications

An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy

An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy

Distinguishing Different Varieties of Oolong Tea by Fluorescence Hyperspectral Technology Combined with Chemometrics

Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review

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