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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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Modeling Textural Properties of Cooked Germinated Brown Rice Using the near-Infrared Spectra of Whole Grain
(2023-12-01)
Kaewsorn, Kannapot
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Phanomsophon, Thitima
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Maichoon, Pisut
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Pokhrel, Dharma Raj
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Pornchaloempong, Pimpen
If a non-destructive and rapid technique to determine the textural properties of cooked germinated brown rice (GBR) was developed, it would hold immense potential for the enhancement of the quality control process in large-scale commercial rice production. We combined the Fourier transform near-infrared (NIR) spectral data of uncooked whole grain GBR with partial least squares (PLS) regression and an artificial neural network (ANN) for an evaluation of the textural properties of cooked germinated brown rice (GBR); in addition, data separation and spectral pretreatment methods were investigated. The ANN was outperformed in the evaluation of hardness by a back extrusion test of cooked GBR using the smoothing combined with the standard normal variate pretreated NIR spectra of 188 whole grain samples in the range of 4000–12,500 cm−1. The calibration sample set was separated from the prediction set by the Kennard–Stone method. The best ANN model for hardness, toughness, and adhesiveness provided R2, r2, RMSEC, RMSEP, Bias, and RPD values of 1.00, 0.94, 0.10 N, 0.77 N, 0.02 N, and 4.3; 1.00, 0.92, 1.40 Nmm, 9.98 Nmm, 1.6 Nmm, and 3.5; and 0.97, 0.91, 1.35 Nmm, 2.63 Nmm, −0.08 Nmm, and 3.4, respectively. The PLS regression of the 64-sample KDML GBR group and the 64-sample GBR group of various varieties provided the optimized models for the hardness of the former and the toughness of the latter. The hardness model was developed by using 5446.3–7506 and 4242.9–4605.4 cm−1, which included the amylose vibration band at 6834.0 cm−1, while the toughness model was from 6094.3 to 9403.8 cm−1 and included the 6834.0 and 8316.0 cm−1 vibration bands of amylose, which influenced the texture of the cooked rice. The PLS regression models for hardness and toughness had the r2 values of 0.85 and 0.82 and the RPDs of 2.9 and 2.4, respectively. The ANN model for the hardness, toughness, and adhesiveness of cooked GBR could be implemented for practical use in GBR production factories for product formulation and quality assurance and for further updating using more samples and several brands to obtain the robust models.
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Evaluation of Precision and Sensitivity of Back Extrusion Test for Measuring Textural Qualities of Cooked Germinated Brown Rice in Production Process
(2023-08-01)
Kaewsorn, Kannapot
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Maichoon, Pisut
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Pornchaloempong, Pimpen
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Krusong, Warawut
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Sirisomboon, Panmanas
The textural qualities of cooked rice may be understood as a dominant property and indicator of eating quality. In this study, we evaluated the precision and sensitivity of a back extrusion (BE) test for the texture of cooked germinated brown rice (GBR) in a production process. BE testing of the textural properties of cooked GBR rice showed a high precision of measurement in hardness, toughness and stickiness tests which indicated by the repeatability and reproductivity test but the sensitivity indicated by coefficient of variation of the texture properties. The findings of our study of the effects on cooked GBR texture of different soaking and incubation durations in the production of Khao Dawk Mali 105 (KDML 105) GBR, as measured by BE testing, confirmed that our original protocol for evaluation of the precision and sensitivity of this texture measurement method. The coefficients of determination (R2) of hardness, toughness and stickiness tests and the incubation time at after 48 hours of soaking were 0.82, 0.81 and 0.64, respectively. The repeatability and reproducibility of reliable measurements, which have a low standard deviation of the greatest difference between replicates, are considered to indicate high precision. A high coefficient of variation where relatively wide variations in the absolute value of the property can be detected indicates high sensitivity when small resolutions can be detected, and vice versa. The sensitivity of the BE tests for stickiness, toughness and hardness all ranked higher, in that order, than the sensitivity of the method for adhesiveness, which ranked lowest. The coefficients of variation of these texture parameters were 31.26, 20.59, 19.41 and 18.72, respectively. However, the correlation coefficients among the texture properties obtained by BE testing were not related to the precision or sensitivity of the test. By obtaining these results, we verified that our original protocol for the determination of the precision and sensitivity of food texture measurements which was successfully used for GBR texture measurement.
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Non-linear viscoelastic behavior of cooked white, brown, and germinated brown Thai jasmine rice by large deformation relaxation test
(2017-07-03)
Sirisomboon, Panmanas
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Kaewsorn, Kannapot
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Thanimkarn, Satthawat
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Phetpan, Kittisak
Stress relaxation tests at high strain were conducted on scoops of cooked white, brown, and germinated brown Thai jasmine rice using a King Mongkut’s Institute of Technology Ladkrabang test rig. The diameter of the scoop was 35 mm and the height was 10 mm. Non-linear modeling, consisting of four relaxation models, was applied to the data obtained for each type of rice. The modeling methods included Peleg and Normand’s; Yadav, Roopa, and Bhattacharya’s; Jaya and Durance’s; and Myhan, Markowski, and Daszkiewicz’s. The cooked white rice showed greater tenderness compared to the others. The toughness of the three types of cooked rice was not found to be different. The Myhan et al. model was the most accurate in describing the non-linear viscoelastic behavior of all types of cooked rice. The cooked brown rice showed the highest initial decay rate, but the lowest relaxation, lowest elasticity, and greatest viscosity. In contrast, the cooked white rice had opposite characteristics.
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Study on evaluation of gamma oryzanol of germinated brown rice by near infrared spectroscopy
(2014-01-01)
Kaewsorn, Kannapot
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Sirisomboon, Panmanas
Germinated brown rice (GBR) is rich in gamma oryzanol which increase its consumption popularity, particularly in the health food market. The objective of this research was to apply the near infrared spectroscopy (NIRS) for evaluation of gamma oryzanol of the germinated brown rice. The germinated brown rice samples were prepared from germinated rough rice (soaked for 24 and 48 h, incubated for 0, 6, 12, 18, 24, 30 and 36 h) and purchased from local supermarkets. The germinated brown rice samples were subjected to NIR scanning before the evaluation of gamma oryzanol by using partial extraction methodology. The prediction model was established by partial least square regression (PLSR) and validated by full cross validation method. The NIRS model established from various varieties of germinated brown rice bought from different markets by first derivatives+vector normalization pretreated spectra showed the optimal prediction with the correlation of determination (R2), root mean squared error of cross validation (RMSECV) and bias of 0.934, 8.84 × 10-5 mg/100 g dry matter and 1.06 × 10-5 mg/100 g dry matter, respectively. This is the first report on the application of NIRS in the evaluation of gamma oryzanol of the germinated brown rice. This information is very useful to the germinated brown rice production factory and consumers. © 2014 The Authors.

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