Khurnpoon, Lampan

Preferred name

Khurnpoon, Lampan

Alternative Name

Khurnpoon, L.

Main Affiliation

School of Agricultural Technology

lampan.kh@kmitl.ac.th

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Total soluble solids, dry matter content prediction and maturity stage classification of durian fruit using long-wavelength NIR reflectance
(2023-12-01)
Saenphon, Chirawan
;
Ditcharoen, Sirirak
;
Malai, Chayuttapong
;
Saengprachatanarug, Khwantri
;
Wongpichet, Seree
The DM and TSS of durian pulp moving on a conveyor belt were measured for their rapid and non-destructive qualities based on quantitative and qualitative measurements. The calibration set and prediction set equaled 209 and 69 pulps, respectively. The quantitative test compared the performance of PLS regression for DM and TSS prediction developed from full wavelength (860–1754 nm) and a few significant variables using SPA, GA, and VIP methods. The qualitative test identified the possibility of maturity stage classification by comparing three supervised machine learning classifiers, namely SVM, random forest (RF), and LDA. Effective models for DM and TSS prediction were developed from second derivatives spectra combined with the GA method, exhibiting r2, SEP, and RPD of 0.85, 4.50%, and 2.64, respectively for DM, and 0.66, 5.15%, and 1.60, respectively, for TSS. The model classifying samples into two distinct groups, namely “reject” and “pass,” utilizing the LDA algorithm, exhibited an impressive accuracy rate of 94.20%, making it a suitable choice for quality assurance purposes. This result indicates that the few effective variables were more efficient than full wavelength and improved model accuracy with greater model stability. Enhancing the classification model could involve data sample balancing in each group, leading to further improvements.
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Classification of N, P, and K concentrations in durian (Durio zibethinus murray cv. mon thong) leaves using near-infrared spectroscopy
(2022-08-24)
Phanomsophon, Thitima
;
Jaisue, Natthapon
;
Tawinteung, Nukoon
;
Khurnpoon, Lampan
;
Sirisomboon, Panmanas
A good durian fruit should be supported by sufficient nutrient concentrations. If farmers can quickly determine the nutrient concentration in leaves, they can control fertilisation. Near-infrared (NIR) spectroscopy is a rapid non-destructive test for estimating nutrient concentration. Measuring nutrient concentrations requires chemical analysis and is a time-consuming process. The aim of this paper is a preliminary test for classifying the N, P, and K concentration levels to determine the possibility of creating models in the future to help farmers obtain information on durian tree nutrient requirements quickly. The models reported in this paper were created by PLS-DA, which had higher accuracy than SVM and the SIMCA method. Fresh durian leaf samples were used, and the spectra provided greater accuracy than the dried ground leaf sample spectra. The classification models had an accuracy of 88.89, 81.25, and 86.36% for predicting N, P, and K, respectively.
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Rapid evaluation of the texture properties of melon (Cucumis melo L. Var. reticulata cv. Green net) using near infrared spectroscopy
(2018-08-01)
Khurnpoon, Lampan
;
Sirisomboon, Panmanas
The near infrared (NIR) spectroscopy as the rapid nondestructive method was aimed to be applied for determination of the texture properties of melon intact fruit and pulp including initial firmness, rupture force, average firmness, rupture distance, toughness, average penetrating force and penetrating energy. The data from the reference method of texture analyzer were correlated with the NIR spectral data. The result showed that, only the two properties including rupture force and penetrating force in pulp could be predicted by NIR spectroscopy technique. The determination coefficient of validation (r2) for prediction of rupture force and penetrating force in the pulp of melon using intact fruit spectra were 0.850 and 0.845, respectively. The r2, for prediction of rupture force and penetrating force in the pulp of melon using pulp spectra were 0.813 and 0.778, respectively. This indicated that the NIR spectroscopy protocol developed here was useful for research works such as breeding and postharvest research, the melon processing factory and also the import and export of melon. Practical applications: The near infrared spectroscopy protocol developed for determination of rupture force and penetrating force in pulp using intact fruit spectra as a nondestructive method will be useful for research works such as breeding and postharvest research, the melon processing factory and also the import and export of melon. There are also the protocol developed using pulp spectra can be used for texture determination of fresh-cut melon.
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Primary assessment of macronutrients in durian (CV Monthong) leaves using near infrared spectroscopy with wavelength selection
(2024-01-05)
Phanomsophon, Thitima
;
Jaisue, Natthapon
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Worphet, Akarawhat
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Tawinteung, Nukoon
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Khurnpoon, Lampan
Farmers would be able to regulate fertilization and produce quality durian if they knew the nutrient concentration in durian leaves. A long period of time for traditional nutritional content determination is needed. Therefore, near-infrared spectroscopy is a good method for nondestructive and quick nutrient content evaluation. The leaf sample matrices (fresh leaves, dried ground leaves, and dried ground leaf pellets) were scanned by Fourier transform near-infrared (FT-NIR) with a wavelength of 12,500–3,600 cm−1. Regression models were developed using partial least squares (PLS) with full wavelength, short wavelength, and selected wavelength by successive projections algorithm (SPA). In this study, the model for N and K concentration was acceptable and the prediction was considered good but for P content not had succeeded. As a result, the PLS-SPA model using fresh leaf samples for evaluating N content in durian leaves exhibited performance of r2 = 0.852, SEP = 0.14%, RPD = 2.63 and bias = −0.020%. The PLS-SPA model using dried ground leaf samples for evaluating K content in durian leaves exhibited performance of r2 = 0.820, SEP = 0.13%, RPD = 2.36 and bias = 0.006%. This research found that it is possible to apply NIR waves to predict N and K concentrations in durian leaves. It is not necessary to predict directly from the wavelengths associated with -N or -K bonds. Instead, NIR can measure them indirectly from the bonding of proteins, which are products formed by N and K. In addition, selecting the wavelength that is related to the value to be measured can produce results that are not significantly different from using full or short wavelengths. These models can assist farmers in rapidly predicting N and K content in durian leaves for immediate fertilizer adjustment.
