Detection of adulteration with duck meat in minced lamb meat by using visible near-infrared hyperspectral imaging

Zheng, Xiaochun; Li, Yongyu; Wei, Wensong; Peng, Yankun

doi:10.1016/j.meatsci.2018.11.005

Cited by 93 publications

(41 citation statements)

References 27 publications

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“…where N is the total number of samples; N right is the number of rightly classified samples; N wrong is the number of wrongly classified samples. For the quantitative analysis, the predictive ability of the quantitative methods was assessed using the coefficient of determination of calibration (R 2 C ), the coefficient of determination of prediction (R 2 P ), and the root mean squared errors of the calibration and prediction (RMSEC and RMSEP) (Quelal-vásconez et al, 2019;Zheng et al, 2019). And, the leave-one-out cross-validation was used to evaluate whether the model has overfitting, so as to ensure the reliability and stability of the model.…”

Section: Model Performance Evaluationmentioning

confidence: 99%

Detection of fraud in high‐quality rice by near‐infrared spectroscopy

Liu

Peng

et al. 2020

Journal of Food Science

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A key feature of food fraud is the use of a lower value ingredient to imitate an authentic product. This study was based on near‐infrared spectroscopy (NIRS) analysis technology, partial least squares discriminant analysis (PLS‐DA), and a support vector machine (SVM) to detect whether high‐quality rice was mixed with other varieties of rice. As an aid to qualitative discrimination, PLS was used to establish the quantitative analysis model to assist in the recognition of the degree of fraud. Due to the direct correlation between the results of NIRS analysis and the homogeneity of the samples, four groups of samples with different physical forms (full granules, 40 mesh, 70 mesh, and 100 mesh) were prepared, each group consisted of 20 pure samples and 140 mixed samples, and the mixing ratio was between 5% and 50%, with an interval of 5%. Regarding qualitative analysis, the performance of the model has no obvious relationship with the physical state of the sample, the qualitative model of PLS‐DA and SVM can detect the fraudulent rice with a 5% detection limit, respectively. Regarding quantitative analysis, the performance of the prediction model was closely related to the particle size of the samples: 100 mesh > 70 mesh > 40 mesh > full grains. The determination coefficient and root mean square errors of the optimal prediction result were 0.96 and 2.93, respectively. These results demonstrate that NIRS analysis technology is a reliable and fast tool to determine whether high‐quality rice contains other varieties of rice. Practical Application The work of this article is based on the current background of increasingly serious rice fraud, using near‐infrared spectroscopy to quickly identify fraudulent rice, to a certain extent, and effectively alleviate the rice fraud. This technology can serve for the supervision of food regulatory agencies on rice fraud, and can also be used in food factories to ensure the authenticity of raw materials of rice.

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Section: Model Performance Evaluationmentioning

confidence: 99%

Detection of fraud in high‐quality rice by near‐infrared spectroscopy

Liu

Peng

et al. 2020

Journal of Food Science

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“…The recognized important wavelengths played an important role in classifying the residual sulfur dioxide on the surface of fresh-cut potato slices immersed in different concentrations of sodium pyrosulfite solution. The spectral band around 985 nm might be related to the second stretching overtone of O-H [30]. The spectral band in the range of 1120-1225 nm might be related to the second overtone of C-H [31].…”

Section: Important Wavelength Recognizementioning

confidence: 96%

Detection of Sulfite Dioxide Residue on the Surface of Fresh-Cut Potato Slices Using Near-Infrared Hyperspectral Imaging System and Portable Near-Infrared Spectrometer

et al. 2020

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Sodium pyrosulfite is a browning inhibitor used for the storage of fresh-cut potato slices. Excessive use of sodium pyrosulfite can lead to sulfur dioxide residue, which is harmful for the human body. The sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentrations of sodium pyrosulfite solution was classified by near-infrared hyperspectral imaging (NIR-HSI) system and portable near-infrared (NIR) spectrometer. Principal component analysis was used to analyze the object-wise spectra, and support vector machine (SVM) model was established. The classification accuracy of calibration set and prediction set were 98.75% and 95%, respectively. Savitzky–Golay algorithm was used to recognize the important wavelengths, and SVM model was established based on the recognized important wavelengths. The final classification accuracy was slightly less than that based on the full spectra. In addition, the pixel-wise spectra extracted from NIR-HSI system could realize the visualization of different samples, and intuitively reflect the differences among the samples. The results showed that it was feasible to classify the sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentration of sodium pyrosulfite solution by NIR spectra. It provided an alternative method for the detection of sulfur dioxide residue on the surface of fresh-cut potato slices.

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“…With regard to the detection of meat adulteration using HSI, several examples have previously been reported, for instance, pork or beef adulterated with chicken [17], chicken adulterated with carrageenan [23], beef adulterated with pork [24,25], prawn adulterated with gelatin [26], lamb adulterated with pork [27], beef adulterated with horsemeat [28,29], lamb adulterated with duck [30], etc. All the above studies achieved good performance through a combination of HSI and chemometrics.…”

Section: Introductionmentioning

confidence: 99%

Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging

Jiang

Cheng

Shi

2020

Foods

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Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with Rp2 = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that Rp2 was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork.

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Detection of adulteration with duck meat in minced lamb meat by using visible near-infrared hyperspectral imaging

Cited by 93 publications

References 27 publications

Detection of fraud in high‐quality rice by near‐infrared spectroscopy

Detection of fraud in high‐quality rice by near‐infrared spectroscopy

Detection of Sulfite Dioxide Residue on the Surface of Fresh-Cut Potato Slices Using Near-Infrared Hyperspectral Imaging System and Portable Near-Infrared Spectrometer

Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging

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