Rapid measurement of classification levels of primary macronutrients in durian (Durio zibethinus Murray CV. Mon Thong) leaves using FT-NIR spectrometer and comparing the effect of imbalanced and balanced data for modelling

Phanomsophon, Thitima; Jaisue, Natthapon; Worphet, Akarawhat; Tawinteung, Nukoon; Shrestha, Bijendra; Posom, Jetsada; Khurnpoon, Lampan; Sirisomboon, Panmanas

doi:10.1016/j.measurement.2022.111975

Cited by 13 publications

(11 citation statements)

References 57 publications

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“…The research data’s TSI level classification was also attempted using partial least squares regression (PLSR) with a method employed by Phanomsophon et al [ 17 ]. When combined with various spectral pre-treatment algorithms, the classification accuracy ranged from 32.14% to 65.12%.…”

Section: Discussionmentioning

confidence: 99%

“…The second time, because this study has imbalanced data, the data augmentation technique will be used by applying the SMOTE to a calibration data set. This technique has been reported several times in research papers as being able to improve the performance of the calibration model being generated, especially when using NIR spectroscopy data for classification problems [ 16 , 17 , 18 ]. A full description of this method can be read in the Brownlee [ 19 ] short report.…”

Section: Methodsmentioning

confidence: 99%

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Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data

Jongyingcharoen,

Howimanporn,

Sitorus

et al. 2024

Polymers

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Classification of the crosslink density level of para rubber medical gloves by using near-infrared spectral data combined with machine learning is the first time reported in this paper. The spectra of medical glove samples with different crosslink densities acquired by an ultra-compact portable MicroNIR spectrometer were correlated with their crosslink density levels, which were referencely evaluated by the toluene swell index (TSI). The machine learning protocols used to classify the 3 groups of TSI were specified as less than 80% TSI, 80–88% TSI, and more than 88% TSI. The 80–88% TSI group was the group in which the compounded latex was suitable for medical glove production, which made the glove specification comply with the requirements of customers as indicated by the tensile test. The results show that when comparing the algorithms used for modeling, the linear discriminant analysis (LDA) developed by 2nd derivative spectra with 15 k-best selected wavelengths fairly accurately predicted the class but was most reliable among other algorithms, i.e., artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (kNN), due to higher prediction accuracy, precision, recall, and F1-score of the same value of 0.76 and no overfitting or underfitting prediction. This developed model can be implemented in the glove factory for screening purposes in the production line. However, deep learning modeling should be explored with a larger sample number required for better model performance.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data

Jongyingcharoen,

Howimanporn,

Sitorus

et al. 2024

Polymers

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“…These techniques include near-infrared spectroscopy (NIRS) and chemometric methods for classifying or quantifying primary macronutrients in plants. [5][6][7][8] Another typical technique is hyperspectral remote sensing, which has been used to monitor changes in leaf traits and estimate soybean leaf carbon and nitrogen content. Additionally, digital images obtained using remotely piloted aircraft have been utilized to detect and monitor abiotic stresses in soybeans using digital aerial images.…”

Section: Introductionmentioning

confidence: 99%

“…To meet the need for plant nutritional diagnosis, some studies have demonstrated the use of non‐destructive techniques for estimating nitrogen content in plant tissue. These techniques include near‐infrared spectroscopy (NIRS) and chemometric methods for classifying or quantifying primary macronutrients in plants 5‐8 . Another typical technique is hyperspectral remote sensing, which has been used to monitor changes in leaf traits and estimate soybean leaf carbon and nitrogen content.…”

Section: Introductionmentioning

confidence: 99%

Fast and low‐cost method for direct and simultaneous determination of nitrogen and carbon in soybean leaves using benchtop and portable near‐infrared devices

