| 基于近红外与中红外光谱技术的淀粉回生度检测 |
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| 引用本文： | 邹小波,崔雪平,石吉勇,胡雪桃,徐艺伟,薛瑾.基于近红外与中红外光谱技术的淀粉回生度检测[J].农业机械学报,2018,49(3):341-346. |
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| 作者姓名： | 邹小波 崔雪平 石吉勇 胡雪桃 徐艺伟 薛瑾 |
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| 作者单位： | 江苏大学,江苏大学,江苏大学,江苏大学,江苏大学,江苏大学 |
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| 基金项目： | “十二五”国家科技支撑计划项目（2015BAD17B04） |
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| 摘 要： | 淀粉食品在加工、运输及储藏过程中会逐渐出现回生,其回生程度是影响淀粉食品品质的重要因素。利用近红外和中红外光谱技术快速、无损检测淀粉回生度。首先采集了储存不同时间淀粉的近红外和中红外光谱,分别利用近红外、中红外以及两者融合的光谱数据结合化学计量学方法(偏最小二乘法(PLS、iPLS、biPLS、siPLS))建立淀粉回生度检测模型。结果显示,近红外和中红外融合光谱技术的biPLS检测模型最佳,校正集和预测集相关系数分别为0.965 5和0.931 3。研究结果表明,红外光谱技术可以快速、无损检测玉米淀粉回生度,保障了富含淀粉食品的质量与安全。
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| 关 键 词： | 淀粉回生度 近红外光谱 中红外光谱 偏最小二乘法 |
| 收稿时间： | 2017/7/8 0:00:00 |
Detection of Retrogradation Degree of Starch Based on Near-infrared and Mid-infrared Spectroscopy |
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| ZOU Xiaobo,CUI Xueping,SHI Jiyong,HU Xuetao,XU Yiwei and XUE Jin.Detection of Retrogradation Degree of Starch Based on Near-infrared and Mid-infrared Spectroscopy[J].Transactions of the Chinese Society of Agricultural Machinery,2018,49(3):341-346. |
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| Authors: | ZOU Xiaobo CUI Xueping SHI Jiyong HU Xuetao XU Yiwei and XUE Jin |
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| Institution: | Jiangsu University,Jiangsu University,Jiangsu University,Jiangsu University,Jiangsu University and Jiangsu University |
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| Abstract: | Starch food is easy to retrograde during processing, transportation and storage, and the degree of retrogradation seriously affects the nutritional value and shelf-life of starch food. Soretrogradation degree is really expected to determine rapidly and non-destructively during storage, that is near-infrared and mid-infrared spectroscopy. The near-infrared and mid-infrared spectra of starch in different storage times (0d, 1d, 2d, 3d, 4d, 5d, 10d, 15d and 20d) were collected. There was a certain associations between spectra data and chemical reference detected by spectrophotometry, then chemometrics (partial least squares, PLS) were used to establish the prediction model of starch retrogradation with near-infrared, mid-infrared and fusion data, the best one that had higher correlation coefficient and lower error was chosen. The results showed that the backward interval partial least squares (biPLS) prediction model of fusion technology was the best one, the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) were 6.79% and 9.52%, and the calibration and prediction correlation coefficient were 0.9655 and 0.9313, respectively. The results indicated that the fusion spectroscopy was superior to any single spectral technique, which could provide more accurately information of starch. Hence, the infrared spectroscopy could detect the retrogradation degree of corn starch rapidly and non-destructively, provide guidance for the processing of starchy food, and ensure the quality and safety of starchy food. |
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| Keywords: | starch retrogradation degree near-infrared spectroscopy mid-infrared spectroscopy partial least squares |
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