Phanomsophon, Thitima

Preferred name

Alternative Name

Phanomsophon, T.

Main Affiliation

School of Agricultural Technology

thitima.ph@kmitl.ac.th

Now showing 1 - 5 of 5

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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
;
Sharma, Sneha
;
Phanomsophon, Thitima
;
Sirisomboon, Panmanas
;
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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Integrating Vis-SWNIR spectrometer in a conveyor system for in-line measurement of dry matter content and soluble solids content of durian pulp
(2021-11-01)
Saechua, Wanphut
;
Sharma, Sneha
;
Nakawajana, Natrapee
;
Leepaitoon, Kritsanaphon
;
Chunsri, Rashphon
The prediction of dry matter content (DMC) and soluble solids content (SSC) in durian pulp were performed using a small laboratory scale in-line visible and short wave near infrared (Vis-SWNIR) spectroscopic system. The fiber optic diode array spectrometer with a charged coupled device (CCD) detector in a wavelength range of 450−1000 nm was used for spectral data acquisition. The spectra of the sample were acquired on the moving conveyor belt in two different orientations, including scanning in the upright position of pulps collected in 2018 and the stable position by scanning on the side of the pulps collected in 2019. Partial least squares regression (PLSR) was used to establish the relationship between the spectra and observed DMC and SSC values using the different wavelength ranges, including 450−1000, 700−1000, and 800−1000 nm for the comparison. The results showed that the durian pulp should be scanned in the upright position at the center of the pulp. Moving average smoothing preprocessing combined with the standard normal variate (SNV) for DMC and multiple scatter correction (MSC) for SSC gave the best result. The suitable wavelength range for model development to predict the DMC and SSC was 700−1000 nm and 800−1000 nm, respectively. After comparing the results, the optimum model showed the coefficient of determination of calibration (R<inf>C</inf>2), and prediction (R<inf>P</inf>2), root mean square error of prediction (RMSEP), bias, and the ratio of performance to interquartile distance (RPIQ) of 0.88, 0.83, 4.32 %, 1.25 %, and 3.52 for DMC and 0.70, 0.70, 4.0 %, 0.4 %, and 2.2 for SSC prediction.
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Effect of Environment Temperature and Relative Humidity on Thermal Emissivity: Study Case of Mango Fruit
(2024-01-01)
Sirisomboon, Panmanas
;
Sripinyowanich Jongyingcharoen, Jiraporn
;
Junto, Apiwat
;
Phanomsophon, Thitima
;
Dachoupakan Sirisomboon, Cheewanun
This research was to study the effect of the environment condition during image captured including temperature and relative humidity in the packaging house of the mango exporting factory and in the orchard on the emissivity of mango fruit. The result showed that in the controlled environment of the packaging house in factory, the emissivity was increased (the mango emitted more energy) when the surface temperature was lower and the emissivity of mango is 0.71-0.84 and in the uncontrolled environment of open-air packaging yard in the mango orchard, the different transpiration rates of mango effected mainly by ambient temperature and relative humidity make the morning condition emissivity of 0.44-0.66 and the afternoon condition of 0.72-0.98. There was the effect of the different measured positions on the fruits where the physiology was different and the fluctuated environment in the latter condition made the wide range of emissivity of mangoes. This indicated the shortcoming of thermal imaging of horticultural product if the correct emissivity varied and difficult to set in the thermal camera setting, hence the inaccurate thermogram to be obtained.
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Non-Destructive Quality Evaluation of Tropical Fruit (Mango and Mangosteen) Purée Using Near-Infrared Spectroscopy Combined with Partial Least Squares Regression
(2022-12-01)
Pornchaloempong, Pimpen
;
Sharma, Sneha
;
Phanomsophon, Thitima
;
Srisawat, Kraisuwit
;
Inta, Wasan
Mango and mangosteen are commercially important tropical fruits with a short shelf life. Fruit processing is one of the alternatives to extend the shelf life of these fruits. Purée is one of the processed products of fresh fruit. In this research, the quality of mango and mangosteen purée was analyzed. Titratable acidity (TA) and total soluble solids (TSS) were predicted using non-destructive near-infrared (NIR) spectroscopy. A partial least squares regression (PLSR) model was developed based on the NIR spectra with a wavelength ranging from 800 to 2500 nm. The PLSR model returned a coefficient of determination (r2) and a ratio of prediction to deviation (RPD) of 0.955 and 4.7 for TSS, and 0.784 and 2.2 for TA, in the mango purée. Similarly, the best model was selected for the TSS prediction in the mangosteen purée through PLSR, with an r2, a root mean square error of cross-validation (RMSECV), and RPD of 0.799, 0.3% malic acid, and 2.2, respectively. The results show the possible application of NIR spectroscopy in the product processing line, although a larger number of samples with wide variation in future studies are needed as an input to update the model, in order to obtain a more robust model.
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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
;
Sharma, Sneha
;
Kaewsorn, Kannapot
;
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.

Phanomsophon, Thitima

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