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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
(2022-11-15)
Phanomsophon, Thitima
;
Jaisue, Natthapon
;
Worphet, Akarawhat
;
Tawinteung, Nukoon
;
Shrestha, Bijendra
For durian growth to produce high-quality fruit, plants should receive sufficient nutrients. Currently, farmers apply various fertilisers to produce a large quantity and quality of durian fruit, irrespective of the actual nutrients that the plant requires. Accordingly, the production cost is high and non-renewable resources. Therefore, this study focused on rapid classification primary macronutrient levels in durian (Durio zibethinus Murray CV. Mon Thong) leaves using Fourier transform near-infrared (FT-NIR) spectroscopy and investigated the effect of imbalanced data on efficient classification models. Contents of N, P, and K in durian leaves were measured via NIR with the wavelength range of 800–2,500 nm. Classification models were developed using partial least squares, k-nearest neighbour, and artificial neural networks (ANNs) with imbalanced and balanced data. The imbalanced data were balanced using a synthetic minority oversampling technique (SMOTE). In this study, the model regarding the fresh leaf sample performed better than that for the dried ground leaf sample. Moreover, the ANN was the best algorithm, exhibiting validation accuracies of classified levels corresponding to N = 0.99 and P = 0.97 when the data were analysed with SMOTE and K = 1.00 from the original balanced data. The imbalanced data affected biased classification when the models could increase the classification accuracy by applying balanced data for modelling.
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Evaluation of physiological properties and texture traits of durian pulp using near-infrared spectra of the pulp and intact fruit
(2021-04-01)
Onsawai, Phalanon
;
Phetpan, Kittisak
;
Khurnpoon, Lampan
;
Sirisomboon, Panmanas
This study aimed to investigate the feasibility of non-destructively predicting physiological properties (color, dry matter, and soluble solids) and texture properties (initial firmness, average firmness, rupture force, rupture distance, toughness, average penetrating force, and penetrating energy) of ‘Monthong’ durian using Fourier transform near-infrared spectroscopy of the pulp of the largest locule, the intact fruit at the largest locule, and the stylar end of the intact fruit. Based on partial least squares regression modeling, the internal quality evaluation of durian obtained by scanning the pulp could provide rough screening capability, with coefficient of determination of validation (r2), root mean square error of prediction (RMSEP), and the ratio of standard error of validation to standard deviation (RPD) values for the dry matter content, average penetrating force, and rupture force of 0.89, 3.60%, and 3.27; 0.73, 5.53 N, and 1.95; and 0.74, 6.15 N, and 1.96, respectively. Only the dry matter content of the pulp could be reasonably predicted based on scanning the intact durian fruit at the largest locule, with r2, RMSEP, and RPD values of 0.79, 5.23%, and 2.18, respectively. This finding could be applied at the first stage of trade between durian agriculturalists and exporters to prevent the exportation of immature durian and would be helpful for the industries producing frozen durian pulp and intact durian fruit for export.
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Comparing Machine Learning and PLSDA Algorithms for Durian Pulp Classification Using Inline NIR Spectra
(2023-06-01)
Pokhrel, Dharma Raj
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Sirisomboon, Panmanas
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Khurnpoon, Lampan
;
Posom, Jetsada
;
Saechua, Wanphut
The aim of this study was to evaluate and compare the performance of multivariate classification algorithms, specifically Partial Least Squares Discriminant Analysis (PLS-DA) and machine learning algorithms, in the classification of Monthong durian pulp based on its dry matter content (DMC) and soluble solid content (SSC), using the inline acquisition of near-infrared (NIR) spectra. A total of 415 durian pulp samples were collected and analyzed. Raw spectra were preprocessed using five different combinations of spectral preprocessing techniques: Moving Average with Standard Normal Variate (MA+SNV), Savitzky–Golay Smoothing with Standard Normal Variate (SG+SNV), Mean Normalization (SG+MN), Baseline Correction (SG+BC), and Multiplicative Scatter Correction (SG+MSC). The results revealed that the SG+SNV preprocessing technique produced the best performance with both the PLS-DA and machine learning algorithms. The optimized wide neural network algorithm of machine learning achieved the highest overall classification accuracy of 85.3%, outperforming the PLS-DA model, with overall classification accuracy of 81.4%. Additionally, evaluation metrics such as recall, precision, specificity, F1-score, AUC ROC, and kappa were calculated and compared between the two models. The findings of this study demonstrate the potential of machine learning algorithms to provide similar or better performance compared to PLS-DA in classifying Monthong durian pulp based on DMC and SSC using NIR spectroscopy, and they can be applied in the quality control and management of durian pulp production and storage.

Khurnpoon, Lampan

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