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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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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
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Shrestha, Bijendra
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Posom, Jetsada
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Pornchaloempong, Pimpen
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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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Revealing the Power of Deep Learning in Quality Assessment of Mango and Mangosteen Purée Using NIR Spectral Data
(2025-09-01)
Pornchaloempong, Pimpen
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Sharma, Sneha
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Phanomsophon, Thitima
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Sirisomboon, Panmanas
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Lapcharoensuk, Ravipat
The quality control of fruit purée products such as mango and mangosteen is crucial for maintaining consumer satisfaction and meeting industry standards. Traditional destructive techniques for assessing key quality parameters like the soluble solid content (SSC) and titratable acidity (TA) are labor-intensive and time-consuming; prompting the need for rapid, nondestructive alternatives. This study investigated the use of deep learning (DL) models including Simple-CNN, AlexNet, EfficientNetB0, MobileNetV2, and ResNeXt for predicting SSC and TA in mango and mangosteen purée and compared their performance with the conventional chemometric method partial least squares regression (PLSR). Spectral data were preprocessed and evaluated using 10-fold cross-validation. For mango purée, the Simple-CNN model achieved the highest predictive accuracy for both SSC (coefficient of determination of cross-validation ((Formula presented.)) = 0.914, root mean square error of cross-validation (RMSE<inf>CV</inf>) = 0.688, the ratio of prediction to deviation of cross-validation (RPD<inf>CV</inf>) = 3.367) and TA ((Formula presented.) = 0.762, RMSE<inf>CV</inf> = 0.037, RPD<inf>CV</inf> = 2.864), demonstrating a statistically significant improvement over PLSR. For the mangosteen purée, AlexNet exhibited the best SSC prediction performance ((Formula presented.) = 0.702, RMSE<inf>CV</inf> = 0.471, RPD<inf>CV</inf> = 1.666), though the RPD<inf>CV</inf> values (<2.0) indicated limited applicability for precise quantification. TA prediction in mangosteen purée showed low variance in the reference values (standard deviation (SD) = 0.048), which may have restricted model performance. These results highlight the potential of DL for improving NIR-based quality evaluation of fruit purée, while also pointing to the need for further refinement to ensure interpretability, robustness, and practical deployment in industrial quality control.
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Shelf-life extension of Thai green papaya salad dressing by hurdle technology
(2024-09-01)
Sriphochanart, Wiramsri
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Krusong, Warawut
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Pornchaloempong, Pimpen
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Chotigavin, Natthaporn
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Srisawat, Kraisuwit
Green papaya salad or Som Tum is the most popular spicy mixed salads in Thailand due to its unique rich flavor. Green papaya salad dressing (GPSD) is made from various ingredients such as fresh chili pepper, fresh garlic, rind tamarind, fish sauce and lime oil, including the limitation in controlling the taste and flavor of salad dressing and its poor shelf-life. In this study, a convenient ready-to-eat GPSD was developed. Hurdle technology was applied to extend shelf-life of the GPSD based on monitoring of microbial contamination and food pathogens throughout the process. Hurdle technology able to decrease total plate count (TPC) from 5.6 ± 0.2 to 1 ± 0.3 log CFU/g and yeast and mold (Y&M) from 4.2 ± 0.3 to <1 log CFU/g. After 12 weeks of storage at 5 ± 2 °C, slightly increase of TPC was detected as 1.5 ± 0.2 log CFU/g and no changes were found for Y&M and other pathogens. At week 12, GPSD stored at 32 ± 2 °C was found to have higher TPC and Y&M (3.7 ± 0.3 and 2.4 ± 0.3 log CFU/g, respectively). Therefore, a combination of hurdles that combines low a<inf>w</inf>, low pH, heat treatment, low temperature after hot filling, and chilled storage could extend the shelf-life of GPSD with satisfy sensorial test result and be suitable for minimally processed salad dressing.
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Analysis of the Pomelo Peel Essential Oils at Different Storage Durations Using a Visible and Near-Infrared Spectroscopic on Intact Fruit
(2024-08-01)
Sirisomboon, Panmanas
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Duangchang, Jittra
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Phanomsophon, Thitima
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Lapcharoensuk, Ravipat
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Shrestha, Bim Prasad
Pomelo fruit pulp mainly is consumed fresh and with very little processing, and its peels are discarded as biological waste, which can cause the environmental problems. The peels contain several bioactive chemical compounds, especially essential oils (EOs). The content of a specific EO is important for the extraction process in industry and in research units such as breeding research. The explanation of the biosynthesis pathway for EO generation and change was included. The chemical bond vibration affected the prediction of EO constituents was comprehensively explained by regression coefficient plots and x-loading plots. Visible and near-infrared spectroscopy (VIS/NIRS) is a prominent rapid technique used for fruit quality assessment. This research work was focused on evaluating the use of VIS/NIRS to predict the composition of EOs found in the peel of the pomelo fruit (Citrus maxima (J. Burm.) Merr. cv Kao Nam Pueng) following storage. The composition of the peel oil was analyzed by gas chromatography–mass spectrometry (GC-MS) at storage durations of 0, 15, 30, 45, 60, 75, 90, 105 and 120 days (at 10 °C and 70% relative humidity). The relationship between the NIR spectral data and the major EO components found in the peel, including nootkatone, geranial, β-phellandrene and limonene, were established using the raw spectral data in conjunction with partial least squares (PLS) regression. Preprocessing of the raw spectra was performed using multiplicative scatter correction (MSC) or second derivative preprocessing. The PLS model of nootkatone with full MSC had the highest correlation coefficient between the predicted and reference values (r = 0.82), with a standard error of prediction (SEP) of 0.11% and bias of 0.01%, while the models of geranial, β-phellandrene and limonene provided too low r values of 0.75, 0.75 and 0.67, respectively. The nootkatone model is only appropriate for use in screening and some other approximate calibrations, though this is the first report of the use of NIR spectroscopy on intact fruit measurement for its peel EO constituents during cold storage.
