| 基于红外光谱技术的蜂蜜理化性质快速检测 |
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| 引用本文： | 张广琪,甘芝霖,杨阳,高玮蔓.基于红外光谱技术的蜂蜜理化性质快速检测[J].农业工程学报,2023,39(10):275-284. |
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| 作者姓名： | 张广琪 甘芝霖 杨阳 高玮蔓 |
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| 作者单位： | 北京林业大学 生物科学与技术学院, 北京, 100083;林业食品加工与安全北京市重点实验室, 北京, 100083;北京便利蜂连锁商业有限公司, 北京, 100089 |
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| 基金项目： | 国家自然科学基金青年项目（32202564） |
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| 摘 要： | 该论文以116个蜂蜜样品为对象,考察蜂蜜理化指标间的相关性,并采用红外光谱技术结合偏最小二乘回归法建立快速定量模型,探讨模型对蜂蜜理化性质指标的定量能力,比较近、中红外光谱在定量检测中的能力,从数据层融合角度考究光谱融合对定量精度的提升。主要研究结论如下：蜂蜜的部分理化性质间具相互关联性,色差、电导率和pH两两呈正相关,pH和可滴定酸,水分与黏度之间彼此呈负相关;近红外光谱技术（near-infrared spectrum,NIR）和中红外光谱技术（mid-infrared spectrum,MIR）对果糖、葡萄糖、还原糖、果糖/葡萄糖、水分、黏度、pH和色差具有良好的定量分析能力（R2＞0.9）,对电导率、蔗糖、麦芽糖和可滴定酸的精度和模型相关系数尚可接受;二者单独对淀粉酶值、脯氨酸的定量结果较差,通过数据融合后,脯氨酸精度有所提升（Rc 0.825,Rp 0.664,RMSEC 38.68,RMSEP 49.57）,淀粉酶值无优化（Rc 0.799,Rp 0.695,RMSEC 2.57,RMSEP 3.02）。MIR对糖的定量分析精度略优于NIR。研究证明将近、中红外光谱用于蜂蜜部分理化指标的快速定量是可行的,数据融合对模型产生积极影响,但仍存在很多理论和算法问题需要解决。
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| 关 键 词： | 近红外光谱 中红外光谱 快速检测 蜂蜜 偏最小二乘回归 定量分析 |
| 收稿时间： | 2022/12/22 0:00:00 |
| 修稿时间： | 2023/4/12 0:00:00 |
Rapid detection of the physicochemical properties of honey based on infrared spectroscopy |
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| ZHANG Guangqi,GAN Zhilin,YANG Yang,GAO Weiman.Rapid detection of the physicochemical properties of honey based on infrared spectroscopy[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(10):275-284. |
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| Authors: | ZHANG Guangqi GAN Zhilin YANG Yang GAO Weiman |
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| Institution: | College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing 100083, China;Beijing Key Laboratory of Forest Food Processing and Safety, Beijing 100083, China;Beijing Convenience Bee chain Business Co. LTD, Beijing 100083, China |
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| Abstract: | Taking 116 honey samples as the research object, this study aims to rapidly detect the physicochemical properties of honey using infrared spectroscopy. The physicochemical properties of honey samples were determined, including the carbohydrate (fructose, glucose, sucrose, maltose, and lactose), moisture content, titration acidity, pH, electrical conductivity, viscosity, amylase value, HMF, proline, and color (L*a*b*). Principal component analysis (PCA) was implemented to explore the correlation among various physicochemical indexes of honey. Near Infrared spectrum (NIR) and mid-infrared spectrum (MIR) were used to obtain the three-dimensional information of "compound, aroma and taste" of honey samples. The Partial least squares (PLS) models were established using physicochemical indexes after NIR and MIR. Correlation coefficient (R2) and predictive root mean square error (RMSEP) were used to evaluate the quantitative accuracy of NIR and MIR indexes. The quantitative analysis of physicochemical properties was also implemented to compare the quality evaluation. Proline and amylase values were taken as the indicators, in order to clarify the effects of spectral technology fusion on the quantitative analysis at the level of information source (data) fusion. The results showed that there was a positive correlation between the color (L*a*b*), electrical conductivity, and pH, indicating the high mineral content with the deeper color and the higher conductivity value. A negative correlation between pH and titration acidity, namely the pH was low, while the titration acidity was high in honey. There was also a negative correlation between the water content and viscosity. The lower the water content of honey was, the higher the viscosity was. some indexes of honey were interrelated as well. The NIR and MIR models were established with the excellent ability of quantitative analysis for the fructose, glucose, reducing sugar, fructose/glucose, water content, viscosity, pH, and color (L*a*b*) (R2>0.9). The R2 and SEP of the model were acceptable for the electrical conductivity, sucrose, maltose, and titration acidity, indicating better precision in the rapid quantitative analysis. There was a low quantitative accuracy of amylase value content, proline, and HMF. There was a low accuracy of the improved model for the amylase value, proline, and HMF after the rapid quantitative analysis. The indexed with the higher prediction accuracy of NIR were ranked as fructose(R2=0.908), sucrose(R2=0.906), water content(R2=0.975), electrical conductivity(R2=0.935), viscosity(R2=0.949), and pH(R2=0.947). The indexes with the higher accuracy of MIR prediction were the glucose(R2=0.813), maltose(R2=0.798), reducing sugar(R2=0.711), fructose/glucose(R2=0.942), titration acidity(R2=0.890), amylase value(R2=0.641) and Color, L*a*b*. The Rc of the proline quantitative model increased from 0.614 to 0.825, whereas the RMSEP decreased from 54.95 to 49.57 after the fusion of NIR and MIR in the data layer. The quantitative model of amylase value was not optimized during this time. From the perspective of instruments, accessories, and spectral acquisition, the ATR accessories used for the MIR spectral acquisition were large and expensive, particularly only limited to laboratory use. The NIR can be expected for the practical application of rapid detection of honey quality under comprehensive conditions. Therefore, it is feasible for the NIR and MIR to rapidly quantify some physical and chemical indexes of honey. Data fusion can posed a positive impact on the prediction model in the implementation process. |
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| Keywords: | NIR MIR rapid detection honey Partial Least Square Regression quantitative analysis |
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