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作 者:刘蕊[1,2] 吴翼 李和帅[1,2] 范海阔 LIU Rui;WU Yi;LI Heshuai;FAN Haikuo(Coconut Research Institute,Chinese Academy of Tropical Agricultural Sciences,Wenchang,Hainan 571339,China;Coconut Germplasm Repository of Ministry of Agriculture and Rural Affairs,Wenchang,Hainan 571339,China)
机构地区:[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
出 处:《福建农业学报》2021年第11期1330-1336,共7页Fujian Journal of Agricultural Sciences
基 金: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)。
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。【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.
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