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Rapid and simultaneous analysis of multiple components in nanostructured lycopene liquid beverages based on near-infrared spectroscopy
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You-Chen WEI1, Pei-Han GAO1, Jing-Yi CHAI1, Hao-Yuan XU1, Yu-Jiao CAI1, Yi-Ran SUN1, A-Bu-Du-Sha-La-Mu AIKEDAN1, Ting YANG1, Er-Long HAN2, Xi-Ning YANG1, *, Feng-Ling LI1, Jing-Jing XIA1, *
Journal of Food Safety & Quality | 2026, 17(15) : 124 - 133
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Journal of Food Safety & Quality | 2026, 17(15): 124-133
• Special Topic: Application of Modern Analysis Instrument in Food Detection •
Rapid and simultaneous analysis of multiple components in nanostructured lycopene liquid beverages based on near-infrared spectroscopy
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You-Chen WEI1, Pei-Han GAO1, Jing-Yi CHAI1, Hao-Yuan XU1, Yu-Jiao CAI1, Yi-Ran SUN1, A-Bu-Du-Sha-La-Mu AIKEDAN1, Ting YANG1, Er-Long HAN2, Xi-Ning YANG1, *, Feng-Ling LI1, Jing-Jing XIA1, *
Affiliations
  • 1.School of Pharmacy, College of Life Science and Technology, Xinjiang University, Urumqi 830017, China
  • 2.Xinjiang Tianshenghe Biotechnology Co., Ltd., Turpan 838000, China
doi: 10.19812/j.cnki.jfsq11-5956/ts.20260330001
Outline
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Objective

To establish rapid prediction models for multiple nutritional component indexes in nanostructured lycopene liquid beverages by near-infrared spectroscopy (NIRS) combined with chemometrics.

Methods

NIRS coupled with partial least squares regression (PLSR) was adopted, and a combined pretreatment optimization strategy was proposed. Rapid quantitative analysis models were constructed separately for key indexes including lycopene, soluble solids, acidity and moisture content in 3 types of samples: lycopene liquid stock solution, nanostructured mannan-modified lycopene liquid beverage and nanostructured dextran-modified lycopene liquid beverage. Model performance was comprehensively evaluated byroot mean square error (RMSE), residual predictive deviation (RPD) and relative error ratio (RER).

Results

After optimized by combined pretreatment, the multi-index quantitative models of the 3 kinds of nanostructured lycopene liquid beverages all showed favorable predictive performance. The model of lycopene liquid stock solution was the best overall, with the test set RMSE of 0.9279, RPD of 5.3659 and RER of 18.3682; the test set RPD of the model for nanostructured mannan-modified samples were 4.7333, respectively; the test set RPD of the model for nanostructured dextran-modified samples were 3.4823, respectively. All models had RPD greater than 3.0, with high prediction accuracy and strong stability, which could realize the simultaneous and rapid quantification of lycopene, soluble solids, acidity and moisture content.

Conclusion

In summary, NIRS combined with chemometrics can effectively detect multiple key indexes in nanostructured lycopene liquid beverages, meet the demand for rapid detection in actual production, and provide efficient technical support for the quality control of such products.

near-infrared spectroscopy  /  nanostructured lycopene liquid beverage  /  combined pretreatment optimization  /  partial least squares regression
韦友晨, 郜珮含, 柴静怡, 徐颢媛, 蔡玉娇, 孙奕然, 艾克旦·阿布都沙拉木, 杨婷, 韩二龙, 杨西宁, 李凤灵, 夏静静. 基于近红外光谱技术同步快速分析纳米化番茄红素液态饮品多成分. 食品安全质量检测学报, 2026 , 17 (15) : 124 -133 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20260330001
You-Chen WEI, Pei-Han GAO, Jing-Yi CHAI, Hao-Yuan XU, Yu-Jiao CAI, Yi-Ran SUN, A-Bu-Du-Sha-La-Mu AIKEDAN, Ting YANG, Er-Long HAN, Xi-Ning YANG, Feng-Ling LI, Jing-Jing XIA. Rapid and simultaneous analysis of multiple components in nanostructured lycopene liquid beverages based on near-infrared spectroscopy[J]. Journal of Food Safety & Quality, 2026 , 17 (15) : 124 -133 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20260330001
Year 2026 volume 17 Issue 15
8
1
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20260330001
  • Receive Date:2026-03-30
  • Online Date:2026-11-05
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  • Received:2026-03-30
Funding
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    1.School of Pharmacy, College of Life Science and Technology, Xinjiang University, Urumqi 830017, China
    2.Xinjiang Tianshenghe Biotechnology Co., Ltd., Turpan 838000, China
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表12种不同金属材料的力学参数
科
Family
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Number of
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种数
Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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