Beijing LICA United Technology Limited

Novel Instruments Provide New Opportunities

Hotline: 010-51292601
Technical Technical
News Technical

Resonon | Application of Resonon Pika L Hyperspectral Imaging on the estimation of amino acid conten

Date: 2022-09-07
浏览次数: 9

Application of Resonon Pika L Hyperspectral Imaging on the estimation of amino acid contents in maize leaves

Application of Resonon Pika L Hyperspectral Imaging on the estimation of amino acid contents in maiz

Maize is one of the most important crops in the world. In maize growth, nitrogen (N) is one of the most important nutrient elements. The nitrogen translocation in maize leaves was mainly in the form of glutamine. The maize yield is correlated well with the amino acids in leaves, such as glutamine, glutamate, alanine, aspartate, and asparagine at the grain filling stage. Therefore, accurate, and rapid estimation of amino acid contents in maize leaves is of great significance in improving maize yield estimation and nitrogen use efficiency. The spectrophotometry, chemical analysis, and mass spectrometry are the main methods for determining the amino acid content. These methods can estimate a variety of amino acids and have the advantages of high sensitivity and accuracy. However, all of them need to damage samples and require complex sample processing, low throughput, and high price. The hyperspectral imaging technology provides a new method for estimating physiological and biochemical parameters of crops with the advantages of being rapid, high throughput, and non-destructive, and it has been used for high-throughput screening of crop phenotypic traits. However, the research on the application of hyperspectral data in estimating the amino acid contents in fresh maize leaves is very limited.

Based on this, in the attached article, a group of scientists from China Agricultural University took maize leaves as the research object, and explored the feasibility of estimating various amino acid contents in fresh maize leaves using hyperspectral imaging data. They conducted two independent experiments with variable N fertilizer applications. A hyperspectral image acquisition system (Resonon Pika L Hyperspectral Imaging) was used to collect the hyperspectral images of maize leaves, and 24 amino acid contents were determined. First, the sensitive band range and sensitive bands of each amino acid were selected by the coefficient of variation (CV) and partial least squares regression (PLSR) coefficient tests. Then, the models of 24 amino acid contents were established based on the reflectance of all bands, sensitive band range, and sensitive bands, respectively. Finally, the samples that were not involved in model construction were used to verify the model accuracy of each amino acid.

Application of Resonon Pika L Hyperspectral Imaging on the estimation of amino acid contents in maiz

Hyperspectral images acquisition system.


Results:

Application of Resonon Pika L Hyperspectral Imaging on the estimation of amino acid contents in maiz

Scatterplot of the measured value against the predicted value of the various amino acid contents by the optimal estimation method using test set.

Conclusions: 

The regression coefficient test of PLSR found that the sensitive bands of most amino acids were in the ranges of 505.39-604.95 nm and 651.21-714.10 nm. The model estimations of the 24 amino acid contents were constructed and validated based on the reflectance of all bands, sensitive band range, and sensitive bands. The authors selected the optimal estimation method for each amino acid. The estimation accuracy of the content of b-aminobutyric acid, ornithine, citrulline, methionine, and histidine was better than other amino acids, with R2, RE, and RPD of the test set in the range of 0.84–0.96, 8.79%–19.77%, and 2.58–5.18. The estimation accuracies of the content of sarcosine, alanine, glutamic acid, proline, threonine, leucine, and aspartic acid were normal, with R2, RE, and RPD of the test set in the range of 0.58–0.73, 23.23%– 39.69%, and 1.56–1.94. The performance of the other amino acid models was relatively poor. This study can provide a reference for monitoring the traits of breeding materials based on hyperspectral technology.



News / Related News More
2023 - 02 - 13
Leaf chlorophyll content (LCC) is an important indicator of foliar nitrogen status and photosynthetic capacity. Accurate and timely mapping of LCC will benefit agronomists to guide fertilizer applications and ecologists to improve carbon flux estimation. In recent decades, remote sensing techniques have been widely used to estimate LCC with empirical and physical models.    Physical...
2022 - 12 - 30
Cherry tomato (Solanum lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. The existing measuring techniques relying upon chemistry reactions can derive the SSC value accurately. However, the destructive methods can not be applied in high volume measurements. Moreover, ...
2022 - 12 - 28
In the Arctic, the air temperature has increased more than twice the global average during the last few decades and may increase by 2-8°C before 2100. Wildfire frequency and expanse in the Arctic have increased in recent years. It can markedly disturb tundra ecosystems in different ways, including above-and belowground plant biomass destruction and alter soil properties through changes in the...
2022 - 12 - 06
Heavy metals in soil are harmful, and their migration and accumulation can seriously threaten ecological environmental security and human health. Arsenic (As) exhibits high neurotoxicity and teratogenicity. Much As is released into soil during human activities, including mining operations and industrial production activities. Determining the As concentration in soil quickly and accurately is impor...
Close window】【Print
Copyright ©2018-2023 LICA United Technology Limited
犀牛云提供企业云服务

LICA United Technology Limited

Address:The 5th.Building,No.18,Anningzhuang East Road,Haidian District, 100085, Beijing, China.

Tel:010-51292601
Fax:010-82899770-8014
E-mail:info@li-ca.com

 


 

 


 
  • Name:
  • *
  • 公司名称:
  • *
  • 地址:
  • *
  • 电话:
  • *
  • 传真:
  • *
  • E-mail:
  • *
  • 邮政编码:
  • *
  • 留言主题:
  • *
  • Details:
  • *
Feedback
Follow us
  • Wechat
  • Mobile Website
友情链接: