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Postharvest detection of anthracnose (Colletotrichum asianum) on mango fruit (Mangifera indica L. cv Namdokmai Sithong) using near-infrared response
(2026-12-01)
Junto, Apiwat
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Phanomsophon, Thitima
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Sharma, Sneha
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Kaewsorn, Kannapot
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Jongyingcharoen, Jiraporn Sripinyowanich
Anthracnose disease, caused by fungi of the genus Colletotrichum, poses a major threat to mango production and export industries, with Colletotrichum asianum being among the most significant pathogenic species. This work proposes the hypothesis that the simple difference in absorption between anthracnose-infected and noninfected mangoes illustrated by the average near-infrared (NIR) spectra obtained from hyperspectral images could be used for simple differentiation of the two groups. The method of depositing fungal spores by spraying the spores over the fruit surface, not a small area or specific point, allows for the number of spores per unit area to be harmonized and to detect infected or noninfected spores on every pixel of the mango surface using a hyperspectral imaging camera. Important wavelengths for differentiation included water bands of 970, 1190, and 1200 nm which resulted in the greatest difference in absorbance, and bands of chitin, the major component of the fungal cell wall; 1195 nm was the most important band. In addition, the vibration bands of 868 (protein in the fungal cell wall), 1134 (sugar and starch of the mango substrate), 1320 (NIR absorbers in the fungus-sprayed and mango substrate, not specifically defined) and 1069 nm (crystallinity and N-acetyl methyl groups in the fungal chitin and constituents of the mango), differed from each other. These wavelengths can be used for modelling, which can lead to high performance in quantifying the concentration of anthracnose and classifying the strength levels of anthracnose infection. The microbiological mechanism of anthracnose growth on infected mangoes corresponding to changes in the NIR spectrum during the 4 days after spore infection is comprehensively discussed. These results can aid in enhancing early detection and classification techniques for anthracnose-infected mangoes from noninfected mangoes using hyperspectral image sensors.
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Author Correction: Predicting biomass global warming potential with FT-NIR spectroscopy (Scientific Reports, (2025), 15, 1, (33725), 10.1038/s41598-025-10584-z)
(2026-12-01)
Gyawali, Prakash
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Shrestha, Bijendra
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Phanomsophon, Thitima
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Posom, Jetsada
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Pornchaloempong, Pimpen
Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-025-10584-z, published online 30 September 2025 The original version of this Article contained errors. In the original version of this article, the climate-carbon effect value was not included as introduced in AR6. As a result, in the Materials and methods section, under the subheading ‘Estimation of global warming potential and emission of greenhouse gas (GHG)’, where: “Step 1: (CO<inf>2</inf>, CH<inf>4</inf>, N<inf>2</inf>O) emissions (kg) = Mass of Fuel (kg) × Carbon emission factor (kg TJ-1) × HHV (kg TJ-1) Step 2: Total GWP = (GWP of CO<inf>2</inf>×CO<inf>2</inf> emissions) +(GWP of CH<inf>4</inf>×CH<inf>4</inf> emissions) +(GWP of N<inf>2</inf>O×N<inf>2</inf>O emissions)” now reads: “Step 1: GHG (CO<inf>2</inf>, CH<inf>4</inf>, or N<inf>2</inf>O) emissions (kg) = Mass of Fuel (kg) × Specific GHG emission factor (kg TJ-1) × HHV (TJ kg-1) Step 2: Total GWP (kg CO<inf>2</inf>e) = (GWP of CO<inf>2</inf>×CO<inf>2</inf> emissions) + (GWP of CH<inf>4</inf>×CH<inf>4</inf> emissions) + (GWP of N<inf>2</inf>O×N<inf>2</inf>O emissions)” In addition, under the subheading ‘Model development and validation’, where: “The model was optimized by selecting wavenumbers through various variable selection methods, including the Correlation Method (CM), Variance Method (VM), Co-Variance Method (COVM), and Variable Importance Projection (VIP). The spectral data were pretreated using raw spectra, standard normal variate (SNV), as well as first derivative and second derivative transformations. The following spectra pretreatment methods: Standard Normal Variate (SNV) is for corrects scatter effects and baseline variations35,” now reads: “The model was optimized by selecting wavenumbers through various variable selection methods, including the Correlation Method (CM), Variance Method (VM), Co-Variance Method (COVM), and Variable Importance Projection (VIP). The following spectra pretreatment methods: Standard Normal Variate (SNV) is for correcting scatter effects and baseline variations35,” In addition, Equations 3, 4 and 6 contained typesetting errors. As a result, Equation 3: (Formula presented.) now reads, (Formula presented.) Equation 4: (Formula presented.) now reads: (Formula presented.) And Equation 6 (Formula presented.) now reads: (Formula presented.) Furthermore, under the Results section, subheading ‘Predicting performance of biomass GWP using PLSR’, where: “The model developed with CM (reduction of 1150 wavenumber of full range to 325 wavenumber) of 1st derivative spectra, gave best performance with R2<inf>P</inf> was 0.87 (Table 4).” now reads: “The model developed with COVM (reduction of 1150 wavenumber of full range to 325 wavenumber) of 1st derivative spectra, gave the best performance with R2<inf>P</inf> was 0.85 (Table 4).” Under the subheading ‘Prediction result of HHV using PLSR’, where: “This approach reduced the number of variables from 1150 to 365 wavenumbers, significantly enhancing the model’s performance (R2<inf>C</inf> of 0.98 and R2<inf>P</inf> of 0.87)” now reads: “This approach reduced the number of variables from 1150 to 365 wavenumbers, significantly enhancing the model’s performance (R2<inf>C</inf> of 0.98 and R2<inf>P</inf> of 0.86)” Under the subheading ‘Regression coefficient and x-loading of GWP model’, where: “Prominent peaks were identified and the bond vibration interpretation is shown in Table 7 and the vibration indicated by Workman and Weyer38 at the wavenumbers in bold were not found or not related to biomass.” now reads: “Prominent peaks were identified, and the bond vibration interpretation is shown in Table 7, which was indicated by Workman and Weyer38.” Equation 8: GWP for CO<inf>2</inf>, or CH<inf>4</inf>, or N<inf>2</inf>O Emissions = (1×CO<inf>2</inf> Emission) or (29.8×CH<inf>4</inf> Emission) or (273×N<inf>2</inf>O Emission) = (1×112 HHV) for CO<inf>2</inf> Emission or (29.8×30 HHV) for CH<inf>4</inf> Emission or (273×4 HHV) for N<inf>2</inf>O Emission = 112.0×HHV for CO<inf>2</inf> Emission or 894.0×HHV for CH<inf>4</inf> Emission or 1092.0×HHV for N<inf>2</inf>O Emission and GWP total = 2098.0 (HHV, TJ kg-1) = 0.000002098 kJ kg-1 = 0.000002098 Jg-1 now reads: GWP for CO<inf>2</inf>, or CH<inf>4</inf>, or N<inf>2</inf>O Emissions = (1×CO<inf>2</inf> Emission) or (27.2×CH<inf>4</inf> Emission) or (273×N<inf>2</inf>O Emission) = (1×112 HHV) for CO<inf>2</inf> Emission or (27.2×30 HHV) for CH<inf>4</inf> Emission or (273×4 HHV) for N<inf>2</inf>O Emission = 112.0×HHV for CO<inf>2</inf> Emission or 816.0×HHV for CH<inf>4</inf> Emission or 1092.0×HHV for N<inf>2</inf>O Emission and GWP total (kg CO<inf>2</inf>e) = 2020.0 (HHV, TJ kg-1) = 0.000002020 kJ kg-1 = 0.000002020 J g-1 Moreover, Tables 1 and 3 have been corrected. Incorrect Table 1: Remarks IPCC guideline Calculation of CO<inf>2</inf> emission Higher Heating Value = 17932000 J kg-1 CO<inf>2</inf> emission factor = 112 kg TJ-1, we can follow these steps: Convert HHV to TJ kg-1: Since 1 TJ = 1012 J, we need to convert the HHV from J kg-1 to TJ kg-1: HHV in TJ kg-1 = 17932000 J kg-1 / 1012 = 1.7932×10-3 TJ kg-1 CO<inf>2</inf> emission (kg) = Mass of fuel (kg) × CO<inf>2</inf> emission factor (kg TJ-1) × HHV (TJ kg-1) CO<inf>2</inf> emission (kg) = 1 kg × 112 kg TJ-1 × 0.017932 TJ kg-1 Therefore, the CO<inf>2</inf> emission from stationary fuel combustion with an HHV of 17932000 J kg-1 and using the default CO<inf>2</inf> emission factor of 112 kg TJ-1 would be approximately 2.0083×10-3 kg of CO<inf>2</inf> per kg of fuel. CO<inf>2</inf> emission factor: 112 kg dry matter TJ-1 (typical for wood combustion) Calculation of CH<inf>4</inf> emission Hight Heating Value = 17932000 J kg-1 CH<inf>4</inf> emission factor = 30 kg TJ-1 Convert HHV unit to TJ kg-1 1 TJ= 1012 J HHV from J kg-1 to TJ kg-1: HHV in TJ kg-1 = 17932000 J kg-1 / 1012 = 1,7932×10-5 TJ kg-1 CH<inf>4</inf> emission (kg) = Mass of Fuel (kg) × CH<inf>4</inf> emission factor (kg TJ-1) × HHV (TJ kg-1) CH<inf>4</inf> emission (kg) = 1 kg × 30 kg TJ-1 × 1,7932×10-5 TJ kg-1 CH<inf>4</inf> emission (kg) = 5.3796×10-4 kg of CH<inf>4</inf> per kg of fuel CH<inf>4</inf> emission factor: 30 kg dry matter TJ-1 Calculation of N<inf>2</inf>O emission Hight Heating Value = 17932000 J kg-1 N<inf>2</inf>O emission factor = 4 kg TJ-1 Convert HHV unit to TJ kg-1 1 TJ = 1012 J HHV from J kg-1 to TJ kg-1: HHV in TJ kg-1 = 17932000 J kg-1 / 1012 = 1.7932 ×10-5 TJ kg-1 N<inf>2</inf>O emission (kg) = Mass of Fuel (kg) × N<inf>2</inf>O emission factor (kg TJ-1) × HHV (TJ kg-1) N<inf>2</inf>O emission (kg) = 1 kg × 4 kg TJ-1 × 1.7932×10-5 TJ kg-1 N<inf>2</inf>O emission (kg) = 7.1728×10-5 kg of N<inf>2</inf>O per kg of fuel N<inf>2</inf>O emission factor 4 kg dry matter /TJ The concept of global warming potential (GWP) was introduced in IPCC –AR1 (Shine et al. 1990) to compare the greenhouse effects of different greenhouse gases relative to a reference gas, normally taken as carbon dioxide, under this definition, CO<inf>2</inf> would have a GWP value of 1. Total GWP = (GWP of CO<inf>2</inf>×CO<inf>2</inf> emissions)+(GWP of CH<inf>4</inf>×CH<inf>4</inf> emissions)+(GWP of N<inf>2</inf>O×N<inf>2</inf>O emissions) Total GWP =1×2.0083×10-3+29.8× 5.379 ×10-4+273×7.1728×10-5 Total GWP=0.0376 kg CO<inf>2</inf>e This calculation is by 100 years based GWP of emission gases followed AR6 7 Correct Table 1: Remarks IPCC guideline Calculation of CO<inf>2</inf> emission Higher Heating Value = 17932000 J kg-1 CO<inf>2</inf> emission factor = 112 kg TJ-1, we can follow these steps: Convert HHV to TJ kg-1: Since 1 TJ = 1012 J, we need to convert the HHV from J kg-1 to TJ kg-1: HHV in TJ kg-1 = 17932000 J kg-1 / 1012 = 1.7932×10-5 TJ kg-1 CO<inf>2</inf> emission (kg) = Mass of fuel (kg) × CO<inf>2</inf> emission factor (kg TJ-1) × HHV (TJ kg-1) CO<inf>2</inf> emission (kg) = 1 kg × 112 kg TJ-1 × 1.7932 × 10-5 TJ kg-1 Therefore, the CO<inf>2</inf> emission from stationary fuel combustion with an HHV of 17932000 J kg-1 and using the default CO<inf>2</inf> emission factor of 112 kg TJ-1 would be approximately 2.0083 × 10-3 kg of CO<inf>2</inf> per kg of fuel. CO<inf>2</inf> emission factor: 112 kg dry matter TJ-1 (typical for wood combustion) Calculation of CH<inf>4</inf> emission Higher Heating Value = 17932000 J kg-1 CH<inf>4</inf> emission factor = 30 kg TJ-1 Convert HHV unit to TJ kg-1 1 TJ= 1012 J HHV from J kg-1 to TJ kg-1: HHV in TJ kg-1 = 17932000 J kg-1 / 1012 = 1,7932×10-5 TJ kg-1 CH<inf>4</inf> emission (kg) = Mass of Fuel (kg) × CH<inf>4</inf> emission factor (kg TJ-1) × HHV (TJ kg-1) CH<inf>4</inf> emission (kg) = 1 kg × 30 kg TJ-1 × 1,7932×10-5 TJ kg-1 CH<inf>4</inf> emission (kg) = 5.3796×10-4 kg of CH<inf>4</inf> per kg of fuel CH<inf>4</inf> emission factor: 30 kg dry matter TJ-1 Calculation of N<inf>2</inf>O emission Higher Heating Value = 17932000 J kg-1 N<inf>2</inf>O emission factor = 4 kg TJ-1 Convert HHV unit to TJ kg-1 1 TJ = 1012 J HHV from J kg-1 to TJ kg-1: HHV in TJ kg-1 = 17932000 J kg-1 / 1012 = 1.7932 × 10-5 TJ kg-1 N<inf>2</inf>O emission (kg) = Mass of Fuel (kg) × N<inf>2</inf>O emission factor (kg TJ-1) × HHV (TJ kg-1) N<inf>2</inf>O emission (kg) = 1 kg × 4 kg TJ-1 × 1.7932×10-5 TJ kg-1 N<inf>2</inf>O emission (kg) = 7.1728×10-5 kg of N<inf>2</inf>O per kg of fuel N<inf>2</inf>O emission factor 4 kg dry matter /TJ-1 The concept of global warming potential (GWP) was introduced in IPCC –AR1 (Shine et al. 1990) to compare the greenhouse effects of different greenhouse gases relative to a reference gas, normally taken as carbon dioxide, under this definition, CO<inf>2</inf> would have a GWP value of 1. Total GWP = (GWP of CO<inf>2</inf>×CO<inf>2</inf> emissions) + (GWP of CH<inf>4</inf>×CH<inf>4</inf> emissions) + (GWP of N<inf>2</inf>O×N<inf>2</inf>O emissions) Total GWP = 1×2.0083×10-3+ 27.2× 5.3796 × 10-4+ 273×7.1728×10-5 Total GWP= 0.03622 kg CO<inf>2</inf>e This calculation is by 100 years based GWP of emission gases followed AR6 7 Incorrect Table 3: Calibration set Prediction set Parameter Method NT NC Max Min Mean SD NP Max Min Mean SD GWP IPCC Guidelines 197 147 0.03905 0.03080 0.03564 0.00180 50 0.038943 0.033002 0.03577 0.00165 HHV (J g-1) Bomb Calorimeter 197 147 18616 16405 16976 910 50 17950 15268 17051 787 Correct Table 3: Parameter Method NT Calibration Set Prediction Set NC Max Min Mean SD NP Max Min Mean SD GWP (kg CO<inf>2</inf>e) IPCC Guidelines 197 147 0.03905 0.03080 0.03564 0.00180 50 0.038943 0.033002 0.03577 0.00165 HHV (J g-1) Bomb Calorimeter 197 147 18616 16405 15268 910 50 17950 16976 17051 787 Finally, the legends of Tables 4 and 7 have been updated: “Table 4: Prediction of GWP of biomass of fast-growing tree and agriculture residue by PLSR. N: Number of samples in calibration set, R2<inf>c</inf>: coefficient of determination of calibration set, n: number of samples in prediction set, R2<inf>p</inf>: coefficient of determination of prediction set, RPD: ratio of prediction to deviation, CM: correlation method, VM: variance method, COVM: co-variance method, VIP: variable. Significant values are in [bold].” now reads: “Table 4. Prediction of GWP of biomass of fast-growing tree and agriculture residue by PLSR. N: Number of samples in calibration set, R2<inf>c</inf>: coefficient of determination of calibration set, n: number of samples in prediction set, R2<inf>p</inf>: coefficient of determination of prediction set, RPD: ratio of prediction to deviation, CM: correlation method, VM: variance method, COVM: co-variance method, VIP: variable important projection, FstDev: 1st derivative, SecDev: 2nd derivative. Significant values are in [bold].” “Table 7. The function groups corresponding to the wavenumber shown in regression coefficient plot and x-loading plot of models for GWP and HHV. *1ν, fundamental stretching vibration; 2ν, 1st overtone of fundamental stretching vibration; 3ν, 2nd overtone of fundamental stretching vibration; 5ν, 4th overtone of fundamental stretching vibration; 1δ, fundamental bending (deformation) vibration; 3δ, 2nd overtone of fundamental bending (deformation) vibration; 1, symmetric stretching vibration; 2, bending vibration; 3, asymmetric stretching vibration; and + is combination. now reads: “Table 7. The function groups corresponding to the wavenumber shown in regression coefficient plot and x-loading plot of models for GWP and HHV. 1ν, fundamental stretching vibration; 2ν, 1st overtone of fundamental stretching vibration; 3ν, 2nd overtone of fundamental stretching vibration; 5ν, 4th overtone of fundamental stretching vibration; 1δ, fundamental bending (deformation) vibration; 3δ, 2nd overtone of fundamental bending (deformation) vibration; and + is combination.” The original version of this Article has been corrected.
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Filter-based short-wave infrared imaging combined with machine learning for non-destructive quality assessment of durian pulp
(2026-09-01)
Promnioy, Surasak
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Phetpan, Kittisak
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Riza, Dimas Firmanda Al
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Sharma, Sneha
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Sirisomboon, Panmanas
The development of affordable, real-time quality monitoring tools is essential for industrial applications involving high-value tropical fruits such as durian. This study presents a cost-effective short-wave infrared multispectral imaging (SWIR-MSI) system employing three discrete bandpass filters (880, 905, and 940 nm) integrated with machine learning algorithms for non-destructive evaluation of fresh-cut durian pulp. Compared with conventional point-based NIR spectroscopy and complex hyperspectral imaging systems, the proposed configuration markedly reduces system complexity and cost while maintaining sensitivity to key compositional variations. It accurately predicted dry matter content (DMC) and starch, demonstrating that limited spectral information within the 860–1100 nm range can effectively capture moisture- and carbohydrate-related features. These findings confirm the feasibility of implementing filter-based SWIR imaging as a practical and scalable alternative to hyperspectral systems for on-line fruit quality assessment. Practically, this approach enables rapid, non-destructive, and spatially adaptable evaluation of durian pulp quality, offering significant potential for on-site grading, ripeness classification, and process control in fresh-cut durian production and packaging operations, particularly for small- and medium-scale agro-processors.
