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Author: search.filters.author.Teerachaichayut, SontisukSubject: search.filters.subject.Image

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    Near-infrared hyperspectral imaging for predicting the quality of SO2 pre-treated and dehydrated mango
    (2025-08-01)
    Aozora, Wayan Dipasasri
    ;
    Tantinantrakun, Achiraya
    ;
    Thompson, Anthony Keith
    ;
    Teerachaichayut, Sontisuk
    Prediction for quality indices of SO<inf>2</inf> pre-treated and dehydrated mango was accessed by NIR-HSI. Models for predicting TSS and SO<inf>2</inf> content achieved R = 0.82; RMSEP = 2.42% and R = 0.83; RMSEP = 56.40 mg/kg, respectively. Visualization of TSS and SO<inf>2</inf> content could be presented by predictive images.
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    Non-destructive quality assessment of hens’ eggs using hyperspectral images
    (2017-12-01)
    Suktanarak, Sineenart
    ;
    Teerachaichayut, Sontisuk
    Freshness of hens’ eggs is important for consumers and the food processing industry and the Haugh unit (HU) is a commonly used index for freshness. Measurement of HU is destructive and also assumes that the sample that is tested accurately reflects the batch of eggs being processed. This research tests the use reflectance near infrared hyperspectral imaging in the wavelength range of 900–1700 nm for nondestructive prediction of eggs freshness and compared these measurements to HU. To achieve this fresh eggs were stored at 25 °C and were measured after storage for 0, 4, 7, 10, 14, 18 and 21 days by hyperspectral imaging technique and compared to HU for each egg. Hyperspectral imaging technique combines between conventional imaging and NIR spectroscopy to achieve spatial and spectral information from eggs. The acquired near infrared hyperspectral imaging data from samples in the calibration set were analyzed in order to develop a calibration model for HU using partial least squares regression (PLSR) and then crossvalidated. The standard normal variate transformation (SNV) spectral pretreatment gave the optimum conditions for establishing the calibration model with a coefficient of determination (R2) of 0.91 and root mean square error of calibration (RMSEC) of 4.58. Distribution maps of HU were generated from the acquired calibration model by interpretation of predicted HU to different colors using image processing algorithms. Displayed colors of acquired image of eggs were different correspond to the freshness of the eggs based on HU. The results show that the near infrared hyperspectral imaging technique can be possible to use for presenting the images of egg related to HU in order to nondestructively evaluate hens’ eggs freshness.
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    Non-destructive prediction of total soluble solids, titratable acidity and maturity index of limes by near infrared hyperspectral imaging
    (2017-11-01)
    Teerachaichayut, Sontisuk
    ;
    Ho, Huong Thanh
    This study was implemented for non-destructive prediction of total soluble solids (TSS), titratable acidity (TA) and calculation of TSS/TA as a measure of maturity index in intact limes using laboratory-based push-broom hyperspectral imaging (HSI) in reflectance mode in the range of 929–1671 nm. Limes were scanned by the HSI system in order to develop calibration models for predicting TSS, TA and TSS/TA using partial least square regression (PLSR). Original spectra obtained optimal conditions for establishing the models for TSS and TA while smoothing spectra for TSS/TA. The accuracy of the models for TSS, TA and TSS/TA provided coefficient of determination of prediction (R<sup>2</sup><inf>p</inf>) of 0.838, 0.694 and 0.775, respectively and root mean square errors of prediction (RMSEP) of 0.237%, 0.288% and 0.049, respectively. Image processing algorithms were then built up by interpreting predictive values, from the models, to colors in each pixel of the images. The predictive visualization of TSS, TA and TSS/TA in all portions of the limes based on a color scale was presented. The results showed that the HSI technique has the capability of predicting TSS, TA and TSS/TA of intact limes non-destructively and the results could be visualized by different colors of the predictive images.