Analysis of Near-infrared (NIR) spectroscopy for chlorophyll prediction in oil palm leaves

Sabri, Mohd. Shafiq Amirul; Endut, R.; Rashidi, C. B. M.; Laili, A. R.; Aljunid, S. A.; Ali, Noraziah

doi:10.11591/eei.v8i2.1412

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…In addition, the SNV method can eliminate the effect of particle size and curvilinear trend issues. However, PLS modeling using different spectral preprocessing techniques to analyze chlorophyll content will produce different accuracy [17]. The performance of the developed model is evaluated using the R 2 value and the RMSE.…”

Section: Statistical and Spectra Analysismentioning

confidence: 99%

Determination of green and red spinach microgreen chlorophyll content using visible spectroscopy and wavelength selection

Nugroho

Zahra

Masithoh

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Microgreens are quickly-grown greens that are suitable for urban areas. The accumulation of chlorophyll in plants is essential for both nutritional compounds and the microgreen’s attractive appearance. A plant factory is a controlled-environment cultivation method that employs LEDs to enhance photosynthesis, vegetative development, seed germination, and chlorophyll accumulation. This work used VIS-NIR for the quantitative determination of chlorophyll content in green and red spinach microgreens grown under four different LED light intensities with four different ratios, with 100% blue, red, and white LED, and 67%:20%:13% of red:blue: white LED, as artificial grow for 12 hours illumination. The performance of MSC, AN, SNV, smoothing, and the first and second Savitzky-Golay’s derivatives (SGD) were examined. PLSR explained accurately predicted R2C, R2P, RMSEC, and RMSEP up to 0.957, 0.892, 1.054, and 1.661 for chlorophyll a using SGD 1st; 0.813, 0.652, 0.979, and 1.338 for chlorophyll b using SNV; and 0.931, 0.876, 1.155, and 1.550 for total chlorophyll using SNV. The wavelength region of 400 to 700 nm was dominant based on the best regression coefficient (β) for predicting green and red spinach microgreens. Results demonstrated that VIS-NIR spectroscopy, combined with chemometric techniques, PLSR, can be used to evaluate the chlorophyll content of green and red spinach microgreens.

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Section: Statistical and Spectra Analysismentioning

confidence: 99%

Determination of green and red spinach microgreen chlorophyll content using visible spectroscopy and wavelength selection

Nugroho

Zahra

Masithoh

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…Spectroradiometer is able to predict nutrient deficiencies in oil palm with reference to frond 17, using partial least square (PLS) analysis [8]. The use of the spectrometer in oil palm is also well recorded [9]. They reported that a correlation of NIR spectral absorbance data and chlorophyll can be achieved, using a PLS regression model with Savitzky-Golay Smoothing (SGS).…”

Section: Introductionmentioning

confidence: 99%

Usage of near infrared spectrometer as an analyzing tool for nutrients in leaf, fertilizer and soil in oil palm industry

Thandapani,

Harahap,

Nadaraj

2024

IOP Conf. Ser.: Earth Environ. Sci.

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In the oil palm industry, a huge money is used for chemical items to examine samples, especially for leaf (foliar), fertilizer, and soil. However, with the advent of FOSS NIRS DS2500 technology, there is a huge reduction in chemical usage in laboratories. Results were attained in less than a minute while covering many parameters in a single scanning. This tool uses the technology of Near Infra-Red Spectroscopy (NIRS) to scan the particles of the substance and thus, elements are detected within the NIRS wavelength region. FOSS NIRS DS2500 uses a broad wavelength range of 400 to 2500nm, covering the highest possible performance across any chemical elements/molecules. Furthermore, there are accurate analysis results for a wide range of parameters, such as oil content, moisture content, N, P, K, Mg, Ca, B, Zn, and Cu. The objective of this paper is to discuss the ‘hands-on’ experience with the usage of FOSS NIRS DS2500 as an analyzing tool for determining the nutrient values for the above-mentioned samples. FOSS NIRS DS2500 is a ‘secondary method’ as this tool needs to be calibrated periodically with a database gathered from a traditional laboratory (primer method/conventional method). Statistics is used to verify the accuracy and acceptance level of data, for example, standard error of prediction, standard error of cross-validation, and standard error lab. Unification usage of applied science of statistics, biochemistry of plant tissues, and appropriate sampling methods in the fields had resulted in a practice where huge use of chemicals is eliminated while the accuracy of data is retained.

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“…Since then, oil palm cultivation has witnessed rapid growth in Malaysia, with a vast crop area of 5.207 million hectares. Notably, Malaysia and Indonesia jointly contribute to 90% of global oil palm production and act as key exporters, accounting for 85% of the total global exports [1]. In 2020, Malaysia's crude palm oil (CPO) exports reached 17.4 million tons, representing 18.3% of the world's total exports of vegetable oils and fats.…”

Section: Introductionmentioning

confidence: 99%

Oil Palm Leaves Phenotyping using Biomarkers Derived from Raman Spectra

Herman,

Hashim,

Mohd Zulkifli

et al. 2024

Mal. J. Fund. Appl. Sci.

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The efficient production of oil from oil palm trees is heavily dependent on their health status, reflected in the oil extraction rate (OER). The 17th frond of the oil palm trees contains a significant amount of organic compounds that directly influence the overall health of the tree. Achieving an optimal balance of essential nutrients such as nitrogen (N), phosphorus (P), and potassium (K) is crucial for classifying a tree as healthy, as it results in an increased oil to bunch and fruit to bunch ratio. To accurately assess the health level of oil palm trees, this study explores the application of Raman spectroscopy, a non-invasive technique in determining the molecular fingerprint of an organic sample. In this research, Raman spectroscopy is employed to determine the health level of oil palm trees, and a machine learning-based health level classification algorithm is developed. The algorithm analyzes the organic compounds found in oil palm leaves, which were collected from 20 different trees. The extracted spectral features from these leaves are used to classify them into two health levels: healthy and not healthy. For this purpose, 31 machine learning models are tested to identify the most accurate classifier. The findings reveal that the Tree and fine K-Nearest Neighbors (KNN) classifier demonstrates the highest overall accuracy of 95% using three significant features, namely the Raman intensity, Full Width at Half Maximum (FWHM), and area under the curve. This result signifies the potential of Raman spectroscopy as a reliable and promising method for non-invasively phenotyping oil palm leaves, enabling precise prediction of the health status of oil palm trees.

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Analysis of Near-infrared (NIR) spectroscopy for chlorophyll prediction in oil palm leaves

Cited by 9 publications

References 13 publications

Determination of green and red spinach microgreen chlorophyll content using visible spectroscopy and wavelength selection

Determination of green and red spinach microgreen chlorophyll content using visible spectroscopy and wavelength selection

Usage of near infrared spectrometer as an analyzing tool for nutrients in leaf, fertilizer and soil in oil palm industry

Oil Palm Leaves Phenotyping using Biomarkers Derived from Raman Spectra

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