A combination of near‐infrared hyperspectral imaging with two‐dimensional correlation analysis for monitoring the content of biogenic amines in mutton

Fan, Naiyun; Liu, Guishan; Wan, Guoling; Ban, Jingjing; Yuan, Ruirui; Sun, Yourui; Li, Yue

doi:10.1111/ijfs.14950

Cited by 12 publications

(8 citation statements)

References 30 publications

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“…The absorption band at 1400–1500 nm represents the first O-H stretching of COOH. The infrared spectrum in the 1600–1700 nm region was related to the amide I band of the protein, which was mainly attributed to the absorption of the C=O bond [ 17 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ].…”

Section: Resultsmentioning

confidence: 99%

“…At the same time, it was able to protect the edge data of the signal. Fan et al [ 27 ] also used MF to optimize the original NIR spectrum to determine the TBA content in Tan mutton, and obtained Rc and Rp values of 0.94 and 0.88, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, the selection of characteristic wavelength variables became a key step in spectral analysis, which was mainly used to simplify the model and eliminate data redundancy. The use of genetic algorithms, principal component algorithms, iterative algorithms, and weight algorithms to extract feature variables has been reported by a large number of researchers [ 27 , 28 , 29 ]. In this study, two weight algorithms (CARS and RC) were used to select characteristic wavelengths to develop simplified models for the full spectral (FS) area and sensitive local areas selected from 2D–COS analysis.…”

Section: Methodsmentioning

confidence: 99%

“…However, beef, as a complex food item, has interactions among various components (proteins, amino acids, lipids, and carbohydrates) [ 26 ]. This makes it difficult to extract the NIR spectral information of the meat, and the spectral signal presents overlapping and complexity [ 27 ]. As a result, quantitative analysis by NIR has relied heavily on the application of chemometric analysis to relate the subtle spectral changes to the variations in concentrations of certain components in the analyte [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

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A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef

Dong

Bi²,

Hao³

et al. 2022

Biosensors

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Alanine (Ala), as the most important free amino acid, plays a significant role in food taste characteristics and human health regulation. The feasibility of using near–infrared hyperspectral imaging (NIR–HSI) combined with two–dimensional correlation spectroscopy (2D–COS) analysis to predict beef Ala content quickly and nondestructively is first proposed in this study. With Ala content as the external disturbance condition, the sequence of chemical bond changes caused by synchronous and asynchronous correlation spectrum changes in 2D–COS was analyzed, and local sensitive variables closely related to Ala content were obtained. On this basis, the simplified linear, nonlinear, and artificial neural network models developed by the weighted coefficient based on the feature wavelength extraction method were compared. The results show that with the change in Ala content in beef, the double-frequency absorption of the C-H bond of CH2 in the chemical bond sequence occurred prior to the third vibration of the C=O bond and the first stretching of O-H in COOH. Furthermore, the wavelength within the 1136–1478 nm spectrum range was obtained as the local study area of Ala content. The linear partial least squares regression (PLSR) model based on effective wavelengths was selected by competitive adaptive reweighted sampling (CARS) from 2D–COS analysis, and provided excellent results (R2C of 0.8141, R2P of 0.8458, and RPDp of 2.54). Finally, the visual distribution of Ala content in beef was produced by the optimal simplified combination model. The results show that 2D–COS combined with NIR–HSI could be used as an effective method to monitor Ala content in beef.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef

Dong

Bi²,

Hao³

et al. 2022

Biosensors

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show abstract

“…Kamruzzaman et al [ 50 ] used HSI and pattern recognition algorithm for different red meat species classification and yielded 98.67% overall classification accuracy. Fan et al [ 51 ] combined HSI with two-dimensional correlation analysis for determining the biogenic amines in mutton during refrigerate storage, and correlation coefficient of 0.91 in prediction set was acquired. As summarized above, previous studies were mainly focused on raw meat samples, however, samples after cooking were rarely covered.…”

Section: Resultsmentioning

confidence: 99%

Chemometrics in Tandem with Hyperspectral Imaging for Detecting Authentication of Raw and Cooked Mutton Rolls

Jiang

Shi

2021

Foods

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Authentication assurance of meat or meat products is critical in the meat industry. Various methods including DNA- or protein-based techniques are accurate for assessing meat authenticity, however, they are destructive, expensive, or laborious. This study explores the feasibility of chemometrics in tandem with hyperspectral imaging (HSI) for identifying raw and cooked mutton rolls substitution by pork and duck rolls. Raw or cooked samples (n = 180) of three meat species were prepared to collect hyperspectral images in range of 400–1000 nm. Spectra were extracted from representative regions of interest (ROIs), and spectral principal component analysis (PCA) revealed that PC1 and PC2 were effective for the identification. Different methods including standard normal variable (SNV), first and second derivatives, and normalization were individually employed for spectral preprocessing, and modeling methods of partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) were also individually applied to develop classification models for both the raw and the cooked. Results showed that PLS-DA model developed by raw spectra presented the highest 100% correct classification rate (CCR) of success in all sets. After that, effective wavelengths selected by successive projections algorithm (SPA) built optimal simplified models which didn’t influence the modeling results compared with full spectra regardless of the meat roll states. Therefore, SPA-PLS-DA models were subsequently used to visualize the raw and cooked meat rolls classification. As a consequence, the general meat species of both raw and cooked meat rolls were readily discernible in pixel-wise manner by generating classification maps. The results showed that HSI combined with chemometrics can be used to identify the authentication of raw and cooked mutton rolls substituted by pork and duck rolls accurately. This promising methodology provides a reference which can be extended to the classification or grading of other meat rolls.

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Predicting the adsorption of ammonia nitrogen by biochar in water bodies using machine learning strategies: Model optimization and analysis of key characteristic variables

Xie,

Zhu,

et al. 2025

Environmental Research

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A combination of near‐infrared hyperspectral imaging with two‐dimensional correlation analysis for monitoring the content of biogenic amines in mutton

Cited by 12 publications

References 30 publications

A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef

A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef

Chemometrics in Tandem with Hyperspectral Imaging for Detecting Authentication of Raw and Cooked Mutton Rolls

Predicting the adsorption of ammonia nitrogen by biochar in water bodies using machine learning strategies: Model optimization and analysis of key characteristic variables

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