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刘 洁,李小昱,李培武,王 为,周 炜,张 军.基于近红外光谱的板栗水分检测方法[J].农业工程学报,2010,26(2):338-341.DOI:
基于近红外光谱的板栗水分检测方法
投稿时间:2009-06-10  修订日期:2010-02-02
中文关键词:  近红外光谱,水分,无损检测,板栗
基金项目:高等学校博士学科点专项科研基金(20090146110018);华中农业大学科研项目(52204-02008)
作者单位
刘 洁 华中农业大学工程技术学院武汉 430070 
李小昱 华中农业大学工程技术学院武汉 430070 
李培武 中国农业科学院武汉油料作物研究所武汉 430062 
王 为 华中农业大学工程技术学院武汉 430070 
周 炜 华中农业大学工程技术学院武汉 430070 
张 军 华中农业大学工程技术学院武汉 430070 
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中文摘要:含水率是影响板栗贮藏、加工的关键指标之一,该文应用近红外光谱技术对板栗含水率进行快速无损检测。试验对240个板栗样本的带壳光谱和栗仁板栗光谱采用SPXY算法进行样本集划分,利用偏最小二乘法建立含水率定量检测模型,并对微分、多元散射校正、变量标准化等多种预处理方法对建模结果的影响进行比较。结果表明:栗仁和带壳板栗的光谱经一阶微分预处理后所建模型性能最佳,其中栗仁的水分检测模型校正集和验证集的相关系数分别为0.9359和0.8473,校正均方根误差为1.44%,验证均方根误差为1.83%;带壳板栗光谱所建模型校正集和验证集的相关系数分别为0.8270和0.7655,校正均方根误差为2.27%,验证均方根误差为2.35%。受栗壳的影响,带壳板栗光谱模型对含水率的预测精度低于栗仁光谱模型的预测精度。研究表明,近红外光谱分析技术可用于板栗含水率的快速无损检测。
.Determination of moisture in chestnuts using near infrared spectroscopy[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2010,26(2):338-341.DOI:
Determination of moisture in chestnuts using near infrared spectroscopy
Author NameAffiliation
  
  
  
  
  
  
Key words:near infrared spectroscopy, moisture, nondestructive measurement, chestnuts
Abstract:Moisture of chestnuts is one of the key indicators affecting their storage and processing. The aim of this study was to evaluate the feasibility of near infrared reflectance (NIR) spectroscopy to determine the moisture of chestnuts with rapid non-destructive testing. The Sample Set Partitioning based on joint X–Y distances, as a method to improve the model performance, was explored when the calibration and validation subset were partitioned. Partial least squares regression models based on a population of 240 samples were established for chestnuts with and without peel respectively and the effect of different preprocessing, namely First Derivative, Second Derivative, Standard Normal Variety Multiplicative Scatter Correction and Non-preprocessing to the performance of models were compared. The results showed that for both peeled and intact chestnuts, the models developed from spectra after First Derivative preprocessing achieved the optimal performance. The model for peeled chestnuts gave more accurate prediction with 1.58% as the root mean square error of calibration (RMSEC), 1.48% as that of prediction (RMSEP), and 0.92 and 0.90 as the correlation coefficient (R) of calibration and validation subset, respectively. These parameters of the model for intact chestnuts were 2.35%, 2.29%, 0.82 and 0.79, respectively. The overall results indicated that NIR spectroscopy could be applied as a nondestructive and accurate alternative method for the determination of chestnuts moisture during orchards and post-harvest process rapidly.
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