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浙江大学学报（农业与生命科学版）
浙江大学学报（农业与生命科学版）  2019, Vol. 45 Issue (6): 760-766    DOI: 10.3785/j.issn.1008-9209.2019.01.111
农业工程     
基于特征波长建模的近红外光谱技术检测辣椒素含量
吕晓菡1(),蒋锦琳2,杨静3,陈建瑛1,岑海燕2,傅鸿妃1,周毅飞1
1.杭州市农业科学研究院，杭州 310024
2.浙江大学生物系统工程与食品科学学院，杭州 310058
3.浙江农林大学农业与食品科学学院，杭州 311300
Detection of capsaicin content by near-infrared spectroscopy combined with optimal wavelengths
Xiaohan Lü1(),Jinlin JIANG2,Jing YANG3,Jianying CHEN1,Haiyan CEN2,Hongfei FU1,Yifei ZHOU1
1.Hangzhou Academy of Agricultural Sciences, Hangzhou 310024, China
2.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
3.College of Agriculture and Food Science, Zhejiang A & F University, Hangzhou 311300, China
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摘要：

为实现近红外光谱技术对新鲜辣椒果实中辣椒素含量的准确预测，以杭椒类辣椒为研究对象，采集新鲜辣椒果实的近红外光谱信息，结合高效液相色谱法，分别采用连续投影算法（successive projection algorithm, SPA）、竞争性自适应重加权采样法（competitive adaptive reweighted sampling, CARS）、无信息变量消除法（uninformation variable elimination, UVE）提取特征波长，建立偏最小二乘法（partial least squares, PLS）预测模型，并比较了全谱建模与特征波长建模的预测效果和运算效率。结果显示：CARS-PLS模型的预测效果最好，预测集相关系数和均方根误差分别为0.838 6和0.014 8 mg/g。此外，与全谱建模的输入变量200相比，基于CARS选择的特征波长建模的输入变量减少了96%，这说明运用特征波长建模大大地简化了模型，提高了运算效率。本试验表明，基于特征波长建模的近红外光谱技术对于新鲜辣椒果实中辣椒素含量的检测是可行的。
关键词： 近红外光谱技术;  辣椒素;  辣椒;  特征波长    
Abstract:

In order to investigate the potential of near-infrared spectroscopy for accurately predicting the capsaicin content in fresh chili peppers, taking Hangzhou chili pepper as a material, the near-infrared spectroscopy was employed to acquire spectral information of chili peppers, and high-performance liquid chromatography was conducted to obtain the reference values of capsaicin content. Three different variable selection methods with successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformation variable elimination (UVE) were performed to select the optimal wavelengths. Partial least square (PLS) models based on full spectra and optimal wavelengths were then developed to predict the capsaicin content, and the prediction performances and operation efficiency were compared. The results showed that the CARS-PLS model yielded the best prediction performances, with the correlation coefficient of 0.838 6 and root-mean-square error of prediction set of 0.014 8 mg/g. In addition, compared with the full spectra of 200 wavelengths, the number of the optimal wavelengths selected by CARS was reduced by 96%, which indicated that optimal wavelengths can be used to simplify the models and improve the operation efficiency. The above results demonstrate that the near-infrared spectroscopy based on optimal wavelengths is feasible for the detection of capsaicin content.
Key words: near-infrared spectroscopy    capsaicin    chili pepper    optimal wavelengths
收稿日期: 2019-01-11 出版日期: 2020-01-20
CLC:  O 433.4  
基金资助: 浙江省农业（蔬菜）新品种选育重大专项(2016C02051);杭州市农业科研主动设计项目(20162012A01)
通讯作者: 吕晓菡     E-mail: 45718653@qq.com
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吕晓菡
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岑海燕
傅鸿妃
周毅飞

引用本文:

吕晓菡,蒋锦琳,杨静,陈建瑛,岑海燕,傅鸿妃,周毅飞. 基于特征波长建模的近红外光谱技术检测辣椒素含量[J]. 浙江大学学报（农业与生命科学版）, 2019, 45(6): 760-766.

Xiaohan Lü,Jinlin JIANG,Jing YANG,Jianying CHEN,Haiyan CEN,Hongfei FU,Yifei ZHOU. Detection of capsaicin content by near-infrared spectroscopy combined with optimal wavelengths. Journal of Zhejiang University (Agriculture and Life Sciences), 2019, 45(6): 760-766.

链接本文:

http://www.zjujournals.com/agr/CN/10.3785/j.issn.1008-9209.2019.01.111    或    http://www.zjujournals.com/agr/CN/Y2019/V45/I6/760

图1  全部样本的辣椒素含量频率直方图
图2  剔除异常样本后的辣椒素含量频率直方图

样本

Samples

最小值Minimum

value

最大值Maximum

value

平均值Mean

标准偏差Standard

deviation

建模集

Modeling set

0.000 40.118 60.033 50.029 9

预测集

Prediction set

0.000 20.122 90.030 70.027 3
表1  建模集和预测集辣椒样本中辣椒素的含量
图3  新鲜辣椒果实的平均光谱
图4  PLS模型建模集（A）和预测集（B）的预测结果

方法

Method

个数

Number

特征波长

Optimal wavelength/nm

SPA61 146, 1 207, 1 281, 1 362, 1 511, 1 565
CARS81 143, 1 200, 1 274, 1 328, 1 382, 1 399, 1 436, 1 622
UVE81 140, 1 206, 1 277, 1 342, 1 402, 1 412, 1 470, 1 612
表2  基于3种方法选择的特征波长
图5  SPA-PLS模型建模集（A）和预测集（B）的预测结果
图6  CARS-PLS模型建模集（A）和预测集（B）的预测结果
图7  UVE-PLS模型建模集（A）和预测集（B）的预测结果
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