Rapid determination of TBARS content by hyperspectral imaging for evaluating lipid oxidation in mutton

Fan, Naiyun; Ma, Xiang; Ban, Jingjing; Yuan, Ruirui; Sun, Yourui

doi:10.1016/j.jfca.2021.104110

Cited by 28 publications

(12 citation statements)

References 35 publications

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“…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%

“…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 ]. In previous studies, the derivative method, weight value method, principal component analysis, and feature variable extraction were the main spectral response analysis methods for studying the spectral characteristic variables of objects [ 29 , 30 ]. It usually requires a lot of experiments to identify sensitive variables and build a stable model effect.…”

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: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…According to Fan et al ( 19 ), the content of thiobarbituric acid reactive substances (TBARS) in sample were determined by CN61M-752 spectrophotometer (Haifuda Instrument Co., LTD.Beijing, China). The results of TBARS value were expressed as milligrams of malondialdehyde (MAD) per kg sample.…”

Section: Methodsmentioning

confidence: 99%

Effect of Traditional Stir-Frying on the Characteristics and Quality of Mutton Sao Zi

et al. 2022

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The effects of stir-frying stage and time on the formation of Maillard reaction products (MRP) and potentially hazardous substances with time in stir-fried mutton sao zi were investigated. Furosine, fluorescence intensity, Nε-(1-carboxymethyl)-L-lysine (CML), Nε-(1-carboxyethyl)-L-lysine (CEL), polyaromatic hydrocarbons PAHs), heterocyclic aromatic amines (HAAs), and acrylamides (AA) mainly presented were of stir-fried mutton sao zi. The furosine decreased after mixed stir-frying (MSF) 160 s due to its degradation as the Maillard reaction (MR) progressed. The fluorescent compound gradually increased with time during the stir-frying process. The CML and CEL peaked in MSF at 200 s. AA reached its maximum at MSF 120 s and then decreased. All the 5 HAAs were detected after MSF 200 s, suggesting that stir-frying mutton sao zi was at its best before MSF for 200 s. When stir-frying exceeded the optimal processing time of (MSF 160 s) 200 s, the benzo[a]pyrene peaked at 0.82 μg/kg, far lower than the maximum permissible value specified by the Commission of the European Communities. Extended stir-frying promoted MRP and some hazardous substances, but the content of potentially hazardous substances was still within the safety range for food.

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“…This makes it more advantageous than the single spectroscopic and imaging techniques in food safety detection and control. Therefore, the hyperspectral imaging technology is widely used in the detection of different substances, such as the total acid, moisture, sugar content of Daqu (Huang, Hu, Tian, Chen, & Huang, 2021; Huang, Hu, Tian, Jiang, Luo, & Huang, 2021; Jiang et al, 2021), oil content of corn kernels (Zhang et al, 2022), stearic acid content, TBARS content of mutton and the fat content of peanut kernels (Fan et al, 2021; Sun et al, 2020; Wang, Wang, et al, 2021). This hyperspectral imaging technology obtained satisfactory results in the detection of substance content, which demonstrates its feasibility for the detection of the starch content of mixed sorghum.…”

Section: Introductionmentioning

confidence: 99%

Rapid and accurate detection of starch content in mixed sorghum by hyperspectral imaging combined with data fusion technology

Jiang

Tian

et al. 2022

J Food Process Engineering

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Sorghum is a type of brewing material. The starch content of different kinds of mixed sorghum affect the quality and yield of liquor. Therefore, an accurate and efficient detection of its starch content is of great significance to obtain high‐quality and high‐yield of liquor. Based on the data of single visible light (Vis), near‐infrared (NIR), and Vis and NIR fusion, this study develops a genetic algorithm optimization BP neural network (GA‐BPNN) model and a particle swarm optimization support vector machine model. It is deduced that the model based on the data from the Vis and NIR fusion has the highest performance in predicting the starch content of mixed sorghum. For the prediction of amylose, the GA‐BPNN model developed after using the Pearson correlation coefficient to select the characteristic wavelength for fusion, has the highest performance (RMSEP = .0298 and = .9948). For the prediction of amylopectin, the GA‐BPNN model developed after fusion of spectral features extracted by principal component analysis, has the highest performance (RMSEP = .0213 and = .9985). In conclusion, the hyperspectral imaging combined with data fusion can rapidly and accurately detect the starch content of mixed sorghum.Practical ApplicationsAs the single raw material of Maotai‐flavor liquor and the main raw material of Luzhou‐flavor liquor, the amylopectin content of sorghum directly affects the quality of liquor. Its amylose content also affects the yield of liquor. Because the different varieties of sorghum have different amylose and amylopectin content, liquor manufacturers often use several mixed varieties of sorghum as a brewing material. In order to ensure the quality and yield of liquor, it is particularly crucial to detect the amylose and amylopectin content variables of blended sorghum. The traditional sorghum starch content determination methods, such as the chemical detection methods and near infrared spectroscopy, have the disadvantages of destructiveness, low efficiency, and low detection accuracy. In the process of liquor brewing, the traditional detection methods cannot guide the timely adjustment of brewing process parameters. The hyperspectral imaging technology is widely used in food material detection, due to its fast and nondestructive advantages. The data fusion technology can fuse data from different sources of the same object to be detected, in order to obtain more comprehensive data for the development of accurate prediction models. This study uses the hyperspectral imaging combined with data fusion in order to quickly and accurately predicts the starch content (amylose, amylopectin) of mixed sorghum, which has a guiding significance for the timely adjustment of process parameters in the brewing process of liquor. In addition, it provides an accurate method for the component detection of other grains.

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Rapid determination of TBARS content by hyperspectral imaging for evaluating lipid oxidation in mutton

Cited by 28 publications

References 35 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

Effect of Traditional Stir-Frying on the Characteristics and Quality of Mutton Sao Zi

Rapid and accurate detection of starch content in mixed sorghum by hyperspectral imaging combined with data fusion technology

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