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Huanglongbing (HLB) disease detection using drone imagery and deep learning neural networks for early management of HLB
(2026-08-01)
Kalbande, Vishal Dashrathrao
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Phanomsophon, Thitima
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Hongwiangjan, Jeerayut
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Sripinyowanich Jongyingcharoen, Jiraporn
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Sirisomboon, Panmanas
In the modern world, machine learning and artificial intelligence have become the foundation of the digital revolution and have a significant role in daily life. It is adopted in various applications, such as object detection, recognition and classification. This research aimed to use deep learning algorithms for the detection of Huanglongbing (HLB)-infected disease, healthy and background patch in citrus orchard. HLB-infected patches need to be identified for appropriate treatments of spraying. However, farmers are unaware of infections on plant leaves and therefore adopt manual disease identification methods. This method results in loss of productivity as the infection spreads throughout the field. However, due to a lack of required facilities, instant identification needs to be improved in many aspects of the agricultural sector. To conduct this research, firstly, a dataset was created that contained drone images of citrus orchards that was categorized into three classes, specifically (i) Healthy, (ii) HLB-infected and (iii) Background. Under this research, 6000 image patches of citrus orchard were collected and categorized 2000 images in each class based on appropriate labels. The next step was to train the deep learning models to identify Healthy, HLB-infected and Background. In this research three models viz. EfficientNetV2B0, DenseNet-121 and ResNet-50 were trained to detect the HLB infection patch in the citrus orchard. The models exhibited exceptional performance, with ResNet-50 achieving the highest overall accuracy of 89%, followed by EfficientNet-V2B0 (87%) and DenseNet-121 (85%). ResNet-50 demonstrated superior balanced performance with macro-average precision of 90%, recall of 89%, and F1-score of 89%. The generated HLB detection map effectively visualized disease distribution patterns, identifying 31% of patches as HLB-infected, 7% as Healthy and 62% as Background across the orchard grid. This research establishes a reliable framework for large-scale, automated citrus disease monitoring that enables precision agriculture interventions, potentially reducing economic losses and optimizing resource allocation in citrus cultivation through early and accurate HLB disease detection.
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NIR Spectroscopy for Non-Destructive Prediction of Greenhouse Gas Emissions and Global Warming Potential by Biomass Combustion
(2026-05-01)
Sirisomboon, Panmanas
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Gyawali, Prakash
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Posom, Jetsada
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Lapcharoensuk, Ravipat
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Shrestha, Bim Prasad
Greenhouse gas (GHG) emissions from biomass combustion include carbon dioxide (CO<inf>2</inf>), methane (CH<inf>4</inf>) and nitrous oxide (N<inf>2</inf>O), which cause climate change and global warming. By measuring GHG emissions by biomass combustion, a potent protocol for the calculation of global warming potential (GWP), which is how much the global temperature has risen due to combustion processes, can be achieved, contributing to determining the mean reduction in global temperature rise and fostering a transition towards more sustainable energy systems. Additionally, warning can be given of the GHG and GWP risks associated with different species of biomass. This review includes the GHG emissions and GWP of biomass combustion and their measurement and estimation directly through biomass sample combustion, using unmanned aerial vehicles (UAVs) and satellite measurements of radiation interacting with atmospheric gases, or satellite-derived data and calculations according to IPCC guidelines. In addition, the relationship of lignocellulosic compounds and elements in biomass to HHV and GHG emissions is described. The key mechanism of molecular vibration of hydrogen bonds in biomass caused by NIR radiation related to GHG emissions is revealed and recorded regarding the possibility of using NIR spectroscopy for the prediction of GHG emissions and GWP. Calculation examples for sugarcane bagasse and other biomass species are shown. The comparative advantages and limitations of NIR spectroscopy with respect to other methods are included. These factors lead to elucidation of the possibility of using NIR spectroscopy for non-destructive prediction of GHG emissions. In this review, the feasibility of using NIR spectroscopy to evaluate GHG emissions, GWP and emission factors (EFs) as an alternative to IPCC estimation methods related to climate change by biomass combustion is confirmed. NIR spectroscopy is a novel methodology for predicting GHG emissions and GWP directly from intact chip or powder biomass spectral data without explicit gas measurement. This article records the essential spectroscopic knowledge of biomass polymer valorization that is of value in polymer science.
