Construction of Fourier Transform Near Infrared Spectroscopy Prediction Model for Main Components in <i>Lycium ruthenicum</i> Murr

ZHOU Zhilei; HENG Yangyang; CHEN Chao; SONG Yaling; KU Jinliang; JI Zhongwei; MAO Jian

doi:10.13386/j.issn1002-0306.2023040248

Volume 45 Issue 5

Feb. 2024

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Science and Technology of Food Industry > 2024 > 45(5): 234-242. > DOI: 10.13386/j.issn1002-0306.2023040248

ZHOU Zhilei, HENG Yangyang, CHEN Chao, et al. Construction of Fourier Transform Near Infrared Spectroscopy Prediction Model for Main Components in Lycium ruthenicum Murr[J]. Science and Technology of Food Industry, 2024, 45(5): 234−242. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023040248.

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Construction of Fourier Transform Near Infrared Spectroscopy Prediction Model for Main Components in Lycium ruthenicum Murr

ZHOU Zhilei^{1, 2, 3,},
HENG Yangyang¹,
CHEN Chao⁴,
SONG Yaling⁴,
KU Jinliang⁴,
JI Zhongwei^{1, 2, 3},
MAO Jian^{1, 2, 3, 5, ,}

1.
National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
2.
Jiangnan University (Shaoxing) Industrial Technology Research Institute, Shaoxing 312000, China
3.
Jiangsu Provincial Engineering Research Center for Bioactive Product Processing, Synergetic Innovation Center of Food Safety and Quality Control, Wuxi 214122, China
4.
Beijing Tong Ren Tang Health Pharmaceutical (Qinghai) Co., Ltd., Delingha 817000, China
5.
National Engineering Research Center for Huangjiu, Zhejiang Shaoxing Huangjiu Industry Innovation Service Complex, Shaoxing 312000, China

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Received Date: April 24, 2023
Available Online: January 04, 2024

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Abstract

Abstract

A quantitative method of main components (total sugar, reducing sugar, total acid, ammonia nitrogen, anthocyanins, procyanidins, total phenols, flavonoids and polysaccharides) in Lycium ruthenicum Murr was developed using Fourier transform near infrared spectroscopy (FT-NIR) combined with chemometric analysis. Firstly, 11 pretreatment methods were compared for the original spectra, and the optimal pretreatment method of each component was selected. Then, the model results established by partial least squares (PLS), interval partial least squares (iPLS) and synergistic interval partial least squares (siPLS) algorithms were compared, and finally siPLS was adopted for modeling. The results showed that the correlation coefficient of calibration (Rc) and prediction (R_P) of total acids, ammonia nitrogen, anthocyanins, procyanidins, total phenols and flavonoids were all greater than 0.9818, and the relative analysis error (RPD) was more than 2.5, indicating an excellent model performance. The modeling effect of total sugar, reducing sugar, and polysaccharide was also good. The established calibration models could be used for actual detection. The predicted values of the verification samples did not significantly differ from the measured values, with a prediction error of only ±0.1%. Therefore, the prediction model had high reliability. The prediction model established in this study can enable nondestructive, rapid and accurate main components in dried and fresh fruits of Lycium ruthenicum Murr.

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