基于近红外光谱的椰子肉含水率检测方法

刘蕊; 吴翼; 李和帅; 范海阔

doi:10.19303/j.issn.1008-0384.2021.11.010

基于近红外光谱的椰子肉含水率检测方法

doi: 10.19303/j.issn.1008-0384.2021.11.010

刘蕊^{1, 2,},
吴翼^{1, 2},
李和帅^{1, 2},
范海阔^{1, 2, ,}

1.
中国热带农业科学院椰子研究所，海南　文昌　571339
2.
农业农村部文昌椰子种质资源圃，海南　文昌　571339

基金项目: 海南省自然科学青年基金项目（318QN275）；海南省重大科技计划项目（ZDKJ2019014）；农业农村部热带作物种质资源保护项目（151721301354052015）

详细信息

作者简介:
刘蕊（1979−），女，博士，副研究员，主要从事椰子种质资源与育种研究（E-mail: sanxingxieyue@126.com）

通讯作者:
范海阔（1976−），男，研究员，主要从事椰子种质资源与育种研究（E-mail: vanheco@163.com）

中图分类号: S 667.4
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- 被引次数: 0
出版历程
- 收稿日期: 2020-11-23
- 修回日期: 2021-11-05
- 网络出版日期: 2021-10-23
- 刊出日期: 2021-11-28

Moisture Determination of Copra by Near Infrared Spectroscopy

LIU Rui^{1, 2
,},
WU Yi^{1, 2},
LI Heshuai^{1, 2},
FAN Haikuo^{1, 2
, ,}

1.
Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang, Hainan　571339, China
2.
Coconut Germplasm Repository of Ministry of Agriculture and Rural Affairs, Wenchang, Hainan　571339, China

摘要

摘要: 目的利用近红外光谱技术建立成熟椰果中椰子肉水分含量的近红外定量检测模型，实现椰子品种椰干含量及椰子种质含水率的高效率实时在线检测，满足椰干产量预测及椰子种质快速鉴定的需求。方法采用国产光栅S400型近红外农产品品质测定仪，对来自不同种质的360个成熟椰果的椰肉样本进行近红外光谱扫描，将采集到的光谱，以建模集∶检验集为1∶1的比例进行样本集划分，利用定量偏最小二乘分析方法建立椰肉含水率定量模型，同时分析一阶导数、二阶导数、散射校正、中心化、极差归一法等预处理方法对定量模型性能的影响。结果椰肉样本的近红外原始光谱所建模型性能最佳，其含水率检测模型建模集和检验集的相关系数分别为0.9963和0.9960，校正标准差和预测标准差分别为0.7605和0.8378。结论试验所建立的椰肉含水率近红外定量检测模型可以实现椰肉含水率的快速检测，满足椰子种质椰干产量的高通量鉴定和实际生产需求，对椰肉蛋白质、脂肪、糖类含量的快速检测有重要借鉴意义。
- 近红外光谱 /
- 含水率 /
- 椰子
Abstract: Objective A high-efficiency, real-time, inline assay using near infrared spectroscopy to determine the moisture content in coconut meat was developed. Method The near infrared (NIR) spectra of 360 mature copra specimens came from varied germplasms were scanned by a grating diffuse NIR instrument made in China (S400 NIR Spectrophotometer for Quality of Agricultural Products). The collected data were separated by half into modeling and test sets. The quantitative partial least squares analysis was applied for the model construction with data pretreatments, such as first derivative, second derivative, scatter correction, zero-centered, and range normalization. Result The developed models achieved optimal performance. The root mean square error of calibration (SEC) and that of prediction (SEP) on the models were 0.7605 and 0.8378, respectively, while the correlation coefficients on the modeling and test sets 0.9963 and 0.9960, respectively. Conclusion The newly developed method for the determination of copra moisture content was capable of instantly and precisely delivering the measurement. It could be employed for real-time detection on a coconut processing line for quality assurance and yield prediction. Moreover, similar approach could conceivably be used to develop protein, fat, and carbohydrate determinations for copra as well.
- near infrared spectroscopy /
- moisture content /
- copra

HTML全文

图 1 样品原始近红外（NIR）漫反射光谱

Figure 1. Original NIR diffuse reflectance spectra

下载: 全尺寸图片幻灯片

图 2 不同预处理方法下建模模型、检验模型中椰肉含水率交叉检验预测值与化学值散点图（横坐标为化学值，纵坐标为预测值）

Figure 2. Scatter plot of predicted (x-axis) and measured (y-axis) moisture contents of copra

下载: 全尺寸图片幻灯片

表 1 椰肉水分检测QPLS模型结果

Table 1. NIR-spectrum-based copra moisture determination obtained by using QPLS models

预处理方法 Pretreatment method	建模集 Modeling set		检验集 Test set
预处理方法 Pretreatment method	相关系数 R	校正标准差 SEC	相关系数 R	预测标准差 SEP
原始光谱 Original spectra	0.9963	0.7605	0.9960	0.8378
一阶导数 First derivative	0.9865	1.4576	0.9880	1.4557
二阶导数 Second derivative	0.9947	0.9126	0.9941	1.0201
散射校正 Scatter correction	0.9932	1.0364	0.9907	1.2787
中心化 Zero-centered	0.9981	0.5538	0.9950	0.9310
极差归一 Range normalization	0.9956	0.8318	0.9953	0.8983

下载: 导出CSV

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