da Silva,

Abich,

Maurer

et al. 2023

J Sci Food Agric

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BackgroundThe current techniques for determining carbon and nitrogen content to provide information about the nutritional status of plants are time‐consuming and expensive. For this reason, the objective of this study was to develop an analytical method for the direct and simultaneous determination of nitrogen and carbon elemental content in soybean leaves using near‐infrared spectroscopy (NIRS) and compare the performance of conventional (1,100–2,500 nm spectral range) and portable equipment (1,100–1,700 nm spectral range). Partial least squares (PLS) regression models were developed using 27 soybean leaf samples collected during the 2021 harvest and applied for the simultaneous determination of carbon and nitrogen in 13 samples collected during the 2022 harvest.ResultsThe benchtop method yielded low root mean square error of prediction (RMSEP) values for nitrogen and carbon (2.42 g kg‐1 and 4.37 g kg‐1 respectively), while the portable method had higher RMSEP values (3.82 g kg‐1 and 10.7 g kg‐1 respectively). The benchtop method did not show significant differences when compared to the reference method for determining nitrogen and carbon. In contrast, the portable methodology showed potential as a screening method for determining nitrogen levels, particularly in fieldwork.ConclusionThe methodologies evaluated in this study were implemented and evaluated under real crop monitoring conditions, using independent sets of calibration and prediction samples. Their utilization enables the acquisition of cost‐effective, safe analytical data aligning with the principles of green analytical chemistry.This article is protected by copyright. All rights reserved.

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“…Due to its rapid detection speed (less than 3 min for each sample), simultaneous detection of many components, no sample pretreatment required, repeatability, non-destructiveness, and environmental friendliness [13][14][15], near infrared (NIR) spectroscopy has demonstrated significant potential in the field of composition analysis and is widely used in food [16][17][18], agriculture [19][20][21], pharmaceutical [22,23], chemical [24,25], and many other fields [26,27]. For example, Tsegay et al [28] developed non-destructive NIR spectroscopy analytical models to predict oil and major fatty acid contents of Ethiopian sesame and Niemi et al [29] applied NIR spectroscopy to determine protein content in North Atlantic seaweed.…”

Section: Introductionmentioning

confidence: 99%

Rapid Analysis of Raw Meal Composition Content Based on NIR Spectroscopy for Cement Raw Material Proportioning Control Process

2022

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Due to fast analysis speed, analyzing composition content of cement raw meal utilizing near infrared (NIR) spectroscopy, combined with partial least squares regression (PLS), is a reliable alternative method for the cement industry to obtain qualified cement products. However, it has hardly been studied. The raw materials employed in different cement plants differ, and the spectral absorption intensity in the NIR range of the raw meal component is weaker than organic substances, although there are obvious absorption peaks, which place high demands on the generality of modeling and accuracy of the analytical model. An effective modeling procedure is proposed, which optimizes the quantitative analytical model from several modeling stages, and two groups of samples with different raw material types and origins are collected to validate it. For the samples in the prediction set from Qufu, the root mean square error of prediction (RMSEP) of CaO, SiO2, Al2O3, and Fe2O3 were 0.1910, 0.2307, 0.0921, and 0.0429, respectively; the average prediction errors for CaO, SiO2, Al2O3, and Fe2O3 were 0.171%, 0.193%, 0.069%, and 0.032%, respectively; for the samples in the prediction set from Linyi, the RMSEP of CaO, SiO2, Al2O3, and Fe2O3 were 0.1995, 0.1267, 0.0336 and 0.0242, respectively, the average prediction errors for CaO, SiO2, Al2O3, and Fe2O3 were 0.154%, 0.100%, 0.022%, and 0.018%, respectively. The standard methods for chemical analysis of cement require that the mean measurement error for CaO, SiO2, Al2O3, and Fe2O3 should be within 0.40%, 0.30%, 0.20%, and 0.15%, respectively. It is obvious that the results of both groups of samples fully satisfied the requirements of raw material proportioning control of the production line, demonstrating that the modeling procedure has excellent generality, the models established have high prediction accuracy, and the NIR spectroscopy combined with the proposed modeling procedure is a rapid and accurate alternative approach for the analysis of cement raw meal composition content.

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Rapid measurement of classification levels of primary macronutrients in durian (Durio zibethinus Murray CV. Mon Thong) leaves using FT-NIR spectrometer and comparing the effect of imbalanced and balanced data for modelling

Cited by 13 publications

References 57 publications

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data

Fast and low‐cost method for direct and simultaneous determination of nitrogen and carbon in soybean leaves using benchtop and portable near‐infrared devices

Rapid Analysis of Raw Meal Composition Content Based on NIR Spectroscopy for Cement Raw Material Proportioning Control Process

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