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| Optimization of Methods for Quantitative Analysis of Unsaturated Fatty Acid in Fresh Meat Based on NIR |
| NIU Xiao-ying1, SHAO Li-min2, ZHAO Zhi-lei1*, JIAO Shen-jiang1, LI Xiao-can1, DONG Fang1 |
1. College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
2. College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071001, China |
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Abstract Unsaturated fatty acids (UFA) are basic composition of fresh meat fat. The composition and content of UFA in fresh meat directly determine its flavor and quality. Differing from being time consuming and causing sample destruction of Gas chromatography, Near-infrared spectroscopy can be used to determine UFA in meat rapidly and non-destructively. NIR diffuse reflectance spectra of sixty-three fresh meat samples including donkey meat, beef, mutton and pork were acquired in the band of 4 000～12 500 cm-1 at temperatures of 5, 10, 15, 20, 25, 30 and 35 ℃. Gas chromatography was used as the reference method to determine the composition and content of UFA in samples. Partial least square (PLS) Calibration models for individual UFA of palmitoleate, linoleic, oleic and tetracosenic acid, and total UFA (TUFA) were developed with all band spectra data of intact and minced samples (diameter of 3 mm) at different temperatures, respectively. The better performances of PLS models for palmitoleate and TUFA were attained with spectra of minced samples at 5 ℃; for linoleic and oleic acid with spectra of minced samples at 35 and 25 ℃ respectively; and for tetracosenic acid with spectra of intact samples at 15 ℃. The influence of sample temperatures on the performances of models for the five indexes was irregularly. Then forward and reverse interval PLS (FiPLS and RiPLS) with interval size of 1 762, 881，440 and 220 variables were performed to select optimal bands based on all band PLS models. For palmitoleate, linoleic, oleic acid and TUFA, the method of RiPLS with interval size of 220 variables gained better prediction, while for tetracosenic acid the performance of FiPLS model with interval of 440 variables was better than the else iPLS models and all band PLS models. The optimal bands were 4 425～4 636, 4 849～5 272, 5 486～5 696.7, 7 398.6～7 818, 8 031.1～8 666.5, 9 947～10 363.6 and 12 495.5～12 498.4 cm-1 for palmitoleate; 4 000.6～4 423.9, 5 273.4～5 698.6, 7 398.6～9 090.8, 10 576.7～10 787.8 and 12 495.5～12 498.4 cm-1 for linoleic; 4 000.6～4 423.9, 4 637～4 848.2, 7 398.6～8 242.3, 8 455.4～9 090.8, 9 947～10 787.8 and 12 495.5～12 498.4 cm-1 for oleic acid; 4 849.1～5 272.4 cm-1 for tetracosenic acid; and 4 000.6～4 423.9, 4 637～5 698.6, 9 097.5～9 515.1, 9 940.3～10 575.7, 11 646～12 060.6 and 12 273.7～12 498.4 cm-1 for TUFA. The spectra data of optimal bands were compressed by PLS. The latent variables obtained from compression were used as input to Least squares-support vector machine (LS-SVM) models for the five indexes. The performances of LS-SVM models were optimal in comparisons with iPLS models. The correlation coefficients and root mean square error of calibration and leave-one-out cross validation, and ratio of prediction to deviation of cross validation (RPDcv) of the optimal models were 0.974, 1.403 mg·(100 g)-1, 0.973, 1.428 mg·(100 g)-1 and 4.31 for palmitoleate; 0.99, 2.233 mg·(100 g)-1, 0.99, 2.263 mg·(100 g)-1 and 7.21 for linoleic; 0.982, 8.194 mg·(100 g)-1, 0.982, 8.223 mg·(100 g)-1 and 5.19 for oleic; 0.921, 0.224 mg·(100 g)-1, 0.92, 0.225 mg·(100 g)-1 and 2.52 for tetracosenic acid; and 0.996, 24.21 mg·(100 g)-1, 0.995, 26.045 mg·(100 g)-1, 10.01 for TUFA. The RPDcv of linoleic, oleic acid and TUFA models were all more than 5, and the one of palmitoleate was near 5, and the one of tetracosenic acid near 3. The prediction performances of NIR models for the five indexes were satisfied. The results show that the method of combination band selection and PLS compression with LS-SVM can optimize the prediction performance of NIR quantitative models for individual UFA and TUFA in fresh meat.
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Received: 2017-12-27
Accepted: 2018-05-06
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Corresponding Authors:
ZHAO Zhi-lei
E-mail: zhaozhilei-3208@163.com
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