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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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    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 (r<sup>2</sup>) 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 r<sup>2</sup>, 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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    Isolation and evaluation of biocontrol agents in controlling anthracnose disease of mango in Thailand
    (2016-07-01)
    Rungjindamai, Nattawut
    The agricultural based economy is a core business in Thailand and food export is one of the main sources of income for the Thai population. However, pesticides are overused and misused. As a result there is an urgent need to reduce the use of synthetic chemicals. Biological control offers an alternative to the use of pesticides. Mango (Mangifera indica L.) is widely planted in Thailand and is one of the major cash crops for international export. However, mango suffers from various diseases especially anthracnose, a fungal disease caused by Colletotrichum gloeosporioides. One hundred and twelve isolates of epiphytic microbes were isolated from healthy leaves and fruits of mangoes; this included 93 and 19 isolates of epiphytic bacteria and yeasts, respectively. They were screened for bioactivity against a pathogenic strain of C. gloeosporioides isolated from diseased mangoes using a dual culture technique. Out of 112 isolates, eight isolates exhibited at least 60% inhibition. These isolates were further screened for their inhibition on mango using fruit inoculation. Two isolates reduced the lesion sizes caused by C. gloeosporioides compared to control treatment. These two isolates, based on phenotypical and biochemical tests, were identified as Bacillus sp. MB61 and Bacillus sp. LB72.