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Subject: search.filters.subject.Fruit quality

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    Investigation of physiological disorder classification in mangosteen fruit using visible and shortwave near-infrared spectroscopy combined with machine learning
    (2025-12-01)
    Ruttanadech, Nuttapong
    ;
    Momin, Abdul
    ;
    Phetpan, Kittisak
    ;
    Chaichanyut, Montree
    ;
    Thongphut, Chitwadee
    Accurate classification of physiological disorders in mangosteen fruit is crucial for ensuring production quality, safety, sustainability, and economic viability. This study investigates the application of visible and shortwave near-infrared (Vis/SWNIR) reflectance spectroscopy, combined with machine learning algorithms, to classify three primary disorders: normal fruit (NF), translucent flesh disorder (TFD), and TFD with yellow gummy latex (TFD & YGL). The study specifically examines the effects of light intensity, spectral pretreatments, and machine learning models on classification performance. Spectral data were collected using two light intensities (50 % and 100 % of a 150 W light source) and processed with three pretreatments: standard normal variate (SNV), second derivative Savitzky-Golay (SGD2), and a combination of SNV and SGD2. Random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms were used for classification. The SGD2 method improved differentiation, especially for the TFD & YGL class, in the 700–725 nm wavelength range, which is associated with xanthone content in the fruit's pericarp. Higher light intensity (100 %) significantly improved classification accuracy, achieving an overall accuracy of 0.71 and an average F1 score of 0.61 with the RF model. Despite these improvements, the model struggled to distinguish the TFD class from NF due to their similar spectral profiles. Overall, the Vis/SWNIR spectroscopy and machine learning combination shows strong potential for the non-destructive classification of mangosteen fruit disorders. Both light intensity and spectral pretreatments play critical roles in enhancing performance. Future studies should focus on improving spectral sensitivity to better capture internal fruit characteristics.
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    Growth and Yield of Watermelon (Citrullus lanatus) in Plastic House in Response to White LED Supplementary Lighting
    (2023-01-01)
    Chamchum, Wasinee
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    Glahan, Somchai
    ;
    Kramchote, Somsak
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    Maniwara, Phonkrit
    ;
    Suwor, Patcharaporn
    Watermelon plants cultivar ‘Kinaree 457’ were grown in plastic house under natural daylight only (control) or with nightly LED supplementary lighting for 6 h (6:00 pm-12:00 pm) or 12 h (6:00 pm-6:00 am) starting from transplanting up to fruit harvest. Plant height, leaf chlorophyll content and fruit yield significantly increased in response to 6 h supplementary LED lighting. Fruit mass, size (length x width) and flesh thickness at 6 h LED treatment were about 2.3 kg, 19.3 ×15.7 cm, and 15.7 cm, respectively, while the fruit of control had 1.7 kg, 16.0 × 14.3 cm, and 13.8 cm, respectively. No significant treatment effect was obtained on peel thickness, flesh color L* and b* values, juice pH and total soluble solids. However, 6 h LED treatment resulted in lower reddening flesh (lower a* values), firmness and higher titratable acidity relative to the control, suggesting the need for improvement in cultural management. Furthermore, multivariate statistics of principal component analysis (PCA) performed on physico-chemical quality revealed the variations among watermelons from lighting and control treatments regardless of lighting hour.
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    Phytochemical screening and fruit quality of commercial eggplants
    (2021-01-01)
    Chokthaweepanich, Hathairat
    ;
    Sriwicha, Suwalak
    ;
    Auvuchanon, Anyamanee
    ;
    Supapvanich, Suriyan
    Ten commercial eggplant cultivars and two allies were collected from the Northeastern region of Thailand in order to examine fruit quality and screen for selected phytochemicals. Eggplants were classified into three species including Solanum torvum, S. violaceum and S. melongena. There were 10 cultivars in S. melongena (commercial eggplants) containing cv. ‘Makhuea kai tao khaw’, ‘Makhuea khuen’, ‘Makhuea pro chao phraya’, ‘Makhuea pro look lai’, ‘Makhuea pro muang’, ‘Makhuea tor lae kaew’, ‘Makhuea tor lae khaw’, ‘Makhuea yao kaew’, ‘Makhuea yao khaw’, and ‘Makhuea yao muang’. The analysis indicated that there were significant differences (p ≤ 0.05) in fruit quality traits including color, thickness, hardness, TSS and moisture. All samples could be divided into three groups based on fruit color including white, purple, and green group. The commercial eggplants had more thickness than S. torvum and S. violaceum, but these two species had more TSS contents than commercial eggplants. The results of phytochemical screening showed that S. torvum tended to have higher alkaloid, tannin, saponin and steroid contents from the staining technique. Furthermore, ‘Makhuea yao muang’ showed the highest DPPH radical scavenging capacity (49.33%) compared to all others. The principal component and cluster analysis based on correlation of fruit traits and phytochemicals showed that all commercial eggplants were clustered in the same group. The correlation analysis indicated that TSS contents positively correlated with saponin and steroid, while TSS contents negatively correlated with thickness and moisture contents. As this study, commercial eggplants showed higher fruit quality and antioxidant activity than related species.