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Mangosteen Pericarp Processing Technology to Create Economic Value and Reduce Biowaste
(2024-07-01)
Soontornwat, Alisa
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Pongsuttiyakorn, Thadchapong
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Rakmae, Samak
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Sritham, Eakasit
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Sirisomboon, Panmanas
This research comparatively investigates different mangosteen pericarp processing schemes. The experimental pericarp processing schemes were hot air drying (HAD; control), quick freezing/HAD (QF + HAD), slow freezing/HAD (SF + HAD), and slow freezing/freeze-drying (SF + FD). For freezing, the QF temperature was −38 °C for 2 h and that of SF was −25 °C for 2 weeks. For drying, the HAD temperature was 60 °C for 7 h. In the FD process, the primary and secondary temperatures were −20 °C and 50 °C for 48 h. The experimental results showed that the freezing method (i.e., QF and SF) affected the physical properties (moisture content, water activity, and color) of dried mangosteen pericarp. The antioxidant activities (DPPH and ABTS) of the SF + HAD scheme (28.20 and 26.86 mg Trolox/g DW of mangosteen pericarp) were lower than the SF + FD scheme (40.68 and 41.20 mg Trolox/g DW of mangosteen pericarp). The α-mangostin contents were 82.3 and 78.9 mg/g DW of mangosteen pericarp for FD and HAD, respectively; and the corresponding TPC were 1065.57 and 783.24 mg GAE/g DW of mangosteen pericarp. The results of this study suggest that the drying process had a negligible effect on bioactive compounds. Essentially, the SF + HAD technology is the most operationally and economically viable scheme to process mangosteen pericarp.
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Near-Infrared Spectroscopy Modeling of Combustion Characteristics in Chip and Ground Biomass from Fast-Growing Trees and Agricultural Residue
(2024-03-01)
Shrestha, Bijendra
;
Posom, Jetsada
;
Pornchaloempong, Pimpen
;
Sirisomboon, Panmanas
;
Shrestha, Bim Prasad
This study focuses on the investigation and comparison of combustion characteristic parameters and combustion performance indices between fast-growing trees and agricultural residues as biomass sources. The investigation is conducted through direct combustion in an air environment using a thermogravimetric analyzer (TGA). Additionally, partial least squares regression (PLSR)-based models were developed to assess combustion performance indices via near-infrared spectroscopy (NIRS), serving as a non-destructive alternative method. The results obtained through the TGA reveal that, specifically, fast-growing trees display higher average ignition temperature (227 °C) and burnout temperature (521 °C) in comparison to agricultural residues, which exhibit the values of 218 °C and 515 °C, respectively. Therefore, fast-growing trees are comparatively difficult to ignite, but sustain combustion over extended periods, yielding higher temperatures. However, despite fast-growing trees having a high ignition index (D<inf>i</inf>) and burnout index (D<inf>f</inf>), the comprehensive combustion performance (S<inf>i</inf>) and flammability index (C<inf>i</inf>) of agricultural residue are higher, indicating the latter possess enhanced thermal and combustion reactivity, coupled with improved combustion stability. Five distinct PLSR-based models were developed using 115 biomass samples for both chip and ground forms, spanning the wavenumber range of 3595–12,489 cm−1. The optimal model was selected by evaluating the coefficients of determination in the prediction set (R2<inf>P</inf>), root mean square error of prediction (RMSEP), and RPD values. The results suggest that the proposed model for D<inf>f</inf>, obtained through GA-PLSR using the first derivative (D1), and S<inf>i</inf>, achieved through full-PLSR with MSC, both in ground biomass, is usable for most applications, including research. The model yielded, respectively, an R2<inf>P</inf>, RMSEP, and RPD, which are 0.8426, 0.4968 wt.% min⁻4, and 2.5; and 0.8808, 0.1566 wt.%2 min⁻2 °C⁻3, and 3.1. The remaining models (D<inf>i</inf> in chip and ground, D<inf>f</inf>, and S<inf>i</inf> in chip, and C<inf>i</inf> in chip and ground biomass) are primarily applicable only for rough screening purposes. However, including more representative samples and exploring a more suitable machine learning algorithm are essential for updating the model to achieve a better nondestructive assessment of biomass combustion behavior.

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