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Prediction of Cross-Link Density of Prevulcanized Latex Using InGaAs NIR Spectroscopy
(2026-02-17)
Dachapan, Suntaree
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Saengprachatanarug, Khwantri
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Phuphaphud, Arthit
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Sirisomboon, Panmanas
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Lim, Chin Hock
Cross-link density plays a critical role in determining the mechanical strength, elasticity, and long-term performance of prevulcanized (PV) latex products. This study aimed to evaluate the feasibility of near-infrared (NIR) spectroscopy for predicting cross-link density in PV latex using InGaAs-based instruments. Two spectrometers operating in the 960–1700 nm range were investigated: a noncontact MicroNIR and a contact AvaSpec system. Partial least-squares regression (PLSR) models were developed using two data sets. Data set I contained only NIR spectra, whereas Data set II combined spectra with industrial process variables, including holding time and latex grade. Cross-link density was referenced using the Prevulcanizate Relaxed Modulus (PRM, 100% and 300% elongation) and Toluene Swelling Index (TSI, 3 and 6 h) methods. Models based solely on Data set I showed limited predictive performance, while Data set II yielded markedly improved accuracy, highlighting the importance of integrating process information. The TSI-based models consistently surpassed PRM, with TSI-3 h identified as the most reliable reference. Using MicroNIR, the best TSI-3 h and TSI-6 h models achieved SEP values of 5.64 × 104 and 5.96 × 104 N·m– 2 and R2 values of 0.96 and 0.95, respectively. Comparable performance between contact and noncontact instruments further confirmed the practicality of noncontact NIR measurement. This work demonstrates, for the first time, that combining InGaAs NIR spectroscopy with process variables enables accurate, rapid, and nondestructive forecasting of TSI-based cross-link density for PV latex manufacturing.
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Identification and quantification of quality of intact durian fruits using NIR spectroscopy
(2026-01-01)
Pitak, Lakkana
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Ditcharoen, Sirirak
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Maraphum, Kanvisit
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Khamwan, Buathip
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Warorost, Nithithada
Quality classification of durian fruits is based on the dry matter (DM) content of the pulp. According to Thai agricultural standards, durian fruit (Monthong variety) must contain at least 32% DM. This study aimed to develop a classification model for assessing durian quality based on DM content, categorizing fruits as either “rejected” (DM < 32%) or “accepted” (DM ≥ 32%). Near-infrared (NIR) spectra were collected as the durian fruits moved along a conveyor belt. The models were developed using two spectral ranges: short-wavelength near-infrared (SWNIR; 4501000 nm) and long-wavelength near-infrared (LWNIR; 8601750 nm). Owing to the imbalance in the dataset between the two classes, the data were adjusted using the synthetic minority oversampling technique to create a balanced dataset. Prediction models were built using different spectral preprocessing methods and algorithms. For the LWNIR range, the models constructed using LDA, SVM, KNN, and SDA achieved accuracies of 95%, 90%, 93%, and 93%, respectively, for the test set. The SWNIR models, developed using the same algorithms, achieved accuracies of 90%, 88%, 90%, and 90%, respectively, for the test set. PLS-regression was used to predict the DM content from both LWNIR and SWNIR data. With the 2nd derivative preprocessing method, the models achieved R² values of 0.89 and 0.79, SEP values of 5% and 6.89%, and RPD values of 2.29 and 1.66, respectively. The wavelength range significantly influenced the model performance, whereas spectral pretreatment had a minor effect on the model's predictive ability. Overall, NIR spectroscopy demonstrated the potential for nondestructive quality grading of whole durian fruits. This work is the first to establish real-time, in-line models for durian grading based on DM content, advancing beyond the previous destructive method. The findings demonstrate the feasibility of automated, nondestructive, and objective quality assessment, supporting industrial automation, precision agriculture, and export quality assurance.
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Moisture content prediction in durian husk biomass via near infrared spectroscopy coupled with aquaphotomics and explainable machine learning
(2025-12-15)
Shrestha, Zenisha
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Shrestha, Bijendra
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Sirisomboon, Panmanas
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Pun, Umed Kumar
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Bajracharya, Tri Ratna
Accurate determination of moisture content is essential for energy efficiency and biomass management for fuel materials such as durian husk. Traditional methods of determining biomass moisture content are time-consuming and require specialized expertise, posing challenges for continuous monitoring. To address this limitation, this study applies Near Infrared Spectroscopy (NIRS) combined with machine learning models to rapidly and accurately assess moisture content. Both linear Partial Least Squares Regression (PLSR) and non-linear approaches were used, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Extreme Gradient Boosting (XGB). The application of preprocessing techniques, notably the Savitzky-Golay second derivative (SD) and Standard Normal Variate (SNV), significantly augmented the predictive performance, highlighting the importance of data preprocessing in spectral analysis. Synthetic spectral augmentation using Gaussian noise revealed that while SVM and ANN exhibited near-perfect performance, SVM demonstrated quantifiable reliability. This study also demonstrates SVM as the most sensitive and reliable method for detecting and quantifying moisture content in durian husk. This research contributes novel insights to biomass analysis, highlighting the benefits of integrating NIRS and feasibility of explainable machine learning techniques to identify water related spectral parameters to advance aquaphotomics, thereby advancing rapid and accurate biomass characterization.
