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利用近红外光谱和化学计量学对芝麻品种和品系进行非破坏性基因型鉴定。

Non-Destructive Genotyping of Cultivars and Strains of Sesame through NIR Spectroscopy and Chemometrics.

机构信息

Chemistry Department, Federal University of Roraima, Boa Vista 69310-000, RR, Brazil.

Brazilian Agricultural Research Corporation (Embrapa), Boa Vista 69301-970, RR, Brazil.

出版信息

Biosensors (Basel). 2022 Jan 26;12(2):69. doi: 10.3390/bios12020069.

DOI:10.3390/bios12020069
PMID:35200330
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8869549/
Abstract

The differentiation of cultivars is carried out by means of morphological descriptors, in addition to molecular markers. In this work, near-infrared spectroscopy (NIR) and chemometric techniques were used to develop classification models for two different commercial sesame cultivars () and 3 different strains. The diffuse reflectance spectra were recorded in the region of 700 to 2500 nm. Based on the application of chemometric techniques: principal component analysis-PCA, hierarchical cluster analysis-HCA, k-nearest neighbor-KNN and the flexible independent modeling of class analogy-SIMCA, from the infrared spectra in the near region, it was possible to perform the genotyping of two sesame cultivars (BRS Seda and BRS Anahí), and to classify these cultivars with 3 different sesame strains, obtaining 100% accurate results. Due to the good results obtained with the implemented models, the potential of the methods for a possible realization of forensic, fast and non-destructive authentication, in intact sesame seeds was evident.

摘要

品种的区分除了使用分子标记外，还可以通过形态描述符来实现。在这项工作中，近红外光谱（NIR）和化学计量学技术被用于为两个不同的商业芝麻品种（）和 3 个不同的品系开发分类模型。漫反射光谱在 700 至 2500nm 范围内记录。基于化学计量学技术的应用：主成分分析-PCA、层次聚类分析-HCA、k-最近邻-KNN 和类间相似性的灵活独立建模-SIMCA，从近红外光谱中，可以对两个芝麻品种（BRS Seda 和 BRS Anahí）进行基因分型，并对这两个品种与 3 个不同的芝麻品系进行分类，得到了 100%准确的结果。由于所实现模型的良好结果，这些方法在完整芝麻种子中的潜在应用，包括法医、快速和无损鉴定，变得显而易见。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/43b80b06659b/biosensors-12-00069-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/eb4493696ed6/biosensors-12-00069-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/ba51f0016a83/biosensors-12-00069-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/3b000bae3b48/biosensors-12-00069-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/f3d5ae67e847/biosensors-12-00069-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/0e46b4f3d3e6/biosensors-12-00069-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/c53c73971532/biosensors-12-00069-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/43b80b06659b/biosensors-12-00069-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/eb4493696ed6/biosensors-12-00069-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/ba51f0016a83/biosensors-12-00069-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/3b000bae3b48/biosensors-12-00069-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/f3d5ae67e847/biosensors-12-00069-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/0e46b4f3d3e6/biosensors-12-00069-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/c53c73971532/biosensors-12-00069-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ccd/8869549/43b80b06659b/biosensors-12-00069-g008.jpg

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