Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in Brassica juncea

高光谱成像 偏最小二乘回归 规范化(社会学) 数学 牡荆素 均方误差 模式识别(心理学) 生物系统 人工智能 计算机科学 生物 统计 生物化学 社会学 抗氧化剂 类黄酮 人类学
作者
Jae-Hyeong Choi,Soo Hyun Park,Soo Hyun Park,Yun Ji Park,Jung‐Seok Yang,Jai-Eok Park,Hyein Lee,Sang Min Kim
出处
期刊:Agriculture [MDPI AG]
卷期号:12 (10): 1515-1515 被引量:4
标识
DOI:10.3390/agriculture12101515
摘要

Partial least squares regression (PLSR) prediction models were developed using hyperspectral imaging for noninvasive detection of the five most representative functional components in Brassica juncea leaves: chlorophyll, carotenoid, phenolic, glucosinolate, and anthocyanin contents. The region of interest for functional component analysis was chosen by polygon selection and the extracted average spectra were used for model development. For pre-processing, 10 combinations of Savitzky–Golay filter (S. G. filter), standard normal variate (SNV), multiplicative scatter correction (MSC), 1st-order derivative (1st-Der), 2nd-order derivative (2nd-Der), and normalization were applied. Root mean square errors of calibration (RMSEP) was used to assess the performance accuracy of the constructed prediction models. The prediction model for total anthocyanins exhibited the highest prediction level (RV2 = 0.8273; RMSEP = 2.4277). Pre-processing combination of SNV and 1st-Der with spectral data resulted in high-performance prediction models for total chlorophyll, carotenoid, and glucosinolate contents. Pre-processing combination of S. G. filter and SNV gave the highest prediction rate for total phenolics. SNV inclusion in the pre-processing conditions was essential for developing high-performance accurate prediction models for functional components. By enabling visualization of the distribution of functional components on the hyperspectral images, PLSR prediction models will prove valuable in determining the harvest time.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
liaomr发布了新的文献求助10
刚刚
刚刚
lalala应助发生了什么树采纳,获得20
1秒前
2秒前
852应助ddl7采纳,获得30
2秒前
科研通AI2S应助物理渣渣采纳,获得10
3秒前
共享精神应助乐橙采纳,获得10
4秒前
4秒前
4秒前
5秒前
6秒前
拓跋雨梅发布了新的文献求助10
7秒前
7秒前
orixero应助远方采纳,获得10
8秒前
Lucas应助Woods采纳,获得10
8秒前
9秒前
9秒前
10秒前
Jalynn2044发布了新的文献求助10
10秒前
11秒前
11秒前
JW完成签到,获得积分10
11秒前
why完成签到,获得积分20
11秒前
李麟发布了新的文献求助10
12秒前
13秒前
wu完成签到,获得积分10
13秒前
优雅的平安完成签到 ,获得积分10
13秒前
无花果应助oops采纳,获得10
13秒前
一叶扁舟发布了新的文献求助10
14秒前
ddl7发布了新的文献求助30
15秒前
乐橙发布了新的文献求助10
16秒前
17秒前
鲤鱼依白发布了新的文献求助30
18秒前
IAMXC发布了新的文献求助10
18秒前
20秒前
20秒前
21秒前
huma1110关注了科研通微信公众号
21秒前
乐橙完成签到,获得积分10
22秒前
8R60d8应助Denmark采纳,获得10
22秒前
高分求助中
The Oxford Handbook of Social Cognition (Second Edition, 2024) 1050
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
Chen Hansheng: China’s Last Romantic Revolutionary 500
COSMETIC DERMATOLOGY & SKINCARE PRACTICE 388
Case Research: The Case Writing Process 300
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3141507
求助须知:如何正确求助?哪些是违规求助? 2792469
关于积分的说明 7803258
捐赠科研通 2448691
什么是DOI,文献DOI怎么找? 1302802
科研通“疑难数据库(出版商)”最低求助积分说明 626665
版权声明 601240