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Predicting biomass global warming potential with FT-NIR spectroscopy
(2025-12-01)
Gyawali, Prakash
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Shrestha, Bijendra
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Phanomsophon, Thitima
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Posom, Jetsada
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Pornchaloempong, Pimpen
This research is to predict the global warming potential (GWP) of biomass by using Fourier transform near-infrared (FT-NIR) spectroscopy. A partial least squares regression model of 197 biomass chip samples was developed for predicting GWP of fast-growing trees and agricultural residues. The reference value of GWP of biomass sample was calculated by the method provided by Intergovernmental Panel on Climate Change (IPCC). After applying different spectral pretreatments and variable selection methods, the best model for predicting GWP was found using the 1st derivative spectrum pretreatment and covariance method (COVM) based variable selection. The results indicate GWP model exhibit good predictive capabilities, where the model can be usable with caution for any purpose including research, by achieving a coefficient of determination for prediction set (R2<inf>P</inf>) of 0.86, and ratio of prediction to deviation (RPD) of 2.6. Additionally, the RMSEP of 0.00063 suggests a low prediction error. This pioneering approach presents a swift and efficient means to determine GWP, the complex functionality parameter, which reveals an optimal relationship model, showcasing its efficacy in a significant advancement in the assessment of biomass functionality related to climate change issue. Additionally, the further research is recommended to integrate FT-NIR data with thermogravimetric analyser to simulate of different thermal conversion of biomass type where different emission gases are generated and with gas chromatography–mass spectrometry for evaluation of concentration of the generated gases for further refine GWP predictions which providing more comprehensive insights and exact content of emission gases affect global warming to support the IPCC.
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Identification and Removal of Negative Biomass Samples via Scatter Plot Analysis to Improve GWP Predictive Modeling
(2025-11-13)
Gyawali, Prakash
;
Shrestha, Bijendra
;
Posom, Jetsada
;
Pornchaloempong, Pimpen
;
Sirisomboon, Panmanas
Accurate prediction of Global Warming Potential (GWP) from biomass constituents is essential for evaluating the sustainability of bioenergy sources. However, the inclusion of biomass samples with weak or negative correlation to key elemental components such as Carbon (C). Hydrogen (H). Nitrogen (N). and Oxygen (O)'can reduce model accuracy and lead to misleading conclusions. This study utilizes scatter plot regression analysis to evaluate and remove "negative biomass samples."defined as those with consistently low R2 values across constituent-GWP relationships using HHV = 0.2949C + 0.82 50H developed for wood biomass by Yin. Regression models were generated for each biomass species using elemental concentrations as predictors of GWP. Notably, several non-wood species (e.g.. Zea Mays-Shell. Bagasse. Bamboo) exhibited very low R- values (often <0.05) for model between elemental composition and GWP. where all elemental correlations indicated weak predictive relationships. In contrast, wood-based species such as Alnus demonstrated significantly higher R2 values, especially with Carbon (R2 = 0.69). Hydrogen (R2 = 0.57). and Oxygen (R2 = 0.68), suggesting a stronger linear influence on GWP. Removing these low-contributing samples improved the clarity and reliability of the predictive model related to HHV and each type of element (C.H.N and O) as evidenced by a sharper regression slope of a graph plotted between predicted GWP and measured GWP of positive species and better fit (increased R2) for the remaining samples. These results highlight the value of preliminary scatter plot analysis in identifying biomass species that obscure rather than support predictive modeling. This filtering step ultimately enhances the robustness and inteipretability of constituent-based GWP prediction frameworks, particularly when applying FT-XIR spectroscopy and chemometric modelling.

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