Rapid Analysis of Apple Leaf Nitrogen using Near Infrared Spectroscopy and Multiple Linear Regression

Zhang, Guangcai; Li, Zhuang; Xiao, Yan; Cui, Cheng; Zhou, Ping; Lin, Guolin; Zhou, Chuanpeng; Liu, Ning; Han, Xianpei

doi:10.1080/00103624.2012.684824

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…Usually first-order calibration data contain fewer samples than variables, because instruments measure hundreds or thousands of variables per sample. In this case one can in principle apply: (1) multiple linear regression (MLR), which requires the selection of a suitable number of wavelengths which should be smaller than the number of samples [27], or (2) PLS/PCR, which involves compression of full spectral data into a few latent variables [28]. The subject 'MLR vs. PLS/PCR' has given rise to considerable debate in the past.…”

Section: First-order Algorithmsmentioning

confidence: 99%

Practical guidelines for reporting results in single- and multi-component analytical calibration: A tutorial

Olivieri

2015

Analytica Chimica Acta

277

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Section: First-order Algorithmsmentioning

confidence: 99%

Practical guidelines for reporting results in single- and multi-component analytical calibration: A tutorial

Olivieri

2015

Analytica Chimica Acta

277

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“…30,31 Zhai et al 32 estimated nitrogen, phosphorus, and potassium levels in leaves of different plants using NIR spectroscopy in the spectral range of 350-2500 nm, combined with chemometrics. Zhang et al 33 have already determined nitrogen in apple leaves by NIR spectroscopy and chemometrics, in the region of 1100-2500 nm. Table 2 presents the results obtained in the calibration models developed for the determination of nitrogen in plant tissue, using the NanoMetrix application and SOLO+MIA software, with different preprocessing methods.…”

Section: Resultsmentioning

confidence: 99%

NanoMetrix: An app for chemometric analysis from near infrared spectra

Baumann

Librelotto

Pappis

et al. 2020

Journal of Chemometrics

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Near-infrared spectroscopy (NIR) associated with chemometric methods has been widely used in the determination of various compounds, as it is a fast, nondestructive, and analytical method with minimal sample preparation and universal application. In addition, the trend of miniaturization has allowed the development of new portable equipment, increasing the possibilities of point-of-use analysis at low cost. In this context, the objective of this study was to develop a portable analytical methodology using a low-cost DLP NIRscan Nano spectrophotometer (Texas Instruments ®) connected to a smartphone for data storage. For this, an app called NanoMetrix was developed, created natively for Android from Android Studio IDE, according to a software design standard known as Model-View-ViewModel (MVVM). To demonstrate the applicability of the method, 36 samples of plant tissue were analyzed to determine total nitrogen content. The calibration models were compared to those obtained with the SOLO+MIA software (Eigenvector Research, Inc.), 8.6.1. The results presented root mean square error of calibration (RMSEC) 1.90 and 1.82 g kg −1 , root mean square error of prediction (RMSEP) 2.00 and 1.97 g kg −1 , and R 2 pred 0.973 and 0.975, for the NanoMetrix application and the SOLO+MIA software, respectively. Therefore, the results indicate that the NanoMetrix integrated with the portable spectrophotometer, besides the acquisition of spectra, allows the processing of data in the field and reducing the time for the analysis and obtaining the results. Also, the chemometric associated with the NIR spectroscopy is a viable alternative to replace, or complement, the methods used for nitrogen determination.

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“…Numerous studies show that the NIRS technique, together with multivariate analysis and partial least squares regression (PLSR), provides a powerful tool for the interpretation and analysis of spectra. For example, NIRS technology has been used successfully to predict the nutritional status of leaves of apple [30], alfalfa [31], sugar cane [32], root crops [33], yerba mate [34], and citrus [35,36]. It has been observed in citrus leaves of different varieties including lemon, mandarin, orange, and grapefruit-high accuracy regarding the estimation of N (R = 0.99) and Ca (R = 0.98) as well as acceptable estimates for K, Mg, Fe, and Zn [37].…”

Section: Plantsmentioning

confidence: 99%

Using Near-Infrared Spectroscopy in Agricultural Systems

Garcı́a-Sánchez¹,

Galvez-Sola²,

Nicolás³

et al. 2017

Developments in Near-Infrared Spectroscopy

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This chapter provides a review on the state of art of the use of the visible near-infrared (vis-NIR) spectroscopy technique to determine mineral nutrients, organic compounds, and other physical and chemical characteristics in samples from agricultural systemssuch as plant tissues, soils, fruits, cocomposted sewage sludge and wastes, cereals, and forage and silage. Currently, all this information is needed to be able to carry out the appropriate fertilization of crops, to handle agricultural soils, determine the organoleptic characteristics of fruit and vegetable products, discover the characteristics of the various substrates obtained in composting processes, and characterize byproducts from the industrial sector. All this needs a large number of samples that must be analyzed; this is a time-consuming work, leading to high economic costs and, obviously, having a negative environmental impact owing to the production of noxious chemicals during the analyses. Therefore, the development of a fast, environmentally friendly, and cheaper method of analysis like vis-NIR is highly desirable. Our intention here is to introduce the main fundamentals of infrared reflectance spectroscopy, and to show that procedures like calibration and validation of data from vis-NIR spectra must be performed, and describe the parameters most commonly measured in the agricultural sector.

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Rapid Analysis of Apple Leaf Nitrogen using Near Infrared Spectroscopy and Multiple Linear Regression

Cited by 10 publications

References 6 publications

Practical guidelines for reporting results in single- and multi-component analytical calibration: A tutorial

Practical guidelines for reporting results in single- and multi-component analytical calibration: A tutorial

NanoMetrix: An app for chemometric analysis from near infrared spectra

Using Near-Infrared Spectroscopy in Agricultural Systems

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