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| Qualitative and Quantitative Analyses of Cooked Donkey Meat
Adulteration Based on NIR Spectroscopy |
| NIU Xiao-ying1, 2, 3, MU Xiao-qing1, 2, 3, SUN Jie1, 2, 3, ZHAO Zhi-lei1, 2, 3*, ZHANG Chun-jiang4 |
1. College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
2. National & Local Joint Engineering Research Center of Metrology Instrument and System, Hebei University, Baoding 071002, China
3. Hebei Key Laboratory of Energy Metering and Safety Testing Technology, Hebei University, Baoding 071002, China
4. Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
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Abstract Donkey meat has excellent flavor and rich nutrition and is in high price and low supply. The problem of cooked donkey meat adulterated with other meat, such as horse and mule meat, needs to be solved urgently. To realize the qualitative and quantitative analysis of cooked donkey meat samples of different adulteration ratios, horse and mule meat samples were used to degrade donkey meat. The gradient was 10%, and the donkey meat contents were 0%~100%. Spectra of samples were collected in the range of 4 000~12 500 cm-1. The methods of linear discriminant analysis, support vector machine, and generalized regression neural network combined with smoothing algorithm (5 points, 15 points, 25 points), multiplicative scattering correction (MSC), standard normal variable (SNV), Baseline correction, normalization, and Detrend were used to establish the NIR discriminant models of adulterated cooked donkey meat samples. Partial least squares regression (PLSR) and backpropagation (BP) were used to establish quantitative models to determine the content of donkey meat in adulterated samples. For minced after cooked meat samples, the results of SNV pretreatment combined with a support vector machine were optimal, and the discriminant accuracy of the calibration set and prediction set was 98.70% and 94.78%. The results of Detrend pretreatment combined with linear discriminant analysis were optimal for minced before cooked meat samples. The discriminant accuracy of the calibration and prediction sets reached 98.47% and 96.23%, respectively. Compared with the PLSR model, the BP model obtained better results, with a higher coefficient of determination (R2), relative percent deviation (RPD), and lower root mean square error (RMSE). For the adulterated samples of minced after cooked meat samples, the BP model of the donkey and mule adulterated samples was better after Detrend pretreatment. R2, RMSE, and RPD of the cross-validation set and prediction set were 0.971, 0.067, 5.844, 0.980, 0.086, 6.984, respectively. After normalized treatment, the results of BP model of donkey and horse adulterated samples were optimal, and the parameters were 0.997, 0.032, 18.026, 0.982, 0.089, 7.454, respectively. For the adulterated samples of minced before cooked meat samples, the results of the BP model with Detrend pretreatment were better, and the optimal quantitative model parameters of donkey and mule adulterated samples were 0.982, 0.041, 7.470, 0.986, 0.103, 8.452, respectively. The best model parameters of donkey and horse adulteration were 0.986, 0.036, 8.348, 0.961, 0.101, and 5.044, respectively. The results show that the NIR spectroscopy combined with different modeling algorithms can realize the rapid, nondestructive detection of different donkey meat contents. The methodology can be used for future qualitative and quantitative analysis of cooked donkey meat adulteration.
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Received: 2023-02-01
Accepted: 2023-09-14
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Corresponding Authors:
ZHAO Zhi-lei
E-mail: zhaozhilei-3208@163.com
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