Rapid quantitative detection of trehalose syrup component based on near infrared spectroscopy

Authors

ZHENG Ruina, College of Chemistry and Biology, Changsha University of Science and Technology, Changsha, Hunan 410114, China
XIE Ding, College of Chemistry and Biology, Changsha University of Science and Technology, Changsha, Hunan 410114, China
YANG Qianyuan, College of Chemistry and Biology, Changsha University of Science and Technology, Changsha, Hunan 410114, China

Abstract

A rapid detection method of using the near-infrared spectra（NIRS）to determine the concentration of syrup (trehalose, maltose and glucose ) in producing trehalose by synthase was established. The 65 NIRS samples of trehalose syrup were divided into a calibration set of 48 samples and a prediction set of 17 samples. Calculation results showed that the first derivative and savitzky-golay filter pretreatment is optimal, and the partial least squares (PLS) was more robust and predictive than the principal component regression (PCR) in building model by TQ analyst. Using PLS, 1D and savitzky-golay filter combination pretreatment model could not only reduce the background noise of the spectrum, but also improve the robustness of the model. The RMSEC, C_c and RMSEP, C_p of trehalose syrup showed in order that the trehalose model was 0.188, 0.995, 0.089, 0.989; the maltose model was 0.143, 0.997, 0.131, 0.969; the glucose model was 0.147, 0.997, 0.094, 0.999. The NIRS detection was fast and non-destructive, could be used to detect the composition of the trehalose syrup in producing it by synthase.

Publication Date

10-28-2017

First Page

60

Last Page

63,134

DOI

10.13652/j.issn.1003-5788.2017.10.013

Recommended Citation

Ruina, ZHENG; Ding, XIE; and Qianyuan, YANG (2017) "Rapid quantitative detection of trehalose syrup component based on near infrared spectroscopy," Food and Machinery: Vol. 33: Iss. 10, Article 13.
DOI: 10.13652/j.issn.1003-5788.2017.10.013
Available at: https://www.ifoodmm.cn/journal/vol33/iss10/13

References

[1] 严衍录. 近红外光谱分析基础与应用[M]. 北京: 中国轻工业出版社, 2005.
[2] TORIT Baran Bagchi, SRIGOPAL Sharma, KRISHNENDU Chattopadhyay. Development of NIRS models to predict protein and amylose content of brown rice and proximate compositions of rice bran[J]. Food Chemistry, 2016, 191: 21-27.
[3] 朱向荣, 李高阳. 基于近红外光谱与组合间隔偏最小二乘法的稻米镉含量快速检测[J]. 食品与机械, 2015, 3(4): 43-50.
[4] 骆莹. 酿酒酵母胞内海藻糖的提取纯化及近红外检测[D]. 咸阳: 西北农林科技大学, 2011:4-8.
[5] 黄日波. 海藻糖——21世纪的新型糖类[M]. 北京: 化学工业出版社, 2010.
[6] 谢定, 张琼, 朱婧, 等. 海藻糖生产方法及其合成酶研究进展[J]. 食品与机械, 2013, 29(2): 223-226.
[7] CASCANT M M, BREIL C, FABIANO-TIXIER A S, et al. Determination of fatty acids and lipid classes in salmon oil by near infrared spectroscopy[J]. Food Chemistry, 2018, 239: 865-871.
[8] 李水芳, 单杨. 近红外透反射光谱用于检测蜂蜜加入麦芽糖浆掺假[J]. 食品科技, 2010, 32(12): 299-303.
[9] 王青云, 王越. 酶解法测定多种糖混合体系中海藻糖的含量[J]. 现代食品科技, 2015, 31(6): 329-333.
[10] CHEN Jia, ZHU Shi-pin. Rapid determination of total protein and wet gluten in commercial wheat flour using siSVR-NIR[J]. Food Chemistry, 2017, 221: 1 939-1 946.
[11] 公丽艳, 孟宪军. 基于主成分与聚类分析的苹果加工品质评价[J]. 农业工程学报, 2014, 30(13): 276-283.
[12] 林海明, 张文霖. 主成分分析与因子分析的异同和SPSS软件: 兼与刘玉玫、卢纹岱等同志商榷[J]. 统计研究, 2005 (3): 65-68.
[13] 李硕, 汪善勤. 基于可见-近红外光谱比较主成分回归、偏最小二乘回归和反向传播神经网络对土壤氮的预测研究[J]. 光学学报, 2012, 32(8): 1-5.
[14] 陈大伟, 闫昭, 刘昊岩. SVD系列算法在评分预测中的过拟合现象[J]. 山东大学学报: 工学版, 2014, 44(3): 15-21.